system

Abstract

We provide the system. [Solution] A means for detecting the presence of a moving object inside a vehicle using wireless signals, A means for analyzing information on detected moving objects and evaluating the biological information of those moving objects, A means for determining an anomaly based on biometric information and sending a warning to an external device if an anomaly is present, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Currently, accidents in which children or pets are left unattended in a vehicle and are at risk of life occur frequently. Although there is a need for technology to prevent such accidents, current monitoring technologies have problems such as detection omissions due to blind spots of cameras and high costs. Therefore, there is a need for a system that can more reliably and economically monitor the situation inside the vehicle and prompt a quick response in case of an abnormality.

Means for Solving the Problems

[0005] This invention is a system that uses wireless signals to detect moving objects inside a vehicle and determines abnormalities based on their biological information. Specifically, it provides a technology that eliminates camera blind spots by utilizing wireless signals and measures and analyzes biological information such as heart rate non-contact. When an abnormality is detected, this system sends a warning to an external device and can send notifications to multiple external devices, enabling more stakeholders to respond quickly.

[0006] "Vehicle interior" refers to the internal space of a car, the area where people and objects are housed.

[0007] A "moving object" refers to an object that is physically present within a vehicle and has the potential to move.

[0008] A "wireless signal" is an electromagnetic wave signal used for communication, a means of sending and receiving information without using wired cables.

[0009] "Biometric information" refers to information that can be obtained from a person's body, including physical data such as heart rate, body temperature, and respiratory rate.

[0010] "Analysis" refers to the process of identifying and evaluating the state and characteristics of an object based on received signals and data.

[0011] "Abnormal" refers to a state that deviates from normal standards or ranges, including cases that require immediate attention.

[0012] An "external device" refers to a device that exists outside the vehicle's system and can receive information through communication.

[0013] "Sending a warning" means sending a message or alert to a target device to notify it of some kind of abnormal situation. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The embodiments for carrying out the present invention will be described in detail. This system monitors movement within a vehicle and provides rapid warnings in the event of an abnormality, and consists of the following elements.

[0036] This system consists of a "terminal" installed inside the vehicle, a "server" located remotely, and an external device such as a smartphone acting as a "user" terminal. The terminal has the function of transmitting wireless signals and receiving the reflected waves to detect the presence of moving objects inside the vehicle. This signal data is transmitted to the server and analyzed.

[0037] The server analyzes signal data sent from the terminal to assess the presence of moving objects and evaluate biometric information. Based on the analysis results, the server distinguishes between normal and abnormal conditions, and generates a warning if an abnormality is detected. The analysis process includes a heart rate estimation function, which is used to determine if biometric information is outside the normal range.

[0038] When an anomaly is detected, the server sends a warning notification to multiple "user" terminals. This allows not only the vehicle owner but all relevant parties to respond quickly. This system can also be used in environments where monitoring and managing multiple people riding in a specific vehicle is necessary, such as to ensure the safety of children in childcare facilities or school buses.

[0039] As a concrete example, let's consider how this system could be implemented on a kindergarten bus. In this case, the terminal automatically activates when the bus departs and begins sending and receiving wireless signals. Monitoring continues even after the bus stops and the engine is turned off, and if a forgotten child is left inside the vehicle, a warning is promptly sent to the relevant personnel. This system makes it possible to quickly rescue children left behind in the vehicle and ensure their safety.

[0040] The invention minimizes privacy concerns because it does not use cameras, reduces costs by utilizing existing wireless technology, and allows for widespread implementation. This system is particularly effective in addressing the security needs of public spaces and individuals where privacy protection is crucial.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The terminal detects that the vehicle has stopped and the engine has been turned off. It begins transmitting a wireless signal and enters motion detection mode inside the vehicle.

[0044] Step 2:

[0045] The terminal receives reflected signals from within the vehicle and generates data from them. It periodically sends the received signal data to the server.

[0046] Step 3:

[0047] The server receives signal data transmitted from the terminal, extracts and analyzes information useful as indicators, and then runs a program to estimate biometric information such as heart rate to determine if a person is present.

[0048] Step 4:

[0049] Based on the analysis results, the server evaluates whether the presence of a moving object and its biometric information are within normal limits. If an anomaly is detected, the process proceeds to the next step.

[0050] Step 5:

[0051] If the server detects an anomaly, it collects pre-configured user contact information and prepares a warning notification.

[0052] Step 6:

[0053] The server sends warning notifications to multiple user terminals. The notifications contain detailed information about the anomaly and are designed to encourage prompt action.

[0054] Step 7:

[0055] The user receives a warning notification on a smartphone or other device and checks its contents. If necessary, they return to the vehicle and take appropriate action.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] Conventional vehicle monitoring systems often rely on cameras, which have drawbacks such as privacy concerns and high equipment costs. Furthermore, the lack of a mechanism for quickly notifying relevant parties in the event of an anomaly means that vehicles may be left unattended, potentially leading to delays in responding to emergencies. Additionally, there are problems with monitoring the individual situations of multiple passengers.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for detecting objects inside a vehicle using wireless signals and without using a camera, means for transmitting the signal data collected by the terminal to an external computing device and performing analysis processing, and means for evaluating the physiological information of the moving object based on the information obtained from the analysis processing and distinguishing between normal and abnormal conditions. This enables efficient and rapid anomaly detection and notification without infringing on privacy.

[0061] A "vehicle" is a device that enables the movement of objects, and primarily refers to vehicles that travel on land.

[0062] An "object" is something that is detected by a wireless signal, and whose existence is associated with its internal movement.

[0063] A "wireless signal" is a medium that uses radio waves to transmit information and is used to detect the presence of objects.

[0064] A "terminal" refers to a device installed inside a vehicle that has the function of transmitting and receiving wireless signals.

[0065] A "server" is a central processing unit that receives and analyzes data, and refers to equipment designed to efficiently perform a large number of calculations.

[0066] A "computational device" is a device used to process data and has the function of analyzing signal data.

[0067] "Physiological information" refers to information that indicates the state of the body, such as heart rate, and is an indicator used to evaluate health status and the presence or absence of abnormalities.

[0068] "Normal state" refers to a normal, healthy condition, and is the opposite of abnormal.

[0069] An "abnormality" refers to a state that deviates from the normal range and is an unusual condition that warrants attention.

[0070] "User device" refers to a terminal used to receive warnings, and is a device carried by the user.

[0071] A "warning" is a cautionary message issued in response to the detection of an anomaly, and it is information that should be promptly communicated to users.

[0072] This invention is a vehicle motion detection system using wireless signal technology, which monitors for anomalies while considering privacy protection. The system consists of a "terminal" installed in the vehicle, a "server" that analyzes and manages data, and a "user" device that receives warnings. The functions of each component are described in detail below.

[0073] The terminal is installed inside the vehicle and is responsible for transmitting and receiving wireless signals. Specifically, it uses radar technology to detect objects and people inside the vehicle. The signal data acquired by the terminal is programmed to be automatically transmitted to a server. At this time, the software built into the terminal processes the reflected wireless signal data in real time and converts it into a format that can be reported immediately.

[0074] The server plays a central role in analyzing signal data sent from terminals. The server is equipped with advanced analysis algorithms that continuously monitor dynamic movement and physiological data (such as heart rate). Based on these results, it quickly identifies normal and abnormal conditions. If the analysis indicates an abnormality, the server immediately generates a warning and sends it to multiple pre-registered user terminals.

[0075] Users typically receive alerts via smartphones or mobile devices. This ensures that notifications reach not only vehicle owners and managers, but also multiple stakeholders who require a quick response. The system has diverse applications and is particularly effective in situations involving multiple passengers, such as public transportation and child transport services.

[0076] A concrete example is a kindergarten bus. A terminal installed inside the bus activates when the engine starts and constantly monitors the situation inside the vehicle. If a child is left behind on the bus after the service has ended, the system can detect the anomaly and immediately send a warning to the relevant parties. This system enables a swift rescue and ensures safety.

[0077] By integrating with the generation AI model, an example of a prompt message is: "Please describe in detail the situations in which a warning regarding the vehicle's motion monitoring system needs to be issued. In particular, please explain the advantages when considering its operation in public transportation." In this way, establishing the operation of the entire system enables the provision of a safer and more efficient monitoring environment.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The terminal automatically activates in conjunction with the vehicle's engine start. It transmits a wireless signal into the vehicle and collects the reflected signal as received data. This input data includes information about the presence and movement of objects inside the vehicle. Specifically, the radar sensor scans for reflected signals from surrounding objects at a high frequency.

[0081] Step 2:

[0082] The terminal transmits the collected signal data to the server in real time. This output data is used as raw data necessary for the analysis of physiological information. Specifically, the communication module sends the data to the server via a wireless network.

[0083] Step 3:

[0084] The server receives signal data transmitted from the terminal. It processes the input data and analyzes physiological information related to the presence of moving objects and heart rate. This analysis is performed using data filtering, signal intensity measurement, and pattern recognition techniques. As output, an index is generated to determine whether the data is normal or abnormal.

[0085] Step 4:

[0086] The server determines whether the situation is normal or abnormal based on the analysis results. If an abnormality is detected, a warning is generated. In this step, the analysis algorithm applies an anomaly detection rule set and interprets the data. This output is organized into a message that includes specific warning details.

[0087] Step 5:

[0088] The server sends the generated warnings to multiple user terminals. The input requires information about the recipient user terminals. The output is a warning notification received by the user on a device such as a smartphone. Specific operations include rapid data distribution using network protocols.

[0089] Step 6:

[0090] The user reviews the received warning notification. The input is the warning message displayed on the device. The output is the action the user should take based on that information, such as contacting the site or proceeding to direct verification. A specific action is the process of tapping the notification to open the details.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] Modern management facilities and apartment buildings require the protection of privacy while ensuring the safety of residents and users. However, existing surveillance systems, such as camera systems, are difficult to implement from a privacy perspective and have challenges in responding quickly to anomalies. In particular, at night or in situations where human vision is unreliable, there is a need for more advanced motion detection and rapid anomaly notification.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] This invention includes a server that includes means for detecting the presence of moving objects inside a vehicle using electromagnetic wave signals, means for analyzing information on the detected moving objects and evaluating the biological information of those moving objects, means for identifying anomalies based on the biological information and sending notifications to external terminals if an anomaly exists, means for monitoring crowd movement in management facilities and apartment buildings based on the detection results, and means for immediately notifying the mobile terminals of relevant parties when an anomaly is detected in the movement of the crowd. This enables rapid and reliable safety measures in the event of an anomaly while protecting privacy.

[0096] "Vehicle interior" refers to the internal space of a structure used as a means of transportation, and is the area used to transport people and goods.

[0097] A "moving object" refers to a physical entity that is not stationary but is constantly moving or changing, and primarily includes humans and animals.

[0098] "Electromagnetic wave signals" are signals that utilize a portion of the electromagnetic spectrum used in wireless communication, and are applied to data communication and object detection.

[0099] "Means of detection" refers to methods and techniques for confirming the existence or state of an object, and includes sensors and analytical algorithms.

[0100] "Biological information" refers to data related to living organisms, such as heart rate, temperature, and movement, and is information that indicates the health and activity level of an individual.

[0101] An "external device" refers to an external device connected to the system that is an electronic device capable of receiving notifications and being operated.

[0102] "Means of sending notifications" refers to means of informing relevant parties of anomalies or important information, and includes alert systems that utilize communication technology.

[0103] "Crowd movement" refers to the movement of a large number of people or objects from one place to another, demonstrating the dynamic behavior of a collective.

[0104] "Privacy" refers to the right of an individual to have their private life and information protected from infringement by others, and it is an ethical concept that is considered important when introducing surveillance technology.

[0105] This system is a motion monitoring device designed to improve safety in management facilities and apartment buildings. The server uses wireless signal transceivers installed inside vehicles or facilities. Terminals continuously transmit electromagnetic signals, and by receiving the reflected signals, the system detects the presence of moving objects, including people. The detected data is sent to the server, where detailed biological information about the moving objects is analyzed.

[0106] The server analyzes this data, evaluates biometric information such as heart rate, and identifies whether an abnormality has occurred. If an abnormality is detected, the server immediately notifies external devices, including smartphones and tablets.

[0107] As a concrete example, let's consider its use in a fitness gym within an apartment building at night. Within this gym, the terminal emits electromagnetic signals at regular intervals to monitor people's movements and conditions. If abnormal movement is detected, the server immediately sends a notification to external terminals of management staff or security guards, enabling a rapid response.

[0108] This system utilizes cloud services such as AWS® Lambda and Google® Cloud Functions to enable real-time data analysis. Furthermore, to protect privacy, it employs a configuration that does not use cameras.

[0109] Examples of prompts for a generative AI model:

[0110] "Consider a scenario for a motion monitoring system in a fitness gym. Explain specifically how the monitoring system within the facility detects anomalies and notifies relevant personnel."

[0111] This configuration allows for both privacy protection and security measures to be achieved, and enables appropriate environmental monitoring.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The terminal uses a wireless signal transceiver installed inside the vehicle or facility to transmit electromagnetic signals at regular intervals and receive the reflected waves. The input is the transmitted wireless signal, and the output is the reflected wave data. Based on this data, basic signal processing is performed to detect the presence of moving objects.

[0115] Step 2:

[0116] The server receives reflected wave data transmitted from the terminal. The input is reflected wave data from the terminal, and the output is information about a moving object. The server analyzes this data and performs data processing such as Fast Fourier Transform (FFT) to measure the distance and speed of the moving object.

[0117] Step 3:

[0118] The server estimates biological information from analyzed moving object information. The input is the analyzed object information, and the output is biological information such as heart rate. Here, biological data is extracted using an algorithm that associates specific movement patterns with heart rate.

[0119] Step 4:

[0120] The server identifies abnormalities based on the obtained biological information. The input is estimated biological information, and the output is the judgment result of whether it is normal or abnormal. If an abnormal value is detected, a corresponding flag is set, and it is classified as abnormal.

[0121] Step 5:

[0122] The server sends a notification to an external device when an anomaly is detected. The input is the result of the anomaly detection, and the output is a warning notification. The server sends the warning to the configured notification destination (smartphone or tablet device) via the network, prompting the user to take prompt action.

[0123] Step 6:

[0124] The user receives a warning notification on an external terminal and, if necessary, checks the site or takes corrective action. The input is the warning notification from the server, and the output is the user's response action. This allows the user to respond quickly to abnormal situations and take necessary actions.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention is a system that detects the presence of moving objects inside a vehicle, their biometric information, and their emotional state, and transmits warnings to an external device as needed. This makes it possible to improve safety while facilitating emergency response.

[0127] This system primarily consists of three elements: a terminal, a server, and a user terminal. The terminal is installed inside the vehicle and uses wireless signals to detect the presence of moving objects and their biometric information. This information is then transmitted to the server.

[0128] The server analyzes biometric information based on the received data and estimates data such as heart rate. The server also incorporates an emotion engine that evaluates the emotional state of the subject through biometric information and movement pattern analysis. Emotional state refers to the psychological state of the subject, such as whether it is stressed or calm.

[0129] If an anomaly is detected, the server considers the evaluation results of the emotion engine and generates a warning that enables a more appropriate and rapid response. The warning is adjusted according to the emotion of the moving object and sent to the user's terminal. This allows not only the user but also multiple stakeholders, as needed, to quickly share information and create a system that enables appropriate responses.

[0130] As a concrete example, consider a case where a child is left behind in a vehicle that has been parked for a long time. The engine is turned off, and the terminal begins transmitting wireless signals. The server analyzes the data from the terminal and confirms that the child is still inside the vehicle. At the same time, the emotion engine analyzes changes in biosignals and behavioral patterns and determines that the child's emotions are unstable. Based on this, the server quickly sends warnings to multiple user terminals urging them to change to a calmer living environment, thereby helping to ensure the child's safety. In this way, the incorporation of the emotion engine enables more appropriate and effective warning notifications, improving the accuracy and speed of responses in emergency situations.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The terminal detects that the vehicle's engine has been turned off. It then switches to a mode that monitors for movement inside the vehicle using a wireless signal.

[0134] Step 2:

[0135] The terminal receives reflected wireless signals and measures the presence of moving objects and related biometric information (e.g., heart rate). This data is then transmitted to the server.

[0136] Step 3:

[0137] The server analyzes the signal data received from the terminal. Based on the biometric information, it evaluates whether the moving body is in a normal state and estimates the heart rate.

[0138] Step 4:

[0139] The server uses an emotion engine to estimate the emotional state of a moving object from its biometric information and other data. This allows it to determine what emotional state the object is in, such as tension, anxiety, or relaxation.

[0140] Step 5:

[0141] Based on an assessment of biometric information and emotional state, the server will begin preparing a warning notification if it determines that there is an abnormality in the moving objects inside the vehicle.

[0142] Step 6:

[0143] Based on the results from the emotion engine, the server generates warnings tailored to the detected emotional state. For example, it adjusts the notification content, prompting a quick response if the user is stressed, or issuing a normal alert if they are relaxed.

[0144] Step 7:

[0145] The server sends pre-configured warning notifications to multiple user terminals, allowing relevant parties to quickly take appropriate action.

[0146] Step 8:

[0147] The user receives a warning notification on their device and checks its contents. Depending on the situation, they can either go to the vehicle or contact relevant parties to confirm the situation and take appropriate action.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] There is a need to provide a system that accurately detects the presence of moving objects, biometric information, and emotional states within a vehicle, and sends rapid and accurate warnings to enhance safety. However, conventional systems are insufficient in detecting anomalies and generating warnings, making it difficult to respond appropriately in emergencies. This necessitates a solution to the problem of situations that could potentially threaten safety.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes means for detecting the presence of a moving object using wireless data, means for analyzing the data of the detected moving object and evaluating its biometric data, means including an artificial intelligence engine that determines the emotional state based on the biometric data, and means for generating detailed warning data and transmitting it to an external computing device when an abnormality is detected. This enables improved safety by accurately monitoring the situation inside the vehicle and allowing for prompt responses as needed.

[0153] "Moving object" refers to objects that may be moving, such as people or animals, that are detected inside a vehicle.

[0154] "Wireless data" refers to data acquired via wireless communication technology used to exchange information between a terminal and a server.

[0155] "Biometric data" refers to physiological data obtained from moving objects, such as heart rate and body temperature.

[0156] "Emotional state" refers to the psychological and emotional state determined by artificial intelligence based on biometric data.

[0157] An "artificial intelligence engine" is a computational means used to analyze biometric data and movement patterns to determine the emotional state of a moving object.

[0158] An "external computing device" is an electronic device, such as a smartphone or tablet, owned by the user that can receive and display warning data.

[0159] "Warning data" refers to notification information that is generated and sent to an external computing device when an anomaly is detected.

[0160] This invention provides a system that improves safety by detecting moving objects within a vehicle and evaluating their biometric data and emotional state. The system mainly consists of three elements: a terminal, a server, and a user terminal.

[0161] The terminal is installed inside the vehicle and detects the presence of moving objects using wireless data. This terminal has the function of collecting biometric data such as heart rate and body temperature using ultrasonic sensors and infrared sensors. The collected data is transmitted to a server via Bluetooth or Wi-Fi.

[0162] The server analyzes the received data. This analysis utilizes a generative AI model based on Python machine learning algorithms. The server then activates an artificial intelligence engine to detect abnormalities in heart rate and body temperature based on biometric data, and further determine emotional states. This engine has the ability to analyze behavioral patterns and time-series data to quantify stress levels and psychological stability.

[0163] If an anomaly is detected, the server promptly generates warning data. This generated warning data is sent to the user's terminal and displayed as a push notification. User terminals are mainly smartphones and tablets, which function as external computing devices.

[0164] As a concrete example, consider the case of a toddler left in a vehicle for an extended period. The device analyzes the biometric data of the moving child and detects abnormalities by sensing unsafe heart rate fluctuations or elevated body temperature. Simultaneously, it assesses the child's emotional state, and if it determines the child is in a state of tension, it immediately warns the user's device, enabling rapid intervention.

[0165] An example of a prompt to the generating AI model is: "Please tell me how to analyze the heart rate and movement patterns of a person trapped inside a vehicle and issue a warning if their emotional state is unstable." By using this system, it is possible to enhance safety inside vehicles and support a quick and appropriate response in emergencies.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The terminal detects the presence of moving objects from the vehicle's environment using wireless data. During this process, ultrasonic and infrared sensors collect location information and movement patterns of the moving objects. The input is data from sensors within the vehicle, and the output is data indicating the presence and initial position of the moving object.

[0169] Step 2:

[0170] The device continuously collects biometric data such as heart rate and body temperature from moving objects. The input is sensor measurement data in the state of the moving object. This data is transmitted to a server via Bluetooth or Wi-Fi, and the output is biometric data that is transferred to the server.

[0171] Step 3:

[0172] After receiving biometric data, the server analyzes the data using a generative AI model. During this process, it detects abnormalities in heart rate and body temperature based on machine learning algorithms implemented in Python. The input is biometric data transmitted from the terminal, and the output is alert data indicating the detected abnormality in the biometric information.

[0173] Step 4:

[0174] The server then activates an artificial intelligence engine to determine the emotional state. This engine analyzes the time-series changes in biometric data and quantifies the stress and stability of the body. The input is the data analyzed in the previous step, and the output is an evaluation result indicating the emotional state.

[0175] Step 5:

[0176] If an abnormal or stressful condition is detected, the server generates appropriate warning data. This includes specific instructions and actions tailored to the individual situation. The input is the abnormality detection and emotion assessment results, and the output is the generated warning message.

[0177] Step 6:

[0178] Warning data is sent to the user's device and displayed as a push notification. The input is warning data from the server, and the output is an alert message displayed on the user's device. This information allows the user to take prompt action.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0181] Conventional autonomous vehicles lacked sufficient safety measures that took into account the occupants' biometric information and emotional state. This resulted in challenges in ensuring safe and comfortable travel for occupants, and making it difficult to respond quickly and appropriately in emergencies. Furthermore, there was a lack of means to reflect the internal state of the occupants, such as fatigue or stress, in the driving system.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0183] In this invention, the server includes means for detecting the presence of moving objects inside the vehicle using wireless signals, means for estimating the emotional state of the occupants and providing information corresponding to that emotional state, and means for adjusting the vehicle's automatic driving based on abnormalities. This ensures both the safety of the occupants and comfortable travel, and enables rapid response to abnormal conditions and adjustment of automatic driving.

[0184] "Moving objects" refers to the movement of occupants or objects detected within a vehicle.

[0185] "Wireless signals" refer to the means of communication used by detection devices within a vehicle, and are used to confirm the presence of moving objects.

[0186] "Biometric information" refers to data obtained from moving objects, including heart rate and body temperature.

[0187] "Emotional state" refers to the psychological state of the crew, and includes emotions such as stress and calmness.

[0188] "External devices" refer to equipment located outside the vehicle, and are devices used to receive warnings and information.

[0189] A "warning" is an alert sent when an anomaly occurs, intended to prompt a quick response.

[0190] "Autonomous driving" refers to technology that allows vehicles to move without driver intervention.

[0191] "Adjustment" means changing the settings and operation of the autonomous driving system based on the detected information.

[0192] This invention begins with a terminal installed inside a vehicle that uses wireless signals to detect the presence of moving objects and their biometric information. The terminal is equipped with various sensors and cameras and is responsible for collecting biometric information. For example, by acquiring information such as heart rate, body temperature, and facial expression, it is possible to evaluate the state of the moving object.

[0193] The server receives data transmitted from the terminal and performs analysis based on that information. Specifically, it uses Python®-based data analysis algorithms and cloud computing services such as AWS Lambda to estimate biometric information and emotional states. For emotion analysis, machine learning models such as TENSORFLOW® and PyTorch are used to evaluate the emotional state of the moving subjects. This process makes it possible to understand the stress levels and calmness levels of the occupants.

[0194] Based on abnormalities or emotions, the server issues warnings. These warnings provide the information necessary to adjust the vehicle's autonomous driving system. The warnings are quickly transmitted to the occupants' smartphones or in-vehicle information displays, allowing them to take direct action. For example, if fatigue from prolonged driving is detected, the server generates a message such as, "We suggest the next rest stop."

[0195] Thus, this invention can enhance the safety of autonomous vehicles and ensure comfortable travel for passengers based on their emotional state and biometric information.

[0196] Example of a prompt:

[0197] "Emotion analysis input: Passenger facial expressions indicate signs of stress. Provide suggestions for calming activities or recommend break locations."

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The terminal uses various sensors and cameras inside the vehicle to acquire biometric information of moving objects. This input includes heart rate, body temperature, and facial expression data. This data is transmitted to a server via wireless signals.

[0201] Step 2:

[0202] The server receives biometric information transmitted from the terminal. Based on the received data, it preprocesses the data using a Python-based data analysis algorithm to calculate the average value and standard deviation of heart rate and body temperature. This data processing makes it possible to detect anomalies.

[0203] Step 3:

[0204] The server inputs pre-processed data into a generating AI model, such as an emotion analysis model built with TensorFlow or PyTorch, to estimate the emotional state of the moving subject. This model determines stress levels and states of calmness from facial expression data. The analysis results are output as an emotional state.

[0205] Step 4:

[0206] The server evaluates for anomalies based on the estimated emotional state. If an anomaly is detected, it generates a warning appropriate to the emotional state and suggests the next action. This warning message is generated as a prompt and used in the next step.

[0207] Step 5:

[0208] The server sends the generated warnings to the user's smartphone or in-vehicle information display via the cloud. This allows the user to receive the warning message and take the suggested action. For example, a message such as "We suggest the next rest stop" might be displayed.

[0209] Step 6:

[0210] The user reviews the information received on the terminal or display device and performs the instructed actions as needed. This process includes operations performed through an interface.

[0211] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0212] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0213] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0217] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0218] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0219] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0221] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0222] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0223] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0224] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0225] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0226] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0227] The embodiments for carrying out the present invention will be described in detail. This system monitors movement within a vehicle and provides rapid warnings in the event of an abnormality, and consists of the following elements.

[0228] This system consists of a "terminal" installed inside the vehicle, a "server" located remotely, and an external device such as a smartphone acting as a "user" terminal. The terminal has the function of transmitting wireless signals and receiving the reflected waves to detect the presence of moving objects inside the vehicle. This signal data is transmitted to the server and analyzed.

[0229] The server analyzes signal data sent from the terminal to assess the presence of moving objects and evaluate biometric information. Based on the analysis results, the server distinguishes between normal and abnormal conditions, and generates a warning if an abnormality is detected. The analysis process includes a heart rate estimation function, which is used to determine if biometric information is outside the normal range.

[0230] When an anomaly is detected, the server sends a warning notification to multiple "user" terminals. This allows not only the vehicle owner but all relevant parties to respond quickly. This system can also be used in environments where monitoring and managing multiple people riding in a specific vehicle is necessary, such as to ensure the safety of children in childcare facilities or school buses.

[0231] As a concrete example, let's consider how this system could be implemented on a kindergarten bus. In this case, the terminal automatically activates when the bus departs and begins sending and receiving wireless signals. Monitoring continues even after the bus stops and the engine is turned off, and if a forgotten child is left inside the vehicle, a warning is promptly sent to the relevant personnel. This system makes it possible to quickly rescue children left behind in the vehicle and ensure their safety.

[0232] The invention minimizes privacy concerns because it does not use cameras, reduces costs by utilizing existing wireless technology, and allows for widespread implementation. This system is particularly effective in addressing the security needs of public spaces and individuals where privacy protection is crucial.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The terminal detects that the vehicle has stopped and the engine has been turned off. It begins transmitting a wireless signal and enters motion detection mode inside the vehicle.

[0236] Step 2:

[0237] The terminal receives reflected signals from within the vehicle and generates data from them. It periodically sends the received signal data to the server.

[0238] Step 3:

[0239] The server receives signal data transmitted from the terminal, extracts and analyzes information useful as indicators, and then runs a program to estimate biometric information such as heart rate to determine if a person is present.

[0240] Step 4:

[0241] Based on the analysis results, the server evaluates whether the presence of a moving object and its biometric information are within normal limits. If an anomaly is detected, the process proceeds to the next step.

[0242] Step 5:

[0243] If the server detects an anomaly, it collects pre-configured user contact information and prepares a warning notification.

[0244] Step 6:

[0245] The server sends warning notifications to multiple user terminals. The notifications contain detailed information about the anomaly and are designed to encourage prompt action.

[0246] Step 7:

[0247] The user receives a warning notification on a smartphone or other device and checks its contents. If necessary, they return to the vehicle and take appropriate action.

[0248] (Example 1)

[0249] Next, we will describe Example 1. 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."

[0250] Conventional vehicle monitoring systems often rely on cameras, which have drawbacks such as privacy concerns and high equipment costs. Furthermore, the lack of a mechanism for quickly notifying relevant parties in the event of an anomaly means that vehicles may be left unattended, potentially leading to delays in responding to emergencies. Additionally, there are problems with monitoring the individual situations of multiple passengers.

[0251] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0252] In this invention, the server includes means for detecting objects inside a vehicle using wireless signals and without using a camera, means for transmitting the signal data collected by the terminal to an external computing device and performing analysis processing, and means for evaluating the physiological information of the moving object based on the information obtained from the analysis processing and distinguishing between normal and abnormal conditions. This enables efficient and rapid anomaly detection and notification without infringing on privacy.

[0253] A "vehicle" is a device that enables the movement of objects, and primarily refers to vehicles that travel on land.

[0254] An "object" is something that is detected by a wireless signal, and whose existence is associated with its internal movement.

[0255] A "wireless signal" is a medium that uses radio waves to transmit information and is used to detect the presence of objects.

[0256] A "terminal" refers to a device installed inside a vehicle that has the function of transmitting and receiving wireless signals.

[0257] A "server" is a central processing unit that receives and analyzes data, and refers to equipment designed to efficiently perform a large number of calculations.

[0258] A "computational device" is a device used to process data and has the function of analyzing signal data.

[0259] "Physiological information" refers to information that indicates the state of the body, such as heart rate, and is an indicator used to evaluate health status and the presence or absence of abnormalities.

[0260] "Normal state" refers to a normal, healthy condition, and is the opposite of abnormal.

[0261] An "abnormality" refers to a state that deviates from the normal range and is an unusual condition that warrants attention.

[0262] "User device" refers to a terminal used to receive warnings, and is a device carried by the user.

[0263] A "warning" is a cautionary message issued in response to the detection of an anomaly, and it is information that should be promptly communicated to users.

[0264] This invention is a vehicle motion detection system using wireless signal technology, which monitors for anomalies while considering privacy protection. The system consists of a "terminal" installed in the vehicle, a "server" that analyzes and manages data, and a "user" device that receives warnings. The functions of each component are described in detail below.

[0265] The terminal is installed inside the vehicle and is responsible for transmitting and receiving wireless signals. Specifically, it uses radar technology to detect objects and people inside the vehicle. The signal data acquired by the terminal is programmed to be automatically transmitted to a server. At this time, the software built into the terminal processes the reflected wireless signal data in real time and converts it into a format that can be reported immediately.

[0266] The server plays a central role in analyzing signal data sent from terminals. The server is equipped with advanced analysis algorithms that continuously monitor dynamic movement and physiological data (such as heart rate). Based on these results, it quickly identifies normal and abnormal conditions. If the analysis indicates an abnormality, the server immediately generates a warning and sends it to multiple pre-registered user terminals.

[0267] Users typically receive alerts via smartphones or mobile devices. This ensures that notifications reach not only vehicle owners and managers, but also multiple stakeholders who require a quick response. The system has diverse applications and is particularly effective in situations involving multiple passengers, such as public transportation and child transport services.

[0268] A concrete example is a kindergarten bus. A terminal installed inside the bus activates when the engine starts and constantly monitors the situation inside the vehicle. If a child is left behind on the bus after the service has ended, the system can detect the anomaly and immediately send a warning to the relevant parties. This system enables a swift rescue and ensures safety.

[0269] By integrating with the generation AI model, an example of a prompt message is: "Please describe in detail the situations in which a warning regarding the vehicle's motion monitoring system needs to be issued. In particular, please explain the advantages when considering its operation in public transportation." In this way, establishing the operation of the entire system enables the provision of a safer and more efficient monitoring environment.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The terminal automatically activates in conjunction with the vehicle's engine start. It transmits a wireless signal into the vehicle and collects the reflected signal as received data. This input data includes information about the presence and movement of objects inside the vehicle. Specifically, the radar sensor scans for reflected signals from surrounding objects at a high frequency.

[0273] Step 2:

[0274] The terminal transmits the collected signal data to the server in real time. This output data is used as raw data necessary for the analysis of physiological information. Specifically, the communication module sends the data to the server via a wireless network.

[0275] Step 3:

[0276] The server receives signal data transmitted from the terminal. It processes the input data and analyzes physiological information related to the presence of moving objects and heart rate. This analysis is performed using data filtering, signal intensity measurement, and pattern recognition techniques. As output, an index is generated to determine whether the data is normal or abnormal.

[0277] Step 4:

[0278] The server determines whether the situation is normal or abnormal based on the analysis results. If an abnormality is detected, a warning is generated. In this step, the analysis algorithm applies an anomaly detection rule set and interprets the data. This output is organized into a message that includes specific warning details.

[0279] Step 5:

[0280] The server sends the generated warnings to multiple user terminals. The input requires information about the recipient user terminals. The output is a warning notification received by the user on a device such as a smartphone. Specific operations include rapid data distribution using network protocols.

[0281] Step 6:

[0282] The user reviews the received warning notification. The input is the warning message displayed on the device. The output is the action the user should take based on that information, such as contacting the site or proceeding to direct verification. A specific action is the process of tapping the notification to open the details.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0285] In modern management facilities and apartment buildings, it is required to ensure the safety of residents and users while protecting privacy. However, existing surveillance devices, such as camera systems, are difficult to introduce from the perspective of privacy, and there are also problems in that it is difficult to respond quickly in case of an abnormality. In particular, in situations where it is difficult to rely on human vision, such as at night, higher-level moving object detection and rapid abnormality notification are required.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0287] In this invention, the server includes means for detecting the presence of a moving object inside the vehicle using an electromagnetic wave signal, means for analyzing the information of the detected moving object and evaluating the biometric information of the moving object, means for identifying an abnormality based on the biometric information and transmitting a notification to an external terminal when an abnormality exists, means for monitoring the movement of a crowd in a management facility or an apartment building based on the detection result, and means for immediately notifying a warning to the mobile terminal of a relevant person when an abnormality is detected in the movement of the crowd. As a result, it is possible to take prompt and reliable safety measures in case of an abnormality while protecting privacy.

[0288] "Inside the vehicle" refers to the internal space of a structure used as a means of transportation and is an area for transporting people and objects.

[0289] "Moving object" refers to a physical entity that moves or changes without staying still, and mainly includes humans, animals, etc.

[0290] [[ID=ID=26]]"Electromagnetic wave signal" refers to a signal that uses a part of the electromagnetic spectrum used for wireless communication and is applied to data communication and object detection.

[0291] "Means of detection" refers to methods and techniques for confirming the existence or state of an object, and includes sensors and analytical algorithms.

[0292] "Biological information" refers to data related to living organisms, such as heart rate, temperature, and movement, and is information that indicates the health and activity level of an individual.

[0293] An "external device" refers to an external device connected to the system that is an electronic device capable of receiving notifications and being operated.

[0294] "Means of sending notifications" refers to means of informing relevant parties of anomalies or important information, and includes alert systems that utilize communication technology.

[0295] "Crowd movement" refers to the movement of a large number of people or objects from one place to another, demonstrating the dynamic behavior of a collective.

[0296] "Privacy" refers to the right of an individual to have their private life and information protected from infringement by others, and it is an ethical concept that is considered important when introducing surveillance technology.

[0297] This system is a motion monitoring device designed to improve safety in management facilities and apartment buildings. The server uses wireless signal transceivers installed inside vehicles or facilities. Terminals continuously transmit electromagnetic signals, and by receiving the reflected signals, the system detects the presence of moving objects, including people. The detected data is sent to the server, where detailed biological information about the moving objects is analyzed.

[0298] The server analyzes this data, evaluates biometric information such as heart rate, and identifies whether an abnormality has occurred. If an abnormality is detected, the server immediately notifies external devices, including smartphones and tablets.

[0299] As a specific example, assume using a fitness gym inside an apartment at night. Inside this gym, the terminal emits electromagnetic wave signals at regular intervals to monitor people's movements and situations. When an abnormal movement is detected, the server can send a notification immediately to the external terminals of the management staff or security guards, enabling a prompt response.

[0300] This system realizes real-time data analysis by using cloud services such as AWS Lambda and Google Cloud Functions. Furthermore, to protect privacy, a configuration that does not use cameras is adopted.

[0301] Example of a prompt sentence for the generative AI model:

[0302] "Please consider the scenario of a moving object monitoring system in a fitness gym. Specifically explain how the monitoring system inside the facility detects abnormalities and notifies the relevant parties."

[0303] With this form, it is possible to achieve both privacy protection and safety measures, and appropriate environmental monitoring can be carried out.

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The terminal uses a wireless signal transceiver installed inside the vehicle or facility to emit electromagnetic wave signals at regular intervals and receive the reflected waves. The input is the transmitted wireless signal, and the output is the reflected wave data. Based on this data, basic signal processing is performed to detect the presence of moving objects.

[0307] Step 2:

[0308] The server receives reflected wave data transmitted from the terminal. The input is reflected wave data from the terminal, and the output is information about a moving object. The server analyzes this data and performs data processing such as Fast Fourier Transform (FFT) to measure the distance and speed of the moving object.

[0309] Step 3:

[0310] The server estimates biological information from analyzed moving object information. The input is the analyzed object information, and the output is biological information such as heart rate. Here, biological data is extracted using an algorithm that associates specific movement patterns with heart rate.

[0311] Step 4:

[0312] The server identifies abnormalities based on the obtained biological information. The input is estimated biological information, and the output is the judgment result of whether it is normal or abnormal. If an abnormal value is detected, a corresponding flag is set, and it is classified as abnormal.

[0313] Step 5:

[0314] The server sends a notification to an external device when an anomaly is detected. The input is the result of the anomaly detection, and the output is a warning notification. The server sends the warning to the configured notification destination (smartphone or tablet device) via the network, prompting the user to take prompt action.

[0315] Step 6:

[0316] The user receives a warning notification on an external terminal and, if necessary, checks the site or takes corrective action. The input is the warning notification from the server, and the output is the user's response action. This allows the user to respond quickly to abnormal situations and take necessary actions.

[0317] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0318] This invention is a system that detects the presence of moving objects inside a vehicle, their biometric information, and their emotional state, and transmits warnings to an external device as needed. This makes it possible to improve safety while facilitating emergency response.

[0319] This system primarily consists of three elements: a terminal, a server, and a user terminal. The terminal is installed inside the vehicle and uses wireless signals to detect the presence of moving objects and their biometric information. This information is then transmitted to the server.

[0320] The server analyzes biometric information based on the received data and estimates data such as heart rate. The server also incorporates an emotion engine that evaluates the emotional state of the subject through biometric information and movement pattern analysis. Emotional state refers to the psychological state of the subject, such as whether it is stressed or calm.

[0321] If an anomaly is detected, the server considers the evaluation results of the emotion engine and generates a warning that enables a more appropriate and rapid response. The warning is adjusted according to the emotion of the moving object and sent to the user's terminal. This allows not only the user but also multiple stakeholders, as needed, to quickly share information and create a system that enables appropriate responses.

[0322] As a concrete example, consider a case where a child is left behind in a vehicle that has been parked for a long time. The engine is turned off, and the terminal begins transmitting wireless signals. The server analyzes the data from the terminal and confirms that the child is still inside the vehicle. At the same time, the emotion engine analyzes changes in biosignals and behavioral patterns and determines that the child's emotions are unstable. Based on this, the server quickly sends warnings to multiple user terminals urging them to change to a calmer living environment, thereby helping to ensure the child's safety. In this way, the incorporation of the emotion engine enables more appropriate and effective warning notifications, improving the accuracy and speed of responses in emergency situations.

[0323] The following describes the processing flow.

[0324] Step 1:

[0325] The terminal detects that the vehicle's engine has been turned off. It then switches to a mode that monitors for movement inside the vehicle using a wireless signal.

[0326] Step 2:

[0327] The terminal receives reflected wireless signals and measures the presence of moving objects and related biometric information (e.g., heart rate). This data is then transmitted to the server.

[0328] Step 3:

[0329] The server analyzes the signal data received from the terminal. Based on the biometric information, it evaluates whether the moving body is in a normal state and estimates the heart rate.

[0330] Step 4:

[0331] The server uses an emotion engine to estimate the emotional state of a moving object from its biometric information and other data. This allows it to determine what emotional state the object is in, such as tension, anxiety, or relaxation.

[0332] Step 5:

[0333] Based on an assessment of biometric information and emotional state, the server will begin preparing a warning notification if it determines that there is an abnormality in the moving objects inside the vehicle.

[0334] Step 6:

[0335] Based on the results from the emotion engine, the server generates warnings tailored to the detected emotional state. For example, it adjusts the notification content, prompting a quick response if the user is stressed, or issuing a normal alert if they are relaxed.

[0336] Step 7:

[0337] The server sends pre-configured warning notifications to multiple user terminals, allowing relevant parties to quickly take appropriate action.

[0338] Step 8:

[0339] The user receives a warning notification on their device and checks its contents. Depending on the situation, they can either go to the vehicle or contact relevant parties to confirm the situation and take appropriate action.

[0340] (Example 2)

[0341] Next, we will describe Example 2. 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".

[0342] There is a need to provide a system that accurately detects the presence of moving objects, biometric information, and emotional states within a vehicle, and sends rapid and accurate warnings to enhance safety. However, conventional systems are insufficient in detecting anomalies and generating warnings, making it difficult to respond appropriately in emergencies. This necessitates a solution to the problem of situations that could potentially threaten safety.

[0343] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0344] In this invention, the server includes means for detecting the presence of a moving object using wireless data, means for analyzing the data of the detected moving object and evaluating its biometric data, means including an artificial intelligence engine that determines the emotional state based on the biometric data, and means for generating detailed warning data and transmitting it to an external computing device when an abnormality is detected. This enables improved safety by accurately monitoring the situation inside the vehicle and allowing for prompt responses as needed.

[0345] "Moving object" refers to objects that may be moving, such as people or animals, that are detected inside a vehicle.

[0346] "Wireless data" refers to data acquired via wireless communication technology used to exchange information between a terminal and a server.

[0347] "Biometric data" refers to physiological data obtained from moving objects, such as heart rate and body temperature.

[0348] "Emotional state" refers to the psychological and emotional state determined by artificial intelligence based on biometric data.

[0349] An "artificial intelligence engine" is a computational means used to analyze biometric data and movement patterns to determine the emotional state of a moving object.

[0350] An "external computing device" is an electronic device, such as a smartphone or tablet, owned by the user that can receive and display warning data.

[0351] "Warning data" refers to notification information that is generated and sent to an external computing device when an anomaly is detected.

[0352] This invention provides a system that improves safety by detecting moving objects within a vehicle and evaluating their biometric data and emotional state. The system mainly consists of three elements: a terminal, a server, and a user terminal.

[0353] The terminal is installed inside the vehicle and detects the presence of moving objects using wireless data. This terminal has the function of collecting biometric data such as heart rate and body temperature using ultrasonic sensors and infrared sensors. The collected data is transmitted to a server via Bluetooth or Wi-Fi.

[0354] The server analyzes the received data. This analysis utilizes a generative AI model based on Python machine learning algorithms. The server then activates an artificial intelligence engine to detect abnormalities in heart rate and body temperature based on biometric data, and further determine emotional states. This engine has the ability to analyze behavioral patterns and time-series data to quantify stress levels and psychological stability.

[0355] If an anomaly is detected, the server promptly generates warning data. This generated warning data is sent to the user's terminal and displayed as a push notification. User terminals are mainly smartphones and tablets, which function as external computing devices.

[0356] As a concrete example, consider the case of a toddler left in a vehicle for an extended period. The device analyzes the biometric data of the moving child and detects abnormalities by sensing unsafe heart rate fluctuations or elevated body temperature. Simultaneously, it assesses the child's emotional state, and if it determines the child is in a state of tension, it immediately warns the user's device, enabling rapid intervention.

[0357] An example of a prompt to the generating AI model is: "Please tell me how to analyze the heart rate and movement patterns of a person trapped inside a vehicle and issue a warning if their emotional state is unstable." By using this system, it is possible to enhance safety inside vehicles and support a quick and appropriate response in emergencies.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] The terminal detects the presence of moving objects from the vehicle's environment using wireless data. During this process, ultrasonic and infrared sensors collect location information and movement patterns of the moving objects. The input is data from sensors within the vehicle, and the output is data indicating the presence and initial position of the moving object.

[0361] Step 2:

[0362] The device continuously collects biometric data such as heart rate and body temperature from moving objects. The input is sensor measurement data in the state of the moving object. This data is transmitted to a server via Bluetooth or Wi-Fi, and the output is biometric data that is transferred to the server.

[0363] Step 3:

[0364] After receiving biometric data, the server analyzes the data using a generative AI model. During this process, it detects abnormalities in heart rate and body temperature based on machine learning algorithms implemented in Python. The input is biometric data transmitted from the terminal, and the output is alert data indicating the detected abnormality in the biometric information.

[0365] Step 4:

[0366] The server then activates an artificial intelligence engine to determine the emotional state. This engine analyzes the time-series changes in biometric data and quantifies the stress and stability of the body. The input is the data analyzed in the previous step, and the output is an evaluation result indicating the emotional state.

[0367] Step 5:

[0368] If an abnormal or stressful condition is detected, the server generates appropriate warning data. This includes specific instructions and actions tailored to the individual situation. The input is the abnormality detection and emotion assessment results, and the output is the generated warning message.

[0369] Step 6:

[0370] Warning data is sent to the user's device and displayed as a push notification. The input is warning data from the server, and the output is an alert message displayed on the user's device. This information allows the user to take prompt action.

[0371] (Application Example 2)

[0372] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0373] Conventional autonomous vehicles lacked sufficient safety measures that took into account the occupants' biometric information and emotional state. This resulted in challenges in ensuring safe and comfortable travel for occupants, and making it difficult to respond quickly and appropriately in emergencies. Furthermore, there was a lack of means to reflect the internal state of the occupants, such as fatigue or stress, in the driving system.

[0374] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0375] In this invention, the server includes means for detecting the presence of moving objects inside the vehicle using wireless signals, means for estimating the emotional state of the occupants and providing information corresponding to that emotional state, and means for adjusting the vehicle's automatic driving based on abnormalities. This ensures both the safety of the occupants and comfortable travel, and enables rapid response to abnormal conditions and adjustment of automatic driving.

[0376] "Moving objects" refers to the movement of occupants or objects detected within a vehicle.

[0377] "Wireless signals" refer to the means of communication used by detection devices within a vehicle, and are used to confirm the presence of moving objects.

[0378] "Biometric information" refers to data obtained from moving objects, including heart rate and body temperature.

[0379] "Emotional state" refers to the psychological state of the crew, and includes emotions such as stress and calmness.

[0380] "External devices" refer to equipment located outside the vehicle, and are devices used to receive warnings and information.

[0381] A "warning" is an alert sent when an anomaly occurs, intended to prompt a quick response.

[0382] "Autonomous driving" refers to technology that allows vehicles to move without driver intervention.

[0383] "Adjustment" means changing the settings and operation of the autonomous driving system based on the detected information.

[0384] This invention begins with a terminal installed inside a vehicle that uses wireless signals to detect the presence of moving objects and their biometric information. The terminal is equipped with various sensors and cameras and is responsible for collecting biometric information. For example, by acquiring information such as heart rate, body temperature, and facial expression, it is possible to evaluate the state of the moving object.

[0385] The server receives data transmitted from the terminal and performs analysis based on that information. Specifically, it uses Python-based data analysis algorithms and cloud computing services such as AWS Lambda to estimate biometric information and emotional states. For emotion analysis, machine learning models such as TensorFlow and PyTorch are used to evaluate the emotional state of the moving subjects. This process makes it possible to understand the stress levels and calmness levels of the occupants.

[0386] Based on abnormalities or emotions, the server issues warnings. These warnings provide the information necessary to adjust the vehicle's autonomous driving system. The warnings are quickly transmitted to the occupants' smartphones or in-vehicle information displays, allowing them to take direct action. For example, if fatigue from prolonged driving is detected, the server generates a message such as, "We suggest the next rest stop."

[0387] Thus, this invention can enhance the safety of autonomous vehicles and ensure comfortable travel for passengers based on their emotional state and biometric information.

[0388] Example of a prompt:

[0389] "Emotion analysis input: Passenger facial expressions indicate signs of stress. Provide suggestions for calming activities or recommend break locations."

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The terminal uses various sensors and cameras inside the vehicle to acquire biometric information of moving objects. This input includes heart rate, body temperature, and facial expression data. This data is transmitted to a server via wireless signals.

[0393] Step 2:

[0394] The server receives biometric information transmitted from the terminal. Based on the received data, it preprocesses the data using a Python-based data analysis algorithm to calculate the average value and standard deviation of heart rate and body temperature. This data processing makes it possible to detect anomalies.

[0395] Step 3:

[0396] The server inputs pre-processed data into a generating AI model, such as an emotion analysis model built with TensorFlow or PyTorch, to estimate the emotional state of the moving subject. This model determines stress levels and states of calmness from facial expression data. The analysis results are output as an emotional state.

[0397] Step 4:

[0398] The server evaluates for anomalies based on the estimated emotional state. If an anomaly is detected, it generates a warning appropriate to the emotional state and suggests the next action. This warning message is generated as a prompt and used in the next step.

[0399] Step 5:

[0400] The server sends the generated warnings to the user's smartphone or in-vehicle information display via the cloud. This allows the user to receive the warning message and take the suggested action. For example, a message such as "We suggest the next rest stop" might be displayed.

[0401] Step 6:

[0402] The user reviews the information received on the terminal or display device and performs the instructed actions as needed. This process includes operations performed through an interface.

[0403] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0404] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0405] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0409] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0410] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0411] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0412] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0413] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0414] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0415] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0416] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0417] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0418] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0419] The embodiments for carrying out the present invention will be described in detail. This system monitors movement within a vehicle and provides rapid warnings in the event of an abnormality, and consists of the following elements.

[0420] This system consists of a "terminal" installed inside the vehicle, a "server" located remotely, and an external device such as a smartphone acting as a "user" terminal. The terminal has the function of transmitting wireless signals and receiving the reflected waves to detect the presence of moving objects inside the vehicle. This signal data is transmitted to the server and analyzed.

[0421] The server analyzes signal data sent from the terminal to assess the presence of moving objects and evaluate biometric information. Based on the analysis results, the server distinguishes between normal and abnormal conditions, and generates a warning if an abnormality is detected. The analysis process includes a heart rate estimation function, which is used to determine if biometric information is outside the normal range.

[0422] When an anomaly is detected, the server sends a warning notification to multiple "user" terminals. This allows not only the vehicle owner but all relevant parties to respond quickly. This system can also be used in environments where monitoring and managing multiple people riding in a specific vehicle is necessary, such as to ensure the safety of children in childcare facilities or school buses.

[0423] As a concrete example, let's consider how this system could be implemented on a kindergarten bus. In this case, the terminal automatically activates when the bus departs and begins sending and receiving wireless signals. Monitoring continues even after the bus stops and the engine is turned off, and if a forgotten child is left inside the vehicle, a warning is promptly sent to the relevant personnel. This system makes it possible to quickly rescue children left behind in the vehicle and ensure their safety.

[0424] The invention minimizes privacy concerns because it does not use cameras, reduces costs by utilizing existing wireless technology, and allows for widespread implementation. This system is particularly effective in addressing the security needs of public spaces and individuals where privacy protection is crucial.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] The terminal detects that the vehicle has stopped and the engine has been turned off. It begins transmitting a wireless signal and enters motion detection mode inside the vehicle.

[0428] Step 2:

[0429] The terminal receives reflected signals from within the vehicle and generates data from them. It periodically sends the received signal data to the server.

[0430] Step 3:

[0431] The server receives signal data transmitted from the terminal, extracts and analyzes information useful as indicators, and then runs a program to estimate biometric information such as heart rate to determine if a person is present.

[0432] Step 4:

[0433] Based on the analysis results, the server evaluates whether the presence of a moving object and its biometric information are within normal limits. If an anomaly is detected, the process proceeds to the next step.

[0434] Step 5:

[0435] If the server detects an anomaly, it collects pre-configured user contact information and prepares a warning notification.

[0436] Step 6:

[0437] The server sends warning notifications to multiple user terminals. The notifications contain detailed information about the anomaly and are designed to encourage prompt action.

[0438] Step 7:

[0439] The user receives a warning notification on a smartphone or other device and checks its contents. If necessary, they return to the vehicle and take appropriate action.

[0440] (Example 1)

[0441] Next, we will describe Example 1. 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."

[0442] Conventional vehicle monitoring systems often rely on cameras, which have drawbacks such as privacy concerns and high equipment costs. Furthermore, the lack of a mechanism for quickly notifying relevant parties in the event of an anomaly means that vehicles may be left unattended, potentially leading to delays in responding to emergencies. Additionally, there are problems with monitoring the individual situations of multiple passengers.

[0443] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0444] In this invention, the server includes means for detecting objects inside a vehicle using wireless signals and without using a camera, means for transmitting the signal data collected by the terminal to an external computing device and performing analysis processing, and means for evaluating the physiological information of the moving object based on the information obtained from the analysis processing and distinguishing between normal and abnormal conditions. This enables efficient and rapid anomaly detection and notification without infringing on privacy.

[0445] A "vehicle" is a device that enables the movement of objects, and primarily refers to vehicles that travel on land.

[0446] An "object" is something that is detected by a wireless signal, and whose existence is associated with its internal movement.

[0447] A "wireless signal" is a medium that uses radio waves to transmit information and is used to detect the presence of objects.

[0448] A "terminal" refers to a device installed inside a vehicle that has the function of transmitting and receiving wireless signals.

[0449] A "server" is a central processing unit that receives and analyzes data, and refers to equipment designed to efficiently perform a large number of calculations.

[0450] A "computational device" is a device used to process data and has the function of analyzing signal data.

[0451] "Physiological information" refers to information that indicates the state of the body, such as heart rate, and is an indicator used to evaluate health status and the presence or absence of abnormalities.

[0452] "Normal state" refers to a normal, healthy condition, and is the opposite of abnormal.

[0453] An "abnormality" refers to a state that deviates from the normal range and is an unusual condition that warrants attention.

[0454] "User device" refers to a terminal used to receive warnings, and is a device carried by the user.

[0455] A "warning" is a cautionary message issued in response to the detection of an anomaly, and it is information that should be promptly communicated to users.

[0456] This invention is a vehicle motion detection system using wireless signal technology, which monitors for anomalies while considering privacy protection. The system consists of a "terminal" installed in the vehicle, a "server" that analyzes and manages data, and a "user" device that receives warnings. The functions of each component are described in detail below.

[0457] The terminal is installed inside the vehicle and is responsible for transmitting and receiving wireless signals. Specifically, it uses radar technology to detect objects and people inside the vehicle. The signal data acquired by the terminal is programmed to be automatically transmitted to a server. At this time, the software built into the terminal processes the reflected wireless signal data in real time and converts it into a format that can be reported immediately.

[0458] The server plays a central role in analyzing signal data sent from terminals. The server is equipped with advanced analysis algorithms that continuously monitor dynamic movement and physiological data (such as heart rate). Based on these results, it quickly identifies normal and abnormal conditions. If the analysis indicates an abnormality, the server immediately generates a warning and sends it to multiple pre-registered user terminals.

[0459] Users typically receive alerts via smartphones or mobile devices. This ensures that notifications reach not only vehicle owners and managers, but also multiple stakeholders who require a quick response. The system has diverse applications and is particularly effective in situations involving multiple passengers, such as public transportation and child transport services.

[0460] A concrete example is a kindergarten bus. A terminal installed inside the bus activates when the engine starts and constantly monitors the situation inside the vehicle. If a child is left behind on the bus after the service has ended, the system can detect the anomaly and immediately send a warning to the relevant parties. This system enables a swift rescue and ensures safety.

[0461] By integrating with the generation AI model, an example of a prompt message is: "Please describe in detail the situations in which a warning regarding the vehicle's motion monitoring system needs to be issued. In particular, please explain the advantages when considering its operation in public transportation." In this way, establishing the operation of the entire system enables the provision of a safer and more efficient monitoring environment.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] The terminal automatically activates in conjunction with the vehicle's engine start. It transmits a wireless signal into the vehicle and collects the reflected signal as received data. This input data includes information about the presence and movement of objects inside the vehicle. Specifically, the radar sensor scans for reflected signals from surrounding objects at a high frequency.

[0465] Step 2:

[0466] The terminal transmits the collected signal data to the server in real time. This output data is used as raw data necessary for the analysis of physiological information. Specifically, the communication module sends the data to the server via a wireless network.

[0467] Step 3:

[0468] The server receives signal data transmitted from the terminal. It processes the input data and analyzes physiological information related to the presence of moving objects and heart rate. This analysis is performed using data filtering, signal intensity measurement, and pattern recognition techniques. As output, an index is generated to determine whether the data is normal or abnormal.

[0469] Step 4:

[0470] The server determines whether the situation is normal or abnormal based on the analysis results. If an abnormality is detected, a warning is generated. In this step, the analysis algorithm applies an anomaly detection rule set and interprets the data. This output is organized into a message that includes specific warning details.

[0471] Step 5:

[0472] The server sends the generated warnings to multiple user terminals. The input requires information about the recipient user terminals. The output is a warning notification received by the user on a device such as a smartphone. Specific operations include rapid data distribution using network protocols.

[0473] Step 6:

[0474] The user reviews the received warning notification. The input is the warning message displayed on the device. The output is the action the user should take based on that information, such as contacting the site or proceeding to direct verification. A specific action is the process of tapping the notification to open the details.

[0475] (Application Example 1)

[0476] Next, we will explain Application Example 1. In the following explanation, 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."

[0477] Modern management facilities and apartment buildings require the protection of privacy while ensuring the safety of residents and users. However, existing surveillance systems, such as camera systems, are difficult to implement from a privacy perspective and have challenges in responding quickly to anomalies. In particular, at night or in situations where human vision is unreliable, there is a need for more advanced motion detection and rapid anomaly notification.

[0478] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0479] This invention includes a server that includes means for detecting the presence of moving objects inside a vehicle using electromagnetic wave signals, means for analyzing information on the detected moving objects and evaluating the biological information of those moving objects, means for identifying anomalies based on the biological information and sending notifications to external terminals if an anomaly exists, means for monitoring crowd movement in management facilities and apartment buildings based on the detection results, and means for immediately notifying the mobile terminals of relevant parties when an anomaly is detected in the movement of the crowd. This enables rapid and reliable safety measures in the event of an anomaly while protecting privacy.

[0480] "Vehicle interior" refers to the internal space of a structure used as a means of transportation, and is the area used to transport people and goods.

[0481] A "moving object" refers to a physical entity that is not stationary but is constantly moving or changing, and primarily includes humans and animals.

[0482] "Electromagnetic wave signals" are signals that utilize a portion of the electromagnetic spectrum used in wireless communication, and are applied to data communication and object detection.

[0483] "Means of detection" refers to methods and techniques for confirming the existence or state of an object, and includes sensors and analytical algorithms.

[0484] "Biological information" refers to data related to living organisms, such as heart rate, temperature, and movement, and is information that indicates the health and activity level of an individual.

[0485] An "external device" refers to an external device connected to the system that is an electronic device capable of receiving notifications and being operated.

[0486] "Means of sending notifications" refers to means of informing relevant parties of anomalies or important information, and includes alert systems that utilize communication technology.

[0487] "Crowd movement" refers to the movement of a large number of people or objects from one place to another, demonstrating the dynamic behavior of a collective.

[0488] "Privacy" refers to the right of an individual to have their private life and information protected from infringement by others, and it is an ethical concept that is considered important when introducing surveillance technology.

[0489] This system is a motion monitoring device designed to improve safety in management facilities and apartment buildings. The server uses wireless signal transceivers installed inside vehicles or facilities. Terminals continuously transmit electromagnetic signals, and by receiving the reflected signals, the system detects the presence of moving objects, including people. The detected data is sent to the server, where detailed biological information about the moving objects is analyzed.

[0490] The server analyzes this data, evaluates biometric information such as heart rate, and identifies whether an abnormality has occurred. If an abnormality is detected, the server immediately notifies external devices, including smartphones and tablets.

[0491] As a concrete example, let's consider its use in a fitness gym within an apartment building at night. Within this gym, the terminal emits electromagnetic signals at regular intervals to monitor people's movements and conditions. If abnormal movement is detected, the server immediately sends a notification to external terminals of management staff or security guards, enabling a rapid response.

[0492] This system utilizes cloud services such as AWS Lambda and Google Cloud Functions to enable real-time data analysis. Furthermore, to protect privacy, it employs a configuration that does not use cameras.

[0493] Examples of prompts for a generative AI model:

[0494] "Please consider a scenario for a motion monitoring system in a fitness gym. Explain specifically how the monitoring system within the facility detects anomalies and notifies relevant personnel."

[0495] This configuration allows for both privacy protection and security measures to be achieved, and enables appropriate environmental monitoring.

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The terminal uses a wireless signal transceiver installed inside the vehicle or facility to transmit electromagnetic signals at regular intervals and receive the reflected waves. The input is the transmitted wireless signal, and the output is the reflected wave data. Based on this data, basic signal processing is performed to detect the presence of moving objects.

[0499] Step 2:

[0500] The server receives reflected wave data transmitted from the terminal. The input is reflected wave data from the terminal, and the output is information about a moving object. The server analyzes this data and performs data processing such as Fast Fourier Transform (FFT) to measure the distance and speed of the moving object.

[0501] Step 3:

[0502] The server estimates biological information from analyzed moving object information. The input is the analyzed object information, and the output is biological information such as heart rate. Here, biological data is extracted using an algorithm that associates specific movement patterns with heart rate.

[0503] Step 4:

[0504] The server identifies abnormalities based on the obtained biological information. The input is estimated biological information, and the output is the judgment result of whether it is normal or abnormal. If an abnormal value is detected, a corresponding flag is set, and it is classified as abnormal.

[0505] Step 5:

[0506] The server sends a notification to an external device when an anomaly is detected. The input is the result of the anomaly detection, and the output is a warning notification. The server sends the warning to the configured notification destination (smartphone or tablet device) via the network, prompting the user to take prompt action.

[0507] Step 6:

[0508] The user receives a warning notification on an external terminal and, if necessary, checks the site or takes corrective action. The input is the warning notification from the server, and the output is the user's response action. This allows the user to respond quickly to abnormal situations and take necessary actions.

[0509] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0510] This invention is a system that detects the presence of moving objects inside a vehicle, their biometric information, and their emotional state, and transmits warnings to an external device as needed. This makes it possible to improve safety while facilitating emergency response.

[0511] This system primarily consists of three elements: a terminal, a server, and a user terminal. The terminal is installed inside the vehicle and uses wireless signals to detect the presence of moving objects and their biometric information. This information is then transmitted to the server.

[0512] The server analyzes biometric information based on the received data and estimates data such as heart rate. The server also incorporates an emotion engine that evaluates the emotional state of the subject through biometric information and movement pattern analysis. Emotional state refers to the psychological state of the subject, such as whether it is stressed or calm.

[0513] If an anomaly is detected, the server considers the evaluation results of the emotion engine and generates a warning that enables a more appropriate and rapid response. The warning is adjusted according to the emotion of the moving object and sent to the user's terminal. This allows not only the user but also multiple stakeholders, as needed, to quickly share information and create a system that enables appropriate responses.

[0514] As a concrete example, consider a case where a child is left behind in a vehicle that has been parked for a long time. The engine is turned off, and the terminal begins transmitting wireless signals. The server analyzes the data from the terminal and confirms that the child is still inside the vehicle. At the same time, the emotion engine analyzes changes in biosignals and behavioral patterns and determines that the child's emotions are unstable. Based on this, the server quickly sends warnings to multiple user terminals urging them to change to a calmer living environment, thereby helping to ensure the child's safety. In this way, the incorporation of the emotion engine enables more appropriate and effective warning notifications, improving the accuracy and speed of responses in emergency situations.

[0515] The following describes the processing flow.

[0516] Step 1:

[0517] The terminal detects that the vehicle's engine has been turned off. It then switches to a mode that monitors for movement inside the vehicle using a wireless signal.

[0518] Step 2:

[0519] The terminal receives reflected wireless signals and measures the presence of moving objects and related biometric information (e.g., heart rate). This data is then transmitted to the server.

[0520] Step 3:

[0521] The server analyzes the signal data received from the terminal. Based on the biometric information, it evaluates whether the moving body is in a normal state and estimates the heart rate.

[0522] Step 4:

[0523] The server uses an emotion engine to estimate the emotional state of a moving object from its biometric information and other data. This allows it to determine what emotional state the object is in, such as tension, anxiety, or relaxation.

[0524] Step 5:

[0525] Based on an assessment of biometric information and emotional state, the server will begin preparing a warning notification if it determines that there is an abnormality in the moving objects inside the vehicle.

[0526] Step 6:

[0527] Based on the results from the emotion engine, the server generates warnings tailored to the detected emotional state. For example, it adjusts the notification content, prompting a quick response if the user is stressed, or issuing a normal alert if they are relaxed.

[0528] Step 7:

[0529] The server sends pre-configured warning notifications to multiple user terminals, allowing relevant parties to quickly take appropriate action.

[0530] Step 8:

[0531] The user receives a warning notification on their device and checks its contents. Depending on the situation, they can either go to the vehicle or contact relevant parties to confirm the situation and take appropriate action.

[0532] (Example 2)

[0533] Next, we will describe Example 2. 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."

[0534] There is a need to provide a system that accurately detects the presence of moving objects, biometric information, and emotional states within a vehicle, and sends rapid and accurate warnings to enhance safety. However, conventional systems are insufficient in detecting anomalies and generating warnings, making it difficult to respond appropriately in emergencies. This necessitates a solution to the problem of situations that could potentially threaten safety.

[0535] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0536] In this invention, the server includes means for detecting the presence of a moving object using wireless data, means for analyzing the data of the detected moving object and evaluating its biometric data, means including an artificial intelligence engine that determines the emotional state based on the biometric data, and means for generating detailed warning data and transmitting it to an external computing device when an abnormality is detected. This enables improved safety by accurately monitoring the situation inside the vehicle and allowing for prompt responses as needed.

[0537] "Moving object" refers to objects that may be moving, such as people or animals, that are detected inside a vehicle.

[0538] "Wireless data" refers to data acquired via wireless communication technology used to exchange information between a terminal and a server.

[0539] "Biometric data" refers to physiological data obtained from moving objects, such as heart rate and body temperature.

[0540] "Emotional state" refers to the psychological and emotional state determined by artificial intelligence based on biometric data.

[0541] An "artificial intelligence engine" is a computational means used to analyze biometric data and movement patterns to determine the emotional state of a moving object.

[0542] An "external computing device" is an electronic device, such as a smartphone or tablet, owned by the user that can receive and display warning data.

[0543] "Warning data" refers to notification information that is generated and sent to an external computing device when an anomaly is detected.

[0544] This invention provides a system that improves safety by detecting moving objects within a vehicle and evaluating their biometric data and emotional state. The system mainly consists of three elements: a terminal, a server, and a user terminal.

[0545] The terminal is installed inside the vehicle and detects the presence of moving objects using wireless data. This terminal has the function of collecting biometric data such as heart rate and body temperature using ultrasonic sensors and infrared sensors. The collected data is transmitted to a server via Bluetooth or Wi-Fi.

[0546] The server analyzes the received data. This analysis utilizes a generative AI model based on Python machine learning algorithms. The server then activates an artificial intelligence engine to detect abnormalities in heart rate and body temperature based on biometric data, and further determine emotional states. This engine has the ability to analyze behavioral patterns and time-series data to quantify stress levels and psychological stability.

[0547] If an anomaly is detected, the server promptly generates warning data. This generated warning data is sent to the user's terminal and displayed as a push notification. User terminals are mainly smartphones and tablets, which function as external computing devices.

[0548] As a concrete example, consider the case of a toddler left in a vehicle for an extended period. The device analyzes the biometric data of the moving child and detects abnormalities by sensing unsafe heart rate fluctuations or elevated body temperature. Simultaneously, it assesses the child's emotional state, and if it determines the child is in a state of tension, it immediately warns the user's device, enabling rapid intervention.

[0549] An example of a prompt to the generating AI model is: "Please tell me how to analyze the heart rate and movement patterns of a person trapped inside a vehicle and issue a warning if their emotional state is unstable." By using this system, it is possible to enhance safety inside vehicles and support a quick and appropriate response in emergencies.

[0550] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0551] Step 1:

[0552] The terminal detects the presence of moving objects from the vehicle's environment using wireless data. During this process, ultrasonic and infrared sensors collect location information and movement patterns of the moving objects. The input is data from sensors within the vehicle, and the output is data indicating the presence and initial position of the moving object.

[0553] Step 2:

[0554] The device continuously collects biometric data such as heart rate and body temperature from moving objects. The input is sensor measurement data in the state of the moving object. This data is transmitted to the server via Bluetooth or Wi-Fi, and the output is biometric data that is transferred to the server.

[0555] Step 3:

[0556] After receiving biometric data, the server analyzes the data using a generative AI model. During this process, it detects abnormalities in heart rate and body temperature based on machine learning algorithms implemented in Python. The input is biometric data transmitted from the terminal, and the output is alert data indicating the detected abnormality in the biometric information.

[0557] Step 4:

[0558] The server then activates an artificial intelligence engine to determine the emotional state. This engine analyzes the time-series changes in biometric data and quantifies the stress and stability of the body. The input is the data analyzed in the previous step, and the output is an evaluation result indicating the emotional state.

[0559] Step 5:

[0560] If an abnormal or stressful condition is detected, the server generates appropriate warning data. This includes specific instructions and actions tailored to the individual situation. The input is the abnormality detection and emotion assessment results, and the output is the generated warning message.

[0561] Step 6:

[0562] Warning data is sent to the user's device and displayed as a push notification. The input is warning data from the server, and the output is an alert message displayed on the user's device. This information allows the user to take prompt action.

[0563] (Application Example 2)

[0564] Next, we will explain application example 2. In the following explanation, 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."

[0565] Conventional autonomous vehicles lacked sufficient safety measures that took into account the occupants' biometric information and emotional state. This resulted in challenges in ensuring safe and comfortable travel for occupants, and making it difficult to respond quickly and appropriately in emergencies. Furthermore, there was a lack of means to reflect the internal state of the occupants, such as fatigue or stress, in the driving system.

[0566] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0567] In this invention, the server includes means for detecting the presence of moving objects inside the vehicle using wireless signals, means for estimating the emotional state of the occupants and providing information corresponding to that emotional state, and means for adjusting the vehicle's automatic driving based on abnormalities. This ensures both the safety of the occupants and comfortable travel, and enables rapid response to abnormal conditions and adjustment of automatic driving.

[0568] "Moving objects" refers to the movement of occupants or objects detected within a vehicle.

[0569] "Wireless signals" refer to the means of communication used by detection devices within a vehicle, and are used to confirm the presence of moving objects.

[0570] "Biometric information" refers to data obtained from moving objects, including heart rate and body temperature.

[0571] "Emotional state" refers to the psychological state of the crew, and includes emotions such as stress and calmness.

[0572] "External devices" refer to equipment located outside the vehicle, and are devices used to receive warnings and information.

[0573] A "warning" is an alert sent when an anomaly occurs, intended to prompt a quick response.

[0574] "Autonomous driving" refers to technology that allows vehicles to move without driver intervention.

[0575] "Adjustment" means changing the settings and operation of the autonomous driving system based on the detected information.

[0576] This invention begins with a terminal installed inside a vehicle that uses wireless signals to detect the presence of moving objects and their biometric information. The terminal is equipped with various sensors and cameras and is responsible for collecting biometric information. For example, by acquiring information such as heart rate, body temperature, and facial expression, it is possible to evaluate the state of the moving object.

[0577] The server receives data transmitted from the terminal and performs analysis based on that information. Specifically, it uses Python-based data analysis algorithms and cloud computing services such as AWS Lambda to estimate biometric information and emotional states. For emotion analysis, machine learning models such as TensorFlow and PyTorch are used to evaluate the emotional state of the moving subjects. This process makes it possible to understand the stress levels and calmness levels of the occupants.

[0578] Based on abnormalities or emotions, the server issues warnings. These warnings provide the information necessary to adjust the vehicle's autonomous driving system. The warnings are quickly transmitted to the occupants' smartphones or in-vehicle information displays, allowing them to take direct action. For example, if fatigue from prolonged driving is detected, the server generates a message such as, "We suggest the next rest stop."

[0579] Thus, this invention can enhance the safety of autonomous vehicles and ensure comfortable travel for passengers based on their emotional state and biometric information.

[0580] Example of a prompt:

[0581] "Emotion analysis input: Passenger facial expressions indicate signs of stress. Provide suggestions for calming activities or recommend break locations."

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] The terminal uses various sensors and cameras inside the vehicle to acquire biometric information of moving objects. This input includes heart rate, body temperature, and facial expression data. This data is transmitted to a server via wireless signals.

[0585] Step 2:

[0586] The server receives biometric information transmitted from the terminal. Based on the received data, it preprocesses the data using a Python-based data analysis algorithm to calculate the average value and standard deviation of heart rate and body temperature. This data processing makes it possible to detect anomalies.

[0587] Step 3:

[0588] The server inputs pre-processed data into a generating AI model, such as an emotion analysis model built with TensorFlow or PyTorch, to estimate the emotional state of the moving subject. This model determines stress levels and states of calmness from facial expression data. The analysis results are output as an emotional state.

[0589] Step 4:

[0590] The server evaluates for anomalies based on the estimated emotional state. If an anomaly is detected, it generates a warning appropriate to the emotional state and suggests the next action. This warning message is generated as a prompt and used in the next step.

[0591] Step 5:

[0592] The server sends the generated warnings to the user's smartphone or in-vehicle information display via the cloud. This allows the user to receive the warning message and take the suggested action. For example, a message such as "We suggest the next rest stop" might be displayed.

[0593] Step 6:

[0594] The user reviews the information received on the terminal or display device and performs the instructed actions as needed. This process includes operations performed through an interface.

[0595] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0596] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0597] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0598] [Fourth Embodiment]

[0599] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0600] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0601] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0602] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0603] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0604] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0605] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0606] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0607] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0608] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0609] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0610] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0611] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] The embodiments for carrying out the present invention will be described in detail. This system monitors movement within a vehicle and provides rapid warnings in the event of an abnormality, and consists of the following elements.

[0613] This system consists of a "terminal" installed inside the vehicle, a "server" located remotely, and an external device such as a smartphone acting as a "user" terminal. The terminal has the function of transmitting wireless signals and receiving the reflected waves to detect the presence of moving objects inside the vehicle. This signal data is transmitted to the server and analyzed.

[0614] The server analyzes signal data sent from the terminal to assess the presence of moving objects and evaluate biometric information. Based on the analysis results, the server distinguishes between normal and abnormal conditions, and generates a warning if an abnormality is detected. The analysis process includes a heart rate estimation function, which is used to determine if biometric information is outside the normal range.

[0615] When an anomaly is detected, the server sends a warning notification to multiple "user" terminals. This allows not only the vehicle owner but all relevant parties to respond quickly. This system can also be used in environments where monitoring and managing multiple people riding in a specific vehicle is necessary, such as to ensure the safety of children in childcare facilities or school buses.

[0616] As a concrete example, let's consider how this system could be implemented on a kindergarten bus. In this case, the terminal automatically activates when the bus departs and begins sending and receiving wireless signals. Monitoring continues even after the bus stops and the engine is turned off, and if a forgotten child is left inside the vehicle, a warning is promptly sent to the relevant personnel. This system makes it possible to quickly rescue children left behind in the vehicle and ensure their safety.

[0617] The invention minimizes privacy concerns because it does not use cameras, reduces costs by utilizing existing wireless technology, and allows for widespread implementation. This system is particularly effective in addressing the security needs of public spaces and individuals where privacy protection is crucial.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] The terminal detects that the vehicle has stopped and the engine has been turned off. It begins transmitting a wireless signal and enters motion detection mode inside the vehicle.

[0621] Step 2:

[0622] The terminal receives reflected signals from within the vehicle and generates data from them. It periodically sends the received signal data to the server.

[0623] Step 3:

[0624] The server receives signal data transmitted from the terminal, extracts and analyzes information useful as indicators, and then runs a program to estimate biometric information such as heart rate to determine if a person is present.

[0625] Step 4:

[0626] Based on the analysis results, the server evaluates whether the presence of a moving object and its biometric information are within normal limits. If an anomaly is detected, the process proceeds to the next step.

[0627] Step 5:

[0628] If the server detects an anomaly, it collects pre-configured user contact information and prepares a warning notification.

[0629] Step 6:

[0630] The server sends warning notifications to multiple user terminals. The notifications contain detailed information about the anomaly and are designed to encourage prompt action.

[0631] Step 7:

[0632] The user receives a warning notification on a smartphone or other device and checks its contents. If necessary, they return to the vehicle and take appropriate action.

[0633] (Example 1)

[0634] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] Conventional vehicle monitoring systems often rely on cameras, which have drawbacks such as privacy concerns and high equipment costs. Furthermore, the lack of a mechanism for quickly notifying relevant parties in the event of an anomaly means that vehicles may be left unattended, potentially leading to delays in responding to emergencies. Additionally, there are problems with monitoring the individual situations of multiple passengers.

[0636] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0637] In this invention, the server includes means for detecting objects inside a vehicle using wireless signals and without using a camera, means for transmitting the signal data collected by the terminal to an external computing device and performing analysis processing, and means for evaluating the physiological information of the moving object based on the information obtained from the analysis processing and distinguishing between normal and abnormal conditions. This enables efficient and rapid anomaly detection and notification without infringing on privacy.

[0638] A "vehicle" is a device that enables the movement of objects, and primarily refers to vehicles that travel on land.

[0639] An "object" is something that is detected by a wireless signal, and whose existence is associated with its internal movement.

[0640] A "wireless signal" is a medium that uses radio waves to transmit information and is used to detect the presence of objects.

[0641] A "terminal" refers to a device installed inside a vehicle that has the function of transmitting and receiving wireless signals.

[0642] A "server" is a central processing unit that receives and analyzes data, and refers to equipment designed to efficiently perform a large number of calculations.

[0643] A "computational device" is a device used to process data and has the function of analyzing signal data.

[0644] "Physiological information" refers to information that indicates the state of the body, such as heart rate, and is an indicator used to evaluate health status and the presence or absence of abnormalities.

[0645] "Normal state" refers to a normal, healthy condition, and is the opposite of abnormal.

[0646] An "abnormality" refers to a state that deviates from the normal range and is an unusual condition that warrants attention.

[0647] "User device" refers to a terminal used to receive warnings, and is a device carried by the user.

[0648] A "warning" is a cautionary message issued in response to the detection of an anomaly, and it is information that should be promptly communicated to users.

[0649] This invention is a vehicle motion detection system using wireless signal technology, which monitors for anomalies while considering privacy protection. The system consists of a "terminal" installed in the vehicle, a "server" that analyzes and manages data, and a "user" device that receives warnings. The functions of each component are described in detail below.

[0650] The terminal is installed inside the vehicle and is responsible for transmitting and receiving wireless signals. Specifically, it uses radar technology to detect objects and people inside the vehicle. The signal data acquired by the terminal is programmed to be automatically transmitted to a server. At this time, the software built into the terminal processes the reflected wireless signal data in real time and converts it into a format that can be reported immediately.

[0651] The server plays a central role in analyzing signal data sent from terminals. The server is equipped with advanced analysis algorithms that continuously monitor dynamic movement and physiological data (such as heart rate). Based on these results, it quickly identifies normal and abnormal conditions. If the analysis indicates an abnormality, the server immediately generates a warning and sends it to multiple pre-registered user terminals.

[0652] Users typically receive alerts via smartphones or mobile devices. This ensures that notifications reach not only vehicle owners and managers, but also multiple stakeholders who require a quick response. The system has diverse applications and is particularly effective in situations involving multiple passengers, such as public transportation and child transport services.

[0653] A concrete example is a kindergarten bus. A terminal installed inside the bus activates when the engine starts and constantly monitors the situation inside the vehicle. If a child is left behind on the bus after the service has ended, the system can detect the anomaly and immediately send a warning to the relevant parties. This system enables a swift rescue and ensures safety.

[0654] By integrating with the generation AI model, an example of a prompt message is: "Please describe in detail the situations in which a warning regarding the vehicle's motion monitoring system needs to be issued. In particular, please explain the advantages when considering its operation in public transportation." In this way, establishing the operation of the entire system enables the provision of a safer and more efficient monitoring environment.

[0655] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0656] Step 1:

[0657] The terminal automatically activates in conjunction with the vehicle's engine start. It transmits a wireless signal into the vehicle and collects the reflected signal as received data. This input data includes information about the presence and movement of objects inside the vehicle. Specifically, the radar sensor scans for reflected signals from surrounding objects at a high frequency.

[0658] Step 2:

[0659] The terminal transmits the collected signal data to the server in real time. This output data is used as raw data necessary for the analysis of physiological information. Specifically, the communication module sends the data to the server via a wireless network.

[0660] Step 3:

[0661] The server receives signal data transmitted from the terminal. It processes the input data and analyzes physiological information related to the presence of moving objects and heart rate. This analysis is performed using data filtering, signal intensity measurement, and pattern recognition techniques. As output, an index is generated to determine whether the data is normal or abnormal.

[0662] Step 4:

[0663] The server determines whether the situation is normal or abnormal based on the analysis results. If an abnormality is detected, a warning is generated. In this step, the analysis algorithm applies an anomaly detection rule set and interprets the data. This output is organized into a message that includes specific warning details.

[0664] Step 5:

[0665] The server sends the generated warnings to multiple user terminals. The input requires information about the recipient user terminals. The output is a warning notification received by the user on a device such as a smartphone. Specific operations include rapid data distribution using network protocols.

[0666] Step 6:

[0667] The user reviews the received warning notification. The input is the warning message displayed on the device. The output is the action the user should take based on that information, such as contacting the site or proceeding to direct verification. A specific action is the process of tapping the notification to open the details.

[0668] (Application Example 1)

[0669] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0670] Modern management facilities and apartment buildings require the protection of privacy while ensuring the safety of residents and users. However, existing surveillance systems, such as camera systems, are difficult to implement from a privacy perspective and have challenges in responding quickly to anomalies. In particular, at night or in situations where human vision is unreliable, there is a need for more advanced motion detection and rapid anomaly notification.

[0671] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0672] This invention includes a server that includes means for detecting the presence of moving objects inside a vehicle using electromagnetic wave signals, means for analyzing information on the detected moving objects and evaluating the biological information of those moving objects, means for identifying anomalies based on the biological information and sending notifications to external terminals if an anomaly exists, means for monitoring crowd movement in management facilities and apartment buildings based on the detection results, and means for immediately notifying the mobile terminals of relevant parties when an anomaly is detected in the movement of the crowd. This enables rapid and reliable safety measures in the event of an anomaly while protecting privacy.

[0673] "Vehicle interior" refers to the internal space of a structure used as a means of transportation, and is the area used to transport people and goods.

[0674] A "moving object" refers to a physical entity that is not stationary but is constantly moving or changing, and primarily includes humans and animals.

[0675] "Electromagnetic wave signals" are signals that utilize a portion of the electromagnetic spectrum used in wireless communication, and are applied to data communication and object detection.

[0676] "Means of detection" refers to methods and techniques for confirming the existence or state of an object, and includes sensors and analytical algorithms.

[0677] "Biological information" refers to data related to living organisms, such as heart rate, temperature, and movement, and is information that indicates the health and activity level of an individual.

[0678] An "external device" refers to an external device connected to the system that is an electronic device capable of receiving notifications and being operated.

[0679] "Means of sending notifications" refers to means of informing relevant parties of anomalies or important information, and includes alert systems that utilize communication technology.

[0680] "Crowd movement" refers to the movement of a large number of people or objects from one place to another, demonstrating the dynamic behavior of a collective.

[0681] "Privacy" refers to the right of an individual to have their private life and information protected from infringement by others, and it is an ethical concept that is considered important when introducing surveillance technology.

[0682] This system is a motion monitoring device designed to improve safety in management facilities and apartment buildings. The server uses wireless signal transceivers installed inside vehicles or facilities. Terminals continuously transmit electromagnetic signals, and by receiving the reflected signals, the system detects the presence of moving objects, including people. The detected data is sent to the server, where detailed biological information about the moving objects is analyzed.

[0683] The server analyzes this data, evaluates biometric information such as heart rate, and identifies whether an abnormality has occurred. If an abnormality is detected, the server immediately notifies external devices, including smartphones and tablets.

[0684] As a concrete example, let's consider its use in a fitness gym within an apartment building at night. Within this gym, the terminal emits electromagnetic signals at regular intervals to monitor people's movements and conditions. If abnormal movement is detected, the server immediately sends a notification to external terminals of management staff or security guards, enabling a rapid response.

[0685] This system utilizes cloud services such as AWS Lambda and Google Cloud Functions to enable real-time data analysis. Furthermore, to protect privacy, it employs a configuration that does not use cameras.

[0686] Examples of prompts for a generative AI model:

[0687] "Please consider a scenario for a motion monitoring system in a fitness gym. Explain specifically how the monitoring system within the facility detects anomalies and notifies relevant personnel."

[0688] This configuration allows for both privacy protection and security measures to be achieved, and enables appropriate environmental monitoring.

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] The terminal uses a wireless signal transceiver installed inside the vehicle or facility to transmit electromagnetic signals at regular intervals and receive the reflected waves. The input is the transmitted wireless signal, and the output is the reflected wave data. Based on this data, basic signal processing is performed to detect the presence of moving objects.

[0692] Step 2:

[0693] The server receives reflected wave data transmitted from the terminal. The input is reflected wave data from the terminal, and the output is information about a moving object. The server analyzes this data and performs data processing such as Fast Fourier Transform (FFT) to measure the distance and speed of the moving object.

[0694] Step 3:

[0695] The server estimates biological information from analyzed moving object information. The input is the analyzed object information, and the output is biological information such as heart rate. Here, biological data is extracted using an algorithm that associates specific movement patterns with heart rate.

[0696] Step 4:

[0697] The server identifies abnormalities based on the obtained biological information. The input is estimated biological information, and the output is the judgment result of whether it is normal or abnormal. If an abnormal value is detected, a corresponding flag is set, and it is classified as abnormal.

[0698] Step 5:

[0699] The server sends a notification to an external device when an anomaly is detected. The input is the result of the anomaly detection, and the output is a warning notification. The server sends the warning to the configured notification destination (smartphone or tablet device) via the network, prompting the user to take prompt action.

[0700] Step 6:

[0701] The user receives a warning notification on an external terminal and, if necessary, checks the site or takes corrective action. The input is the warning notification from the server, and the output is the user's response action. This allows the user to respond quickly to abnormal situations and take necessary actions.

[0702] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0703] This invention is a system that detects the presence of moving objects inside a vehicle, their biometric information, and their emotional state, and transmits warnings to an external device as needed. This makes it possible to improve safety while facilitating emergency response.

[0704] This system primarily consists of three elements: a terminal, a server, and a user terminal. The terminal is installed inside the vehicle and uses wireless signals to detect the presence of moving objects and their biometric information. This information is then transmitted to the server.

[0705] The server analyzes biometric information based on the received data and estimates data such as heart rate. The server also incorporates an emotion engine that evaluates the emotional state of the subject through biometric information and movement pattern analysis. Emotional state refers to the psychological state of the subject, such as whether it is stressed or calm.

[0706] If an anomaly is detected, the server considers the evaluation results of the emotion engine and generates a warning that enables a more appropriate and rapid response. The warning is adjusted according to the emotion of the moving object and sent to the user's terminal. This allows not only the user but also multiple stakeholders, as needed, to quickly share information and create a system that enables appropriate responses.

[0707] As a concrete example, consider a case where a child is left behind in a vehicle that has been parked for a long time. The engine is turned off, and the terminal begins transmitting wireless signals. The server analyzes the data from the terminal and confirms that the child is still inside the vehicle. At the same time, the emotion engine analyzes changes in biosignals and behavioral patterns and determines that the child's emotions are unstable. Based on this, the server quickly sends warnings to multiple user terminals urging them to change to a calmer living environment, thereby helping to ensure the child's safety. In this way, the incorporation of the emotion engine enables more appropriate and effective warning notifications, improving the accuracy and speed of responses in emergency situations.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] The terminal detects that the vehicle's engine has been turned off. It then switches to a mode that monitors for movement inside the vehicle using a wireless signal.

[0711] Step 2:

[0712] The terminal receives reflected wireless signals and measures the presence of moving objects and related biometric information (e.g., heart rate). This data is then transmitted to the server.

[0713] Step 3:

[0714] The server analyzes the signal data received from the terminal. Based on the biometric information, it evaluates whether the moving body is in a normal state and estimates the heart rate.

[0715] Step 4:

[0716] The server uses an emotion engine to estimate the emotional state of a moving object from its biometric information and other data. This allows it to determine what emotional state the object is in, such as tension, anxiety, or relaxation.

[0717] Step 5:

[0718] Based on an assessment of biometric information and emotional state, the server will begin preparing a warning notification if it determines that there is an abnormality in the moving objects inside the vehicle.

[0719] Step 6:

[0720] Based on the results from the emotion engine, the server generates warnings tailored to the detected emotional state. For example, it adjusts the notification content, prompting a quick response if the user is stressed, or issuing a normal alert if they are relaxed.

[0721] Step 7:

[0722] The server sends pre-configured warning notifications to multiple user terminals, allowing relevant parties to quickly take appropriate action.

[0723] Step 8:

[0724] The user receives a warning notification on their device and checks its contents. Depending on the situation, they can either go to the vehicle or contact relevant parties to confirm the situation and take appropriate action.

[0725] (Example 2)

[0726] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0727] There is a need to provide a system that accurately detects the presence of moving objects, biometric information, and emotional states within a vehicle, and sends rapid and accurate warnings to enhance safety. However, conventional systems are insufficient in detecting anomalies and generating warnings, making it difficult to respond appropriately in emergencies. This necessitates a solution to the problem of situations that could potentially threaten safety.

[0728] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0729] In this invention, the server includes means for detecting the presence of a moving object using wireless data, means for analyzing the data of the detected moving object and evaluating its biometric data, means including an artificial intelligence engine that determines the emotional state based on the biometric data, and means for generating detailed warning data and transmitting it to an external computing device when an abnormality is detected. This enables improved safety by accurately monitoring the situation inside the vehicle and allowing for prompt responses as needed.

[0730] "Moving object" refers to objects that may be moving, such as people or animals, that are detected inside a vehicle.

[0731] "Wireless data" refers to data acquired via wireless communication technology used to exchange information between a terminal and a server.

[0732] "Biometric data" refers to physiological data obtained from moving objects, such as heart rate and body temperature.

[0733] "Emotional state" refers to the psychological and emotional state determined by artificial intelligence based on biometric data.

[0734] An "artificial intelligence engine" is a computational means used to analyze biometric data and movement patterns to determine the emotional state of a moving object.

[0735] An "external computing device" is an electronic device, such as a smartphone or tablet, owned by the user that can receive and display warning data.

[0736] "Warning data" refers to notification information that is generated and sent to an external computing device when an anomaly is detected.

[0737] This invention provides a system that improves safety by detecting moving objects within a vehicle and evaluating their biometric data and emotional state. The system mainly consists of three elements: a terminal, a server, and a user terminal.

[0738] The terminal is installed inside the vehicle and detects the presence of moving objects using wireless data. This terminal has the function of collecting biometric data such as heart rate and body temperature using ultrasonic sensors and infrared sensors. The collected data is transmitted to a server via Bluetooth or Wi-Fi.

[0739] The server analyzes the received data. This analysis utilizes a generative AI model based on Python machine learning algorithms. The server then activates an artificial intelligence engine to detect abnormalities in heart rate and body temperature based on biometric data, and further determine emotional states. This engine has the ability to analyze behavioral patterns and time-series data to quantify stress levels and psychological stability.

[0740] If an anomaly is detected, the server promptly generates warning data. This generated warning data is sent to the user's terminal and displayed as a push notification. User terminals are mainly smartphones and tablets, which function as external computing devices.

[0741] As a concrete example, consider the case of a toddler left in a vehicle for an extended period. The device analyzes the biometric data of the moving child and detects abnormalities by sensing unsafe heart rate fluctuations or elevated body temperature. Simultaneously, it assesses the child's emotional state, and if it determines the child is in a state of tension, it immediately warns the user's device, enabling rapid intervention.

[0742] An example of a prompt to the generating AI model is: "Please tell me how to analyze the heart rate and movement patterns of a person trapped inside a vehicle and issue a warning if their emotional state is unstable." By using this system, it is possible to enhance safety inside vehicles and support a quick and appropriate response in emergencies.

[0743] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0744] Step 1:

[0745] The terminal detects the presence of moving objects from the vehicle's environment using wireless data. During this process, ultrasonic and infrared sensors collect location information and movement patterns of the moving objects. The input is data from sensors within the vehicle, and the output is data indicating the presence and initial position of the moving object.

[0746] Step 2:

[0747] The device continuously collects biometric data such as heart rate and body temperature from moving objects. The input is sensor measurement data in the state of the moving object. This data is transmitted to the server via Bluetooth or Wi-Fi, and the output is biometric data that is transferred to the server.

[0748] Step 3:

[0749] After receiving biometric data, the server analyzes the data using a generative AI model. During this process, it detects abnormalities in heart rate and body temperature based on machine learning algorithms implemented in Python. The input is biometric data transmitted from the terminal, and the output is alert data indicating the detected abnormality in the biometric information.

[0750] Step 4:

[0751] The server then activates an artificial intelligence engine to determine the emotional state. This engine analyzes the time-series changes in biometric data and quantifies the stress and stability of the body. The input is the data analyzed in the previous step, and the output is an evaluation result indicating the emotional state.

[0752] Step 5:

[0753] If an abnormal or stressful condition is detected, the server generates appropriate warning data. This includes specific instructions and actions tailored to the individual situation. The input is the abnormality detection and emotion assessment results, and the output is the generated warning message.

[0754] Step 6:

[0755] Warning data is sent to the user's device and displayed as a push notification. The input is warning data from the server, and the output is an alert message displayed on the user's device. This information allows the user to take prompt action.

[0756] (Application Example 2)

[0757] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0758] Conventional autonomous vehicles lacked sufficient safety measures that took into account the occupants' biometric information and emotional state. This resulted in challenges in ensuring safe and comfortable travel for occupants, and making it difficult to respond quickly and appropriately in emergencies. Furthermore, there was a lack of means to reflect the internal state of the occupants, such as fatigue or stress, in the driving system.

[0759] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0760] In this invention, the server includes means for detecting the presence of moving objects inside the vehicle using wireless signals, means for estimating the emotional state of the occupants and providing information corresponding to that emotional state, and means for adjusting the vehicle's automatic driving based on abnormalities. This ensures both the safety of the occupants and comfortable travel, and enables rapid response to abnormal conditions and adjustment of automatic driving.

[0761] "Moving objects" refers to the movement of occupants or objects detected within a vehicle.

[0762] "Wireless signals" refer to the means of communication used by detection devices within a vehicle, and are used to confirm the presence of moving objects.

[0763] "Biometric information" refers to data obtained from moving objects, including heart rate and body temperature.

[0764] "Emotional state" refers to the psychological state of the crew, and includes emotions such as stress and calmness.

[0765] "External devices" refer to equipment located outside the vehicle, and are devices used to receive warnings and information.

[0766] A "warning" is an alert sent when an anomaly occurs, intended to prompt a quick response.

[0767] "Autonomous driving" refers to technology that allows vehicles to move without driver intervention.

[0768] "Adjustment" means changing the settings and operation of the autonomous driving system based on the detected information.

[0769] This invention begins with a terminal installed inside a vehicle that uses wireless signals to detect the presence of moving objects and their biometric information. The terminal is equipped with various sensors and cameras and is responsible for collecting biometric information. For example, by acquiring information such as heart rate, body temperature, and facial expression, it is possible to evaluate the state of the moving object.

[0770] The server receives data transmitted from the terminal and performs analysis based on that information. Specifically, it uses Python-based data analysis algorithms and cloud computing services such as AWS Lambda to estimate biometric information and emotional states. For emotion analysis, machine learning models such as TensorFlow and PyTorch are used to evaluate the emotional state of the moving subjects. This process makes it possible to understand the stress levels and calmness levels of the occupants.

[0771] Based on abnormalities or emotions, the server issues warnings. These warnings provide the information necessary to adjust the vehicle's autonomous driving system. The warnings are quickly transmitted to the occupants' smartphones or in-vehicle information displays, allowing them to take direct action. For example, if fatigue from prolonged driving is detected, the server generates a message such as, "We suggest the next rest stop."

[0772] Thus, this invention can enhance the safety of autonomous vehicles and ensure comfortable travel for passengers based on their emotional state and biometric information.

[0773] Example of a prompt:

[0774] "Emotion analysis input: Passenger facial expressions indicate signs of stress. Provide suggestions for calming activities or recommend break locations."

[0775] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0776] Step 1:

[0777] The terminal uses various sensors and cameras inside the vehicle to acquire biometric information of moving objects. This input includes heart rate, body temperature, and facial expression data. This data is transmitted to a server via wireless signals.

[0778] Step 2:

[0779] The server receives biometric information transmitted from the terminal. Based on the received data, it preprocesses the data using a Python-based data analysis algorithm to calculate the average value and standard deviation of heart rate and body temperature. This data processing makes it possible to detect anomalies.

[0780] Step 3:

[0781] The server inputs pre-processed data into a generating AI model, such as an emotion analysis model built with TensorFlow or PyTorch, to estimate the emotional state of the moving subject. This model determines stress levels and states of calmness from facial expression data. The analysis results are output as an emotional state.

[0782] Step 4:

[0783] The server evaluates for anomalies based on the estimated emotional state. If an anomaly is detected, it generates a warning appropriate to the emotional state and suggests the next action. This warning message is generated as a prompt and used in the next step.

[0784] Step 5:

[0785] The server sends the generated warnings to the user's smartphone or in-vehicle information display via the cloud. This allows the user to receive the warning message and take the suggested action. For example, a message such as "We suggest the next rest stop" might be displayed.

[0786] Step 6:

[0787] The user reviews the information received on the terminal or display device and performs the instructed actions as needed. This process includes operations performed through an interface.

[0788] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0789] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0790] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0791] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0792] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0793] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0794] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0795] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0796] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0797] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0798] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0799] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0800] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0801] 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.

[0802] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0803] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0804] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0805] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0806] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0807] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0808] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0809] The following is further disclosed regarding the embodiments described above.

[0810] (Claim 1)

[0811] A means for detecting the presence of a moving object inside a vehicle using wireless signals,

[0812] A means for analyzing information on detected moving objects and evaluating the biological information of those moving objects,

[0813] A means for determining an anomaly based on biometric information and sending a warning to an external device if an anomaly is present,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, comprising means for estimating heart rate as a means for detecting biological information.

[0817] (Claim 3)

[0818] The system according to claim 1, which allows multiple external devices to be configured as destinations for sending warnings.

[0819] "Example 1"

[0820] (Claim 1)

[0821] A means for detecting objects inside a vehicle using wireless signals and without using a camera,

[0822] A means for transmitting signal data collected by a terminal to an external computing device and performing analysis processing,

[0823] A means for evaluating the physiological information of a moving body based on the information obtained through analysis processing, and for distinguishing between normal and abnormal conditions,

[0824] A means for sending warnings to multiple user devices when an abnormality is detected,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, comprising means for estimating heart rate as an evaluation of physiological information.

[0828] (Claim 3)

[0829] The system according to claim 1, which allows multiple user devices to be set as destinations for sending warnings.

[0830] "Application Example 1"

[0831] (Claim 1)

[0832] A means for detecting the presence of moving objects inside a vehicle using electromagnetic wave signals,

[0833] A means for analyzing information about detected moving objects and evaluating the biological information of those moving objects,

[0834] A means for identifying abnormalities based on biological information and sending notifications to an external terminal if an abnormality is present,

[0835] Based on the detection results, a means of monitoring crowd movement in management facilities and apartment buildings,

[0836] A means of immediately notifying the mobile devices of those involved when abnormalities are detected in the movement of a crowd,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, comprising means for estimating heart rate as a means for detecting biological information.

[0840] (Claim 3)

[0841] The system according to claim 1, which allows setting multiple external terminals as destinations for notifications.

[0842] "Example 2 of combining an emotion engine"

[0843] (Claim 1)

[0844] A means of detecting the presence of a moving object inside a vehicle using wireless data,

[0845] A means for analyzing data from detected moving objects and evaluating the biometric data of those moving objects,

[0846] A means including an artificial intelligence engine that determines emotional state based on biometric data,

[0847] A means for generating warning data that matches the details of the anomaly when an anomaly is detected and transmitting it to an external computing device,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, comprising means for estimating heart rate as part of the analysis of biological data.

[0851] (Claim 3)

[0852] The system according to claim 1, which allows setting multiple external computing devices as destinations for sending warning data.

[0853] "Application example 2 when combining with an emotional engine"

[0854] (Claim 1)

[0855] A means for detecting the presence of a moving object inside a vehicle using wireless signals,

[0856] A means for analyzing information on detected moving objects and evaluating the biological information of those moving objects,

[0857] A means for determining abnormalities based on biological information and sending a warning to an external device if an abnormality is present,

[0858] A means for estimating the emotional state of the crew and providing information corresponding to that emotional state,

[0859] A means for adjusting the automated driving of a vehicle based on abnormalities,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, comprising means for estimating heart rate as a means for detecting biological information.

[0863] (Claim 3)

[0864] The system according to claim 1, which allows multiple external devices to be set as destinations for sending warnings, and displays warnings on the occupants' electronic devices and in-vehicle information display devices. [Explanation of symbols]

[0865] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for detecting the presence of a moving object inside a vehicle using wireless signals, A means for analyzing information on detected moving objects and evaluating the biological information of those moving objects, A means for determining an anomaly based on biometric information and sending a warning to an external device if an anomaly is present, A system that includes this.

2. The system according to claim 1, comprising means for estimating heart rate as a means for detecting biological information.

3. The system according to claim 1, wherein multiple external devices can be configured as destinations for sending warnings.

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