system

Abstract

Provide a system. 【Solution means】 A system for preventing a person from being left behind inside a vehicle, Image acquisition means for acquiring image data of a person, Face recognition means for recognizing a person from the acquired image data, Position information acquisition means for acquiring position information inside the vehicle, Temperature measurement means for measuring the temperature inside the vehicle, Data analysis means for integrating data from the face recognition means, position information acquisition means, and temperature measurement means and detecting abnormalities, Alarm output means for outputting an alarm when an abnormality is detected, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 the chatbot's 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] Accidents where infants or pets are left behind in a vehicle occur every year, especially in a high-temperature environment, which poses a life-threatening problem. It is required to prevent such accidents and ensure the safety of people and animals in the vehicle. However, conventional systems relied on locking the vehicle and checking at the time of departure, etc., so there was a problem that the response due to human error was insufficient. The present invention aims to solve such problems in the prior art and prevent accidents where people are left behind in a vehicle by an automated system.

Means for Solving the Problems

[0005] This invention is a system that uses facial recognition technology to detect people inside a vehicle and monitors the temperature and location information inside the vehicle, thereby preventing the risk of people being trapped inside. Specifically, it integrates and analyzes information acquired from cameras, temperature sensors, and GPS devices inside the vehicle, and immediately issues an alarm when an anomaly is detected. This allows users to immediately recognize the anomaly via a smartphone or other device and take a quick response. These measures can improve safety in the event that a person is trapped inside a vehicle.

[0006] "Image acquisition means" refers to a camera and its associated devices positioned to acquire image data of people and objects inside a vehicle.

[0007] "Face recognition means" refers to a processing function that identifies a person's face based on acquired image data and performs the function of identifying and recognizing the person's presence.

[0008] A "location information acquisition means" is a device that uses GPS technology to acquire location information inside and around a vehicle, and to determine the precise location of a person.

[0009] A "temperature measurement means" is a sensor device that continuously measures the ambient temperature inside a vehicle and transmits that data to a server or other device.

[0010] "Data analysis means" refers to a device or system that has the function of integrating data obtained from face recognition means, location information acquisition means, and temperature measurement means, and performing necessary analysis to determine whether or not there is an abnormality.

[0011] An "alarm output means" is a function or device designed to issue an alarm and notify the user when an abnormality is detected. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled 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), etc.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled 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.

[0018] In the following embodiments, the labeled 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 applied 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.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a safety management system for preventing people or animals from being left behind inside a vehicle. Specific embodiments are shown below.

[0034] 1. System Configuration

[0035] The system consists of multiple terminals and servers. The terminals include image acquisition devices, temperature measurement devices, and location information acquisition devices installed inside the vehicle. Smartphones are also used as alarm output devices.

[0036] 2. Data Collection

[0037] Terminal (in-vehicle camera): Functions as a means of acquiring images and photographs people inside the vehicle. The captured images are converted into identification information of the people by a facial recognition system.

[0038] Terminal (temperature sensor): Measures the temperature inside the vehicle and transmits data at regular intervals.

[0039] Terminal (GPS device): As a means of acquiring location information, it continuously acquires location information of the person inside the vehicle.

[0040] 3. Data Analysis and Decision-Making

[0041] Server: Performs facial recognition on acquired image data and integrates and analyzes temperature data and location information. This allows it to determine whether a person is trapped inside the vehicle or whether the temperature exceeds a safe range.

[0042] The server uses this information to maintain a state where it can respond quickly when an anomaly is detected.

[0043] 4. Alert Notifications and Responses

[0044] Server: If an anomaly is detected, an alert is immediately sent to the smartphone of the registered user (such as a parent or guardian).

[0045] User: Upon receiving an alarm, return to your vehicle, assess the situation, and take rescue action as necessary.

[0046] 5. Specific Examples

[0047] For example, in a case where this system is implemented in a vehicle used to transport children to and from a childcare facility, the number of children would be counted using facial recognition when they board the vehicle, and the same method would be used to reconfirm their presence when they disembark. If it is determined that someone has been left inside the vehicle, an alarm will be immediately sent to the user's smartphone, even if the temperature data is within a safe range.

[0048] This system configuration enables safe and highly efficient monitoring within the vehicle.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The terminal (in-vehicle camera) monitors the situation inside the vehicle in real time and operates as a means of acquiring images. It collects image data of people inside the vehicle and transmits the information to the server.

[0052] Step 2:

[0053] The server analyzes the received image data using facial recognition technology. This extracts identifiable information about the people inside the vehicle and records the current number of passengers. The resulting facial recognition results are then stored in a database.

[0054] Step 3:

[0055] The terminal (GPS device) continuously acquires the location information of all people inside the vehicle and periodically transmits it to the server. This ensures that the location of everyone inside the vehicle is always known.

[0056] Step 4:

[0057] The terminal (temperature sensor) measures the temperature inside the vehicle and periodically sends this data to the server. The server uses this temperature information as basic data for making safety decisions.

[0058] Step 5:

[0059] The server then integrates facial recognition, location information, and temperature data to determine if there are any anomalies. If a person is left behind or the temperature exceeds a set safe value, it flags the situation as an anomaly.

[0060] Step 6:

[0061] If an abnormal flag is set on the server, it immediately activates the alarm output mechanism. The alarm is sent to the user's smartphone, and a warning message appropriate to the situation is sent.

[0062] Step 7:

[0063] The user checks the alert received on their smartphone and returns to the designated vehicle or takes appropriate action. After resolving the situation, the user provides feedback to the server via the application, confirming that the problem has been resolved and updating the system record.

[0064] (Example 1)

[0065] 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."

[0066] There is a problem of accidents and dangers occurring when humans or animals are trapped inside vehicles. In particular, in high-temperature environments or enclosed spaces, situations that endanger lives can develop over time, so it is necessary to quickly and accurately assess the situation and respond appropriately. However, conventional technology is insufficient to address this problem.

[0067] 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.

[0068] In this invention, the server includes an information acquisition means, an identification means, a location acquisition device, an observation means, an integrated processing means, and a notification means. This makes it possible to monitor the situation inside the vehicle in real time, prevent people or animals from being left behind, and quickly send an alarm to the user when an abnormality is detected.

[0069] "Information acquisition means" refers to devices installed to understand the situation inside a vehicle and to detect the presence of people or animals.

[0070] "Identification means" refers to technology used to identify people or animals inside a vehicle based on data obtained from information acquisition means.

[0071] A "position acquisition device" is a device used to determine the current location of a vehicle and its position within the vehicle.

[0072] "Observation means" refers to a device that measures and records environmental data such as temperature inside a vehicle.

[0073] An "integrated processing means" is a system that integrates and analyzes acquired data to perform processing for detecting anomalies.

[0074] A "notification means" is a device or system for transmitting relevant information to the user when an anomaly is detected.

[0075] This invention is a safety management system for preventing people or animals from being left behind inside a vehicle. The system consists of multiple terminals and a server.

[0076] The device includes an in-vehicle camera, a temperature sensor, and a location acquisition device. The in-vehicle camera functions as an image acquisition tool, capturing images of people and animals inside the vehicle. The captured images are analyzed through facial recognition software installed on the device and converted into person identification information.

[0077] A temperature sensor continuously measures the ambient temperature inside the vehicle. The acquired temperature data is transmitted to a server at regular intervals. A GPS device tracks the vehicle's current location in real time.

[0078] The server collects this data and performs analysis using an integrated processing system. This analysis utilizes facial recognition software and a location data integration system. Based on the analysis results, it determines whether a person or animal is trapped inside the vehicle, or whether the temperature inside the vehicle exceeds a safe range.

[0079] If an anomaly is detected, the server will connect to a smartphone as a notification method and send an alert to the user. Upon receiving the alert, the user will check the situation and, if necessary, quickly go to the site to take action.

[0080] For example, if this system were installed in a vehicle used to transport children to and from a childcare facility, facial recognition technology would be used to verify the number of children boarding and disembarking in the same way. If it is determined that someone is still inside the vehicle, an alert would be immediately sent to a smartphone.

[0081] Examples of prompts for a generative AI model:

[0082] "Please describe the processing steps of a system that uses data from in-vehicle cameras and temperature sensors to determine the safety status of people inside the vehicle and sends alerts to registered users in case of abnormalities."

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The terminal (in-vehicle camera) captures images of the interior of the vehicle. The input is the current visual information inside the vehicle, which is captured by the camera. The output is a captured image file. This image data is used in the subsequent face recognition process.

[0086] Step 2:

[0087] The terminal (temperature sensor) measures the temperature inside the vehicle. The input is the current temperature of the vehicle's interior. The output is temperature information in numerical data format, which is then sent to the server. This temperature data is used for safety assessments of humans and animals.

[0088] Step 3:

[0089] The terminal (GPS device) obtains the current location of the vehicle. Its input is a signal from GPS satellites. The output is location data indicating the vehicle's specific location. This location information is used for situation assessment and emergency notifications.

[0090] Step 4:

[0091] The server receives image data transmitted from the in-vehicle camera and processes the images using facial recognition software. An image file is provided as input, and a facial recognition algorithm is applied. The output is identifying information about the identified person.

[0092] Step 5:

[0093] The server integrates and analyzes the collected temperature and location data. Temperature and location data are used as inputs, and these are integrated and analyzed. As a result of the analysis, safety assessment data for the vehicle's interior is obtained as output. This assessment is used to determine whether a person or animal is trapped inside the vehicle.

[0094] Step 6:

[0095] If an anomaly is detected based on the analysis results, the server sends a notification to the user. The input is the safety assessment data from the previous step. The output is an alert notification sent as a warning message to a mobile device such as a smartphone.

[0096] Step 7:

[0097] The user receives notifications on their smartphone and returns to their vehicle to check the situation depending on the warning status. The input is the received alert information. The output is user activity data (e.g., travel to the site). The user takes the necessary actions at the site and ensures safety.

[0098] (Application Example 1)

[0099] 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."

[0100] In autonomous vehicles, the absence of human intervention makes it difficult to detect the presence of people or animals trapped inside the vehicle at an early stage. In particular, in environments where the temperature inside the vehicle rises, safety cannot be guaranteed if someone is trapped inside. Therefore, there is a need to establish a system that can quickly and accurately detect people or animals inside the vehicle and issue warnings as needed.

[0101] 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.

[0102] In this invention, the server includes a shooting means for acquiring video data of a person, an authentication means for identifying a person from the acquired video data, and a location data acquisition means for acquiring location data inside the vehicle. This makes it possible to efficiently detect the presence of a person or animal left inside the vehicle and to quickly issue a warning when an abnormality occurs.

[0103] "Filming equipment" refers to devices used to acquire video data of people or animals inside a vehicle.

[0104] "Authentication means" refers to technology used to identify individuals from acquired video data.

[0105] "Location data acquisition means" refers to a device for acquiring location data inside a vehicle.

[0106] A "temperature measuring device" is a device used to measure the ambient temperature inside a vehicle.

[0107] The "data analysis means" is a processing device that integrates data obtained from authentication means, location data acquisition means, and temperature measurement means to detect anomalies.

[0108] An "alarm notification device" is a device that transmits an alarm to an external source when an abnormality is detected.

[0109] A "portable information terminal" refers to a portable information device such as a smartphone or tablet.

[0110] One embodiment of this invention is an integrated system for safety management within an autonomous vehicle. The system includes means for taking photographs, means for authentication, means for acquiring location data, means for measuring temperature, means for analyzing data, and means for notifying alarms, all of which work in coordination.

[0111] The server uses cameras installed inside the vehicle to perform a recording procedure and collect video data. This data is then passed to an authentication system that uses specific software libraries (e.g., a Python library for image analysis) to identify people. This makes it possible to extract information about the presence or absence of people and their identification from the video.

[0112] Furthermore, the server implements a temperature measurement system that uses sensors placed inside the vehicle to measure temperature and acquire data in real time. The location data acquisition system acquires the vehicle's current location and detailed internal location information via a GPS module and provides the necessary data.

[0113] The various acquired data are integrated by a data analysis system. This system performs calculations to detect abnormal conditions, such as high temperatures or leftover items. When an abnormality is detected, an alarm notification system is activated to immediately send a warning to the mobile device.

[0114] Users can receive alerts and take action via their mobile devices. Specifically, users can remotely check on their vehicles and arrange for rescue if necessary.

[0115] For example, when a family is in a self-driving car, this system could detect a child left inside the vehicle and immediately send an alert to the parent's mobile device. To prevent such misuse, vehicle interior safety monitoring would be strengthened.

[0116] An example of a prompt message would be, "How do I integrate facial recognition and environmental monitoring into an application that enhances in-vehicle monitoring capabilities, and how do I send alerts in case of anomalies?"

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The server receives video data sent from the terminal (in-car camera) as input. Using this data, it applies a face recognition algorithm using video analysis software. This process outputs information about the presence or absence of people and their identification. Specifically, the in-car camera continuously captures video of the inside of the car, and this video is transmitted to the server.

[0120] Step 2:

[0121] The server acquires ambient temperature data measured by terminals (temperature sensors) at regular intervals. Using this data as input, it performs temperature analysis processing and outputs a determination of whether or not the temperature exceeds the set safe temperature range. Specifically, temperature information is transmitted to the server from temperature sensors installed in multiple locations inside the vehicle, and safety is evaluated based on this information.

[0122] Step 3:

[0123] The server obtains location data from the terminal (GPS device) within the vehicle. Using this location information as input, it performs a matching process and outputs the current location of each person. This includes tracking the vehicle's position while it is moving and confirming the location of each occupant.

[0124] Step 4:

[0125] The server integrates the facial recognition results, temperature data, and location information obtained from the above processes and performs data analysis. It detects anomalies, such as people being left behind or abnormal temperature increases, and outputs the results. Specifically, it uses a data integration algorithm and flags any data that exceeds a set threshold.

[0126] Step 5:

[0127] The system outputs an alert to the user's mobile device indicating that a security anomaly has been detected. The input is the detection of an anomaly from data analysis, and the output is an alert notification. Specifically, this includes sending an alert to the user via email or app notification, prompting them to take necessary action.

[0128] 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.

[0129] This invention is a system that prevents people from being left behind inside a vehicle and provides more appropriate warnings by recognizing the user's emotions. The system is implemented with the following elements:

[0130] 1. System Configuration

[0131] The system consists of multiple terminals installed inside the vehicle and a remote server. Key components include image acquisition means, temperature measurement means, location information acquisition means, emotion recognition engine, and alarm output means.

[0132] 2. Data Collection

[0133] Terminal (in-vehicle camera): Continuously monitors people inside the vehicle, acquires images, and transmits them to the server. This accumulates foundational data for facial recognition and emotion recognition.

[0134] Terminal (temperature sensor) and GPS device: Measure environmental data inside the vehicle and transmit the acquired information to the server.

[0135] 3. Data Analysis and Sentiment Recognition

[0136] Server: Uses received image data to perform facial recognition and identify people inside the vehicle. Next, uses an emotion recognition engine to identify the user's emotions from the facial image data.

[0137] The server integrates location information and temperature data to detect anomalies such as people being left behind or high temperatures.

[0138] 4. Alarm notification and response

[0139] Server: If an anomaly is detected, it generates an optimized alarm that takes into account the user's sentiment data and sends a notification to the user's smartphone.

[0140] User: After receiving the notification, follow the situation-specific instructions provided by the system and take the necessary actions.

[0141] 5. Specific Examples

[0142] For example, in a case where a child is left behind while waiting at a traffic light, the emotion recognition engine can detect the child's stress and anxiety from the camera data. Based on this, the server can send a high-priority alert to the user, prompting a quick response.

[0143] This system incorporates emotion recognition technology to achieve more advanced safety management regarding people inside vehicles.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The terminal (in-vehicle camera) continuously captures images of the vehicle's interior and transmits the image data to the server in real time. This data is used for facial recognition and emotion recognition.

[0147] Step 2:

[0148] The server performs a facial recognition process on the received image data to identify the number of people inside the vehicle and their respective identifiers. This information can then be used to assess the risk of being left behind.

[0149] Step 3:

[0150] The server then uses an emotion recognition engine to analyze the emotional state of the person whose face has been recognized. In this process, emotions such as stress and anxiety are identified from facial expressions, and the results are stored in a database.

[0151] Step 4:

[0152] The terminal (GPS device) acquires the location information of each person inside the vehicle and transmits it to the server. This location information is used to determine whether a person is trapped inside the vehicle.

[0153] Step 5:

[0154] The terminal (temperature sensor) measures the temperature inside the vehicle and continuously transmits this data to the server. This temperature data is important information for determining safety.

[0155] Step 6:

[0156] The server integrates and analyzes collected facial recognition, emotion recognition, location information, and temperature data to determine if there are any anomalies. It then assesses safety based on the risk of being left behind and the level of risk calculated from emotional states.

[0157] Step 7:

[0158] If an anomaly is detected, the server generates an alert with an appropriate level of urgency based on the user's emotional state. This alert is set to be sent to the user's smartphone to inform them of the situation.

[0159] Step 8:

[0160] Users check the alerts displayed on their smartphones and take appropriate action based on the designated urgency level. This includes actions such as returning to the vehicle or initiating rescue operations.

[0161] Through these steps, the system utilizes facial recognition and emotion data to quickly and accurately manage the safety status inside the vehicle.

[0162] (Example 2)

[0163] 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".

[0164] Conventional systems designed to prevent people from being trapped inside vehicles only detect environmental anomalies and are unable to provide warnings that take into account the emotional state of the person being trapped. Therefore, prompt and appropriate responses are difficult, and reducing the risk, especially when children or the elderly are trapped, remains a challenge.

[0165] 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.

[0166] In this invention, the server includes means for acquiring image information of a person, means for recognizing a person from the acquired image information, and means for analyzing the emotional state of the person. This enables the creation of an optimal alarm that takes the emotional state of the person into consideration, allowing for a quick and appropriate response.

[0167] "Image acquisition means" refers to a device that acquires image information of people inside a vehicle.

[0168] "Facial recognition means" refers to technology used to identify individuals from acquired image information.

[0169] A "spatial information acquisition means" is a device for acquiring location information inside a vehicle.

[0170] A "temperature measuring device" is a device that measures the temperature inside a vehicle.

[0171] An "information analysis system" is a system that integrates and analyzes information obtained from various acquisition methods to detect anomalies.

[0172] "Emotion recognition means" refers to a technology that analyzes a person's emotional state, and it identifies emotions based on image information.

[0173] A "warning generation means" is a system that creates and outputs the most appropriate warning based on detected abnormalities or emotional states.

[0174] This invention is a system that prevents people from being left behind inside a vehicle and provides more appropriate warnings through emotion recognition. The system consists of multiple terminals installed inside the vehicle and a remote server.

[0175] The device includes an in-car camera, a temperature sensor, and a GPS device, each performing the following roles: The in-car camera continuously photographs people inside the vehicle and collects image information. The temperature sensor measures the ambient temperature inside the vehicle, and the GPS device obtains the vehicle's location information. This data is transmitted to the server via wireless communication.

[0176] The server is responsible for analyzing the received data. First, the server uses facial recognition to identify individuals from image information. Then, emotion recognition is used to determine the user's emotional state from the facial image. This analysis process uses technology to detect facial feature points and compare them with existing databases. The server also integrates and analyzes spatial information and temperature data to detect anomalies such as being left behind or overheating.

[0177] If an anomaly is detected, the server uses an alarm generation mechanism to create an optimal alarm based on the user's emotional state. This alarm is sent to the mobile device, prompting the user to take a quick and appropriate action.

[0178] For example, if a child is left alone in a vehicle while waiting at a traffic light, the device's camera will detect this, and the server will use emotion recognition to determine the child's level of anxiety. Based on this, the server can send an emergency message to the user saying, "The child in the back seat is anxious. Please check on them immediately."

[0179] This system enables advanced safety management for people inside the vehicle, reducing risks, especially when children or the elderly are present.

[0180] Example of a prompt

[0181] "Please explain the system for detecting a person trapped inside a vehicle. Specifically, please explain in detail how it recognizes emotions and issues an alarm."

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] The terminal (in-vehicle camera) acquires image information from inside the vehicle. In this step, the camera periodically takes pictures of the interior of the vehicle, collecting image data in real time. The acquired image information is transmitted to the server via wireless communication. The server uses this input (raw image data) for subsequent face recognition processing.

[0185] Step 2:

[0186] The server receives image information transmitted from the terminal as input and identifies the person using facial recognition technology. Specifically, the server quantifies the facial feature points and compares them with a database. As output, an ID of the identified person is generated. This allows the server to identify which person is inside the vehicle.

[0187] Step 3:

[0188] The server receives location and temperature data sent from the terminal. In this step, the information analysis means operates based on the input spatial information and ambient temperature. The server integrates and analyzes this data to detect abnormal conditions inside the vehicle (e.g., excessively high temperature, prolonged stoppage). If an abnormality is detected, a flag corresponding to the situation is output.

[0189] Step 4:

[0190] The server uses the face recognition results and anomaly detection flags to activate the emotion recognition mechanism. As input, it estimates the emotional state (e.g., anxiety, anger, joy) based on the identified person's face image. The server analyzes this data and identifies the person's emotional state as output.

[0191] Step 5:

[0192] The server generates the optimal alarm based on the person's emotional state and the results of anomaly detection. Specifically, the server considers the emotional state and environmental anomalies and selects appropriate wording. The generated alarm message is sent as output to the user's mobile device.

[0193] Step 6:

[0194] The user receives an alarm notification sent from the server. The user checks the notification and takes appropriate action based on its content (e.g., return to the vehicle to check, open the windows to lower the temperature). This enables a quick response to situations such as being trapped inside the vehicle or encountering abnormal conditions.

[0195] (Application Example 2)

[0196] 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".

[0197] In modern vehicles, occupant safety management is crucial, but technologies that can completely prevent the danger of occupants being unintentionally trapped inside a vehicle are limited. Especially with the proliferation of autonomous vehicles, there is a growing need to consider the emotions of occupants inside vehicles and manage safety at a higher level. However, conventional systems do not take into account the occupants' state or emotions, making it difficult to provide appropriate warnings.

[0198] 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.

[0199] In this invention, the server includes an emotion recognition engine means for identifying the emotions of the occupant, means for optimizing alarms by taking into account the information from the emotion recognition engine means, and means for transmitting notifications to a general-purpose information terminal. This makes it possible to provide the occupant's information terminal with an optimized alarm based on the occupant's emotional state in real time when an abnormal event or danger occurs.

[0200] "Occupant" refers to a person inside a vehicle.

[0201] "Image acquisition means" refers to a device or method for capturing image data of an occupant inside a vehicle.

[0202] "Facial recognition means" refers to technology for identifying the face of an occupant from acquired image data.

[0203] "Location information acquisition means" refers to a device or technology for acquiring location data inside a vehicle.

[0204] "Temperature measuring means" refers to a device or method for measuring the temperature inside a vehicle.

[0205] "Data analysis means" refers to a technology that integrates data from facial recognition means, location information acquisition means, and temperature measurement means to detect abnormal events.

[0206] "Alarm output means" refers to a device or method that issues an alarm when an abnormal event is detected.

[0207] An "emotion recognition engine" is a technology for identifying the emotions of an occupant.

[0208] "General-purpose information terminals" refer to commonly available portable electronic devices, including smartphones.

[0209] To realize this invention, a system is needed to monitor the occupant inside the vehicle and ensure their safety. The server continuously acquires image data from cameras installed inside the vehicle and identifies the occupant using facial recognition software. An emotion recognition engine analyzes the occupant's emotions from the images and provides the data to the server. In addition, a temperature sensor measures the temperature inside the vehicle, and a GPS module acquires location data.

[0210] The server comprehensively analyzes this data to detect abnormal events. For example, if a child is left unattended in a vehicle and the conditions are dangerously high in temperature, the server will immediately issue an alarm. The alarm is sent to a general-purpose information terminal, prompting the user to take a quick response. The notification is optimized based on the occupant's emotional state, allowing the user to take an appropriate action according to the situation.

[0211] As a concrete example, consider a situation where an adult gets out of a vehicle while it's parked in a shopping center parking lot. If a child is left inside the vehicle, the system analyzes the child's emotions as stress or anxiety and issues an alarm. The user receives a notification via their smartphone and can ensure their safety by quickly returning to the vehicle.

[0212] An example of a prompt message is: "Please describe how to use Emotion Recognizer to analyze the occupant's emotions in real time and optimize notifications when anomalies are detected."

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The terminal continuously acquires image data of the occupant using a camera inside the vehicle. The input is image frames from the camera, and the output is image data sent to the server. This data acquisition forms the basis for visually understanding the occupant's current state.

[0216] Step 2:

[0217] The server processes the received image data using facial recognition software to identify the occupant. The input is the image data acquired in step 1, and the output is the information of the identified occupant. By identifying the occupant through facial recognition, the system can track the occupant's status.

[0218] Step 3:

[0219] The terminal measures the temperature inside the vehicle using a temperature sensor and transmits the data to the server. The input is numerical data obtained from the temperature sensor, and the output is temperature information sent to the server. This temperature information is important for understanding the environment inside the vehicle.

[0220] Step 4:

[0221] The server uses an emotion recognition engine to analyze the occupant's emotions from image data. The input is the image data obtained in step 1, and the output is the analyzed emotion data. Emotion recognition makes it possible to take the occupant's psychological state into consideration.

[0222] Step 5:

[0223] The server integrates facial recognition, temperature, and emotion data to perform data analysis for detecting abnormal events. Input consists of information provided by each data source, and output is a determination of whether or not an abnormal event occurred. Data integration enables more accurate situational assessment.

[0224] Step 6:

[0225] If an abnormal event is detected, the server sends an optimized alarm notification to a general-purpose information terminal. The input is the result of the abnormal event detection and the optimized alarm message, and the output is a notification to the user's terminal. This notification allows the user to understand the situation in real time and take appropriate action.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention is a safety management system for preventing people or animals from being left behind inside a vehicle. Specific embodiments are shown below.

[0243] 1. System Configuration

[0244] The system consists of multiple terminals and servers. The terminals include image acquisition devices, temperature measurement devices, and location information acquisition devices installed inside the vehicle. Smartphones are also used as alarm output devices.

[0245] 2. Data Collection

[0246] Terminal (in-vehicle camera): Functions as a means of acquiring images and photographs people inside the vehicle. The captured images are converted into identification information of the people by a facial recognition system.

[0247] Terminal (temperature sensor): Measures the temperature inside the vehicle and transmits data at regular intervals.

[0248] Terminal (GPS device): As a means of acquiring location information, it continuously acquires location information of the person inside the vehicle.

[0249] 3. Data Analysis and Decision-Making

[0250] Server: Performs facial recognition on acquired image data and integrates and analyzes temperature data and location information. This allows it to determine whether a person is trapped inside the vehicle or whether the temperature exceeds a safe range.

[0251] The server uses this information to maintain a state where it can respond quickly when an anomaly is detected.

[0252] 4. Alert Notifications and Responses

[0253] Server: If an anomaly is detected, an alert is immediately sent to the smartphone of the registered user (such as a parent or guardian).

[0254] User: Upon receiving an alarm, return to your vehicle, assess the situation, and take rescue action as necessary.

[0255] 5. Specific Examples

[0256] For example, in a case where this system is implemented in a vehicle used to transport children to and from a childcare facility, the number of children would be counted using facial recognition when they board the vehicle, and the same method would be used to reconfirm their presence when they disembark. If it is determined that someone has been left inside the vehicle, an alarm will be immediately sent to the user's smartphone, even if the temperature data is within a safe range.

[0257] This system configuration enables safe and highly efficient monitoring within the vehicle.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] The terminal (in-vehicle camera) monitors the situation inside the vehicle in real time and operates as a means of acquiring images. It collects image data of people inside the vehicle and transmits the information to the server.

[0261] Step 2:

[0262] The server analyzes the received image data using facial recognition technology. This extracts identifiable information about the people inside the vehicle and records the current number of passengers. The resulting facial recognition results are then stored in a database.

[0263] Step 3:

[0264] The terminal (GPS device) continuously acquires the location information of all people inside the vehicle and periodically transmits it to the server. This ensures that the location of everyone inside the vehicle is always known.

[0265] Step 4:

[0266] The terminal (temperature sensor) measures the temperature inside the vehicle and periodically sends this data to the server. The server uses this temperature information as basic data for making safety decisions.

[0267] Step 5:

[0268] The server then integrates facial recognition, location information, and temperature data to determine if there are any anomalies. If a person is left behind or the temperature exceeds a set safe value, it flags the situation as an anomaly.

[0269] Step 6:

[0270] If an abnormal flag is set on the server, it immediately activates the alarm output mechanism. The alarm is sent to the user's smartphone, and a warning message appropriate to the situation is sent.

[0271] Step 7:

[0272] The user checks the alert received on their smartphone and returns to the designated vehicle or takes appropriate action. After resolving the situation, the user provides feedback to the server via the application, confirming that the problem has been resolved and updating the system record.

[0273] (Example 1)

[0274] 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."

[0275] There is a problem of accidents and dangers occurring when humans or animals are trapped inside vehicles. In particular, in high-temperature environments or enclosed spaces, situations that endanger lives can develop over time, so it is necessary to quickly and accurately assess the situation and respond appropriately. However, conventional technology is insufficient to address this problem.

[0276] 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.

[0277] In this invention, the server includes an information acquisition means, an identification means, a location acquisition device, an observation means, an integrated processing means, and a notification means. This makes it possible to monitor the situation inside the vehicle in real time, prevent people or animals from being left behind, and quickly send an alarm to the user when an abnormality is detected.

[0278] "Information acquisition means" refers to devices installed to understand the situation inside a vehicle and to detect the presence of people or animals.

[0279] The "identification means" is a technology used to identify people and animals inside a vehicle based on the data obtained from the information acquisition means.

[0280] The "position acquisition device" is a device for identifying the current location of a vehicle and its position inside the vehicle.

[0281] The "observation means" is a device for measuring and recording environmental data such as the temperature inside a vehicle.

[0282] The "integrated processing means" is a system that performs processing for detecting abnormalities by integrating and analyzing the acquired data.

[0283] The "notification means" is a device or system for transmitting relevant information to a user when an abnormality is detected.

[0284] This invention is a safety management system for preventing humans and animals from being left behind inside a vehicle. The system consists of a plurality of terminals and a server.

[0285] The terminals include an in-vehicle camera, a temperature sensor, and a position acquisition device. The in-vehicle camera functions as an image acquisition means and captures images of people and animals inside the vehicle. The captured images are analyzed through the face recognition software installed on the terminal and converted into identification information of the people.

[0286] The temperature sensor continuously measures the environmental temperature inside the vehicle. The acquired temperature data is transmitted to the server at regular time intervals. The position acquisition device using a GPS device tracks the current position of the vehicle in real time.

[0287] The server collects these data and performs analysis by the integrated processing means. In this analysis, face recognition software and a position data integration system are utilized. Based on the analysis results, it is determined whether there are people or animals left behind inside the vehicle or whether the temperature inside the vehicle exceeds the safe range.

[0288] If an anomaly is detected, the server will connect to a smartphone as a notification method and send an alert to the user. Upon receiving the alert, the user will check the situation and, if necessary, quickly go to the site to take action.

[0289] For example, if this system were installed in a vehicle used to transport children to and from a childcare facility, facial recognition technology would be used to verify the number of children boarding and disembarking in the same way. If it is determined that someone is still inside the vehicle, an alert would be immediately sent to a smartphone.

[0290] Examples of prompts for a generative AI model:

[0291] "Please describe the processing steps of a system that uses data from in-vehicle cameras and temperature sensors to determine the safety status of people inside the vehicle and sends alerts to registered users in case of abnormalities."

[0292] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0293] Step 1:

[0294] The terminal (in-vehicle camera) captures images of the interior of the vehicle. The input is the current visual information inside the vehicle, which is captured by the camera. The output is a captured image file. This image data is used in the subsequent face recognition process.

[0295] Step 2:

[0296] The terminal (temperature sensor) measures the temperature inside the vehicle. The input is the current temperature of the vehicle's interior. The output is temperature information in numerical data format, which is then sent to the server. This temperature data is used for safety assessments of humans and animals.

[0297] Step 3:

[0298] The terminal (GPS device) obtains the current position of the vehicle. As input, there is a signal from a GPS satellite. The output is position data indicating the specific position of the vehicle. This position information is used for situation judgment and emergency notification.

[0299] Step 4:

[0300] The server receives the image data transmitted from the in-vehicle camera and processes the image using face recognition software. As input, an image file is provided, and a face recognition algorithm is applied. The output is the specific information of the identified person.

[0301] Step 5:

[0302] The server integrates and analyzes the collected temperature data and position data. As input, there is temperature data and position data, and these are integrated and analyzed. As a result of the analysis, in-vehicle safety evaluation data is obtained as output. This evaluation is used to determine whether there are people or animals left in the vehicle.

[0303] Step 6:

[0304] If an abnormality is detected based on the analysis result, the server sends a notification to the user. As input, there is the safety evaluation data from the previous step. The output is that an alert notification is sent to a mobile terminal such as a smartphone as a warning message.

[0305] Step 7:

[0306] The user receives the notification on the smartphone and returns to the vehicle to check according to the warning situation. As input, there is the received alert information. The output is that the user's action data (for example, moving to the scene) is generated. The user takes necessary actions on-site to ensure safety.

[0307] (Application Example 1)

[0308] 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 glasses 214 will be referred to as the "terminal."

[0309] In autonomous vehicles, the absence of human intervention makes it difficult to detect the presence of people or animals trapped inside the vehicle at an early stage. In particular, in environments where the temperature inside the vehicle rises, safety cannot be guaranteed if someone is trapped inside. Therefore, there is a need to establish a system that can quickly and accurately detect people or animals inside the vehicle and issue warnings as needed.

[0310] 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.

[0311] In this invention, the server includes a shooting means for acquiring video data of a person, an authentication means for identifying a person from the acquired video data, and a location data acquisition means for acquiring location data inside the vehicle. This makes it possible to efficiently detect the presence of a person or animal left inside the vehicle and to quickly issue a warning when an abnormality occurs.

[0312] "Filming equipment" refers to devices used to acquire video data of people or animals inside a vehicle.

[0313] "Authentication means" refers to technology used to identify individuals from acquired video data.

[0314] "Location data acquisition means" refers to a device for acquiring location data inside a vehicle.

[0315] A "temperature measuring device" is a device used to measure the ambient temperature inside a vehicle.

[0316] "Data analysis means" refers to a processing device that integrates data obtained from authentication means, location data acquisition means, and temperature measurement means to detect anomalies.

[0317] An "alarm notification device" is a device that transmits an alarm to an external source when an abnormality is detected.

[0318] A "portable information terminal" refers to a portable information device such as a smartphone or tablet.

[0319] One embodiment of this invention is an integrated system for safety management within an autonomous vehicle. The system includes means for taking photographs, means for authentication, means for acquiring location data, means for measuring temperature, means for analyzing data, and means for notifying alarms, all of which work in coordination.

[0320] The server uses cameras installed inside the vehicle to perform imaging and collect video data. This data is then passed to an authentication system that uses specific software libraries (e.g., a Python library for image analysis) to identify people. This makes it possible to extract information about the presence or absence of people and their identification from the video.

[0321] Furthermore, the server implements a temperature measurement system that uses sensors placed inside the vehicle to measure temperature and acquire data in real time. The location data acquisition system acquires the vehicle's current location and detailed internal location information via a GPS module and provides the necessary data.

[0322] The various acquired data are integrated by a data analysis system. This system performs calculations to detect abnormal conditions, such as high temperatures or leftover items. When an abnormality is detected, an alarm notification system is activated to immediately send a warning to the mobile device.

[0323] Users can receive alerts and take action via their mobile devices. Specifically, users can remotely check on their vehicles and arrange for rescue if necessary.

[0324] For example, when a family is in a self-driving car, this system could detect a child left inside the vehicle and immediately send an alert to the parent's mobile device. To prevent such misuse, vehicle interior safety monitoring would be strengthened.

[0325] An example of a prompt message would be, "How do I integrate facial recognition and environmental monitoring into an application that enhances in-vehicle monitoring capabilities, and how do I send alerts in case of anomalies?"

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] The server receives video data sent from the terminal (in-car camera) as input. Using this data, it applies a face recognition algorithm using video analysis software. This process outputs information about the presence or absence of people and their identification. Specifically, the in-car camera continuously captures video of the inside of the car, and this video is transmitted to the server.

[0329] Step 2:

[0330] The server acquires ambient temperature data measured by terminals (temperature sensors) at regular intervals. Using this data as input, it performs temperature analysis processing and outputs a determination of whether or not the temperature exceeds the set safe temperature range. Specifically, temperature information is transmitted to the server from temperature sensors installed in multiple locations inside the vehicle, and safety is evaluated based on this information.

[0331] Step 3:

[0332] The server obtains location data from the terminal (GPS device) within the vehicle. Using this location information as input, it performs a matching process and outputs the current location of each person. This includes tracking the vehicle's position while it is moving and confirming the location of each occupant.

[0333] Step 4:

[0334] The server integrates the facial recognition results, temperature data, and location information obtained from the above processes and performs data analysis. It detects anomalies, such as people being left behind or abnormal temperature increases, and outputs the results. Specifically, it uses a data integration algorithm and flags any data that exceeds a set threshold.

[0335] Step 5:

[0336] The system outputs an alert to the user's mobile device indicating that a security anomaly has been detected. The input is the detection of an anomaly from data analysis, and the output is an alert notification. Specifically, this includes sending an alert to the user via email or app notification, prompting them to take necessary action.

[0337] 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.

[0338] This invention is a system that prevents people from being left behind inside a vehicle and provides more appropriate warnings by recognizing the user's emotions. The system is implemented with the following elements:

[0339] 1. System Configuration

[0340] The system consists of multiple terminals installed inside the vehicle and a remote server. Key components include image acquisition means, temperature measurement means, location information acquisition means, emotion recognition engine, and alarm output means.

[0341] 2. Data Collection

[0342] Terminal (in-vehicle camera): Continuously monitors people inside the vehicle, acquires images, and transmits them to the server. This accumulates foundational data for facial recognition and emotion recognition.

[0343] Terminal (temperature sensor) and GPS device: Measure environmental data inside the vehicle and transmit the acquired information to the server.

[0344] 3. Data Analysis and Sentiment Recognition

[0345] Server: Uses received image data to perform facial recognition and identify people inside the vehicle. Next, uses an emotion recognition engine to determine the user's emotions from the facial image data.

[0346] The server integrates location information and temperature data to detect anomalies such as people being left behind or high temperatures.

[0347] 4. Alarm notification and response

[0348] Server: If an anomaly is detected, it generates an optimized alarm that takes into account the user's sentiment data and sends a notification to the user's smartphone.

[0349] User: After receiving the notification, follow the situation-specific instructions provided by the system and take the necessary actions.

[0350] 5. Specific Examples

[0351] For example, in a case where a child is left behind while waiting at a traffic light, the emotion recognition engine can detect the child's stress and anxiety from the camera data. Based on this, the server can send a high-priority alert to the user, prompting a quick response.

[0352] This system incorporates emotion recognition technology to achieve more advanced safety management regarding people inside vehicles.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] The terminal (in-vehicle camera) continuously captures images of the vehicle's interior and transmits the image data to the server in real time. This data is used for facial recognition and emotion recognition.

[0356] Step 2:

[0357] The server performs a facial recognition process on the received image data to identify the number of people inside the vehicle and their respective identifiers. This information can then be used to assess the risk of being left behind.

[0358] Step 3:

[0359] The server then uses an emotion recognition engine to analyze the emotional state of the person whose face has been recognized. In this process, emotions such as stress and anxiety are identified from facial expressions, and the results are stored in a database.

[0360] Step 4:

[0361] The terminal (GPS device) acquires the location information of each person inside the vehicle and transmits it to the server. This location information is used to determine whether a person is trapped inside the vehicle.

[0362] Step 5:

[0363] The terminal (temperature sensor) measures the temperature inside the vehicle and continuously transmits this data to the server. This temperature data is important information for determining safety.

[0364] Step 6:

[0365] The server integrates and analyzes collected facial recognition, emotion recognition, location information, and temperature data to determine if there are any anomalies. It then assesses safety based on the risk of being left behind and the level of risk calculated from emotional states.

[0366] Step 7:

[0367] If an anomaly is detected, the server generates an alert with an appropriate level of urgency based on the user's emotional state. This alert is set to be sent to the user's smartphone to inform them of the situation.

[0368] Step 8:

[0369] Users check the alerts displayed on their smartphones and take appropriate action based on the designated urgency level. This includes actions such as returning to the vehicle or initiating rescue operations.

[0370] Through these steps, the system utilizes facial recognition and emotion data to quickly and accurately manage the safety status inside the vehicle.

[0371] (Example 2)

[0372] 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".

[0373] Conventional systems designed to prevent people from being trapped inside vehicles only detect environmental anomalies and are unable to provide warnings that take into account the emotional state of the person being trapped. Therefore, prompt and appropriate responses are difficult, and reducing the risk, especially when children or the elderly are trapped, remains a challenge.

[0374] 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.

[0375] In this invention, the server includes means for acquiring image information of a person, means for recognizing the person from the acquired image information, and means for analyzing the person's emotional state. This enables the creation of an optimal alarm that takes the person's emotional state into account, allowing for a quick and appropriate response.

[0376] "Image acquisition means" refers to a device that acquires image information of people inside a vehicle.

[0377] "Facial recognition means" refers to technology used to identify individuals from acquired image information.

[0378] A "spatial information acquisition means" is a device for acquiring location information inside a vehicle.

[0379] A "temperature measuring device" is a device that measures the temperature inside a vehicle.

[0380] An "information analysis system" is a system that integrates and analyzes information obtained from various acquisition methods to detect anomalies.

[0381] "Emotion recognition means" refers to a technology that analyzes a person's emotional state, and it identifies emotions based on image information.

[0382] A "warning generation means" is a system that creates and outputs the most appropriate warning based on detected abnormalities or emotional states.

[0383] This invention is a system that prevents people from being left behind inside a vehicle and provides more appropriate warnings through emotion recognition. The system consists of multiple terminals installed inside the vehicle and a remote server.

[0384] The device includes an in-car camera, a temperature sensor, and a GPS device, each performing the following roles: The in-car camera continuously photographs people inside the vehicle and collects image information. The temperature sensor measures the ambient temperature inside the vehicle, and the GPS device obtains the vehicle's location information. This data is transmitted to the server via wireless communication.

[0385] The server is responsible for analyzing the received data. First, the server uses facial recognition to identify individuals from image information. Then, emotion recognition is used to determine the user's emotional state from the facial image. This analysis process uses technology to detect facial feature points and compare them with existing databases. The server also integrates and analyzes spatial information and temperature data to detect anomalies such as being left behind or overheating.

[0386] If an anomaly is detected, the server uses an alarm generation mechanism to create an optimal alarm based on the user's emotional state. This alarm is sent to the mobile device, prompting the user to take a quick and appropriate action.

[0387] For example, if a child is left alone in a vehicle while waiting at a traffic light, the device's camera will detect this, and the server will use emotion recognition to determine the child's level of anxiety. Based on this, the server can send an emergency message to the user saying, "The child in the back seat is anxious. Please check on them immediately."

[0388] This system enables advanced safety management for people inside the vehicle, reducing risks, especially when children or the elderly are present.

[0389] Example of a prompt

[0390] "Please explain the system for detecting a person trapped inside a vehicle. Specifically, please explain in detail how it recognizes emotions and issues an alarm."

[0391] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0392] Step 1:

[0393] The terminal (in-vehicle camera) acquires image information from inside the vehicle. In this step, the camera periodically takes pictures of the interior of the vehicle, collecting image data in real time. The acquired image information is transmitted to the server via wireless communication. The server uses this input (raw image data) for subsequent face recognition processing.

[0394] Step 2:

[0395] The server receives image information transmitted from the terminal as input and identifies the person using facial recognition technology. Specifically, the server quantifies the facial feature points and compares them with a database. As output, an ID of the identified person is generated. This allows the server to identify which person is inside the vehicle.

[0396] Step 3:

[0397] The server receives location and temperature data sent from the terminal. In this step, the information analysis means operates based on the input spatial information and ambient temperature. The server integrates and analyzes this data to detect abnormal conditions inside the vehicle (e.g., excessively high temperature, prolonged stoppage). If an abnormality is detected, a flag corresponding to the situation is output.

[0398] Step 4:

[0399] The server uses the face recognition results and anomaly detection flags to activate the emotion recognition mechanism. As input, it estimates the emotional state (e.g., anxiety, anger, joy) based on the identified person's face image. The server analyzes this data and identifies the person's emotional state as output.

[0400] Step 5:

[0401] The server generates the optimal alarm based on the person's emotional state and the anomaly detection results. Specifically, the server considers the emotional state and environmental anomalies and selects appropriate wording. The generated alarm message is sent as output to the user's mobile device.

[0402] Step 6:

[0403] The user receives an alarm notification sent from the server. The user checks the notification and takes appropriate action based on its content (e.g., return to the vehicle to check, open the windows to lower the temperature). This enables a quick response to situations such as being trapped inside the vehicle or encountering abnormal conditions.

[0404] (Application Example 2)

[0405] 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."

[0406] In modern vehicles, occupant safety management is crucial, but technologies that can completely prevent the danger of occupants being unintentionally trapped inside a vehicle are limited. Especially with the proliferation of autonomous vehicles, there is a growing need to consider the emotions of occupants inside vehicles and manage safety at a higher level. However, conventional systems do not take into account the occupants' state or emotions, making it difficult to provide appropriate warnings.

[0407] 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.

[0408] In this invention, the server includes an emotion recognition engine means for identifying the emotions of the occupant, means for optimizing alarms by taking into account the information from the emotion recognition engine means, and means for transmitting notifications to a general-purpose information terminal. This makes it possible to provide the occupant's information terminal with an optimized alarm based on the occupant's emotional state in real time when an abnormal event or danger occurs.

[0409] "Occupant" refers to a person inside a vehicle.

[0410] "Image acquisition means" refers to a device or method for capturing image data of an occupant inside a vehicle.

[0411] "Facial recognition means" refers to technology for identifying the face of an occupant from acquired image data.

[0412] "Location information acquisition means" refers to a device or technology for acquiring location data inside a vehicle.

[0413] "Temperature measuring means" refers to a device or method for measuring the temperature inside a vehicle.

[0414] "Data analysis means" refers to a technology that integrates data from facial recognition means, location information acquisition means, and temperature measurement means to detect abnormal events.

[0415] "Alarm output means" refers to a device or method that issues an alarm when an abnormal event is detected.

[0416] An "emotion recognition engine" is a technology for identifying the emotions of an occupant.

[0417] "General-purpose information terminals" refer to commonly available portable electronic devices, including smartphones.

[0418] To realize this invention, a system is needed to monitor the occupant inside the vehicle and ensure their safety. The server continuously acquires image data from cameras installed inside the vehicle and identifies the occupant using facial recognition software. An emotion recognition engine analyzes the occupant's emotions from the images and provides the data to the server. In addition, a temperature sensor measures the temperature inside the vehicle, and a GPS module acquires location data.

[0419] The server comprehensively analyzes this data to detect abnormal events. For example, if a child is left unattended in a vehicle and the conditions are dangerously high in temperature, the server will immediately issue an alarm. The alarm is sent to a general-purpose information terminal, prompting the user to take a quick response. The notification is optimized based on the occupant's emotional state, allowing the user to take an appropriate action according to the situation.

[0420] As a concrete example, consider a situation where an adult gets out of a vehicle while it's parked in a shopping center parking lot. If a child is left inside the vehicle, the system analyzes the child's emotions as stress or anxiety and issues an alarm. The user receives a notification via their smartphone and can ensure their safety by quickly returning to the vehicle.

[0421] An example of a prompt message is: "Please describe how to use Emotion Recognizer to analyze the occupant's emotions in real time and optimize notifications when anomalies are detected."

[0422] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0423] Step 1:

[0424] The terminal continuously acquires image data of the occupant using a camera inside the vehicle. The input is image frames from the camera, and the output is image data sent to the server. This data acquisition forms the basis for visually understanding the occupant's current state.

[0425] Step 2:

[0426] The server processes the received image data using facial recognition software to identify the occupant. The input is the image data acquired in step 1, and the output is the information of the identified occupant. By identifying the occupant through facial recognition, the system can track the occupant's status.

[0427] Step 3:

[0428] The terminal measures the temperature inside the vehicle using a temperature sensor and transmits the data to the server. The input is numerical data obtained from the temperature sensor, and the output is temperature information sent to the server. This temperature information is important for understanding the environment inside the vehicle.

[0429] Step 4:

[0430] The server uses an emotion recognition engine to analyze the occupant's emotions from image data. The input is the image data obtained in step 1, and the output is the analyzed emotion data. Emotion recognition makes it possible to take the occupant's psychological state into consideration.

[0431] Step 5:

[0432] The server integrates facial recognition, temperature, and emotion data to perform data analysis for detecting abnormal events. Input consists of information provided by each data source, and output is a determination of whether or not an abnormal event occurred. Data integration enables more accurate situational assessment.

[0433] Step 6:

[0434] If an abnormal event is detected, the server sends an optimized alarm notification to a general-purpose information terminal. The input is the result of the abnormal event detection and the optimized alarm message, and the output is a notification to the user's terminal. This notification allows the user to understand the situation in real time and take appropriate action.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] [Third Embodiment]

[0439] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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".

[0451] This invention is a safety management system for preventing people or animals from being left behind inside a vehicle. Specific embodiments are shown below.

[0452] 1. System Configuration

[0453] The system consists of multiple terminals and servers. The terminals include image acquisition devices, temperature measurement devices, and location information acquisition devices installed inside the vehicle. Smartphones are also used as alarm output devices.

[0454] 2. Data Collection

[0455] Terminal (in-vehicle camera): Functions as a means of acquiring images and photographs people inside the vehicle. The captured images are converted into identification information of the people by a facial recognition system.

[0456] Terminal (temperature sensor): Measures the temperature inside the vehicle and transmits data at regular intervals.

[0457] Terminal (GPS device): As a means of acquiring location information, it continuously acquires location information of the person inside the vehicle.

[0458] 3. Data Analysis and Decision-Making

[0459] Server: Performs facial recognition on acquired image data and integrates and analyzes temperature data and location information. This allows it to determine whether a person is trapped inside the vehicle or whether the temperature exceeds a safe range.

[0460] The server uses this information to maintain a state where it can respond quickly when an anomaly is detected.

[0461] 4. Alert Notifications and Responses

[0462] Server: If an anomaly is detected, an alert is immediately sent to the smartphone of the registered user (such as a parent or guardian).

[0463] User: Upon receiving an alarm, return to your vehicle, assess the situation, and take rescue action as necessary.

[0464] 5. Specific Examples

[0465] For example, in a case where this system is implemented in a vehicle used to transport children to and from a childcare facility, the number of children would be counted using facial recognition when they board the vehicle, and the same method would be used to reconfirm their presence when they disembark. If it is determined that someone has been left inside the vehicle, an alarm will be immediately sent to the user's smartphone, even if the temperature data is within a safe range.

[0466] This system configuration enables safe and highly efficient monitoring within the vehicle.

[0467] The following describes the processing flow.

[0468] Step 1:

[0469] The terminal (in-vehicle camera) monitors the situation inside the vehicle in real time and operates as a means of acquiring images. It collects image data of people inside the vehicle and transmits the information to the server.

[0470] Step 2:

[0471] The server analyzes the received image data using facial recognition technology. This extracts identifiable information about the people inside the vehicle and records the current number of passengers. The resulting facial recognition results are then stored in a database.

[0472] Step 3:

[0473] The terminal (GPS device) continuously acquires the location information of all people inside the vehicle and periodically transmits it to the server. This ensures that the location of everyone inside the vehicle is always known.

[0474] Step 4:

[0475] The terminal (temperature sensor) measures the temperature inside the vehicle and periodically sends this data to the server. The server uses this temperature information as basic data for making safety decisions.

[0476] Step 5:

[0477] The server then integrates facial recognition, location information, and temperature data to determine if there are any anomalies. If a person is left behind or the temperature exceeds a set safe value, it flags the situation as an anomaly.

[0478] Step 6:

[0479] If an abnormal flag is set on the server, it immediately activates the alarm output mechanism. The alarm is sent to the user's smartphone, and a warning message appropriate to the situation is sent.

[0480] Step 7:

[0481] The user checks the alert received on their smartphone and returns to the designated vehicle or takes appropriate action. After resolving the situation, the user provides feedback to the server via the application, confirming that the problem has been resolved and updating the system record.

[0482] (Example 1)

[0483] 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."

[0484] There is a problem of accidents and dangers occurring when humans or animals are trapped inside vehicles. In particular, in high-temperature environments or enclosed spaces, situations that endanger lives can develop over time, so it is necessary to quickly and accurately assess the situation and respond appropriately. However, conventional technology is insufficient to address this problem.

[0485] 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.

[0486] In this invention, the server includes an information acquisition means, an identification means, a location acquisition device, an observation means, an integrated processing means, and a notification means. This makes it possible to monitor the situation inside the vehicle in real time, prevent people or animals from being left behind, and quickly send an alarm to the user when an abnormality is detected.

[0487] "Information acquisition means" refers to devices installed to understand the situation inside a vehicle and to detect the presence of people or animals.

[0488] "Identification means" refers to technology used to identify people or animals inside a vehicle based on data obtained from information acquisition means.

[0489] A "position acquisition device" is a device used to determine the current location of a vehicle and its position within the vehicle.

[0490] "Observation means" refers to a device that measures and records environmental data such as temperature inside a vehicle.

[0491] An "integrated processing means" is a system that integrates and analyzes acquired data to perform processing for detecting anomalies.

[0492] A "notification means" is a device or system for transmitting relevant information to the user when an anomaly is detected.

[0493] This invention is a safety management system for preventing people or animals from being trapped inside a vehicle. The system consists of multiple terminals and a server.

[0494] The device includes an in-vehicle camera, a temperature sensor, and a location acquisition device. The in-vehicle camera functions as an image acquisition tool, capturing images of people and animals inside the vehicle. The captured images are analyzed through facial recognition software installed on the device and converted into person identification information.

[0495] A temperature sensor continuously measures the ambient temperature inside the vehicle. The acquired temperature data is transmitted to a server at regular intervals. A GPS device tracks the vehicle's current location in real time.

[0496] The server collects this data and performs analysis using an integrated processing system. This analysis utilizes facial recognition software and a location data integration system. Based on the analysis results, it determines whether a person or animal is trapped inside the vehicle, or whether the temperature inside the vehicle exceeds a safe range.

[0497] If an anomaly is detected, the server will connect to a smartphone as a notification method and send an alert to the user. Upon receiving the alert, the user will check the situation and, if necessary, quickly go to the site to take action.

[0498] For example, if this system were installed in a vehicle used to transport children to and from a childcare facility, facial recognition technology would be used to verify the number of children boarding and disembarking in the same way. If it is determined that someone is still inside the vehicle, an alert would be immediately sent to a smartphone.

[0499] Examples of prompts for a generative AI model:

[0500] "Please describe the processing steps of a system that uses data from in-vehicle cameras and temperature sensors to determine the safety status of people inside the vehicle and sends alerts to registered users in case of abnormalities."

[0501] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0502] Step 1:

[0503] The terminal (in-vehicle camera) captures images of the interior of the vehicle. The input is the current visual information inside the vehicle, which is captured by the camera. The output is a captured image file. This image data is used in the subsequent face recognition process.

[0504] Step 2:

[0505] The terminal (temperature sensor) measures the temperature inside the vehicle. The input is the current temperature of the vehicle's interior. The output is temperature information in numerical data format, which is then sent to the server. This temperature data is used for safety assessments of humans and animals.

[0506] Step 3:

[0507] The terminal (GPS device) obtains the current location of the vehicle. Its input is a signal from GPS satellites. The output is location data indicating the vehicle's specific location. This location information is used for situation assessment and emergency notifications.

[0508] Step 4:

[0509] The server receives image data transmitted from the in-vehicle camera and processes the images using facial recognition software. An image file is provided as input, and a facial recognition algorithm is applied. The output is identifying information about the identified person.

[0510] Step 5:

[0511] The server integrates and analyzes the collected temperature and location data. Temperature and location data are used as inputs, and these are integrated and analyzed. As a result of the analysis, safety assessment data for the vehicle's interior is obtained as output. This assessment is used to determine whether a person or animal is trapped inside the vehicle.

[0512] Step 6:

[0513] If an anomaly is detected based on the analysis results, the server sends a notification to the user. The input is the safety assessment data from the previous step. The output is an alert notification sent as a warning message to a mobile device such as a smartphone.

[0514] Step 7:

[0515] The user receives notifications on their smartphone and returns to their vehicle to check the situation depending on the warning status. The input is the received alert information. The output generates user activity data (e.g., travel to the site). The user takes the necessary actions at the site and ensures safety.

[0516] (Application Example 1)

[0517] 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."

[0518] In autonomous vehicles, the absence of human intervention makes it difficult to detect the presence of people or animals trapped inside the vehicle at an early stage. In particular, in environments where the temperature inside the vehicle rises, safety cannot be guaranteed if someone is trapped inside. Therefore, there is a need to establish a system that can quickly and accurately detect people or animals inside the vehicle and issue warnings as needed.

[0519] 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.

[0520] In this invention, the server includes a shooting means for acquiring video data of a person, an authentication means for identifying a person from the acquired video data, and a location data acquisition means for acquiring location data inside the vehicle. This makes it possible to efficiently detect the presence of a person or animal left inside the vehicle and to quickly issue a warning when an abnormality occurs.

[0521] "Filming equipment" refers to devices used to acquire video data of people or animals inside a vehicle.

[0522] "Authentication means" refers to technology used to identify individuals from acquired video data.

[0523] "Location data acquisition means" refers to a device for acquiring location data inside a vehicle.

[0524] A "temperature measuring device" is a device used to measure the ambient temperature inside a vehicle.

[0525] "Data analysis means" refers to a processing device that integrates data obtained from authentication means, location data acquisition means, and temperature measurement means to detect anomalies.

[0526] An "alarm notification device" is a device that transmits an alarm to an external source when an abnormality is detected.

[0527] A "portable information terminal" refers to a portable information device such as a smartphone or tablet.

[0528] One embodiment of this invention is an integrated system for safety management within an autonomous vehicle. The system includes means for taking photographs, means for authentication, means for acquiring location data, means for measuring temperature, means for analyzing data, and means for notifying alarms, all of which work in coordination.

[0529] The server uses cameras installed inside the vehicle to perform imaging and collect video data. This data is then passed to an authentication system that uses specific software libraries (e.g., a Python library for image analysis) to identify people. This makes it possible to extract information about the presence or absence of people and their identification from the video.

[0530] Furthermore, the server implements a temperature measurement system that uses sensors placed inside the vehicle to measure temperature and acquire data in real time. The location data acquisition system acquires the vehicle's current location and detailed internal location information via a GPS module and provides the necessary data.

[0531] The various acquired data are integrated by a data analysis system. This system performs calculations to detect abnormal conditions, such as high temperatures or leftover items. When an abnormality is detected, an alarm notification system is activated to immediately send a warning to the mobile device.

[0532] Users can receive alerts and take action via their mobile devices. Specifically, users can remotely check on their vehicles and arrange for rescue if necessary.

[0533] For example, when a family is in a self-driving car, this system could detect a child left inside the vehicle and immediately send an alert to the parent's mobile device. To prevent such misuse, vehicle interior safety monitoring would be strengthened.

[0534] An example of a prompt message would be, "How do I integrate facial recognition and environmental monitoring into an application that enhances in-vehicle monitoring capabilities, and how do I send alerts in case of anomalies?"

[0535] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0536] Step 1:

[0537] The server receives video data sent from the terminal (in-car camera) as input. Using this data, it applies a face recognition algorithm using video analysis software. This process outputs information about the presence or absence of people and their identification. Specifically, the in-car camera continuously captures video of the inside of the car, and this video is transmitted to the server.

[0538] Step 2:

[0539] The server acquires ambient temperature data measured by terminals (temperature sensors) at regular intervals. Using this data as input, it performs temperature analysis processing and outputs a determination of whether or not the temperature exceeds the set safe temperature range. Specifically, temperature information is transmitted to the server from temperature sensors installed in multiple locations inside the vehicle, and safety is evaluated based on this information.

[0540] Step 3:

[0541] The server obtains location data from the terminal (GPS device) within the vehicle. Using this location information as input, it performs a matching process and outputs the current location of each person. This includes tracking the vehicle's position while it is moving and confirming the location of each occupant.

[0542] Step 4:

[0543] The server integrates the facial recognition results, temperature data, and location information obtained from the above processes and performs data analysis. It detects anomalies, such as people being left behind or abnormal temperature increases, and outputs the results. Specifically, it uses a data integration algorithm and flags any data that exceeds a set threshold.

[0544] Step 5:

[0545] The system outputs an alert to the user's mobile device indicating that a security anomaly has been detected. The input is the detection of an anomaly from data analysis, and the output is an alert notification. Specifically, this includes sending an alert to the user via email or app notification, prompting them to take necessary action.

[0546] 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.

[0547] This invention is a system that prevents people from being left behind inside a vehicle and provides more appropriate warnings by recognizing the user's emotions. The system is implemented with the following elements:

[0548] 1. System Configuration

[0549] The system consists of multiple terminals installed inside the vehicle and a remote server. Key components include image acquisition means, temperature measurement means, location information acquisition means, emotion recognition engine, and alarm output means.

[0550] 2. Data Collection

[0551] Terminal (in-vehicle camera): Continuously monitors people inside the vehicle, acquires images, and transmits them to the server. This accumulates foundational data for facial recognition and emotion recognition.

[0552] Terminal (temperature sensor) and GPS device: Measure environmental data inside the vehicle and transmit the acquired information to the server.

[0553] 3. Data Analysis and Sentiment Recognition

[0554] Server: Uses received image data to perform facial recognition and identify people inside the vehicle. Next, uses an emotion recognition engine to determine the user's emotions from the facial image data.

[0555] The server integrates location information and temperature data to detect anomalies such as people being left behind or high temperatures.

[0556] 4. Alarm notification and response

[0557] Server: If an anomaly is detected, it generates an optimized alarm that takes into account the user's sentiment data and sends a notification to the user's smartphone.

[0558] User: After receiving the notification, follow the situation-specific instructions provided by the system and take the necessary actions.

[0559] 5. Specific Examples

[0560] For example, in a case where a child is left behind while waiting at a traffic light, the emotion recognition engine can detect the child's stress and anxiety from the camera data. Based on this, the server can send a high-priority alert to the user, prompting a quick response.

[0561] This system incorporates emotion recognition technology to achieve more advanced safety management regarding people inside vehicles.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] The terminal (in-vehicle camera) continuously captures images of the vehicle's interior and transmits the image data to the server in real time. This data is used for facial recognition and emotion recognition.

[0565] Step 2:

[0566] The server performs a facial recognition process on the received image data to identify the number of people inside the vehicle and their respective identifiers. This information can then be used to assess the risk of being left behind.

[0567] Step 3:

[0568] The server then uses an emotion recognition engine to analyze the emotional state of the person whose face has been recognized. In this process, emotions such as stress and anxiety are identified from facial expressions, and the results are stored in a database.

[0569] Step 4:

[0570] The terminal (GPS device) acquires the location information of each person inside the vehicle and transmits it to the server. This location information is used to determine whether a person is trapped inside the vehicle.

[0571] Step 5:

[0572] The terminal (temperature sensor) measures the temperature inside the vehicle and continuously transmits this data to the server. This temperature data is important information for determining safety.

[0573] Step 6:

[0574] The server integrates and analyzes collected facial recognition, emotion recognition, location information, and temperature data to determine if there are any anomalies. It then assesses safety based on the risk of being left behind and the level of risk calculated from emotional states.

[0575] Step 7:

[0576] If an anomaly is detected, the server generates an alert with an appropriate level of urgency based on the user's emotional state. This alert is set to be sent to the user's smartphone to inform them of the situation.

[0577] Step 8:

[0578] Users check the alerts displayed on their smartphones and take appropriate action based on the designated urgency level. This includes actions such as returning to the vehicle or initiating rescue operations.

[0579] Through these steps, the system utilizes facial recognition and emotion data to quickly and accurately manage the safety status inside the vehicle.

[0580] (Example 2)

[0581] 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."

[0582] Conventional systems designed to prevent people from being trapped inside vehicles only detect environmental anomalies and are unable to provide warnings that take into account the emotional state of the person being trapped. Therefore, prompt and appropriate responses are difficult, and reducing the risk, especially when children or the elderly are trapped, remains a challenge.

[0583] 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.

[0584] In this invention, the server includes means for acquiring image information of a person, means for recognizing the person from the acquired image information, and means for analyzing the person's emotional state. This enables the creation of an optimal alarm that takes the person's emotional state into account, allowing for a quick and appropriate response.

[0585] "Image acquisition means" refers to a device that acquires image information of people inside a vehicle.

[0586] "Facial recognition means" refers to technology used to identify individuals from acquired image information.

[0587] A "spatial information acquisition means" is a device for acquiring location information inside a vehicle.

[0588] A "temperature measuring device" is a device that measures the temperature inside a vehicle.

[0589] An "information analysis system" is a system that integrates and analyzes information obtained from various acquisition methods to detect anomalies.

[0590] "Emotion recognition means" refers to a technology that analyzes a person's emotional state, and it identifies emotions based on image information.

[0591] A "warning generation means" is a system that creates and outputs the most appropriate warning based on detected abnormalities or emotional states.

[0592] This invention is a system that prevents people from being left behind inside a vehicle and provides more appropriate warnings through emotion recognition. The system consists of multiple terminals installed inside the vehicle and a remote server.

[0593] The device includes an in-car camera, a temperature sensor, and a GPS device, each performing the following roles: The in-car camera continuously photographs people inside the vehicle and collects image information. The temperature sensor measures the ambient temperature inside the vehicle, and the GPS device obtains the vehicle's location information. This data is transmitted to the server via wireless communication.

[0594] The server is responsible for analyzing the received data. First, the server uses facial recognition to identify individuals from image information. Then, emotion recognition is used to determine the user's emotional state from the facial image. This analysis process uses technology to detect facial feature points and compare them with existing databases. The server also integrates and analyzes spatial information and temperature data to detect anomalies such as being left behind or overheating.

[0595] If an anomaly is detected, the server uses an alarm generation mechanism to create an optimal alarm based on the user's emotional state. This alarm is sent to the mobile device, prompting the user to take a quick and appropriate action.

[0596] For example, if a child is left alone in a vehicle while waiting at a traffic light, the device's camera will detect this, and the server will use emotion recognition to determine the child's level of anxiety. Based on this, the server can send an emergency message to the user saying, "The child in the back seat is anxious. Please check on them immediately."

[0597] This system enables advanced safety management for people inside the vehicle, reducing risks, especially when children or the elderly are present.

[0598] Example of a prompt

[0599] "Please explain the system for detecting a person trapped inside a vehicle. Specifically, please explain in detail how it recognizes emotions and issues an alarm."

[0600] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0601] Step 1:

[0602] The terminal (in-vehicle camera) acquires image information from inside the vehicle. In this step, the camera periodically takes pictures of the interior of the vehicle, collecting image data in real time. The acquired image information is transmitted to the server via wireless communication. The server uses this input (raw image data) for subsequent face recognition processing.

[0603] Step 2:

[0604] The server receives image information transmitted from the terminal as input and identifies the person using facial recognition technology. Specifically, the server quantifies the facial feature points and compares them with a database. As output, an ID of the identified person is generated. This allows the server to identify which person is inside the vehicle.

[0605] Step 3:

[0606] The server receives location and temperature data sent from the terminal. In this step, the information analysis means operates based on the input spatial information and ambient temperature. The server integrates and analyzes this data to detect abnormal conditions inside the vehicle (e.g., excessively high temperature, prolonged stoppage). If an abnormality is detected, a flag corresponding to the situation is output.

[0607] Step 4:

[0608] The server uses the face recognition results and anomaly detection flags to activate the emotion recognition mechanism. As input, it estimates the emotional state (e.g., anxiety, anger, joy) based on the identified person's face image. The server analyzes this data and identifies the person's emotional state as output.

[0609] Step 5:

[0610] The server generates the optimal alarm based on the person's emotional state and the anomaly detection results. Specifically, the server considers the emotional state and environmental anomalies and selects appropriate wording. The generated alarm message is sent as output to the user's mobile device.

[0611] Step 6:

[0612] The user receives an alarm notification sent from the server. The user checks the notification and takes appropriate action based on its content (e.g., return to the vehicle to check, open the windows to lower the temperature). This enables a quick response to situations such as being trapped inside the vehicle or encountering abnormal conditions.

[0613] (Application Example 2)

[0614] 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."

[0615] In modern vehicles, occupant safety management is crucial, but technologies that can completely prevent the danger of occupants being unintentionally trapped inside a vehicle are limited. Especially with the proliferation of autonomous vehicles, there is a growing need to consider the emotions of occupants inside vehicles and manage safety at a higher level. However, conventional systems do not take into account the occupants' state or emotions, making it difficult to provide appropriate warnings.

[0616] 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.

[0617] In this invention, the server includes an emotion recognition engine means for identifying the emotions of the occupant, means for optimizing alarms by taking into account the information from the emotion recognition engine means, and means for transmitting notifications to a general-purpose information terminal. This makes it possible to provide the occupant's information terminal with an optimized alarm based on the occupant's emotional state in real time when an abnormal event or danger occurs.

[0618] "Occupant" refers to a person inside a vehicle.

[0619] "Image acquisition means" refers to a device or method for capturing image data of an occupant inside a vehicle.

[0620] "Facial recognition means" refers to technology for identifying the face of an occupant from acquired image data.

[0621] "Location information acquisition means" refers to a device or technology for acquiring location data inside a vehicle.

[0622] "Temperature measuring means" refers to a device or method for measuring the temperature inside a vehicle.

[0623] "Data analysis means" refers to a technology that integrates data from facial recognition means, location information acquisition means, and temperature measurement means to detect abnormal events.

[0624] "Alarm output means" refers to a device or method that issues an alarm when an abnormal event is detected.

[0625] An "emotion recognition engine" is a technology for identifying the emotions of an occupant.

[0626] "General-purpose information terminals" refer to commonly available portable electronic devices, including smartphones.

[0627] To realize this invention, a system is needed to monitor the occupant inside the vehicle and ensure their safety. The server continuously acquires image data from cameras installed inside the vehicle and identifies the occupant using facial recognition software. An emotion recognition engine analyzes the occupant's emotions from the images and provides the data to the server. In addition, a temperature sensor measures the temperature inside the vehicle, and a GPS module acquires location data.

[0628] The server comprehensively analyzes this data to detect abnormal events. For example, if a child is left unattended in a vehicle and the conditions are dangerously high in temperature, the server will immediately issue an alarm. The alarm is sent to a general-purpose information terminal, prompting the user to take a quick response. The notification is optimized based on the occupant's emotional state, allowing the user to take an appropriate action according to the situation.

[0629] As a concrete example, consider a situation where an adult gets out of a vehicle while it's parked in a shopping center parking lot. If a child is left inside the vehicle, the system analyzes the child's emotions as stress or anxiety and issues an alarm. The user receives a notification via their smartphone and can ensure their safety by quickly returning to the vehicle.

[0630] An example of a prompt message is: "Please describe how to use Emotion Recognizer to analyze the occupant's emotions in real time and optimize notifications when anomalies are detected."

[0631] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0632] Step 1:

[0633] The terminal continuously acquires image data of the occupant using a camera inside the vehicle. The input is image frames from the camera, and the output is image data sent to the server. This data acquisition forms the basis for visually understanding the occupant's current state.

[0634] Step 2:

[0635] The server processes the received image data using facial recognition software to identify the occupant. The input is the image data acquired in step 1, and the output is the information of the identified occupant. By identifying the occupant through facial recognition, the system can track the occupant's status.

[0636] Step 3:

[0637] The terminal measures the temperature inside the vehicle using a temperature sensor and transmits the data to the server. The input is numerical data obtained from the temperature sensor, and the output is temperature information sent to the server. This temperature information is important for understanding the environment inside the vehicle.

[0638] Step 4:

[0639] The server uses an emotion recognition engine to analyze the occupant's emotions from image data. The input is the image data obtained in step 1, and the output is the analyzed emotion data. Emotion recognition makes it possible to take the occupant's psychological state into consideration.

[0640] Step 5:

[0641] The server integrates facial recognition, temperature, and emotion data to perform data analysis for detecting abnormal events. Input consists of information provided by each data source, and output is a determination of whether or not an abnormal event occurred. Data integration enables more accurate situational assessment.

[0642] Step 6:

[0643] If an abnormal event is detected, the server sends an optimized alarm notification to a general-purpose information terminal. The input is the result of the abnormal event detection and the optimized alarm message, and the output is a notification to the user's terminal. This notification allows the user to understand the situation in real time and take appropriate action.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] [Fourth Embodiment]

[0648] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0649] 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.

[0650] 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).

[0651] 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.

[0652] 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.

[0653] 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).

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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".

[0661] This invention is a safety management system for preventing people or animals from being left behind inside a vehicle. Specific embodiments are shown below.

[0662] 1. System Configuration

[0663] The system consists of multiple terminals and servers. The terminals include image acquisition devices, temperature measurement devices, and location information acquisition devices installed inside the vehicle. Smartphones are also used as alarm output devices.

[0664] 2. Data Collection

[0665] Terminal (in-vehicle camera): Functions as a means of acquiring images and photographs people inside the vehicle. The captured images are converted into identification information of the people by a facial recognition system.

[0666] Terminal (temperature sensor): Measures the temperature inside the vehicle and transmits data at regular intervals.

[0667] Terminal (GPS device): As a means of acquiring location information, it continuously acquires location information of the person inside the vehicle.

[0668] 3. Data Analysis and Decision-Making

[0669] Server: Performs facial recognition on acquired image data and integrates and analyzes temperature data and location information. This allows it to determine whether a person is trapped inside the vehicle or whether the temperature exceeds a safe range.

[0670] The server uses this information to maintain a state where it can respond quickly when an anomaly is detected.

[0671] 4. Alert Notifications and Responses

[0672] Server: If an anomaly is detected, an alert is immediately sent to the smartphone of the registered user (such as a parent or guardian).

[0673] User: Upon receiving an alarm, return to your vehicle, assess the situation, and take rescue action as necessary.

[0674] 5. Specific Examples

[0675] For example, in a case where this system is implemented in a vehicle used to transport children to and from a childcare facility, the number of children would be counted using facial recognition when they board the vehicle, and the same method would be used to reconfirm their presence when they disembark. If it is determined that someone has been left inside the vehicle, an alarm will be immediately sent to the user's smartphone, even if the temperature data is within a safe range.

[0676] This system configuration enables safe and highly efficient monitoring within the vehicle.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] The terminal (in-vehicle camera) monitors the situation inside the vehicle in real time and operates as a means of acquiring images. It collects image data of people inside the vehicle and transmits the information to the server.

[0680] Step 2:

[0681] The server analyzes the received image data using facial recognition technology. This extracts identifiable information about the people inside the vehicle and records the current number of passengers. The resulting facial recognition results are then stored in a database.

[0682] Step 3:

[0683] The terminal (GPS device) continuously acquires the location information of all people inside the vehicle and periodically transmits it to the server. This ensures that the location of everyone inside the vehicle is always known.

[0684] Step 4:

[0685] The terminal (temperature sensor) measures the temperature inside the vehicle and periodically sends this data to the server. The server uses this temperature information as basic data for making safety decisions.

[0686] Step 5:

[0687] The server then integrates facial recognition, location information, and temperature data to determine if there are any anomalies. If a person is left behind or the temperature exceeds a set safe value, it flags the situation as an anomaly.

[0688] Step 6:

[0689] If an abnormal flag is set on the server, it immediately activates the alarm output mechanism. The alarm is sent to the user's smartphone, and a warning message appropriate to the situation is sent.

[0690] Step 7:

[0691] The user checks the alert received on their smartphone and returns to the designated vehicle or takes appropriate action. After resolving the situation, the user provides feedback to the server via the application, confirming that the problem has been resolved and updating the system record.

[0692] (Example 1)

[0693] 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".

[0694] There is a problem of accidents and dangers occurring when humans or animals are trapped inside vehicles. In particular, in high-temperature environments or enclosed spaces, situations that endanger lives can develop over time, so it is necessary to quickly and accurately assess the situation and respond appropriately. However, conventional technology is insufficient to address this problem.

[0695] 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.

[0696] In this invention, the server includes an information acquisition means, an identification means, a location acquisition device, an observation means, an integrated processing means, and a notification means. This makes it possible to monitor the situation inside the vehicle in real time, prevent people or animals from being left behind, and quickly send an alarm to the user when an abnormality is detected.

[0697] "Information acquisition means" refers to devices installed to understand the situation inside a vehicle and to detect the presence of people or animals.

[0698] "Identification means" refers to technology used to identify people or animals inside a vehicle based on data obtained from information acquisition means.

[0699] A "position acquisition device" is a device used to determine the current location of a vehicle and its position within the vehicle.

[0700] "Observation means" refers to a device that measures and records environmental data such as temperature inside a vehicle.

[0701] An "integrated processing means" is a system that integrates and analyzes acquired data to perform processing for detecting anomalies.

[0702] A "notification means" is a device or system for transmitting relevant information to the user when an anomaly is detected.

[0703] This invention is a safety management system for preventing people or animals from being trapped inside a vehicle. The system consists of multiple terminals and a server.

[0704] The device includes an in-vehicle camera, a temperature sensor, and a location acquisition device. The in-vehicle camera functions as an image acquisition tool, capturing images of people and animals inside the vehicle. The captured images are analyzed through facial recognition software installed on the device and converted into person identification information.

[0705] A temperature sensor continuously measures the ambient temperature inside the vehicle. The acquired temperature data is transmitted to a server at regular intervals. A GPS device tracks the vehicle's current location in real time.

[0706] The server collects this data and performs analysis using an integrated processing system. This analysis utilizes facial recognition software and a location data integration system. Based on the analysis results, it determines whether a person or animal is trapped inside the vehicle, or whether the temperature inside the vehicle exceeds a safe range.

[0707] If an anomaly is detected, the server will connect to a smartphone as a notification method and send an alert to the user. Upon receiving the alert, the user will check the situation and, if necessary, quickly go to the site to take action.

[0708] For example, if this system were installed in a vehicle used to transport children to and from a childcare facility, facial recognition technology would be used to verify the number of children boarding and disembarking in the same way. If it is determined that someone is still inside the vehicle, an alert would be immediately sent to a smartphone.

[0709] Examples of prompts for a generative AI model:

[0710] "Please describe the processing steps of a system that uses data from in-vehicle cameras and temperature sensors to determine the safety status of people inside the vehicle and sends alerts to registered users in case of abnormalities."

[0711] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0712] Step 1:

[0713] The terminal (in-vehicle camera) captures images of the interior of the vehicle. The input is the current visual information inside the vehicle, which is captured by the camera. The output is a captured image file. This image data is used in the subsequent face recognition process.

[0714] Step 2:

[0715] The terminal (temperature sensor) measures the temperature inside the vehicle. The input is the current temperature of the vehicle's interior. The output is temperature information in numerical data format, which is then sent to the server. This temperature data is used for safety assessments of humans and animals.

[0716] Step 3:

[0717] The terminal (GPS device) obtains the current location of the vehicle. Its input is a signal from GPS satellites. The output is location data indicating the vehicle's specific location. This location information is used for situation assessment and emergency notifications.

[0718] Step 4:

[0719] The server receives image data transmitted from the in-vehicle camera and processes the images using facial recognition software. An image file is provided as input, and a facial recognition algorithm is applied. The output is identifying information about the identified person.

[0720] Step 5:

[0721] The server integrates and analyzes the collected temperature and location data. Temperature and location data are used as inputs, and these are integrated and analyzed. As a result of the analysis, safety assessment data for the vehicle's interior is obtained as output. This assessment is used to determine whether a person or animal is trapped inside the vehicle.

[0722] Step 6:

[0723] If an anomaly is detected based on the analysis results, the server sends a notification to the user. The input is the safety assessment data from the previous step. The output is an alert notification sent as a warning message to a mobile device such as a smartphone.

[0724] Step 7:

[0725] The user receives notifications on their smartphone and returns to their vehicle to check the situation depending on the warning status. The input is the received alert information. The output generates user activity data (e.g., travel to the site). The user takes the necessary actions at the site and ensures safety.

[0726] (Application Example 1)

[0727] 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".

[0728] In autonomous vehicles, the absence of human intervention makes it difficult to detect the presence of people or animals trapped inside the vehicle at an early stage. In particular, in environments where the temperature inside the vehicle rises, safety cannot be guaranteed if someone is trapped inside. Therefore, there is a need to establish a system that can quickly and accurately detect people or animals inside the vehicle and issue warnings as needed.

[0729] 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.

[0730] In this invention, the server includes a shooting means for acquiring video data of a person, an authentication means for identifying a person from the acquired video data, and a location data acquisition means for acquiring location data inside the vehicle. This makes it possible to efficiently detect the presence of a person or animal left inside the vehicle and to quickly issue a warning when an abnormality occurs.

[0731] "Filming equipment" refers to devices used to acquire video data of people or animals inside a vehicle.

[0732] "Authentication means" refers to technology used to identify individuals from acquired video data.

[0733] "Location data acquisition means" refers to a device for acquiring location data inside a vehicle.

[0734] A "temperature measuring device" is a device used to measure the ambient temperature inside a vehicle.

[0735] "Data analysis means" refers to a processing device that integrates data obtained from authentication means, location data acquisition means, and temperature measurement means to detect anomalies.

[0736] An "alarm notification device" is a device that transmits an alarm to an external source when an abnormality is detected.

[0737] A "portable information terminal" refers to a portable information device such as a smartphone or tablet.

[0738] One embodiment of this invention is an integrated system for safety management within an autonomous vehicle. The system includes means for taking photographs, means for authentication, means for acquiring location data, means for measuring temperature, means for analyzing data, and means for notifying alarms, all of which work in coordination.

[0739] The server uses cameras installed inside the vehicle to perform imaging and collect video data. This data is then passed to an authentication system that uses specific software libraries (e.g., a Python library for image analysis) to identify people. This makes it possible to extract information about the presence or absence of people and their identification from the video.

[0740] Furthermore, the server implements a temperature measurement system that uses sensors placed inside the vehicle to measure temperature and acquire data in real time. The location data acquisition system acquires the vehicle's current location and detailed internal location information via a GPS module and provides the necessary data.

[0741] The various acquired data are integrated by a data analysis system. This system performs calculations to detect abnormal conditions, such as high temperatures or leftover items. When an abnormality is detected, an alarm notification system is activated to immediately send a warning to the mobile device.

[0742] Users can receive alerts and take action via their mobile devices. Specifically, users can remotely check on their vehicles and arrange for rescue if necessary.

[0743] For example, when a family is in a self-driving car, this system could detect a child left inside the vehicle and immediately send an alert to the parent's mobile device. To prevent such misuse, vehicle interior safety monitoring would be strengthened.

[0744] An example of a prompt message would be, "How do I integrate facial recognition and environmental monitoring into an application that enhances in-vehicle monitoring capabilities, and how do I send alerts in case of anomalies?"

[0745] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0746] Step 1:

[0747] The server receives video data sent from the terminal (in-car camera) as input. Using this data, it applies a face recognition algorithm using video analysis software. This process outputs information about the presence or absence of people and their identification. Specifically, the in-car camera continuously captures video of the inside of the car, and this video is transmitted to the server.

[0748] Step 2:

[0749] The server acquires ambient temperature data measured by terminals (temperature sensors) at regular intervals. Using this data as input, it performs temperature analysis processing and outputs a determination of whether or not the temperature exceeds the set safe temperature range. Specifically, temperature information is transmitted to the server from temperature sensors installed in multiple locations inside the vehicle, and safety is evaluated based on this information.

[0750] Step 3:

[0751] The server obtains location data from the terminal (GPS device) within the vehicle. Using this location information as input, it performs a matching process and outputs the current location of each person. This includes tracking the vehicle's position while it is moving and confirming the location of each occupant.

[0752] Step 4:

[0753] The server integrates the facial recognition results, temperature data, and location information obtained from the above processes and performs data analysis. It detects anomalies, such as people being left behind or abnormal temperature increases, and outputs the results. Specifically, it uses a data integration algorithm and flags any data that exceeds a set threshold.

[0754] Step 5:

[0755] The system outputs an alert to the user's mobile device indicating that a security anomaly has been detected. The input is the detection of an anomaly from data analysis, and the output is an alert notification. Specifically, this includes sending an alert to the user via email or app notification, prompting them to take necessary action.

[0756] 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.

[0757] This invention is a system that prevents people from being left behind inside a vehicle and provides more appropriate warnings by recognizing the user's emotions. The system is implemented with the following elements:

[0758] 1. System Configuration

[0759] The system consists of multiple terminals installed inside the vehicle and a remote server. Key components include image acquisition means, temperature measurement means, location information acquisition means, emotion recognition engine, and alarm output means.

[0760] 2. Data Collection

[0761] Terminal (in-vehicle camera): Continuously monitors people inside the vehicle, acquires images, and transmits them to the server. This accumulates foundational data for facial recognition and emotion recognition.

[0762] Terminal (temperature sensor) and GPS device: Measure environmental data inside the vehicle and transmit the acquired information to the server.

[0763] 3. Data Analysis and Sentiment Recognition

[0764] Server: Uses received image data to perform facial recognition and identify people inside the vehicle. Next, uses an emotion recognition engine to determine the user's emotions from the facial image data.

[0765] The server integrates location information and temperature data to detect anomalies such as people being left behind or high temperatures.

[0766] 4. Alarm notification and response

[0767] Server: If an anomaly is detected, it generates an optimized alarm that takes into account the user's sentiment data and sends a notification to the user's smartphone.

[0768] User: After receiving the notification, follow the situation-specific instructions provided by the system and take the necessary actions.

[0769] 5. Specific Examples

[0770] For example, in a case where a child is left behind while waiting at a traffic light, the emotion recognition engine can detect the child's stress and anxiety from the camera data. Based on this, the server can send a high-priority alert to the user, prompting a quick response.

[0771] This system incorporates emotion recognition technology to achieve more advanced safety management regarding people inside vehicles.

[0772] The following describes the processing flow.

[0773] Step 1:

[0774] The terminal (in-vehicle camera) continuously captures images of the vehicle's interior and transmits the image data to the server in real time. This data is used for facial recognition and emotion recognition.

[0775] Step 2:

[0776] The server performs a facial recognition process on the received image data to identify the number of people inside the vehicle and their respective identifiers. This information can then be used to assess the risk of being left behind.

[0777] Step 3:

[0778] The server then uses an emotion recognition engine to analyze the emotional state of the person whose face has been recognized. In this process, emotions such as stress and anxiety are identified from facial expressions, and the results are stored in a database.

[0779] Step 4:

[0780] The terminal (GPS device) acquires the location information of each person inside the vehicle and transmits it to the server. This location information is used to determine whether a person is trapped inside the vehicle.

[0781] Step 5:

[0782] The terminal (temperature sensor) measures the temperature inside the vehicle and continuously transmits this data to the server. This temperature data is important information for determining safety.

[0783] Step 6:

[0784] The server integrates and analyzes collected facial recognition, emotion recognition, location information, and temperature data to determine if there are any anomalies. It then assesses safety based on the risk of being left behind and the level of risk calculated from emotional states.

[0785] Step 7:

[0786] If an anomaly is detected, the server generates an alert with an appropriate level of urgency based on the user's emotional state. This alert is set to be sent to the user's smartphone to inform them of the situation.

[0787] Step 8:

[0788] Users check the alerts displayed on their smartphones and take appropriate action based on the designated urgency level. This includes actions such as returning to the vehicle or initiating rescue operations.

[0789] Through these steps, the system utilizes facial recognition and emotion data to quickly and accurately manage the safety status inside the vehicle.

[0790] (Example 2)

[0791] 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".

[0792] Conventional systems designed to prevent people from being trapped inside vehicles only detect environmental anomalies and are unable to provide warnings that take into account the emotional state of the person being trapped. Therefore, prompt and appropriate responses are difficult, and reducing the risk, especially when children or the elderly are trapped, remains a challenge.

[0793] 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.

[0794] In this invention, the server includes means for acquiring image information of a person, means for recognizing the person from the acquired image information, and means for analyzing the person's emotional state. This enables the creation of an optimal alarm that takes the person's emotional state into account, allowing for a quick and appropriate response.

[0795] "Image acquisition means" refers to a device that acquires image information of people inside a vehicle.

[0796] "Facial recognition means" refers to technology used to identify individuals from acquired image information.

[0797] A "spatial information acquisition means" is a device for acquiring location information inside a vehicle.

[0798] A "temperature measuring device" is a device that measures the temperature inside a vehicle.

[0799] An "information analysis system" is a system that integrates and analyzes information obtained from various acquisition methods to detect anomalies.

[0800] "Emotion recognition means" refers to a technology that analyzes a person's emotional state, and it identifies emotions based on image information.

[0801] A "warning generation means" is a system that creates and outputs the most appropriate warning based on detected abnormalities or emotional states.

[0802] This invention is a system that prevents people from being left behind inside a vehicle and provides more appropriate warnings through emotion recognition. The system consists of multiple terminals installed inside the vehicle and a remote server.

[0803] The device includes an in-car camera, a temperature sensor, and a GPS device, each performing the following roles: The in-car camera continuously photographs people inside the vehicle and collects image information. The temperature sensor measures the ambient temperature inside the vehicle, and the GPS device obtains the vehicle's location information. This data is transmitted to the server via wireless communication.

[0804] The server is responsible for analyzing the received data. First, the server uses facial recognition to identify individuals from image information. Then, emotion recognition is used to determine the user's emotional state from the facial image. This analysis process uses technology to detect facial feature points and compare them with existing databases. The server also integrates and analyzes spatial information and temperature data to detect anomalies such as being left behind or overheating.

[0805] If an anomaly is detected, the server uses an alarm generation mechanism to create an optimal alarm based on the user's emotional state. This alarm is sent to the mobile device, prompting the user to take a quick and appropriate action.

[0806] For example, if a child is left alone in a vehicle while waiting at a traffic light, the device's camera will detect this, and the server will use emotion recognition to determine the child's level of anxiety. Based on this, the server can send an emergency message to the user saying, "The child in the back seat is anxious. Please check on them immediately."

[0807] This system enables advanced safety management for people inside the vehicle, reducing risks, especially when children or the elderly are present.

[0808] Example of a prompt

[0809] "Please explain the system for detecting a person trapped inside a vehicle. Specifically, please explain in detail how it recognizes emotions and issues an alarm."

[0810] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0811] Step 1:

[0812] The terminal (in-vehicle camera) acquires image information from inside the vehicle. In this step, the camera periodically takes pictures of the interior of the vehicle, collecting image data in real time. The acquired image information is transmitted to the server via wireless communication. The server uses this input (raw image data) for subsequent face recognition processing.

[0813] Step 2:

[0814] The server receives image information transmitted from the terminal as input and identifies the person using facial recognition technology. Specifically, the server quantifies the facial feature points and compares them with a database. As output, an ID of the identified person is generated. This allows the server to identify which person is inside the vehicle.

[0815] Step 3:

[0816] The server receives location and temperature data sent from the terminal. In this step, the information analysis means operates based on the input spatial information and ambient temperature. The server integrates and analyzes this data to detect abnormal conditions inside the vehicle (e.g., excessively high temperature, prolonged stoppage). If an abnormality is detected, a flag corresponding to the situation is output.

[0817] Step 4:

[0818] The server uses the face recognition results and anomaly detection flags to activate the emotion recognition mechanism. As input, it estimates the emotional state (e.g., anxiety, anger, joy) based on the identified person's face image. The server analyzes this data and identifies the person's emotional state as output.

[0819] Step 5:

[0820] The server generates the optimal alarm based on the person's emotional state and the anomaly detection results. Specifically, the server considers the emotional state and environmental anomalies and selects appropriate wording. The generated alarm message is sent as output to the user's mobile device.

[0821] Step 6:

[0822] The user receives an alarm notification sent from the server. The user checks the notification and takes appropriate action based on its content (e.g., return to the vehicle to check, open the windows to lower the temperature). This enables a quick response to situations such as being trapped inside the vehicle or encountering abnormal conditions.

[0823] (Application Example 2)

[0824] 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".

[0825] In modern vehicles, occupant safety management is crucial, but technologies that can completely prevent the danger of occupants being unintentionally trapped inside a vehicle are limited. Especially with the proliferation of autonomous vehicles, there is a growing need to consider the emotions of occupants inside vehicles and manage safety at a higher level. However, conventional systems do not take into account the occupants' state or emotions, making it difficult to provide appropriate warnings.

[0826] 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.

[0827] In this invention, the server includes an emotion recognition engine means for identifying the emotions of the occupant, means for optimizing alarms by taking into account the information from the emotion recognition engine means, and means for transmitting notifications to a general-purpose information terminal. This makes it possible to provide the occupant's information terminal with an optimized alarm based on the occupant's emotional state in real time when an abnormal event or danger occurs.

[0828] "Occupant" refers to a person inside a vehicle.

[0829] "Image acquisition means" refers to a device or method for capturing image data of an occupant inside a vehicle.

[0830] "Facial recognition means" refers to technology for identifying the face of an occupant from acquired image data.

[0831] "Location information acquisition means" refers to a device or technology for acquiring location data inside a vehicle.

[0832] "Temperature measuring means" refers to a device or method for measuring the temperature inside a vehicle.

[0833] "Data analysis means" refers to a technology that integrates data from facial recognition means, location information acquisition means, and temperature measurement means to detect abnormal events.

[0834] "Alarm output means" refers to a device or method that issues an alarm when an abnormal event is detected.

[0835] An "emotion recognition engine" is a technology for identifying the emotions of an occupant.

[0836] "General-purpose information terminals" refer to commonly available portable electronic devices, including smartphones.

[0837] To realize this invention, a system is needed to monitor the occupant inside the vehicle and ensure their safety. The server continuously acquires image data from cameras installed inside the vehicle and identifies the occupant using facial recognition software. An emotion recognition engine analyzes the occupant's emotions from the images and provides the data to the server. In addition, a temperature sensor measures the temperature inside the vehicle, and a GPS module acquires location data.

[0838] The server comprehensively analyzes this data to detect abnormal events. For example, if a child is left unattended in a vehicle and the conditions are dangerously high in temperature, the server will immediately issue an alarm. The alarm is sent to a general-purpose information terminal, prompting the user to take a quick response. The notification is optimized based on the occupant's emotional state, allowing the user to take an appropriate action according to the situation.

[0839] As a concrete example, consider a situation where an adult gets out of a vehicle while it's parked in a shopping center parking lot. If a child is left inside the vehicle, the system analyzes the child's emotions as stress or anxiety and issues an alarm. The user receives a notification via their smartphone and can ensure their safety by quickly returning to the vehicle.

[0840] An example of a prompt message is: "Please describe how to use Emotion Recognizer to analyze the occupant's emotions in real time and optimize notifications when anomalies are detected."

[0841] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0842] Step 1:

[0843] The terminal continuously acquires image data of the occupant using a camera inside the vehicle. The input is image frames from the camera, and the output is image data sent to the server. This data acquisition forms the basis for visually understanding the occupant's current state.

[0844] Step 2:

[0845] The server processes the received image data using facial recognition software to identify the occupant. The input is the image data acquired in step 1, and the output is the information of the identified occupant. By identifying the occupant through facial recognition, the system can track the occupant's status.

[0846] Step 3:

[0847] The terminal measures the temperature inside the vehicle using a temperature sensor and transmits the data to the server. The input is numerical data obtained from the temperature sensor, and the output is temperature information sent to the server. This temperature information is important for understanding the environment inside the vehicle.

[0848] Step 4:

[0849] The server uses an emotion recognition engine to analyze the occupant's emotions from image data. The input is the image data obtained in step 1, and the output is the analyzed emotion data. Emotion recognition makes it possible to take the occupant's psychological state into consideration.

[0850] Step 5:

[0851] The server integrates facial recognition, temperature, and emotion data to perform data analysis for detecting abnormal events. Input consists of information provided by each data source, and output is a determination of whether or not an abnormal event occurred. Data integration enables more accurate situational assessment.

[0852] Step 6:

[0853] If an abnormal event is detected, the server sends an optimized alarm notification to a general-purpose information terminal. The input is the result of the abnormal event detection and the optimized alarm message, and the output is a notification to the user's terminal. This notification allows the user to understand the situation in real time and take appropriate action.

[0854] 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.

[0855] 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.

[0856] 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 robot 414.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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."

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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 as being incorporated by reference.

[0875] The following is further disclosed regarding the embodiments described above.

[0876] (Claim 1)

[0877] A system to prevent people from being trapped inside a vehicle,

[0878] Image acquisition means for acquiring image data of a person,

[0879] A facial recognition method that recognizes a person from acquired image data,

[0880] A means for acquiring location information to obtain location information inside the vehicle,

[0881] A temperature measuring means for measuring the temperature inside a vehicle,

[0882] A data analysis means that integrates data from the aforementioned face recognition means, location information acquisition means, and temperature measurement means to detect anomalies,

[0883] An alarm output means that outputs an alarm when an abnormality is detected,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, wherein the alarm output means has a function of sending a notification to a smartphone.

[0887] (Claim 3)

[0888] The system according to claim 1, wherein the data analysis means has a function to compare face recognition results with location information in order to confirm that a person has been left behind.

[0889] "Example 1"

[0890] (Claim 1)

[0891] A device for preventing the abandonment of humans and animals inside a vehicle,

[0892] A means of acquiring information to understand the situation inside the vehicle,

[0893] An identification means for identifying a target based on the acquired information,

[0894] A location acquisition device that collects location information,

[0895] Observation methods for measuring temperature,

[0896] An integrated processing means for integrating and analyzing this data,

[0897] A notification means that notifies when an anomaly is detected,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, wherein the notification means has the function of transmitting a notification to a mobile information terminal.

[0901] (Claim 3)

[0902] The system according to claim 1, wherein the integrated processing means has a function to compare identification results with location information in order to confirm the presence of a person or animal.

[0903] "Application Example 1"

[0904] (Claim 1)

[0905] A device for preventing people from being trapped inside a vehicle,

[0906] A means of capturing video data of a person,

[0907] Authentication means for identifying a person from acquired video data,

[0908] A location data acquisition means for acquiring location data inside a vehicle,

[0909] A temperature measuring means for measuring the ambient temperature inside a vehicle,

[0910] A data analysis means that integrates data from the authentication means, location data acquisition means, and temperature measurement means to detect anomalies,

[0911] An alarm notification means that outputs an alarm when an abnormality is detected,

[0912] A device that includes this.

[0913] (Claim 2)

[0914] The apparatus according to claim 1, wherein the alarm notification means has a function of transmitting a warning to a portable information terminal.

[0915] (Claim 3)

[0916] The apparatus according to claim 1, wherein the data analysis means has a function to compare the authentication result with location data in order to verify that a person has been left behind.

[0917] "Example 2 of combining an emotion engine"

[0918] (Claim 1)

[0919] An image acquisition method for obtaining image information of a person,

[0920] A facial recognition means for recognizing a person from acquired image information,

[0921] A spatial information acquisition means for acquiring location information inside the vehicle,

[0922] A temperature measuring means for measuring the temperature inside the vehicle,

[0923] An information analysis means that integrates information from facial recognition means, spatial information acquisition means, and temperature measurement means to detect anomalies,

[0924] An emotion recognition method for analyzing a person's emotional state,

[0925] An alarm generation means that outputs an alarm based on the emotional state when an abnormality is detected,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, wherein the alarm generation means has a function of sending a notification to a mobile terminal.

[0929] (Claim 3)

[0930] The system according to claim 1, wherein the information analysis means has a function to match facial recognition results with spatial information in order to confirm that a person has been left behind.

[0931] "Application example 2 of combining emotional engines"

[0932] (Claim 1)

[0933] A device for preventing occupants from being left behind inside a vehicle,

[0934] An image acquisition means for acquiring image data of the occupant,

[0935] A facial recognition means for identifying the occupant from acquired image data,

[0936] A location information acquisition means for acquiring location data inside a vehicle,

[0937] A temperature measuring means for measuring the temperature inside a vehicle,

[0938] A data analysis means that integrates data from the aforementioned face recognition means, location information acquisition means, and temperature measurement means to detect abnormal events,

[0939] An alarm output means that issues an alarm when an abnormal event is detected,

[0940] An emotion recognition engine means for identifying the emotions of the occupant,

[0941] A means for optimizing the alarm considering information from the emotion recognition engine means,

[0942] A device that includes this.

[0943] (Claim 2)

[0944] The apparatus according to claim 1, wherein the alarm output means has a function of transmitting a notification to a general-purpose information terminal.

[0945] (Claim 3)

[0946] The apparatus according to claim 1, wherein the data analysis means has a function to compare facial recognition results with location data in order to determine that an occupant has been left behind. [Explanation of Symbols]

[0947] 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 system to prevent people from being trapped inside a vehicle, An image acquisition method for acquiring image data of a person, A facial recognition method that recognizes a person from acquired image data, A means for acquiring location information to obtain location information inside the vehicle, A temperature measuring means for measuring the temperature inside a vehicle, A data analysis means that integrates data from the aforementioned face recognition means, location information acquisition means, and temperature measurement means to detect anomalies, An alarm output means that outputs an alarm when an abnormality is detected, A system that includes this.

2. The system according to claim 1, wherein the alarm output means has a function of sending a notification to a smartphone.

3. The system according to claim 1, wherein the data analysis means has a function to compare face recognition results with location information in order to confirm that a person has been left behind.

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