system

Abstract

We provide the system. [Solution] A means for acquiring location information and movement speed of the user, An analysis means for analyzing data acquired by a location information acquisition means to determine whether the user is in motion, A distance measurement means for managing the location information of emergency vehicles and measuring the relative distance to the user's location information, A notification mechanism to notify the user's device when it is determined that the user is approaching an emergency vehicle within a certain distance, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, when emergency vehicles respond to emergencies, collision accidents with general vehicles and bicycles occur frequently. This problem is often caused by the failure to notice the approach of emergency vehicles, and an urgent solution is required. These accidents that impede the rapid movement of emergency vehicles may ultimately lead to serious situations involving human lives. The present invention aims to prevent such collisions between emergency vehicles and other moving objects.

Means for Solving the Problems

[0005] This invention utilizes a location information acquisition means to obtain the user's location information and movement speed, and includes an analysis means to analyze this data, thereby determining in real time whether the user is currently moving. Furthermore, it includes a distance measurement means to manage the location information of emergency vehicles and measure the relative distance to the user's location information, and a notification means to notify the user's terminal with a voice or alarm sound when the user approaches an emergency vehicle within a certain distance, thereby enabling early warning to the user. With this system, the user can immediately recognize the approach of an emergency vehicle and take appropriate evasive action.

[0006] "Location information acquisition means" refers to a device or method for acquiring the user's current geographical location and movement speed in real time.

[0007] "Analysis means" refers to technical means that determine whether a user is moving or not based on acquired location information and movement speed data.

[0008] "Distance measurement means" refers to a device or method that uses the location information of an emergency vehicle and the location information of a user to measure the relative distance between them.

[0009] "Notification means" refers to a device or method that issues a warning, such as by voice or alarm, to inform the user of the approach of an emergency vehicle. [Brief explanation of the drawing]

[0010] [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]

[0011] 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.

[0012] First, let's explain the terminology used in the following explanation.

[0013] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0014] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0015] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0016] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards 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), and the like.

[0017] 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."

[0018] [First Embodiment]

[0019] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0020] 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.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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".

[0031] This invention includes a location information acquisition means that utilizes the GPS function of the user's device to obtain the user's current location and speed in real time. The device transmits this data to a server, which analyzes the location information and speed data. Based on the analysis means, the server determines whether the user is moving and stores the result.

[0032] The server also continuously acquires and manages the location of emergency vehicles through cooperation with external emergency vehicle tracking systems. Using distance measurement devices, the server compares the user's current location with the location of the emergency vehicle and measures the relative distance between them. If it is determined that the user has entered a certain distance, the server generates warning notification data and sends it to the user's terminal via the notification device.

[0033] The device uses voice or an alarm to notify the user of the approaching emergency vehicle based on notifications received from the server. For example, the device might announce, "An emergency vehicle is approaching ahead," allowing the user to recognize the approach and take a quick and safe action.

[0034] As a concrete example, consider a scenario where a user is driving a car. When the terminal detects that the user's speed exceeds a certain threshold, the server compares that data with the location information of an emergency vehicle. For example, if an emergency vehicle approaches within 500 meters, the server issues a warning, and an alarm sounds through the terminal. This allows the user to consciously reduce their speed or pull over to the side of the road.

[0035] The system of the present invention not only assists emergency vehicles in smoothly reaching emergency situations, but also reduces the risk of accidents in normal traffic conditions.

[0036] The following describes the processing flow.

[0037] Step 1:

[0038] The device obtains the user's location and speed via GPS. It then processes the acquired data and prepares it for transmission to the server.

[0039] Step 2:

[0040] The server receives location information and movement speed data transmitted from the terminal. The received data is passed to an analysis tool to determine whether the user is moving.

[0041] Step 3:

[0042] When the server confirms that the user is on the move, it retrieves the latest location data from an external emergency vehicle tracking system to obtain the location information of emergency vehicles.

[0043] Step 4:

[0044] The server compares the user's location information with the emergency vehicle's location information and uses distance measurement equipment to measure the relative distance between the two.

[0045] Step 5:

[0046] If the server determines that the distance between the user and the emergency vehicle is below a certain threshold, it generates warning notification data and sends it to the user's device.

[0047] Step 6:

[0048] Based on notification data received from the server, the device warns the user of the approaching emergency vehicle via voice or alarm. The purpose of the notification is to enable the user to respond appropriately.

[0049] Step 7:

[0050] The user receives a warning notification on their device and takes appropriate action, such as slowing down or pulling over to the side of the road to ensure safety.

[0051] (Example 1)

[0052] 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."

[0053] In today's traffic environment, a major obstacle to the rapid and safe movement of emergency vehicles is that regular vehicles may not notice their approach. This leads to traffic congestion and delays, reducing the effectiveness of emergency responses. Therefore, there is a need for a system that quickly notifies drivers of regular vehicles when an emergency vehicle is approaching and encourages safe evasive action.

[0054] 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.

[0055] In this invention, the server includes a device for acquiring location information, means for analyzing the information acquired by the device to determine whether the target is in motion, and a measuring device for acquiring the location of an external emergency vehicle and measuring the distance to the target's location. This makes it possible to immediately notify the target's device and issue a warning with voice or alarm sound when it is determined that the target is approaching the emergency vehicle within a certain distance.

[0056] A "device for acquiring location information" is a device that has the function of detecting geographical location and is used to determine the current location of a target in real time.

[0057] "Means for analyzing acquired information" refers to a system that includes processes and algorithms for analyzing acquired data and determining the state or situation of the subject.

[0058] "Means for determining whether an object is in motion" refers to technologies that provide criteria and methods for determining whether an object is actually moving physically, based on analyzed information.

[0059] A "measuring device" is a piece of equipment and technology used to accurately measure the physical distance between different points.

[0060] "When it is determined that the object is approaching within a specific distance" refers to a situation in which the analysis means recognizes that the object has exceeded a predetermined distance threshold and is in extremely close proximity.

[0061] A "notification" is the process of communicating specific information to a target device or user, drawing their attention visually or audibly.

[0062] "Voice or alarm sound" refers to a signal sound that is perceived by human hearing and is an audible notification emitted from a device for the purpose of warning or alerting.

[0063] This invention is a system designed to improve safety and efficiency in the traffic environment. In implementing the invention, terminals, servers, and external systems work together in coordination.

[0064] First, the user's device functions as a device for acquiring location information. The device uses its built-in GPS sensor to measure the user's current location and speed of movement in real time. This information is converted into a digital format that can be analyzed.

[0065] Next, location and speed data are sent from the terminal to the server. The server receives this data and uses software called an analysis engine to determine whether the object is moving. Based on this analysis, the server decides whether to continue further processing. In particular, movement is recognized when the speed exceeds a certain threshold value.

[0066] Simultaneously, the server communicates with an external emergency vehicle tracking system and uses measuring devices to obtain the current location of the emergency vehicle. Based on this information, the server compares the user's location with the emergency vehicle's location and measures the distance. If the server determines that the emergency vehicle is approaching within a set distance, a notification is sent to the user's terminal.

[0067] Specifically, the device has a function that notifies the user of the approaching emergency vehicle through voice messages or alarm sounds based on the notification data it receives. For example, by announcing "An emergency vehicle is approaching" via voice, the user can immediately pay attention and take safe evasive action.

[0068] An example of a prompt message might be: "Use the user's current location and the location of the emergency vehicle to design the optimal program algorithm for issuing a warning when an emergency vehicle approaches."

[0069] Thus, the invention can support the smooth operation of emergency vehicles and improve safety on the road.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The device uses a built-in GPS sensor to measure the user's current location and speed in real time. The input is the signal from the GPS sensor, and the output is location information (latitude and longitude) and speed data. Specifically, the device updates the location and speed every second and stores this data digitally.

[0073] Step 2:

[0074] The terminal transmits the acquired location information and speed data to the server via the internet. The input is the location information and speed data obtained in step 1, and the output is the digital data packets sent to the server. Specifically, the communication module within the terminal packets this data and securely transmits it to the server using the HTTPS protocol.

[0075] Step 3:

[0076] The server analyzes the received location and speed data. The input is data sent from the terminal, and the output is a determination indicating whether the user is moving or not. Specifically, the server uses an "analysis engine" to determine that the user is moving if the speed exceeds a certain threshold. This determination is made directly using a standard algorithm within the server.

[0077] Step 4:

[0078] The server communicates with an external emergency vehicle tracking system to obtain the current location of the emergency vehicle. The input is location information from the external system, and the output is data on the current location of the emergency vehicle. Specifically, the server obtains data via API communication and updates its location in real time.

[0079] Step 5:

[0080] The server compares the user's location information with the location of the emergency vehicle and measures the relative distance between them. The input is the location data of both parties held by the server, and the output is the result of the distance measurement. The server uses a "distance calculation module" to calculate the straight-line distance and determines whether it has entered within the set reference distance.

[0081] Step 6:

[0082] The server sends a notification to the user's device when it determines that it has approached within a set distance. The input is the result of the determination that the distance has exceeded the threshold, and the output is the notification data sent to the user's device. Specifically, the server generates a warning message and sends it to the device again using the HTTPS protocol.

[0083] Step 7:

[0084] Based on received notifications, the device alerts the user to the approaching emergency vehicle via voice message or alarm sound. The input is notification data from the server, and the output is a voice or alarm warning delivered to the user. As a concrete example of operation, the device starts playing a voice message saying "An emergency vehicle is approaching" and sounds an alarm sound if necessary.

[0085] (Application Example 1)

[0086] 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."

[0087] When autonomous vehicles travel on public roads, they are required to appropriately detect approaching emergency vehicles and automatically adjust their course and speed. Conventional systems only provide warnings to the driver and lack autonomous driving capabilities, which can prevent a quick response. As a result, they may hinder the smooth passage of emergency vehicles. Therefore, providing a system that allows autonomous vehicles to respond appropriately when emergency vehicles approach is a crucial challenge.

[0088] 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.

[0089] In this invention, the server includes positioning means for acquiring user location data and movement data; calculation means for analyzing the data acquired by the positioning means and determining whether the user is moving; measuring means for managing the location data of emergency vehicles and calculating the relative distance to the user's location data; warning means for issuing a warning to the user's information processing device when it is determined that the user is approaching an emergency vehicle within a certain distance; and route adjustment means mounted on the autonomous vehicle for automatically adjusting the course and speed in response to the approach of an emergency vehicle. This enables the autonomous vehicle to immediately sense the approach of an emergency vehicle and automatically change its course and adjust its speed, thereby supporting the smooth passage of the emergency vehicle.

[0090] "User" refers to an individual or vehicle driver using this system.

[0091] "Location data" refers to information indicating the geographical location of a user or emergency vehicle.

[0092] "Movement data" refers to information about a user's speed and movement patterns.

[0093] "Positioning means" refers to a technical device for acquiring the user's location data and movement data.

[0094] A "computation means" is a device that analyzes acquired location data and movement data to determine whether the user is moving.

[0095] A "measurement device" is a device that compares the location data of an emergency vehicle and a user and calculates the relative distance.

[0096] A "warning device" is a device or function that sends a warning to the user's information processing device when an emergency vehicle approaches within a certain distance.

[0097] "Route adjustment means" refers to a device or function in an autonomous vehicle that automatically changes its course or adjusts its speed in response to the approach of an emergency vehicle.

[0098] An "autonomous vehicle" is a vehicle designed to operate on its own without the need for human intervention.

[0099] An "information processing device" is a device or computer system used to communicate warnings to a user.

[0100] "Emergency vehicles" refer to vehicles such as fire trucks, ambulances, and police cars that are permitted to travel preferentially in emergency situations.

[0101] To realize this application, the server manipulates location data using various hardware and software. First, the server acquires the user's location and movement data using a GPS module. This data is transmitted from the user's terminal to the server. The server analyzes the received data and uses a computational means to determine whether the user is moving.

[0102] Next, the server collaborates with an external emergency vehicle tracking system to continuously acquire emergency vehicle location data. Based on this, it measures the relative distance between the user's location data and the emergency vehicle's location data. If the distance falls within a certain threshold, the server uses an alert mechanism to send a warning to the user's information processing device.

[0103] Furthermore, in autonomous vehicles, when the server detects the approach of an emergency vehicle, the vehicle's route adjustment mechanism activates, automatically adjusting its course and speed. This system allows autonomous vehicles to respond flexibly to the approach of emergency vehicles.

[0104] As a concrete example, consider a scenario where a fire truck approaches on a congested road near a shopping mall on a holiday. In this case, the server immediately detects the approaching emergency vehicle, adjusts the autonomous vehicle's course, and changes its speed as needed. This allows the fire truck to pass smoothly. An example of a prompt message for considering such a case would be: "Tell the autonomous driving AI model how to respond to an approaching emergency vehicle on the road. For example, specify how to change course and adjust speed."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server receives location and movement data transmitted from the terminal. This includes the user's current location and speed information. This data is collected by a GPS module and transmitted to the server via wireless communication.

[0108] Step 2:

[0109] The server uses computational means to analyze the received location and movement data. Based on the analysis, it checks if the movement data exceeds a certain threshold and determines whether the user is moving. The result of this determination is then carried over to the next step.

[0110] Step 3:

[0111] The server works in conjunction with the emergency vehicle tracking system to obtain the latest location data of emergency vehicles. The location data of emergency vehicles is updated in real time and stored in the server's database.

[0112] Step 4:

[0113] The server measures the distance between the user's current location data and the emergency vehicle's location data. This process is performed using distance measurement equipment, and mathematical calculations are made based on the coordinate information of both to determine the actual distance.

[0114] Step 5:

[0115] The server checks whether the calculated distance falls within a specific threshold. If it does, the server uses a warning mechanism to generate a warning on the user's device and sends warning data to notify them of the proximity.

[0116] Step 6:

[0117] Based on warning data received from the server, the terminal performs specific actions, such as sending an audio or alarm to the user. This allows the user to recognize the approach of an emergency vehicle both visually and audibly.

[0118] Step 7:

[0119] When an autonomous vehicle detects an approaching emergency vehicle, it changes its course or adjusts its speed based on route adjustment data from the server. This ensures that the vehicle can safely and quickly pass through the emergency vehicle's path.

[0120] 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.

[0121] This invention includes a system that notifies the user of the approach of an emergency vehicle while they are driving, as well as an emotion engine that recognizes the user's emotional state in real time. This system is activated by utilizing a location information acquisition means that obtains the user's location information and speed using GPS functionality, and transmitting the acquired data to a server. The server processes this data using an analysis means to determine whether or not the user is moving.

[0122] Furthermore, this system works in conjunction with an external emergency vehicle tracking system to obtain the location of emergency vehicles and measures the relative position of the emergency vehicle and the user using distance measurement devices. When the server detects that the distance has fallen within a certain range, it sends a warning notification to the user.

[0123] The emotion recognition means in this invention determines the user's emotional state by analyzing the user's facial expressions and voice in real time using the user's smartphone or cameras and sensors installed in the vehicle. The emotion engine uses this information to adjust the content of warning notifications. For example, if the user is feeling stressed, the tone of the voice notification is made gentler to reduce the user's psychological burden.

[0124] As a concrete example, consider a situation where the user is driving a vehicle. The device sends location and speed data to the server, which analyzes it to confirm that the user is moving. The server then assumes that when an emergency vehicle approaches within 500 meters, the emotion recognition system detects tension from the user's facial expression. In this case, the notification system will issue an audio alarm in a gentle voice saying, "Please stay calm, an emergency vehicle is approaching ahead."

[0125] This system supports safe and calm driving by providing flexible responses tailored to the user's emotional state, and assists in the smooth progress of emergency vehicles.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The device uses GPS functionality to acquire the user's location and speed, and prepares to send this data to the server.

[0129] Step 2:

[0130] The server receives location information and movement data transmitted from the terminal and uses analysis tools to determine whether the user is moving. If a speed exceeding a pre-set threshold is detected, it is determined that the user is moving.

[0131] Step 3:

[0132] The server obtains real-time location information of emergency vehicles from the emergency vehicle tracking system and compares it with the user's location information to measure the relative distance.

[0133] Step 4:

[0134] The emotion recognition system acquires the user's facial expressions and voice data using the device's camera or microphone, and analyzes the user's emotional state. The analyzed emotional data is then sent to a server.

[0135] Step 5:

[0136] The server verifies that the distance between the user and the emergency vehicle is below a certain threshold and adjusts the notification content based on the user's emotional state. If the user is anxious, the notification will be made gentler, for example.

[0137] Step 6:

[0138] The server generates warning notification data and sends it to the device, which then uses voice or an alarm sound to warn the user of the approaching emergency vehicle. The tone and content of the notification are optimized according to the user's emotional state.

[0139] Step 7:

[0140] After the user receives a notification on their device, they take appropriate action, such as slowing down or yielding the right of way to ensure safety.

[0141] (Example 2)

[0142] 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".

[0143] While it is crucial to create an environment where emergency vehicles can move smoothly through traffic, the current system has a challenge in that its notifications of approaching emergency vehicles are not flexible enough and cannot respond in accordance with the user's emotions and state of mind. Therefore, user support is needed to alleviate traffic congestion and allow emergency vehicles to pass quickly and safely. Furthermore, it is necessary to optimize notifications that take into account the user's mental state so that they can react appropriately to emergency vehicles.

[0144] 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.

[0145] In this invention, the server includes data acquisition means, analysis means, and information acquisition means. This makes it possible to acquire emergency vehicle information from an external system while monitoring the user's location information and movement speed, and to provide accurate and flexible notifications to the user. Furthermore, by using emotion determination means and adjustment means, a notification method that takes the user's mental state into account can be provided, enabling appropriate warnings while reducing stress.

[0146] "Data acquisition means" refers to a device or system that has the function of collecting the user's location information and movement speed.

[0147] "Analysis means" refers to a device or system for analyzing acquired data to determine whether or not the user is exercising.

[0148] "Information acquisition means" refers to a device or system that has the function of acquiring location information of emergency vehicles in cooperation with an external system.

[0149] "Measuring means" refers to a device or system for calculating the relative position of the user and the emergency vehicle.

[0150] "Communication means" refers to a device or system that has the function of sending a warning to the user's terminal.

[0151] An "emotion determination device" is a device or system that analyzes a user's facial expressions and voice to identify the user's emotional state.

[0152] "Adjustment means" refers to a device or system that has the function of adjusting the content and tone of a warning based on the emotion identified by the emotion determination means.

[0153] To implement this invention, the user's device must be equipped with a GPS module for acquiring location information, and a camera and voice sensor for determining emotional state. Using this hardware, the device collects location information, movement speed, facial expressions, and voice data and transmits them to a server.

[0154] The server receives location information and motion speed data from the terminal via a data acquisition means, and processes this data using an analysis means. The analysis means uses a specific algorithm and a generative AI model to determine whether the user is moving. Specifically, the user is recognized as moving if their motion speed exceeds a certain threshold.

[0155] Furthermore, the server obtains location information of emergency vehicles from an external emergency vehicle monitoring system through an information acquisition means. Based on this, the server calculates the relative positional relationship between the user and the emergency vehicle using a measurement means. If it is determined that an emergency vehicle is approaching within 500 meters of the user, the server sends a warning to the user's terminal using a communication means. The warning is provided in the form of voice or alarm sound.

[0156] Furthermore, the device's emotion detection mechanism uses a generative AI model to analyze the user's emotional state from their facial expressions and voice, determining whether the user is experiencing stress, etc. Based on this, the adjustment mechanism appropriately adjusts the content and tone of the warning.

[0157] As a concrete example, consider a situation where the user is driving a vehicle. In this case, the device sends location and speed data to the server, which analyzes it to confirm that the user is moving. Furthermore, suppose that when an emergency vehicle approaches within 500 meters, the emotion detection system detects the user's state of tension. In this case, the notification system will issue an audio alarm in a gentle voice saying, "Please stay calm, an emergency vehicle is approaching ahead."

[0158] An example of a prompt message is, "We want to optimize the emergency vehicle approach notification system while driving a vehicle according to the user's emotional state." Such a system configuration and operation would enable users to act safely and calmly even in emergency situations.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The device uses a GPS module to obtain the user's location and speed.

[0162] Input: GPS data (latitude, longitude, speed)

[0163] Specific operation: The device activates its built-in GPS sensor and periodically records the user's current location and speed. This allows the device to obtain accurate location information in real time and monitor the user's movements.

[0164] Output: Acquired location information and speed data

[0165] Step 2:

[0166] The device sends the acquired location information and speed data to the server.

[0167] Input: Location information and speed data from the device.

[0168] Specific operation: The terminal transmits the data it collects to the server via wireless communication. This communication occurs at regular intervals and is updated more frequently if the user is on the move.

[0169] Output: Location and speed data sent to the server

[0170] Step 3:

[0171] The server analyzes the received location and speed data to determine if the user is moving.

[0172] Input: Location information and speed data

[0173] Specific operation: The server uses an algorithm to determine if the speed exceeds a set threshold. If the threshold is exceeded, it recognizes that the user is moving. This data is stored for use in the next step.

[0174] Output: The result indicates that the user is in transit.

[0175] Step 4:

[0176] The server works in conjunction with an external emergency vehicle monitoring system to obtain location information for emergency vehicles.

[0177] Input: Location data of emergency vehicles from an external source.

[0178] Specific operation: The server periodically receives location information of emergency vehicles using an API. This allows it to check if an emergency vehicle is nearby and enables immediate response.

[0179] Output: Location data of acquired emergency vehicles

[0180] Step 5:

[0181] The server calculates the relative positions of the user and the emergency vehicle, and issues a warning if they approach within a certain distance.

[0182] Input: User's location information and emergency vehicle location information

[0183] Specific operation: Calculate the distance between coordinates and set a trigger to send a warning signal to the user's terminal if an emergency vehicle approaches within 500 meters.

[0184] Output: Trigger for warning notification

[0185] Step 6:

[0186] The device uses a camera and voice sensors to analyze the user's facial expressions and voice in real time and determine their emotions.

[0187] Input: User's facial expression data, voice data

[0188] Specific operation: Using a generative AI model, the system analyzes the user's emotional state in real time from collected data and determines emotions such as "stress" and "tension."

[0189] Output: User's emotional state

[0190] Step 7:

[0191] Based on the emotion assessment results, the server sends instructions to the device to adjust the content and tone of the warning notification.

[0192] Input: Sentiment assessment result

[0193] Specific operation: The server generates and sends notification instructions to the device based on the user's stress and anxiety level. For example, if the user is feeling anxious, the server will be configured to send notifications in a calm tone.

[0194] Output: Adjusted warning notification content and tone

[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] With the recent advancements in autonomous driving technology, ensuring safety during travel has become a critical issue. In particular, if users are unable to recognize the approach of an emergency vehicle, there is a risk of hindering its smooth progress. Furthermore, the psychological stress of the driver must also be considered. Conventional systems only provide emergency information notifications and lack flexible responses tailored to the user's psychological state. Therefore, it is necessary to improve situations that easily cause anxiety and promote safe and smooth traffic flow.

[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 positioning means for acquiring location information and speed of movement; information analysis means for analyzing the data acquired by the positioning means and determining whether the user is moving; distance evaluation means for managing the location information of emergency vehicles and measuring the relative distance to the user's location information; and emotion recognition means for recognizing the user's emotional state and adjusting the content of notifications. This makes it possible to provide an environment in which users can respond to emergency vehicles safely and calmly, and to support the smooth progress of emergency vehicles.

[0200] "Positioning means" refers to a function for accurately acquiring the user's location information and speed of movement.

[0201] "Information analysis means" refers to a processing function that uses acquired location information and movement speed data to determine whether the user is currently moving.

[0202] The "distance evaluation means" is a function that measures the relative distance between an emergency vehicle and a user based on their respective location information.

[0203] A "warning mechanism" is a function that notifies the user when it is determined that the user has approached an emergency vehicle within a certain distance.

[0204] "Emotion recognition means" refers to a function that recognizes the user's emotional state, adjusts the content of notifications based on the analysis results, and reduces the user's psychological burden.

[0205] This system consists of a user, a terminal, and a server. It is designed to alert users to the presence of emergency vehicles while they are on the move, facilitating safe travel. Specifically, it takes into account situations where the user is wearing smart glasses while driving a vehicle.

[0206] The terminal first uses positioning means to accurately acquire the user's location and speed. This information is transmitted to the server in real time, and information analysis means determine whether the user is moving. The server then collaborates with an external emergency vehicle tracking system to collect its location information and measures the relative distance between the two using distance evaluation means.

[0207] When the system detects that the user has approached an emergency vehicle within a certain distance, a warning system activates, displaying a notification on the smart glasses' screen. At this time, the emotion recognition system uses the smart glasses' built-in camera and microphone to capture the user's facial expressions and voice, analyzing their emotions in real time. For example, it uses a facial recognition and emotion analysis library based on OpenCV to evaluate the user's psychological state. If the user is experiencing stress, the warning voice tone is softened to reduce their psychological burden.

[0208] As a concrete example, consider a scenario where a user is driving a vehicle towards the suburbs with a friend. Suddenly, an emergency vehicle is detected approaching from behind. The smart glasses immediately notify the user in a gentle voice, "Please stay calm, an emergency vehicle is approaching ahead," and display its location on a map. In this way, the user can remain calm and continue operating the vehicle.

[0209] An example of a prompt is, "Create a notification that will help the driver feel at ease while driving. The situation is that an emergency vehicle is approaching." This prompt provides important instructions to the generation AI model when creating calming notifications that prioritize user comfort.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The device uses positioning to acquire the user's location information and speed. During this process, a GPS sensor is used to collect accurate data in real time. This allows the user's location coordinates and speed to be obtained and transmitted to the server.

[0213] Step 2:

[0214] The server receives the transmitted location and speed data and processes it using an information analysis tool. This process determines whether the user is currently moving by checking if the speed is above a set threshold. Based on this result, the server decides whether to proceed to the next step.

[0215] Step 3:

[0216] The server works in conjunction with the emergency vehicle tracking system to obtain the location information of emergency vehicles. Using a distance evaluation method, it compares this location information with the user's location information and measures the relative distance between the two. Here, it evaluates whether the calculated distance falls within a certain range.

[0217] Step 4:

[0218] If the server determines, based on its assessment, that the distance has entered a certain range, the warning system is activated. It sends a notification signal to the terminal and prepares to display visual and audible warnings on the smart glasses. At this point, it identifies that an emergency alert is necessary for the terminal.

[0219] Step 5:

[0220] The device uses a camera and microphone built into smart glasses to capture the user's facial expressions and voice in real time. Emotion recognition technology is used to analyze this data and identify the user's emotional state. An emotion analysis library is used to assess the stress and anxiety the user is experiencing.

[0221] Step 6:

[0222] If emotion recognition determines that the user is experiencing stress, the server instructs the warning system to adjust the tone of the notification. Using a generative AI model, it generates a voice notification based on the prompt: "Create a notification that will help the driver feel at ease while driving. The situation is that an emergency vehicle is approaching," delivering the appropriate message to the user in a gentle tone.

[0223] Step 7:

[0224] After notifying the user, the server verifies that all processes have completed successfully and returns to its initial state to prepare for future emergencies or events during transit. This is a process of continuous monitoring of the system to continuously receive and analyze critical information.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention includes a location information acquisition means that utilizes the GPS function of the user's device to obtain the user's current location and speed in real time. The device transmits this data to a server, which analyzes the location information and speed data. Based on the analysis means, the server determines whether the user is moving and stores the result.

[0242] The server also continuously acquires and manages the location of emergency vehicles through cooperation with external emergency vehicle tracking systems. Using distance measurement devices, the server compares the user's current location with the location of the emergency vehicle and measures the relative distance between them. If it is determined that the user has entered a certain distance, the server generates warning notification data and sends it to the user's terminal via the notification device.

[0243] The device uses voice or an alarm to notify the user of the approaching emergency vehicle based on notifications received from the server. For example, the device might announce, "An emergency vehicle is approaching ahead," allowing the user to recognize the approach and take a quick and safe action.

[0244] As a concrete example, consider a scenario where a user is driving a car. When the terminal detects that the user's speed exceeds a certain threshold, the server compares that data with the location information of an emergency vehicle. For example, if an emergency vehicle approaches within 500 meters, the server issues a warning, and an alarm sounds through the terminal. This allows the user to consciously reduce their speed or pull over to the side of the road.

[0245] The system of the present invention not only assists emergency vehicles in smoothly reaching emergency situations, but also reduces the risk of accidents in normal traffic conditions.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] The device obtains the user's location and speed via GPS. It then processes the acquired data and prepares it for transmission to the server.

[0249] Step 2:

[0250] The server receives location information and movement speed data transmitted from the terminal. The received data is passed to an analysis tool to determine whether the user is moving.

[0251] Step 3:

[0252] When the server confirms that the user is on the move, it retrieves the latest location data from an external emergency vehicle tracking system to obtain the location information of emergency vehicles.

[0253] Step 4:

[0254] The server compares the user's location information with the emergency vehicle's location information and uses distance measurement equipment to measure the relative distance between the two.

[0255] Step 5:

[0256] If the server determines that the distance between the user and the emergency vehicle is below a certain threshold, it generates warning notification data and sends it to the user's device.

[0257] Step 6:

[0258] Based on notification data received from the server, the device warns the user of the approaching emergency vehicle via voice or alarm. The purpose of the notification is to enable the user to respond appropriately.

[0259] Step 7:

[0260] The user receives a warning notification on their device and takes appropriate action, such as slowing down or pulling over to the side of the road to ensure safety.

[0261] (Example 1)

[0262] 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."

[0263] In today's traffic environment, a major obstacle to the rapid and safe movement of emergency vehicles is that regular vehicles may not notice their approach. This leads to traffic congestion and delays, reducing the effectiveness of emergency responses. Therefore, there is a need for a system that quickly notifies drivers of regular vehicles when an emergency vehicle is approaching and encourages safe evasive action.

[0264] 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.

[0265] In this invention, the server includes a device for acquiring location information, means for analyzing the information acquired by the device to determine whether the target is in motion, and a measuring device for acquiring the location of an external emergency vehicle and measuring the distance to the target's location. This makes it possible to immediately notify the target's device and issue a warning with voice or alarm sound when it is determined that the target is approaching the emergency vehicle within a certain distance.

[0266] A "device for acquiring location information" is a device that has the function of detecting geographical location and is used to determine the current location of a target in real time.

[0267] "Means for analyzing acquired information" refers to a system that includes processes and algorithms for analyzing acquired data and determining the state or situation of the subject.

[0268] "Means for determining whether an object is in motion" refers to technologies that provide criteria and methods for determining whether an object is actually moving physically, based on analyzed information.

[0269] A "measuring device" is a piece of equipment and technology used to accurately measure the physical distance between different points.

[0270] "When it is determined that the object is approaching within a specific distance" refers to a situation in which the analysis means recognizes that the object has exceeded a predetermined distance threshold and is in extremely close proximity.

[0271] A "notification" is the process of communicating specific information to a target device or user, drawing their attention visually or audibly.

[0272] "Voice or alarm sound" refers to a signal sound that is perceived by human hearing and is an audible notification emitted from a device for the purpose of warning or alerting.

[0273] This invention is a system designed to improve safety and efficiency in the traffic environment. In implementing the invention, terminals, servers, and external systems work together in coordination.

[0274] First, the user's device functions as a device for acquiring location information. The device uses its built-in GPS sensor to measure the user's current location and speed of movement in real time. This information is converted into a digital format that can be analyzed.

[0275] Next, location and speed data are sent from the terminal to the server. The server receives this data and uses software called an analysis engine to determine whether the object is moving. Based on this analysis, the server decides whether to continue further processing. In particular, movement is recognized when the speed exceeds a certain threshold value.

[0276] Simultaneously, the server communicates with an external emergency vehicle tracking system and uses measuring devices to obtain the current location of the emergency vehicle. Based on this information, the server compares the user's location with the emergency vehicle's location and measures the distance. If the server determines that the emergency vehicle is approaching within a set distance, a notification is sent to the user's terminal.

[0277] Specifically, based on the received notification data, the terminal has a function to notify the user of the approach of an emergency moving object with a voice message or an alarm sound. For example, by notifying the user with a voice message such as "An emergency moving object is approaching", the user can immediately pay attention and take safe avoidance actions.

[0278] As an example of a prompt sentence, "Please design an optimal program algorithm for issuing a warning when approaching an emergency, using the current position of the user and the position of the emergency moving object" can be considered.

[0279] In this way, the invention can support the smooth operation of emergency moving objects and improve safety on the road.

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The terminal measures the current position and moving speed of the user in real time using the built-in GPS sensor. The input is the signal from the GPS sensor, and the output is the position information (latitude, longitude) and speed data. As a specific operation, the terminal updates the position and speed every second and saves them as digital data.

[0283] Step 2:

[0284] The terminal transmits the acquired position information and speed data to the server via the Internet. The input is the position information and speed data obtained in Step 1, and the output is the digital data packet transmitted to the server. Specifically, the communication module in the terminal packets these data and securely transmits them to the server using the HTTPS protocol.

[0285] Step 3:

[0286] The server analyzes the received location information and speed data. The input is the data transmitted from the terminal, and the output is a judgment result indicating whether the user is moving. As a specific operation, the server uses an "analysis engine" to determine that the user is moving when the speed exceeds a certain reference value. This judgment is made directly using the reference algorithm in the server.

[0287] Step 4:

[0288] The server communicates with an external emergency moving object tracking system to obtain the current position of the emergency moving object. The input is the location information from the external system, and the output is the data of the current position of the emergency moving object. Specifically, the server obtains the data through API communication and updates its position in real time.

[0289] Step 5:

[0290] The server compares the user's location information with the position of the emergency moving object and measures the relative distance between the two. The input is the position data of both held by the server, and the output is the result of the distance measurement. The server uses a "distance calculation module" to calculate the straight-line distance and determines whether it falls within the set reference distance.

[0291] Step 6:

[0292] When the server determines that it has approached within the set distance, it sends a notification to the user's terminal. The input is the judgment result that the distance has exceeded the standard, and the output is the notification data sent to the user's terminal. As a specific operation, the server generates a warning message and sends it to the terminal again using the HTTPS protocol.

[0293] Step 7:

[0294] Based on received notifications, the device alerts the user to the approaching emergency vehicle via voice message or alarm sound. The input is notification data from the server, and the output is a voice or alarm warning delivered to the user. As a concrete example of operation, the device starts playing a voice message saying "An emergency vehicle is approaching" and sounds an alarm sound if necessary.

[0295] (Application Example 1)

[0296] 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."

[0297] When autonomous vehicles travel on public roads, they are required to appropriately detect approaching emergency vehicles and automatically adjust their course and speed. Conventional systems only provide warnings to the driver and lack autonomous driving capabilities, which can prevent a quick response. As a result, they may hinder the smooth passage of emergency vehicles. Therefore, providing a system that allows autonomous vehicles to respond appropriately when emergency vehicles approach is a crucial challenge.

[0298] 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.

[0299] In this invention, the server includes positioning means for acquiring user location data and movement data; calculation means for analyzing the data acquired by the positioning means and determining whether the user is moving; measuring means for managing the location data of emergency vehicles and calculating the relative distance to the user's location data; warning means for issuing a warning to the user's information processing device when it is determined that the user is approaching an emergency vehicle within a certain distance; and route adjustment means mounted on the autonomous vehicle for automatically adjusting the course and speed in response to the approach of an emergency vehicle. This enables the autonomous vehicle to immediately sense the approach of an emergency vehicle and automatically change its course and adjust its speed, thereby supporting the smooth passage of the emergency vehicle.

[0300] "User" refers to an individual using this system or the driver of a vehicle.

[0301] "Location data" refers to information indicating the geographical location of a user or an emergency vehicle.

[0302] "Movement data" refers to information regarding the speed and movement trend of a user.

[0303] "Positioning means" is a technical device for acquiring the location data and movement data of a user.

[0304] "Calculation means" is a device that analyzes the acquired location data and movement data to determine whether the user is in motion.

[0305] "Measurement means" is a device that compares the location data of an emergency vehicle and a user to calculate the relative distance.

[0306] "Warning means" is a device or function for transmitting a warning to the user's information processing device when an emergency vehicle approaches within a certain distance.

[0307] "Route adjustment means" is a device or function in an autonomous vehicle for automatically changing the route or adjusting the speed in response to the approach of an emergency vehicle.

[0308] "Autonomous vehicle" is a vehicle designed to operate on its own without the need for a passenger to operate it.

[0309] "Information processing device" is a device or computer system used to transmit a warning to a user.

[0310] "Emergency vehicle" refers to vehicles such as fire trucks, ambulances, and police cars that are permitted to have priority travel during emergencies.

[0311] To realize this application, the server manipulates location data using various hardware and software. First, the server acquires the user's location and movement data using a GPS module. This data is transmitted from the user's terminal to the server. The server analyzes the received data and uses a computational means to determine whether the user is moving.

[0312] Next, the server collaborates with an external emergency vehicle tracking system to continuously acquire emergency vehicle location data. Based on this, it measures the relative distance between the user's location data and the emergency vehicle's location data. If the distance falls within a certain threshold, the server uses an alert mechanism to send a warning to the user's information processing device.

[0313] Furthermore, in autonomous vehicles, when the server detects the approach of an emergency vehicle, the vehicle's route adjustment mechanism activates, automatically adjusting its course and speed. This system allows autonomous vehicles to respond flexibly to the approach of emergency vehicles.

[0314] As a concrete example, consider a scenario where a fire truck approaches on a congested road near a shopping mall on a holiday. In this case, the server immediately detects the approaching emergency vehicle, adjusts the autonomous vehicle's course, and changes its speed as needed. This allows the fire truck to pass smoothly. An example of a prompt message for considering such a case would be: "Tell the autonomous driving AI model how to respond to an approaching emergency vehicle on the road. For example, specify how to change course and adjust speed."

[0315] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0316] Step 1:

[0317] The server receives location and movement data transmitted from the terminal. This includes the user's current location and speed information. This data is collected by a GPS module and transmitted to the server via wireless communication.

[0318] Step 2:

[0319] The server uses computational means to analyze the received location and movement data. Based on the analysis, it checks if the movement data exceeds a certain threshold and determines whether the user is moving. The result of this determination is then carried over to the next step.

[0320] Step 3:

[0321] The server works in conjunction with the emergency vehicle tracking system to obtain the latest location data of emergency vehicles. The location data of emergency vehicles is updated in real time and stored in the server's database.

[0322] Step 4:

[0323] The server measures the distance between the user's current location data and the emergency vehicle's location data. This process is performed using distance measurement equipment, and mathematical calculations are made based on the coordinate information of both to determine the actual distance.

[0324] Step 5:

[0325] The server checks whether the calculated distance falls within a specific threshold. If it does, the server uses a warning mechanism to generate a warning on the user's device and sends warning data to notify them of the proximity.

[0326] Step 6:

[0327] Based on warning data received from the server, the terminal performs specific actions, such as sending an audio or alarm to the user. This allows the user to recognize the approach of an emergency vehicle both visually and audibly.

[0328] Step 7:

[0329] When an autonomous vehicle detects an approaching emergency vehicle, it changes its course or adjusts its speed based on route adjustment data from the server. This ensures that the vehicle can safely and quickly pass through the emergency vehicle's path.

[0330] 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.

[0331] This invention includes a system that notifies the user of the approach of an emergency vehicle while they are driving, as well as an emotion engine that recognizes the user's emotional state in real time. This system is activated by utilizing a location information acquisition means that obtains the user's location information and speed using GPS functionality, and transmitting the acquired data to a server. The server processes this data using an analysis means to determine whether or not the user is moving.

[0332] Furthermore, this system works in conjunction with an external emergency vehicle tracking system to obtain the location of emergency vehicles and measures the relative position of the emergency vehicle and the user using distance measurement devices. When the server detects that the distance has fallen within a certain range, it sends a warning notification to the user.

[0333] The emotion recognition means in this invention determines the user's emotional state by analyzing the user's facial expressions and voice in real time using the user's smartphone or cameras and sensors installed in the vehicle. The emotion engine uses this information to adjust the content of warning notifications. For example, if the user is feeling stressed, the tone of the voice notification is made gentler to reduce the user's psychological burden.

[0334] As a concrete example, consider a situation where the user is driving a vehicle. The device sends location and speed data to the server, which analyzes it to confirm that the user is moving. The server then assumes that when an emergency vehicle approaches within 500 meters, the emotion recognition system detects tension from the user's facial expression. In this case, the notification system will issue an audio alarm in a gentle voice saying, "Please stay calm, an emergency vehicle is approaching ahead."

[0335] This system supports safe and calm driving by providing flexible responses tailored to the user's emotional state, and assists in the smooth progress of emergency vehicles.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The device uses GPS functionality to acquire the user's location and speed, and prepares to send this data to the server.

[0339] Step 2:

[0340] The server receives location information and movement data transmitted from the terminal and uses analysis tools to determine whether the user is moving. If a speed exceeding a pre-set threshold is detected, it is determined that the user is moving.

[0341] Step 3:

[0342] The server obtains real-time location information of emergency vehicles from the emergency vehicle tracking system and compares it with the user's location information to measure the relative distance.

[0343] Step 4:

[0344] The emotion recognition system acquires the user's facial expressions and voice data using the device's camera or microphone, and analyzes the user's emotional state. The analyzed emotional data is then sent to a server.

[0345] Step 5:

[0346] The server verifies that the distance between the user and the emergency vehicle is below a certain threshold and adjusts the notification content based on the user's emotional state. If the user is anxious, the notification will be made gentler, for example.

[0347] Step 6:

[0348] The server generates warning notification data and sends it to the device, which then uses voice or an alarm sound to warn the user of the approaching emergency vehicle. The tone and content of the notification are optimized according to the user's emotional state.

[0349] Step 7:

[0350] After the user receives a notification on their device, they take appropriate action, such as slowing down or yielding the right of way to ensure safety.

[0351] (Example 2)

[0352] 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".

[0353] While it is crucial to create an environment where emergency vehicles can move smoothly through traffic, the current system has a challenge in that its notifications of approaching emergency vehicles are not flexible enough and cannot respond in accordance with the user's emotions and state of mind. Therefore, user support is needed to alleviate traffic congestion and allow emergency vehicles to pass quickly and safely. Furthermore, it is necessary to optimize notifications that take into account the user's mental state so that they can react appropriately to emergency vehicles.

[0354] 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.

[0355] In this invention, the server includes data acquisition means, analysis means, and information acquisition means. This makes it possible to acquire emergency vehicle information from an external system while monitoring the user's location information and movement speed, and to provide accurate and flexible notifications to the user. Furthermore, by using emotion determination means and adjustment means, a notification method that takes the user's mental state into account can be provided, enabling appropriate warnings while reducing stress.

[0356] "Data acquisition means" refers to a device or system that has the function of collecting the user's location information and movement speed.

[0357] "Analysis means" refers to a device or system for analyzing acquired data to determine whether or not the user is exercising.

[0358] "Information acquisition means" refers to a device or system that has the function of acquiring location information of emergency vehicles in cooperation with an external system.

[0359] "Measuring means" refers to a device or system for calculating the relative position of the user and the emergency vehicle.

[0360] "Communication means" refers to a device or system that has the function of sending a warning to the user's terminal.

[0361] An "emotion determination device" is a device or system that analyzes a user's facial expressions and voice to identify the user's emotional state.

[0362] "Adjustment means" refers to a device or system that has the function of adjusting the content and tone of a warning based on the emotion identified by the emotion determination means.

[0363] To implement this invention, the user's device must be equipped with a GPS module for acquiring location information, and a camera and voice sensor for determining emotional state. Using this hardware, the device collects location information, movement speed, facial expressions, and voice data and transmits them to a server.

[0364] The server receives location information and motion speed data from the terminal via a data acquisition means, and processes this data using an analysis means. The analysis means uses a specific algorithm and a generative AI model to determine whether the user is moving. Specifically, the user is recognized as moving if their motion speed exceeds a certain threshold.

[0365] Furthermore, the server obtains location information of emergency vehicles from an external emergency vehicle monitoring system through an information acquisition means. Based on this, the server calculates the relative positional relationship between the user and the emergency vehicle using a measurement means. If it is determined that an emergency vehicle is approaching within 500 meters of the user, the server sends a warning to the user's terminal using a communication means. The warning is provided in the form of voice or alarm sound.

[0366] Furthermore, the device's emotion detection mechanism uses a generative AI model to analyze the user's emotional state from their facial expressions and voice, determining whether the user is experiencing stress, etc. Based on this, the adjustment mechanism appropriately adjusts the content and tone of the warning.

[0367] As a concrete example, consider a situation where the user is driving a vehicle. In this case, the device sends location and speed data to the server, which analyzes it to confirm that the user is moving. Furthermore, suppose that when an emergency vehicle approaches within 500 meters, the emotion detection system detects the user's state of tension. In this case, the notification system will issue an audio alarm in a gentle voice saying, "Please stay calm, an emergency vehicle is approaching ahead."

[0368] An example of a prompt message is, "We want to optimize the emergency vehicle approach notification system while driving a vehicle according to the user's emotional state." Such a system configuration and operation would enable users to act safely and calmly even in emergency situations.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The device uses a GPS module to obtain the user's location and speed.

[0372] Input: GPS data (latitude, longitude, speed)

[0373] Specific operation: The device activates its built-in GPS sensor and periodically records the user's current location and speed. This allows the device to obtain accurate location information in real time and monitor the user's movements.

[0374] Output: Acquired location information and speed data

[0375] Step 2:

[0376] The device sends the acquired location information and speed data to the server.

[0377] Input: Location information and speed data from the device.

[0378] Specific operation: The terminal transmits the data it collects to the server via wireless communication. This communication occurs at regular intervals and is updated more frequently if the user is on the move.

[0379] Output: Location and speed data sent to the server

[0380] Step 3:

[0381] The server analyzes the received location and speed data to determine if the user is moving.

[0382] Input: Location information and speed data

[0383] Specific operation: The server uses an algorithm to determine if the speed exceeds a set threshold. If the threshold is exceeded, it recognizes that the user is moving. This data is stored for use in the next step.

[0384] Output: The result indicates that the user is in transit.

[0385] Step 4:

[0386] The server works in conjunction with an external emergency vehicle monitoring system to obtain location information for emergency vehicles.

[0387] Input: Location data of emergency vehicles from an external source.

[0388] Specific operation: The server periodically receives location information of emergency vehicles using an API. This allows it to check if an emergency vehicle is nearby and enables immediate response.

[0389] Output: Location data of acquired emergency vehicles

[0390] Step 5:

[0391] The server calculates the relative positions of the user and the emergency vehicle, and issues a warning if they approach within a certain distance.

[0392] Input: User's location information and emergency vehicle location information

[0393] Specific operation: Calculate the distance between coordinates and set a trigger to send a warning signal to the user's terminal if an emergency vehicle approaches within 500 meters.

[0394] Output: Trigger for warning notification

[0395] Step 6:

[0396] The device uses a camera and voice sensors to analyze the user's facial expressions and voice in real time and determine their emotions.

[0397] Input: User's facial expression data, voice data

[0398] Specific operation: Using a generative AI model, the system analyzes the user's emotional state in real time from collected data and determines emotions such as "stress" and "tension."

[0399] Output: User's emotional state

[0400] Step 7:

[0401] Based on the emotion assessment results, the server sends instructions to the device to adjust the content and tone of the warning notification.

[0402] Input: Sentiment assessment result

[0403] Specific operation: The server generates and sends notification instructions to the device based on the user's stress and anxiety level. For example, if the user is feeling anxious, the server will be configured to send notifications in a calm tone.

[0404] Output: Adjusted warning notification content and tone

[0405] (Application Example 2)

[0406] 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."

[0407] With the recent advancements in autonomous driving technology, ensuring safety during travel has become a critical issue. In particular, if users are unable to recognize the approach of an emergency vehicle, there is a risk of hindering its smooth progress. Furthermore, the psychological stress of the driver must also be considered. Conventional systems only provide emergency information notifications and lack flexible responses tailored to the user's psychological state. Therefore, it is necessary to improve situations that easily cause anxiety and promote safe and smooth traffic flow.

[0408] 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.

[0409] In this invention, the server includes positioning means for acquiring location information and speed of movement; information analysis means for analyzing the data acquired by the positioning means and determining whether the user is moving; distance evaluation means for managing the location information of emergency vehicles and measuring the relative distance to the user's location information; and emotion recognition means for recognizing the user's emotional state and adjusting the content of notifications. This makes it possible to provide an environment in which users can respond to emergency vehicles safely and calmly, and to support the smooth progress of emergency vehicles.

[0410] "Positioning means" refers to a function for accurately acquiring the user's location information and speed of movement.

[0411] "Information analysis means" refers to a processing function that uses acquired location information and movement speed data to determine whether the user is currently moving.

[0412] The "distance evaluation means" is a function that measures the relative distance between an emergency vehicle and a user based on their respective location information.

[0413] A "warning mechanism" is a function that notifies the user when it is determined that the user has approached an emergency vehicle within a certain distance.

[0414] "Emotion recognition means" refers to a function that recognizes the user's emotional state, adjusts the content of notifications based on the analysis results, and reduces the user's psychological burden.

[0415] This system consists of a user, a terminal, and a server. It is designed to alert users to the presence of emergency vehicles while they are on the move, facilitating safe travel. Specifically, it takes into account situations where the user is wearing smart glasses while driving a vehicle.

[0416] The terminal first uses positioning means to accurately acquire the user's location and speed. This information is transmitted to the server in real time, and information analysis means determine whether the user is moving. The server then collaborates with an external emergency vehicle tracking system to collect its location information and measures the relative distance between the two using distance evaluation means.

[0417] When the system detects that the user has approached an emergency vehicle within a certain distance, a warning system activates, displaying a notification on the smart glasses' screen. At this time, the emotion recognition system uses the smart glasses' built-in camera and microphone to capture the user's facial expressions and voice, analyzing their emotions in real time. For example, it uses a facial recognition and emotion analysis library based on OpenCV to evaluate the user's psychological state. If the user is experiencing stress, the warning voice tone is softened to reduce their psychological burden.

[0418] As a concrete example, consider a scenario where a user is driving a vehicle towards the suburbs with a friend. Suddenly, an emergency vehicle is detected approaching from behind. The smart glasses immediately notify the user in a gentle voice, "Please stay calm, an emergency vehicle is approaching ahead," and display its location on a map. In this way, the user can remain calm and continue operating the vehicle.

[0419] An example of a prompt is, "Create a notification that will help the driver feel at ease while driving. The situation is that an emergency vehicle is approaching." This prompt provides important instructions to the generation AI model when creating calming notifications that prioritize user comfort.

[0420] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0421] Step 1:

[0422] The device uses positioning to acquire the user's location information and speed. During this process, a GPS sensor is used to collect accurate data in real time. This allows the user's location coordinates and speed to be obtained and transmitted to the server.

[0423] Step 2:

[0424] The server receives the transmitted location and speed data and processes it using an information analysis tool. This process determines whether the user is currently moving by checking if the speed is above a set threshold. Based on this result, the server decides whether to proceed to the next step.

[0425] Step 3:

[0426] The server works in conjunction with the emergency vehicle tracking system to obtain the location information of emergency vehicles. Using a distance evaluation method, it compares this location information with the user's location information and measures the relative distance between the two. Here, it evaluates whether the calculated distance falls within a certain range.

[0427] Step 4:

[0428] If the server determines, based on its assessment, that the distance has entered a certain range, the warning system is activated. It sends a notification signal to the terminal and prepares to display visual and audible warnings on the smart glasses. At this point, it identifies that an emergency alert is necessary for the terminal.

[0429] Step 5:

[0430] The device uses a camera and microphone built into smart glasses to capture the user's facial expressions and voice in real time. Emotion recognition technology is used to analyze this data and identify the user's emotional state. An emotion analysis library is used to assess the stress and anxiety the user is experiencing.

[0431] Step 6:

[0432] If emotion recognition determines that the user is experiencing stress, the server instructs the warning system to adjust the tone of the notification. Using a generative AI model, it generates a voice notification based on the prompt: "Create a notification that will help the driver feel at ease while driving. The situation is that an emergency vehicle is approaching," delivering the appropriate message to the user in a gentle tone.

[0433] Step 7:

[0434] After notifying the user, the server verifies that all processes have completed successfully and returns to its initial state to prepare for future emergencies or events during transit. This is a process of continuous monitoring of the system to continuously receive and analyze critical information.

[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 includes a location information acquisition means that utilizes the GPS function of the user's device to obtain the user's current location and speed in real time. The device transmits this data to a server, which analyzes the location information and speed data. Based on the analysis means, the server determines whether the user is moving and stores the result.

[0452] The server also continuously acquires and manages the location of emergency vehicles through cooperation with external emergency vehicle tracking systems. Using distance measurement devices, the server compares the user's current location with the location of the emergency vehicle and measures the relative distance between them. If it is determined that the user has entered a certain distance, the server generates warning notification data and sends it to the user's terminal via the notification device.

[0453] The device uses voice or an alarm to notify the user of the approaching emergency vehicle based on notifications received from the server. For example, the device might announce, "An emergency vehicle is approaching ahead," allowing the user to recognize the approach and take a quick and safe action.

[0454] As a concrete example, consider a scenario where a user is driving a car. When the terminal detects that the user's speed exceeds a certain threshold, the server compares that data with the location information of an emergency vehicle. For example, if an emergency vehicle approaches within 500 meters, the server issues a warning, and an alarm sounds through the terminal. This allows the user to consciously reduce their speed or pull over to the side of the road.

[0455] The system of the present invention not only assists emergency vehicles in smoothly reaching emergency situations, but also reduces the risk of accidents in normal traffic conditions.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] The device obtains the user's location and speed via GPS. It then processes the acquired data and prepares it for transmission to the server.

[0459] Step 2:

[0460] The server receives location information and movement speed data transmitted from the terminal. The received data is passed to an analysis tool to determine whether the user is moving.

[0461] Step 3:

[0462] When the server confirms that the user is on the move, it retrieves the latest location data from an external emergency vehicle tracking system to obtain the location information of emergency vehicles.

[0463] Step 4:

[0464] The server compares the user's location information with the emergency vehicle's location information and uses distance measurement equipment to measure the relative distance between the two.

[0465] Step 5:

[0466] If the server determines that the distance between the user and the emergency vehicle is below a certain threshold, it generates warning notification data and sends it to the user's device.

[0467] Step 6:

[0468] Based on notification data received from the server, the device warns the user of the approaching emergency vehicle via voice or alarm. The purpose of the notification is to enable the user to respond appropriately.

[0469] Step 7:

[0470] The user receives a warning notification on their device and takes appropriate action, such as slowing down or pulling over to the side of the road to ensure safety.

[0471] (Example 1)

[0472] 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."

[0473] In today's traffic environment, a major obstacle to the rapid and safe movement of emergency vehicles is that regular vehicles may not notice their approach. This leads to traffic congestion and delays, reducing the effectiveness of emergency responses. Therefore, there is a need for a system that quickly notifies drivers of regular vehicles when an emergency vehicle is approaching and encourages safe evasive action.

[0474] 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.

[0475] In this invention, the server includes a device for acquiring location information, means for analyzing the information acquired by the device to determine whether the target is in motion, and a measuring device for acquiring the location of an external emergency vehicle and measuring the distance to the target's location. This makes it possible to immediately notify the target's device and issue a warning with voice or alarm sound when it is determined that the target is approaching the emergency vehicle within a certain distance.

[0476] A "device for acquiring location information" is a device that has the function of detecting geographical location and is used to determine the current location of a target in real time.

[0477] "Means for analyzing acquired information" refers to a system that includes processes and algorithms for analyzing acquired data and determining the state or situation of the subject.

[0478] "Means for determining whether an object is in motion" refers to technologies that provide criteria and methods for determining whether an object is actually moving physically, based on analyzed information.

[0479] A "measuring device" is a piece of equipment and technology used to accurately measure the physical distance between different points.

[0480] "When it is determined that the object is approaching within a specific distance" refers to a situation in which the analysis means recognizes that the object has exceeded a predetermined distance threshold and is in extremely close proximity.

[0481] A "notification" is the process of communicating specific information to a target device or user, drawing their attention visually or audibly.

[0482] "Voice or alarm sound" refers to a signal sound that is perceived by human hearing and is an audible notification emitted from a device for the purpose of warning or alerting.

[0483] This invention is a system designed to improve safety and efficiency in the traffic environment. In implementing the invention, terminals, servers, and external systems work together in coordination.

[0484] First, the user's device functions as a device for acquiring location information. The device uses its built-in GPS sensor to measure the user's current location and speed of movement in real time. This information is converted into a digital format that can be analyzed.

[0485] Next, location and speed data are sent from the terminal to the server. The server receives this data and uses software called an analysis engine to determine whether the object is moving. Based on this analysis, the server decides whether to continue further processing. In particular, movement is recognized when the speed exceeds a certain threshold value.

[0486] Simultaneously, the server communicates with an external emergency vehicle tracking system and uses measuring devices to obtain the current location of the emergency vehicle. Based on this information, the server compares the user's location with the emergency vehicle's location and measures the distance. If the server determines that the emergency vehicle is approaching within a set distance, a notification is sent to the user's terminal.

[0487] Specifically, the device has a function that notifies the user of the approaching emergency vehicle through voice messages or alarm sounds based on the notification data it receives. For example, by announcing "An emergency vehicle is approaching" via voice, the user can immediately pay attention and take safe evasive action.

[0488] An example of a prompt message might be: "Use the user's current location and the location of the emergency vehicle to design the optimal program algorithm for issuing a warning when an emergency vehicle approaches."

[0489] Thus, the invention can support the smooth operation of emergency vehicles and improve safety on the road.

[0490] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0491] Step 1:

[0492] The device uses a built-in GPS sensor to measure the user's current location and speed in real time. The input is the signal from the GPS sensor, and the output is location information (latitude and longitude) and speed data. Specifically, the device updates the location and speed every second and stores this data digitally.

[0493] Step 2:

[0494] The terminal transmits the acquired location information and speed data to the server via the internet. The input is the location information and speed data obtained in step 1, and the output is the digital data packets sent to the server. Specifically, the communication module within the terminal packets this data and securely transmits it to the server using the HTTPS protocol.

[0495] Step 3:

[0496] The server analyzes the received location and speed data. The input is data sent from the terminal, and the output is a determination indicating whether the user is moving or not. Specifically, the server uses an "analysis engine" to determine that the user is moving if the speed exceeds a certain threshold. This determination is made directly using a standard algorithm within the server.

[0497] Step 4:

[0498] The server communicates with an external emergency vehicle tracking system to obtain the current location of the emergency vehicle. The input is location information from the external system, and the output is data on the current location of the emergency vehicle. Specifically, the server obtains data via API communication and updates its location in real time.

[0499] Step 5:

[0500] The server compares the user's location information with the location of the emergency vehicle and measures the relative distance between them. The input is the location data of both parties held by the server, and the output is the result of the distance measurement. The server uses a "distance calculation module" to calculate the straight-line distance and determines whether it has entered within the set reference distance.

[0501] Step 6:

[0502] The server sends a notification to the user's device when it determines that it has approached within a set distance. The input is the result of the determination that the distance has exceeded the threshold, and the output is the notification data sent to the user's device. Specifically, the server generates a warning message and sends it to the device again using the HTTPS protocol.

[0503] Step 7:

[0504] Based on received notifications, the device alerts the user to the approaching emergency vehicle via voice message or alarm sound. The input is notification data from the server, and the output is a voice or alarm warning delivered to the user. As a concrete example of operation, the device starts playing a voice message saying "An emergency vehicle is approaching" and sounds an alarm sound if necessary.

[0505] (Application Example 1)

[0506] 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."

[0507] When autonomous vehicles travel on public roads, they are required to appropriately detect approaching emergency vehicles and automatically adjust their course and speed. Conventional systems only provide warnings to the driver and lack autonomous driving capabilities, which can prevent a quick response. As a result, they may hinder the smooth passage of emergency vehicles. Therefore, providing a system that allows autonomous vehicles to respond appropriately when emergency vehicles approach is a crucial challenge.

[0508] 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.

[0509] In this invention, the server includes positioning means for acquiring user location data and movement data; calculation means for analyzing the data acquired by the positioning means and determining whether the user is moving; measuring means for managing the location data of emergency vehicles and calculating the relative distance to the user's location data; warning means for issuing a warning to the user's information processing device when it is determined that the user is approaching an emergency vehicle within a certain distance; and route adjustment means mounted on the autonomous vehicle for automatically adjusting the course and speed in response to the approach of an emergency vehicle. This enables the autonomous vehicle to immediately sense the approach of an emergency vehicle and automatically change its course and adjust its speed, thereby supporting the smooth passage of the emergency vehicle.

[0510] "User" refers to an individual or vehicle driver using this system.

[0511] "Location data" refers to information indicating the geographical location of a user or emergency vehicle.

[0512] "Movement data" refers to information about a user's speed and movement patterns.

[0513] "Positioning means" refers to a technical device for acquiring the user's location data and movement data.

[0514] A "computation means" is a device that analyzes acquired location data and movement data to determine whether the user is moving.

[0515] A "measurement device" is a device that compares the location data of an emergency vehicle and a user and calculates the relative distance.

[0516] A "warning device" is a device or function that sends a warning to the user's information processing device when an emergency vehicle approaches within a certain distance.

[0517] "Route adjustment means" refers to a device or function in an autonomous vehicle that automatically changes its course or adjusts its speed in response to the approach of an emergency vehicle.

[0518] An "autonomous vehicle" is a vehicle designed to operate on its own without the need for human intervention.

[0519] An "information processing device" is a device or computer system used to communicate warnings to a user.

[0520] "Emergency vehicles" refer to vehicles such as fire trucks, ambulances, and police cars that are permitted to travel preferentially in emergency situations.

[0521] To realize this application, the server manipulates location data using various hardware and software. First, the server acquires the user's location and movement data using a GPS module. This data is transmitted from the user's terminal to the server. The server analyzes the received data and uses a computational means to determine whether the user is moving.

[0522] Next, the server collaborates with an external emergency vehicle tracking system to continuously acquire emergency vehicle location data. Based on this, it measures the relative distance between the user's location data and the emergency vehicle's location data. If the distance falls within a certain threshold, the server uses an alert mechanism to send a warning to the user's information processing device.

[0523] Furthermore, in autonomous vehicles, when the server detects the approach of an emergency vehicle, the vehicle's route adjustment mechanism activates, automatically adjusting its course and speed. This system allows autonomous vehicles to respond flexibly to the approach of emergency vehicles.

[0524] As a concrete example, consider a scenario where a fire truck approaches on a congested road near a shopping mall on a holiday. In this case, the server immediately detects the approaching emergency vehicle, adjusts the autonomous vehicle's course, and changes its speed as needed. This allows the fire truck to pass smoothly. An example of a prompt message for considering such a case would be: "Tell the autonomous driving AI model how to respond to an approaching emergency vehicle on the road. For example, specify how to change course and adjust speed."

[0525] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0526] Step 1:

[0527] The server receives location and movement data transmitted from the terminal. This includes the user's current location and speed information. This data is collected by a GPS module and transmitted to the server via wireless communication.

[0528] Step 2:

[0529] The server uses computational means to analyze the received location and movement data. Based on the analysis, it checks if the movement data exceeds a certain threshold and determines whether the user is moving. The result of this determination is then carried over to the next step.

[0530] Step 3:

[0531] The server works in conjunction with the emergency vehicle tracking system to obtain the latest location data of emergency vehicles. The location data of emergency vehicles is updated in real time and stored in the server's database.

[0532] Step 4:

[0533] The server measures the distance between the user's current location data and the emergency vehicle's location data. This process is performed using distance measurement equipment, and mathematical calculations are made based on the coordinate information of both to determine the actual distance.

[0534] Step 5:

[0535] The server checks whether the calculated distance falls within a specific threshold. If it does, the server uses a warning mechanism to generate a warning on the user's device and sends warning data to notify them of the proximity.

[0536] Step 6:

[0537] Based on warning data received from the server, the terminal performs specific actions, such as sending an audio or alarm to the user. This allows the user to recognize the approach of an emergency vehicle both visually and audibly.

[0538] Step 7:

[0539] When an autonomous vehicle detects an approaching emergency vehicle, it changes its course or adjusts its speed based on route adjustment data from the server. This ensures that the vehicle can safely and quickly pass through the emergency vehicle's path.

[0540] 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.

[0541] This invention includes a system that notifies the user of the approach of an emergency vehicle while they are driving, as well as an emotion engine that recognizes the user's emotional state in real time. This system is activated by utilizing a location information acquisition means that obtains the user's location information and speed using GPS functionality, and transmitting the acquired data to a server. The server processes this data using an analysis means to determine whether or not the user is moving.

[0542] Furthermore, this system works in conjunction with an external emergency vehicle tracking system to obtain the location of emergency vehicles and measures the relative position of the emergency vehicle and the user using distance measurement devices. When the server detects that the distance has fallen within a certain range, it sends a warning notification to the user.

[0543] The emotion recognition means in this invention determines the user's emotional state by analyzing the user's facial expressions and voice in real time using the user's smartphone or cameras and sensors installed in the vehicle. The emotion engine uses this information to adjust the content of warning notifications. For example, if the user is feeling stressed, the tone of the voice notification is made gentler to reduce the user's psychological burden.

[0544] As a concrete example, consider a situation where the user is driving a vehicle. The device sends location and speed data to the server, which analyzes it to confirm that the user is moving. The server then assumes that when an emergency vehicle approaches within 500 meters, the emotion recognition system detects tension from the user's facial expression. In this case, the notification system will issue an audio alarm in a gentle voice saying, "Please stay calm, an emergency vehicle is approaching ahead."

[0545] This system supports safe and calm driving by providing flexible responses tailored to the user's emotional state, and assists in the smooth progress of emergency vehicles.

[0546] The following describes the processing flow.

[0547] Step 1:

[0548] The device uses GPS functionality to acquire the user's location and speed, and prepares to send this data to the server.

[0549] Step 2:

[0550] The server receives location information and movement data transmitted from the terminal and uses analysis tools to determine whether the user is moving. If a speed exceeding a pre-set threshold is detected, it is determined that the user is moving.

[0551] Step 3:

[0552] The server obtains real-time location information of emergency vehicles from the emergency vehicle tracking system and compares it with the user's location information to measure the relative distance.

[0553] Step 4:

[0554] The emotion recognition system acquires the user's facial expressions and voice data using the device's camera or microphone, and analyzes the user's emotional state. The analyzed emotional data is then sent to a server.

[0555] Step 5:

[0556] The server verifies that the distance between the user and the emergency vehicle is below a certain threshold and adjusts the notification content based on the user's emotional state. If the user is anxious, the notification will be made gentler, for example.

[0557] Step 6:

[0558] The server generates warning notification data and sends it to the device, which then uses voice or an alarm sound to warn the user of the approaching emergency vehicle. The tone and content of the notification are optimized according to the user's emotional state.

[0559] Step 7:

[0560] After the user receives a notification on their device, they take appropriate action, such as slowing down or yielding the right of way to ensure safety.

[0561] (Example 2)

[0562] 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."

[0563] While it is crucial to create an environment where emergency vehicles can move smoothly through traffic, the current system has a challenge in that its notifications of approaching emergency vehicles are not flexible enough and cannot respond in accordance with the user's emotions and state of mind. Therefore, user support is needed to alleviate traffic congestion and allow emergency vehicles to pass quickly and safely. Furthermore, it is necessary to optimize notifications that take into account the user's mental state so that they can react appropriately to emergency vehicles.

[0564] 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.

[0565] In this invention, the server includes data acquisition means, analysis means, and information acquisition means. This makes it possible to acquire emergency vehicle information from an external system while monitoring the user's location information and movement speed, and to provide accurate and flexible notifications to the user. Furthermore, by using emotion determination means and adjustment means, a notification method that takes the user's mental state into account can be provided, enabling appropriate warnings while reducing stress.

[0566] "Data acquisition means" refers to a device or system that has the function of collecting the user's location information and movement speed.

[0567] "Analysis means" refers to a device or system for analyzing acquired data to determine whether or not the user is exercising.

[0568] "Information acquisition means" refers to a device or system that has the function of acquiring location information of emergency vehicles in cooperation with an external system.

[0569] "Measuring means" refers to a device or system for calculating the relative position of the user and the emergency vehicle.

[0570] "Communication means" refers to a device or system that has the function of sending a warning to the user's terminal.

[0571] An "emotion determination device" is a device or system that analyzes a user's facial expressions and voice to identify the user's emotional state.

[0572] "Adjustment means" refers to a device or system that has the function of adjusting the content and tone of a warning based on the emotion identified by the emotion determination means.

[0573] To implement this invention, the user's device must be equipped with a GPS module for acquiring location information, and a camera and voice sensor for determining emotional state. Using this hardware, the device collects location information, movement speed, facial expressions, and voice data and transmits them to a server.

[0574] The server receives location information and motion speed data from the terminal via a data acquisition means, and processes this data using an analysis means. The analysis means uses a specific algorithm and a generative AI model to determine whether the user is moving. Specifically, the user is recognized as moving if their motion speed exceeds a certain threshold.

[0575] Furthermore, the server obtains location information of emergency vehicles from an external emergency vehicle monitoring system through an information acquisition means. Based on this, the server calculates the relative positional relationship between the user and the emergency vehicle using a measurement means. If it is determined that an emergency vehicle is approaching within 500 meters of the user, the server sends a warning to the user's terminal using a communication means. The warning is provided in the form of voice or alarm sound.

[0576] Furthermore, the device's emotion detection mechanism uses a generative AI model to analyze the user's emotional state from their facial expressions and voice, determining whether the user is experiencing stress, etc. Based on this, the adjustment mechanism appropriately adjusts the content and tone of the warning.

[0577] As a concrete example, consider a situation where the user is driving a vehicle. In this case, the device sends location and speed data to the server, which analyzes it to confirm that the user is moving. Furthermore, suppose that when an emergency vehicle approaches within 500 meters, the emotion detection system detects the user's state of tension. In this case, the notification system will issue an audio alarm in a gentle voice saying, "Please stay calm, an emergency vehicle is approaching ahead."

[0578] An example of a prompt message is, "We want to optimize the emergency vehicle approach notification system while driving a vehicle according to the user's emotional state." Such a system configuration and operation would enable users to act safely and calmly even in emergency situations.

[0579] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0580] Step 1:

[0581] The device uses a GPS module to obtain the user's location and speed.

[0582] Input: GPS data (latitude, longitude, speed)

[0583] Specific operation: The device activates its built-in GPS sensor and periodically records the user's current location and speed. This allows the device to obtain accurate location information in real time and monitor the user's movements.

[0584] Output: Acquired location information and speed data

[0585] Step 2:

[0586] The device sends the acquired location information and speed data to the server.

[0587] Input: Location information and speed data from the device.

[0588] Specific operation: The terminal transmits the data it collects to the server via wireless communication. This communication occurs at regular intervals and is updated more frequently if the user is on the move.

[0589] Output: Location and speed data sent to the server

[0590] Step 3:

[0591] The server analyzes the received location and speed data to determine if the user is moving.

[0592] Input: Location information and speed data

[0593] Specific operation: The server uses an algorithm to determine if the speed exceeds a set threshold. If the threshold is exceeded, it recognizes that the user is moving. This data is stored for use in the next step.

[0594] Output: The result indicates that the user is in transit.

[0595] Step 4:

[0596] The server works in conjunction with an external emergency vehicle monitoring system to obtain location information for emergency vehicles.

[0597] Input: Location data of emergency vehicles from an external source.

[0598] Specific operation: The server periodically receives location information of emergency vehicles using an API. This allows it to check if an emergency vehicle is nearby and enables immediate response.

[0599] Output: Location data of acquired emergency vehicles

[0600] Step 5:

[0601] The server calculates the relative positions of the user and the emergency vehicle, and issues a warning if they approach within a certain distance.

[0602] Input: User's location information and emergency vehicle location information

[0603] Specific operation: Calculate the distance between coordinates and set a trigger to send a warning signal to the user's terminal if an emergency vehicle approaches within 500 meters.

[0604] Output: Trigger for warning notification

[0605] Step 6:

[0606] The device uses a camera and voice sensors to analyze the user's facial expressions and voice in real time and determine their emotions.

[0607] Input: User's facial expression data, voice data

[0608] Specific operation: Using a generative AI model, the system analyzes the user's emotional state in real time from collected data and determines emotions such as "stress" and "tension."

[0609] Output: User's emotional state

[0610] Step 7:

[0611] Based on the emotion assessment results, the server sends instructions to the device to adjust the content and tone of the warning notification.

[0612] Input: Sentiment assessment result

[0613] Specific operation: The server generates and sends notification instructions to the device based on the user's stress and anxiety level. For example, if the user is feeling anxious, the server will be configured to send notifications in a calm tone.

[0614] Output: Adjusted warning notification content and tone

[0615] (Application Example 2)

[0616] 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."

[0617] With the recent advancements in autonomous driving technology, ensuring safety during travel has become a critical issue. In particular, if users are unable to recognize the approach of an emergency vehicle, there is a risk of hindering its smooth progress. Furthermore, the psychological stress of the driver must also be considered. Conventional systems only provide emergency information notifications and lack flexible responses tailored to the user's psychological state. Therefore, it is necessary to improve situations that easily cause anxiety and promote safe and smooth traffic flow.

[0618] 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.

[0619] In this invention, the server includes positioning means for acquiring location information and speed of movement; information analysis means for analyzing the data acquired by the positioning means and determining whether the user is moving; distance evaluation means for managing the location information of emergency vehicles and measuring the relative distance to the user's location information; and emotion recognition means for recognizing the user's emotional state and adjusting the content of notifications. This makes it possible to provide an environment in which users can respond to emergency vehicles safely and calmly, and to support the smooth progress of emergency vehicles.

[0620] "Positioning means" refers to a function for accurately acquiring the user's location information and speed of movement.

[0621] "Information analysis means" refers to a processing function that uses acquired location information and movement speed data to determine whether the user is currently moving.

[0622] The "distance evaluation means" is a function that measures the relative distance between an emergency vehicle and a user based on their respective location information.

[0623] A "warning mechanism" is a function that notifies the user when it is determined that the user has approached an emergency vehicle within a certain distance.

[0624] "Emotion recognition means" refers to a function that recognizes the user's emotional state, adjusts the content of notifications based on the analysis results, and reduces the user's psychological burden.

[0625] This system consists of a user, a terminal, and a server. It is designed to alert users to the presence of emergency vehicles while they are on the move, facilitating safe travel. Specifically, it takes into account situations where the user is wearing smart glasses while driving a vehicle.

[0626] The terminal first uses positioning means to accurately acquire the user's location and speed. This information is transmitted to the server in real time, and information analysis means determine whether the user is moving. The server then collaborates with an external emergency vehicle tracking system to collect its location information and measures the relative distance between the two using distance evaluation means.

[0627] When the system detects that the user has approached an emergency vehicle within a certain distance, a warning system activates, displaying a notification on the smart glasses' screen. At this time, the emotion recognition system uses the smart glasses' built-in camera and microphone to capture the user's facial expressions and voice, analyzing their emotions in real time. For example, it uses a facial recognition and emotion analysis library based on OpenCV to evaluate the user's psychological state. If the user is experiencing stress, the warning voice tone is softened to reduce their psychological burden.

[0628] As a concrete example, consider a scenario where a user is driving a vehicle towards the suburbs with a friend. Suddenly, an emergency vehicle is detected approaching from behind. The smart glasses immediately notify the user in a gentle voice, "Please stay calm, an emergency vehicle is approaching ahead," and display its location on a map. In this way, the user can remain calm and continue operating the vehicle.

[0629] An example of a prompt is, "Create a notification that will help the driver feel at ease while driving. The situation is that an emergency vehicle is approaching." This prompt provides important instructions to the generation AI model when creating calming notifications that prioritize user comfort.

[0630] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0631] Step 1:

[0632] The device uses positioning to acquire the user's location information and speed. During this process, a GPS sensor is used to collect accurate data in real time. This allows the user's location coordinates and speed to be obtained and transmitted to the server.

[0633] Step 2:

[0634] The server receives the transmitted location and speed data and processes it using an information analysis tool. This process determines whether the user is currently moving by checking if the speed is above a set threshold. Based on this result, the server decides whether to proceed to the next step.

[0635] Step 3:

[0636] The server works in conjunction with the emergency vehicle tracking system to obtain the location information of emergency vehicles. Using a distance evaluation method, it compares this location information with the user's location information and measures the relative distance between the two. Here, it evaluates whether the calculated distance falls within a certain range.

[0637] Step 4:

[0638] If the server determines, based on its assessment, that the distance has entered a certain range, the warning system is activated. It sends a notification signal to the terminal and prepares to display visual and audible warnings on the smart glasses. At this point, it identifies that an emergency alert is necessary for the terminal.

[0639] Step 5:

[0640] The device uses a camera and microphone built into smart glasses to capture the user's facial expressions and voice in real time. Emotion recognition technology is used to analyze this data and identify the user's emotional state. An emotion analysis library is used to assess the stress and anxiety the user is experiencing.

[0641] Step 6:

[0642] If emotion recognition determines that the user is experiencing stress, the server instructs the warning system to adjust the tone of the notification. Using a generative AI model, it generates a voice notification based on the prompt: "Create a notification that will help the driver feel at ease while driving. The situation is that an emergency vehicle is approaching," delivering the appropriate message to the user in a gentle tone.

[0643] Step 7:

[0644] After notifying the user, the server verifies that all processes have completed successfully and returns to its initial state to prepare for future emergencies or events during transit. This is a process of continuous monitoring of the system to continuously receive and analyze critical information.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] [Fourth Embodiment]

[0649] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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".

[0662] This invention includes a location information acquisition means that utilizes the GPS function of the user's device to obtain the user's current location and speed in real time. The device transmits this data to a server, which analyzes the location information and speed data. Based on the analysis means, the server determines whether the user is moving and stores the result.

[0663] The server also continuously acquires and manages the location of emergency vehicles through cooperation with external emergency vehicle tracking systems. Using distance measurement devices, the server compares the user's current location with the location of the emergency vehicle and measures the relative distance between them. If it is determined that the user has entered a certain distance, the server generates warning notification data and sends it to the user's terminal via the notification device.

[0664] The device uses voice or an alarm to notify the user of the approaching emergency vehicle based on notifications received from the server. For example, the device might announce, "An emergency vehicle is approaching ahead," allowing the user to recognize the approach and take a quick and safe action.

[0665] As a concrete example, consider a scenario where a user is driving a car. When the terminal detects that the user's speed exceeds a certain threshold, the server compares that data with the location information of an emergency vehicle. For example, if an emergency vehicle approaches within 500 meters, the server issues a warning, and an alarm sounds through the terminal. This allows the user to consciously reduce their speed or pull over to the side of the road.

[0666] The system of the present invention not only assists emergency vehicles in smoothly reaching emergency situations, but also reduces the risk of accidents in normal traffic conditions.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] The device obtains the user's location and speed via GPS. It then processes the acquired data and prepares it for transmission to the server.

[0670] Step 2:

[0671] The server receives location information and movement speed data transmitted from the terminal. The received data is passed to an analysis tool to determine whether the user is moving.

[0672] Step 3:

[0673] When the server confirms that the user is on the move, it retrieves the latest location data from an external emergency vehicle tracking system to obtain the location information of emergency vehicles.

[0674] Step 4:

[0675] The server compares the user's location information with the emergency vehicle's location information and uses distance measurement equipment to measure the relative distance between the two.

[0676] Step 5:

[0677] If the server determines that the distance between the user and the emergency vehicle is below a certain threshold, it generates warning notification data and sends it to the user's device.

[0678] Step 6:

[0679] Based on notification data received from the server, the device warns the user of the approaching emergency vehicle via voice or alarm. The purpose of the notification is to enable the user to respond appropriately.

[0680] Step 7:

[0681] The user receives a warning notification on their device and takes appropriate action, such as slowing down or pulling over to the side of the road to ensure safety.

[0682] (Example 1)

[0683] 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".

[0684] In today's traffic environment, a major obstacle to the rapid and safe movement of emergency vehicles is that regular vehicles may not notice their approach. This leads to traffic congestion and delays, reducing the effectiveness of emergency responses. Therefore, there is a need for a system that quickly notifies drivers of regular vehicles when an emergency vehicle is approaching and encourages safe evasive action.

[0685] 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.

[0686] In this invention, the server includes a device for acquiring location information, means for analyzing the information acquired by the device to determine whether the target is in motion, and a measuring device for acquiring the location of an external emergency vehicle and measuring the distance to the target's location. This makes it possible to immediately notify the target's device and issue a warning with voice or alarm sound when it is determined that the target is approaching the emergency vehicle within a certain distance.

[0687] A "device for acquiring location information" is a device that has the function of detecting geographical location and is used to determine the current location of a target in real time.

[0688] "Means for analyzing acquired information" refers to a system that includes processes and algorithms for analyzing acquired data and determining the state or situation of the subject.

[0689] "Means for determining whether an object is in motion" refers to technologies that provide criteria and methods for determining whether an object is actually moving physically, based on analyzed information.

[0690] A "measuring device" is a piece of equipment and technology used to accurately measure the physical distance between different points.

[0691] "When it is determined that the object is approaching within a specific distance" refers to a situation in which the analysis means recognizes that the object has exceeded a predetermined distance threshold and is in extremely close proximity.

[0692] A "notification" is the process of communicating specific information to a target device or user, drawing their attention visually or audibly.

[0693] "Voice or alarm sound" refers to a signal sound that is perceived by human hearing and is an audible notification emitted from a device for the purpose of warning or alerting.

[0694] This invention is a system designed to improve safety and efficiency in the traffic environment. In implementing the invention, terminals, servers, and external systems work together in coordination.

[0695] First, the user's device functions as a device for acquiring location information. The device uses its built-in GPS sensor to measure the user's current location and speed of movement in real time. This information is converted into a digital format that can be analyzed.

[0696] Next, location and speed data are sent from the terminal to the server. The server receives this data and uses software called an analysis engine to determine whether the object is moving. Based on this analysis, the server decides whether to continue further processing. In particular, movement is recognized when the speed exceeds a certain threshold value.

[0697] Simultaneously, the server communicates with an external emergency vehicle tracking system and uses measuring devices to obtain the current location of the emergency vehicle. Based on this information, the server compares the user's location with the emergency vehicle's location and measures the distance. If the server determines that the emergency vehicle is approaching within a set distance, a notification is sent to the user's terminal.

[0698] Specifically, the device has a function that notifies the user of the approaching emergency vehicle through voice messages or alarm sounds based on the notification data it receives. For example, by announcing "An emergency vehicle is approaching" via voice, the user can immediately pay attention and take safe evasive action.

[0699] An example of a prompt message might be: "Use the user's current location and the location of the emergency vehicle to design the optimal program algorithm for issuing a warning when an emergency vehicle approaches."

[0700] Thus, the invention can support the smooth operation of emergency vehicles and improve safety on the road.

[0701] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0702] Step 1:

[0703] The device uses a built-in GPS sensor to measure the user's current location and speed in real time. The input is the signal from the GPS sensor, and the output is location information (latitude and longitude) and speed data. Specifically, the device updates the location and speed every second and stores this data digitally.

[0704] Step 2:

[0705] The terminal transmits the acquired location information and speed data to the server via the internet. The input is the location information and speed data obtained in step 1, and the output is the digital data packets sent to the server. Specifically, the communication module within the terminal packets this data and securely transmits it to the server using the HTTPS protocol.

[0706] Step 3:

[0707] The server analyzes the received location and speed data. The input is data sent from the terminal, and the output is a determination indicating whether the user is moving or not. Specifically, the server uses an "analysis engine" to determine that the user is moving if the speed exceeds a certain threshold. This determination is made directly using a standard algorithm within the server.

[0708] Step 4:

[0709] The server communicates with an external emergency vehicle tracking system to obtain the current location of the emergency vehicle. The input is location information from the external system, and the output is data on the current location of the emergency vehicle. Specifically, the server obtains data via API communication and updates its location in real time.

[0710] Step 5:

[0711] The server compares the user's location information with the location of the emergency vehicle and measures the relative distance between them. The input is the location data of both parties held by the server, and the output is the result of the distance measurement. The server uses a "distance calculation module" to calculate the straight-line distance and determines whether it has entered within the set reference distance.

[0712] Step 6:

[0713] The server sends a notification to the user's device when it determines that it has approached within a set distance. The input is the result of the determination that the distance has exceeded the threshold, and the output is the notification data sent to the user's device. Specifically, the server generates a warning message and sends it to the device again using the HTTPS protocol.

[0714] Step 7:

[0715] Based on received notifications, the device alerts the user to the approaching emergency vehicle via voice message or alarm sound. The input is notification data from the server, and the output is a voice or alarm warning delivered to the user. As a concrete example of operation, the device starts playing a voice message saying "An emergency vehicle is approaching" and sounds an alarm sound if necessary.

[0716] (Application Example 1)

[0717] 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".

[0718] When autonomous vehicles travel on public roads, they are required to appropriately detect approaching emergency vehicles and automatically adjust their course and speed. Conventional systems only provide warnings to the driver and lack autonomous driving capabilities, which can prevent a quick response. As a result, they may hinder the smooth passage of emergency vehicles. Therefore, providing a system that allows autonomous vehicles to respond appropriately when emergency vehicles approach is a crucial challenge.

[0719] 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.

[0720] In this invention, the server includes positioning means for acquiring user location data and movement data; calculation means for analyzing the data acquired by the positioning means and determining whether the user is moving; measuring means for managing the location data of emergency vehicles and calculating the relative distance to the user's location data; warning means for issuing a warning to the user's information processing device when it is determined that the user is approaching an emergency vehicle within a certain distance; and route adjustment means mounted on the autonomous vehicle for automatically adjusting the course and speed in response to the approach of an emergency vehicle. This enables the autonomous vehicle to immediately sense the approach of an emergency vehicle and automatically change its course and adjust its speed, thereby supporting the smooth passage of the emergency vehicle.

[0721] "User" refers to an individual or vehicle driver using this system.

[0722] "Location data" refers to information indicating the geographical location of a user or emergency vehicle.

[0723] "Movement data" refers to information about a user's speed and movement patterns.

[0724] "Positioning means" refers to a technical device for acquiring the user's location data and movement data.

[0725] A "computation means" is a device that analyzes acquired location data and movement data to determine whether the user is moving.

[0726] A "measurement device" is a device that compares the location data of an emergency vehicle and a user and calculates the relative distance.

[0727] A "warning device" is a device or function that sends a warning to the user's information processing device when an emergency vehicle approaches within a certain distance.

[0728] "Route adjustment means" refers to a device or function in an autonomous vehicle that automatically changes its course or adjusts its speed in response to the approach of an emergency vehicle.

[0729] An "autonomous vehicle" is a vehicle designed to operate on its own without the need for human intervention.

[0730] An "information processing device" is a device or computer system used to communicate warnings to a user.

[0731] "Emergency vehicles" refer to vehicles such as fire trucks, ambulances, and police cars that are permitted to travel preferentially in emergency situations.

[0732] To realize this application, the server manipulates location data using various hardware and software. First, the server acquires the user's location and movement data using a GPS module. This data is transmitted from the user's terminal to the server. The server analyzes the received data and uses a computational means to determine whether the user is moving.

[0733] Next, the server collaborates with an external emergency vehicle tracking system to continuously acquire emergency vehicle location data. Based on this, it measures the relative distance between the user's location data and the emergency vehicle's location data. If the distance falls within a certain threshold, the server uses an alert mechanism to send a warning to the user's information processing device.

[0734] Furthermore, in autonomous vehicles, when the server detects the approach of an emergency vehicle, the vehicle's route adjustment mechanism activates, automatically adjusting its course and speed. This system allows autonomous vehicles to respond flexibly to the approach of emergency vehicles.

[0735] As a concrete example, consider a scenario where a fire truck approaches on a congested road near a shopping mall on a holiday. In this case, the server immediately detects the approaching emergency vehicle, adjusts the autonomous vehicle's course, and changes its speed as needed. This allows the fire truck to pass smoothly. An example of a prompt message for considering such a case would be: "Tell the autonomous driving AI model how to respond to an approaching emergency vehicle on the road. For example, specify how to change course and adjust speed."

[0736] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0737] Step 1:

[0738] The server receives location and movement data transmitted from the terminal. This includes the user's current location and speed information. This data is collected by a GPS module and transmitted to the server via wireless communication.

[0739] Step 2:

[0740] The server uses computational means to analyze the received location and movement data. Based on the analysis, it checks if the movement data exceeds a certain threshold and determines whether the user is moving. The result of this determination is then carried over to the next step.

[0741] Step 3:

[0742] The server works in conjunction with the emergency vehicle tracking system to obtain the latest location data of emergency vehicles. The location data of emergency vehicles is updated in real time and stored in the server's database.

[0743] Step 4:

[0744] The server measures the distance between the user's current location data and the emergency vehicle's location data. This process is performed using distance measurement equipment, and mathematical calculations are made based on the coordinate information of both to determine the actual distance.

[0745] Step 5:

[0746] The server checks whether the calculated distance falls within a specific threshold. If it does, the server uses a warning mechanism to generate a warning on the user's device and sends warning data to notify them of the proximity.

[0747] Step 6:

[0748] Based on warning data received from the server, the terminal performs specific actions, such as sending an audio or alarm to the user. This allows the user to recognize the approach of an emergency vehicle both visually and audibly.

[0749] Step 7:

[0750] When an autonomous vehicle detects an approaching emergency vehicle, it changes its course or adjusts its speed based on route adjustment data from the server. This ensures that the vehicle can safely and quickly pass through the emergency vehicle's path.

[0751] 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.

[0752] This invention includes a system that notifies the user of the approach of an emergency vehicle while they are driving, as well as an emotion engine that recognizes the user's emotional state in real time. This system is activated by utilizing a location information acquisition means that obtains the user's location information and speed using GPS functionality, and transmitting the acquired data to a server. The server processes this data using an analysis means to determine whether or not the user is moving.

[0753] Furthermore, this system works in conjunction with an external emergency vehicle tracking system to obtain the location of emergency vehicles and measures the relative position of the emergency vehicle and the user using distance measurement devices. When the server detects that the distance has fallen within a certain range, it sends a warning notification to the user.

[0754] The emotion recognition means in this invention determines the user's emotional state by analyzing the user's facial expressions and voice in real time using the user's smartphone or cameras and sensors installed in the vehicle. The emotion engine uses this information to adjust the content of warning notifications. For example, if the user is feeling stressed, the tone of the voice notification is made gentler to reduce the user's psychological burden.

[0755] As a concrete example, consider a situation where the user is driving a vehicle. The device sends location and speed data to the server, which analyzes it to confirm that the user is moving. The server then assumes that when an emergency vehicle approaches within 500 meters, the emotion recognition system detects tension from the user's facial expression. In this case, the notification system will issue an audio alarm in a gentle voice saying, "Please stay calm, an emergency vehicle is approaching ahead."

[0756] This system supports safe and calm driving by providing flexible responses tailored to the user's emotional state, and assists in the smooth progress of emergency vehicles.

[0757] The following describes the processing flow.

[0758] Step 1:

[0759] The device uses GPS functionality to acquire the user's location and speed, and prepares to send this data to the server.

[0760] Step 2:

[0761] The server receives location information and movement data transmitted from the terminal and uses analysis tools to determine whether the user is moving. If a speed exceeding a pre-set threshold is detected, it is determined that the user is moving.

[0762] Step 3:

[0763] The server obtains real-time location information of emergency vehicles from the emergency vehicle tracking system and compares it with the user's location information to measure the relative distance.

[0764] Step 4:

[0765] The emotion recognition system acquires the user's facial expressions and voice data using the device's camera or microphone, and analyzes the user's emotional state. The analyzed emotional data is then sent to a server.

[0766] Step 5:

[0767] The server verifies that the distance between the user and the emergency vehicle is below a certain threshold and adjusts the notification content based on the user's emotional state. If the user is anxious, the notification will be made gentler, for example.

[0768] Step 6:

[0769] The server generates warning notification data and sends it to the device, which then uses voice or an alarm sound to warn the user of the approaching emergency vehicle. The tone and content of the notification are optimized according to the user's emotional state.

[0770] Step 7:

[0771] After the user receives a notification on their device, they take appropriate action, such as slowing down or yielding the right of way to ensure safety.

[0772] (Example 2)

[0773] 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".

[0774] While it is crucial to create an environment where emergency vehicles can move smoothly through traffic, the current system has a challenge in that its notifications of approaching emergency vehicles are not flexible enough and cannot respond in accordance with the user's emotions and state of mind. Therefore, user support is needed to alleviate traffic congestion and allow emergency vehicles to pass quickly and safely. Furthermore, it is necessary to optimize notifications that take into account the user's mental state so that they can react appropriately to emergency vehicles.

[0775] 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.

[0776] In this invention, the server includes data acquisition means, analysis means, and information acquisition means. This makes it possible to acquire emergency vehicle information from an external system while monitoring the user's location information and movement speed, and to provide accurate and flexible notifications to the user. Furthermore, by using emotion determination means and adjustment means, a notification method that takes the user's mental state into account can be provided, enabling appropriate warnings while reducing stress.

[0777] "Data acquisition means" refers to a device or system that has the function of collecting the user's location information and movement speed.

[0778] "Analysis means" refers to a device or system for analyzing acquired data to determine whether or not the user is exercising.

[0779] "Information acquisition means" refers to a device or system that has the function of acquiring location information of emergency vehicles in cooperation with an external system.

[0780] "Measuring means" refers to a device or system for calculating the relative position of the user and the emergency vehicle.

[0781] "Communication means" refers to a device or system that has the function of sending a warning to the user's terminal.

[0782] An "emotion determination device" is a device or system that analyzes a user's facial expressions and voice to identify the user's emotional state.

[0783] "Adjustment means" refers to a device or system that has the function of adjusting the content and tone of a warning based on the emotion identified by the emotion determination means.

[0784] To implement this invention, the user's device must be equipped with a GPS module for acquiring location information, and a camera and voice sensor for determining emotional state. Using this hardware, the device collects location information, movement speed, facial expressions, and voice data and transmits them to a server.

[0785] The server receives location information and motion speed data from the terminal via a data acquisition means, and processes this data using an analysis means. The analysis means uses a specific algorithm and a generative AI model to determine whether the user is moving. Specifically, the user is recognized as moving if their motion speed exceeds a certain threshold.

[0786] Furthermore, the server obtains location information of emergency vehicles from an external emergency vehicle monitoring system through an information acquisition means. Based on this, the server calculates the relative positional relationship between the user and the emergency vehicle using a measurement means. If it is determined that an emergency vehicle is approaching within 500 meters of the user, the server sends a warning to the user's terminal using a communication means. The warning is provided in the form of voice or alarm sound.

[0787] Furthermore, the device's emotion detection mechanism uses a generative AI model to analyze the user's emotional state from their facial expressions and voice, determining whether the user is experiencing stress, etc. Based on this, the adjustment mechanism appropriately adjusts the content and tone of the warning.

[0788] As a concrete example, consider a situation where the user is driving a vehicle. In this case, the device sends location and speed data to the server, which analyzes it to confirm that the user is moving. Furthermore, suppose that when an emergency vehicle approaches within 500 meters, the emotion detection system detects the user's state of tension. In this case, the notification system will issue an audio alarm in a gentle voice saying, "Please stay calm, an emergency vehicle is approaching ahead."

[0789] An example of a prompt message is, "We want to optimize the emergency vehicle approach notification system while driving a vehicle according to the user's emotional state." Such a system configuration and operation would enable users to act safely and calmly even in emergency situations.

[0790] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0791] Step 1:

[0792] The device uses a GPS module to obtain the user's location and speed.

[0793] Input: GPS data (latitude, longitude, speed)

[0794] Specific operation: The device activates its built-in GPS sensor and periodically records the user's current location and speed. This allows the device to obtain accurate location information in real time and monitor the user's movements.

[0795] Output: Acquired location information and speed data

[0796] Step 2:

[0797] The device sends the acquired location information and speed data to the server.

[0798] Input: Location information and speed data from the device.

[0799] Specific operation: The terminal transmits the data it collects to the server via wireless communication. This communication occurs at regular intervals and is updated more frequently if the user is on the move.

[0800] Output: Location and speed data sent to the server

[0801] Step 3:

[0802] The server analyzes the received location and speed data to determine if the user is moving.

[0803] Input: Location information and speed data

[0804] Specific operation: The server uses an algorithm to determine if the speed exceeds a set threshold. If the threshold is exceeded, it recognizes that the user is moving. This data is stored for use in the next step.

[0805] Output: The result indicates that the user is in transit.

[0806] Step 4:

[0807] The server works in conjunction with an external emergency vehicle monitoring system to obtain location information for emergency vehicles.

[0808] Input: Location data of emergency vehicles from an external source.

[0809] Specific operation: The server periodically receives location information of emergency vehicles using an API. This allows it to check if an emergency vehicle is nearby and enables immediate response.

[0810] Output: Location data of acquired emergency vehicles

[0811] Step 5:

[0812] The server calculates the relative positions of the user and the emergency vehicle, and issues a warning if they approach within a certain distance.

[0813] Input: User's location information and emergency vehicle location information

[0814] Specific operation: Calculate the distance between coordinates and set a trigger to send a warning signal to the user's terminal if an emergency vehicle approaches within 500 meters.

[0815] Output: Trigger for warning notification

[0816] Step 6:

[0817] The device uses a camera and voice sensors to analyze the user's facial expressions and voice in real time and determine their emotions.

[0818] Input: User's facial expression data, voice data

[0819] Specific operation: Using a generative AI model, the system analyzes the user's emotional state in real time from collected data and determines emotions such as "stress" and "tension."

[0820] Output: User's emotional state

[0821] Step 7:

[0822] Based on the emotion assessment results, the server sends instructions to the device to adjust the content and tone of the warning notification.

[0823] Input: Sentiment assessment result

[0824] Specific operation: The server generates and sends notification instructions to the device based on the user's stress and anxiety level. For example, if the user is feeling anxious, the server will be configured to send notifications in a calm tone.

[0825] Output: Adjusted warning notification content and tone

[0826] (Application Example 2)

[0827] 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".

[0828] With the recent advancements in autonomous driving technology, ensuring safety during travel has become a critical issue. In particular, if users are unable to recognize the approach of an emergency vehicle, there is a risk of hindering its smooth progress. Furthermore, the psychological stress of the driver must also be considered. Conventional systems only provide emergency information notifications and lack flexible responses tailored to the user's psychological state. Therefore, it is necessary to improve situations that easily cause anxiety and promote safe and smooth traffic flow.

[0829] 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.

[0830] In this invention, the server includes positioning means for acquiring location information and speed of movement; information analysis means for analyzing the data acquired by the positioning means and determining whether the user is moving; distance evaluation means for managing the location information of emergency vehicles and measuring the relative distance to the user's location information; and emotion recognition means for recognizing the user's emotional state and adjusting the content of notifications. This makes it possible to provide an environment in which users can respond to emergency vehicles safely and calmly, and to support the smooth progress of emergency vehicles.

[0831] "Positioning means" refers to a function for accurately acquiring the user's location information and speed of movement.

[0832] "Information analysis means" refers to a processing function that uses acquired location information and movement speed data to determine whether the user is currently moving.

[0833] The "distance evaluation means" is a function that measures the relative distance between an emergency vehicle and a user based on their respective location information.

[0834] A "warning mechanism" is a function that notifies the user when it is determined that the user has approached an emergency vehicle within a certain distance.

[0835] "Emotion recognition means" refers to a function that recognizes the user's emotional state, adjusts the content of notifications based on the analysis results, and reduces the user's psychological burden.

[0836] This system consists of a user, a terminal, and a server. It is designed to alert users to the presence of emergency vehicles while they are on the move, facilitating safe travel. Specifically, it takes into account situations where the user is wearing smart glasses while driving a vehicle.

[0837] The terminal first uses positioning means to accurately acquire the user's location and speed. This information is transmitted to the server in real time, and information analysis means determine whether the user is moving. The server then collaborates with an external emergency vehicle tracking system to collect its location information and measures the relative distance between the two using distance evaluation means.

[0838] When the system detects that the user has approached an emergency vehicle within a certain distance, a warning system activates, displaying a notification on the smart glasses' screen. At this time, the emotion recognition system uses the smart glasses' built-in camera and microphone to capture the user's facial expressions and voice, analyzing their emotions in real time. For example, it uses a facial recognition and emotion analysis library based on OpenCV to evaluate the user's psychological state. If the user is experiencing stress, the warning voice tone is softened to reduce their psychological burden.

[0839] As a concrete example, consider a scenario where a user is driving a vehicle towards the suburbs with a friend. Suddenly, an emergency vehicle is detected approaching from behind. The smart glasses immediately notify the user in a gentle voice, "Please stay calm, an emergency vehicle is approaching ahead," and display its location on a map. In this way, the user can remain calm and continue operating the vehicle.

[0840] An example of a prompt is, "Create a notification that will help the driver feel at ease while driving. The situation is that an emergency vehicle is approaching." This prompt provides important instructions to the generation AI model when creating calming notifications that prioritize user comfort.

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

[0842] Step 1:

[0843] The device uses positioning to acquire the user's location information and speed. During this process, a GPS sensor is used to collect accurate data in real time. This allows the user's location coordinates and speed to be obtained and transmitted to the server.

[0844] Step 2:

[0845] The server receives the transmitted location and speed data and processes it using an information analysis tool. This process determines whether the user is currently moving by checking if the speed is above a set threshold. Based on this result, the server decides whether to proceed to the next step.

[0846] Step 3:

[0847] The server works in conjunction with the emergency vehicle tracking system to obtain the location information of emergency vehicles. Using a distance evaluation method, it compares this location information with the user's location information and measures the relative distance between the two. Here, it evaluates whether the calculated distance falls within a certain range.

[0848] Step 4:

[0849] If the server determines, based on its assessment, that the distance has entered a certain range, the warning system is activated. It sends a notification signal to the terminal and prepares to display visual and audible warnings on the smart glasses. At this point, it identifies that an emergency alert is necessary for the terminal.

[0850] Step 5:

[0851] The device uses a camera and microphone built into smart glasses to capture the user's facial expressions and voice in real time. Emotion recognition technology is used to analyze this data and identify the user's emotional state. An emotion analysis library is used to assess the stress and anxiety the user is experiencing.

[0852] Step 6:

[0853] If emotion recognition determines that the user is experiencing stress, the server instructs the warning system to adjust the tone of the notification. Using a generative AI model, it generates a voice notification based on the prompt: "Create a notification that will help the driver feel at ease while driving. The situation is that an emergency vehicle is approaching," delivering the appropriate message to the user in a gentle tone.

[0854] Step 7:

[0855] After notifying the user, the server verifies that all processes have completed successfully and returns to its initial state to prepare for future emergencies or events during transit. This is a process of continuous monitoring of the system to continuously receive and analyze critical information.

[0856] 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.

[0857] 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.

[0858] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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."

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0877] The following is further disclosed regarding the embodiments described above.

[0878] (Claim 1)

[0879] A means for acquiring location information and movement speed of the user,

[0880] An analysis means for analyzing data acquired by a location information acquisition means to determine whether the user is in motion,

[0881] A distance measurement means for managing the location information of emergency vehicles and measuring the relative distance to the user's location information,

[0882] A notification mechanism to notify the user's device when it is determined that the user is approaching an emergency vehicle within a certain distance,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, wherein the analysis means determines that the user is moving when it detects that the user's movement speed is greater than or equal to a preset threshold.

[0886] (Claim 3)

[0887] The system according to claim 1, wherein the notification means warns the user of the approach of an emergency vehicle by voice or alarm sound.

[0888] "Example 1"

[0889] (Claim 1)

[0890] A device for acquiring location information,

[0891] A means for analyzing information acquired by the device and determining whether the object is moving,

[0892] A measuring device for acquiring the position of an external emergency mobile object and measuring the distance to the target's position,

[0893] A means for notifying the target's device when it is determined that the target is approaching an emergency vehicle within a certain distance,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, wherein the analysis means determines that the object is in motion when it detects that the object's movement speed is greater than or equal to a set reference value.

[0897] (Claim 3)

[0898] The system according to claim 1, wherein the notification means warns a target of the approach of an emergency moving object by voice or alarm sound.

[0899] "Application Example 1"

[0900] (Claim 1)

[0901] A positioning means for acquiring user location data and movement data,

[0902] A calculation means for analyzing data acquired by a positioning means and determining whether the user is moving,

[0903] A measurement means for managing the location data of emergency vehicles and calculating the relative distance to the user's location data,

[0904] A warning means for issuing a warning to the user's information processing device when it is determined that the user is approaching an emergency vehicle within a certain distance,

[0905] A route adjustment mechanism installed in an autonomous vehicle to automatically adjust the course and speed in response to the approach of an emergency vehicle,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, wherein the calculation means determines that a user is moving when it senses that the user's movement data is above a predetermined threshold value.

[0909] (Claim 3)

[0910] The system according to claim 1, wherein the warning means notifies the user of the approach of an emergency vehicle by voice or alarm sound and activates the route adjustment means.

[0911] "Example 2 of combining an emotion engine"

[0912] (Claim 1)

[0913] A data acquisition method for obtaining the user's location information and movement speed,

[0914] An analysis means for analyzing information acquired by a data acquisition means to determine whether or not the person is in motion,

[0915] A means for acquiring information to obtain location information of moving objects in cooperation with an external emergency vehicle monitoring system,

[0916] A measurement means for calculating the relative positional relationship between the user and the moving object,

[0917] A communication means for sending a warning to the user's device when it is determined that the user has approached a moving object within a certain distance,

[0918] To analyze the user's emotional state, the device uses mounted imaging and audio acquisition equipment to analyze data, and the emotion determination means is used.

[0919] An adjustment means for adjusting the content and tone of a warning based on the emotion identified by the emotion determination means,

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, wherein the analysis means determines that the user is in motion when the user's movement speed exceeds a predetermined reference value.

[0923] (Claim 3)

[0924] The system according to claim 1, wherein the notification means warns the user of the approach of a moving object by voice or alarm sound and adjusts the characteristics of the notification according to the user's emotional state.

[0925] "Application example 2 when combining with an emotional engine"

[0926] (Claim 1)

[0927] A positioning means for obtaining location information and movement speed,

[0928] An information analysis means for analyzing data acquired by a positioning means to determine whether the user is moving,

[0929] A distance evaluation means for managing the location information of emergency vehicles and measuring the relative distance to the location information of users,

[0930] A warning mechanism to notify the user's device when it is determined that the user is approaching an emergency vehicle within a certain distance,

[0931] A means for recognizing the user's emotional state and adjusting the content of notifications,

[0932] A system that includes this.

[0933] (Claim 2)

[0934] The system according to claim 1, wherein the information analysis means determines that a user is moving when it detects that the user's movement speed is greater than or equal to a preset threshold.

[0935] (Claim 3)

[0936] The system according to claim 1, wherein the warning means warns the user of the approach of an emergency vehicle by voice or warning sound, and further adjusts according to the user's emotional state. [Explanation of Symbols]

[0937] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring location information and movement speed of the user, An analysis means for analyzing data acquired by a location information acquisition means to determine whether the user is in motion, A distance measurement means for managing the location information of emergency vehicles and measuring the relative distance to the user's location information, A notification mechanism to notify the user's device when it is determined that the user is approaching an emergency vehicle within a certain distance, A system that includes this.

2. The system according to claim 1, wherein the analysis means determines that the user is moving when it detects that the user's movement speed is greater than or equal to a preset threshold.

3. The system according to claim 1, wherein the notification means warns the user of the approach of an emergency vehicle by voice or alarm sound.

Images