system

Abstract

We provide the system. [Solution] A means for monitoring the passenger's state using video acquisition means inside the vehicle and detecting drowsiness or relaxation, An audio output device that alerts passengers when a certain condition is detected, A means of communication that attracts the attention of passengers through dialogue and provides information suitable for safe and comfortable travel, A data communication method that obtains traffic conditions and facility information from external information sources, A route guidance means that provides optimal travel route information to passengers based on acquired information, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] Traffic accidents caused by drowsy driving or decreased attention not only endanger the lives of drivers but also cause significant social losses. In particular, these accidents, which are likely to occur during long-distance driving or on monotonous roads, are difficult to fully prevent with conventional warning systems, so there is a need for a new system to support safe driving.

Means for Solving the Problems

[0005] This invention incorporates a means for acquiring video footage from inside the vehicle to understand the driver's state and an audio output means for immediately alerting the driver upon detecting drowsiness. Furthermore, it provides the driver with optimal route information by utilizing traffic conditions and commercial facility information acquired from an external database, while also raising awareness through continuous dialogue with the driver. In addition, it enhances attention through conversation content tailored to the driver's preferences and suggests appropriate rest stops considering the road conditions during the drive. The aim is to support the driver in continuing to drive safely.

[0006] "Image acquisition means" refers to a device that uses cameras and sensors installed inside the vehicle to capture images of the driver's face and movements.

[0007] "Methods for detecting drowsiness" refers to algorithms that analyze data collected by video acquisition devices to determine whether the driver is drowsy or has a decreased level of attention.

[0008] "Voice output means" refers to a speaker or output device that provides voice warnings to the driver based on the detected results.

[0009] "Dialogue means" refers to AI or conversational systems that maintain the driver's attention through voice conversations with the driver.

[0010] A "data communication device" is a communication device that allows a vehicle to communicate with an external database to obtain traffic information and commercial facility information.

[0011] A "route guidance system" is a navigation system that provides the driver with the optimal route based on acquired information.

[0012] "Voice conversation based on driver preferences" refers to a service that engages in voice conversations based on topics and subjects that interest the driver.

[0013] The "rest stop suggestion" function suggests appropriate rest locations based on the driver's fatigue level and driving conditions. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0035] The system implementing this invention is centered around a terminal installed inside the vehicle. This terminal is equipped with a camera as a means of acquiring video to monitor the driver's condition, continuously capturing the driver's facial movements and eye opening / closing states, and analyzing the data. A server obtains the latest traffic conditions and commercial facility information from an external database and transmits this information to the terminal. The driver, as the user, is guided to the optimal driving route via the information provided by this terminal.

[0036] Based on video data, an algorithm detects drowsiness, and the device issues a voice warning if the driver appears sleepy. For example, the device might ask the user, "Why don't you take a short break?" to encourage attention. It also learns the driver's preferences based on their profile information and has a dialogue mechanism to enhance attention through appropriate voice conversations. It can provide personalized responses, such as playing music appropriate to the situation or offering topics that the driver might be interested in.

[0037] Based on information received from the server, the terminal considers the road conditions the user is traveling on and the distance to their destination, and suggests rest stops as needed. For example, it might suggest, "There's a cafe 5 kilometers ahead," supporting a safer driving environment. Furthermore, directions to appropriate rest stops are smoothly provided through route guidance systems.

[0038] This allows the system to provide appropriate support according to the driver's condition, reducing the risk of accidents caused by drowsy driving or decreased attention. This invention provides an effective means for drivers to achieve safe and comfortable driving. Specifically, for example, during long drives on a highway, a camera can detect how often the driver closes their eyes, and if drowsiness is detected, the AI ​​can prompt the driver to take a break via voice and begin guiding them to a nearby parking area.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device acquires real-time video of the driver via an in-car camera, monitoring eye movements and facial orientation. An algorithm analyzes the biometric information obtained from the video to detect signs of driver drowsiness or decreased attention.

[0042] Step 2:

[0043] The server connects to an external database to collect the latest traffic information and information on nearby commercial facilities. This information includes congestion levels, weather conditions, and the locations of rest areas. The server sends this information to terminals as needed, updating it in real time.

[0044] Step 3:

[0045] If the device detects signs of drowsiness or dangerous driving, the AI ​​generates a voice warning and tells the user a message such as "You need to be careful while driving." At this time, the device initiates a voice conversation based on the user's preferences, providing dialogue to attract the driver's attention.

[0046] Step 4:

[0047] If the user continues driving, the terminal uses its navigation system based on traffic information obtained from the server to advise on the optimal route. It also displays information on rest stops and cafes along the driving route to help the user decide where to take a break.

[0048] Step 5:

[0049] If the user selects a rest stop, the terminal provides navigation to the selected rest area using route guidance. The server maintains up-to-date information about the vehicle's surroundings even while the driver is resting, and provides updates to the terminal as needed.

[0050] Through this series of processes, the system enhances driver safety and comfort and effectively reduces the risk of potential accidents.

[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] When operators operate machinery for extended periods or in situations requiring high levels of concentration, the risk of accidents due to decreased attention or fatigue is a significant concern. There is a need to reduce these risks and provide an environment where operators can safely and efficiently operate machinery.

[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 means for monitoring the operator's actions using a video acquisition device and detecting a decrease in reaction speed, an audio output device that issues a warning to the operator when a decrease in reaction speed is detected, and a dialogue device that attracts the operator's attention through conversation and provides information appropriate to the operating situation. This makes it possible to detect a decrease in the operator's attention early and prompt appropriate action.

[0056] A "video acquisition device" is a device installed to monitor the actions of an operator and has the function of collecting video data.

[0057] "Reduced reaction speed" refers to a state in which the operator's actions and reactions are slower than usual, and is an indicator of decreased attention and concentration.

[0058] A "voice output device" is a device that provides information or warnings to the operator via voice.

[0059] A "dialogue device" is a device that communicates with the operator by voice or other means, providing necessary information and drawing attention.

[0060] A "data communication device" is a device that has the function of acquiring situational information and facility data from external information sources and sending and receiving such data between devices.

[0061] A "route guidance device" is a device that, based on acquired data, presents the operator with optimal route information and supports their operation.

[0062] "Operator" refers to an individual who operates a machine or vehicle and is a person who is supported by the system of the present invention.

[0063] This invention was developed to monitor the operator's condition using a system installed inside a vehicle and reduce the risk of accidents. The system consists of a "terminal" and a "server" and provides comprehensive support to improve safety and efficiency in driving.

[0064] The terminal is installed inside the vehicle and is equipped with a camera to monitor the operator's facial movements and eye movements. The video data from the camera is analyzed by a built-in evaluation program. This program uses machine learning algorithms to detect decreased attention or drowsiness in the operator. If the operator shows signs of drowsiness, the terminal provides an audio warning via an audio output device, such as "Why don't you take a short break?"

[0065] The server accesses an external database to obtain real-time traffic conditions and commercial facility data. Based on this, it sends information on optimal driving routes and rest stops to the terminal and provides it to the operator. It also generates content to attract the operator's attention through music and topics based on the operator's profile information. Information tailored to the operator's interests and preferences is provided through prompts generated by an AI model.

[0066] The user, as the operator, can utilize the information provided by the system to drive safely and comfortably. By following route guidance based on external information and being offered timely breaks, the risk of accidents can be significantly reduced.

[0067] For example, if the camera monitors that the driver's eyes are closing slowly during prolonged driving, the system will immediately alert the driver with a voice message saying, "Please use a nearby rest area," and smoothly guide them to such a facility. Furthermore, a generative AI model can provide topics that pique the driver's interest, such as, "Would you like to hear about recent news?", thereby maintaining the driver's attention.

[0068] An example of a prompt message is, "Please describe the algorithm for detecting drowsiness from camera footage and explain how to implement a safe driving support system that includes suggesting rest stops tailored to the driver." Such inventions enable operators to avoid accidents and achieve safe and efficient driving.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] The terminal uses a camera installed inside the vehicle to capture the operator's face. The input is real-time video data, and this data is used to detect facial movements and eye opening / closing states. Specifically, the camera captures images at regular intervals and sends this data to an analysis program.

[0072] Step 2:

[0073] The device performs data analysis to evaluate the operator's attention level based on the acquired video data. It uses a machine learning algorithm to analyze features such as eye opening / closing time and face orientation in the input video data to determine the operator's decreased attention or drowsiness. The output is the evaluation result regarding the operator's state.

[0074] Step 3:

[0075] The server accesses an external database to obtain real-time information on traffic conditions and commercial facilities. The input is the operator's current location, and based on this, it searches for nearby facilities and traffic conditions to output appropriate data. Specifically, it uses APIs to retrieve the latest traffic information from the database.

[0076] Step 4:

[0077] The terminal suggests appropriate rest stops and routes to the operator based on traffic conditions and facility information received from the server. Input is information data from the server. Output is a voice guidance message to the operator, such as "There is a rest stop 5 kilometers ahead." Guidance begins immediately using the voice output device.

[0078] Step 5:

[0079] The device uses a generative AI model to generate conversational content tailored to the user's profile and preferences. Input consists of the user's personal information and preference data, which is used to generate content such as music and topics to capture the user's attention. Output is audio content designed to relax the user or restore their attention. A specific example of its operation is the prompt, "Would you like to hear the latest news?"

[0080] Step 6:

[0081] The user, acting as the operator, ensures safe and comfortable driving by following voice instructions and guidance from the terminal. The operator can prevent a decline in attention by taking appropriate breaks at rest stops suggested by the terminal. The operator will decide and implement specific breaks based on the terminal's suggestions.

[0082] (Application Example 1)

[0083] 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."

[0084] In today's world of widespread autonomous vehicles, there is a need for technologies that can mitigate passenger fatigue and decreased attention span during long journeys, providing a safe and comfortable travel environment. In particular, there is a risk that passengers may become too relaxed or drowsy during the ride, preventing them from responding appropriately in emergencies. Furthermore, there is a need to enhance the travel experience by providing services tailored to individual passenger preferences. There is a demand for systems that can effectively address these challenges.

[0085] 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.

[0086] In this invention, the server includes means for monitoring the passenger's state using in-vehicle video acquisition means and detecting drowsiness or relaxation; means for outputting audio to alert the passenger when such a state is detected; and means for dialogue with the passenger to alert them and provide information suitable for safe and comfortable travel. This makes it possible to provide specific alerts and relaxation methods based on preferences so that passengers can continue their journey safely.

[0087] A "video acquisition device" is a device installed inside a vehicle that captures the faces and body movements of passengers in real time and monitors their condition.

[0088] A "passenger" is someone riding in an autonomous vehicle, a user who enjoys the service safely and comfortably on their way to their destination.

[0089] "Means for detecting drowsiness or a relaxed state" refers to a technology that analyzes the eye movements and facial expressions of passengers based on acquired video data to identify drowsiness or an excessively relaxed state.

[0090] A "voice output means" is a device that can convey appropriate warnings and information to passengers by voice based on the detection results.

[0091] "Dialogue methods" refer to technologies that facilitate communication with passengers, providing information and engaging in conversations tailored to their individual needs and attention.

[0092] "Data communication means" refers to technology that allows for the acquisition of traffic conditions and facility information between the inside of an autonomous vehicle and external information sources, and provides the latest information to the vehicle's internal systems.

[0093] A "route guidance system" is a system that calculates the optimal route for passengers based on acquired information and provides visual or audio guidance along that route.

[0094] To ensure passenger safety and comfort in autonomous vehicles, the server provides a system that combines various technologies. This system continuously monitors the passengers' condition using cameras mounted on the vehicle and processes the video data in real time. Specifically, it uses high-performance cameras such as Sony IMX series cameras to detect the passengers' eye movements and facial expressions and acquire data.

[0095] The server uses the OpenCV library, built in Python, to analyze face position and eye opening / closing status from video data to detect drowsiness and relaxation. Based on the analysis results, a generative AI model using TENSORFLOW® is activated to generate a voice message to warn passengers as needed. This voice message is then converted into natural-sounding speech by speech synthesis software such as Amazon Polly and delivered to the passengers.

[0096] Furthermore, the system obtains traffic conditions and information on commercial facilities from external sources via data communication, and uses this information to provide optimal route guidance. This guidance is customized according to the passenger's preferences. For example, by using the Spotify API to select and play music that the passenger likes, the system helps passengers travel in a relaxed state.

[0097] For example, if a server detects that a passenger is too relaxed during a long journey, it might prompt them with "Would you like to take a break soon?" and guide them to a nearby rest stop. It can also suggest conversations on topics that might interest the passenger to help them maintain their focus.

[0098] An example of a prompt message is: "Create an AI model for an application that detects when passengers in an autonomous vehicle are drowsy and suggests appropriate rest stops."

[0099] The introduction of this system will enable autonomous vehicles to provide passengers with a safe and comfortable travel experience, thereby improving passenger satisfaction.

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The server acquires video data of passengers using cameras mounted on the vehicle. The video data is transmitted to the server in real time as input from the camera sensor. At this time, the video resolution and frame rate are adjusted to convert it into a data format optimized for subsequent processing.

[0103] Step 2:

[0104] The server analyzes the acquired video data using the OpenCV library to detect the position of passengers' faces and the state of their eyes (open / closed). In this step, a face recognition algorithm is applied to identify feature points of the passengers' faces and eyes. The input to this process is the video data obtained in step 1, and the output is numerical data of face position information and eye open / closed state.

[0105] Step 3:

[0106] The server runs an AI model using TensorFlow and analyzes the face and eye state data obtained in step 2. This analysis estimates drowsiness and excessive relaxation. In this step, the AI ​​model classifies the passenger's state by comparing it to patterns it has learned in advance. The input is face and eye feature data, and the output is label data indicating the state (e.g., "drowsy," "relaxed," etc.).

[0107] Step 4:

[0108] The server generates necessary warning messages based on the analysis results of the AI ​​model. Amazon Polly is used for speech synthesis to create messages in natural-sounding voices. In this step, the input is the text of the message to be generated, and the output is an audio file. Specifically, this involves selecting appropriate text and converting it into audio data.

[0109] Step 5:

[0110] The server transmits the generated warning message to the user through an audio output device. In this step, the speaker is controlled so that the message is played at an appropriate volume for the passengers. The input is the audio file generated in step 4, and the output is the playback of the audio.

[0111] Step 6:

[0112] The server retrieves traffic conditions and facility information from external sources and performs route guidance. The information obtained via data communication is updated in real time. Input is information from an external database, and output is optimized route information. Specifically, its operation includes retrieving information via an API and calculating the optimal route.

[0113] 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.

[0114] This invention is a system that monitors the driver's condition in real time and comprehensively analyzes their emotions and drowsiness, centered around a terminal installed inside the vehicle. The terminal is equipped with a camera as a means of acquiring images, and by capturing the driver's facial expressions and body movements, an emotion engine determines the driver's emotional state. It also incorporates an algorithm that detects signs of drowsiness based on eye movements.

[0115] The server manages databases related to emotions, in addition to traffic and commercial facility information, in conjunction with external resources. Based on the information received from the server, the terminal informs the driver of the latest traffic conditions and provides optimal route guidance. To alleviate the stress and frustration the driver is experiencing, a voice conversation is initiated that takes into account the driver's emotional state and preferences.

[0116] If the driver shows any signs of stress, the device uses that emotional data to select and play music or audio content that will help the driver relax. It also suggests rest stops that take into account the driver's emotional state, creating a safe and comfortable driving environment.

[0117] Specifically, for example, if a driver shows signs of frustration due to long hours of driving, the device uses an emotion engine to recognize this and plays a music playlist that reflects the driver's preferences. Also, if a user says they want to take a break, the device will guide them to a nearby cafe or park and provide directions for refreshing themselves. Meanwhile, the server monitors the latest traffic conditions and continues to update the device with information as needed.

[0118] In this way, this invention addresses both the driver's emotions and drowsiness, thereby supporting safe driving and providing a comfortable driving experience.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The device continuously acquires driver facial data through cameras installed inside the vehicle and analyzes it in real time using an emotion engine. It recognizes the driver's emotions using facial recognition technology and stores the data.

[0122] Step 2:

[0123] The server links emotional data with external traffic information databases to collect information tailored to the driver's emotional state. For example, if a driver is showing signs of frustration, the server prioritizes searching for information on nearby rest facilities where they can relax.

[0124] Step 3:

[0125] The device provides appropriate voice output to the driver based on the detected emotional state. For example, if it determines that the driver is feeling stressed, it will prompt music playback with a message such as, "I'll play some music to help you relax."

[0126] Step 4:

[0127] The user follows suggestions from the device, starting a music playlist or accepting suggestions for resting places as needed. The emotion engine continuously evaluates the user's responses and receives feedback.

[0128] Step 5:

[0129] If the user selects a rest, the terminal will provide optimal route guidance. The server checks the latest traffic conditions and provides the user with the safest and most efficient route via the terminal.

[0130] Through this series of processes, the system takes the driver's emotions into consideration, ensuring a safe and comfortable driving experience.

[0131] (Example 2)

[0132] 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".

[0133] In recent years, there has been a growing demand for technologies that monitor the driver's condition in real time while driving, thereby improving safety and comfort. However, while conventional systems can detect driver drowsiness and attention levels, they have struggled to provide driving support that takes emotional states into account. Furthermore, systems that provide actions based on the driver's emotions have remained insufficient, making it a challenge to improve the quality of the driving experience.

[0134] 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.

[0135] In this invention, the server includes means for receiving data from a video acquisition unit to analyze the driver's emotional state, means for selecting voice output and audio content as a response to the driver's emotional state, and means for evaluating the driver's emotional state using an emotional database. This makes it possible to analyze the driver's emotional state in real time and take appropriate measures quickly.

[0136] "Driver's emotional state" refers to the psychological and emotional activity state of the driver while driving, and includes mental states such as stress, relaxation, and irritation.

[0137] The "video acquisition unit" refers to a device that captures the driver's facial expressions and body movements in real time and records the video data.

[0138] "Means for selecting audio output and audio content" include a process for selecting appropriate music or voice messages according to the driver's condition and playing them through the in-vehicle audio system.

[0139] An "emotional database" is a database that stores various emotional states and related data, and is used to evaluate the psychological state of drivers.

[0140] "Means for evaluating emotional state" refers to algorithms that analyze a driver's emotions based on collected data and make quantitative or qualitative judgments about that state.

[0141] This system is built around a terminal installed inside the vehicle and aims to monitor and analyze the driver's condition in real time. Specifically, a camera mounted on the in-vehicle terminal captures the driver's facial expressions and body movements, and an emotion engine analyzes this data to understand the driver's emotional state. The terminal also has an algorithm implemented to detect signs of drowsiness based on eye movements, which allows for an appropriate assessment of the driver's attention level.

[0142] The server manages databases containing traffic information, commercial facility information, and emotion-related data, updating this information in real time in conjunction with external sources. The terminal provides drivers with the latest traffic conditions and optimal route guidance based on the information received from the server. Furthermore, if the driver's emotional state indicates stress or frustration, the terminal selects and plays relaxing music or audio content based on that information, helping to maintain a comfortable driving environment.

[0143] The driver, as the user, can receive music and rest stop suggestions from the system, which can reduce fatigue and stress while driving. For example, if the driver shows signs of frustration after driving for a long time, the device will automatically play a playlist of relaxing music such as jazz or classical music. Also, if the driver says, "I want to take a short break," the device will suggest nearby cafes or parks and provide directions to appropriate rest spots.

[0144] When using a generative AI model, you can use prompt statements like the following:

[0145] "When a driver is feeling tired, what kind of music should be suggested to have a relaxing effect?"

[0146] "When a driver is feeling stressed, what kind of rest stop would be best to suggest?"

[0147] Through these functions, the system can provide drivers with a safe and comfortable driving experience.

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] The device monitors the driver's condition and acquires video footage.

[0151] The input consists of real-time video data of the driver's facial expressions and body movements, captured by cameras inside the vehicle.

[0152] This video data is processed as a digital image using a facial recognition algorithm and then passed to the emotion engine.

[0153] Specifically, the camera focuses on the driver's face and takes continuous shots.

[0154] Step 2:

[0155] The device uses an emotion engine to analyze the driver's emotional state.

[0156] The input is processed digital image data.

[0157] The emotion engine uses facial expression analysis algorithms to assess the driver's emotional state and determine whether they are stressed or relaxed.

[0158] The output is analyzed emotion data, consisting of numerical or categorical information indicating the driver's emotional state.

[0159] In terms of its specific operation, the emotion engine analyzes the facial features of each frame and calculates metrics related to emotion.

[0160] Step 3:

[0161] The server interacts with an external database to manage and update related information.

[0162] The inputs include traffic information, commercial facility information, and database information related to emotions, all obtained from external sources.

[0163] Using database management software, information is organized in real time, and necessary data is selected and prepared for transmission to the terminal.

[0164] The output is a dataset of organized and up-to-date information.

[0165] In terms of specific operations, the server queries the database and extracts and updates data based on the specified conditions.

[0166] Step 4:

[0167] The device takes appropriate action based on the driver's condition.

[0168] The input consists of emotion data obtained as output from the emotion engine and the latest information sent from the server.

[0169] Based on the analysis data, the device plays selected music or audio content and activates a navigation function to suggest rest stops.

[0170] The output consists of voice guidance and music playback tailored to the driver's emotional state.

[0171] Specifically, the device plays music through its speaker and displays graphic navigation to rest stops on its screen.

[0172] Step 5:

[0173] The user accepts and selects the system's proposal.

[0174] Input consists of user input through the terminal interface and voice commands.

[0175] The user selects the best option from the system's recommended choices, including rest stops and music selection.

[0176] The output is a decision on the action to be taken based on the user's choice.

[0177] In terms of specific actions, the user makes their desired selection using a touch panel or voice recognition technology.

[0178] (Application Example 2)

[0179] 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".

[0180] In automobile driving, the driver's emotions and drowsiness are important factors that significantly affect safety. However, currently, there are limited means to comprehensively monitor these factors and address them appropriately in real time. As a result, there are concerns that driver fatigue and stress accumulate, increasing the risk of accidents. The present invention aims to provide a system that detects and analyzes the driver's emotional state and drowsiness, and provides a safe and comfortable driving environment as a solution.

[0181] 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.

[0182] In this invention, the server includes means for monitoring the driver's condition using in-vehicle video acquisition means, means for analyzing the driver's emotional state, and means for providing sound output that promotes driver relaxation based on the emotional state. This enables real-time monitoring of the driver's emotions and drowsiness, and supports stress reduction and safe driving.

[0183] "Video acquisition means" refers to a device installed inside a vehicle that acquires video data to monitor the driver's condition.

[0184] A "means of detecting drowsiness" is a system that analyzes the driver's eye movements and facial expressions to determine signs of drowsiness.

[0185] A "voice output device" is a device that issues a voice warning to alert the driver when drowsiness is detected.

[0186] An "emotional analysis system" is a system that analyzes the driver's emotional state from their facial expressions and actions to understand the driver's emotions.

[0187] "Dialogue means" refers to a function that communicates with the driver via voice and attracts the driver's attention.

[0188] "Data communication means" refers to a method for connecting a vehicle to an external database to obtain information on traffic conditions and commercial facilities.

[0189] A "route guidance system" is a device that provides the driver with optimal route information based on acquired information and assists with navigation.

[0190] "Audio output means" refers to a device that plays music or audio content to promote relaxation, according to the driver's emotional state.

[0191] This invention realizes a system that provides a safe and comfortable driving environment by monitoring the driver's emotional state and drowsiness in real time. The server uses video acquisition means installed in the vehicle to capture the driver's facial expressions and movements and monitor the driver's condition. Through emotion analysis means that analyze the driver's emotional state, the system grasps the driver's stress and frustration. In addition, drowsiness detection means analyzes eye movements and other factors to determine signs of drowsiness.

[0192] The vehicle uses cameras equipped with common image sensors from companies like Sony to acquire driver facial data with high accuracy. Emotional analysis is performed using emotion recognition software such as Affectiva to analyze the driver's emotional state in real time. For data communication, location information acquisition services such as Google® Maps API are used via an in-vehicle computer (e.g., NVIDIA Jetson Nano).

[0193] The device takes appropriate action based on the driver's state. Depending on their emotional state, it selects and provides relaxing music from a music streaming service. Furthermore, it uses an audio output device to provide audio content tailored to the driver's preferences. This reduces driver stress and helps maintain concentration while driving.

[0194] For example, if a driver shows signs of frustration while driving on a highway for extended periods, the system can play classical music to help them relax. Also, if signs of eye fatigue appear during driving, the system can guide the driver to a nearby service area and recommend a break.

[0195] An example of a prompt from the generated AI model is, "Design an in-car system that analyzes the driver's emotional state in real time and suggests appropriate music and rest stops to reduce stress." In this way, the present invention contributes to realizing a safe and comfortable driving environment by combining driver emotion analysis with the provision of relaxing content.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The server acquires video footage through cameras inside the vehicle. The input is real-time video data, and the output generates facial feature data of the driver. This clearly identifies the facial landmark points necessary for facial expression analysis.

[0199] Step 2:

[0200] The server uses emotion analysis tools to analyze the driver's emotional state from acquired facial feature data. It takes facial feature data as input and generates emotion labels for the driver (e.g., joy, anger, sadness, frustration, etc.) as output. For data processing, it uses an emotion recognition algorithm to calculate a probability score for each emotion.

[0201] Step 3:

[0202] The server analyzes signs of drowsiness based on eye movements using a means of detecting drowsiness. The input data is continuous eye movement information from a camera, and the output is a score indicating the degree of drowsiness. Specifically, drowsiness is detected through an algorithm that analyzes the frequency and duration of blinking.

[0203] Step 4:

[0204] The device provides appropriate audio output to the driver based on their emotional state and drowsiness score. Using emotional labels and the drowsiness score as input, it selects and plays music or audio content with a high relaxing effect as output. For example, if the driver indicates stress, it will select a relaxing music playlist.

[0205] Step 5:

[0206] The server retrieves traffic conditions and commercial facility information from an external database and provides drivers with the latest information. Inputs include location information and responses from external APIs, and output generates route information and rest stop suggestions necessary for the driver. Specifically, it searches for and guides drivers to the most suitable rest areas and service areas based on their current location.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] The system implementing this invention is centered around a terminal installed inside the vehicle. This terminal is equipped with a camera as a means of acquiring video to monitor the driver's condition, continuously capturing the driver's facial movements and eye opening / closing states, and analyzing the data. A server obtains the latest traffic conditions and commercial facility information from an external database and transmits this information to the terminal. The driver, as the user, is guided to the optimal driving route via the information provided by this terminal.

[0224] Based on video data, an algorithm detects drowsiness, and the device issues a voice warning if the driver appears sleepy. For example, the device might ask the user, "Why don't you take a short break?" to encourage attention. It also learns the driver's preferences based on their profile information and has a dialogue mechanism to enhance attention through appropriate voice conversations. It can provide personalized responses, such as playing music appropriate to the situation or offering topics that the driver might be interested in.

[0225] Based on information received from the server, the terminal considers the road conditions the user is traveling on and the distance to their destination, and suggests rest stops as needed. For example, it might suggest, "There's a cafe 5 kilometers ahead," supporting a safer driving environment. Furthermore, directions to appropriate rest stops are smoothly provided through route guidance systems.

[0226] This allows the system to provide appropriate support according to the driver's condition, reducing the risk of accidents caused by drowsy driving or decreased attention. This invention provides an effective means for drivers to achieve safe and comfortable driving. Specifically, for example, during long drives on a highway, a camera can detect how often the driver closes their eyes, and if drowsiness is detected, the AI ​​can prompt the driver to take a break via voice and begin guiding them to a nearby parking area.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The device acquires real-time video of the driver via an in-car camera, monitoring eye movements and facial orientation. An algorithm analyzes the biometric information obtained from the video to detect signs of driver drowsiness or decreased attention.

[0230] Step 2:

[0231] The server connects to an external database to collect the latest traffic information and information on nearby commercial facilities. This information includes congestion levels, weather conditions, and the locations of rest areas. The server sends this information to terminals as needed, updating it in real time.

[0232] Step 3:

[0233] If the device detects signs of drowsiness or dangerous driving, the AI ​​generates a voice warning and tells the user a message such as "You need to be careful while driving." At this time, the device initiates a voice conversation based on the user's preferences, providing dialogue to attract the driver's attention.

[0234] Step 4:

[0235] If the user continues driving, the terminal uses its navigation system based on traffic information obtained from the server to advise on the optimal route. It also displays information on rest stops and cafes along the driving route to help the user decide where to take a break.

[0236] Step 5:

[0237] If the user selects a rest stop, the terminal provides navigation to the selected rest area using route guidance. The server maintains up-to-date information about the vehicle's surroundings even while the driver is resting, and provides updates to the terminal as needed.

[0238] Through this series of processes, the system enhances driver safety and comfort and effectively reduces the risk of potential accidents.

[0239] (Example 1)

[0240] 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."

[0241] When operators operate machinery for extended periods or in situations requiring high levels of concentration, the risk of accidents due to decreased attention or fatigue is a significant concern. There is a need to reduce these risks and provide an environment where operators can safely and efficiently operate machinery.

[0242] 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.

[0243] In this invention, the server includes means for monitoring the operator's actions using a video acquisition device and detecting a decrease in reaction speed, an audio output device that issues a warning to the operator when a decrease in reaction speed is detected, and a dialogue device that attracts the operator's attention through conversation and provides information appropriate to the operating situation. This makes it possible to detect a decrease in the operator's attention early and prompt appropriate action.

[0244] A "video acquisition device" is a device installed to monitor the actions of an operator and has the function of collecting video data.

[0245] "Reduced reaction speed" refers to a state in which the operator's actions and reactions are slower than usual, and is an indicator of decreased attention and concentration.

[0246] A "voice output device" is a device that provides information or warnings to the operator via voice.

[0247] A "dialogue device" is a device that communicates with the operator by voice or other means, providing necessary information and drawing attention.

[0248] A "data communication device" is a device that has the function of acquiring situational information and facility data from external information sources and sending and receiving such data between devices.

[0249] A "route guidance device" is a device that, based on acquired data, presents the operator with optimal route information and supports their operation.

[0250] "Operator" refers to an individual who operates a machine or vehicle and is a person who is supported by the system of the present invention.

[0251] This invention was developed to monitor the operator's condition using a system installed inside a vehicle and reduce the risk of accidents. The system consists of a "terminal" and a "server" and provides comprehensive support to improve safety and efficiency in driving.

[0252] The terminal is installed inside the vehicle and is equipped with a camera to monitor the operator's facial movements and eye movements. The video data from the camera is analyzed by a built-in evaluation program. This program uses machine learning algorithms to detect decreased attention or drowsiness in the operator. If the operator shows signs of drowsiness, the terminal provides an audio warning via an audio output device, such as "Why don't you take a short break?"

[0253] The server accesses an external database to obtain real-time traffic conditions and commercial facility data. Based on this, it sends information on optimal driving routes and rest stops to the terminal and provides it to the operator. It also generates content to attract the operator's attention through music and topics based on the operator's profile information. Information tailored to the operator's interests and preferences is provided through prompts generated by an AI model.

[0254] The user, as the operator, can utilize the information provided by the system to drive safely and comfortably. By following route guidance based on external information and being offered timely breaks, the risk of accidents can be significantly reduced.

[0255] For example, if the camera monitors that the driver's eyes are closing slowly during prolonged driving, the system will immediately alert the driver with a voice message saying, "Please use a nearby rest area," and smoothly guide them to such a facility. Furthermore, a generative AI model can provide topics that pique the driver's interest, such as, "Would you like to hear about recent news?", thereby maintaining the driver's attention.

[0256] An example of a prompt message is, "Please describe the algorithm for detecting drowsiness from camera footage and explain how to implement a safe driving support system that includes suggesting rest stops tailored to the driver." Such inventions enable operators to avoid accidents and achieve safe and efficient driving.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The terminal uses a camera installed inside the vehicle to capture the operator's face. The input is real-time video data, and this data is used to detect facial movements and eye opening / closing states. Specifically, the camera captures images at regular intervals and sends this data to an analysis program.

[0260] Step 2:

[0261] The device performs data analysis to evaluate the operator's attention level based on the acquired video data. It uses a machine learning algorithm to analyze features such as eye opening / closing time and face orientation in the input video data to determine the operator's decreased attention or drowsiness. The output is the evaluation result regarding the operator's state.

[0262] Step 3:

[0263] The server accesses an external database to obtain real-time information on traffic conditions and commercial facilities. The input is the operator's current location, and based on this, it searches for nearby facilities and traffic conditions to output appropriate data. Specifically, it uses APIs to retrieve the latest traffic information from the database.

[0264] Step 4:

[0265] The terminal suggests appropriate rest stops and routes to the operator based on traffic conditions and facility information received from the server. Input is information data from the server. Output is a voice guidance message to the operator, such as "There is a rest stop 5 kilometers ahead." Guidance begins immediately using the voice output device.

[0266] Step 5:

[0267] The device uses a generative AI model to generate conversational content tailored to the user's profile and preferences. Input consists of the user's personal information and preference data, which is used to generate content such as music and topics to capture the user's attention. Output is audio content designed to relax the user or restore their attention. A specific example of its operation is the prompt, "Would you like to hear the latest news?"

[0268] Step 6:

[0269] The user, acting as the operator, ensures safe and comfortable driving by following voice instructions and guidance from the terminal. The operator can prevent a decline in attention by taking appropriate breaks at rest stops suggested by the terminal. The operator will decide and implement specific breaks based on the terminal's suggestions.

[0270] (Application Example 1)

[0271] 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."

[0272] In today's world of widespread autonomous vehicles, there is a need for technologies that can mitigate passenger fatigue and decreased attention span during long journeys, providing a safe and comfortable travel environment. In particular, there is a risk that passengers may become too relaxed or drowsy during the ride, preventing them from responding appropriately in emergencies. Furthermore, there is a need to enhance the travel experience by providing services tailored to individual passenger preferences. There is a demand for systems that can effectively address these challenges.

[0273] 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.

[0274] In this invention, the server includes means for monitoring the passenger's state using in-vehicle video acquisition means and detecting drowsiness or relaxation; means for outputting audio to alert the passenger when such a state is detected; and means for dialogue with the passenger to alert them and provide information suitable for safe and comfortable travel. This makes it possible to provide specific alerts and relaxation methods based on preferences so that passengers can continue their journey safely.

[0275] A "video acquisition device" is a device installed inside a vehicle that captures the faces and body movements of passengers in real time and monitors their condition.

[0276] A "passenger" is someone riding in an autonomous vehicle, a user who enjoys the service safely and comfortably on their way to their destination.

[0277] "Means for detecting drowsiness or a relaxed state" refers to a technology that analyzes the eye movements and facial expressions of passengers based on acquired video data to identify drowsiness or an excessively relaxed state.

[0278] A "voice output means" is a device that can convey appropriate warnings and information to passengers by voice based on the detection results.

[0279] The "dialogue means" is a technology for providing information and conducting conversations according to the passenger's condition through communication with the passenger, and promoting attention.

[0280] The "data communication means" is a technology for obtaining traffic conditions and facility information between the inside of an autonomous vehicle and external information sources, and providing the latest information to the vehicle's internal system.

[0281] The "route guidance means" is a system that can calculate the optimal route for a passenger based on the obtained information and guide the route visually or aurally.

[0282] In order to realize the safety and comfort of passengers in an autonomous vehicle, the server provides a system that combines various technologies. This system continuously monitors the condition of passengers by cameras installed in the vehicle and processes video data in real time. Specifically, high-performance cameras such as Sony IMX series cameras are used to detect the movement of passengers' eyes and the condition of their faces, and acquire data.

[0283] The server uses the OpenCV library built in Python to analyze the position of the face and the open / closed state of the eyes from the video data, and detect drowsiness and relaxation states. Based on the analysis results, a generative AI model using TensorFlow operates to generate voice messages for attention calls as needed. This voice message is converted into a natural voice by voice synthesis software such as Amazon Polly and transmitted to the passenger.

[0284] Also, traffic conditions and information on commercial facilities are obtained from external information sources through the data communication means, and based on this, the route guidance means implements optimal route guidance. This guidance is customized according to the passenger's preference information. For example, by using the Spotify API to select and play the music preferred by the passenger, it supports the passenger to move in a relaxed state.

[0285] As a specific example, when it is detected that a passenger is in a state of being too relaxed during a long-distance journey, the server prompts, "Won't you take a break soon?" and guides the passenger to a nearby rest point. Also, the server proposes conversations about topics that passengers are likely to be interested in to help maintain their concentration.

[0286] As an example of a prompt sentence, "Please create an AI model for an application that detects the drowsy state of passengers in an autonomous vehicle and proposes appropriate rest points." can be cited.

[0287] By introducing this system, the autonomous vehicle can provide a safe and comfortable travel experience for passengers and improve passenger satisfaction.

[0288] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0289] Step 1:

[0290] The server uses a camera mounted on the vehicle to acquire video data of the passengers. The video data is transmitted to the server in real time as an input from the camera sensor. At this time, by adjusting the resolution and frame rate of the video, it is converted into an optimal data format for subsequent processing.

[0291] Step 2:

[0292] The server analyzes the acquired video data using the OpenCV library to detect the position of the passengers' faces and the open / closed state of their eyes. In this step, a face recognition algorithm is applied to identify the feature points of the passengers' faces and eyes. The input of this process is the video data obtained in Step 1, and the output is the numerical data of the face position information and the open / closed state of the eyes.

[0293] Step 3:

[0294] The server runs an AI model using TensorFlow and analyzes the face and eye state data obtained in step 2. This analysis estimates drowsiness and excessive relaxation. In this step, the AI ​​model classifies the passenger's state by comparing it to patterns it has learned in advance. The input is face and eye feature data, and the output is label data indicating the state (e.g., "drowsy," "relaxed," etc.).

[0295] Step 4:

[0296] The server generates necessary warning messages based on the analysis results of the AI ​​model. Amazon Polly is used for speech synthesis to create messages in natural-sounding voices. In this step, the input is the text of the message to be generated, and the output is an audio file. Specifically, this involves selecting appropriate text and converting it into audio data.

[0297] Step 5:

[0298] The server transmits the generated warning message to the user through an audio output device. In this step, the speaker is controlled so that the message is played at an appropriate volume for the passengers. The input is the audio file generated in step 4, and the output is the playback of the audio.

[0299] Step 6:

[0300] The server retrieves traffic conditions and facility information from external sources and performs route guidance. The information obtained via data communication is updated in real time. Input is information from an external database, and output is optimized route information. Specifically, its operation includes retrieving information via an API and calculating the optimal route.

[0301] 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.

[0302] This invention is a system that monitors the driver's condition in real time and comprehensively analyzes their emotions and drowsiness, centered around a terminal installed inside the vehicle. The terminal is equipped with a camera as a means of acquiring images, and by capturing the driver's facial expressions and body movements, an emotion engine determines the driver's emotional state. It also incorporates an algorithm that detects signs of drowsiness based on eye movements.

[0303] The server manages databases related to emotions, in addition to traffic and commercial facility information, in conjunction with external resources. Based on the information received from the server, the terminal informs the driver of the latest traffic conditions and provides optimal route guidance. To alleviate the stress and frustration the driver is experiencing, a voice conversation is initiated that takes into account the driver's emotional state and preferences.

[0304] If the driver shows any signs of stress, the device uses that emotional data to select and play music or audio content that will help the driver relax. It also suggests rest stops that take into account the driver's emotional state, creating a safe and comfortable driving environment.

[0305] Specifically, for example, if a driver shows signs of frustration due to long hours of driving, the device uses an emotion engine to recognize this and plays a music playlist that reflects the driver's preferences. Also, if a user says they want to take a break, the device will guide them to a nearby cafe or park and provide directions for refreshing themselves. Meanwhile, the server monitors the latest traffic conditions and continues to update the device with information as needed.

[0306] In this way, this invention addresses both the driver's emotions and drowsiness, thereby supporting safe driving and providing a comfortable driving experience.

[0307] The process flow will be described below.

[0308] Step 1:

[0309] The terminal continuously acquires the facial expression data of the driver through a camera installed inside the vehicle and analyzes it in real time with an emotion engine. The emotion recognition technology is used to recognize the driver's emotion and save it as data.

[0310] Step 2:

[0311] The server links the emotion data with an external traffic information database and collects information according to the driver's emotional state. For example, when the driver shows impatience, it preferentially searches for information on nearby relaxation facilities where one can relax.

[0312] Step 3:

[0313] Based on the detected emotional state, the terminal performs appropriate voice output to the driver. For example, when it is determined that the driver is feeling stressed, it prompts music playback in the form of "I will play music for you to relax".

[0314] Step 4:

[0315] The user follows the suggestions from the terminal and, if necessary, starts a music playlist or accepts the suggestion of a rest point. The emotion engine continuously evaluates the user's response to obtain feedback.

[0316] Step 5:

[0317] If the user selects to rest, the terminal executes optimal route guidance. The server checks the latest traffic situation and provides the user with the safest and most efficient route via the terminal.

[0318] Through this series of processes, the system takes into account the driver's emotion and realizes a safe and comfortable driving experience.

[0319] (Example 2)

[0320] 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".

[0321] In recent years, there has been a growing demand for technologies that monitor the driver's condition in real time while driving, thereby improving safety and comfort. However, while conventional systems can detect driver drowsiness and attention levels, they have struggled to provide driving support that takes emotional states into account. Furthermore, systems that provide actions based on the driver's emotions have remained insufficient, making it a challenge to improve the quality of the driving experience.

[0322] 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.

[0323] In this invention, the server includes means for receiving data from a video acquisition unit to analyze the driver's emotional state, means for selecting voice output and audio content as a response to the driver's emotional state, and means for evaluating the driver's emotional state using an emotional database. This makes it possible to analyze the driver's emotional state in real time and take appropriate measures quickly.

[0324] "Driver's emotional state" refers to the psychological and emotional activity state of the driver while driving, and includes mental states such as stress, relaxation, and irritation.

[0325] The "video acquisition unit" refers to a device that captures the driver's facial expressions and body movements in real time and records the video data.

[0326] "Means for selecting audio output and audio content" include a process for selecting appropriate music or voice messages according to the driver's condition and playing them through the in-vehicle audio system.

[0327] An "emotional database" is a database that stores various emotional states and related data, and is used to evaluate the psychological state of drivers.

[0328] "Means for evaluating emotional state" refers to algorithms that analyze a driver's emotions based on collected data and make quantitative or qualitative judgments about that state.

[0329] This system is built around a terminal installed inside the vehicle and aims to monitor and analyze the driver's condition in real time. Specifically, a camera mounted on the in-vehicle terminal captures the driver's facial expressions and body movements, and an emotion engine analyzes this data to understand the driver's emotional state. The terminal also has an algorithm implemented to detect signs of drowsiness based on eye movements, which allows for an appropriate assessment of the driver's attention level.

[0330] The server manages databases containing traffic information, commercial facility information, and emotion-related data, updating this information in real time in conjunction with external sources. The terminal provides drivers with the latest traffic conditions and optimal route guidance based on the information received from the server. Furthermore, if the driver's emotional state indicates stress or frustration, the terminal selects and plays relaxing music or audio content based on that information, helping to maintain a comfortable driving environment.

[0331] The driver, as the user, can receive music and rest stop suggestions from the system, which can reduce fatigue and stress while driving. For example, if the driver shows signs of frustration after driving for a long time, the device will automatically play a playlist of relaxing music such as jazz or classical music. Also, if the driver says, "I want to take a short break," the device will suggest nearby cafes or parks and provide directions to appropriate rest spots.

[0332] When using a generative AI model, you can use prompt statements like the following:

[0333] "When a driver is feeling tired, what kind of music should be suggested to have a relaxing effect?"

[0334] "When a driver is feeling stressed, what kind of rest stop would be best to suggest?"

[0335] Through these functions, the system can provide drivers with a safe and comfortable driving experience.

[0336] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0337] Step 1:

[0338] The device monitors the driver's condition and acquires video footage.

[0339] The input consists of real-time video data of the driver's facial expressions and body movements, captured by cameras inside the vehicle.

[0340] This video data is processed as a digital image using a facial recognition algorithm and then passed to the emotion engine.

[0341] Specifically, the camera focuses on the driver's face and takes continuous shots.

[0342] Step 2:

[0343] The device uses an emotion engine to analyze the driver's emotional state.

[0344] The input is processed digital image data.

[0345] The emotion engine uses facial expression analysis algorithms to assess the driver's emotional state and determine whether they are stressed or relaxed.

[0346] The output is analyzed emotion data, consisting of numerical or categorical information indicating the driver's emotional state.

[0347] In terms of its specific operation, the emotion engine analyzes the facial features of each frame and calculates metrics related to emotion.

[0348] Step 3:

[0349] The server interacts with an external database to manage and update related information.

[0350] The inputs include traffic information, commercial facility information, and database information related to emotions, all obtained from external sources.

[0351] Using database management software, information is organized in real time, and necessary data is selected and prepared for transmission to the terminal.

[0352] The output is a dataset of organized and up-to-date information.

[0353] In terms of specific operations, the server queries the database and extracts and updates data based on the specified conditions.

[0354] Step 4:

[0355] The device takes appropriate action based on the driver's condition.

[0356] The input consists of emotion data obtained as output from the emotion engine and the latest information sent from the server.

[0357] Based on the analysis data, the device plays selected music or audio content and activates a navigation function to suggest rest stops.

[0358] The output consists of voice guidance and music playback tailored to the driver's emotional state.

[0359] Specifically, the device plays music through its speaker and displays graphic navigation to rest stops on its screen.

[0360] Step 5:

[0361] The user accepts and selects the system's proposal.

[0362] Input consists of user input through the terminal interface and voice commands.

[0363] The user selects the best option from the system's recommended choices, including rest stops and music selection.

[0364] The output is a decision on the action to be taken based on the user's choice.

[0365] In terms of specific actions, the user makes their desired selection using a touch panel or voice recognition technology.

[0366] (Application Example 2)

[0367] 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."

[0368] In automobile driving, the driver's emotions and drowsiness are important factors that significantly affect safety. However, currently, there are limited means to comprehensively monitor these factors and address them appropriately in real time. As a result, there are concerns that driver fatigue and stress accumulate, increasing the risk of accidents. The present invention aims to provide a system that detects and analyzes the driver's emotional state and drowsiness, and provides a safe and comfortable driving environment as a solution.

[0369] 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.

[0370] In this invention, the server includes means for monitoring the driver's condition using in-vehicle video acquisition means, means for analyzing the driver's emotional state, and means for providing sound output that promotes driver relaxation based on the emotional state. This enables real-time monitoring of the driver's emotions and drowsiness, and supports stress reduction and safe driving.

[0371] "Video acquisition means" refers to a device installed inside a vehicle that acquires video data to monitor the driver's condition.

[0372] A "means of detecting drowsiness" is a system that analyzes the driver's eye movements and facial expressions to determine signs of drowsiness.

[0373] A "voice output device" is a device that issues a voice warning to alert the driver when drowsiness is detected.

[0374] An "emotional analysis system" is a system that analyzes the driver's emotional state from their facial expressions and actions to understand the driver's emotions.

[0375] "Dialogue means" refers to a function that communicates with the driver via voice and attracts the driver's attention.

[0376] "Data communication means" refers to a method for connecting a vehicle to an external database to obtain information on traffic conditions and commercial facilities.

[0377] A "route guidance system" is a device that provides the driver with optimal route information based on acquired information and assists with navigation.

[0378] "Audio output means" refers to a device that plays music or audio content to promote relaxation, according to the driver's emotional state.

[0379] This invention realizes a system that provides a safe and comfortable driving environment by monitoring the driver's emotional state and drowsiness in real time. The server uses video acquisition means installed in the vehicle to capture the driver's facial expressions and movements and monitor the driver's condition. Through emotion analysis means that analyze the driver's emotional state, the system grasps the driver's stress and frustration. In addition, drowsiness detection means analyzes eye movements and other factors to determine signs of drowsiness.

[0380] The vehicle uses cameras equipped with common image sensors from companies like Sony to acquire driver facial data with high accuracy. Emotional analysis is performed using emotion recognition software such as Affectiva to analyze the driver's emotional state in real time. For data communication, the vehicle's computer (e.g., NVIDIA Jetson Nano) is used to access location information services such as the Google Maps API.

[0381] The device takes appropriate action based on the driver's state. Depending on their emotional state, it selects and provides relaxing music from a music streaming service. Furthermore, it uses an audio output device to provide audio content tailored to the driver's preferences. This reduces driver stress and helps maintain concentration while driving.

[0382] For example, if a driver shows signs of frustration while driving on a highway for extended periods, the system can play classical music to help them relax. Also, if signs of eye fatigue appear during driving, the system can guide the driver to a nearby service area and recommend a break.

[0383] An example of a prompt from the generated AI model is, "Design an in-car system that analyzes the driver's emotional state in real time and suggests appropriate music and rest stops to reduce stress." In this way, the present invention contributes to realizing a safe and comfortable driving environment by combining driver emotion analysis with the provision of relaxing content.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The server acquires video footage through cameras inside the vehicle. The input is real-time video data, and the output generates facial feature data of the driver. This clearly identifies the facial landmark points necessary for facial expression analysis.

[0387] Step 2:

[0388] The server uses emotion analysis tools to analyze the driver's emotional state from acquired facial feature data. It takes facial feature data as input and generates emotion labels for the driver (e.g., joy, anger, sadness, frustration, etc.) as output. For data processing, it uses an emotion recognition algorithm to calculate a probability score for each emotion.

[0389] Step 3:

[0390] The server analyzes signs of drowsiness based on eye movements using a means of detecting drowsiness. The input data is continuous eye movement information from a camera, and the output is a score indicating the degree of drowsiness. Specifically, drowsiness is detected through an algorithm that analyzes the frequency and duration of blinking.

[0391] Step 4:

[0392] The device provides appropriate audio output to the driver based on their emotional state and drowsiness score. Using emotional labels and the drowsiness score as input, it selects and plays music or audio content with a high relaxing effect as output. For example, if the driver indicates stress, it will select a relaxing music playlist.

[0393] Step 5:

[0394] The server retrieves traffic conditions and commercial facility information from an external database and provides drivers with the latest information. Inputs include location information and responses from external APIs, and output generates route information and rest stop suggestions necessary for the driver. Specifically, it searches for and guides drivers to the most suitable rest areas and service areas based on their current location.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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".

[0411] The system implementing this invention is centered around a terminal installed inside the vehicle. This terminal is equipped with a camera as a means of acquiring video to monitor the driver's condition, continuously capturing the driver's facial movements and eye opening / closing states, and analyzing the data. A server obtains the latest traffic conditions and commercial facility information from an external database and transmits this information to the terminal. The driver, as the user, is guided to the optimal driving route via the information provided by this terminal.

[0412] Based on video data, an algorithm detects drowsiness, and the device issues a voice warning if the driver appears sleepy. For example, the device might ask the user, "Why don't you take a short break?" to encourage attention. It also learns the driver's preferences based on their profile information and has a dialogue mechanism to enhance attention through appropriate voice conversations. It can provide personalized responses, such as playing music appropriate to the situation or offering topics that the driver might be interested in.

[0413] Based on information received from the server, the terminal considers the road conditions the user is traveling on and the distance to their destination, and suggests rest stops as needed. For example, it might suggest, "There's a cafe 5 kilometers ahead," supporting a safer driving environment. Furthermore, directions to appropriate rest stops are smoothly provided through route guidance systems.

[0414] This allows the system to provide appropriate support according to the driver's condition, reducing the risk of accidents caused by drowsy driving or decreased attention. This invention provides an effective means for drivers to achieve safe and comfortable driving. Specifically, for example, during long drives on a highway, a camera can detect how often the driver closes their eyes, and if drowsiness is detected, the AI ​​can prompt the driver to take a break via voice and begin guiding them to a nearby parking area.

[0415] The following describes the processing flow.

[0416] Step 1:

[0417] The device acquires real-time video of the driver via an in-car camera, monitoring eye movements and facial orientation. An algorithm analyzes the biometric information obtained from the video to detect signs of driver drowsiness or decreased attention.

[0418] Step 2:

[0419] The server connects to an external database to collect the latest traffic information and information on nearby commercial facilities. This information includes congestion levels, weather conditions, and the locations of rest areas. The server sends this information to terminals as needed, updating it in real time.

[0420] Step 3:

[0421] If the device detects signs of drowsiness or dangerous driving, the AI ​​generates a voice warning and tells the user a message such as "You need to be careful while driving." At this time, the device initiates a voice conversation based on the user's preferences, providing dialogue to attract the driver's attention.

[0422] Step 4:

[0423] If the user continues driving, the terminal uses its navigation system based on traffic information obtained from the server to advise on the optimal route. It also displays information on rest stops and cafes along the driving route to help the user decide where to take a break.

[0424] Step 5:

[0425] If the user selects a rest stop, the terminal provides navigation to the selected rest area using route guidance. The server maintains up-to-date information about the vehicle's surroundings even while the driver is resting, and provides updates to the terminal as needed.

[0426] Through this series of processes, the system enhances driver safety and comfort and effectively reduces the risk of potential accidents.

[0427] (Example 1)

[0428] 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."

[0429] When operators operate machinery for extended periods or in situations requiring high levels of concentration, the risk of accidents due to decreased attention or fatigue is a significant concern. There is a need to reduce these risks and provide an environment where operators can safely and efficiently operate machinery.

[0430] 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.

[0431] In this invention, the server includes means for monitoring the operator's actions using a video acquisition device and detecting a decrease in reaction speed, an audio output device that issues a warning to the operator when a decrease in reaction speed is detected, and a dialogue device that attracts the operator's attention through conversation and provides information appropriate to the operating situation. This makes it possible to detect a decrease in the operator's attention early and prompt appropriate action.

[0432] A "video acquisition device" is a device installed to monitor the actions of an operator and has the function of collecting video data.

[0433] "Reduced reaction speed" refers to a state in which the operator's actions and reactions are slower than usual, and is an indicator of decreased attention and concentration.

[0434] A "voice output device" is a device that provides information or warnings to the operator via voice.

[0435] A "dialogue device" is a device that communicates with the operator by voice or other means, providing necessary information and drawing attention.

[0436] A "data communication device" is a device that has the function of acquiring situational information and facility data from external information sources and sending and receiving such data between devices.

[0437] A "route guidance device" is a device that, based on acquired data, presents the operator with optimal route information and supports their operation.

[0438] "Operator" refers to an individual who operates a machine or vehicle and is a person who is supported by the system of the present invention.

[0439] This invention was developed to monitor the operator's condition using a system installed inside a vehicle and reduce the risk of accidents. The system consists of a "terminal" and a "server" and provides comprehensive support to improve safety and efficiency in driving.

[0440] The terminal is installed inside the vehicle and is equipped with a camera to monitor the operator's facial movements and eye movements. The video data from the camera is analyzed by a built-in evaluation program. This program uses machine learning algorithms to detect decreased attention or drowsiness in the operator. If the operator shows signs of drowsiness, the terminal provides an audio warning via an audio output device, such as "Why don't you take a short break?"

[0441] The server accesses an external database to obtain real-time traffic conditions and commercial facility data. Based on this, it sends information on optimal driving routes and rest stops to the terminal and provides it to the operator. It also generates content to attract the operator's attention through music and topics based on the operator's profile information. Information tailored to the operator's interests and preferences is provided through prompts generated by an AI model.

[0442] The user, as the operator, can utilize the information provided by the system to drive safely and comfortably. By following route guidance based on external information and being offered timely breaks, the risk of accidents can be significantly reduced.

[0443] For example, if the camera monitors that the driver's eyes are closing slowly during prolonged driving, the system will immediately alert the driver with a voice message saying, "Please use a nearby rest area," and smoothly guide them to such a facility. Furthermore, a generative AI model can provide topics that pique the driver's interest, such as, "Would you like to hear about recent news?", thereby maintaining the driver's attention.

[0444] An example of a prompt message is, "Please describe the algorithm for detecting drowsiness from camera footage and explain how to implement a safe driving support system that includes suggesting rest stops tailored to the driver." Such inventions enable operators to avoid accidents and achieve safe and efficient driving.

[0445] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0446] Step 1:

[0447] The terminal uses a camera installed inside the vehicle to capture the operator's face. The input is real-time video data, and this data is used to detect facial movements and eye opening / closing states. Specifically, the camera captures images at regular intervals and sends this data to an analysis program.

[0448] Step 2:

[0449] The device performs data analysis to evaluate the operator's attention level based on the acquired video data. It uses a machine learning algorithm to analyze features such as eye opening / closing time and face orientation in the input video data to determine the operator's decreased attention or drowsiness. The output is the evaluation result regarding the operator's state.

[0450] Step 3:

[0451] The server accesses an external database to obtain real-time information on traffic conditions and commercial facilities. The input is the operator's current location, and based on this, it searches for nearby facilities and traffic conditions to output appropriate data. Specifically, it uses APIs to retrieve the latest traffic information from the database.

[0452] Step 4:

[0453] The terminal suggests appropriate rest stops and routes to the operator based on traffic conditions and facility information received from the server. Input is information data from the server. Output is a voice guidance message to the operator, such as "There is a rest stop 5 kilometers ahead." Guidance begins immediately using the voice output device.

[0454] Step 5:

[0455] The device uses a generative AI model to generate conversational content tailored to the user's profile and preferences. Input consists of the user's personal information and preference data, which is used to generate content such as music and topics to capture the user's attention. Output is audio content designed to relax the user or restore their attention. A specific example of its operation is the prompt, "Would you like to hear the latest news?"

[0456] Step 6:

[0457] The user, acting as the operator, ensures safe and comfortable driving by following voice instructions and guidance from the terminal. The operator can prevent a decline in attention by taking appropriate breaks at rest stops suggested by the terminal. The operator will decide and implement specific breaks based on the terminal's suggestions.

[0458] (Application Example 1)

[0459] 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."

[0460] In today's world of widespread autonomous vehicles, there is a need for technologies that can mitigate passenger fatigue and decreased attention span during long journeys, providing a safe and comfortable travel environment. In particular, there is a risk that passengers may become too relaxed or drowsy during the ride, preventing them from responding appropriately in emergencies. Furthermore, there is a need to enhance the travel experience by providing services tailored to individual passenger preferences. There is a demand for systems that can effectively address these challenges.

[0461] 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.

[0462] In this invention, the server includes means for monitoring the passenger's state using in-vehicle video acquisition means and detecting drowsiness or relaxation; means for outputting audio to alert the passenger when such a state is detected; and means for dialogue with the passenger to alert them and provide information suitable for safe and comfortable travel. This makes it possible to provide specific alerts and relaxation methods based on preferences so that passengers can continue their journey safely.

[0463] A "video acquisition device" is a device installed inside a vehicle that captures the faces and body movements of passengers in real time and monitors their condition.

[0464] A "passenger" is someone riding in an autonomous vehicle, a user who enjoys the service safely and comfortably on their way to their destination.

[0465] "Means for detecting drowsiness or a relaxed state" refers to a technology that analyzes the eye movements and facial expressions of passengers based on acquired video data to identify drowsiness or an excessively relaxed state.

[0466] A "voice output means" is a device that can convey appropriate warnings and information to passengers by voice based on the detection results.

[0467] "Dialogue methods" refer to technologies that facilitate communication with passengers, providing information and engaging in conversations tailored to their individual needs and attention.

[0468] "Data communication means" refers to technology that allows for the acquisition of traffic conditions and facility information between the inside of an autonomous vehicle and external information sources, and provides the latest information to the vehicle's internal systems.

[0469] A "route guidance system" is a system that calculates the optimal route for passengers based on acquired information and provides visual or audio guidance along that route.

[0470] To ensure passenger safety and comfort in autonomous vehicles, the server provides a system that combines various technologies. This system continuously monitors the passengers' condition using cameras mounted on the vehicle and processes the video data in real time. Specifically, it uses high-performance cameras such as Sony IMX series cameras to detect the passengers' eye movements and facial expressions and acquire data.

[0471] The server uses the OpenCV library, built in Python, to analyze face position and eye opening / closing status from video data to detect drowsiness and relaxation. Based on the analysis results, a generative AI model using TensorFlow is activated to generate a voice message to warn passengers as needed. This voice message is then converted into natural-sounding speech by speech synthesis software such as Amazon Polly and delivered to the passengers.

[0472] Furthermore, the system obtains traffic conditions and information on commercial facilities from external sources via data communication, and uses this information to provide optimal route guidance. This guidance is customized according to the passenger's preferences. For example, by using the Spotify API to select and play music that the passenger likes, the system helps passengers travel in a relaxed state.

[0473] For example, if a server detects that a passenger is too relaxed during a long journey, it might prompt them with "Would you like to take a break soon?" and guide them to a nearby rest stop. It can also suggest conversations on topics that might interest the passenger to help them maintain their focus.

[0474] An example of a prompt message is: "Create an AI model for an application that detects when passengers in an autonomous vehicle are drowsy and suggests appropriate rest stops."

[0475] The introduction of this system will enable autonomous vehicles to provide passengers with a safe and comfortable travel experience, thereby improving passenger satisfaction.

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The server acquires video data of passengers using cameras mounted on the vehicle. The video data is transmitted to the server in real time as input from the camera sensor. At this time, the video resolution and frame rate are adjusted to convert it into a data format optimized for subsequent processing.

[0479] Step 2:

[0480] The server analyzes the acquired video data using the OpenCV library to detect the position of passengers' faces and the state of their eyes (open / closed). In this step, a face recognition algorithm is applied to identify feature points of the passengers' faces and eyes. The input to this process is the video data obtained in step 1, and the output is numerical data of face position information and eye open / closed state.

[0481] Step 3:

[0482] The server runs an AI model using TensorFlow and analyzes the face and eye state data obtained in step 2. This analysis estimates drowsiness and excessive relaxation. In this step, the AI ​​model classifies the passenger's state by comparing it to patterns it has learned in advance. The input is face and eye feature data, and the output is label data indicating the state (e.g., "drowsy," "relaxed," etc.).

[0483] Step 4:

[0484] The server generates necessary warning messages based on the analysis results of the AI ​​model. Amazon Polly is used for speech synthesis to create messages in natural-sounding voices. In this step, the input is the text of the message to be generated, and the output is an audio file. Specifically, this involves selecting appropriate text and converting it into audio data.

[0485] Step 5:

[0486] The server transmits the generated warning message to the user through an audio output device. In this step, the speaker is controlled so that the message is played at an appropriate volume for the passengers. The input is the audio file generated in step 4, and the output is the playback of the audio.

[0487] Step 6:

[0488] The server retrieves traffic conditions and facility information from external sources and performs route guidance. The information obtained via data communication is updated in real time. Input is information from an external database, and output is optimized route information. Specifically, its operation includes retrieving information via an API and calculating the optimal route.

[0489] 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.

[0490] This invention is a system that monitors the driver's condition in real time and comprehensively analyzes their emotions and drowsiness, centered around a terminal installed inside the vehicle. The terminal is equipped with a camera as a means of acquiring images, and by capturing the driver's facial expressions and body movements, an emotion engine determines the driver's emotional state. It also incorporates an algorithm that detects signs of drowsiness based on eye movements.

[0491] The server manages databases related to emotions, in addition to traffic and commercial facility information, in conjunction with external resources. Based on the information received from the server, the terminal informs the driver of the latest traffic conditions and provides optimal route guidance. To alleviate the stress and frustration the driver is experiencing, a voice conversation is initiated that takes into account the driver's emotional state and preferences.

[0492] If the driver shows any signs of stress, the device uses that emotional data to select and play music or audio content that will help the driver relax. It also suggests rest stops that take into account the driver's emotional state, creating a safe and comfortable driving environment.

[0493] Specifically, for example, if a driver shows signs of frustration due to long hours of driving, the device uses an emotion engine to recognize this and plays a music playlist that reflects the driver's preferences. Also, if a user says they want to take a break, the device will guide them to a nearby cafe or park and provide directions for refreshing themselves. Meanwhile, the server monitors the latest traffic conditions and continues to update the device with information as needed.

[0494] In this way, this invention addresses both the driver's emotions and drowsiness, thereby supporting safe driving and providing a comfortable driving experience.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] The device continuously acquires driver facial data through cameras installed inside the vehicle and analyzes it in real time using an emotion engine. It recognizes the driver's emotions using facial recognition technology and stores the data.

[0498] Step 2:

[0499] The server links emotional data with external traffic information databases to collect information tailored to the driver's emotional state. For example, if a driver is showing signs of frustration, the server prioritizes searching for information on nearby rest facilities where they can relax.

[0500] Step 3:

[0501] The device provides appropriate voice output to the driver based on the detected emotional state. For example, if it determines that the driver is feeling stressed, it will prompt music playback with a message such as, "I'll play some music to help you relax."

[0502] Step 4:

[0503] The user follows suggestions from the device, starting a music playlist or accepting suggestions for resting places as needed. The emotion engine continuously evaluates the user's responses and receives feedback.

[0504] Step 5:

[0505] If the user selects a rest, the terminal will provide optimal route guidance. The server checks the latest traffic conditions and provides the user with the safest and most efficient route via the terminal.

[0506] Through this series of processes, the system takes the driver's emotions into consideration, ensuring a safe and comfortable driving experience.

[0507] (Example 2)

[0508] 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."

[0509] In recent years, there has been a growing demand for technologies that monitor the driver's condition in real time while driving, thereby improving safety and comfort. However, while conventional systems can detect driver drowsiness and attention levels, they have struggled to provide driving support that takes emotional states into account. Furthermore, systems that provide actions based on the driver's emotions have remained insufficient, making it a challenge to improve the quality of the driving experience.

[0510] 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.

[0511] In this invention, the server includes means for receiving data from a video acquisition unit to analyze the driver's emotional state, means for selecting voice output and audio content as a response to the driver's emotional state, and means for evaluating the driver's emotional state using an emotional database. This makes it possible to analyze the driver's emotional state in real time and take appropriate measures quickly.

[0512] "Driver's emotional state" refers to the psychological and emotional activity state of the driver while driving, and includes mental states such as stress, relaxation, and irritation.

[0513] The "video acquisition unit" refers to a device that captures the driver's facial expressions and body movements in real time and records the video data.

[0514] "Means for selecting audio output and audio content" include a process for selecting appropriate music or voice messages according to the driver's condition and playing them through the in-vehicle audio system.

[0515] An "emotional database" is a database that stores various emotional states and related data, and is used to evaluate the psychological state of drivers.

[0516] "Means for evaluating emotional state" refers to algorithms that analyze a driver's emotions based on collected data and make quantitative or qualitative judgments about that state.

[0517] This system is built around a terminal installed inside the vehicle and aims to monitor and analyze the driver's condition in real time. Specifically, a camera mounted on the in-vehicle terminal captures the driver's facial expressions and body movements, and an emotion engine analyzes this data to understand the driver's emotional state. The terminal also has an algorithm implemented to detect signs of drowsiness based on eye movements, which allows for an appropriate assessment of the driver's attention level.

[0518] The server manages databases containing traffic information, commercial facility information, and emotion-related data, updating this information in real time in conjunction with external sources. The terminal provides drivers with the latest traffic conditions and optimal route guidance based on the information received from the server. Furthermore, if the driver's emotional state indicates stress or frustration, the terminal selects and plays relaxing music or audio content based on that information, helping to maintain a comfortable driving environment.

[0519] The driver, as the user, can receive music and rest stop suggestions from the system, which can reduce fatigue and stress while driving. For example, if the driver shows signs of frustration after driving for a long time, the device will automatically play a playlist of relaxing music such as jazz or classical music. Also, if the driver says, "I want to take a short break," the device will suggest nearby cafes or parks and provide directions to appropriate rest spots.

[0520] When using a generative AI model, you can use prompt statements like the following:

[0521] "When a driver is feeling tired, what kind of music should be suggested to have a relaxing effect?"

[0522] "When a driver is feeling stressed, what kind of rest stop would be best to suggest?"

[0523] Through these functions, the system can provide drivers with a safe and comfortable driving experience.

[0524] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0525] Step 1:

[0526] The device monitors the driver's condition and acquires video footage.

[0527] The input consists of real-time video data of the driver's facial expressions and body movements, captured by cameras inside the vehicle.

[0528] This video data is processed as a digital image using a facial recognition algorithm and then passed to the emotion engine.

[0529] Specifically, the camera focuses on the driver's face and takes continuous shots.

[0530] Step 2:

[0531] The device uses an emotion engine to analyze the driver's emotional state.

[0532] The input is processed digital image data.

[0533] The emotion engine uses facial expression analysis algorithms to assess the driver's emotional state and determine whether they are stressed or relaxed.

[0534] The output is analyzed emotion data, consisting of numerical or categorical information indicating the driver's emotional state.

[0535] In terms of its specific operation, the emotion engine analyzes the facial features of each frame and calculates metrics related to emotion.

[0536] Step 3:

[0537] The server interacts with an external database to manage and update related information.

[0538] The inputs include traffic information, commercial facility information, and database information related to emotions, all obtained from external sources.

[0539] Using database management software, information is organized in real time, and necessary data is selected and prepared for transmission to the terminal.

[0540] The output is a dataset of organized and up-to-date information.

[0541] In terms of specific operations, the server queries the database and extracts and updates data based on the specified conditions.

[0542] Step 4:

[0543] The device takes appropriate action based on the driver's condition.

[0544] The input consists of emotion data obtained as output from the emotion engine and the latest information sent from the server.

[0545] Based on the analysis data, the device plays selected music or audio content and activates a navigation function to suggest rest stops.

[0546] The output consists of voice guidance and music playback tailored to the driver's emotional state.

[0547] Specifically, the device plays music through its speaker and displays graphic navigation to rest stops on its screen.

[0548] Step 5:

[0549] The user accepts and selects the system's proposal.

[0550] Input consists of user input through the terminal interface and voice commands.

[0551] The user selects the best option from the system's recommended choices, including rest stops and music selection.

[0552] The output is a decision on the action to be taken based on the user's choice.

[0553] In terms of specific actions, the user makes their desired selection using a touch panel or voice recognition technology.

[0554] (Application Example 2)

[0555] 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."

[0556] In automobile driving, the driver's emotions and drowsiness are important factors that significantly affect safety. However, currently, there are limited means to comprehensively monitor these factors and address them appropriately in real time. As a result, there are concerns that driver fatigue and stress accumulate, increasing the risk of accidents. The present invention aims to provide a system that detects and analyzes the driver's emotional state and drowsiness, and provides a safe and comfortable driving environment as a solution.

[0557] 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.

[0558] In this invention, the server includes means for monitoring the driver's condition using in-vehicle video acquisition means, means for analyzing the driver's emotional state, and means for providing sound output that promotes driver relaxation based on the emotional state. This enables real-time monitoring of the driver's emotions and drowsiness, and supports stress reduction and safe driving.

[0559] "Video acquisition means" refers to a device installed inside a vehicle that acquires video data to monitor the driver's condition.

[0560] A "means of detecting drowsiness" is a system that analyzes the driver's eye movements and facial expressions to determine signs of drowsiness.

[0561] A "voice output device" is a device that issues a voice warning to alert the driver when drowsiness is detected.

[0562] An "emotional analysis system" is a system that analyzes the driver's emotional state from their facial expressions and actions to understand the driver's emotions.

[0563] "Dialogue means" refers to a function that communicates with the driver via voice and attracts the driver's attention.

[0564] "Data communication means" refers to a method for connecting a vehicle to an external database to obtain information on traffic conditions and commercial facilities.

[0565] A "route guidance system" is a device that provides the driver with optimal route information based on acquired information and assists with navigation.

[0566] "Audio output means" refers to a device that plays music or audio content to promote relaxation, according to the driver's emotional state.

[0567] This invention realizes a system that provides a safe and comfortable driving environment by monitoring the driver's emotional state and drowsiness in real time. The server uses video acquisition means installed in the vehicle to capture the driver's facial expressions and movements and monitor the driver's condition. Through emotion analysis means that analyze the driver's emotional state, the system grasps the driver's stress and frustration. In addition, drowsiness detection means analyzes eye movements and other factors to determine signs of drowsiness.

[0568] The vehicle uses cameras equipped with common image sensors from companies like Sony to acquire driver facial data with high accuracy. Emotional analysis is performed using emotion recognition software such as Affectiva to analyze the driver's emotional state in real time. For data communication, the vehicle's computer (e.g., NVIDIA Jetson Nano) is used to access location information services such as the Google Maps API.

[0569] The device takes appropriate action based on the driver's state. Depending on their emotional state, it selects and provides relaxing music from a music streaming service. Furthermore, it uses an audio output device to provide audio content tailored to the driver's preferences. This reduces driver stress and helps maintain concentration while driving.

[0570] For example, if a driver shows signs of frustration while driving on a highway for extended periods, the system can play classical music to help them relax. Also, if signs of eye fatigue appear during driving, the system can guide the driver to a nearby service area and recommend a break.

[0571] An example of a prompt from the generated AI model is, "Design an in-car system that analyzes the driver's emotional state in real time and suggests appropriate music and rest stops to reduce stress." In this way, the present invention contributes to realizing a safe and comfortable driving environment by combining driver emotion analysis with the provision of relaxing content.

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The server acquires video footage through cameras inside the vehicle. The input is real-time video data, and the output generates facial feature data of the driver. This clearly identifies the facial landmark points necessary for facial expression analysis.

[0575] Step 2:

[0576] The server uses emotion analysis tools to analyze the driver's emotional state from acquired facial feature data. It takes facial feature data as input and generates emotion labels for the driver (e.g., joy, anger, sadness, frustration, etc.) as output. For data processing, it uses an emotion recognition algorithm to calculate a probability score for each emotion.

[0577] Step 3:

[0578] The server analyzes signs of drowsiness based on eye movements using a means of detecting drowsiness. The input data is continuous eye movement information from a camera, and the output is a score indicating the degree of drowsiness. Specifically, drowsiness is detected through an algorithm that analyzes the frequency and duration of blinking.

[0579] Step 4:

[0580] The device provides appropriate audio output to the driver based on their emotional state and drowsiness score. Using emotional labels and the drowsiness score as input, it selects and plays music or audio content with a high relaxing effect as output. For example, if the driver indicates stress, it will select a relaxing music playlist.

[0581] Step 5:

[0582] The server retrieves traffic conditions and commercial facility information from an external database and provides drivers with the latest information. Inputs include location information and responses from external APIs, and output generates route information and rest stop suggestions necessary for the driver. Specifically, it searches for and guides drivers to the most suitable rest areas and service areas based on their current location.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] [Fourth Embodiment]

[0587] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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".

[0600] The system implementing this invention is centered around a terminal installed inside the vehicle. This terminal is equipped with a camera as a means of acquiring video to monitor the driver's condition, continuously capturing the driver's facial movements and eye opening / closing states, and analyzing the data. A server obtains the latest traffic conditions and commercial facility information from an external database and transmits this information to the terminal. The driver, as the user, is guided to the optimal driving route via the information provided by this terminal.

[0601] Based on video data, an algorithm detects drowsiness, and the device issues a voice warning if the driver appears sleepy. For example, the device might ask the user, "Why don't you take a short break?" to encourage attention. It also learns the driver's preferences based on their profile information and has a dialogue mechanism to enhance attention through appropriate voice conversations. It can provide personalized responses, such as playing music appropriate to the situation or offering topics that the driver might be interested in.

[0602] Based on information received from the server, the terminal considers the road conditions the user is traveling on and the distance to their destination, and suggests rest stops as needed. For example, it might suggest, "There's a cafe 5 kilometers ahead," supporting a safer driving environment. Furthermore, directions to appropriate rest stops are smoothly provided through route guidance systems.

[0603] This allows the system to provide appropriate support according to the driver's condition, reducing the risk of accidents caused by drowsy driving or decreased attention. This invention provides an effective means for drivers to achieve safe and comfortable driving. Specifically, for example, during long drives on a highway, a camera can detect how often the driver closes their eyes, and if drowsiness is detected, the AI ​​can prompt the driver to take a break via voice and begin guiding them to a nearby parking area.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] The device acquires real-time video of the driver via an in-car camera, monitoring eye movements and facial orientation. An algorithm analyzes the biometric information obtained from the video to detect signs of driver drowsiness or decreased attention.

[0607] Step 2:

[0608] The server connects to an external database to collect the latest traffic information and information on nearby commercial facilities. This information includes congestion levels, weather conditions, and the locations of rest areas. The server sends this information to terminals as needed, updating it in real time.

[0609] Step 3:

[0610] If the device detects signs of drowsiness or dangerous driving, the AI ​​generates a voice warning and tells the user a message such as "You need to be careful while driving." At this time, the device initiates a voice conversation based on the user's preferences, providing dialogue to attract the driver's attention.

[0611] Step 4:

[0612] If the user continues driving, the terminal uses its navigation system based on traffic information obtained from the server to advise on the optimal route. It also displays information on rest stops and cafes along the driving route to help the user decide where to take a break.

[0613] Step 5:

[0614] If the user selects a rest stop, the terminal provides navigation to the selected rest area using route guidance. The server maintains up-to-date information about the vehicle's surroundings even while the driver is resting, and provides updates to the terminal as needed.

[0615] Through this series of processes, the system enhances driver safety and comfort and effectively reduces the risk of potential accidents.

[0616] (Example 1)

[0617] 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".

[0618] When operators operate machinery for extended periods or in situations requiring high levels of concentration, the risk of accidents due to decreased attention or fatigue is a significant concern. There is a need to reduce these risks and provide an environment where operators can safely and efficiently operate machinery.

[0619] 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.

[0620] In this invention, the server includes means for monitoring the operator's actions using a video acquisition device and detecting a decrease in reaction speed, an audio output device that issues a warning to the operator when a decrease in reaction speed is detected, and a dialogue device that attracts the operator's attention through conversation and provides information appropriate to the operating situation. This makes it possible to detect a decrease in the operator's attention early and prompt appropriate action.

[0621] A "video acquisition device" is a device installed to monitor the actions of an operator and has the function of collecting video data.

[0622] "Reduced reaction speed" refers to a state in which the operator's actions and reactions are slower than usual, and is an indicator of decreased attention and concentration.

[0623] A "voice output device" is a device that provides information or warnings to the operator via voice.

[0624] A "dialogue device" is a device that communicates with the operator by voice or other means, providing necessary information and drawing attention.

[0625] A "data communication device" is a device that has the function of acquiring situational information and facility data from external information sources and sending and receiving such data between devices.

[0626] A "route guidance device" is a device that, based on acquired data, presents the operator with optimal route information and supports their operation.

[0627] "Operator" refers to an individual who operates a machine or vehicle and is a person who is supported by the system of the present invention.

[0628] This invention was developed to monitor the operator's condition using a system installed inside a vehicle and reduce the risk of accidents. The system consists of a "terminal" and a "server" and provides comprehensive support to improve safety and efficiency in driving.

[0629] The terminal is installed inside the vehicle and is equipped with a camera to monitor the operator's facial movements and eye movements. The video data from the camera is analyzed by a built-in evaluation program. This program uses machine learning algorithms to detect decreased attention or drowsiness in the operator. If the operator shows signs of drowsiness, the terminal provides an audio warning via an audio output device, such as "Why don't you take a short break?"

[0630] The server accesses an external database to obtain real-time traffic conditions and commercial facility data. Based on this, it sends information on optimal driving routes and rest stops to the terminal and provides it to the operator. It also generates content to attract the operator's attention through music and topics based on the operator's profile information. Information tailored to the operator's interests and preferences is provided through prompts generated by an AI model.

[0631] The user, as the operator, can utilize the information provided by the system to drive safely and comfortably. By following route guidance based on external information and being offered timely breaks, the risk of accidents can be significantly reduced.

[0632] For example, if the camera monitors that the driver's eyes are closing slowly during prolonged driving, the system will immediately alert the driver with a voice message saying, "Please use a nearby rest area," and smoothly guide them to such a facility. Furthermore, a generative AI model can provide topics that pique the driver's interest, such as, "Would you like to hear about recent news?", thereby maintaining the driver's attention.

[0633] An example of a prompt message is, "Please describe the algorithm for detecting drowsiness from camera footage and explain how to implement a safe driving support system that includes suggesting rest stops tailored to the driver." Such inventions enable operators to avoid accidents and achieve safe and efficient driving.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] The terminal uses a camera installed inside the vehicle to capture the operator's face. The input is real-time video data, and this data is used to detect facial movements and eye opening / closing states. Specifically, the camera captures images at regular intervals and sends this data to an analysis program.

[0637] Step 2:

[0638] The device performs data analysis to evaluate the operator's attention level based on the acquired video data. It uses a machine learning algorithm to analyze features such as eye opening / closing time and face orientation in the input video data to determine the operator's decreased attention or drowsiness. The output is the evaluation result regarding the operator's state.

[0639] Step 3:

[0640] The server accesses an external database to obtain real-time information on traffic conditions and commercial facilities. The input is the operator's current location, and based on this, it searches for nearby facilities and traffic conditions to output appropriate data. Specifically, it uses APIs to retrieve the latest traffic information from the database.

[0641] Step 4:

[0642] The terminal suggests appropriate rest stops and routes to the operator based on traffic conditions and facility information received from the server. Input is information data from the server. Output is a voice guidance message to the operator, such as "There is a rest stop 5 kilometers ahead." Guidance begins immediately using the voice output device.

[0643] Step 5:

[0644] The device uses a generative AI model to generate conversational content tailored to the user's profile and preferences. Input consists of the user's personal information and preference data, which is used to generate content such as music and topics to capture the user's attention. Output is audio content designed to relax the user or restore their attention. A specific example of its operation is the prompt, "Would you like to hear the latest news?"

[0645] Step 6:

[0646] The user, acting as the operator, ensures safe and comfortable driving by following voice instructions and guidance from the terminal. The operator can prevent a decline in attention by taking appropriate breaks at rest stops suggested by the terminal. The operator will decide and implement specific breaks based on the terminal's suggestions.

[0647] (Application Example 1)

[0648] 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".

[0649] In today's world of widespread autonomous vehicles, there is a need for technologies that can mitigate passenger fatigue and decreased attention span during long journeys, providing a safe and comfortable travel environment. In particular, there is a risk that passengers may become too relaxed or drowsy during the ride, preventing them from responding appropriately in emergencies. Furthermore, there is a need to enhance the travel experience by providing services tailored to individual passenger preferences. There is a demand for systems that can effectively address these challenges.

[0650] 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.

[0651] In this invention, the server includes means for monitoring the passenger's state using in-vehicle video acquisition means and detecting drowsiness or relaxation; means for outputting audio to alert the passenger when such a state is detected; and means for dialogue with the passenger to alert them and provide information suitable for safe and comfortable travel. This makes it possible to provide specific alerts and relaxation methods based on preferences so that passengers can continue their journey safely.

[0652] A "video acquisition device" is a device installed inside a vehicle that captures the faces and body movements of passengers in real time and monitors their condition.

[0653] A "passenger" is someone riding in an autonomous vehicle, a user who enjoys the service safely and comfortably on their way to their destination.

[0654] "Means for detecting drowsiness or a relaxed state" refers to a technology that analyzes the eye movements and facial expressions of passengers based on acquired video data to identify drowsiness or an excessively relaxed state.

[0655] A "voice output means" is a device that can convey appropriate warnings and information to passengers by voice based on the detection results.

[0656] "Dialogue methods" refer to technologies that facilitate communication with passengers, providing information and engaging in conversations tailored to their individual needs and attention.

[0657] "Data communication means" refers to technology that allows for the acquisition of traffic conditions and facility information between the inside of an autonomous vehicle and external information sources, and provides the latest information to the vehicle's internal systems.

[0658] A "route guidance system" is a system that calculates the optimal route for passengers based on acquired information and provides visual or audio guidance along that route.

[0659] To ensure passenger safety and comfort in autonomous vehicles, the server provides a system that combines various technologies. This system continuously monitors the passengers' condition using cameras mounted on the vehicle and processes the video data in real time. Specifically, it uses high-performance cameras such as Sony IMX series cameras to detect the passengers' eye movements and facial expressions and acquire data.

[0660] The server uses the OpenCV library, built in Python, to analyze face position and eye opening / closing status from video data to detect drowsiness and relaxation. Based on the analysis results, a generative AI model using TensorFlow is activated to generate a voice message to warn passengers as needed. This voice message is then converted into natural-sounding speech by speech synthesis software such as Amazon Polly and delivered to the passengers.

[0661] Furthermore, the system obtains traffic conditions and information on commercial facilities from external sources via data communication, and uses this information to provide optimal route guidance. This guidance is customized according to the passenger's preferences. For example, by using the Spotify API to select and play music that the passenger likes, the system helps passengers travel in a relaxed state.

[0662] For example, if a server detects that a passenger is too relaxed during a long journey, it might prompt them with "Would you like to take a break soon?" and guide them to a nearby rest stop. It can also suggest conversations on topics that might interest the passenger to help them maintain their focus.

[0663] An example of a prompt message is: "Create an AI model for an application that detects when passengers in an autonomous vehicle are drowsy and suggests appropriate rest stops."

[0664] The introduction of this system will enable autonomous vehicles to provide passengers with a safe and comfortable travel experience, thereby improving passenger satisfaction.

[0665] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0666] Step 1:

[0667] The server acquires video data of passengers using cameras mounted on the vehicle. The video data is transmitted to the server in real time as input from the camera sensor. At this time, the video resolution and frame rate are adjusted to convert it into a data format optimized for subsequent processing.

[0668] Step 2:

[0669] The server analyzes the acquired video data using the OpenCV library to detect the position of passengers' faces and the state of their eyes (open / closed). In this step, a face recognition algorithm is applied to identify feature points of the passengers' faces and eyes. The input to this process is the video data obtained in step 1, and the output is numerical data of face position information and eye open / closed state.

[0670] Step 3:

[0671] The server runs an AI model using TensorFlow and analyzes the face and eye state data obtained in step 2. This analysis estimates drowsiness and excessive relaxation. In this step, the AI ​​model classifies the passenger's state by comparing it to patterns it has learned in advance. The input is face and eye feature data, and the output is label data indicating the state (e.g., "drowsy," "relaxed," etc.).

[0672] Step 4:

[0673] The server generates necessary warning messages based on the analysis results of the AI ​​model. Amazon Polly is used for speech synthesis to create messages in natural-sounding voices. In this step, the input is the text of the message to be generated, and the output is an audio file. Specifically, this involves selecting appropriate text and converting it into audio data.

[0674] Step 5:

[0675] The server transmits the generated warning message to the user through an audio output device. In this step, the speaker is controlled so that the message is played at an appropriate volume for the passengers. The input is the audio file generated in step 4, and the output is the playback of the audio.

[0676] Step 6:

[0677] The server retrieves traffic conditions and facility information from external sources and performs route guidance. The information obtained via data communication is updated in real time. Input is information from an external database, and output is optimized route information. Specifically, its operation includes retrieving information via an API and calculating the optimal route.

[0678] 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.

[0679] This invention is a system that monitors the driver's condition in real time and comprehensively analyzes their emotions and drowsiness, centered around a terminal installed inside the vehicle. The terminal is equipped with a camera as a means of acquiring images, and by capturing the driver's facial expressions and body movements, an emotion engine determines the driver's emotional state. It also incorporates an algorithm that detects signs of drowsiness based on eye movements.

[0680] The server manages databases related to emotions, in addition to traffic and commercial facility information, in conjunction with external resources. Based on the information received from the server, the terminal informs the driver of the latest traffic conditions and provides optimal route guidance. To alleviate the stress and frustration the driver is experiencing, a voice conversation is initiated that takes into account the driver's emotional state and preferences.

[0681] If the driver shows any signs of stress, the device uses that emotional data to select and play music or audio content that will help the driver relax. It also suggests rest stops that take into account the driver's emotional state, creating a safe and comfortable driving environment.

[0682] Specifically, for example, if a driver shows signs of frustration due to long hours of driving, the device uses an emotion engine to recognize this and plays a music playlist that reflects the driver's preferences. Also, if a user says they want to take a break, the device will guide them to a nearby cafe or park and provide directions for refreshing themselves. Meanwhile, the server monitors the latest traffic conditions and continues to update the device with information as needed.

[0683] In this way, this invention addresses both the driver's emotions and drowsiness, thereby supporting safe driving and providing a comfortable driving experience.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] The device continuously acquires driver facial data through cameras installed inside the vehicle and analyzes it in real time using an emotion engine. It recognizes the driver's emotions using facial recognition technology and stores the data.

[0687] Step 2:

[0688] The server links emotional data with external traffic information databases to collect information tailored to the driver's emotional state. For example, if a driver is showing signs of frustration, the server prioritizes searching for information on nearby rest facilities where they can relax.

[0689] Step 3:

[0690] The device provides appropriate voice output to the driver based on the detected emotional state. For example, if it determines that the driver is feeling stressed, it will prompt music playback with a message such as, "I'll play some music to help you relax."

[0691] Step 4:

[0692] The user follows suggestions from the device, starting a music playlist or accepting suggestions for resting places as needed. The emotion engine continuously evaluates the user's responses and receives feedback.

[0693] Step 5:

[0694] If the user selects a rest, the terminal will provide optimal route guidance. The server checks the latest traffic conditions and provides the user with the safest and most efficient route via the terminal.

[0695] Through this series of processes, the system takes the driver's emotions into consideration, ensuring a safe and comfortable driving experience.

[0696] (Example 2)

[0697] 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".

[0698] In recent years, there has been a growing demand for technologies that monitor the driver's condition in real time while driving, thereby improving safety and comfort. However, while conventional systems can detect driver drowsiness and attention levels, they have struggled to provide driving support that takes emotional states into account. Furthermore, systems that provide actions based on the driver's emotions have remained insufficient, making it a challenge to improve the quality of the driving experience.

[0699] 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.

[0700] In this invention, the server includes means for receiving data from a video acquisition unit to analyze the driver's emotional state, means for selecting voice output and audio content as a response to the driver's emotional state, and means for evaluating the driver's emotional state using an emotional database. This makes it possible to analyze the driver's emotional state in real time and take appropriate measures quickly.

[0701] "Driver's emotional state" refers to the psychological and emotional activity state of the driver while driving, and includes mental states such as stress, relaxation, and irritation.

[0702] The "video acquisition unit" refers to a device that captures the driver's facial expressions and body movements in real time and records the video data.

[0703] "Means for selecting audio output and audio content" include a process for selecting appropriate music or voice messages according to the driver's condition and playing them through the in-vehicle audio system.

[0704] An "emotional database" is a database that stores various emotional states and related data, and is used to evaluate the psychological state of drivers.

[0705] "Means for evaluating emotional state" refers to algorithms that analyze a driver's emotions based on collected data and make quantitative or qualitative judgments about that state.

[0706] This system is built around a terminal installed inside the vehicle and aims to monitor and analyze the driver's condition in real time. Specifically, a camera mounted on the in-vehicle terminal captures the driver's facial expressions and body movements, and an emotion engine analyzes this data to understand the driver's emotional state. The terminal also has an algorithm implemented to detect signs of drowsiness based on eye movements, which allows for an appropriate assessment of the driver's attention level.

[0707] The server manages databases containing traffic information, commercial facility information, and emotion-related data, updating this information in real time in conjunction with external sources. The terminal provides drivers with the latest traffic conditions and optimal route guidance based on the information received from the server. Furthermore, if the driver's emotional state indicates stress or frustration, the terminal selects and plays relaxing music or audio content based on that information, helping to maintain a comfortable driving environment.

[0708] The driver, as the user, can receive music and rest stop suggestions from the system, which can reduce fatigue and stress while driving. For example, if the driver shows signs of frustration after driving for a long time, the device will automatically play a playlist of relaxing music such as jazz or classical music. Also, if the driver says, "I want to take a short break," the device will suggest nearby cafes or parks and provide directions to appropriate rest spots.

[0709] When using a generative AI model, you can use prompt statements like the following:

[0710] "When a driver is feeling tired, what kind of music should be suggested to have a relaxing effect?"

[0711] "When a driver is feeling stressed, what kind of rest stop would be best to suggest?"

[0712] Through these functions, the system can provide drivers with a safe and comfortable driving experience.

[0713] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0714] Step 1:

[0715] The device monitors the driver's condition and acquires video footage.

[0716] The input consists of real-time video data of the driver's facial expressions and body movements, captured by cameras inside the vehicle.

[0717] This video data is processed as a digital image using a facial recognition algorithm and then passed to the emotion engine.

[0718] Specifically, the camera focuses on the driver's face and takes continuous shots.

[0719] Step 2:

[0720] The device uses an emotion engine to analyze the driver's emotional state.

[0721] The input is processed digital image data.

[0722] The emotion engine uses facial expression analysis algorithms to assess the driver's emotional state and determine whether they are stressed or relaxed.

[0723] The output is analyzed emotion data, consisting of numerical or categorical information indicating the driver's emotional state.

[0724] In terms of its specific operation, the emotion engine analyzes the facial features of each frame and calculates metrics related to emotion.

[0725] Step 3:

[0726] The server interacts with an external database to manage and update related information.

[0727] The inputs include traffic information, commercial facility information, and database information related to emotions, all obtained from external sources.

[0728] Using database management software, information is organized in real time, and necessary data is selected and prepared for transmission to the terminal.

[0729] The output is a dataset of organized and up-to-date information.

[0730] In terms of specific operations, the server queries the database and extracts and updates data based on the specified conditions.

[0731] Step 4:

[0732] The device takes appropriate action based on the driver's condition.

[0733] The input consists of emotion data obtained as output from the emotion engine and the latest information sent from the server.

[0734] Based on the analysis data, the device plays selected music or audio content and activates a navigation function to suggest rest stops.

[0735] The output consists of voice guidance and music playback tailored to the driver's emotional state.

[0736] Specifically, the device plays music through its speaker and displays graphic navigation to rest stops on its screen.

[0737] Step 5:

[0738] The user accepts and selects the system's proposal.

[0739] Input consists of user input through the terminal interface and voice commands.

[0740] The user selects the best option from the system's recommended choices, including rest stops and music selection.

[0741] The output is a decision on the action to be taken based on the user's choice.

[0742] In terms of specific actions, the user makes their desired selection using a touch panel or voice recognition technology.

[0743] (Application Example 2)

[0744] 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".

[0745] In automobile driving, the driver's emotions and drowsiness are important factors that significantly affect safety. However, currently, there are limited means to comprehensively monitor these factors and address them appropriately in real time. As a result, there are concerns that driver fatigue and stress accumulate, increasing the risk of accidents. The present invention aims to provide a system that detects and analyzes the driver's emotional state and drowsiness, and provides a safe and comfortable driving environment as a solution.

[0746] 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.

[0747] In this invention, the server includes means for monitoring the driver's condition using in-vehicle video acquisition means, means for analyzing the driver's emotional state, and means for providing sound output that promotes driver relaxation based on the emotional state. This enables real-time monitoring of the driver's emotions and drowsiness, and supports stress reduction and safe driving.

[0748] "Video acquisition means" refers to a device installed inside a vehicle that acquires video data to monitor the driver's condition.

[0749] A "means of detecting drowsiness" is a system that analyzes the driver's eye movements and facial expressions to determine signs of drowsiness.

[0750] A "voice output device" is a device that issues a voice warning to alert the driver when drowsiness is detected.

[0751] An "emotional analysis system" is a system that analyzes the driver's emotional state from their facial expressions and actions to understand the driver's emotions.

[0752] "Dialogue means" refers to a function that communicates with the driver via voice and attracts the driver's attention.

[0753] "Data communication means" refers to a method for connecting a vehicle to an external database to obtain information on traffic conditions and commercial facilities.

[0754] A "route guidance system" is a device that provides the driver with optimal route information based on acquired information and assists with navigation.

[0755] "Audio output means" refers to a device that plays music or audio content to promote relaxation, according to the driver's emotional state.

[0756] This invention realizes a system that provides a safe and comfortable driving environment by monitoring the driver's emotional state and drowsiness in real time. The server uses video acquisition means installed in the vehicle to capture the driver's facial expressions and movements and monitor the driver's condition. Through emotion analysis means that analyze the driver's emotional state, the system grasps the driver's stress and frustration. In addition, drowsiness detection means analyzes eye movements and other factors to determine signs of drowsiness.

[0757] The vehicle uses cameras equipped with common image sensors from companies like Sony to acquire driver facial data with high accuracy. Emotional analysis is performed using emotion recognition software such as Affectiva to analyze the driver's emotional state in real time. For data communication, the vehicle's computer (e.g., NVIDIA Jetson Nano) is used to access location information services such as the Google Maps API.

[0758] The device takes appropriate action based on the driver's state. Depending on their emotional state, it selects and provides relaxing music from a music streaming service. Furthermore, it uses an audio output device to provide audio content tailored to the driver's preferences. This reduces driver stress and helps maintain concentration while driving.

[0759] For example, if a driver shows signs of frustration while driving on a highway for extended periods, the system can play classical music to help them relax. Also, if signs of eye fatigue appear during driving, the system can guide the driver to a nearby service area and recommend a break.

[0760] An example of a prompt from the generated AI model is, "Design an in-car system that analyzes the driver's emotional state in real time and suggests appropriate music and rest stops to reduce stress." In this way, the present invention contributes to realizing a safe and comfortable driving environment by combining driver emotion analysis with the provision of relaxing content.

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The server acquires video footage through cameras inside the vehicle. The input is real-time video data, and the output generates facial feature data of the driver. This clearly identifies the facial landmark points necessary for facial expression analysis.

[0764] Step 2:

[0765] The server uses emotion analysis tools to analyze the driver's emotional state from acquired facial feature data. It takes facial feature data as input and generates emotion labels for the driver (e.g., joy, anger, sadness, frustration, etc.) as output. For data processing, it uses an emotion recognition algorithm to calculate a probability score for each emotion.

[0766] Step 3:

[0767] The server analyzes signs of drowsiness based on eye movements using a means of detecting drowsiness. The input data is continuous eye movement information from a camera, and the output is a score indicating the degree of drowsiness. Specifically, drowsiness is detected through an algorithm that analyzes the frequency and duration of blinking.

[0768] Step 4:

[0769] The device provides appropriate audio output to the driver based on their emotional state and drowsiness score. Using emotional labels and the drowsiness score as input, it selects and plays music or audio content with a high relaxing effect as output. For example, if the driver indicates stress, it will select a relaxing music playlist.

[0770] Step 5:

[0771] The server retrieves traffic conditions and commercial facility information from an external database and provides drivers with the latest information. Inputs include location information and responses from external APIs, and output generates route information and rest stop suggestions necessary for the driver. Specifically, it searches for and guides drivers to the most suitable rest areas and service areas based on their current location.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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."

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0793] The following is further disclosed regarding the embodiments described above.

[0794] (Claim 1)

[0795] A means for monitoring the driver's condition using a means for acquiring video footage inside the vehicle and detecting drowsiness,

[0796] A voice output device that alerts the driver when drowsiness is detected,

[0797] A dialogue means that, through conversation with the driver, attracts the driver's attention and provides information appropriate to the driving situation,

[0798] A data communication method for obtaining traffic conditions and commercial facility information from an external database,

[0799] A route guidance means that provides the driver with optimal route information based on acquired information,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, which enhances driver alertness by conducting voice conversations based on the driver's preferences.

[0803] (Claim 3)

[0804] The system according to claim 1, which analyzes road conditions while the driver is driving and suggests the optimal rest stop.

[0805] "Example 1"

[0806] (Claim 1)

[0807] A means for monitoring the operator's movements using a video acquisition device and detecting a decrease in reaction speed,

[0808] An audio output device that issues a warning to the operator when a decrease in response speed is detected,

[0809] A dialogue device that attracts the operator's attention through conversation and provides information appropriate to the operating situation,

[0810] A data communication device that acquires situation information and facility data from external information sources,

[0811] A route guidance device that provides the operator with optimal route data based on acquired data,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, which enhances the operator's awareness by conducting voice conversations based on the operator's personal information.

[0815] (Claim 3)

[0816] The system according to claim 1, in which the operator analyzes the path conditions while moving and proposes an optimal stopping point.

[0817] "Application Example 1"

[0818] (Claim 1)

[0819] A means for monitoring the passenger's state using video acquisition means inside the vehicle and detecting drowsiness or relaxation,

[0820] An audio output device that alerts passengers when a certain condition is detected,

[0821] A means of communication that attracts the attention of passengers through dialogue and provides information suitable for safe and comfortable travel,

[0822] A data communication method that obtains traffic conditions and facility information from external information sources,

[0823] A route guidance means that provides optimal travel route information to passengers based on acquired information,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, which enhances passenger attention by providing voice conversation and music playback based on passenger preferences.

[0827] (Claim 3)

[0828] The system according to claim 1, which analyzes the passenger's situation while they are traveling and suggests the optimal rest stop.

[0829] "Example 2 of combining an emotion engine"

[0830] (Claim 1)

[0831] A means for receiving data from a video acquisition unit in order to analyze the driver's emotional state,

[0832] A means for selecting voice output and audio content as a response to the driver's emotional state,

[0833] A means of evaluating a driver's emotional state using an emotion database,

[0834] A method for detecting signs of drowsiness from eye movement patterns,

[0835] A means of managing information in real time by linking an emotional database with external information,

[0836] A means of suggesting rest stops based on the driver's emotional state,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, which enhances warnings through a voice dialogue function that takes into account the driver's emotional state.

[0840] (Claim 3)

[0841] The system according to claim 1, which automatically suggests a suitable rest stop based on the emotional state indicated by the driver.

[0842] "Application example 2 when combining with an emotional engine"

[0843] (Claim 1)

[0844] A means for monitoring the driver's condition using a means for acquiring video footage inside the vehicle and detecting drowsiness,

[0845] A voice output device that alerts the driver when drowsiness is detected,

[0846] An emotion analysis tool that analyzes the driver's emotional state and presents information to reduce the driver's stress,

[0847] A dialogue means that, through conversation with the driver, attracts the driver's attention and provides information appropriate to the driving situation,

[0848] A data communication method for obtaining traffic conditions and commercial facility information from an external database,

[0849] A route guidance means that provides the driver with optimal route information based on acquired information,

[0850] An acoustic output means that provides an acoustic output that promotes driver relaxation based on emotional state,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, which enhances driver alertness by conducting voice conversations based on the driver's preferences.

[0854] (Claim 3)

[0855] The system according to claim 1, which analyzes road conditions while the driver is driving and suggests the optimal rest stop. [Explanation of Symbols]

[0856] 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 monitoring the passenger's state using video acquisition means inside the vehicle and detecting drowsiness or relaxation, An audio output device that alerts passengers when a certain condition is detected, A means of communication that attracts the attention of passengers through dialogue and provides information suitable for safe and comfortable travel, A data communication method that obtains traffic conditions and facility information from external information sources, A route guidance means that provides optimal travel route information to passengers based on acquired information, A system that includes this.

2. The system according to claim 1, which enhances passenger attention by providing voice conversation and music playback based on passenger preferences.

3. The system according to claim 1, which analyzes the passenger's situation while they are traveling and suggests the optimal rest stop.

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