system

Abstract

An object of a system according to an embodiment is to constantly monitor safety in a vehicle and to quickly cope with a problem when the problem occurs.SOLUTION: A system includes a monitoring AI camera, an analysis unit, and a reporting unit. The monitoring AI camera constantly monitors the situation in the vehicle. The analysis unit analyzes the video acquired by the monitoring AI camera and detects a problem. The notification unit notifies the police together with the GPS information based on the problem detected by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to constantly monitor the safety of the vehicle interior and to respond quickly when a problem occurs.

[0005] The system according to the embodiment aims to constantly monitor the safety of the vehicle interior and to respond quickly when a problem occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes a surveillance AI camera, an analysis unit, and a reporting unit. The surveillance AI camera constantly monitors the situation inside the vehicle. The analysis unit analyzes the video data acquired by the surveillance AI camera and detects problems. The reporting unit reports the problem detected by the analysis unit to the police along with GPS information. [Effects of the Invention]

[0007] The system according to the embodiment constantly monitors the safety of the vehicle interior and can quickly respond to any problems that may arise. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A safety monitoring system according to an embodiment of the present invention is a system that improves safety by installing a surveillance AI camera inside a vehicle and providing constant monitoring. This system has a function in which the surveillance AI camera constantly monitors the situation inside the vehicle, and if a problem occurs, the camera automatically reports the incident to the police along with GPS information. This safety monitoring system ensures safety in the ride-sharing service and taxi industries and reduces the anxiety of both drivers and users.

[0029] A safety monitoring system according to an embodiment includes a surveillance AI camera, an analysis unit, and a reporting unit. The surveillance AI camera constantly monitors the situation inside the vehicle. For example, the surveillance AI camera analyzes video footage from inside the vehicle in real time to detect abnormal behavior or situations. The surveillance AI camera can also analyze audio data from inside the vehicle to detect abnormal audio. The surveillance AI camera can also acquire environmental data such as the temperature and humidity inside the vehicle to detect abnormal environmental changes. The analysis unit analyzes the video data acquired by the surveillance AI camera to detect problems. For example, the analysis unit detects violent acts or suspicious movements. The analysis unit is also equipped with an emotion estimation function, and can analyze the emotions of passengers and the driver in real time to detect abnormal emotional changes. The analysis unit can also analyze audio data from inside the vehicle to detect abnormal behavior or situations. The reporting unit reports the problem detected by the analysis unit along with GPS information to the police. For example, if the reporting unit detects violent behavior, it transmits the vehicle's location information along with the video to the police. The reporting unit can also add an emotion estimation function and transmit the emotional state of the passenger or driver to the police when reporting. Furthermore, the reporting unit can automatically summarize the content of the report to provide concise and clear information to the police. This allows the safety monitoring system according to the embodiment to improve in-vehicle safety. For example, in the ride-sharing service and taxi industries, the system can ensure the safety of both drivers and users and reduce their anxiety.

[0030] The analysis unit can detect violent acts or suspicious behavior. For example, the analysis unit analyzes video data acquired by a surveillance AI camera to detect violent acts. For example, it detects acts such as punching and kicking. The analysis unit also analyzes abnormal behavioral patterns to detect suspicious behavior. For example, it detects sudden movements or abnormal behavioral patterns. Furthermore, the analysis unit integrates and analyzes multiple emotional indicators (facial expressions, voice, and body movements) to detect abnormal emotional changes with high accuracy. For example, a blushing face, a louder voice, and trembling body are recognized as abnormal emotional changes. This enables rapid response by detecting violent acts and suspicious behavior.

[0031] The analysis unit can analyze voice data and detect abnormal behavior or abnormal situations. For example, the analysis unit adds a voice recognition function to a surveillance AI camera and analyzes conversations and voices inside the car. For example, if it detects a scream or a cry for help, it will recognize this as an abnormal situation. The analysis unit also analyzes voice data and detects specific keywords and phrases. For example, if it detects words such as "help" or "danger," it will recognize this as abnormal behavior. Furthermore, the analysis unit analyzes changes in the tone and volume of the voice to detect abnormal behavior. For example, if the volume suddenly increases or the tone of the voice changes, it will recognize this as an abnormal situation. In this way, abnormal behavior and situations can be detected by analyzing voice data.

[0032] The analysis unit can acquire environmental data such as temperature or humidity and detect abnormal environmental changes. For example, the analysis unit may be equipped with a temperature sensor in a surveillance AI camera and constantly monitor the temperature inside the vehicle. For example, if a sudden rise or fall in temperature is detected, it will recognize this as an abnormal environmental change. The analysis unit may also be equipped with a humidity sensor and constantly monitor the humidity inside the vehicle. For example, if a sudden change in humidity is detected, it will recognize this as an abnormal environmental change. Furthermore, the analysis unit integrates and analyzes the temperature and humidity data to detect abnormal environmental changes with high accuracy. For example, if the temperature rises suddenly and the humidity also changes suddenly, it will recognize this as an abnormal environmental change. In this way, environmental data can be acquired and abnormal environmental changes can be detected.

[0033] Surveillance AI cameras can also be installed outside the vehicle, allowing for constant monitoring of the situation around the vehicle. For example, a surveillance AI camera can be installed outside the vehicle and analyze video footage of the area around the vehicle in real time. For example, if a suspicious person approaches the vehicle, it will recognize this as an abnormal situation. Surveillance AI cameras also cover 360 degrees around the vehicle, constantly monitoring the situation in all directions. For example, they can detect a person approaching from behind the vehicle. Furthermore, surveillance AI cameras can analyze audio data around the vehicle to detect abnormal sounds. For example, if they detect the sound of glass breaking or metal colliding, they will recognize this as an abnormal situation. This constantly monitors the situation around the vehicle, improving the safety of the entire vehicle.

[0034] Video data from surveillance AI cameras can be stored in the cloud for later analysis. For example, surveillance AI cameras can automatically upload video data to the cloud so that it can be accessed later. For example, past footage can be played back to check for abnormal behavior. Video data stored in the cloud can also be analyzed to detect abnormal behavior or situations later. For example, video from a specific time period can be analyzed to identify abnormal behavior. Furthermore, video data stored in the cloud can be accessed from multiple devices to quickly check for abnormal situations. For example, video can be viewed from a smartphone or tablet. This allows video data to be stored in the cloud so that it can be analyzed later, making it possible to investigate past situations in detail.

[0035] The reporting unit can automatically summarize the contents of the report and send the information to the police concisely and clearly. For example, the reporting unit adds a summarization function to the automatic reporting function and automatically summarizes the contents of the report and sends it to the police. For example, if an act of violence occurs, the reporting unit briefly summarizes the main points. The reporting unit also analyzes the contents of the report and extracts and summarizes important information. For example, the reporting unit briefly summarizes the vehicle's location information and details of the problem that occurred. Furthermore, the reporting unit uses the summarization function to make the contents of the report concise and clear. For example, the reporting unit shortens long reports and sends them to the police. In this way, by summarizing the contents of the report, the police can be provided with concise and clear information.

[0036] The notification unit can extend the automatic notification function to emergency services, enabling it to respond to multiple emergency situations. For example, the notification unit extends the automatic notification function to other emergency services such as ambulances and fire departments, enabling it to respond to various emergency situations. For example, it automatically notifies in the event of a traffic accident or fire. The notification unit also automatically generates different notification content for each emergency service and notifies the appropriate service. For example, it sends information about injured people to an ambulance and information about the fire to a fire department. Furthermore, the notification unit integrates the automatic notification function to build a system that simultaneously notifies multiple emergency services. For example, in the event of a traffic accident, it notifies the police, ambulance, and fire department simultaneously. In this way, by extending the automatic notification function to other emergency services, it is possible to respond to various emergency situations.

[0037] The reporting unit can automatically translate the report content into multiple languages ​​to accommodate international users. The reporting unit, for example, adds an automatic translation function to the automatic reporting function to translate the report content into multiple languages. For example, the report is translated into English, Spanish, Chinese, etc. The reporting unit also builds a system that analyzes the report content and automatically translates it into an appropriate language. For example, the report content is translated based on the language setting of the reporter. Furthermore, the reporting unit uses the automatic translation function to accommodate international users. For example, when used by a foreign tourist, the report is made in an appropriate language. In this way, the report content can be automatically translated into multiple languages, making it possible to accommodate international users.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The analysis unit can also acquire air quality data inside the vehicle and detect abnormal changes in air quality. For example, the analysis unit constantly monitors carbon dioxide concentration and harmful gas levels and detects sudden changes. For example, a sudden increase in carbon dioxide concentration is recognized as an abnormal change in air quality. The analysis unit also analyzes the air quality data and detects the presence of specific harmful gases. For example, detection of carbon monoxide is recognized as an abnormal change in air quality. Furthermore, the analysis unit integrates and analyzes the air quality data to detect abnormal changes in air quality with high accuracy. For example, simultaneous detection of multiple harmful gases is recognized as an abnormal change in air quality. In this way, acquiring air quality data and detecting abnormal changes in air quality can improve safety inside the vehicle.

[0040] The analysis unit can also acquire lighting data inside the vehicle and detect abnormal lighting changes. For example, the analysis unit constantly monitors the brightness and color of lighting inside the vehicle and detects sudden changes. For example, if the lighting suddenly becomes dark, it recognizes this as an abnormal lighting change. The analysis unit also analyzes the lighting data and detects specific patterns. For example, if the lighting flashes, it recognizes this as an abnormal lighting change. Furthermore, the analysis unit integrates and analyzes the lighting data to detect abnormal lighting changes with high accuracy. For example, if the lighting color suddenly changes, it recognizes this as an abnormal lighting change. In this way, by acquiring lighting data and detecting abnormal lighting changes, safety inside the vehicle can be improved.

[0041] The analysis unit can acquire vibration data inside the vehicle and detect abnormal vibrations. For example, the analysis unit constantly monitors vibrations inside the vehicle and detects sudden changes. For example, if the vehicle suddenly shakes, it recognizes this as abnormal vibration. The analysis unit also analyzes the vibration data and detects specific patterns. For example, if continuous vibrations occur, it recognizes this as abnormal vibration. Furthermore, the analysis unit integrates and analyzes the vibration data to detect abnormal vibrations with high accuracy. For example, if the vibration intensity changes suddenly, it recognizes this as abnormal vibration. In this way, by acquiring vibration data and detecting abnormal vibrations, safety inside the vehicle can be improved.

[0042] Surveillance AI cameras can monitor seat occupancy status within a vehicle and detect abnormal seat use. For example, surveillance AI cameras constantly monitor seat usage and detect sudden changes. For example, if a seat suddenly becomes vacant, it will recognize this as abnormal seat use. Surveillance AI cameras also analyze seat usage data and detect specific patterns. For example, if a seat is occupied continuously, it will recognize this as abnormal seat use. Furthermore, surveillance AI cameras integrate and analyze seat usage data to detect abnormal seat use with high accuracy. For example, if there is a sudden change in seat usage status, it will recognize this as abnormal seat use. In this way, by monitoring seat usage and detecting abnormal seat use, safety within the vehicle can be improved.

[0043] Surveillance AI cameras can monitor the status of luggage inside a vehicle and detect abnormal luggage movement. For example, a surveillance AI camera constantly monitors the location of luggage and detects sudden changes. For example, if luggage moves suddenly, it will recognize this as abnormal luggage movement. Surveillance AI cameras also analyze luggage movement data and detect specific patterns. For example, if luggage moves continuously, it will recognize this as abnormal luggage movement. Furthermore, surveillance AI cameras integrate and analyze luggage movement data to detect abnormal luggage movement with high accuracy. For example, if the location of luggage changes suddenly, it will recognize this as abnormal luggage movement. In this way, by monitoring the status of luggage and detecting abnormal luggage movement, safety inside the vehicle can be improved.

[0044] The reporting unit can automatically classify the content of a call and send it to the appropriate emergency service. For example, the reporting unit analyzes the content of a call and automatically classifies it to the appropriate emergency service, such as police, ambulance, or fire department, before sending it. For example, if a violent act occurs, the police will be notified, and if there are any injuries, an ambulance will be notified. The reporting unit can also analyze the content of a call and notify multiple emergency services simultaneously. For example, if a traffic accident occurs, the police and an ambulance will be notified simultaneously. Furthermore, the reporting unit automatically summarizes the content of a call and provides concise and clear information to emergency services. For example, it can summarize long content of a call and send it in a shorter form. This allows the content of a call to be automatically classified and sent to the appropriate emergency service, enabling a quick and appropriate response.

[0045] The reporting unit can automatically set the priority of the emergency response based on the content of the report, enabling a rapid response. For example, the reporting unit analyzes the content of the report and sets the priority according to the urgency. For example, if an act of violence has occurred, a high priority is set, encouraging a rapid response. The reporting unit also sets the priority of the emergency response based on the content of the report and notifies the appropriate emergency service. For example, if there is an injured person, an ambulance is notified with high priority. Furthermore, the reporting unit sets the priority of the emergency response based on the content of the report and notifies multiple emergency services simultaneously. For example, if a fire has occurred, the fire department and police are notified with high priority. This allows the priority of the emergency response to be automatically set based on the content of the report, enabling a rapid and appropriate response.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The AI ​​surveillance camera constantly monitors the situation inside the vehicle. For example, the AI ​​surveillance camera analyzes the video footage inside the vehicle in real time to detect abnormal behavior or situations. The AI ​​surveillance camera can also analyze the audio data inside the vehicle to detect abnormal sounds. Furthermore, the AI ​​surveillance camera can acquire environmental data such as the temperature and humidity inside the vehicle to detect abnormal environmental changes. Step 2: The analysis unit analyzes the video data captured by the surveillance AI camera and detects problems. For example, the analysis unit detects violent acts or suspicious movements. The analysis unit is also equipped with an emotion estimation function, which can analyze the emotions of passengers and drivers in real time and detect abnormal emotional changes. Furthermore, the analysis unit can analyze audio data inside the vehicle to detect abnormal behavior or situations. Step 3: The reporting unit reports the incident to the police along with GPS information based on the problem detected by the analysis unit. For example, if the reporting unit detects a violent act, it will send the vehicle's location information along with a video of the incident to the police. The reporting unit can also add an emotion estimation function to send the emotional state of the passengers and driver to the police when reporting. Furthermore, the reporting unit can automatically summarize the report content to make the information sent to the police concise and clear.

[0048] (Example 2) A safety monitoring system according to an embodiment of the present invention is a system that improves safety by installing a surveillance AI camera inside a vehicle and providing constant monitoring. This system has a function in which the surveillance AI camera constantly monitors the situation inside the vehicle, and if a problem occurs, the camera automatically reports the incident to the police along with GPS information. This safety monitoring system ensures safety in the ride-sharing service and taxi industries and reduces the anxiety of both drivers and users.

[0049] A safety monitoring system according to an embodiment includes a surveillance AI camera, an analysis unit, and a reporting unit. The surveillance AI camera constantly monitors the situation inside the vehicle. For example, the surveillance AI camera analyzes video footage from inside the vehicle in real time to detect abnormal behavior or situations. The surveillance AI camera can also analyze audio data from inside the vehicle to detect abnormal audio. The surveillance AI camera can also acquire environmental data such as the temperature and humidity inside the vehicle to detect abnormal environmental changes. The analysis unit analyzes the video data acquired by the surveillance AI camera to detect problems. For example, the analysis unit detects violent acts or suspicious movements. The analysis unit is also equipped with an emotion estimation function, and can analyze the emotions of passengers and the driver in real time to detect abnormal emotional changes. The analysis unit can also analyze audio data from inside the vehicle to detect abnormal behavior or situations. The reporting unit reports the problem detected by the analysis unit along with GPS information to the police. For example, if the reporting unit detects violent behavior, it transmits the vehicle's location information along with the video to the police. The reporting unit can also add an emotion estimation function and transmit the emotional state of the passenger or driver to the police when reporting. Furthermore, the reporting unit can automatically summarize the content of the report to provide concise and clear information to the police. This allows the safety monitoring system according to the embodiment to improve in-vehicle safety. For example, in the ride-sharing service and taxi industries, the system can ensure the safety of both drivers and users and reduce their anxiety.

[0050] The analysis unit can detect violent acts or suspicious behavior. For example, the analysis unit analyzes video data acquired by a surveillance AI camera to detect violent acts. For example, it detects acts such as punching and kicking. The analysis unit also analyzes abnormal behavioral patterns to detect suspicious behavior. For example, it detects sudden movements or abnormal behavioral patterns. Furthermore, the analysis unit integrates and analyzes multiple emotional indicators (facial expressions, voice, and body movements) to detect abnormal emotional changes with high accuracy. For example, a blushing face, a louder voice, and trembling body are recognized as abnormal emotional changes. This enables rapid response by detecting violent acts and suspicious behavior.

[0051] The analysis unit is equipped with an emotion estimation function and can analyze the emotions of passengers or drivers in real time and detect abnormal emotional changes. For example, the analysis unit is equipped with an emotion estimation function in a surveillance AI camera and analyzes the facial expressions of passengers and drivers to estimate their emotions in real time. For example, if an expression of anger or fear is detected, it is recognized as an abnormal emotional change. The analysis unit also analyzes audio data and estimates emotions from the tone of voice and vocabulary. For example, a trembling voice or yelling is detected as an abnormal emotional change. Furthermore, the analysis unit integrates and analyzes multiple emotional indicators (facial expressions, voice, and body movements) to detect abnormal emotional changes with high accuracy. For example, a red face, a louder voice, and trembling body are recognized as abnormal emotional changes. This allows for the detection of abnormal emotional changes and enables rapid response.

[0052] The analysis unit can analyze voice data and detect abnormal behavior or abnormal situations. For example, the analysis unit adds a voice recognition function to a surveillance AI camera and analyzes conversations and voices inside the car. For example, if it detects a scream or a cry for help, it will recognize this as an abnormal situation. The analysis unit also analyzes voice data and detects specific keywords and phrases. For example, if it detects words such as "help" or "danger," it will recognize this as abnormal behavior. Furthermore, the analysis unit analyzes changes in the tone and volume of the voice to detect abnormal behavior. For example, if the volume suddenly increases or the tone of the voice changes, it will recognize this as an abnormal situation. In this way, abnormal behavior and situations can be detected by analyzing voice data.

[0053] The analysis unit can acquire environmental data such as temperature or humidity and detect abnormal environmental changes. For example, the analysis unit may be equipped with a temperature sensor in a surveillance AI camera and constantly monitor the temperature inside the vehicle. For example, if a sudden rise or fall in temperature is detected, it will recognize this as an abnormal environmental change. The analysis unit may also be equipped with a humidity sensor and constantly monitor the humidity inside the vehicle. For example, if a sudden change in humidity is detected, it will recognize this as an abnormal environmental change. Furthermore, the analysis unit integrates and analyzes the temperature and humidity data to detect abnormal environmental changes with high accuracy. For example, if the temperature rises suddenly and the humidity also changes suddenly, it will recognize this as an abnormal environmental change. In this way, environmental data can be acquired and abnormal environmental changes can be detected.

[0054] Surveillance AI cameras can also be installed outside the vehicle, allowing for constant monitoring of the situation around the vehicle. For example, a surveillance AI camera can be installed outside the vehicle and analyze video footage of the area around the vehicle in real time. For example, if a suspicious person approaches the vehicle, it will recognize this as an abnormal situation. Surveillance AI cameras also cover 360 degrees around the vehicle, constantly monitoring the situation in all directions. For example, they can detect a person approaching from behind the vehicle. Furthermore, surveillance AI cameras can analyze audio data around the vehicle to detect abnormal sounds. For example, if they detect the sound of glass breaking or metal colliding, they will recognize this as an abnormal situation. This constantly monitors the situation around the vehicle, improving the safety of the entire vehicle.

[0055] Video data from surveillance AI cameras can be stored in the cloud for later analysis. For example, surveillance AI cameras can automatically upload video data to the cloud so that it can be accessed later. For example, past footage can be played back to check for abnormal behavior. Video data stored in the cloud can also be analyzed to detect abnormal behavior or situations later. For example, video from a specific time period can be analyzed to identify abnormal behavior. Furthermore, video data stored in the cloud can be accessed from multiple devices to quickly check for abnormal situations. For example, video can be viewed from a smartphone or tablet. This allows video data to be stored in the cloud so that it can be analyzed later, making it possible to investigate past situations in detail.

[0056] Surveillance AI cameras are equipped with emotion estimation functions and can detect anxiety or stress felt by passengers in the vehicle in real time and notify the driver. For example, surveillance AI cameras are equipped with emotion estimation functions and can analyze passenger facial expressions to detect anxiety or stress in real time. For example, if a passenger looks anxious, the driver will be notified. Surveillance AI cameras can also analyze passenger audio data to detect anxiety or stress from the tone of voice and choice of words. For example, if a passenger speaks in a tense voice, the driver will be notified. Surveillance AI cameras can also analyze passenger body movements to detect anxiety or stress. For example, if a passenger is moving their body frequently, the driver will be notified. This makes it possible to detect passenger anxiety or stress in real time and notify the driver, enabling a rapid response.

[0057] The reporting unit may add an emotion estimation function and transmit the emotional state of a passenger or driver to the police when reporting. For example, the reporting unit adds the emotion estimation function to the automatic reporting function and analyzes the facial expressions of a passenger or driver when reporting and transmits the emotional state to the police. For example, if an expression of anger or fear is detected, the information is included in the report. The reporting unit may also analyze audio data and transmit the emotional state of a passenger or driver to the police from the tone of voice and language used when reporting. For example, if a nervous voice or a scream is detected, the information is included in the report. Furthermore, the reporting unit may use the emotion estimation function to transmit the emotional state of a passenger or driver to the police from the body movements of the passenger or driver when reporting. For example, if the body is trembling, the information is included in the report. In this way, transmitting the emotional state when reporting makes it possible to provide the police with more detailed information about the situation.

[0058] The reporting unit can automatically summarize the contents of the report and send the information to the police concisely and clearly. For example, the reporting unit adds a summarization function to the automatic reporting function and automatically summarizes the contents of the report and sends it to the police. For example, if an act of violence occurs, the reporting unit briefly summarizes the main points. The reporting unit also analyzes the contents of the report and extracts and summarizes important information. For example, the reporting unit briefly summarizes the vehicle's location information and details of the problem that occurred. Furthermore, the reporting unit uses the summarization function to make the contents of the report concise and clear. For example, the reporting unit shortens long reports and sends them to the police. In this way, by summarizing the contents of the report, the police can be provided with concise and clear information.

[0059] The notification unit can extend the automatic notification function to emergency services, enabling it to respond to multiple emergency situations. For example, the notification unit extends the automatic notification function to other emergency services such as ambulances and fire departments, enabling it to respond to various emergency situations. For example, it automatically notifies in the event of a traffic accident or fire. The notification unit also automatically generates different notification content for each emergency service and notifies the appropriate service. For example, it sends information about injured people to an ambulance and information about the fire to a fire department. Furthermore, the notification unit integrates the automatic notification function to build a system that simultaneously notifies multiple emergency services. For example, in the event of a traffic accident, it notifies the police, ambulance, and fire department simultaneously. In this way, by extending the automatic notification function to other emergency services, it is possible to respond to various emergency situations.

[0060] The reporting unit can automatically translate the report content into multiple languages ​​to accommodate international users. The reporting unit, for example, adds an automatic translation function to the automatic reporting function to translate the report content into multiple languages. For example, the report is translated into English, Spanish, Chinese, etc. The reporting unit also builds a system that analyzes the report content and automatically translates it into an appropriate language. For example, the report content is translated based on the language setting of the reporter. Furthermore, the reporting unit uses the automatic translation function to accommodate international users. For example, when used by a foreign tourist, the report is made in an appropriate language. In this way, the report content can be automatically translated into multiple languages, making it possible to accommodate international users.

[0061] The reporting unit can add an emotion estimation function and propose an appropriate emergency response based on the emotional state of the passenger or driver at the time of the report. The reporting unit, for example, adds the emotion estimation function to the automatic reporting function and analyzes the emotional state of the passenger or driver at the time of the report to propose an appropriate emergency response. For example, if the emotion of fear is strong, a prompt response is proposed. The reporting unit also uses the emotion estimation function to build a system that proposes an appropriate emergency response based on the content of the report. For example, if the emotion of anger is strong, a prompt police intervention is proposed. Furthermore, the reporting unit proposes an emergency response based on the emotion estimation data at the time of the report. For example, if a tense voice or a trembling body is detected, a prompt emergency response is proposed. This makes it possible to propose an appropriate emergency response based on the emotional state at the time of the report, enabling a prompt and appropriate response.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The analysis unit can also acquire air quality data inside the vehicle and detect abnormal changes in air quality. For example, the analysis unit constantly monitors carbon dioxide concentration and harmful gas levels and detects sudden changes. For example, a sudden increase in carbon dioxide concentration is recognized as an abnormal change in air quality. The analysis unit also analyzes the air quality data and detects the presence of specific harmful gases. For example, detection of carbon monoxide is recognized as an abnormal change in air quality. Furthermore, the analysis unit integrates and analyzes the air quality data to detect abnormal changes in air quality with high accuracy. For example, simultaneous detection of multiple harmful gases is recognized as an abnormal change in air quality. In this way, acquiring air quality data and detecting abnormal changes in air quality can improve safety inside the vehicle.

[0064] The analysis unit can also acquire lighting data inside the vehicle and detect abnormal lighting changes. For example, the analysis unit constantly monitors the brightness and color of lighting inside the vehicle and detects sudden changes. For example, if the lighting suddenly becomes dark, it recognizes this as an abnormal lighting change. The analysis unit also analyzes the lighting data and detects specific patterns. For example, if the lighting flashes, it recognizes this as an abnormal lighting change. Furthermore, the analysis unit integrates and analyzes the lighting data to detect abnormal lighting changes with high accuracy. For example, if the lighting color suddenly changes, it recognizes this as an abnormal lighting change. In this way, by acquiring lighting data and detecting abnormal lighting changes, safety inside the vehicle can be improved.

[0065] The analysis unit can monitor passengers' health conditions in real time and detect abnormal health conditions. For example, the analysis unit constantly monitors passengers' heart rates and blood pressure to detect sudden changes. For example, a sudden increase in heart rate is recognized as an abnormal health condition. The analysis unit also analyzes passengers' respiratory data to detect abnormal breathing patterns. For example, a sudden shallowness in breathing is recognized as an abnormal health condition. Furthermore, the analysis unit integrates and analyzes multiple health indicators to detect abnormal health conditions with high accuracy. For example, if both heart rate and breathing show abnormal patterns simultaneously, it is recognized as an abnormal health condition. This enables passengers' health conditions to be monitored in real time and abnormal health conditions to be detected, enabling prompt response.

[0066] The analysis unit can acquire vibration data inside the vehicle and detect abnormal vibrations. For example, the analysis unit constantly monitors vibrations inside the vehicle and detects sudden changes. For example, if the vehicle suddenly shakes, it recognizes this as abnormal vibration. The analysis unit also analyzes the vibration data and detects specific patterns. For example, if continuous vibrations occur, it recognizes this as abnormal vibration. Furthermore, the analysis unit integrates and analyzes the vibration data to detect abnormal vibrations with high accuracy. For example, if the vibration intensity changes suddenly, it recognizes this as abnormal vibration. In this way, by acquiring vibration data and detecting abnormal vibrations, safety inside the vehicle can be improved.

[0067] The analysis unit can monitor passenger stress levels in real time and detect abnormal changes in stress. For example, the analysis unit constantly monitors passenger heart rate variability and electrodermal response to detect sudden changes. For example, a sudden increase in heart rate variability is recognized as an abnormal change in stress. The analysis unit also analyzes passenger voice data and estimates stress levels from voice tone and speech. For example, a trembling voice is detected as an abnormal change in stress. Furthermore, the analysis unit integrates and analyzes multiple stress indicators to detect abnormal changes in stress with high accuracy. For example, if heart rate variability and voice tone simultaneously show abnormal patterns, it is recognized as an abnormal change in stress. This makes it possible to monitor passenger stress levels in real time and detect abnormal changes in stress, enabling rapid response.

[0068] Surveillance AI cameras can monitor seat occupancy status within a vehicle and detect abnormal seat use. For example, surveillance AI cameras constantly monitor seat usage and detect sudden changes. For example, if a seat suddenly becomes vacant, it will recognize this as abnormal seat use. Surveillance AI cameras also analyze seat usage data and detect specific patterns. For example, if a seat is occupied continuously, it will recognize this as abnormal seat use. Furthermore, surveillance AI cameras integrate and analyze seat usage data to detect abnormal seat use with high accuracy. For example, if there is a sudden change in seat usage status, it will recognize this as abnormal seat use. In this way, by monitoring seat usage and detecting abnormal seat use, safety within the vehicle can be improved.

[0069] Surveillance AI cameras can monitor the status of luggage inside a vehicle and detect abnormal luggage movement. For example, a surveillance AI camera constantly monitors the location of luggage and detects sudden changes. For example, if luggage moves suddenly, it will recognize this as abnormal luggage movement. Surveillance AI cameras also analyze luggage movement data and detect specific patterns. For example, if luggage moves continuously, it will recognize this as abnormal luggage movement. Furthermore, surveillance AI cameras integrate and analyze luggage movement data to detect abnormal luggage movement with high accuracy. For example, if the location of luggage changes suddenly, it will recognize this as abnormal luggage movement. In this way, by monitoring the status of luggage and detecting abnormal luggage movement, safety inside the vehicle can be improved.

[0070] AI surveillance cameras can monitor passengers' fatigue levels in real time and detect abnormal changes in fatigue. For example, AI surveillance cameras can analyze passengers' facial expressions to estimate their fatigue levels in real time. For example, if a passenger frequently rubs their eyes, it will recognize this as an abnormal change in fatigue. AI surveillance cameras can also analyze passengers' voice data to estimate their fatigue levels from their tone of voice and choice of words. For example, if a passenger's voice is hoarse, it will detect this as an abnormal change in fatigue. AI surveillance cameras can also analyze passengers' body movements to detect their fatigue levels. For example, if a passenger frequently moves their body, it will recognize this as an abnormal change in fatigue. This makes it possible to monitor passengers' fatigue levels in real time and detect abnormal changes in fatigue, enabling rapid response.

[0071] The reporting unit can automatically classify the content of a call and send it to the appropriate emergency service. For example, the reporting unit analyzes the content of a call and automatically classifies it to the appropriate emergency service, such as police, ambulance, or fire department, before sending it. For example, if a violent act occurs, the police will be notified, and if there are any injuries, an ambulance will be notified. The reporting unit can also analyze the content of a call and notify multiple emergency services simultaneously. For example, if a traffic accident occurs, the police and an ambulance will be notified simultaneously. Furthermore, the reporting unit automatically summarizes the content of a call and provides concise and clear information to emergency services. For example, it can summarize long content of a call and send it in a shorter form. This allows the content of a call to be automatically classified and sent to the appropriate emergency service, enabling a quick and appropriate response.

[0072] The reporting unit can automatically set the priority of the emergency response based on the content of the report, enabling a rapid response. For example, the reporting unit analyzes the content of the report and sets the priority according to the urgency. For example, if an act of violence has occurred, a high priority is set, encouraging a rapid response. The reporting unit also sets the priority of the emergency response based on the content of the report and notifies the appropriate emergency service. For example, if there is an injured person, an ambulance is notified with high priority. Furthermore, the reporting unit sets the priority of the emergency response based on the content of the report and notifies multiple emergency services simultaneously. For example, if a fire has occurred, the fire department and police are notified with high priority. This allows the priority of the emergency response to be automatically set based on the content of the report, enabling a rapid and appropriate response.

[0073] The reporting department can add an emotion estimation function and suggest an appropriate emergency response based on the emotional state of the passenger or driver at the time of the report. For example, the reporting department analyzes the content of the report and suggests an appropriate emergency response based on the emotional state of the passenger or driver. For example, if the emotion of fear is strong, a swift response is suggested. The reporting department also uses the emotion estimation function to build a system that suggests an appropriate emergency response based on the content of the report. For example, if the emotion of anger is strong, a swift police intervention is suggested. Furthermore, the reporting department suggests an emergency response based on the emotion estimation data at the time of the report. For example, if a tense voice or trembling body is detected, a swift emergency response is suggested. This makes it possible to suggest an appropriate emergency response based on the emotional state at the time of the report, enabling a swift and appropriate response.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The AI ​​surveillance camera constantly monitors the situation inside the vehicle. For example, the AI ​​surveillance camera analyzes the video footage inside the vehicle in real time to detect abnormal behavior or situations. The AI ​​surveillance camera can also analyze the audio data inside the vehicle to detect abnormal sounds. Furthermore, the AI ​​surveillance camera can acquire environmental data such as the temperature and humidity inside the vehicle to detect abnormal environmental changes. Step 2: The analysis unit analyzes the video data captured by the surveillance AI camera and detects problems. For example, the analysis unit detects violent acts or suspicious movements. The analysis unit is also equipped with an emotion estimation function, which can analyze the emotions of passengers and drivers in real time and detect abnormal emotional changes. Furthermore, the analysis unit can analyze audio data inside the vehicle to detect abnormal behavior or situations. Step 3: The reporting unit reports the incident to the police along with GPS information based on the problem detected by the analysis unit. For example, if the reporting unit detects a violent act, it will send the vehicle's location information along with a video of the incident to the police. The reporting unit can also add an emotion estimation function to send the emotional state of the passengers and driver to the police when reporting. Furthermore, the reporting unit can automatically summarize the report content to make the information sent to the police concise and clear.

[0076] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0077] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0078] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0082] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0083] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0084] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0085] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0086] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0087] 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.

[0088] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0089] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0090] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] 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.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0117] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0126] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0127] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0128] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0129] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0130] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0131] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0132] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0133] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0134] 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.

[0135] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0136] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0137] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0138] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0139] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0140] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0141] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0142] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0143] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An AI camera constantly monitors the situation inside the vehicle, an analysis unit that analyzes video data acquired by the surveillance AI camera and detects problems; a reporting unit that reports the problem detected by the analysis unit to the police together with the GPS information. A system characterized by:

2. The analysis unit Equipped with an emotion estimation function, it analyzes the emotions of passengers or drivers in real time and detects abnormal emotional changes.

2. The system of claim 1.

3. The AI ​​surveillance camera is It is also installed outside the vehicle to constantly monitor the situation around the vehicle.

2. The system of claim 1.

4. The reporting unit Add emotion estimation function to send passenger or driver emotional state to police when reporting.

2. The system of claim 1.

5. The analysis unit Analyzing the audio data to detect abnormal behavior or abnormal situations 2. The system of claim 1.

6. The video data from the AI ​​surveillance camera is stored in the cloud, allowing it to be analyzed later.

2. The system of claim 1.

7. The reporting unit Extending automated notification capabilities to emergency services to handle multiple emergencies 2. The system of claim 1.

8. The reporting unit Adds emotion estimation functionality to suggest appropriate emergency responses based on the emotional state of the passenger or driver when reporting an emergency.

2. The system of claim 1.

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