System

Abstract

An object of a system according to an embodiment is to quickly and appropriately detect and deal with a trouble in a vehicle.SOLUTION: A system according to an embodiment includes a video analysis unit, a trouble detection unit, a warning message generation unit, and a speech unit. The video analysis unit analyzes the in-vehicle video. The trouble detection unit detects a trouble from the in-vehicle video analyzed by the video analysis unit. The warning message generation unit generates a warning message according to the content of the trouble detected by the trouble detection unit. The utterance unit utters the warning message generated by the warning message generation unit to the target person.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 quickly and appropriately detect and respond to in-vehicle problems.

[0005] The system according to the embodiment aims to quickly and appropriately detect and respond to troubles inside a vehicle. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, a trouble detection unit, a warning message generation unit, and a speech unit. The video analysis unit analyzes in-vehicle video. The trouble detection unit detects trouble from the in-vehicle video analyzed by the video analysis unit. The warning message generation unit generates a warning message according to the content of the trouble detected by the trouble detection unit. The speech unit speaks the warning message generated by the warning message generation unit to a target person. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and appropriately detect and respond to troubles inside the vehicle. [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) The trouble detection system according to the embodiment of the present invention is a system that analyzes in-car video, detects trouble, generates and issues a warning message, and thereby prevents trouble between passengers and suppresses further trouble from occurring.

[0029] A trouble detection system according to an embodiment includes a video analysis unit, a trouble detection unit, a warning message generation unit, and a speech unit. The video analysis unit analyzes in-vehicle video. For example, the video analysis unit analyzes video from a camera installed inside the vehicle in real time to monitor people's movements and behavior. The video analysis unit can also detect abnormal behavior and signs of trouble. For example, the video analysis unit detects behaviors such as harassment, seat occupation, assault, pickpocketing, and voyeurism. The trouble detection unit detects trouble from the in-vehicle video analyzed by the video analysis unit. For example, the trouble detection unit analyzes people's movements and behavior to detect troubles such as harassment and assault. The trouble detection unit can also identify abnormal behavior based on trouble patterns learned in advance. The warning message generation unit generates a warning message according to the content of the trouble detected by the trouble detection unit. For example, if harassment is detected, the warning message generation unit generates a warning message such as, "Customer, please refrain from causing trouble to other passengers." Furthermore, when seat occupancy is detected, the warning message generation unit can generate a warning message such as "Customer, please refrain from occupying seats." The speech unit speaks the warning message generated by the warning message generation unit to the target. For example, the speech unit speaks the warning message through a speaker in the vehicle. Furthermore, by directly communicating the warning message to the target, the speech unit can prevent trouble between passengers. In this way, the trouble detection system according to the embodiment can prevent trouble between passengers and suppress the occurrence of further trouble. For example, when a sexual assault or an act of assault is detected, the trouble detection system can immediately issue a warning to prevent further trouble from occurring. Furthermore, it can prevent manner violations such as seat occupancy and pickpocketing.

[0030] The video analysis unit can detect passengers' facial expressions and changes in body temperature to analyze signs of stress or tension. The video analysis unit, for example, analyzes camera footage to detect passengers' facial expressions in real time. For example, it analyzes facial muscle movements and eye movements to identify signs of stress or tension. The video analysis unit can also use an infrared camera to detect changes in body temperature. For example, it can analyze body temperature fluctuations to identify signs of stress or tension. The video analysis unit can also combine data on facial expressions and changes in body temperature to comprehensively analyze signs of stress or tension. This makes it possible to analyze passengers' signs of stress or tension and detect problems early.

[0031] The video analysis unit can identify passenger clothing and belongings and detect risk factors. For example, the video analysis unit analyzes camera footage to identify passenger clothing. For example, it detects heavy coats and hooded clothing and evaluates them as risk factors. The video analysis unit can also detect the shape and size of bags and luggage to identify belongings. For example, it can identify large luggage and suspicious behavior as risk factors. The video analysis unit can also combine data on clothing and belongings to comprehensively analyze risk factors. This allows specific risk factors to be detected early by identifying passenger clothing and belongings.

[0032] In addition to analyzing in-vehicle video, the video analysis unit can also simultaneously analyze audio data to detect abnormal sounds. The video analysis unit, for example, simultaneously analyzes camera video and audio data from a microphone to detect abnormal sounds. For example, it can detect screams or fighting sounds in real time. The video analysis unit can also analyze the frequency and volume of audio data to identify abnormal sounds. For example, it can detect sounds with frequencies and volumes that differ from normal sounds. The video analysis unit can also combine video and audio data to comprehensively analyze abnormal sounds. This allows for the simultaneous analysis of in-vehicle video and audio data to detect abnormal sounds early on.

[0033] The video analysis unit can also analyze environmental data such as temperature and humidity inside the vehicle and determine whether an abnormal environmental change is a sign of trouble. The video analysis unit, for example, analyzes temperature data inside the vehicle to detect an abnormal temperature change. For example, it identifies a sudden increase or decrease in temperature as a sign of trouble. The video analysis unit can also analyze humidity data to detect an abnormal humidity change. For example, it identifies a sudden increase or decrease in humidity as a sign of trouble. The video analysis unit can also combine temperature and humidity data to comprehensively analyze abnormal environmental changes. This allows for early detection of abnormal environmental changes by analyzing environmental data such as temperature and humidity inside the vehicle.

[0034] The trouble detection unit can refer to past trouble data and identify similar trouble patterns. The trouble detection unit, for example, analyzes past trouble data and identifies similar trouble patterns. For example, it detects new acts of harassment based on data on past acts of harassment. The trouble detection unit can also compare with past trouble data and identify abnormal behavior. For example, it detects new acts of violence based on data on past acts of violence. The trouble detection unit can also predict the probability of trouble occurring based on past trouble data. This makes it possible to identify similar trouble patterns by referring to past trouble data.

[0035] The trouble detection unit can integrate footage from multiple cameras to perform highly accurate trouble detection. For example, the trouble detection unit can integrate footage from multiple cameras to improve the accuracy of trouble detection. For example, it can analyze footage from different angles to identify the details of the trouble. The trouble detection unit can also synchronize footage from multiple cameras to grasp the occurrence of a trouble in real time. For example, it can simultaneously analyze footage from multiple cameras to identify the location and time of the trouble. The trouble detection unit can also identify the cause of the trouble based on the footage from multiple cameras. In this way, by integrating footage from multiple cameras, more accurate trouble detection is possible.

[0036] The trouble detection unit can also analyze data from passengers' smartphones and wearable devices to detect abnormal behavior. The trouble detection unit, for example, analyzes data from passengers' smartphones to detect abnormal behavior. For example, it can identify cases where a passenger suddenly takes out their smartphone and makes suspicious movements. The trouble detection unit can also analyze data from wearable devices to identify abnormal behavior. For example, it can analyze fluctuations in heart rate and activity level to identify abnormal behavior. The trouble detection unit can also combine data from smartphones and wearable devices to comprehensively analyze abnormal behavior. This allows for early detection of abnormal behavior by analyzing data from smartphones and wearable devices.

[0037] The trouble detection unit can analyze the Wi-Fi connection status in the vehicle and detect abnormal device connections. The trouble detection unit, for example, analyzes the Wi-Fi connection status in the vehicle and detects abnormal device connections. For example, it identifies device connections that exceed the normal number of connections. The trouble detection unit can also analyze the type of connected device and the connection time to identify abnormal device connections. For example, it can detect connections of unauthorized devices. The trouble detection unit can also comprehensively analyze abnormal connection patterns based on data on the Wi-Fi connection status. As a result, abnormal device connections can be detected early by analyzing the Wi-Fi connection status.

[0038] The warning message generation unit can select appropriate language depending on the age and gender of the target person. The warning message generation unit can, for example, analyze the age of the target person and select appropriate language. For example, gentle language is selected for children and polite language is selected for adults. The warning message generation unit can also analyze the gender of the target person and select appropriate language. For example, neutral language is selected for men and polite language is selected for women. The warning message generation unit can also combine age and gender data to comprehensively select appropriate language. In this way, by selecting appropriate language depending on the age and gender of the target person, the effectiveness of the warning message can be increased.

[0039] The warning message generation unit can learn the effectiveness of past warning messages and select the most effective message. The warning message generation unit, for example, analyzes data on past warning messages and identifies effective messages. For example, it generates new warning messages based on messages that were effective in the past. The warning message generation unit can also evaluate the effectiveness of past warning messages and select the most effective message. For example, it can score the effectiveness of warning messages and select the message with the highest score. The warning message generation unit can also comprehensively analyze effective message patterns based on data on past warning messages. This allows the most effective message to be selected by learning the effectiveness of past warning messages.

[0040] The warning message generation unit can generate a visual warning in addition to generating the warning message. For example, when generating a warning message, the warning message generation unit displays a warning message on the display. For example, it displays a message such as, "Customer, please refrain from causing a nuisance to other passengers." The warning message generation unit can also adjust the content displayed on the display to generate the visual warning. For example, it can change the font size or color of the warning message. The warning message generation unit can also evaluate the effectiveness of the visual warning and select the optimal display method. In this way, the effectiveness of the warning message can be enhanced by generating a visual warning.

[0041] In addition to generating the warning message, the warning message generation unit can also send a notification to the smartphone of the target person to individually warn the target person. For example, when generating the warning message, the warning message generation unit sends a notification to the smartphone of the target person. For example, it sends a message such as, "Customer, please refrain from causing a nuisance to other passengers." The warning message generation unit can also adjust the method of sending the notification. For example, it can adjust the timing and content of the notification. The warning message generation unit can also evaluate the effectiveness of the notification and select the optimal sending method. This makes it possible to individually warn the target person by sending a notification to their smartphone.

[0042] The speech unit can identify the location of the target person and speak from the most effective speaker. The speech unit, for example, analyzes camera footage to identify the location of the target person. For example, if the target person is at the front of the vehicle, speech is spoken from the front speaker. The speech unit can also adjust the speaker volume according to the target person's location. For example, if the target person is far away, the volume is increased. The speech unit can also synchronize multiple speakers to provide the optimal acoustic environment. This allows the target person's location to be identified and speech to be spoken from the most effective speaker, thereby increasing the effectiveness of the warning message.

[0043] The speech unit can adjust the tone and speed of the voice to speak in a way that is most easily understood by the target person. The speech unit, for example, analyzes the target person's situation and adjusts the tone and speed of the voice. For example, if the target person is nervous, it will speak slowly and in a calm tone. The speech unit can also adjust the tone and speed of the voice in real time. For example, it changes the tone and speed based on the target person's reaction. The speech unit can also comprehensively analyze the optimal speaking method based on data on the tone and speed of the voice. As a result, by adjusting the tone and speed of the voice, it is possible to speak in a way that is most easily understood by the target person.

[0044] In addition to speaking the warning message, the speech unit can also display a visual warning on a display inside the vehicle. For example, when speaking the warning message, the speech unit displays a visual warning on a display inside the vehicle. For example, a message such as "Customer, please refrain from causing a nuisance to other passengers" is displayed. The speech unit can also adjust the content of the display to provide the visual warning. For example, the speech unit can change the font size or color of the warning message. The speech unit can also evaluate the effectiveness of the visual warning and select the optimal display method. This allows the effectiveness of the warning message to be enhanced by displaying the visual warning.

[0045] In addition to speaking the warning message, the speech unit can also send a notification to the smartphone of the target person to individually warn them. For example, when speaking the warning message, the speech unit sends a notification to the smartphone of the target person. For example, it sends a message such as, "Customer, please refrain from causing a nuisance to other passengers." The speech unit can also adjust the method of sending the notification. For example, it can adjust the timing and content of the notification. The speech unit can also evaluate the effectiveness of the notification and select the optimal sending method. This makes it possible to individually warn the target person by sending a notification to their smartphone.

[0046] The trouble recording unit generates a detailed timeline of the trouble, making it easier to analyze it later. For example, the trouble recording unit records the time the trouble occurred and the elapsed time, and generates a detailed timeline. For example, it records the start time, end time, and timestamps of important events of the trouble. Furthermore, the trouble recording unit can comprehensively analyze the circumstances under which the trouble occurred based on the timeline data. For example, it can identify the cause and impact of the trouble. Furthermore, the trouble recording unit can consider measures to prevent the trouble from recurring based on the timeline data. In this way, by generating a detailed timeline of the trouble, it can make it easier to analyze it later.

[0047] The trouble recording unit can also store related video data and audio data, making it possible to use it as evidence. The trouble recording unit, for example, records video data of a trouble and stores it as evidence. For example, camera footage taken when a trouble occurs can be stored and used for later analysis. The trouble recording unit can also record audio data and store it as evidence. For example, audio data taken when a trouble occurs can be stored and used for later analysis. The trouble recording unit can also combine video data and audio data to record a detailed record of a trouble. In this way, by storing related video data and audio data, it can be used as evidence.

[0048] In addition to recording troubles, the trouble recording unit can notify the transportation operating company in real time, enabling an immediate response. For example, when a trouble is detected, the trouble recording unit notifies the transportation operating company in real time. For example, it can immediately report the occurrence of the trouble and prompt a response. The trouble recording unit can also adjust the content and format of the notification. For example, it can describe the content of the notification in detail and clarify the response procedures. The trouble recording unit can also evaluate the effectiveness of the notification and select the optimal notification method. This makes it possible to notify the transportation operating company in real time, enabling an immediate response.

[0049] In addition to recording the trouble, the trouble recording unit can also conduct a questionnaire with passengers to collect detailed information about the trouble. For example, the trouble recording unit conducts a questionnaire with passengers after recording the trouble. For example, detailed information about the trouble and passenger opinions are collected. The trouble recording unit can also adjust the content of the questions in the questionnaire and the timing of conducting the questionnaire. For example, a questionnaire can be conducted immediately after the trouble occurs to collect detailed information. The trouble recording unit can also analyze the content of the questionnaire responses and identify the cause and impact of the trouble. In this way, detailed information about the trouble can be collected by conducting a questionnaire with passengers.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The trouble detection system can further include a health condition monitoring unit that monitors the health condition of passengers. For example, the health condition monitoring unit can measure passengers' heart rate and blood pressure in real time and detect abnormal fluctuations. If the heart rate rises suddenly or blood pressure becomes abnormally high, the health condition monitoring unit can issue a warning as a sign of trouble. The health condition monitoring unit can also accumulate passenger health data and use it for long-term health management. In this way, by monitoring the passenger's health condition, health problems can be prevented before they occur.

[0052] The trouble detection system can further include a behavior history analysis unit that analyzes passenger behavior history. For example, the behavior history analysis unit can analyze passenger past behavior patterns and identify abnormal behavior. It can learn the behavior patterns of passengers who have caused trouble in the past and issue a warning if similar behavior is observed. The behavior history analysis unit can also predict the probability of trouble occurring based on passenger behavior history. In this way, by analyzing passenger behavior history, it is possible to prevent trouble from occurring.

[0053] The trouble detection system can further include a device data analysis unit that analyzes data from passengers' smartphones and wearable devices to detect abnormal behavior. For example, the device data analysis unit can analyze data from passengers' smartphones to identify cases where the passenger suddenly takes out their smartphone and makes suspicious movements. It can also analyze data from wearable devices to analyze fluctuations in heart rate and activity level to identify abnormal behavior. This allows for early detection of abnormal behavior by analyzing data from smartphones and wearable devices.

[0054] The trouble detection system may further include an environmental data analysis unit that analyzes environmental data such as temperature and humidity inside the vehicle and determines whether an abnormal environmental change is a sign of trouble. For example, the environmental data analysis unit may analyze temperature data inside the vehicle and identify a sudden increase or decrease in temperature as a sign of trouble. It may also analyze humidity data and identify a sudden increase or decrease in humidity as a sign of trouble. This allows for early detection of abnormal environmental changes by analyzing environmental data such as temperature and humidity inside the vehicle.

[0055] The trouble detection system can further include a clothing recognition unit that identifies passenger clothing and belongings to detect risk factors. For example, the clothing recognition unit analyzes camera footage to identify passenger clothing. It can detect heavy coats and hooded clothing and evaluate them as risk factors. It can also detect the shape and size of bags and luggage to identify belongings. This allows for early detection of specific risk factors by identifying passenger clothing and belongings.

[0056] The trouble detection system can further include a Wi-Fi analysis unit that analyzes the Wi-Fi connection status in the vehicle and detects abnormal device connections. For example, the Wi-Fi analysis unit can analyze the Wi-Fi connection status in the vehicle and identify device connections that exceed the normal number of connections. It can also analyze the type of connected device and the connection time to detect unauthorized device connections. This allows for early detection of abnormal device connections by analyzing the Wi-Fi connection status.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The video analysis unit analyzes the video footage from inside the vehicle. For example, it analyzes video footage from cameras installed inside the vehicle in real time to monitor people's movements and behavior. It can also detect abnormal behavior and signs of trouble. Specifically, it can detect behaviors such as sexual harassment, seat domination, assault, pickpocketing, and voyeurism. Step 2: The trouble detection unit detects trouble from the in-car video analyzed by the video analysis unit. For example, it analyzes people's movements and behavior to detect trouble such as harassment and assault. It can also identify abnormal behavior based on trouble patterns learned in advance. Step 3: The warning message generation unit generates a warning message according to the content of the trouble detected by the trouble detection unit. For example, if harassment is detected, a warning message such as "Customer, please refrain from causing trouble to other passengers" is generated. Also, if seat occupancy is detected, a warning message such as "Customer, please refrain from occupying seats" can be generated. Step 4: The speech unit speaks the warning message generated by the warning message generation unit to the target person. For example, the warning message is spoken through a speaker inside the vehicle. In addition, by directly communicating the warning message to the target person, it is possible to prevent trouble between passengers.

[0059] (Example 2) The trouble detection system according to the embodiment of the present invention is a system that analyzes in-car video, detects trouble, generates and issues a warning message, and thereby prevents trouble between passengers and suppresses further trouble from occurring.

[0060] A trouble detection system according to an embodiment includes a video analysis unit, a trouble detection unit, a warning message generation unit, and a speech unit. The video analysis unit analyzes in-vehicle video. For example, the video analysis unit analyzes video from a camera installed inside the vehicle in real time to monitor people's movements and behavior. The video analysis unit can also detect abnormal behavior and signs of trouble. For example, the video analysis unit detects behaviors such as harassment, seat occupation, assault, pickpocketing, and voyeurism. The trouble detection unit detects trouble from the in-vehicle video analyzed by the video analysis unit. For example, the trouble detection unit analyzes people's movements and behavior to detect troubles such as harassment and assault. The trouble detection unit can also identify abnormal behavior based on trouble patterns learned in advance. The warning message generation unit generates a warning message according to the content of the trouble detected by the trouble detection unit. For example, if harassment is detected, the warning message generation unit generates a warning message such as, "Customer, please refrain from causing trouble to other passengers." Furthermore, when seat occupancy is detected, the warning message generation unit can generate a warning message such as "Customer, please refrain from occupying seats." The speech unit speaks the warning message generated by the warning message generation unit to the target. For example, the speech unit speaks the warning message through a speaker in the vehicle. Furthermore, by directly communicating the warning message to the target, the speech unit can prevent trouble between passengers. In this way, the trouble detection system according to the embodiment can prevent trouble between passengers and suppress the occurrence of further trouble. For example, when a sexual assault or an act of assault is detected, the trouble detection system can immediately issue a warning to prevent further trouble from occurring. Furthermore, it can prevent manner violations such as seat occupancy and pickpocketing.

[0061] The video analysis unit can detect passengers' facial expressions and changes in body temperature to analyze signs of stress or tension. The video analysis unit, for example, analyzes camera footage to detect passengers' facial expressions in real time. For example, it analyzes facial muscle movements and eye movements to identify signs of stress or tension. The video analysis unit can also use an infrared camera to detect changes in body temperature. For example, it can analyze body temperature fluctuations to identify signs of stress or tension. The video analysis unit can also combine data on facial expressions and changes in body temperature to comprehensively analyze signs of stress or tension. This makes it possible to analyze passengers' signs of stress or tension and detect problems early.

[0062] The video analysis unit can identify passenger clothing and belongings and detect risk factors. For example, the video analysis unit analyzes camera footage to identify passenger clothing. For example, it detects heavy coats and hooded clothing and evaluates them as risk factors. The video analysis unit can also detect the shape and size of bags and luggage to identify belongings. For example, it can identify large luggage and suspicious behavior as risk factors. The video analysis unit can also combine data on clothing and belongings to comprehensively analyze risk factors. This allows specific risk factors to be detected early by identifying passenger clothing and belongings.

[0063] The video analysis unit can use an emotion estimation function to analyze passengers' emotional states in real time and detect abnormal emotional changes. The video analysis unit, for example, analyzes camera footage and estimates passengers' emotional states from their facial expressions. For example, it detects smiling and angry expressions in real time. The video analysis unit can also use audio analysis technology to analyze the tone and speed of passengers' voices and estimate their emotional states. For example, it calculates an emotion score based on changes in voice tone and speed. The video analysis unit can also combine facial expression and audio data to comprehensively analyze passengers' emotional states. This allows passengers' emotional states to be analyzed in real time and abnormal emotional changes to be detected early.

[0064] In addition to analyzing in-vehicle video, the video analysis unit can also simultaneously analyze audio data to detect abnormal sounds. The video analysis unit, for example, simultaneously analyzes camera video and audio data from a microphone to detect abnormal sounds. For example, it can detect screams or fighting sounds in real time. The video analysis unit can also analyze the frequency and volume of audio data to identify abnormal sounds. For example, it can detect sounds with frequencies and volumes that differ from normal sounds. The video analysis unit can also combine video and audio data to comprehensively analyze abnormal sounds. This allows for the simultaneous analysis of in-vehicle video and audio data to detect abnormal sounds early on.

[0065] The video analysis unit can also analyze environmental data such as temperature and humidity inside the vehicle and determine whether an abnormal environmental change is a sign of trouble. The video analysis unit, for example, analyzes temperature data inside the vehicle to detect an abnormal temperature change. For example, it identifies a sudden increase or decrease in temperature as a sign of trouble. The video analysis unit can also analyze humidity data to detect an abnormal humidity change. For example, it identifies a sudden increase or decrease in humidity as a sign of trouble. The video analysis unit can also combine temperature and humidity data to comprehensively analyze abnormal environmental changes. This allows for early detection of abnormal environmental changes by analyzing environmental data such as temperature and humidity inside the vehicle.

[0066] The video analysis unit can use an emotion estimation function to analyze the emotional state of passengers and adjust the environment to elicit positive emotions. The video analysis unit, for example, analyzes camera footage to estimate the emotional state of passengers. For example, it detects whether the passenger is relaxed. The video analysis unit can also use audio analysis technology to analyze the tone and speed of the passenger's voice to estimate the emotional state. For example, it can calculate an emotion score based on changes in the tone and speed of the voice. The video analysis unit can also combine facial expression and audio data to comprehensively analyze the emotional state. Furthermore, the video analysis unit can adjust the environment according to the emotional state. For example, it can change the lighting or music to elicit positive emotions. This makes it possible to analyze the emotional state of passengers and adjust the environment to elicit positive emotions.

[0067] The trouble detection unit can refer to past trouble data and identify similar trouble patterns. The trouble detection unit, for example, analyzes past trouble data and identifies similar trouble patterns. For example, it detects new acts of harassment based on data on past acts of harassment. The trouble detection unit can also compare with past trouble data and identify abnormal behavior. For example, it detects new acts of violence based on data on past acts of violence. The trouble detection unit can also predict the probability of trouble occurring based on past trouble data. This makes it possible to identify similar trouble patterns by referring to past trouble data.

[0068] The trouble detection unit can integrate footage from multiple cameras to perform highly accurate trouble detection. For example, the trouble detection unit can integrate footage from multiple cameras to improve the accuracy of trouble detection. For example, it can analyze footage from different angles to identify the details of the trouble. The trouble detection unit can also synchronize footage from multiple cameras to grasp the occurrence of a trouble in real time. For example, it can simultaneously analyze footage from multiple cameras to identify the location and time of the trouble. The trouble detection unit can also identify the cause of the trouble based on the footage from multiple cameras. In this way, by integrating footage from multiple cameras, more accurate trouble detection is possible.

[0069] The trouble detection unit uses the emotion estimation function to analyze changes in passenger emotions and can detect signs of emotional trouble at an early stage. The trouble detection unit, for example, analyzes camera footage to detect changes in passenger emotions in real time. For example, it identifies signs of emotional trouble, such as a sudden change in facial expression to anger. The trouble detection unit can also use voice analysis technology to analyze the tone and speed of passengers' voices and identify changes in emotion. For example, it identifies signs of emotional trouble based on changes in voice tone and speed. The trouble detection unit can also combine facial expression and voice data to comprehensively analyze signs of emotional trouble. As a result, the emotion estimation function can be used to detect signs of emotional trouble at an early stage.

[0070] The trouble detection unit can also analyze data from passengers' smartphones and wearable devices to detect abnormal behavior. The trouble detection unit, for example, analyzes data from passengers' smartphones to detect abnormal behavior. For example, it can identify cases where a passenger suddenly takes out their smartphone and makes suspicious movements. The trouble detection unit can also analyze data from wearable devices to identify abnormal behavior. For example, it can analyze fluctuations in heart rate and activity level to identify abnormal behavior. The trouble detection unit can also combine data from smartphones and wearable devices to comprehensively analyze abnormal behavior. This allows for early detection of abnormal behavior by analyzing data from smartphones and wearable devices.

[0071] The trouble detection unit can analyze the Wi-Fi connection status in the vehicle and detect abnormal device connections. The trouble detection unit, for example, analyzes the Wi-Fi connection status in the vehicle and detects abnormal device connections. For example, it identifies device connections that exceed the normal number of connections. The trouble detection unit can also analyze the type of connected device and the connection time to identify abnormal device connections. For example, it can detect connections of unauthorized devices. The trouble detection unit can also comprehensively analyze abnormal connection patterns based on data on the Wi-Fi connection status. As a result, abnormal device connections can be detected early by analyzing the Wi-Fi connection status.

[0072] The warning message generation unit can select appropriate language depending on the age and gender of the target person. The warning message generation unit can, for example, analyze the age of the target person and select appropriate language. For example, gentle language is selected for children and polite language is selected for adults. The warning message generation unit can also analyze the gender of the target person and select appropriate language. For example, neutral language is selected for men and polite language is selected for women. The warning message generation unit can also combine age and gender data to comprehensively select appropriate language. In this way, by selecting appropriate language depending on the age and gender of the target person, the effectiveness of the warning message can be increased.

[0073] The warning message generation unit can learn the effectiveness of past warning messages and select the most effective message. The warning message generation unit, for example, analyzes data on past warning messages and identifies effective messages. For example, it generates new warning messages based on messages that were effective in the past. The warning message generation unit can also evaluate the effectiveness of past warning messages and select the most effective message. For example, it can score the effectiveness of warning messages and select the message with the highest score. The warning message generation unit can also comprehensively analyze effective message patterns based on data on past warning messages. This allows the most effective message to be selected by learning the effectiveness of past warning messages.

[0074] The warning message generation unit can use the emotion estimation function to select a gentle expression or a harsh expression according to the emotional state of the subject. The warning message generation unit, for example, analyzes the emotional state of the subject and selects a gentle expression or a harsh expression. For example, if the subject is angry, a gentle expression can be selected. Also, if the subject is relaxed, a harsh expression can be selected. The warning message generation unit can also use the emotion estimation function to analyze the emotional state of the subject in real time and select an appropriate expression. For example, the expression can be selected based on an emotion score. The warning message generation unit can also comprehensively analyze patterns of gentle and harsh expressions based on data on the emotional state. This can enhance the effectiveness of the warning message by selecting a gentle or harsh expression according to the emotional state of the subject.

[0075] The warning message generation unit can generate a visual warning in addition to generating the warning message. For example, when generating a warning message, the warning message generation unit displays a warning message on the display. For example, it displays a message such as, "Customer, please refrain from causing a nuisance to other passengers." The warning message generation unit can also adjust the content displayed on the display to generate the visual warning. For example, it can change the font size or color of the warning message. The warning message generation unit can also evaluate the effectiveness of the visual warning and select the optimal display method. In this way, the effectiveness of the warning message can be enhanced by generating a visual warning.

[0076] In addition to generating the warning message, the warning message generation unit can also send a notification to the smartphone of the target person to individually warn the target person. For example, when generating the warning message, the warning message generation unit sends a notification to the smartphone of the target person. For example, it sends a message such as, "Customer, please refrain from causing a nuisance to other passengers." The warning message generation unit can also adjust the method of sending the notification. For example, it can adjust the timing and content of the notification. The warning message generation unit can also evaluate the effectiveness of the notification and select the optimal sending method. This makes it possible to individually warn the target person by sending a notification to their smartphone.

[0077] The speech unit can identify the location of the target person and speak from the most effective speaker. The speech unit, for example, analyzes camera footage to identify the location of the target person. For example, if the target person is at the front of the vehicle, speech is spoken from the front speaker. The speech unit can also adjust the speaker volume according to the target person's location. For example, if the target person is far away, the volume is increased. The speech unit can also synchronize multiple speakers to provide the optimal acoustic environment. This allows the target person's location to be identified and speech to be spoken from the most effective speaker, thereby increasing the effectiveness of the warning message.

[0078] The speech unit can adjust the tone and speed of the voice to speak in a way that is most easily understood by the target person. The speech unit, for example, analyzes the target person's situation and adjusts the tone and speed of the voice. For example, if the target person is nervous, it will speak slowly and in a calm tone. The speech unit can also adjust the tone and speed of the voice in real time. For example, it changes the tone and speed based on the target person's reaction. The speech unit can also comprehensively analyze the optimal speaking method based on data on the tone and speed of the voice. As a result, by adjusting the tone and speed of the voice, it is possible to speak in a way that is most easily understood by the target person.

[0079] The speech unit can use the emotion estimation function to speak at a tone and speed that corresponds to the emotional state of the subject. The speech unit, for example, analyzes the emotional state of the subject and adjusts the tone and speed. For example, if the subject is angry, it speaks slowly in a calm tone. The speech unit can also use the emotion estimation function to analyze the emotional state of the subject in real time and select an appropriate tone and speed. For example, it adjusts the tone and speed based on the emotion score. The speech unit can also comprehensively analyze the tone and speed patterns based on the emotional state data. As a result, by using the emotion estimation function, it is possible to speak at a tone and speed that corresponds to the emotional state of the subject.

[0080] In addition to speaking the warning message, the speech unit can also display a visual warning on a display inside the vehicle. For example, when speaking the warning message, the speech unit displays a visual warning on a display inside the vehicle. For example, a message such as "Customer, please refrain from causing a nuisance to other passengers" is displayed. The speech unit can also adjust the content of the display to provide the visual warning. For example, the speech unit can change the font size or color of the warning message. The speech unit can also evaluate the effectiveness of the visual warning and select the optimal display method. This allows the effectiveness of the warning message to be enhanced by displaying the visual warning.

[0081] In addition to speaking the warning message, the speech unit can also send a notification to the smartphone of the target person to individually warn them. For example, when speaking the warning message, the speech unit sends a notification to the smartphone of the target person. For example, it sends a message such as, "Customer, please refrain from causing a nuisance to other passengers." The speech unit can also adjust the method of sending the notification. For example, it can adjust the timing and content of the notification. The speech unit can also evaluate the effectiveness of the notification and select the optimal sending method. This makes it possible to individually warn the target person by sending a notification to their smartphone.

[0082] The trouble recording unit generates a detailed timeline of the trouble, making it easier to analyze it later. For example, the trouble recording unit records the time the trouble occurred and the elapsed time, and generates a detailed timeline. For example, it records the start time, end time, and timestamps of important events of the trouble. Furthermore, the trouble recording unit can comprehensively analyze the circumstances under which the trouble occurred based on the timeline data. For example, it can identify the cause and impact of the trouble. Furthermore, the trouble recording unit can consider measures to prevent the trouble from recurring based on the timeline data. In this way, by generating a detailed timeline of the trouble, it can make it easier to analyze it later.

[0083] The trouble recording unit can also store related video data and audio data, making it possible to use it as evidence. The trouble recording unit, for example, records video data of a trouble and stores it as evidence. For example, camera footage taken when a trouble occurs can be stored and used for later analysis. The trouble recording unit can also record audio data and store it as evidence. For example, audio data taken when a trouble occurs can be stored and used for later analysis. The trouble recording unit can also combine video data and audio data to record a detailed record of a trouble. In this way, by storing related video data and audio data, it can be used as evidence.

[0084] The trouble recording unit also uses the emotion estimation function to record the emotional state of passengers when a trouble occurs, allowing for a more detailed understanding of the background of the trouble. The trouble recording unit, for example, records the emotional state of passengers when a trouble occurs and understands the background of the trouble. For example, it records the passengers' emotions of anger and fear when a trouble occurs. The trouble recording unit can also use the emotion estimation function to analyze the passengers' emotional state in real time and record the background of the trouble. For example, it records the emotional state based on an emotion score. The trouble recording unit can also comprehensively analyze the cause and impact of the trouble based on the emotional state data. In this way, by recording the passengers' emotional state when a trouble occurs, it is possible to understand the background of the trouble in more detail.

[0085] In addition to recording troubles, the trouble recording unit can notify the transportation operating company in real time, enabling an immediate response. For example, when a trouble is detected, the trouble recording unit notifies the transportation operating company in real time. For example, it can immediately report the occurrence of the trouble and prompt a response. The trouble recording unit can also adjust the content and format of the notification. For example, it can describe the content of the notification in detail and clarify the response procedures. The trouble recording unit can also evaluate the effectiveness of the notification and select the optimal notification method. This makes it possible to notify the transportation operating company in real time, enabling an immediate response.

[0086] In addition to recording the trouble, the trouble recording unit can also conduct a questionnaire with passengers to collect detailed information about the trouble. For example, the trouble recording unit conducts a questionnaire with passengers after recording the trouble. For example, detailed information about the trouble and passenger opinions are collected. The trouble recording unit can also adjust the content of the questions in the questionnaire and the timing of conducting the questionnaire. For example, a questionnaire can be conducted immediately after the trouble occurs to collect detailed information. The trouble recording unit can also analyze the content of the questionnaire responses and identify the cause and impact of the trouble. In this way, detailed information about the trouble can be collected by conducting a questionnaire with passengers.

[0087] The trouble recording unit also uses the emotion estimation function to record the emotional state of passengers when a trouble occurs, allowing for a more detailed understanding of the background of the trouble. The trouble recording unit, for example, records the emotional state of passengers when a trouble occurs and understands the background of the trouble. For example, it records the passengers' emotions of anger and fear when a trouble occurs. The trouble recording unit can also use the emotion estimation function to analyze the passengers' emotional state in real time and record the background of the trouble. For example, it records the emotional state based on an emotion score. The trouble recording unit can also comprehensively analyze the cause and impact of the trouble based on the emotional state data. In this way, by recording the passengers' emotional state when a trouble occurs, it is possible to understand the background of the trouble in more detail.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The trouble detection system can further include a health condition monitoring unit that monitors the health condition of passengers. For example, the health condition monitoring unit can measure passengers' heart rate and blood pressure in real time and detect abnormal fluctuations. If the heart rate rises suddenly or blood pressure becomes abnormally high, the health condition monitoring unit can issue a warning as a sign of trouble. The health condition monitoring unit can also accumulate passenger health data and use it for long-term health management. In this way, by monitoring the passenger's health condition, health problems can be prevented before they occur.

[0090] The trouble detection system can further include a behavior history analysis unit that analyzes passenger behavior history. For example, the behavior history analysis unit can analyze passenger past behavior patterns and identify abnormal behavior. It can learn the behavior patterns of passengers who have caused trouble in the past and issue a warning if similar behavior is observed. The behavior history analysis unit can also predict the probability of trouble occurring based on passenger behavior history. In this way, by analyzing passenger behavior history, it is possible to prevent trouble from occurring.

[0091] The trouble detection system can further include an emotion analysis unit that analyzes the emotional state of passengers and detects signs of emotional trouble at an early stage. For example, the emotion analysis unit analyzes camera footage and estimates the emotional state of passengers from their facial expressions. It can identify signs of emotional trouble, such as a passenger's sudden change in facial expression to anger. The emotion analysis unit can also use voice analysis technology to analyze the tone and speed of passengers' voices and identify changes in their emotions. This makes it possible to prevent trouble from occurring by detecting signs of emotional trouble at an early stage.

[0092] The trouble detection system can further include a device data analysis unit that analyzes data from passengers' smartphones and wearable devices to detect abnormal behavior. For example, the device data analysis unit can analyze data from passengers' smartphones to identify cases where the passenger suddenly takes out their smartphone and makes suspicious movements. It can also analyze data from wearable devices to analyze fluctuations in heart rate and activity level to identify abnormal behavior. This allows for early detection of abnormal behavior by analyzing data from smartphones and wearable devices.

[0093] The trouble detection system can further include an environmental adjustment unit that analyzes the emotional state of passengers and adjusts the environment to elicit positive emotions. For example, the environmental adjustment unit analyzes camera footage to estimate the emotional state of passengers. It can detect whether passengers are relaxed and elicit positive emotions by changing the lighting or music. It can also use voice analysis technology to analyze the tone and speed of passengers' voices and estimate their emotional state. This makes it possible to analyze passengers' emotional states and adjust the environment to elicit positive emotions.

[0094] The trouble detection system may further include an environmental data analysis unit that analyzes environmental data such as temperature and humidity inside the vehicle and determines whether an abnormal environmental change is a sign of trouble. For example, the environmental data analysis unit may analyze temperature data inside the vehicle and identify a sudden increase or decrease in temperature as a sign of trouble. It may also analyze humidity data and identify a sudden increase or decrease in humidity as a sign of trouble. This allows for early detection of abnormal environmental changes by analyzing environmental data such as temperature and humidity inside the vehicle.

[0095] The trouble detection system can further include an emotion estimation unit that analyzes the emotional state of passengers and detects signs of emotional trouble at an early stage. For example, the emotion estimation unit analyzes camera footage and estimates the emotional state of passengers from their facial expressions. It can identify signs of emotional trouble, such as a passenger's sudden change in facial expression to anger. It can also use voice analysis technology to analyze the tone and speed of passengers' voices and identify changes in their emotions. This makes it possible to detect signs of emotional trouble at an early stage and prevent trouble from occurring.

[0096] The trouble detection system can further include a clothing recognition unit that identifies passenger clothing and belongings to detect risk factors. For example, the clothing recognition unit analyzes camera footage to identify passenger clothing. It can detect heavy coats and hooded clothing and evaluate them as risk factors. It can also detect the shape and size of bags and luggage to identify belongings. This allows for early detection of specific risk factors by identifying passenger clothing and belongings.

[0097] The trouble detection system can further include an emotion estimation unit that analyzes the emotional state of passengers and detects signs of emotional trouble at an early stage. For example, the emotion estimation unit analyzes camera footage and estimates the emotional state of passengers from their facial expressions. It can identify signs of emotional trouble, such as a passenger's sudden change in facial expression to anger. It can also use voice analysis technology to analyze the tone and speed of passengers' voices and identify changes in their emotions. This makes it possible to detect signs of emotional trouble at an early stage and prevent trouble from occurring.

[0098] The trouble detection system can further include a Wi-Fi analysis unit that analyzes the Wi-Fi connection status in the vehicle and detects abnormal device connections. For example, the Wi-Fi analysis unit can analyze the Wi-Fi connection status in the vehicle and identify device connections that exceed the normal number of connections. It can also analyze the type of connected device and the connection time to detect unauthorized device connections. This allows for early detection of abnormal device connections by analyzing the Wi-Fi connection status.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The video analysis unit analyzes the video footage from inside the vehicle. For example, it analyzes video footage from cameras installed inside the vehicle in real time to monitor people's movements and behavior. It can also detect abnormal behavior and signs of trouble. Specifically, it can detect behaviors such as sexual harassment, seat domination, assault, pickpocketing, and voyeurism. Step 2: The trouble detection unit detects trouble from the in-car video analyzed by the video analysis unit. For example, it analyzes people's movements and behavior to detect trouble such as harassment and assault. It can also identify abnormal behavior based on trouble patterns learned in advance. Step 3: The warning message generation unit generates a warning message according to the content of the trouble detected by the trouble detection unit. For example, if harassment is detected, a warning message such as "Customer, please refrain from causing trouble to other passengers" is generated. Also, if seat occupancy is detected, a warning message such as "Customer, please refrain from occupying seats" can be generated. Step 4: The speech unit speaks the warning message generated by the warning message generation unit to the target person. For example, the warning message is spoken through a speaker inside the vehicle. In addition, by directly communicating the warning message to the target person, it is possible to prevent trouble between passengers.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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. A video analysis unit that analyzes in-vehicle video; a trouble detection unit that detects trouble from the in-vehicle video analyzed by the video analysis unit; a warning message generation unit that generates a warning message according to the content of the trouble detected by the trouble detection unit; a speaking unit that speaks the warning message generated by the warning message generating unit to a target person. A system characterized by:

2. The video analysis unit Using emotion estimation functionality, passengers' emotional states are analyzed in real time and abnormal emotional changes are detected.

2. The system of claim 1.

3. The video analysis unit In addition to analyzing the in-car video, audio data is also analyzed at the same time to detect abnormal sounds.

2. The system of claim 1.

4. The trouble detection unit Refer to past trouble data and identify similar trouble patterns 2. The system of claim 1.

5. The warning message generation unit Choose appropriate language depending on the age and gender of the target audience 2. The system of claim 1.

6. The speech unit is Identifying the target person's location and speaking from the most effective speaker 2. The system of claim 1.

7. The trouble recording section Using the emotion estimation function, the emotional state of passengers when a problem occurs is also recorded, allowing for a more detailed understanding of the background of the problem.

2. The system of claim 1.

8. The trouble detection unit Using emotion estimation functionality, changes in passenger emotions are analyzed to detect early signs of emotional trouble.

2. The system of claim 1.

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