System

Abstract

An object of a system according to an embodiment is to provide quick and appropriate action information when an accident occurs.SOLUTION: A system according to an embodiment includes a voice recognition unit, an information collection unit, and an action information provision unit. The speech recognition unit recognizes the common language. The information collection unit collects peripheral information based on the common language recognized by the voice recognition unit. The action information providing unit provides appropriate action information based on the peripheral information collected by the information collecting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide prompt and appropriate action information when an accident occurs.

[0005] The system according to the embodiment aims to provide prompt and appropriate action information when an accident occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice recognition unit, an information collection unit, and a behavioral information providing unit. The voice recognition unit recognizes a common language. The information collection unit collects peripheral information based on the common language recognized by the voice recognition unit. The behavioral information providing unit provides appropriate behavioral information based on the peripheral information collected by the information collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide prompt and appropriate action information when an accident occurs. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 emergency response system according to the embodiment of the present invention is a system that uses the common language "Help!!!" as a trigger to gather information about the surrounding area and provide appropriate action information. This allows the emergency response system to respond quickly when an accident occurs, regardless of time or place.

[0029] An emergency response system according to an embodiment includes a speech recognition unit, an information collection unit, and a behavioral information provision unit. The speech recognition unit recognizes the common language "Help!!!." For example, the generation AI recognizes the common language "Help!!!" using speech recognition technology. The generation AI has previously learned specific keywords that indicate an emergency and responds immediately when it detects the voice "Help!!!." For example, if someone shouts "Help!!!" in the street, the generation AI picks up the voice and recognizes that an emergency has occurred. The information collection unit collects surrounding information based on the common language "Help!!!" recognized by the speech recognition unit. For example, the generation AI collects surrounding information about the location where the emergency has occurred. The inputs to the generation AI are the detection of "Help!!!" by speech recognition and data from surrounding sensors and cameras. This allows the generation AI to understand the detailed situation of the emergency. The behavioral information provision unit provides appropriate behavioral information based on the surrounding information collected by the information collection unit. For example, the generation AI provides appropriate behavioral information based on the collected surrounding information. The generation AI instructs people near the location of the emergency on how to act. Specifically, the generation AI provides instructions such as "If anyone nearby, please evacuate to a safe place" or "Please call an ambulance" in voice or text. As a result, the emergency response system according to the embodiment can provide prompt and appropriate information on actions to take when triggered by the common language "Help!!!".

[0030] The speech recognition unit is trained to recognize multiple languages ​​and dialects that indicate emergencies. For example, the speech recognition unit trains the generation AI to recognize languages ​​and dialects that indicate emergencies other than "Help!!!" (for example, "Help!" in English, "! Ayuda!" in Spanish, and "Lifesaving!" in Chinese). The generation AI is also trained to recognize regional dialects and slang. For example, it recognizes regional expressions that indicate emergencies, such as "Help me!" in the Southern United States and "Help us!" in the United Kingdom. The generation AI is also trained on multilingual speech data to enable it to recognize emergency phrases with high accuracy. Examples include "Au secours!" in French and "Hilfe!" in German. This allows for support for multiple languages ​​and dialects, enabling a wide range of emergency situations to be recognized.

[0031] In addition to speech recognition, the speech recognition unit has the ability to recognize non-speech emergency signs in sign language or gestures. For example, the speech recognition unit trains the generation AI in sign language emergency signs and uses them in conjunction with speech recognition to recognize emergencies. For example, it recognizes the sign language gesture indicating "help." A gesture recognition function can also be added to the generation AI to detect specific actions that indicate an emergency. For example, it can recognize the gesture of waving both hands or specific hand shapes. The generation AI can also be trained in non-speech emergency signs, allowing it to recognize emergencies even in environments where speech cannot be heard. For example, it can detect visual signs or flashing lights. This allows it to recognize non-speech emergency signs as well.

[0032] The information gathering unit can analyze surrounding audio data and grasp the detailed situation of the emergency. For example, the information gathering unit has the generation AI analyze the surrounding audio data and identify the direction of the screams. For example, it uses multiple microphones to triangulate the location of the sound source. The generation AI also analyzes the surrounding audio data to grasp the number of people involved in the emergency. For example, it identifies multiple voices and counts the number of people. In addition, an audio analysis function can be added to the generation AI to grasp the direction of the screams and the number of people in real time. For example, it can identify the details of the emergency based on the strength and direction of the sound. This makes it possible to analyze the surrounding audio data and grasp the detailed situation of the emergency.

[0033] The information collection unit also collects data from nearby smart devices and can perform a multifaceted analysis of the emergency situation. For example, the generation AI collects data from nearby smart devices and performs a multifaceted analysis of the emergency situation. For example, it uses GPS data and acceleration sensor information from smartphones. In addition, the generation AI is given a smart device data collection function to grasp the detailed situation of the emergency. For example, it analyzes heart rate data and activity logs from smartwatches. In addition, the generation AI collects data from nearby smart devices in real time and performs a multifaceted analysis of the emergency situation. For example, it integrates sensor information from smart devices to identify the emergency situation. This allows data from nearby smart devices to be collected and a multifaceted analysis of the emergency situation.

[0034] The behavioral information providing unit can customize the behavioral information provided by the generation AI according to the location information or situation of each individual user. For example, the behavioral information providing unit adds a function to the generation AI that customizes behavioral information based on location information. For example, it may suggest an evacuation route according to the user's current location. The generation AI may also analyze the user's situation and provide individually customized behavioral information. For example, it may issue instructions that take into account the user's health condition and surrounding circumstances. The generation AI may also integrate location information and situation data to provide optimal behavioral information for each individual user. For example, it may issue specific behavioral instructions according to the user's location and situation. This makes it possible to provide behavioral information customized according to the location information and situation of each individual user.

[0035] The behavioral information providing unit can also use visual guides when providing behavioral information. For example, the behavioral information providing unit adds a visual guide function using AR technology to the generation AI to provide behavioral information. For example, evacuation routes are displayed using a smartphone camera. The generation AI also provides visual guides to enable the user to intuitively understand. For example, emergency evacuation locations are displayed using AR technology. The generation AI also integrates a visual guide function to provide behavioral information. For example, an arrow indicating the user's direction of travel is displayed using AR technology. This allows behavioral information to be provided in conjunction with visual guides.

[0036] The behavioral information providing unit can provide behavioral information through a smart speaker or a smart display. For example, the behavioral information providing unit links a smart speaker to the generation AI and provides behavioral information by voice. For example, evacuation instructions are issued through Amazon Echo or Google (registered trademark) Home. In addition, a smart display is linked to the generation AI and behavioral information is provided visually. For example, evacuation routes are displayed through Nest Hub or Echo Show. In addition, a smart speaker or smart display is integrated into the generation AI and behavioral information is provided in various formats. For example, behavioral instructions are issued by combining voice and visual information. This makes it possible to provide behavioral information through a smart speaker or smart display.

[0037] The behavioral information provision unit can provide behavioral information in multiple languages ​​or dialects, enabling international response. For example, the behavioral information provision unit adds a multilingual support function to the generation AI and provides behavioral information in multiple languages. For example, evacuation instructions can be issued in English, Spanish, Chinese, etc. The generation AI can also learn regional dialects and slang and provide behavioral information. For example, instructions can be issued using southern American dialects or British slang. Furthermore, multilingual support functions can be integrated into the generation AI to enable international response. For example, behavioral information can be provided to travelers and foreign residents in their native language. This makes it possible to provide behavioral information in multiple languages ​​and dialects and enable international response.

[0038] The information collection unit can collect peripheral information over a wide area using drones or robots. For example, the information collection unit links a drone to the generation AI to collect peripheral information over a wide area. For example, a camera or sensor mounted on the drone can be used to grasp the situation of an emergency. The generation AI can also link a robot to collect peripheral information over a wide area. For example, a camera or microphone mounted on the robot can be used to collect details of the emergency. The generation AI can also link a drone or robot to collect peripheral information over a wide area in real time. For example, information can be collected while controlling the movement of the drone or robot. This makes it possible to collect peripheral information over a wide area using a drone or robot.

[0039] The information collection unit can also collect surrounding traffic or weather information to help respond to emergencies. The information collection unit, for example, has the generation AI collect traffic information to help respond to emergencies. For example, it grasps information about surrounding traffic congestion and road closures. It also has the generation AI collect weather information to help respond to emergencies. For example, it proposes countermeasures based on the current weather and forecast. It also integrates traffic and weather information into the generation AI to help respond to emergencies. For example, it proposes evacuation routes that take traffic congestion and bad weather into consideration. In this way, surrounding traffic and weather information is also collected to help respond to emergencies.

[0040] The information gathering unit enables the generating AI to automatically contact the nearest emergency response agency regardless of time or location. For example, the information gathering unit adds an automatic contact function to the generating AI, which contacts the nearest emergency response agency when an emergency occurs. For example, it automatically notifies the police or fire department. The generating AI also identifies the location of the emergency and contacts the nearest emergency response agency. For example, it automatically contacts a hospital or ambulance. The generating AI also integrates an automatic contact function, which contacts the nearest emergency response agency regardless of time or location. For example, it identifies the nearest agency based on GPS data and contacts them. This makes it possible to automatically contact the nearest emergency response agency regardless of time or location.

[0041] The information gathering unit can also notify residents in the vicinity of where an emergency has occurred and encourage their cooperation. For example, the information gathering unit adds a notification function to the generation AI to notify residents in the vicinity of where an emergency has occurred. For example, it can send a notification via a smartphone app. The generation AI can also identify the location of the emergency and send a notification to nearby residents encouraging their cooperation. For example, it can send evacuation instructions and requests for cooperation to nearby residents. Furthermore, by integrating a notification function into the generation AI, it can notify residents in the vicinity of where an emergency has occurred in real time. For example, it can send a notification including detailed information about the emergency. This makes it possible to notify residents in the vicinity of where an emergency has occurred and encourage their cooperation.

[0042] The information collection unit automatically generates notification content to emergency contacts, accurately conveying details of the emergency. For example, the information collection unit adds a function to the generation AI to automatically generate notification content, accurately conveying details of the emergency to emergency contacts. For example, sending a notification that includes the current situation and necessary responses. The generation AI also analyzes the details of the emergency and automatically generates appropriate notification content to emergency contacts. For example, sending a notification that includes the location and situation of the emergency. The generation AI also integrates a function to automatically generate notification content, accurately conveying details of the emergency to emergency contacts. For example, sending a notification that includes detailed information about the emergency. This allows notification content to be automatically generated to emergency contacts, accurately conveying details of the emergency.

[0043] The information collection unit can notify emergency contacts not only by voice call, but also by text message or email. For example, the information collection unit adds various notification methods to the generation AI, so that emergency contacts are notified not only by voice call, but also by text message or email. For example, details of the emergency are communicated via SMS or email. The generation AI also analyzes the details of the emergency and selects an appropriate notification method to notify the emergency contact. For example, if a voice call is difficult, a text message or email is sent. Furthermore, various notification methods can be integrated into the generation AI, so that emergency contacts are notified by voice call, text message, or email. For example, a notification including details of the emergency is sent via multiple methods. This allows emergency contacts to be notified not only by voice call, but also by text message or email.

[0044] The information collection unit can notify the emergency contacts through social media or messaging apps. For example, the information collection unit adds a social media integration function to the generation AI to notify the emergency contacts. For example, details of the emergency are communicated through Facebook (registered trademark) or Twitter (registered trademark). The generation AI also notifies the emergency contacts through messaging apps. For example, details of the emergency are sent through WhatsApp or LINE (registered trademark). The generation AI can also integrate social media and messaging apps to notify the emergency contacts through various means. For example, details of the emergency are sent across multiple platforms. This allows the emergency contacts to be notified through social media and messaging apps.

[0045] The information collection unit can provide notification content to emergency contacts in multiple languages, enabling international response. For example, the information collection unit adds multilingual support functionality to the generation AI to provide notification content to emergency contacts in multiple languages. For example, details of the emergency can be communicated in English, Spanish, Chinese, etc. The generation AI can also learn regional dialects and slang and provide notification content to emergency contacts. For example, notifications can be made using southern American dialects or British slang. The generation AI can also integrate multilingual support functionality to enable international response. For example, notifications can be provided in the native language of travelers and foreign residents. This allows notification content to be provided to emergency contacts in multiple languages, enabling international response.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The emergency response system may further include a location information acquisition unit. The location information acquisition unit acquires the user's current location in real time and identifies the location of the emergency. For example, GPS data may be used to identify the user's location and quickly determine the location of the emergency. The location information acquisition unit may also track the user's movement route and identify the location of the emergency. For example, if an emergency occurs while the user is moving, the location of the emergency can be identified based on the user's movement route. Furthermore, the location information acquisition unit may integrate the location information of multiple users and identify the location of the emergency. For example, if multiple users simultaneously shout "Help!!!", the location of the emergency can be identified by integrating the location information.

[0048] The emergency response system may further include a battery monitoring unit. The battery monitoring unit monitors the remaining battery level of the user's device and provides appropriate notifications in the event of an emergency. For example, if the remaining battery level is low, emergency notifications are given priority. The battery monitoring unit also changes the notification method depending on the remaining battery level of the user's device. For example, if the remaining battery level is low, a lightweight notification method can be selected instead of a text message or voice call. Furthermore, the battery monitoring unit suggests emergency response measures based on the remaining battery level of the user's device. For example, if the remaining battery level is low, it can guide the user to nearby charging spots.

[0049] The emergency response system may further include a health monitoring unit. The health monitoring unit monitors the user's health condition in real time and takes appropriate action in the event of an emergency. For example, it may monitor the user's heart rate and blood pressure and issue an emergency notification if an abnormality is detected. The health monitoring unit may also suggest emergency response measures based on the user's health data. For example, if the user's heart rate suddenly increases, it may instruct the user to rest. Furthermore, the health monitoring unit may evaluate the severity of the emergency based on the user's health condition. For example, if the user's health condition is deteriorating, it may set the severity of the emergency to a high level.

[0050] The emergency response system may further include an environmental monitoring unit. The environmental monitoring unit collects surrounding environmental data and takes appropriate action in the event of an emergency. For example, it may monitor temperature, humidity, and air quality, and issue an emergency notification if an abnormality is detected. The environmental monitoring unit may also suggest emergency response measures based on the surrounding environmental data. For example, if the temperature suddenly rises, it may instruct people to evacuate to a cooler place. Furthermore, the environmental monitoring unit may evaluate the severity of the emergency based on the surrounding environmental data. For example, if the air quality is deteriorating, it may set the severity of the emergency to a higher level.

[0051] The emergency response system may further include a data backup unit. The data backup unit periodically backs up important user data to prevent data loss in the event of an emergency. For example, the data backup unit may use cloud storage to back up the data. The data backup unit may also encrypt and store the user data to ensure security. For example, the data may be protected using AES encryption. The data backup unit may also include a function to quickly restore the backed up data in the event of an emergency. For example, if data is lost, the data may be quickly restored from the cloud storage.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The speech recognition unit recognizes the common language "Help!!!". For example, the generation AI uses speech recognition technology to recognize the common language "Help!!!". The generation AI has learned in advance specific keywords that indicate emergencies, and when it detects the voice "Help!!!", it responds immediately. For example, if someone shouts "Help!!!" on the street, the generation AI will pick up on the voice and recognize that an emergency has occurred. Step 2: The information gathering unit collects surrounding information based on the common language "Help!!!" recognized by the voice recognition unit. For example, the generation AI collects surrounding information about the location where the emergency occurred. The input to the generation AI is the detection of "Help!!!" by voice recognition and data from surrounding sensors and cameras. This allows the generation AI to understand the detailed situation of the emergency. Step 3: The behavioral information provision unit provides appropriate behavioral information based on the surrounding information collected by the information collection unit. For example, the generation AI provides appropriate behavioral information based on the surrounding information it has collected. The generation AI instructs people near the location where the emergency has occurred on how to act. Specifically, the generation AI provides instructions in voice or text, such as "Those nearby should evacuate to a safe place" or "Call an ambulance."

[0054] (Example 2) The emergency response system according to the embodiment of the present invention is a system that uses the common language "Help!!!" as a trigger to gather information about the surrounding area and provide appropriate action information. This allows the emergency response system to respond quickly when an accident occurs, regardless of time or place.

[0055] An emergency response system according to an embodiment includes a speech recognition unit, an information collection unit, and a behavioral information provision unit. The speech recognition unit recognizes the common language "Help!!!." For example, the generation AI recognizes the common language "Help!!!" using speech recognition technology. The generation AI has previously learned specific keywords that indicate an emergency and responds immediately when it detects the voice "Help!!!." For example, if someone shouts "Help!!!" in the street, the generation AI picks up the voice and recognizes that an emergency has occurred. The information collection unit collects surrounding information based on the common language "Help!!!" recognized by the speech recognition unit. For example, the generation AI collects surrounding information about the location where the emergency has occurred. The inputs to the generation AI are the detection of "Help!!!" by speech recognition and data from surrounding sensors and cameras. This allows the generation AI to understand the detailed situation of the emergency. The behavioral information provision unit provides appropriate behavioral information based on the surrounding information collected by the information collection unit. For example, the generation AI provides appropriate behavioral information based on the collected surrounding information. The generation AI instructs people near the location of the emergency on how to act. Specifically, the generation AI provides instructions such as "If anyone nearby, please evacuate to a safe place" or "Please call an ambulance" in voice or text. As a result, the emergency response system according to the embodiment can provide prompt and appropriate information on actions to take when triggered by the common language "Help!!!".

[0056] The speech recognition unit is trained to recognize multiple languages ​​and dialects that indicate emergencies. For example, the speech recognition unit trains the generation AI to recognize languages ​​and dialects that indicate emergencies other than "Help!!!" (for example, "Help!" in English, "! Ayuda!" in Spanish, and "Lifesaving!" in Chinese). The generation AI is also trained to recognize regional dialects and slang. For example, it recognizes regional expressions that indicate emergencies, such as "Help me!" in the Southern United States and "Help us!" in the United Kingdom. The generation AI is also trained on multilingual speech data to enable it to recognize emergency phrases with high accuracy. Examples include "Au secours!" in French and "Hilfe!" in German. This allows for support for multiple languages ​​and dialects, enabling a wide range of emergency situations to be recognized.

[0057] In addition to speech recognition, the speech recognition unit has the ability to recognize non-speech emergency signs in sign language or gestures. For example, the speech recognition unit trains the generation AI in sign language emergency signs and uses them in conjunction with speech recognition to recognize emergencies. For example, it recognizes the sign language gesture indicating "help." A gesture recognition function can also be added to the generation AI to detect specific actions that indicate an emergency. For example, it can recognize the gesture of waving both hands or specific hand shapes. The generation AI can also be trained in non-speech emergency signs, allowing it to recognize emergencies even in environments where speech cannot be heard. For example, it can detect visual signs or flashing lights. This allows it to recognize non-speech emergency signs as well.

[0058] The voice recognition unit uses an emotion estimation function to analyze the tone or tension of the voice and determine the level of urgency. For example, the voice recognition unit may incorporate an emotion estimation function into the generation AI, which may analyze the tone and tension of the voice to determine the level of urgency. For example, it may detect voice tremors or changes in the high-pitched range. The generation AI may also analyze the emotional state of the voice in real time and prioritize recognizing situations with a high level of urgency. For example, it may detect emotions such as fear or panic. The generation AI may also be equipped with a voice emotion analysis function to evaluate the seriousness of the emergency. For example, it may determine the level of urgency based on changes in the volume and speed of the voice. This allows the tone and tension of the voice to be analyzed and the level of urgency to be determined.

[0059] The information gathering unit can analyze surrounding audio data and grasp the detailed situation of the emergency. For example, the information gathering unit has the generation AI analyze the surrounding audio data and identify the direction of the screams. For example, it uses multiple microphones to triangulate the location of the sound source. The generation AI also analyzes the surrounding audio data to grasp the number of people involved in the emergency. For example, it identifies multiple voices and counts the number of people. In addition, an audio analysis function can be added to the generation AI to grasp the direction of the screams and the number of people in real time. For example, it can identify the details of the emergency based on the strength and direction of the sound. This makes it possible to analyze the surrounding audio data and grasp the detailed situation of the emergency.

[0060] The information collection unit also collects data from nearby smart devices and can perform a multifaceted analysis of the emergency situation. For example, the generation AI collects data from nearby smart devices and performs a multifaceted analysis of the emergency situation. For example, it uses GPS data and acceleration sensor information from smartphones. In addition, the generation AI is given a smart device data collection function to grasp the detailed situation of the emergency. For example, it analyzes heart rate data and activity logs from smartwatches. In addition, the generation AI collects data from nearby smart devices in real time and performs a multifaceted analysis of the emergency situation. For example, it integrates sensor information from smart devices to identify the emergency situation. This allows data from nearby smart devices to be collected and a multifaceted analysis of the emergency situation.

[0061] The information gathering unit can use the emotion estimation function to analyze the emotional state of people in the vicinity and assess the severity of the emergency. For example, the information gathering unit can equip the generation AI with an emotion estimation function to analyze the emotional state of people in the vicinity in real time. For example, emotions can be identified by analyzing facial expressions and voice tones. The generation AI can also analyze the emotional state of people in the vicinity and assess the severity of the emergency. For example, the severity can be set high if there are many feelings of fear or panic. The emotion estimation function can also be added to the generation AI to monitor the emotional state of people in the vicinity in real time. For example, the severity of the emergency can be assessed based on the emotion score. This makes it possible to analyze the emotional state of people in the vicinity and assess the severity of the emergency.

[0062] The behavioral information providing unit can customize the behavioral information provided by the generation AI according to the location information or situation of each individual user. For example, the behavioral information providing unit adds a function to the generation AI that customizes behavioral information based on location information. For example, it may suggest an evacuation route according to the user's current location. The generation AI may also analyze the user's situation and provide individually customized behavioral information. For example, it may issue instructions that take into account the user's health condition and surrounding circumstances. The generation AI may also integrate location information and situation data to provide optimal behavioral information for each individual user. For example, it may issue specific behavioral instructions according to the user's location and situation. This makes it possible to provide behavioral information customized according to the location information and situation of each individual user.

[0063] The behavioral information providing unit can also use visual guides when providing behavioral information. For example, the behavioral information providing unit adds a visual guide function using AR technology to the generation AI to provide behavioral information. For example, evacuation routes are displayed using a smartphone camera. The generation AI also provides visual guides to enable the user to intuitively understand. For example, emergency evacuation locations are displayed using AR technology. The generation AI also integrates a visual guide function to provide behavioral information. For example, an arrow indicating the user's direction of travel is displayed using AR technology. This allows behavioral information to be provided in conjunction with visual guides.

[0064] The behavioral information providing unit can use the emotion estimation function to provide a message of encouragement or reassurance according to the user's emotional state. For example, the behavioral information providing unit can equip the generation AI with an emotion estimation function and provide an encouraging message according to the user's emotional state. For example, a message of reassurance can be sent to a user who is feeling scared. The generation AI can also analyze the user's emotional state in real time and provide an appropriate encouraging message. For example, a message urging a user who is panicking to stay calm can be sent. The generation AI can also be added with an emotion estimation function to provide a message of reassurance according to the user's emotional state. For example, a message encouraging the user to act calmly even in an emergency can be sent. This makes it possible to provide a message of encouragement or reassurance according to the user's emotional state.

[0065] The behavioral information providing unit can provide behavioral information through a smart speaker or a smart display. For example, the behavioral information providing unit links a smart speaker to the generation AI and provides behavioral information by voice. For example, evacuation instructions are issued through Amazon Echo or Google (registered trademark) Home. In addition, a smart display is linked to the generation AI and behavioral information is provided visually. For example, evacuation routes are displayed through Nest Hub or Echo Show. In addition, a smart speaker or smart display is integrated into the generation AI and behavioral information is provided in various formats. For example, behavioral instructions are issued by combining voice and visual information. This makes it possible to provide behavioral information through a smart speaker or smart display.

[0066] The behavioral information provision unit can provide behavioral information in multiple languages ​​or dialects, enabling international response. For example, the behavioral information provision unit adds a multilingual support function to the generation AI and provides behavioral information in multiple languages. For example, evacuation instructions can be issued in English, Spanish, Chinese, etc. The generation AI can also learn regional dialects and slang and provide behavioral information. For example, instructions can be issued using southern American dialects or British slang. Furthermore, multilingual support functions can be integrated into the generation AI to enable international response. For example, behavioral information can be provided to travelers and foreign residents in their native language. This makes it possible to provide behavioral information in multiple languages ​​and dialects and enable international response.

[0067] The behavioral information providing unit can use the emotion estimation function to provide behavioral information in a format that is most acceptable to the user. For example, the behavioral information providing unit may equip the generation AI with an emotion estimation function and provide behavioral information in a format that is most acceptable to the user. For example, if voice instructions are effective, the information is provided by voice. The generation AI may also analyze the user's emotional state in real time and provide behavioral information in an appropriate format. For example, if visual information is effective, the information is provided visually. The generation AI may also be added with an emotion estimation function and provide behavioral information in a format that is most acceptable to the user. For example, if text instructions are effective, the information is provided by text. This allows behavioral information to be provided in a format that is most acceptable to the user.

[0068] The information collection unit can collect peripheral information over a wide area using drones or robots. For example, the information collection unit links a drone to the generation AI to collect peripheral information over a wide area. For example, a camera or sensor mounted on the drone can be used to grasp the situation of an emergency. The generation AI can also link a robot to collect peripheral information over a wide area. For example, a camera or microphone mounted on the robot can be used to collect details of the emergency. The generation AI can also link a drone or robot to collect peripheral information over a wide area in real time. For example, information can be collected while controlling the movement of the drone or robot. This makes it possible to collect peripheral information over a wide area using a drone or robot.

[0069] The information collection unit can also collect surrounding traffic or weather information to help respond to emergencies. The information collection unit, for example, has the generation AI collect traffic information to help respond to emergencies. For example, it grasps information about surrounding traffic congestion and road closures. It also has the generation AI collect weather information to help respond to emergencies. For example, it proposes countermeasures based on the current weather and forecast. It also integrates traffic and weather information into the generation AI to help respond to emergencies. For example, it proposes evacuation routes that take traffic congestion and bad weather into consideration. In this way, surrounding traffic and weather information is also collected to help respond to emergencies.

[0070] The information gathering unit can use the emotion estimation function to monitor the emotional reactions of people in the vicinity in real time and provide appropriate behavioral information. For example, the information gathering unit can equip the generation AI with an emotion estimation function and monitor the emotional reactions of people in the vicinity in real time. For example, emotions can be identified by analyzing facial expressions and voice tones. The generation AI can then analyze the emotional reactions of people in the vicinity and provide appropriate behavioral information. For example, evacuation orders can be issued if there are many feelings of fear or panic. The generation AI can also be added with an emotion estimation function to monitor the emotional reactions of people in the vicinity in real time. For example, appropriate behavioral information can be provided based on the emotion score. This makes it possible to monitor the emotional reactions of people in the vicinity in real time and provide appropriate behavioral information.

[0071] The information gathering unit enables the generating AI to automatically contact the nearest emergency response agency regardless of time or location. For example, the information gathering unit adds an automatic contact function to the generating AI, which contacts the nearest emergency response agency when an emergency occurs. For example, it automatically notifies the police or fire department. The generating AI also identifies the location of the emergency and contacts the nearest emergency response agency. For example, it automatically contacts a hospital or ambulance. The generating AI also integrates an automatic contact function, which contacts the nearest emergency response agency regardless of time or location. For example, it identifies the nearest agency based on GPS data and contacts them. This makes it possible to automatically contact the nearest emergency response agency regardless of time or location.

[0072] The information gathering unit can also notify residents in the vicinity of where an emergency has occurred and encourage their cooperation. For example, the information gathering unit adds a notification function to the generation AI to notify residents in the vicinity of where an emergency has occurred. For example, it can send a notification via a smartphone app. The generation AI can also identify the location of the emergency and send a notification to nearby residents encouraging their cooperation. For example, it can send evacuation instructions and requests for cooperation to nearby residents. Furthermore, by integrating a notification function into the generation AI, it can notify residents in the vicinity of where an emergency has occurred in real time. For example, it can send a notification including detailed information about the emergency. This makes it possible to notify residents in the vicinity of where an emergency has occurred and encourage their cooperation.

[0073] The information gathering unit can use the emotion estimation function to adjust countermeasures in real time according to the user's emotional state. For example, the information gathering unit may equip the generation AI with an emotion estimation function and adjust countermeasures in real time according to the user's emotional state. For example, it may propose countermeasures that give a sense of security to a user who is feeling scared. The generation AI may also analyze the user's emotional state in real time and provide appropriate countermeasures. For example, it may propose countermeasures that encourage a user who is panicking to remain calm. The generation AI may also add an emotion estimation function to adjust countermeasures in real time according to the user's emotional state. For example, it may propose countermeasures that encourage the user to act calmly even in an emergency. This makes it possible to adjust countermeasures in real time according to the user's emotional state.

[0074] The information collection unit automatically generates notification content to emergency contacts, accurately conveying details of the emergency. For example, the information collection unit adds a function to the generation AI to automatically generate notification content, accurately conveying details of the emergency to emergency contacts. For example, sending a notification that includes the current situation and necessary responses. The generation AI also analyzes the details of the emergency and automatically generates appropriate notification content to emergency contacts. For example, sending a notification that includes the location and situation of the emergency. The generation AI also integrates a function to automatically generate notification content, accurately conveying details of the emergency to emergency contacts. For example, sending a notification that includes detailed information about the emergency. This allows notification content to be automatically generated to emergency contacts, accurately conveying details of the emergency.

[0075] The information collection unit can notify emergency contacts not only by voice call, but also by text message or email. For example, the information collection unit adds various notification methods to the generation AI, so that emergency contacts are notified not only by voice call, but also by text message or email. For example, details of the emergency are communicated via SMS or email. The generation AI also analyzes the details of the emergency and selects an appropriate notification method to notify the emergency contact. For example, if a voice call is difficult, a text message or email is sent. Furthermore, various notification methods can be integrated into the generation AI, so that emergency contacts are notified by voice call, text message, or email. For example, a notification including details of the emergency is sent via multiple methods. This allows emergency contacts to be notified not only by voice call, but also by text message or email.

[0076] The information collection unit can use the emotion estimation function to provide a message that gives a sense of security to emergency contacts. For example, the information collection unit can equip the generation AI with an emotion estimation function to provide a message that gives a sense of security to emergency contacts. For example, a message can be sent that includes details of the emergency situation and conveys that the situation is under control. The generation AI can also analyze the emotional state of the emergency contacts in real time to provide an appropriate message that gives a sense of security. For example, a message can be sent that includes the progress of the emergency situation and countermeasures. The emotion estimation function can also be added to the generation AI to provide a message that gives a sense of security to emergency contacts. For example, a message can be sent that encourages the emergency contacts to remain calm even in an emergency. This makes it possible to provide a message that gives a sense of security to emergency contacts.

[0077] The information collection unit can notify the emergency contacts through social media or messaging apps. For example, the information collection unit adds a social media integration function to the generation AI to notify the emergency contacts. For example, details of the emergency are communicated through Facebook (registered trademark) or Twitter (registered trademark). The generation AI also notifies the emergency contacts through messaging apps. For example, details of the emergency are sent through WhatsApp or LINE (registered trademark). The generation AI can also integrate social media and messaging apps to notify the emergency contacts through various means. For example, details of the emergency are sent across multiple platforms. This allows the emergency contacts to be notified through social media and messaging apps.

[0078] The information collection unit can provide notification content to emergency contacts in multiple languages, enabling international response. For example, the information collection unit adds multilingual support functionality to the generation AI to provide notification content to emergency contacts in multiple languages. For example, details of the emergency can be communicated in English, Spanish, Chinese, etc. The generation AI can also learn regional dialects and slang and provide notification content to emergency contacts. For example, notifications can be made using southern American dialects or British slang. The generation AI can also integrate multilingual support functionality to enable international response. For example, notifications can be provided in the native language of travelers and foreign residents. This allows notification content to be provided to emergency contacts in multiple languages, enabling international response.

[0079] The information collection unit can use the emotion estimation function to analyze the emotional reactions of emergency contacts and provide appropriate follow-up. For example, the information collection unit can equip the generation AI with an emotion estimation function and analyze the emotional reactions of emergency contacts in real time. For example, if the emergency contact is feeling anxious, it can provide follow-up that reassures them. The generation AI can also analyze the emotional state of the emergency contact and provide appropriate follow-up. For example, it can send a follow-up message that includes the progress of the emergency situation and countermeasures. The generation AI can also add an emotion estimation function to analyze the emotional reactions of emergency contacts and provide appropriate follow-up. For example, it can send a follow-up message that encourages them to remain calm even in an emergency. This makes it possible to analyze the emotional reactions of emergency contacts and provide appropriate follow-up.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The emergency response system may further include a location information acquisition unit. The location information acquisition unit acquires the user's current location in real time and identifies the location of the emergency. For example, GPS data may be used to identify the user's location and quickly determine the location of the emergency. The location information acquisition unit may also track the user's movement route and identify the location of the emergency. For example, if an emergency occurs while the user is moving, the location of the emergency can be identified based on the user's movement route. Furthermore, the location information acquisition unit may integrate the location information of multiple users and identify the location of the emergency. For example, if multiple users simultaneously shout "Help!!!", the location of the emergency can be identified by integrating the location information.

[0082] The emergency response system may further include a battery monitoring unit. The battery monitoring unit monitors the remaining battery level of the user's device and provides appropriate notifications in the event of an emergency. For example, if the remaining battery level is low, emergency notifications are given priority. The battery monitoring unit also changes the notification method depending on the remaining battery level of the user's device. For example, if the remaining battery level is low, a lightweight notification method can be selected instead of a text message or voice call. Furthermore, the battery monitoring unit suggests emergency response measures based on the remaining battery level of the user's device. For example, if the remaining battery level is low, it can guide the user to nearby charging spots.

[0083] The emergency response system may further include a health monitoring unit. The health monitoring unit monitors the user's health condition in real time and takes appropriate action in the event of an emergency. For example, it may monitor the user's heart rate and blood pressure and issue an emergency notification if an abnormality is detected. The health monitoring unit may also suggest emergency response measures based on the user's health data. For example, if the user's heart rate suddenly increases, it may instruct the user to rest. Furthermore, the health monitoring unit may evaluate the severity of the emergency based on the user's health condition. For example, if the user's health condition is deteriorating, it may set the severity of the emergency to a high level.

[0084] The emergency response system may further include an environmental monitoring unit. The environmental monitoring unit collects surrounding environmental data and takes appropriate action in the event of an emergency. For example, it may monitor temperature, humidity, and air quality, and issue an emergency notification if an abnormality is detected. The environmental monitoring unit may also suggest emergency response measures based on the surrounding environmental data. For example, if the temperature suddenly rises, it may instruct people to evacuate to a cooler place. Furthermore, the environmental monitoring unit may evaluate the severity of the emergency based on the surrounding environmental data. For example, if the air quality is deteriorating, it may set the severity of the emergency to a higher level.

[0085] The emergency response system may further include a data backup unit. The data backup unit periodically backs up important user data to prevent data loss in the event of an emergency. For example, the data backup unit may use cloud storage to back up the data. The data backup unit may also encrypt and store the user data to ensure security. For example, the data may be protected using AES encryption. The data backup unit may also include a function to quickly restore the backed up data in the event of an emergency. For example, if data is lost, the data may be quickly restored from the cloud storage.

[0086] The emergency response system can further use an emotion estimation function to adjust emergency response measures based on the user's emotional state. For example, if the user is feeling scared, a message that provides reassurance can be provided. The emotion estimation function can also be used to provide behavioral information according to the user's emotional state. For example, a message urging a panicked user to stay calm can be sent. The emotion estimation function can also be used to evaluate the severity of the emergency based on the user's emotional state. For example, if the emotion score is high, the severity of the emergency can be set high.

[0087] The emergency response system can further use emotion estimation to monitor the emotional state of people in the vicinity in real time and provide appropriate behavioral information. For example, if people in the vicinity are feeling fear, evacuation orders can be issued. The emotion estimation function can also be used to provide behavioral information according to the emotional state of people in the vicinity. For example, a message urging people in a panic to remain calm can be sent. The emotion estimation function can also be used to evaluate the severity of the emergency based on the emotional state of people in the vicinity. For example, if the emotion score is high, the severity of the emergency can be set high.

[0088] The emergency response system can further use the emotion estimation function to provide an encouraging or reassuring message according to the user's emotional state. For example, a reassuring message can be sent to a user who is feeling scared. The emotion estimation function can also be used to provide an encouraging message according to the user's emotional state. For example, a message encouraging a user who is panicking can be sent to stay calm. The emotion estimation function can also be used to suggest emergency response measures based on the user's emotional state. For example, if the emotion score is high, a message encouraging the user to stay calm can be sent.

[0089] The emergency response system can further use emotion estimation to provide reassurance messages to emergency contacts, such as sending a message with details of the emergency situation and letting them know the situation is under control. The system can also use emotion estimation to analyze the emergency contact's emotional state in real time and provide appropriate reassurance messages, such as sending a message that includes the progress of the emergency situation and countermeasures. The system can also use emotion estimation to follow up based on the emergency contact's emotional state, such as sending a message encouraging them to stay calm if the emotion score is high.

[0090] The emergency response system can further use an emotion estimation function to provide behavioral information in a format most acceptable to the user. For example, if voice instructions are effective, the information can be provided by voice. The emotion estimation function can also be used to analyze the user's emotional state in real time and provide behavioral information in an appropriate format. For example, if visual information is effective, the information can be provided visually. The emotion estimation function can also be used to provide behavioral information in a format most acceptable to the user. For example, if text instructions are effective, the information can be provided by text.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The speech recognition unit recognizes the common language "Help!!!". For example, the generation AI uses speech recognition technology to recognize the common language "Help!!!". The generation AI has learned in advance specific keywords that indicate emergencies, and when it detects the voice "Help!!!", it responds immediately. For example, if someone shouts "Help!!!" on the street, the generation AI will pick up on the voice and recognize that an emergency has occurred. Step 2: The information gathering unit collects surrounding information based on the common language "Help!!!" recognized by the voice recognition unit. For example, the generation AI collects surrounding information about the location where the emergency occurred. The input to the generation AI is the detection of "Help!!!" by voice recognition and data from surrounding sensors and cameras. This allows the generation AI to understand the detailed situation of the emergency. Step 3: The behavioral information provision unit provides appropriate behavioral information based on the surrounding information collected by the information collection unit. For example, the generation AI provides appropriate behavioral information based on the surrounding information it has collected. The generation AI instructs people near the location where the emergency has occurred on how to act. Specifically, the generation AI provides instructions in voice or text, such as "Those nearby should evacuate to a safe place" or "Call an ambulance."

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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.

[0108] 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.

[0109] 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 AI 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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 AI 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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 AI 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 speech recognition unit that recognizes a common language; an information collection unit that collects peripheral information based on the common language recognized by the voice recognition unit; a behavior information providing unit that provides appropriate behavior information based on the surrounding information collected by the information collecting unit. A system characterized by:

2. The voice recognition unit Trained to recognize emergency signals in multiple languages ​​or dialects 2. The system of claim 1.

3. The voice recognition unit In addition to voice recognition, it also has the ability to recognize non-vocal emergency signs in sign language or gestures.

2. The system of claim 1.

4. The voice recognition unit Analyze the tone or tension of the voice to determine the level of urgency 2. The system of claim 1.

5. The information collecting unit Analyze surrounding audio data to understand the details of an emergency situation 2. The system of claim 1.

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