system

Abstract

The system according to this embodiment aims to improve safety at large-scale events and in offices. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a warning unit, and a communication unit. The data collection unit monitors the environment. The analysis unit analyzes the data collected by the data collection unit. The warning unit rushes to the site and issues a warning when an anomaly is detected by the analysis unit. The communication unit communicates with participants and employees in natural language.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, monitoring and anomaly detection for ensuring the safety of large-scale events and offices have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the safety of large-scale events and offices. [[ID=4I]]

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a warning unit, and a communication unit. The data collection unit monitors the environment. The analysis unit analyzes the data collected by the data collection unit. The warning unit rushes to the site and issues a warning when an anomaly is detected by the analysis unit. The communication unit communicates with participants and employees in natural language. [Effects of the Invention]

[0007] The system according to this embodiment can improve safety at large-scale events and in offices. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The safety enhancement system according to an embodiment of the present invention is a system that improves the safety of large-scale events and offices by utilizing an autonomous mobile robot equipped with a generative AI. In this safety enhancement system, the robot patrols the venue or office and monitors the environment with cameras and sensors. The generative AI integrates and analyzes multiple data sources such as camera images, audio, and vibration sensors to detect anomalies. When an anomaly is detected, the robot rushes to the scene and issues a warning. In addition, a security AI agent communicates with participants and employees in natural language and provides necessary information in real time, thereby improving a sense of security. This mechanism enables a more flexible and rapid response compared to human security or fixed cameras, achieving cost reduction and improved safety. For example, as the robot moves through corridors or venues, it takes images with cameras and detects audio and vibrations with sensors. This data is input to the generative AI. The generative AI integrates and analyzes multiple data sources such as camera images, audio, and vibration sensors to detect anomalies. For example, if suspicious movement or an unusual sound is detected, the generative AI determines that an anomaly has occurred. When an anomaly is detected, the robot rushes to the scene and issues a warning using voice and lights, enabling a rapid response. Furthermore, a security AI agent communicates with participants and employees in natural language, providing necessary information in real time. For example, the robot can warn participants that "this area is off-limits" or instruct employees that "an anomaly has been detected. Please evacuate to a safe location." This system allows for a more flexible and rapid response compared to human security or fixed cameras, providing participants and employees with a sense of security. It also enables cost reduction and improved safety. For example, costs can be reduced by decreasing the number of human security personnel, and safety can be improved by having robots patrol 24 hours a day. In this way, the enhanced security system provides participants and employees with a sense of security, while achieving cost reduction and improved safety.

[0029] The safety improvement system according to this embodiment comprises a data collection unit, an analysis unit, a warning unit, and a communication unit. The data collection unit monitors the environment. The data collection unit collects multiple data sources, such as camera images, audio, and vibration sensors. The data collection unit can capture images with a camera and detect audio and vibrations with sensors. The data collection unit can collect more detailed data by, for example, setting a high resolution for the camera and a high sampling rate for audio. The data collection unit can also detect minute vibrations by adjusting the sensitivity of the vibration sensors. The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generative AI to integrate and analyze multiple data sources, such as camera images, audio, and vibration sensors. The analysis unit can, for example, use deep learning technology to detect suspicious movements from camera images. The analysis unit can use natural language processing technology to detect abnormal sounds from audio data. The analysis unit can analyze vibration data and detect abnormal vibrations. The warning unit rushes to the scene and issues a warning when an abnormality is detected by the analysis unit. The warning unit can, for example, move to the location where a robot has detected an anomaly and issue a warning using voice and lights. The warning unit can alert those in the surrounding area to an anomaly by issuing a voice alert and flashing lights. The warning unit can, for example, set the content of the voice message to "An anomaly has been detected. Please evacuate to a safe place." The warning unit can issue warnings according to the type of anomaly by adjusting the color and flashing pattern of the lights. The communication unit communicates with participants and employees in natural language. The communication unit can, for example, use voice recognition technology to respond to questions from participants and employees. The communication unit can also communicate in text-based mode using a chatbot. The communication unit provides information in real time. For example, if an anomaly is detected, the communication unit can issue warnings and instructions to participants and employees in real time. As a result, the safety improvement system according to the embodiment can improve safety by consistently performing everything from environmental monitoring to anomaly detection, warning, and communication.

[0030] The data collection unit monitors the environment. It collects data from multiple sources, such as camera footage, audio, and vibration sensors. Specifically, the camera captures high-resolution images and covers a wide field of view. This allows for the acquisition of clear images with fine detail, enabling the detection of unusual movements and suspicious individuals. The audio sensor collects audio at a high sampling rate, recording ambient sounds and conversations in detail. This allows for the detection of unusual sounds and suspicious conversations. The vibration sensor is set to high sensitivity to detect even minute vibrations, enabling rapid detection of abnormal vibrations such as earthquakes and shocks. The data collection unit transmits this data to a central database in real time, allowing the analysis unit to access it immediately. Furthermore, the data collection unit can adjust the data collection frequency and sensitivity according to the situation. For example, if anomalies are likely to occur at a particular time or location, the camera's frame rate can be increased or the audio sensor's sensitivity increased to collect more detailed data. This allows the data collection unit to efficiently and effectively monitor the environment and contribute to the early detection of anomalies.

[0031] The analysis unit analyzes the data collected by the collection unit. Using generative AI, the analysis unit integrates and analyzes multiple data sources, including camera footage, audio, and vibration sensors. Specifically, it uses deep learning technology to detect suspicious movements from camera footage. For example, it can track the movements of people in the footage and identify patterns that differ from normal movements. This allows for the rapid identification of abnormal behavior or suspicious individuals. For audio data, it uses natural language processing technology to detect unusual sounds and conversations. For example, it can identify loud noises that differ from normal ambient sounds or conversations containing specific keywords. For vibration data, it uses an anomaly detection algorithm to detect abnormal vibrations that differ from normal vibration patterns. This allows for the rapid identification of abnormal vibrations such as earthquakes and shocks. By integrating and analyzing this data, the analysis unit achieves more accurate anomaly detection. Furthermore, the analysis unit can utilize past data and statistical information to analyze anomaly occurrence patterns and trends. This can be used for long-term risk assessment and the planning of preventive measures. The analysis unit analyzes data in real time and immediately notifies the warning unit if an anomaly is detected. This allows the analysis unit to quickly and accurately detect anomalies, thereby improving the overall safety of the system.

[0032] The warning unit rushes to the scene and issues a warning when an anomaly is detected by the analysis unit. Specifically, the robot moves to the location where the anomaly was detected and can issue a warning using voice and lights. The robot autonomously moves along a pre-set route and quickly reaches the location where the anomaly occurred. Upon arrival, it issues a voice alert and flashes lights to notify those in the surrounding area of ​​the anomaly. The voice alert is pre-set to say, "An anomaly has been detected. Please evacuate to a safe place," providing clear instructions to people in the vicinity. The color and flashing pattern of the lights are adjusted according to the type of anomaly. For example, a red light flashes in the case of a fire, and a blue light flashes in the case of an earthquake, visually conveying the type of anomaly. By combining these warning methods, the warning unit can quickly and effectively notify people in the surrounding area of ​​the anomaly. Furthermore, the warning unit can transmit video and audio from the location where the anomaly occurred to a monitoring center in real time to support remote response. This allows the warning unit to receive support not only from the field but also from remote locations. The warning unit can stop the audio alerts and flashing lights, returning to normal operation once the abnormality is resolved. This allows the warning unit to quickly and effectively handle the entire process from the occurrence of an abnormality to its resolution.

[0033] The communications department communicates with participants and employees using natural language. Specifically, it can use speech recognition technology to respond to questions from participants and employees. For example, if a participant asks, "What is the current situation?", the communications department can explain the situation based on data collected in real time. Furthermore, it can also communicate in text-based mode using a chatbot. The chatbot provides appropriate answers to questions from participants and employees based on pre-configured scenarios. For example, in response to the question, "Where is the evacuation site?", the chatbot can answer, "The nearest evacuation site is XX." Because the communications department provides information in real time, it can quickly issue warnings and instructions to participants and employees if an anomaly is detected. For example, it can send a warning message such as, "An anomaly has been detected. Please evacuate immediately." In addition, the communications department can collect feedback from participants and employees and use it to improve the system. For example, it can collect opinions on responses to evacuation instructions and the appropriateness of evacuation routes and reflect them in future responses. In this way, the communications department can maintain smooth communication with participants and employees and improve the overall safety and reliability of the system.

[0034] The data collection unit can collect multiple data sources, such as camera images, audio, and vibration sensors. For example, by setting a high resolution for the camera and a high sampling rate for the audio, the data collection unit can collect more detailed data. By adjusting the sensitivity of the vibration sensor, the data collection unit can detect even minute vibrations. For example, the data collection unit can capture camera images at high resolution and collect audio data at a high sampling rate. By setting the sensitivity of the vibration sensor high, the data collection unit can detect even minute vibrations. This improves the accuracy of anomaly detection by collecting multiple data sources. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input camera images and audio data into a generating AI, which can analyze the data to detect anomalies.

[0035] The analysis unit can integrate and analyze multiple data sources, such as camera footage, audio, and vibration sensors, using generative AI. For example, the analysis unit can detect suspicious movements from camera footage using deep learning technology. The analysis unit can detect abnormal sounds from audio data using natural language processing technology. The analysis unit can analyze vibration data and detect abnormal vibrations. This improves the accuracy of anomaly detection by using generative AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or without it. For example, the analysis unit can input camera footage and audio data into the generative AI, which can then analyze the data and detect anomalies.

[0036] The warning unit can rush to the scene when it detects an anomaly and issue a warning using voice and lights. For example, the warning unit can move to the location where a robot has detected an anomaly and issue a warning using voice and lights. The warning unit can alert those in the surrounding area to an anomaly by issuing a voice alert and flashing lights. For example, the content of the voice alert can be set to "An anomaly has been detected. Please evacuate to a safe place." The warning unit can issue warnings according to the type of anomaly by adjusting the color and flashing pattern of the lights. For example, the warning unit can issue a voice alert and flash lights. The warning unit can set the content of the voice alert to "An anomaly has been detected. Please evacuate to a safe place." The warning unit can adjust the color and flashing pattern of the lights. This allows for a rapid response when an anomaly is detected and improves safety by issuing a warning. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can issue a warning using an AI model that moves to the location where an anomaly is detected and issues a warning using voice and lights.

[0037] The communications department can issue warnings and instructions to participants and employees in natural language. The communications department can respond to questions from participants and employees using, for example, speech recognition technology. The communications department can also conduct text-based communication using a chatbot. The communications department responds to questions from participants and employees using, for example, speech recognition technology. The communications department conducts text-based communication using a chatbot. This enables the provision of quick and accurate information to participants and employees by issuing warnings and instructions in natural language. Some or all of the above processing in the communications department may be performed using, for example, AI, or not using AI. For example, the communications department can issue warnings and instructions using an AI model that responds to questions from participants and employees using speech recognition technology.

[0038] The communications department can provide information in real time. For example, if an anomaly is detected, the communications department can issue warnings or instructions to participants or employees in real time. By providing information in real time, the communications department can respond quickly. Some or all of the above processes in the communications department may be performed using AI, for example, or not using AI. For example, the communications department can issue warnings or instructions using an AI model that provides information in real time.

[0039] The data collection unit can dynamically change the frequency of data collection in response to environmental changes during data collection. For example, the data collection unit can set a lower data collection frequency when the environment is quiet, and a higher data collection frequency when the environment is noisy. The data collection unit can dynamically adjust the data collection frequency when the environment changes. This allows for efficient data collection by adjusting the data collection frequency in response to environmental changes. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental data into a generating AI, which can then dynamically change the data collection frequency.

[0040] The data collection unit can add a filtering function during collection to focus on collecting data in specific areas. For example, the data collection unit can prioritize data collection in specific areas. The data collection unit can focus on data collection in specific areas. The data collection unit can filter data collection in specific areas. This strengthens monitoring of important areas by focusing on data collection in specific areas. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data from a specific area into a generating AI, which can then filter the data collection.

[0041] The data collection unit can change the type of data it collects depending on the weather and time of day. For example, the data collection unit may prioritize collecting audio data when the weather is bad, or prioritize collecting video data when the weather is good. The data collection unit can also change the type of data it collects depending on the time of day. This allows for appropriate data collection by changing the type of data according to the weather and time of day. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather and time-of-day data into a generating AI, which can then change the type of data.

[0042] The collection unit can adjust the method of collecting audio data while taking into account the ambient noise level. For example, the collection unit can adjust the method of collecting audio data when the ambient noise level is high. The collection unit can adjust the method of collecting audio data when the ambient noise level is low. The collection unit can adjust the method of collecting audio data according to the ambient noise level. This makes it possible to collect accurate audio data by adjusting the method of collecting audio data according to the ambient noise level. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit can input the ambient noise level to a generating AI, and the generating AI can adjust the method of collecting audio data.

[0043] The analysis unit can optimize the analysis algorithm by referring to past anomaly detection data during analysis. For example, the analysis unit optimizes the analysis algorithm based on past anomaly detection data. The analysis unit can optimize the analysis algorithm by referring to past anomaly detection data. The analysis unit can optimize the analysis algorithm using past anomaly detection data. This improves the accuracy of the analysis algorithm by referring to past anomaly detection data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input past anomaly detection data into a generating AI, and the generating AI can optimize the analysis algorithm.

[0044] The analysis unit can apply different analysis methods depending on the type of anomaly during analysis. For example, the analysis unit applies different analysis methods depending on the type of anomaly. The analysis unit can change the analysis method depending on the type of anomaly. The analysis unit can apply the optimal analysis method depending on the type of anomaly. This makes it possible to perform appropriate analysis by changing the analysis method depending on the type of anomaly. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the type of anomaly into the generating AI, and the generating AI can apply different analysis methods.

[0045] The analysis unit can be modified to dynamically adjust the anomaly detection threshold during analysis. For example, the analysis unit can be modified to dynamically adjust the anomaly detection threshold. The analysis unit can be modified to adjust the anomaly detection threshold in real time. The analysis unit can be modified to automatically adjust the anomaly detection threshold. This enables appropriate anomaly detection by dynamically adjusting the anomaly detection threshold. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the anomaly detection threshold to the generating AI, which can then dynamically adjust the threshold.

[0046] The analysis unit can add a function to share the results of anomaly detection with other systems in real time during analysis. For example, the analysis unit can add a function to share the results of anomaly detection with other systems in real time. The analysis unit can add a function to share the results of anomaly detection with other systems. This enables a rapid response by sharing the results of anomaly detection in real time. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the results of anomaly detection into a generating AI, which can then share them with other systems.

[0047] The warning unit can apply different warning methods (such as sound, light, or vibration) depending on the type of anomaly when an alarm is issued. For example, the warning unit can issue a warning by sound depending on the type of anomaly. The warning unit can issue a warning by light depending on the type of anomaly. The warning unit can issue a warning by vibration depending on the type of anomaly. This enables quick and effective warnings by applying the appropriate warning method according to the type of anomaly. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the type of anomaly into a generating AI, and the generating AI can apply different warning methods.

[0048] The warning unit can add a function to dynamically change the priority of warnings when a warning is issued. For example, the warning unit can add a function to dynamically change the priority of warnings. The warning unit can add a function to change the priority of warnings in real time. The warning unit can add a function to automatically change the priority of warnings. This allows important warnings to be given priority by dynamically changing the priority of warnings. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the priority of warnings to a generating AI, and the generating AI can dynamically change the priority.

[0049] The warning unit can be equipped with a function to automatically adjust the volume of the warning sound according to the ambient noise when a warning is issued. For example, the warning unit can automatically adjust the volume of the warning sound if the ambient noise is loud. The warning unit can also automatically adjust the volume of the warning sound if the ambient noise is quiet. The warning unit can automatically adjust the volume of the warning sound according to the ambient noise. This allows warnings to be issued at an appropriate volume by automatically adjusting the volume of the warning sound according to the ambient noise. Some or all of the above processing in the warning unit may be performed using AI, for example, or without using AI. For example, the warning unit can input ambient noise into a generating AI, and the generating AI can automatically adjust the volume of the warning sound.

[0050] The warning unit can add a function to provide warning content in multiple languages ​​when a warning is issued. For example, the warning unit can add a function to provide warning content in multiple languages. The warning unit can add a function to provide warning content in multiple languages. The warning unit can add a function to display warning content in multiple languages. This makes multilingual warnings possible by providing warning content in multiple languages. Some or all of the above processing in the warning unit may be performed using AI, for example, or without using AI. For example, the warning unit can input the warning content to a generating AI, and the generating AI can provide it in multiple languages.

[0051] The communication unit can provide the optimal response by referring to the user's past response history during communication. For example, the communication unit can provide the optimal response based on the user's past response history. The communication unit can provide the optimal response by referring to the user's past response history. The communication unit can provide the optimal response by utilizing the user's past response history. In this way, the communication unit can provide the optimal response by referring to the user's past response history. Some or all of the above processing in the communication unit may be performed using AI, for example, or without using AI. For example, the communication unit can input the user's past response history into a generating AI, and the generating AI can provide the optimal response.

[0052] The communication unit can apply different response methods (voice, text, etc.) depending on the type of anomaly during communication. For example, the communication unit can respond by voice depending on the type of anomaly. The communication unit can respond by text depending on the type of anomaly. The communication unit can apply the most appropriate response method depending on the type of anomaly. This enables a quick and effective response by applying the appropriate response method according to the type of anomaly. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the type of anomaly into a generating AI, and the generating AI can apply different response methods.

[0053] The communication unit can select the optimal response method during communication, taking into account the user's device information. For example, if the user is using a smartphone, the communication unit can provide a response method that matches the screen size. If the user is using a tablet, the communication unit can provide a response method optimized for a larger screen. If the user is using a smartwatch, the communication unit can provide a concise and highly visible response method. This enables the provision of appropriate information by selecting the optimal response method according to the user's device information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's device information into a generating AI, which can then select the optimal response method.

[0054] The communications department can add a function to provide information in real time by coordinating with other systems during communication. For example, the communications department can provide the latest information by coordinating with other systems in real time. The communications department can share information in real time by coordinating with other systems. The communications department can provide optimal information by coordinating with other systems in real time. As a result, the latest information can be provided quickly by coordinating with other systems in real time. Some or all of the above processing in the communications department may be performed using AI, for example, or without using AI. For example, the communications department can input information from other systems into a generating AI, and the generating AI can provide the information in real time.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The analysis unit can propose different countermeasures depending on the type of anomaly detected. For example, if a fire is detected, it can provide guidance indicating the location of fire extinguishers. If an intruder is detected, it can indicate nearby evacuation routes. Furthermore, by proposing appropriate countermeasures according to the type of anomaly, a quick and effective response becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the type of anomaly into a generation AI, and the generation AI can propose an appropriate countermeasure.

[0057] The analysis unit can evaluate the importance of an anomaly based on its frequency of occurrence when detecting an anomaly. For example, frequently occurring anomalies are evaluated as highly important, prompting a rapid response. Rarely occurring anomalies are evaluated as less important, allowing for a normal response. This enables appropriate responses by evaluating importance based on the frequency of anomaly occurrence. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the frequency of anomaly occurrences into a generation AI, which can then evaluate its importance.

[0058] The warning unit can emit different warning sounds depending on the type of anomaly detected. For example, if a fire is detected, it can emit a siren sound. If an intruder is detected, it can emit an alarm sound. This enables quick and effective warning by emitting a warning sound appropriate to the type of anomaly. Some or all of the above-described processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the type of anomaly into a generating AI, and the generating AI can emit different warning sounds.

[0059] The data collection unit can be equipped with a scheduling function to prioritize data collection during specific time periods. For example, it can prioritize data collection during nighttime hours. The data collection unit can schedule data collection during specific time periods. This allows for efficient data collection by prioritizing data collection during specific time periods. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for specific time periods into a generating AI, which can then schedule data collection.

[0060] The warning unit can apply different warning methods depending on the location of the anomaly when it detects one. For example, if an anomaly occurs indoors, it can issue an audible warning. If an anomaly occurs outdoors, it can issue a warning using lights. This enables quick and effective warnings by applying the appropriate warning method according to the location of the anomaly. Some or all of the above-described processes in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the location of the anomaly into a generating AI, and the generating AI can apply different warning methods.

[0061] The data collection unit can add an event-based collection function to focus on collecting data for specific events during the collection process. For example, it can focus on collecting data during large-scale events. The data collection unit can perform event-based data collection for specific events. This enables efficient data collection by focusing on data collection for specific events. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for a specific event into a generating AI, which can then perform event-based data collection.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit monitors the environment. The data collection unit collects data from multiple sources, such as camera images, audio, and vibration sensors. For example, it captures video with a camera and detects audio and vibration with sensors. By setting a high resolution for the camera and a high sampling rate for the audio, more detailed data can be collected. In addition, by adjusting the sensitivity of the vibration sensor, even minute vibrations can be detected. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to integrate and analyze multiple data sources such as camera images, audio, and vibration sensors. For example, it can use deep learning technology to detect suspicious movements from camera images and natural language processing technology to detect abnormal sounds from audio data. Furthermore, it can analyze vibration data to detect abnormal vibrations. Step 3: The warning unit rushes to the scene and issues a warning when an anomaly is detected by the analysis unit. For example, a robot moves to the location where the anomaly was detected and issues a warning with voice and lights. It alerts the surroundings to the anomaly by issuing a voice alert and flashing lights. By setting the voice message to "An anomaly has been detected. Please evacuate to a safe place" and adjusting the color and flashing pattern of the lights, it is possible to issue warnings according to the type of anomaly. Step 4: The communications department communicates with participants and employees using natural language. For example, it can use speech recognition technology to respond to questions from participants and employees, and a chatbot to conduct text-based communication. If an anomaly is detected, warnings and instructions can be issued to participants and employees in real time.

[0064] (Example of form 2) The safety enhancement system according to an embodiment of the present invention is a system that improves the safety of large-scale events and offices by utilizing an autonomous mobile robot equipped with a generative AI. In this safety enhancement system, the robot patrols the venue or office and monitors the environment with cameras and sensors. The generative AI integrates and analyzes multiple data sources such as camera images, audio, and vibration sensors to detect anomalies. When an anomaly is detected, the robot rushes to the scene and issues a warning. In addition, a security AI agent communicates with participants and employees in natural language and provides necessary information in real time, thereby improving a sense of security. This mechanism enables a more flexible and rapid response compared to human security or fixed cameras, achieving cost reduction and improved safety. For example, as the robot moves through corridors or venues, it takes images with cameras and detects audio and vibrations with sensors. This data is input to the generative AI. The generative AI integrates and analyzes multiple data sources such as camera images, audio, and vibration sensors to detect anomalies. For example, if suspicious movement or an unusual sound is detected, the generative AI determines that an anomaly has occurred. When an anomaly is detected, the robot rushes to the scene and issues a warning using voice and lights, enabling a rapid response. Furthermore, a security AI agent communicates with participants and employees in natural language, providing necessary information in real time. For example, the robot can warn participants that "this area is off-limits" or instruct employees that "an anomaly has been detected. Please evacuate to a safe location." This system allows for a more flexible and rapid response compared to human security or fixed cameras, providing participants and employees with a sense of security. It also enables cost reduction and improved safety. For example, costs can be reduced by decreasing the number of human security personnel, and safety can be improved by having robots patrol 24 hours a day. In this way, the enhanced security system provides participants and employees with a sense of security, while achieving cost reduction and improved safety.

[0065] The safety improvement system according to this embodiment comprises a data collection unit, an analysis unit, a warning unit, and a communication unit. The data collection unit monitors the environment. The data collection unit collects multiple data sources, such as camera images, audio, and vibration sensors. The data collection unit can capture images with a camera and detect audio and vibrations with sensors. The data collection unit can collect more detailed data by, for example, setting a high resolution for the camera and a high sampling rate for audio. The data collection unit can also detect minute vibrations by adjusting the sensitivity of the vibration sensors. The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generative AI to integrate and analyze multiple data sources, such as camera images, audio, and vibration sensors. The analysis unit can, for example, use deep learning technology to detect suspicious movements from camera images. The analysis unit can use natural language processing technology to detect abnormal sounds from audio data. The analysis unit can analyze vibration data and detect abnormal vibrations. The warning unit rushes to the scene and issues a warning when an abnormality is detected by the analysis unit. The warning unit can, for example, move to the location where a robot has detected an anomaly and issue a warning using voice and lights. The warning unit can alert those in the surrounding area to an anomaly by issuing a voice alert and flashing lights. The warning unit can, for example, set the content of the voice message to "An anomaly has been detected. Please evacuate to a safe place." The warning unit can issue warnings according to the type of anomaly by adjusting the color and flashing pattern of the lights. The communication unit communicates with participants and employees in natural language. The communication unit can, for example, use voice recognition technology to respond to questions from participants and employees. The communication unit can also communicate in text-based mode using a chatbot. The communication unit provides information in real time. For example, if an anomaly is detected, the communication unit can issue warnings and instructions to participants and employees in real time. As a result, the safety improvement system according to the embodiment can improve safety by consistently performing everything from environmental monitoring to anomaly detection, warning, and communication.

[0066] The data collection unit monitors the environment. It collects data from multiple sources, such as camera footage, audio, and vibration sensors. Specifically, the camera captures high-resolution images and covers a wide field of view. This allows for the acquisition of clear images with fine detail, enabling the detection of unusual movements and suspicious individuals. The audio sensor collects audio at a high sampling rate, recording ambient sounds and conversations in detail. This allows for the detection of unusual sounds and suspicious conversations. The vibration sensor is set to high sensitivity to detect even minute vibrations, enabling rapid detection of abnormal vibrations such as earthquakes and shocks. The data collection unit transmits this data to a central database in real time, allowing the analysis unit to access it immediately. Furthermore, the data collection unit can adjust the data collection frequency and sensitivity according to the situation. For example, if anomalies are likely to occur at a particular time or location, the camera's frame rate can be increased or the audio sensor's sensitivity increased to collect more detailed data. This allows the data collection unit to efficiently and effectively monitor the environment and contribute to the early detection of anomalies.

[0067] The analysis unit analyzes the data collected by the collection unit. Using generative AI, the analysis unit integrates and analyzes multiple data sources, including camera footage, audio, and vibration sensors. Specifically, it uses deep learning technology to detect suspicious movements from camera footage. For example, it can track the movements of people in the footage and identify patterns that differ from normal movements. This allows for the rapid identification of abnormal behavior or suspicious individuals. For audio data, it uses natural language processing technology to detect unusual sounds and conversations. For example, it can identify loud noises that differ from normal ambient sounds or conversations containing specific keywords. For vibration data, it uses an anomaly detection algorithm to detect abnormal vibrations that differ from normal vibration patterns. This allows for the rapid identification of abnormal vibrations such as earthquakes and shocks. By integrating and analyzing this data, the analysis unit achieves more accurate anomaly detection. Furthermore, the analysis unit can utilize past data and statistical information to analyze anomaly occurrence patterns and trends. This can be used for long-term risk assessment and the planning of preventive measures. The analysis unit analyzes data in real time and immediately notifies the warning unit if an anomaly is detected. This allows the analysis unit to quickly and accurately detect anomalies, thereby improving the overall safety of the system.

[0068] The warning unit rushes to the scene and issues a warning when an anomaly is detected by the analysis unit. Specifically, the robot moves to the location where the anomaly was detected and can issue a warning using voice and lights. The robot autonomously moves along a pre-set route and quickly reaches the location where the anomaly occurred. Upon arrival, it issues a voice alert and flashes lights to notify those in the surrounding area of ​​the anomaly. The voice alert is pre-set to say, "An anomaly has been detected. Please evacuate to a safe place," providing clear instructions to people in the vicinity. The color and flashing pattern of the lights are adjusted according to the type of anomaly. For example, a red light flashes in the case of a fire, and a blue light flashes in the case of an earthquake, visually conveying the type of anomaly. By combining these warning methods, the warning unit can quickly and effectively notify people in the surrounding area of ​​the anomaly. Furthermore, the warning unit can transmit video and audio from the location where the anomaly occurred to a monitoring center in real time to support remote response. This allows the warning unit to receive support not only from the field but also from remote locations. The warning unit can stop the audio alerts and flashing lights, returning to normal operation once the abnormality is resolved. This allows the warning unit to quickly and effectively handle the entire process from the occurrence of an abnormality to its resolution.

[0069] The communications department communicates with participants and employees using natural language. Specifically, it can use speech recognition technology to respond to questions from participants and employees. For example, if a participant asks, "What is the current situation?", the communications department can explain the situation based on data collected in real time. Furthermore, it can also communicate in text-based mode using a chatbot. The chatbot provides appropriate answers to questions from participants and employees based on pre-configured scenarios. For example, in response to the question, "Where is the evacuation site?", the chatbot can answer, "The nearest evacuation site is XX." Because the communications department provides information in real time, it can quickly issue warnings and instructions to participants and employees if an anomaly is detected. For example, it can send a warning message such as, "An anomaly has been detected. Please evacuate immediately." In addition, the communications department can collect feedback from participants and employees and use it to improve the system. For example, it can collect opinions on responses to evacuation instructions and the appropriateness of evacuation routes and reflect them in future responses. In this way, the communications department can maintain smooth communication with participants and employees and improve the overall safety and reliability of the system.

[0070] The data collection unit can collect multiple data sources, such as camera images, audio, and vibration sensors. For example, by setting a high resolution for the camera and a high sampling rate for the audio, the data collection unit can collect more detailed data. By adjusting the sensitivity of the vibration sensor, the data collection unit can detect even minute vibrations. For example, the data collection unit can capture camera images at high resolution and collect audio data at a high sampling rate. By setting the sensitivity of the vibration sensor high, the data collection unit can detect even minute vibrations. This improves the accuracy of anomaly detection by collecting multiple data sources. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input camera images and audio data into a generating AI, which can analyze the data to detect anomalies.

[0071] The analysis unit can integrate and analyze multiple data sources, such as camera footage, audio, and vibration sensors, using generative AI. For example, the analysis unit can detect suspicious movements from camera footage using deep learning technology. The analysis unit can detect abnormal sounds from audio data using natural language processing technology. The analysis unit can analyze vibration data and detect abnormal vibrations. This improves the accuracy of anomaly detection by using generative AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or without it. For example, the analysis unit can input camera footage and audio data into the generative AI, which can then analyze the data and detect anomalies.

[0072] The warning unit can rush to the scene when it detects an anomaly and issue a warning using voice and lights. For example, the warning unit can move to the location where a robot has detected an anomaly and issue a warning using voice and lights. The warning unit can alert those in the surrounding area to an anomaly by issuing a voice alert and flashing lights. For example, the content of the voice alert can be set to "An anomaly has been detected. Please evacuate to a safe place." The warning unit can issue warnings according to the type of anomaly by adjusting the color and flashing pattern of the lights. For example, the warning unit can issue a voice alert and flash lights. The warning unit can set the content of the voice alert to "An anomaly has been detected. Please evacuate to a safe place." The warning unit can adjust the color and flashing pattern of the lights. This allows for a rapid response when an anomaly is detected and improves safety by issuing a warning. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can issue a warning using an AI model that moves to the location where an anomaly is detected and issues a warning using voice and lights.

[0073] The communications department can issue warnings and instructions to participants and employees in natural language. The communications department can respond to questions from participants and employees using, for example, speech recognition technology. The communications department can also conduct text-based communication using a chatbot. The communications department responds to questions from participants and employees using, for example, speech recognition technology. The communications department conducts text-based communication using a chatbot. This enables the provision of quick and accurate information to participants and employees by issuing warnings and instructions in natural language. Some or all of the above processing in the communications department may be performed using, for example, AI, or not using AI. For example, the communications department can issue warnings and instructions using an AI model that responds to questions from participants and employees using speech recognition technology.

[0074] The communications department can provide information in real time. For example, if an anomaly is detected, the communications department can issue warnings or instructions to participants or employees in real time. By providing information in real time, the communications department can respond quickly. Some or all of the above processes in the communications department may be performed using AI, for example, or not using AI. For example, the communications department can issue warnings or instructions using an AI model that provides information in real time.

[0075] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is tense, the data collection unit can prioritize collecting audio data and detect abnormal sounds. If the user is relaxed, the data collection unit can prioritize collecting video data and detect visual anomalies. If the user is excited, the data collection unit can prioritize collecting vibration data and detect abnormal vibrations. This allows for more appropriate data collection by adjusting the type of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into the generative AI, which can then adjust the type of data.

[0076] The data collection unit can dynamically change the frequency of data collection in response to environmental changes during data collection. For example, the data collection unit can set a lower data collection frequency when the environment is quiet, and a higher data collection frequency when the environment is noisy. The data collection unit can dynamically adjust the data collection frequency when the environment changes. This allows for efficient data collection by adjusting the data collection frequency in response to environmental changes. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental data into a generating AI, which can then dynamically change the data collection frequency.

[0077] The data collection unit can add a filtering function during collection to focus on collecting data in specific areas. For example, the data collection unit can prioritize data collection in specific areas. The data collection unit can focus on data collection in specific areas. The data collection unit can filter data collection in specific areas. This strengthens monitoring of important areas by focusing on data collection in specific areas. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data from a specific area into a generating AI, which can then filter the data collection.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is tense, the data collection unit may prioritize collecting audio data. If the user is relaxed, the data collection unit may prioritize collecting video data. If the user is excited, the data collection unit may prioritize collecting vibration data. This allows for the priority collection of important data by determining the data priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI, which can then determine the data priority.

[0079] The data collection unit can change the type of data it collects depending on the weather and time of day. For example, the data collection unit may prioritize collecting audio data when the weather is bad, or prioritize collecting video data when the weather is good. The data collection unit can also change the type of data it collects depending on the time of day. This allows for appropriate data collection by changing the type of data according to the weather and time of day. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather and time-of-day data into a generating AI, which can then change the type of data.

[0080] The collection unit can adjust the method of collecting audio data while taking into account the ambient noise level. For example, the collection unit can adjust the method of collecting audio data when the ambient noise level is high. The collection unit can adjust the method of collecting audio data when the ambient noise level is low. The collection unit can adjust the method of collecting audio data according to the ambient noise level. This makes it possible to collect accurate audio data by adjusting the method of collecting audio data according to the ambient noise level. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit can input the ambient noise level to a generating AI, and the generating AI can adjust the method of collecting audio data.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the display method of the analysis results.

[0082] The analysis unit can optimize the analysis algorithm by referring to past anomaly detection data during analysis. For example, the analysis unit optimizes the analysis algorithm based on past anomaly detection data. The analysis unit can optimize the analysis algorithm by referring to past anomaly detection data. The analysis unit can optimize the analysis algorithm using past anomaly detection data. This improves the accuracy of the analysis algorithm by referring to past anomaly detection data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input past anomaly detection data into a generating AI, and the generating AI can optimize the analysis algorithm.

[0083] The analysis unit can apply different analysis methods depending on the type of anomaly during analysis. For example, the analysis unit applies different analysis methods depending on the type of anomaly. The analysis unit can change the analysis method depending on the type of anomaly. The analysis unit can apply the optimal analysis method depending on the type of anomaly. This makes it possible to perform appropriate analysis by changing the analysis method depending on the type of anomaly. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the type of anomaly into the generating AI, and the generating AI can apply different analysis methods.

[0084] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can prioritize displaying important analysis results. If the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. If the user is in a hurry, the analysis unit can prioritize displaying concise analysis results. In this way, by prioritizing analysis results according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can determine the priority of analysis results.

[0085] The analysis unit can be modified to dynamically adjust the anomaly detection threshold during analysis. For example, the analysis unit can be modified to dynamically adjust the anomaly detection threshold. The analysis unit can be modified to adjust the anomaly detection threshold in real time. The analysis unit can be modified to automatically adjust the anomaly detection threshold. This enables appropriate anomaly detection by dynamically adjusting the anomaly detection threshold. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the anomaly detection threshold to the generating AI, which can then dynamically adjust the threshold.

[0086] The analysis unit can add a function to share the results of anomaly detection with other systems in real time during analysis. For example, the analysis unit can add a function to share the results of anomaly detection with other systems in real time. The analysis unit can add a function to share the results of anomaly detection with other systems. This enables a rapid response by sharing the results of anomaly detection in real time. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the results of anomaly detection into a generating AI, which can then share them with other systems.

[0087] The warning unit can estimate the user's emotions and adjust the warning method based on the estimated emotions. For example, if the user is tense, the warning unit can issue a warning in a calm voice. If the user is relaxed, the warning unit can issue a warning in a cheerful voice. If the user is in a hurry, the warning unit can issue a quick and concise warning. This allows for appropriate warnings by adjusting the warning method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI, for example, or not using AI. For example, the warning unit can input user emotion data into the generative AI, which can then adjust the warning method.

[0088] The warning unit can apply different warning methods (such as sound, light, or vibration) depending on the type of anomaly when an alarm is issued. For example, the warning unit can issue a warning by sound depending on the type of anomaly. The warning unit can issue a warning by light depending on the type of anomaly. The warning unit can issue a warning by vibration depending on the type of anomaly. This enables quick and effective warnings by applying the appropriate warning method according to the type of anomaly. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the type of anomaly into a generating AI, and the generating AI can apply different warning methods.

[0089] The warning unit can add a function to dynamically change the priority of warnings when a warning is issued. For example, the warning unit can add a function to dynamically change the priority of warnings. The warning unit can add a function to change the priority of warnings in real time. The warning unit can add a function to automatically change the priority of warnings. This allows important warnings to be given priority by dynamically changing the priority of warnings. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the priority of warnings to a generating AI, and the generating AI can dynamically change the priority.

[0090] The warning unit can estimate the user's emotions and adjust the intensity of the warning based on the estimated emotions. For example, if the user is tense, the warning unit can weaken the warning intensity. If the user is relaxed, the warning unit can strengthen the warning intensity. If the user is in a hurry, the warning unit can adjust the warning intensity. This allows for appropriate warnings by adjusting the warning intensity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI, for example, or not using AI. For example, the warning unit can input user emotion data into the generative AI, which can then adjust the warning intensity.

[0091] The warning unit can be equipped with a function to automatically adjust the volume of the warning sound according to the ambient noise when a warning is issued. For example, the warning unit can automatically adjust the volume of the warning sound if the ambient noise is loud. The warning unit can also automatically adjust the volume of the warning sound if the ambient noise is quiet. The warning unit can automatically adjust the volume of the warning sound according to the ambient noise. This allows warnings to be issued at an appropriate volume by automatically adjusting the volume of the warning sound according to the ambient noise. Some or all of the above processing in the warning unit may be performed using AI, for example, or without using AI. For example, the warning unit can input ambient noise into a generating AI, and the generating AI can automatically adjust the volume of the warning sound.

[0092] The warning unit can add a function to provide warning content in multiple languages ​​when a warning is issued. For example, the warning unit can add a function to provide warning content in multiple languages. The warning unit can add a function to provide warning content in multiple languages. The warning unit can add a function to display warning content in multiple languages. This makes multilingual warnings possible by providing warning content in multiple languages. Some or all of the above processing in the warning unit may be performed using AI, for example, or without using AI. For example, the warning unit can input the warning content to a generating AI, and the generating AI can provide it in multiple languages.

[0093] The communication unit can estimate the user's emotions and adjust the content of the communication based on the estimated emotions. For example, if the user is nervous, the communication unit can communicate in a calm manner. If the user is relaxed, the communication unit can communicate in a cheerful manner. If the user is in a hurry, the communication unit can communicate quickly and concisely. This allows for appropriate communication by adjusting the content of the communication according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, or not using AI. For example, the communication unit can input user emotion data into a generative AI, and the generative AI can adjust the content of the communication.

[0094] The communication unit can provide the optimal response by referring to the user's past response history during communication. For example, the communication unit can provide the optimal response based on the user's past response history. The communication unit can provide the optimal response by referring to the user's past response history. The communication unit can provide the optimal response by utilizing the user's past response history. In this way, the communication unit can provide the optimal response by referring to the user's past response history. Some or all of the above processing in the communication unit may be performed using AI, for example, or without using AI. For example, the communication unit can input the user's past response history into a generating AI, and the generating AI can provide the optimal response.

[0095] The communication unit can apply different response methods (voice, text, etc.) depending on the type of anomaly during communication. For example, the communication unit can respond by voice depending on the type of anomaly. The communication unit can respond by text depending on the type of anomaly. The communication unit can apply the most appropriate response method depending on the type of anomaly. This enables a quick and effective response by applying the appropriate response method according to the type of anomaly. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the type of anomaly into a generating AI, and the generating AI can apply different response methods.

[0096] The communication unit can estimate the user's emotions and adjust the tone of communication based on the estimated emotions. For example, if the user is nervous, the communication unit will communicate in a calm tone. If the user is relaxed, the communication unit can communicate in a cheerful tone. If the user is in a hurry, the communication unit can communicate in a quick and concise tone. This allows for appropriate communication by adjusting the tone of communication according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input user emotion data into the generative AI, and the generative AI can adjust the tone of communication.

[0097] The communication unit can select the optimal response method during communication, taking into account the user's device information. For example, if the user is using a smartphone, the communication unit can provide a response method that matches the screen size. If the user is using a tablet, the communication unit can provide a response method optimized for a larger screen. If the user is using a smartwatch, the communication unit can provide a concise and highly visible response method. This enables the provision of appropriate information by selecting the optimal response method according to the user's device information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's device information into a generating AI, which can then select the optimal response method.

[0098] The communications department can add a function to provide information in real time by coordinating with other systems during communication. For example, the communications department can provide the latest information by coordinating with other systems in real time. The communications department can share information in real time by coordinating with other systems. The communications department can provide optimal information by coordinating with other systems in real time. As a result, the latest information can be provided quickly by coordinating with other systems in real time. Some or all of the above processing in the communications department may be performed using AI, for example, or without using AI. For example, the communications department can input information from other systems into a generating AI, and the generating AI can provide the information in real time.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The analysis unit can propose different countermeasures depending on the type of anomaly detected. For example, if a fire is detected, it can provide guidance indicating the location of fire extinguishers. If an intruder is detected, it can indicate nearby evacuation routes. Furthermore, by proposing appropriate countermeasures according to the type of anomaly, a quick and effective response becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the type of anomaly into a generation AI, and the generation AI can propose an appropriate countermeasure.

[0101] The data collection unit can estimate the user's emotions and adjust the accuracy of the collected data based on the estimated emotions. For example, if the user is tense, the accuracy of audio data collection can be increased. If the user is relaxed, the accuracy of video data collection can be increased. This allows for more appropriate data collection by adjusting the data accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the accuracy of the data.

[0102] The analysis unit can evaluate the importance of an anomaly based on its frequency of occurrence when detecting an anomaly. For example, frequently occurring anomalies are evaluated as highly important, prompting a rapid response. Rarely occurring anomalies are evaluated as less important, allowing for a normal response. This enables appropriate responses by evaluating importance based on the frequency of anomaly occurrence. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the frequency of anomaly occurrences into a generation AI, which can then evaluate its importance.

[0103] The warning unit can emit different warning sounds depending on the type of anomaly detected. For example, if a fire is detected, it can emit a siren sound. If an intruder is detected, it can emit an alarm sound. This enables quick and effective warning by emitting a warning sound appropriate to the type of anomaly. Some or all of the above-described processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the type of anomaly into a generating AI, and the generating AI can emit different warning sounds.

[0104] The communication unit can estimate the user's emotions and adjust the timing of communication based on the estimated emotions. For example, if the user is nervous, communication can be initiated at a calm time. If the user is relaxed, communication can be initiated immediately. This allows for appropriate communication by adjusting the timing of communication according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above processing in the communication unit may be performed using AI, or not using AI. For example, the communication unit can input user emotion data into a generative AI, which can then adjust the timing of communication.

[0105] The data collection unit can be equipped with a scheduling function to prioritize data collection during specific time periods. For example, it can prioritize data collection during nighttime hours. The data collection unit can schedule data collection during specific time periods. This allows for efficient data collection by prioritizing data collection during specific time periods. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for specific time periods into a generating AI, which can then schedule data collection.

[0106] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is tense, a simple and highly visible notification method can be provided. If the user is relaxed, a notification method containing detailed information can be provided. This allows for notifications that are easy for the user to understand by adjusting the notification method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user emotion data into the generative AI, and the generative AI can adjust the notification method of the analysis results.

[0107] The warning unit can apply different warning methods depending on the location of the anomaly when it detects one. For example, if an anomaly occurs indoors, it can issue an audible warning. If an anomaly occurs outdoors, it can issue a warning using lights. This enables quick and effective warnings by applying the appropriate warning method according to the location of the anomaly. Some or all of the above-described processes in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the location of the anomaly into a generating AI, and the generating AI can apply different warning methods.

[0108] The communication unit can estimate the user's emotions and adjust the frequency of communication based on the estimated emotions. For example, if the user is nervous, the frequency of communication can be reduced. If the user is relaxed, the frequency of communication can be increased. This allows for appropriate communication by adjusting the frequency of communication according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input user emotion data into a generative AI, which can then adjust the frequency of communication.

[0109] The data collection unit can add an event-based collection function to focus on collecting data for specific events during the collection process. For example, it can focus on collecting data during large-scale events. The data collection unit can perform event-based data collection for specific events. This enables efficient data collection by focusing on data collection for specific events. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for a specific event into a generating AI, which can then perform event-based data collection.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit monitors the environment. The data collection unit collects data from multiple sources, such as camera images, audio, and vibration sensors. For example, it captures video with a camera and detects audio and vibration with sensors. By setting a high resolution for the camera and a high sampling rate for the audio, more detailed data can be collected. In addition, by adjusting the sensitivity of the vibration sensor, even minute vibrations can be detected. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to integrate and analyze multiple data sources such as camera images, audio, and vibration sensors. For example, it can use deep learning technology to detect suspicious movements from camera images and natural language processing technology to detect abnormal sounds from audio data. Furthermore, it can analyze vibration data to detect abnormal vibrations. Step 3: The warning unit rushes to the scene and issues a warning when an anomaly is detected by the analysis unit. For example, a robot moves to the location where the anomaly was detected and issues a warning with voice and lights. It alerts the surroundings to the anomaly by issuing a voice alert and flashing lights. By setting the voice message to "An anomaly has been detected. Please evacuate to a safe place" and adjusting the color and flashing pattern of the lights, it is possible to issue warnings according to the type of anomaly. Step 4: The communications department communicates with participants and employees using natural language. For example, it can use speech recognition technology to respond to questions from participants and employees, and a chatbot to conduct text-based communication. If an anomaly is detected, warnings and instructions can be issued to participants and employees in real time.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, warning unit, and communication unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit monitors the environment and collects data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using a generation AI. The warning unit is implemented in the control unit 46A of the smart device 14 and issues a warning with sound or light when an anomaly is detected. The communication unit is implemented in the control unit 46A of the smart device 14 and communicates with participants and employees in natural language. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, warning unit, and communication unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit monitors the environment and collects data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The warning unit is implemented in the control unit 46A of the smart glasses 214 and issues a warning with voice or light when an anomaly is detected. The communication unit is implemented in the control unit 46A of the smart glasses 214 and communicates with participants or employees in natural language. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, warning unit, and communication unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit monitors the environment and collects data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using a generation AI. The warning unit is implemented in the control unit 46A of the headset terminal 314 and issues a warning with voice or light when an anomaly is detected. The communication unit is implemented in the control unit 46A of the headset terminal 314 and communicates with participants or employees in natural language. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, warning unit, and communication unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit monitors the environment and collects data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using a generation AI. The warning unit is implemented in the control unit 46A of the robot 414 and issues a warning with sound or light when an abnormality is detected. The communication unit is implemented in the control unit 46A of the robot 414 and communicates with participants or employees in natural language. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A collection unit that monitors the environment, An analysis unit analyzes the data collected by the aforementioned collection unit, A warning unit that rushes to the scene and issues a warning when an abnormality is detected by the aforementioned analysis unit, It includes a communications department that communicates with participants and employees using natural language. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data from multiple sources, including camera footage, audio, and vibration sensors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Using generative AI, multiple data sources such as camera footage, audio, and vibration sensors are integrated and analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned warning unit is If an anomaly is detected, it will rush to the scene and issue a warning using voice and lights. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned communications department, Issue warnings and instructions to participants and employees in natural language. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned communications department, Providing information in real time The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the frequency of data collection is dynamically changed in response to environmental changes. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, add a filtering function to focus on collecting data from specific areas. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the type of data collected is changed depending on the weather and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the method of collecting audio data is adjusted to take into account the ambient noise level. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past anomaly detection data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, add a function to dynamically adjust the anomaly detection threshold. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, add a function to share anomaly detection results with other systems in real time. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned warning unit is It estimates the user's emotions and adjusts the warning method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned warning unit is When a warning is issued, different warning methods are applied depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is Add a feature to dynamically change the priority of warnings when they are issued. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is It estimates the user's emotions and adjusts the intensity of the warning based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is Add a feature that automatically adjusts the volume of the warning sound according to the surrounding ambient noise when a warning is issued. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned warning unit is Add a feature to provide warnings in multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned communications department, It estimates the user's emotions and adjusts the content of communication based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned communications department, During communication, the system provides the most appropriate response by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned communications department, During communication, different response methods are applied depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned communications department, It estimates the user's emotions and adjusts the tone of communication based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned communications department, During communication, the system selects the optimal response method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned communications department, Add a feature that provides information in real time by coordinating with other systems during communication. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that monitors the environment, An analysis unit analyzes the data collected by the aforementioned collection unit, A warning unit that rushes to the scene and issues a warning when an abnormality is detected by the aforementioned analysis unit, It includes a communications department that communicates with participants and employees using natural language. A system characterized by the following features.

2. The aforementioned collection unit is It collects data from multiple sources, including camera footage, audio, and vibration sensors. The system according to feature 1.

3. The aforementioned analysis unit, Using generative AI, multiple data sources such as camera footage, audio, and vibration sensors are integrated and analyzed. The system according to feature 1.

4. The aforementioned warning unit is If an anomaly is detected, it will rush to the scene and issue a warning using voice and lights. The system according to feature 1.

5. The aforementioned communications department, Issue warnings and instructions to participants and employees in natural language. The system according to feature 1.

6. The aforementioned communications department, Providing information in real time The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the frequency of data collection is dynamically changed in response to environmental changes. The system according to feature 1.

9. The aforementioned collection unit is During data collection, add a filtering function to focus on collecting data from specific areas. The system according to feature 1.

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