system

Abstract

The system according to this embodiment aims to detect signs of bullying early and respond quickly. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a monitoring unit, a notification unit, and a proposal unit. The collection unit collects audio data from cameras, microphones, and smartphones in real time. The analysis unit analyzes the data collected by the collection unit and detects signs of bullying. The monitoring unit continuously monitors the scene based on the signs of bullying detected by the analysis unit. The notification unit notifies school administrators or the police if the abnormality persists as detected by the monitoring unit. The proposal unit proposes countermeasures based on the information reported by the notification unit.

Description

Technical Field

[0004] ,

[0006] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, there is a problem that it is difficult to detect signs of bullying at an early stage and respond promptly.

[0005] The system according to the embodiment aims to detect signs of bullying at an early stage and respond promptly.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, a monitoring unit, a notification unit, and a proposal unit. The collection unit collects audio data from cameras, microphones, and smartphones in real time. The analysis unit analyzes the data collected by the collection unit and detects signs of bullying. The monitoring unit continuously monitors the scene based on the signs of bullying detected by the analysis unit. The notification unit notifies school administrators or the police if the abnormality persists as detected by the monitoring unit. The proposal unit proposes countermeasures based on the information reported by the notification unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect signs of bullying early and respond quickly. [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 such as 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[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 bullying detection system according to an embodiment of the present invention is a system that collects audio data from cameras, microphones, and smartphones installed in schools and public spaces in real time, and an AI agent analyzes this data to detect signs of bullying. This bullying detection system collects audio data from cameras, microphones, and smartphones in real time, and the AI ​​agent analyzes the collected data to detect signs of bullying. If signs of bullying are detected, the bullying detection system autonomously continues to monitor the scene, and if the abnormality persists, it notifies school administrators and the police in real time. Furthermore, the bullying detection system comprehensively analyzes various data such as the content of conversations, tone of voice, facial expressions, and body movements, comprehensively evaluates the situation at the scene, and proposes countermeasures to the reporting destinations. For example, the bullying detection system collects audio data from cameras, microphones, and smartphones in real time. For example, the bullying detection system has an AI agent analyze the collected data to detect signs of bullying. If signs of bullying are detected, the bullying detection system autonomously continues to monitor the scene, and if the abnormality persists, it notifies school administrators and the police in real time. Furthermore, the bullying detection system comprehensively analyzes diverse data such as conversation content, tone of voice, facial expressions, and body movements to evaluate the situation on-site and propose countermeasures to the reporting party. This enables early detection and rapid response to bullying, which is expected to improve victim protection and overall school safety. As a result, the bullying detection system can detect signs of bullying in real time and respond quickly.

[0029] The bullying detection system according to the embodiment comprises a collection unit, an analysis unit, a monitoring unit, a notification unit, and a suggestion unit. The collection unit collects audio data from cameras, microphones, and smartphones in real time. The collection unit collects video data using, for example, a camera. The collection unit collects audio data using, for example, a microphone. The collection unit collects audio data from, for example, a smartphone. The analysis unit analyzes the data collected by the collection unit and detects signs of bullying. The analysis unit analyzes the collected audio data and detects specific words or tones. The analysis unit analyzes the collected video data and detects specific behavioral patterns. The analysis unit comprehensively analyzes the collected audio and video data and detects signs of bullying. The monitoring unit continuously monitors the scene based on the signs of bullying detected by the analysis unit. The monitoring unit continuously monitors the scene using, for example, a camera when signs of bullying are detected. The monitoring unit continuously monitors the scene using, for example, a microphone when signs of bullying are detected. The monitoring unit, for example, if signs of bullying are detected, continuously monitors the scene using voice data from a smartphone. The reporting unit notifies school administrators or the police if the abnormality persists as detected by the monitoring unit. The reporting unit notifies school administrators if the abnormality persists. The reporting unit notifies the police if the abnormality persists. The reporting unit notifies parents if the abnormality persists. The proposal unit proposes countermeasures based on the information reported by the reporting unit. The proposal unit proposes countermeasures to school administrators based on the reported information. The proposal unit proposes countermeasures to the police based on the reported information. The proposal unit proposes countermeasures to parents based on the reported information. As a result, the bullying detection system according to the embodiment can detect signs of bullying in real time and respond quickly.

[0030] The data collection unit collects audio data from cameras, microphones, and smartphones in real time. Specifically, cameras installed within the school constantly monitor classrooms, hallways, and school grounds, collecting video data. This allows for the identification of locations and times when bullying is likely to occur. Microphones are installed in classrooms and hallways to collect conversations and sounds. This allows for the detection of words and tones associated with bullying. Audio data from smartphones is collected from smartphones carried by students, and the data is collected through specific applications. This allows for the detection of signs of bullying outside the classroom as well. The data collection unit centrally manages this data and transmits it to the analysis unit in real time. The frequency and accuracy of data collection can be adjusted according to the system settings, allowing for flexible responses to specific situations and conditions. As a result, the data collection unit efficiently and effectively collects data, providing a foundation for the early detection of signs of bullying.

[0031] The analysis unit analyzes data collected by the collection unit to detect signs of bullying. Specifically, it analyzes collected audio data to detect specific words and tones. For example, it uses AI to analyze audio data and detect aggressive words and threatening tones. This allows for early detection of signs of bullying. It also analyzes collected video data to detect specific behavioral patterns. For example, it uses AI to analyze video data and detect violent behavior and group harassment. Furthermore, it integrates and analyzes audio and video data to more accurately detect signs of bullying. For example, by combining and analyzing audio and video data, it can confirm the consistency between the content of words and actions, increasing the likelihood of bullying. The analysis unit processes this data in real time, enabling rapid detection of signs of bullying. In addition, it can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The monitoring unit continuously monitors the scene based on signs of bullying detected by the analysis unit. Specifically, when signs of bullying are detected, the monitoring unit continuously monitors the scene using cameras. For example, it monitors camera footage in real time in specific areas or time periods to check for abnormal behavior or situations. It also continuously monitors the scene using microphones. For example, when specific conversations or sounds are detected, it monitors microphone audio data in real time to check for abnormal words or tones. It also continuously monitors the scene using smartphone audio data. For example, it monitors audio data collected from students' smartphones in real time to check for abnormal words or tones. The monitoring unit centrally manages this data and notifies the reporting unit if abnormalities persist. This provides the monitoring unit with a foundation for early detection of signs of bullying and rapid response. Furthermore, when abnormalities are detected, the monitoring unit can record the situation at the scene for later analysis and use as evidence. This allows the monitoring unit to continuously monitor signs of bullying and support a quick and appropriate response.

[0033] The reporting department will notify school administrators and the police if abnormalities persist as detected by the monitoring department. Specifically, if abnormalities persist, it will notify school administrators. For example, if signs of bullying are detected and the abnormalities persist, it will notify school administrators to encourage a swift response. It will also notify the police. For example, if signs of bullying are serious and the abnormalities persist, it will notify the police and request legal action. It will also notify parents. For example, if signs of bullying are detected and the abnormalities persist, it will notify parents to encourage action at home. The reporting department has a system in place to make these reports quickly and accurately, and can immediately report any abnormalities detected. Furthermore, the reporting department can record the content of the reports and use them for later analysis and as evidence. This allows the reporting department to detect signs of bullying early and support a swift and appropriate response.

[0034] The Proposal Department proposes countermeasures based on information reported by the Reporting Department. Specifically, it proposes countermeasures to school administrators based on the reported information. For example, if signs of bullying are detected and reported, it proposes bullying prevention and response measures to school administrators. It also proposes countermeasures to the police. For example, if signs of bullying are serious and reported, it proposes legal countermeasures and intervention measures to the police. It also proposes countermeasures to parents. For example, if signs of bullying are detected and reported, it proposes home-based countermeasures and support measures to parents. The Proposal Department has a system in place to make these proposals quickly and accurately, and can immediately propose countermeasures based on the reported information. Furthermore, the Proposal Department can record the content of the proposals and use them for later analysis and as evidence. This allows the Proposal Department to detect signs of bullying early and support a swift and appropriate response.

[0035] The data collection unit can collect audio data from cameras, microphones, and smartphones in real time. For example, the data collection unit can collect video data using a camera. For example, the data collection unit can collect audio data using a microphone. For example, the data collection unit can collect audio data from a smartphone. This allows the data collection unit to quickly detect signs of bullying by collecting data in real time. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input video data acquired by a camera into a generating AI and have the generating AI perform analysis of the video data.

[0036] The analysis unit can analyze the collected data and detect signs of bullying. For example, the analysis unit can analyze the collected audio data and detect specific words or tones. For example, the analysis unit can analyze the collected video data and detect specific behavioral patterns. For example, the analysis unit can comprehensively analyze the collected audio and video data to detect signs of bullying. In this way, the analysis unit can accurately detect signs of bullying by analyzing the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected audio data into a generating AI and have the generating AI perform the analysis of the audio data.

[0037] The monitoring unit can continuously monitor the scene when signs of bullying are detected. For example, the monitoring unit can continuously monitor the scene using a camera when signs of bullying are detected. For example, the monitoring unit can continuously monitor the scene using a microphone when signs of bullying are detected. For example, the monitoring unit can continuously monitor the scene using audio data from a smartphone when signs of bullying are detected. This makes it easier for the monitoring unit to understand the situation by continuously monitoring when signs of bullying are detected. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input video data acquired by a camera into a generating AI and have the generating AI perform analysis of the video data.

[0038] The reporting department can report to school administrators or the police if the abnormality persists. For example, the reporting department may report to school administrators if the abnormality persists. For example, the reporting department may report to the police if the abnormality persists. For example, the reporting department may report to parents if the abnormality persists. This allows the reporting department to respond quickly by reporting when the abnormality persists. Some or all of the above processes in the reporting department may be performed using AI or not. For example, if the abnormality persists, the reporting department may input the report content into a generating AI and have the generating AI summarize the report content.

[0039] The proposal department can propose countermeasures based on the reported information. For example, the proposal department can propose countermeasures to school administrators based on the reported information. For example, the proposal department can propose countermeasures to the police based on the reported information. For example, the proposal department can propose countermeasures to parents based on the reported information. In this way, the proposal department can take appropriate action by proposing countermeasures based on the reported information. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the reported information into a generating AI and have the generating AI execute the proposal of countermeasures.

[0040] The analysis unit can comprehensively analyze various data, such as the content of conversations, tone of voice, facial expressions, and body movements. For example, the analysis unit can analyze the content of conversations and detect specific words or phrases. For example, the analysis unit can analyze tone of voices and detect changes in emotion. For example, the analysis unit can analyze facial expressions and detect changes in emotion. For example, the analysis unit can analyze body movements and detect specific behavioral patterns. In this way, the analysis unit can more accurately detect signs of bullying by comprehensively analyzing various data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the content of conversations into a generating AI and have the generating AI perform the detection of specific words or phrases.

[0041] The proposal department can comprehensively evaluate the situation on site and propose countermeasures to the reporting party. For example, the proposal department can evaluate the situation on site and propose the optimal countermeasure. For example, the proposal department can evaluate the situation on site and propose a rapid countermeasure. For example, the proposal department can evaluate the situation on site and propose an effective countermeasure. In this way, the proposal department can propose more appropriate countermeasures by comprehensively evaluating the situation on site. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the situation on site into a generating AI and have the generating AI execute a countermeasure proposal.

[0042] The data collection unit can add a filtering function during collection to prioritize the collection of specific audio or video patterns. For example, the data collection unit may prioritize the collection of audio data containing specific keywords related to bullying. For example, the data collection unit may prioritize the collection of video data if it detects specific facial expressions or body movements. For example, the data collection unit may prioritize the collection of data from specific time periods or locations. This allows the data collection unit to detect signs of bullying earlier by prioritizing the collection of specific audio or video patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit may input specific audio or video patterns into a generating AI and have the generating AI perform the filtering.

[0043] The data collection unit can dynamically change the placement of data collection devices and automatically select the optimal data collection point. For example, the data collection unit can change the placement of devices in real time and select the optimal data collection point. For example, the data collection unit can analyze past data to predict the optimal data collection point. For example, the data collection unit can analyze user movement patterns to select the optimal data collection point. In this way, the data collection unit can select the optimal data collection point by dynamically changing the placement of data collection devices. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the placement of data collection devices into a generating AI and have the generating AI perform the selection of the optimal data collection point.

[0044] The data collection unit can adjust its collection method during collection, taking into account the device's battery level and communication status. For example, if the device's battery level is low, the data collection unit may reduce the frequency of data collection. For example, if the communication status is poor, the data collection unit may change the data collection method. For example, the data collection unit may monitor the device's battery level and communication status in real time and select the optimal collection method. This enables efficient data collection by adjusting the collection method while considering the device's battery level and communication status. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit may input data on the device's battery level and communication status into a generating AI and have the generating AI perform the adjustment of the collection method.

[0045] The data collection unit can increase the types of collection devices it uses, for example, by collecting data from wearable devices. For example, the data collection unit can collect heart rate data from wearable devices. For example, the data collection unit can collect location data from wearable devices. For example, the data collection unit can collect activity level data from wearable devices. This allows the data collection unit to collect a wider variety of data by increasing the types of collection devices. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from wearable devices into a generating AI and have the generating AI perform the data collection.

[0046] The analysis unit can improve the accuracy of its analysis by referring to past bullying case data during the analysis process. For example, the analysis unit can refer to past bullying case data to detect similar patterns. For example, the analysis unit can detect early signs of bullying based on past bullying case data. For example, the analysis unit can use past bullying case data to improve the accuracy of its analysis algorithm. In this way, the analysis unit can improve the accuracy of its analysis by referring to past bullying case data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past bullying case data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0047] The analysis unit can enhance its methods for integrating and analyzing different data sources during analysis. For example, the analysis unit can integratively analyze audio and video data to detect signs of bullying. For example, the analysis unit can integratively analyze text and audio data to detect signs of bullying. For example, the analysis unit can integratively analyze video and text data to detect signs of bullying. This allows the analysis unit to more accurately detect signs of bullying by integratively analyzing different data sources. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input different data sources into a generating AI and have the generating AI perform the integrated analysis.

[0048] The analysis unit can respond to real-time data updates during analysis and sequentially update the analysis results. For example, the analysis unit updates data in real time and reflects the analysis results sequentially. For example, the analysis unit can respond to real-time data updates and detect signs of bullying early. For example, the analysis unit can respond to real-time data updates and provide analysis results quickly. In this way, the analysis unit can provide the latest analysis results by responding to real-time data updates. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input real-time data updates to the generating AI and have the generating AI perform sequential updates of the analysis results.

[0049] The analysis unit can prepare multiple analysis algorithms and select the most suitable algorithm depending on the situation. For example, the analysis unit can prepare multiple algorithms for detecting signs of bullying and select the most suitable algorithm depending on the situation. For example, the analysis unit can prepare multiple algorithms for handling different data sources and select the most suitable algorithm. For example, the analysis unit can prepare multiple algorithms for improving analysis accuracy and select the most suitable algorithm. In this way, the analysis unit can improve analysis accuracy by selecting the most suitable analysis algorithm depending on the situation. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input multiple analysis algorithms into a generating AI and have the generating AI select the most suitable algorithm.

[0050] The monitoring unit can be equipped with a function to issue alerts when it detects specific behavioral patterns during monitoring. For example, the monitoring unit may issue an alert when it detects specific behavioral patterns related to bullying. For example, the monitoring unit may issue an alert when it detects abnormal behavior at a specific time or place. For example, the monitoring unit may monitor the behavioral patterns of a specific person and issue an alert when an anomaly is detected. This allows the monitoring unit to respond quickly by issuing alerts when it detects specific behavioral patterns. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit may input specific behavioral patterns into a generating AI and have the generating AI issue alerts.

[0051] The monitoring unit can dynamically change the placement of monitoring devices and automatically select the optimal monitoring point. For example, the monitoring unit can change the placement of devices in real time and select the optimal monitoring point. For example, the monitoring unit can analyze past data to predict the optimal monitoring point. For example, the monitoring unit can analyze user movement patterns to select the optimal monitoring point. In this way, the monitoring unit can select the optimal monitoring point by dynamically changing the placement of monitoring devices. Some or all of the above processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input monitoring device placement data into a generating AI and have the generating AI perform the selection of the optimal monitoring point.

[0052] The monitoring unit can be equipped with a function to automatically start recording when an anomaly is detected during monitoring. For example, the monitoring unit may automatically start recording when signs of bullying are detected. For example, the monitoring unit may automatically start recording when a specific behavioral pattern is detected. For example, the monitoring unit may automatically start recording when an anomaly is detected and save it as evidence. This allows the monitoring unit to secure evidence by automatically starting recording when an anomaly is detected. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, when an anomaly is detected, the monitoring unit may input the recording to a generating AI and have the generating AI start recording.

[0053] The monitoring unit can increase the types of monitoring devices it uses, for example, to perform monitoring using drones. The monitoring unit can, for example, use drones to perform wide-area monitoring. The monitoring unit can, for example, use drones to perform monitoring from high places. The monitoring unit can, for example, use drones to perform real-time monitoring. This allows the monitoring unit to perform monitoring over a wider area by increasing the types of monitoring devices it uses. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input drone monitoring data into a generating AI and have the generating AI perform the monitoring.

[0054] The reporting unit can select the most appropriate reporting method by referring to the past response history of the person to whom the report is made. For example, if the person to whom the report is made has responded promptly in the past, the reporting unit will prioritize notifying that person. For example, if the person to whom the report is made has responded appropriately in the past, the reporting unit will notify that person. For example, the reporting unit will analyze the past response history of the person to whom the report is made and select the most appropriate reporting method. In this way, the reporting unit can select the most appropriate reporting method by referring to the past response history of the person to whom the report is made. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input the past response history of the person to whom the report is made into a generating AI and have the generating AI perform the selection of the most appropriate reporting method.

[0055] The reporting unit can be equipped with a function to automatically summarize the reported content and highlight important information. For example, the reporting unit can automatically summarize the reported content and highlight important information. For example, the reporting unit can concisely summarize the reported content and quickly submit the report. For example, the reporting unit can summarize the reported content and highlight important information when communicating it to the recipient. This allows the reporting unit to quickly communicate important information by automatically summarizing the reported content. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input the reported content into a generating AI and have the generating AI perform summarization and highlighting.

[0056] The reporting unit can select the optimal timing for reporting, taking into account the schedule of the person to whom the report is made. For example, the reporting unit considers the schedule of the person to whom the report is made and selects the optimal timing for reporting. For example, the reporting unit makes a report during a time when the person to whom the report is made is available. For example, the reporting unit checks the schedule of the person to whom the report is made in real time and selects the optimal timing for reporting. In this way, the reporting unit can select the optimal timing for reporting by taking into account the schedule of the person to whom the report is made. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input the schedule data of the person to whom the report is made into a generating AI and have the generating AI perform the selection of the optimal timing for reporting.

[0057] The reporting unit can increase the number of reporting methods, for example, by enabling reporting via SMS or messaging apps. For example, the reporting unit can quickly report using SMS. For example, the reporting unit can report using a messaging app. For example, the reporting unit can prepare multiple reporting methods and select the most appropriate method depending on the situation. This allows the reporting unit to report more quickly and in a wider variety of ways by increasing the number of reporting methods. Some or all of the above-described processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input the selection of reporting methods into a generating AI and have the generating AI perform the selection of the most appropriate reporting method.

[0058] The proposal department can make the optimal proposal by referring to the effectiveness of past countermeasures when making a proposal. For example, the proposal department can make the optimal proposal by referring to the effectiveness of past countermeasures. For example, the proposal department can make an effective proposal based on data from past countermeasures. For example, the proposal department can make the optimal proposal by referring to successful cases of past countermeasures. In this way, the proposal department can make the optimal proposal by referring to the effectiveness of past countermeasures. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data on the effectiveness of past countermeasures into a generating AI and have the generating AI select the optimal proposal.

[0059] The proposal department can add a function to automatically summarize the proposal and highlight important information. For example, the proposal department can automatically summarize the proposal and highlight important information. For example, the proposal department can concisely summarize the proposal and submit it quickly. For example, the proposal department can summarize the proposal and highlight important information when communicating it to the recipient. This allows the proposal department to quickly communicate important information by automatically summarizing the proposal. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the proposal into a generation AI and have the generation AI perform summarization and highlighting.

[0060] The proposal department can select the optimal timing for a proposal by considering the schedule of the person in charge at the target company. For example, the proposal department selects the optimal timing by considering the schedule of the person in charge at the target company. For example, the proposal department makes a proposal during a time when the person in charge at the target company is available. For example, the proposal department checks the schedule of the person in charge at the target company in real time and selects the optimal timing for a proposal. In this way, the proposal department can select the optimal timing for a proposal by considering the schedule of the person in charge at the target company. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the schedule data of the person in charge at the target company into a generating AI and have the generating AI perform the selection of the optimal timing for a proposal.

[0061] The proposal unit can increase its proposal methods, for example, by using video calls or chatbots. The proposal unit can, for example, make proposals using video calls. The proposal unit can, for example, make proposals using chatbots. The proposal unit can, for example, prepare multiple proposal methods and select the most suitable one depending on the situation. In this way, the proposal unit can make more diverse proposals by increasing its proposal methods. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the selection of proposal methods into a generation AI and have the generation AI perform the selection of the most suitable proposal method.

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

[0063] The analysis unit can improve its analysis accuracy by referring to past bullying case data during the analysis process. For example, it can detect similar patterns by referring to past bullying case data. Based on past bullying case data, it can detect signs of bullying at an early stage. It can also improve the accuracy of the analysis algorithm by using past bullying case data. In this way, the analysis unit can improve its analysis accuracy by referring to past bullying case data.

[0064] The monitoring unit can be equipped with a function to issue alerts when it detects specific behavioral patterns during monitoring. For example, it can issue alerts when it detects specific behavioral patterns related to bullying. It can also issue alerts when it detects abnormal behavior at specific times or locations. It can monitor the behavioral patterns of specific individuals and issue alerts when abnormalities are detected. This allows the monitoring unit to respond quickly by issuing alerts when it detects specific behavioral patterns.

[0065] The reporting department can select the most appropriate reporting method by referring to the past response history of the person being reported to. For example, if the person being reported to has responded promptly in the past, the report can be prioritized for reporting to that person. If the person being reported to has responded appropriately in the past, the report can be directed to that person. The reporting department can analyze the past response history of the person being reported to and select the most appropriate reporting method. In this way, the reporting department can select the most appropriate reporting method by referring to the past response history of the person being reported to.

[0066] The proposal department can make optimal proposals by referring to the effectiveness of past countermeasures. For example, they can make optimal proposals by referring to the effectiveness of past countermeasures. They can make effective proposals based on data from past countermeasures. They can make optimal proposals by referring to successful examples of past countermeasures. In this way, the proposal department can make optimal proposals by referring to the effectiveness of past countermeasures.

[0067] The data collection unit can increase the types of data collection devices it can use, for example, to collect data from wearable devices. For instance, it can collect heart rate data from wearable devices, location data from wearable devices, and activity level data from wearable devices. This allows the data collection unit to collect a wider variety of data by increasing the types of data collection devices it can use.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The collection unit collects audio data from cameras, microphones, and smartphones in real time. For example, it collects video data using a camera, audio data using a microphone, and audio data from a smartphone. Step 2: The analysis unit analyzes the data collected by the collection unit to detect signs of bullying. For example, it analyzes the collected audio data to detect specific words or tones, analyzes the collected video data to detect specific behavioral patterns, and analyzes the audio and video data in an integrated manner to detect signs of bullying. Step 3: The monitoring unit continues to monitor the scene based on the signs of bullying detected by the analysis unit. For example, if signs of bullying are detected, the unit continues to monitor the scene using cameras, microphones, and audio data from smartphones. Step 4: The reporting unit will notify school administrators and the police if the abnormality persists as detected by the monitoring unit. For example, if the abnormality persists, they will notify school administrators, the police, and parents. Step 5: The proposal department proposes countermeasures based on the information reported by the reporting department. For example, they propose countermeasures to school administrators, the police, and parents based on the reported information.

[0070] (Example of form 2) The bullying detection system according to an embodiment of the present invention is a system that collects audio data from cameras, microphones, and smartphones installed in schools and public spaces in real time, and an AI agent analyzes this data to detect signs of bullying. This bullying detection system collects audio data from cameras, microphones, and smartphones in real time, and the AI ​​agent analyzes the collected data to detect signs of bullying. If signs of bullying are detected, the bullying detection system autonomously continues to monitor the scene, and if the abnormality persists, it notifies school administrators and the police in real time. Furthermore, the bullying detection system comprehensively analyzes various data such as the content of conversations, tone of voice, facial expressions, and body movements, comprehensively evaluates the situation at the scene, and proposes countermeasures to the reporting destinations. For example, the bullying detection system collects audio data from cameras, microphones, and smartphones in real time. For example, the bullying detection system has an AI agent analyze the collected data to detect signs of bullying. If signs of bullying are detected, the bullying detection system autonomously continues to monitor the scene, and if the abnormality persists, it notifies school administrators and the police in real time. Furthermore, the bullying detection system comprehensively analyzes diverse data such as conversation content, tone of voice, facial expressions, and body movements to evaluate the situation on-site and propose countermeasures to the reporting party. This enables early detection and rapid response to bullying, which is expected to improve victim protection and overall school safety. As a result, the bullying detection system can detect signs of bullying in real time and respond quickly.

[0071] The bullying detection system according to the embodiment comprises a collection unit, an analysis unit, a monitoring unit, a notification unit, and a suggestion unit. The collection unit collects audio data from cameras, microphones, and smartphones in real time. The collection unit collects video data using, for example, a camera. The collection unit collects audio data using, for example, a microphone. The collection unit collects audio data from, for example, a smartphone. The analysis unit analyzes the data collected by the collection unit and detects signs of bullying. The analysis unit analyzes the collected audio data and detects specific words or tones. The analysis unit analyzes the collected video data and detects specific behavioral patterns. The analysis unit comprehensively analyzes the collected audio and video data and detects signs of bullying. The monitoring unit continuously monitors the scene based on the signs of bullying detected by the analysis unit. The monitoring unit continuously monitors the scene using, for example, a camera when signs of bullying are detected. The monitoring unit continuously monitors the scene using, for example, a microphone when signs of bullying are detected. The monitoring unit, for example, if signs of bullying are detected, continuously monitors the scene using voice data from a smartphone. The reporting unit notifies school administrators or the police if the abnormality persists as detected by the monitoring unit. The reporting unit notifies school administrators if the abnormality persists. The reporting unit notifies the police if the abnormality persists. The reporting unit notifies parents if the abnormality persists. The proposal unit proposes countermeasures based on the information reported by the reporting unit. The proposal unit proposes countermeasures to school administrators based on the reported information. The proposal unit proposes countermeasures to the police based on the reported information. The proposal unit proposes countermeasures to parents based on the reported information. As a result, the bullying detection system according to the embodiment can detect signs of bullying in real time and respond quickly.

[0072] The data collection unit collects audio data from cameras, microphones, and smartphones in real time. Specifically, cameras installed within the school constantly monitor classrooms, hallways, and school grounds, collecting video data. This allows for the identification of locations and times when bullying is likely to occur. Microphones are installed in classrooms and hallways to collect conversations and sounds. This allows for the detection of words and tones associated with bullying. Audio data from smartphones is collected from smartphones carried by students, and the data is collected through specific applications. This allows for the detection of signs of bullying outside the classroom as well. The data collection unit centrally manages this data and transmits it to the analysis unit in real time. The frequency and accuracy of data collection can be adjusted according to the system settings, allowing for flexible responses to specific situations and conditions. As a result, the data collection unit efficiently and effectively collects data, providing a foundation for the early detection of signs of bullying.

[0073] The analysis unit analyzes data collected by the collection unit to detect signs of bullying. Specifically, it analyzes collected audio data to detect specific words and tones. For example, it uses AI to analyze audio data and detect aggressive words and threatening tones. This allows for early detection of signs of bullying. It also analyzes collected video data to detect specific behavioral patterns. For example, it uses AI to analyze video data and detect violent behavior and group harassment. Furthermore, it integrates and analyzes audio and video data to more accurately detect signs of bullying. For example, by combining and analyzing audio and video data, it can confirm the consistency between the content of words and actions, increasing the likelihood of bullying. The analysis unit processes this data in real time, enabling rapid detection of signs of bullying. In addition, it can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0074] The monitoring unit continuously monitors the scene based on signs of bullying detected by the analysis unit. Specifically, when signs of bullying are detected, the monitoring unit continuously monitors the scene using cameras. For example, it monitors camera footage in real time in specific areas or time periods to check for abnormal behavior or situations. It also continuously monitors the scene using microphones. For example, when specific conversations or sounds are detected, it monitors microphone audio data in real time to check for abnormal words or tones. It also continuously monitors the scene using smartphone audio data. For example, it monitors audio data collected from students' smartphones in real time to check for abnormal words or tones. The monitoring unit centrally manages this data and notifies the reporting unit if abnormalities persist. This provides the monitoring unit with a foundation for early detection of signs of bullying and rapid response. Furthermore, when abnormalities are detected, the monitoring unit can record the situation at the scene for later analysis and use as evidence. This allows the monitoring unit to continuously monitor signs of bullying and support a quick and appropriate response.

[0075] The reporting department will notify school administrators and the police if abnormalities persist as detected by the monitoring department. Specifically, if abnormalities persist, it will notify school administrators. For example, if signs of bullying are detected and the abnormalities persist, it will notify school administrators to encourage a swift response. It will also notify the police. For example, if signs of bullying are serious and the abnormalities persist, it will notify the police and request legal action. It will also notify parents. For example, if signs of bullying are detected and the abnormalities persist, it will notify parents to encourage action at home. The reporting department has a system in place to make these reports quickly and accurately, and can immediately report any abnormalities detected. Furthermore, the reporting department can record the content of the reports and use them for later analysis and as evidence. This allows the reporting department to detect signs of bullying early and support a swift and appropriate response.

[0076] The Proposal Department proposes countermeasures based on information reported by the Reporting Department. Specifically, it proposes countermeasures to school administrators based on the reported information. For example, if signs of bullying are detected and reported, it proposes bullying prevention and response measures to school administrators. It also proposes countermeasures to the police. For example, if signs of bullying are serious and reported, it proposes legal countermeasures and intervention measures to the police. It also proposes countermeasures to parents. For example, if signs of bullying are detected and reported, it proposes home-based countermeasures and support measures to parents. The Proposal Department has a system in place to make these proposals quickly and accurately, and can immediately propose countermeasures based on the reported information. Furthermore, the Proposal Department can record the content of the proposals and use them for later analysis and as evidence. This allows the Proposal Department to detect signs of bullying early and support a swift and appropriate response.

[0077] The data collection unit can collect audio data from cameras, microphones, and smartphones in real time. For example, the data collection unit can collect video data using a camera. For example, the data collection unit can collect audio data using a microphone. For example, the data collection unit can collect audio data from a smartphone. This allows the data collection unit to quickly detect signs of bullying by collecting data in real time. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input video data acquired by a camera into a generating AI and have the generating AI perform analysis of the video data.

[0078] The analysis unit can analyze the collected data and detect signs of bullying. For example, the analysis unit can analyze the collected audio data and detect specific words or tones. For example, the analysis unit can analyze the collected video data and detect specific behavioral patterns. For example, the analysis unit can comprehensively analyze the collected audio and video data to detect signs of bullying. In this way, the analysis unit can accurately detect signs of bullying by analyzing the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected audio data into a generating AI and have the generating AI perform the analysis of the audio data.

[0079] The monitoring unit can continuously monitor the scene when signs of bullying are detected. For example, the monitoring unit can continuously monitor the scene using a camera when signs of bullying are detected. For example, the monitoring unit can continuously monitor the scene using a microphone when signs of bullying are detected. For example, the monitoring unit can continuously monitor the scene using audio data from a smartphone when signs of bullying are detected. This makes it easier for the monitoring unit to understand the situation by continuously monitoring when signs of bullying are detected. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input video data acquired by a camera into a generating AI and have the generating AI perform analysis of the video data.

[0080] The reporting department can report to school administrators or the police if the abnormality persists. For example, the reporting department may report to school administrators if the abnormality persists. For example, the reporting department may report to the police if the abnormality persists. For example, the reporting department may report to parents if the abnormality persists. This allows the reporting department to respond quickly by reporting when the abnormality persists. Some or all of the above processes in the reporting department may be performed using AI or not. For example, if the abnormality persists, the reporting department may input the report content into a generating AI and have the generating AI summarize the report content.

[0081] The proposal department can propose countermeasures based on the reported information. For example, the proposal department can propose countermeasures to school administrators based on the reported information. For example, the proposal department can propose countermeasures to the police based on the reported information. For example, the proposal department can propose countermeasures to parents based on the reported information. In this way, the proposal department can take appropriate action by proposing countermeasures based on the reported information. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the reported information into a generating AI and have the generating AI execute the proposal of countermeasures.

[0082] The analysis unit can comprehensively analyze various data, such as the content of conversations, tone of voice, facial expressions, and body movements. For example, the analysis unit can analyze the content of conversations and detect specific words or phrases. For example, the analysis unit can analyze tone of voices and detect changes in emotion. For example, the analysis unit can analyze facial expressions and detect changes in emotion. For example, the analysis unit can analyze body movements and detect specific behavioral patterns. In this way, the analysis unit can more accurately detect signs of bullying by comprehensively analyzing various data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the content of conversations into a generating AI and have the generating AI perform the detection of specific words or phrases.

[0083] The proposal department can comprehensively evaluate the situation on site and propose countermeasures to the reporting party. For example, the proposal department can evaluate the situation on site and propose the optimal countermeasure. For example, the proposal department can evaluate the situation on site and propose a rapid countermeasure. For example, the proposal department can evaluate the situation on site and propose an effective countermeasure. In this way, the proposal department can propose more appropriate countermeasures by comprehensively evaluating the situation on site. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the situation on site into a generating AI and have the generating AI execute a countermeasure proposal.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit increases the frequency of data collection and collects more detailed data. For example, if the user is relaxed, the data collection unit decreases the frequency of data collection and collects only the minimum necessary data. For example, if the user is in a hurry, the data collection unit prioritizes collecting only important data. This allows the data collection unit to collect more appropriate data by adjusting the timing of data collection based on 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0085] The data collection unit can add a filtering function during collection to prioritize the collection of specific audio or video patterns. For example, the data collection unit may prioritize the collection of audio data containing specific keywords related to bullying. For example, the data collection unit may prioritize the collection of video data if it detects specific facial expressions or body movements. For example, the data collection unit may prioritize the collection of data from specific time periods or locations. This allows the data collection unit to detect signs of bullying earlier by prioritizing the collection of specific audio or video patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit may input specific audio or video patterns into a generating AI and have the generating AI perform the filtering.

[0086] The data collection unit can dynamically change the placement of data collection devices and automatically select the optimal data collection point. For example, the data collection unit can change the placement of devices in real time and select the optimal data collection point. For example, the data collection unit can analyze past data to predict the optimal data collection point. For example, the data collection unit can analyze user movement patterns to select the optimal data collection point. In this way, the data collection unit can select the optimal data collection point by dynamically changing the placement of data collection devices. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the placement of data collection devices into a generating AI and have the generating AI perform the selection of the optimal data collection point.

[0087] 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 stressed, the data collection unit will prioritize collecting audio data. For example, if the user is relaxed, the data collection unit will prioritize collecting video data. For example, if the user is in a hurry, the data collection unit will prioritize collecting only important data. In this way, the data collection unit can prioritize collecting important data by determining the priority of data to collect based on 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0088] The data collection unit can adjust its collection method during collection, taking into account the device's battery level and communication status. For example, if the device's battery level is low, the data collection unit may reduce the frequency of data collection. For example, if the communication status is poor, the data collection unit may change the data collection method. For example, the data collection unit may monitor the device's battery level and communication status in real time and select the optimal collection method. This enables efficient data collection by adjusting the collection method while considering the device's battery level and communication status. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit may input data on the device's battery level and communication status into a generating AI and have the generating AI perform the adjustment of the collection method.

[0089] The data collection unit can increase the types of collection devices it uses, for example, by collecting data from wearable devices. For example, the data collection unit can collect heart rate data from wearable devices. For example, the data collection unit can collect location data from wearable devices. For example, the data collection unit can collect activity level data from wearable devices. This allows the data collection unit to collect a wider variety of data by increasing the types of collection devices. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from wearable devices into a generating AI and have the generating AI perform the data collection.

[0090] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit uses an algorithm that emphasizes emotional changes. For example, if the user is relaxed, the analysis unit uses an algorithm that performs detailed data analysis. For example, if the user is in a hurry, the analysis unit uses an algorithm that performs rapid analysis. This allows the analysis unit to perform more accurate analysis by adjusting the analysis algorithm based on 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0091] The analysis unit can improve the accuracy of its analysis by referring to past bullying case data during the analysis process. For example, the analysis unit can refer to past bullying case data to detect similar patterns. For example, the analysis unit can detect early signs of bullying based on past bullying case data. For example, the analysis unit can use past bullying case data to improve the accuracy of its analysis algorithm. In this way, the analysis unit can improve the accuracy of its analysis by referring to past bullying case data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past bullying case data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0092] The analysis unit can enhance its methods for integrating and analyzing different data sources during analysis. For example, the analysis unit can integratively analyze audio and video data to detect signs of bullying. For example, the analysis unit can integratively analyze text and audio data to detect signs of bullying. For example, the analysis unit can integratively analyze video and text data to detect signs of bullying. This allows the analysis unit to more accurately detect signs of bullying by integratively analyzing different data sources. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input different data sources into a generating AI and have the generating AI perform the integrated analysis.

[0093] 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 stressed, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, the analysis unit can provide a more appropriate display by adjusting the display method of the analysis results based on the user's emotions. 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 AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0094] The analysis unit can respond to real-time data updates during analysis and sequentially update the analysis results. For example, the analysis unit updates data in real time and reflects the analysis results sequentially. For example, the analysis unit can respond to real-time data updates and detect signs of bullying early. For example, the analysis unit can respond to real-time data updates and provide analysis results quickly. In this way, the analysis unit can provide the latest analysis results by responding to real-time data updates. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input real-time data updates to the generating AI and have the generating AI perform sequential updates of the analysis results.

[0095] The analysis unit can prepare multiple analysis algorithms and select the most suitable algorithm depending on the situation. For example, the analysis unit can prepare multiple algorithms for detecting signs of bullying and select the most suitable algorithm depending on the situation. For example, the analysis unit can prepare multiple algorithms for handling different data sources and select the most suitable algorithm. For example, the analysis unit can prepare multiple algorithms for improving analysis accuracy and select the most suitable algorithm. In this way, the analysis unit can improve analysis accuracy by selecting the most suitable analysis algorithm depending on the situation. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input multiple analysis algorithms into a generating AI and have the generating AI select the most suitable algorithm.

[0096] The monitoring unit can estimate the user's emotions and adjust the monitoring intensity based on the estimated emotions. For example, if the user is stressed, the monitoring unit increases the monitoring intensity and collects detailed data. For example, if the user is relaxed, the monitoring unit reduces the monitoring intensity and collects only the minimum necessary data. For example, if the user is in a hurry, the monitoring unit prioritizes collecting only important data. This allows the monitoring unit to perform more appropriate monitoring by adjusting the monitoring intensity based on 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 monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring intensity.

[0097] The monitoring unit can be equipped with a function to issue alerts when it detects specific behavioral patterns during monitoring. For example, the monitoring unit may issue an alert when it detects specific behavioral patterns related to bullying. For example, the monitoring unit may issue an alert when it detects abnormal behavior at a specific time or place. For example, the monitoring unit may monitor the behavioral patterns of a specific person and issue an alert when an anomaly is detected. This allows the monitoring unit to respond quickly by issuing alerts when it detects specific behavioral patterns. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit may input specific behavioral patterns into a generating AI and have the generating AI issue alerts.

[0098] The monitoring unit can dynamically change the placement of monitoring devices and automatically select the optimal monitoring point. For example, the monitoring unit can change the placement of devices in real time and select the optimal monitoring point. For example, the monitoring unit can analyze past data to predict the optimal monitoring point. For example, the monitoring unit can analyze user movement patterns to select the optimal monitoring point. In this way, the monitoring unit can select the optimal monitoring point by dynamically changing the placement of monitoring devices. Some or all of the above processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input monitoring device placement data into a generating AI and have the generating AI perform the selection of the optimal monitoring point.

[0099] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is stressed, the monitoring unit provides a simple and highly visible display method. For example, if the user is relaxed, the monitoring unit provides a display method that includes detailed information. For example, if the user is in a hurry, the monitoring unit provides a display method that gets straight to the point. In this way, the monitoring unit can provide a more appropriate display by adjusting the display method of the monitoring results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the monitoring results.

[0100] The monitoring unit can be equipped with a function to automatically start recording when an anomaly is detected during monitoring. For example, the monitoring unit may automatically start recording when signs of bullying are detected. For example, the monitoring unit may automatically start recording when a specific behavioral pattern is detected. For example, the monitoring unit may automatically start recording when an anomaly is detected and save it as evidence. This allows the monitoring unit to secure evidence by automatically starting recording when an anomaly is detected. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, when an anomaly is detected, the monitoring unit may input the recording to a generating AI and have the generating AI start recording.

[0101] The monitoring unit can increase the types of monitoring devices it uses, for example, to perform monitoring using drones. The monitoring unit can, for example, use drones to perform wide-area monitoring. The monitoring unit can, for example, use drones to perform monitoring from high places. The monitoring unit can, for example, use drones to perform real-time monitoring. This allows the monitoring unit to perform monitoring over a wider area by increasing the types of monitoring devices it uses. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input drone monitoring data into a generating AI and have the generating AI perform the monitoring.

[0102] The reporting unit can estimate the user's emotions and adjust the urgency of the report based on the estimated emotions. For example, if the user is stressed, the reporting unit will increase the urgency of the report and report quickly. For example, if the user is relaxed, the reporting unit will lower the urgency of the report and report only the minimum necessary information. For example, if the user is in a hurry, the reporting unit will prioritize reporting only important information. In this way, the reporting unit can make more appropriate reports by adjusting the urgency of the report based on 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 reporting unit may be performed using AI or not. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the urgency of the report.

[0103] The reporting unit can select the most appropriate reporting method by referring to the past response history of the person to whom the report is made. For example, if the person to whom the report is made has responded promptly in the past, the reporting unit will prioritize notifying that person. For example, if the person to whom the report is made has responded appropriately in the past, the reporting unit will notify that person. For example, the reporting unit will analyze the past response history of the person to whom the report is made and select the most appropriate reporting method. In this way, the reporting unit can select the most appropriate reporting method by referring to the past response history of the person to whom the report is made. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input the past response history of the person to whom the report is made into a generating AI and have the generating AI perform the selection of the most appropriate reporting method.

[0104] The reporting unit can be equipped with a function to automatically summarize the reported content and highlight important information. For example, the reporting unit can automatically summarize the reported content and highlight important information. For example, the reporting unit can concisely summarize the reported content and quickly submit the report. For example, the reporting unit can summarize the reported content and highlight important information when communicating it to the recipient. This allows the reporting unit to quickly communicate important information by automatically summarizing the reported content. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input the reported content into a generating AI and have the generating AI perform summarization and highlighting.

[0105] The reporting unit can estimate the user's emotions and adjust the level of detail in the report based on the estimated emotions. For example, if the user is stressed, the reporting unit will provide a detailed report. For example, if the user is relaxed, the reporting unit will provide a concise report. For example, if the user is in a hurry, the reporting unit will provide a to-the-point report. This allows the reporting unit to provide more appropriate reports by adjusting the level of detail in the report based on 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 reporting unit may be performed using AI or not. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail in the report.

[0106] The reporting unit can select the optimal timing for reporting, taking into account the schedule of the person to whom the report is made. For example, the reporting unit considers the schedule of the person to whom the report is made and selects the optimal timing for reporting. For example, the reporting unit makes a report during a time when the person to whom the report is made is available. For example, the reporting unit checks the schedule of the person to whom the report is made in real time and selects the optimal timing for reporting. In this way, the reporting unit can select the optimal timing for reporting by taking into account the schedule of the person to whom the report is made. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input the schedule data of the person to whom the report is made into a generating AI and have the generating AI perform the selection of the optimal timing for reporting.

[0107] The reporting unit can increase the number of reporting methods, for example, by enabling reporting via SMS or messaging apps. For example, the reporting unit can quickly report using SMS. For example, the reporting unit can report using a messaging app. For example, the reporting unit can prepare multiple reporting methods and select the most appropriate method depending on the situation. This allows the reporting unit to report more quickly and in a wider variety of ways by increasing the number of reporting methods. Some or all of the above-described processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input the selection of reporting methods into a generating AI and have the generating AI perform the selection of the most appropriate reporting method.

[0108] The suggestion unit can estimate the user's emotions and adjust its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will offer suggestions to reduce stress. If the user is relaxed, the suggestion unit will offer detailed suggestions. If the user is in a hurry, the suggestion unit will offer suggestions that allow for quick action. By adjusting its suggestions based on the user's emotions, the suggestion unit can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the suggestions.

[0109] The proposal department can make the optimal proposal by referring to the effectiveness of past countermeasures when making a proposal. For example, the proposal department can make the optimal proposal by referring to the effectiveness of past countermeasures. For example, the proposal department can make an effective proposal based on data from past countermeasures. For example, the proposal department can make the optimal proposal by referring to successful cases of past countermeasures. In this way, the proposal department can make the optimal proposal by referring to the effectiveness of past countermeasures. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data on the effectiveness of past countermeasures into a generating AI and have the generating AI select the optimal proposal.

[0110] The proposal department can add a function to automatically summarize the proposal and highlight important information. For example, the proposal department can automatically summarize the proposal and highlight important information. For example, the proposal department can concisely summarize the proposal and submit it quickly. For example, the proposal department can summarize the proposal and highlight important information when communicating it to the recipient. This allows the proposal department to quickly communicate important information by automatically summarizing the proposal. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the proposal into a generation AI and have the generation AI perform summarization and highlighting.

[0111] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize suggestions to reduce stress. If the user is relaxed, the suggestion unit will prioritize detailed suggestions. If the user is in a hurry, the suggestion unit will prioritize suggestions that can be addressed quickly. This allows the suggestion unit to provide more appropriate suggestions by prioritizing suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0112] The proposal department can select the optimal timing for a proposal by considering the schedule of the person in charge at the target company. For example, the proposal department selects the optimal timing by considering the schedule of the person in charge at the target company. For example, the proposal department makes a proposal during a time when the person in charge at the target company is available. For example, the proposal department checks the schedule of the person in charge at the target company in real time and selects the optimal timing for a proposal. In this way, the proposal department can select the optimal timing for a proposal by considering the schedule of the person in charge at the target company. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the schedule data of the person in charge at the target company into a generating AI and have the generating AI perform the selection of the optimal timing for a proposal.

[0113] The proposal unit can increase its proposal methods, for example, by using video calls or chatbots. The proposal unit can, for example, make proposals using video calls. The proposal unit can, for example, make proposals using chatbots. The proposal unit can, for example, prepare multiple proposal methods and select the most suitable one depending on the situation. In this way, the proposal unit can make more diverse proposals by increasing its proposal methods. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the selection of proposal methods into a generation AI and have the generation AI perform the selection of the most suitable proposal method.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, data related to changes in emotions can be prioritized for analysis. If the user is relaxed, detailed data analysis can be performed. If the user is in a hurry, rapid analysis can be performed. In this way, the analysis unit can perform more appropriate analysis by determining the priority of analysis based on the user's emotions.

[0116] The monitoring unit can estimate the user's emotions and adjust the monitoring intensity based on that estimation. For example, if the user is stressed, the monitoring intensity can be increased to collect more detailed data. If the user is relaxed, the monitoring intensity can be reduced to collect only the minimum necessary data. If the user is in a hurry, only important data can be prioritized for collection. This allows the monitoring unit to perform more appropriate monitoring by adjusting the monitoring intensity based on the user's emotions.

[0117] The reporting system can estimate the user's emotions and adjust the urgency of the report based on those emotions. For example, if a user is stressed, the urgency of the report can be increased, allowing for a quicker report. If a user is relaxed, the urgency can be reduced, allowing for a report containing only the essentials. If a user is in a hurry, only important information can be prioritized for reporting. This allows the reporting system to make more appropriate reports by adjusting the urgency of the report based on the user's emotions.

[0118] The suggestion function can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is stressed, it can offer suggestions to reduce stress. If the user is relaxed, it can offer more detailed suggestions. If the user is in a hurry, it can offer suggestions that allow for a quick response. In this way, the suggestion function can provide more appropriate suggestions by adjusting the suggestions based on the user's emotions.

[0119] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be increased to collect more detailed data. If the user is relaxed, the frequency of data collection can be decreased to collect only the minimum necessary data. If the user is in a hurry, only important data can be prioritized for collection. In this way, the data collection unit can collect more appropriate data by adjusting the timing of data collection based on the user's emotions.

[0120] The analysis unit can improve its analysis accuracy by referring to past bullying case data during the analysis process. For example, it can detect similar patterns by referring to past bullying case data. Based on past bullying case data, it can detect signs of bullying at an early stage. It can also improve the accuracy of the analysis algorithm by using past bullying case data. In this way, the analysis unit can improve its analysis accuracy by referring to past bullying case data.

[0121] The monitoring unit can be equipped with a function to issue alerts when it detects specific behavioral patterns during monitoring. For example, it can issue alerts when it detects specific behavioral patterns related to bullying. It can also issue alerts when it detects abnormal behavior at specific times or locations. It can monitor the behavioral patterns of specific individuals and issue alerts when abnormalities are detected. This allows the monitoring unit to respond quickly by issuing alerts when it detects specific behavioral patterns.

[0122] The reporting department can select the most appropriate reporting method by referring to the past response history of the person being reported to. For example, if the person being reported to has responded promptly in the past, the report can be prioritized for reporting to that person. If the person being reported to has responded appropriately in the past, the report can be directed to that person. The reporting department can analyze the past response history of the person being reported to and select the most appropriate reporting method. In this way, the reporting department can select the most appropriate reporting method by referring to the past response history of the person being reported to.

[0123] The proposal department can make optimal proposals by referring to the effectiveness of past countermeasures. For example, they can make optimal proposals by referring to the effectiveness of past countermeasures. They can make effective proposals based on data from past countermeasures. They can make optimal proposals by referring to successful examples of past countermeasures. In this way, the proposal department can make optimal proposals by referring to the effectiveness of past countermeasures.

[0124] The data collection unit can increase the types of data collection devices it can use, for example, to collect data from wearable devices. For instance, it can collect heart rate data from wearable devices, location data from wearable devices, and activity level data from wearable devices. This allows the data collection unit to collect a wider variety of data by increasing the types of data collection devices it can use.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The collection unit collects audio data from cameras, microphones, and smartphones in real time. For example, it collects video data using a camera, audio data using a microphone, and audio data from a smartphone. Step 2: The analysis unit analyzes the data collected by the collection unit to detect signs of bullying. For example, it analyzes the collected audio data to detect specific words or tones, analyzes the collected video data to detect specific behavioral patterns, and analyzes the audio and video data in an integrated manner to detect signs of bullying. Step 3: The monitoring unit continues to monitor the scene based on the signs of bullying detected by the analysis unit. For example, if signs of bullying are detected, the unit continues to monitor the scene using cameras, microphones, and audio data from smartphones. Step 4: The reporting unit will notify school administrators and the police if the abnormality persists as detected by the monitoring unit. For example, if the abnormality persists, they will notify school administrators, the police, and parents. Step 5: The proposal department proposes countermeasures based on the information reported by the reporting department. For example, they propose countermeasures to school administrators, the police, and parents based on the reported information.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, monitoring unit, notification unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects video and audio data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to detect signs of bullying. The monitoring unit is implemented in the specific processing unit 46A of the smart device 14, and continuously monitors the scene when signs of bullying are detected. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, and notifies school administrators or the police if the abnormality persists. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes countermeasures based on the reported information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, monitoring unit, notification unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects video and audio data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to detect signs of bullying. The monitoring unit is implemented in the control unit 46A of the smart glasses 214, for example, and continuously monitors the scene when signs of bullying are detected. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and notifies school administrators or the police if the abnormality persists. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes countermeasures based on the reported information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, monitoring unit, notification unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects video and audio data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to detect signs of bullying. The monitoring unit is implemented in the control unit 46A of the headset terminal 314, for example, and continuously monitors the scene when signs of bullying are detected. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and notifies school administrators or the police if the abnormality persists. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes countermeasures based on the reported information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the collection unit, analysis unit, monitoring unit, notification unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects video and audio data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to detect signs of bullying. The monitoring unit is implemented in the control unit 46A of the robot 414, for example, and continuously monitors the scene when signs of bullying are detected. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and notifies school administrators or the police if the abnormality persists. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes countermeasures based on the reported information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) The collection unit collects audio data from cameras, microphones, and smartphones in real time, An analysis unit analyzes the data collected by the aforementioned collection unit and detects signs of bullying, A monitoring unit that continuously monitors the scene based on the signs of bullying detected by the aforementioned analysis unit, The aforementioned monitoring unit will notify the school administrator or the police if the abnormality persists, The system comprises a proposal unit that proposes countermeasures based on the information reported by the reporting unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects audio data from cameras, microphones, and smartphones in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to detect signs of bullying. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, If signs of bullying are detected, the scene will be continuously monitored. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reporting unit, If the abnormality persists, report it to the school administrator or the police. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We will propose countermeasures based on the information reported. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It comprehensively analyzes diverse data such as the content of the conversation, tone of voice, facial expressions, and body movements. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, We will comprehensively assess the situation on-site and propose countermeasures to the reporting party. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, add a filtering function that prioritizes the collection of specific audio or video patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Dynamically change the placement of collection devices and automatically select the optimal collection point. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned collection unit is During data collection, the collection method is adjusted considering the device's battery level and communication status. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is We will increase the types of devices used for data collection and also collect data from wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, past bullying case data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, we are strengthening methods for integrating and analyzing different data sources. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned analysis unit, During analysis, the system supports real-time data updates and continuously updates the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, We prepare multiple analysis algorithms and select the most suitable one depending on the situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring intensity based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, We will add a feature that issues an alert when a specific behavioral pattern is detected during monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned monitoring unit, Dynamically change the placement of monitoring devices and automatically select the optimal monitoring points. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned monitoring unit, Add a feature that automatically starts recording when an anomaly is detected during monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned monitoring unit, We will increase the types of surveillance devices and also use drones for surveillance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the urgency of the report based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reporting unit, When making a report, the system will select the most appropriate reporting method by referring to the past response history of the person handling the report. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reporting unit, Add a feature that automatically summarizes reports and highlights important information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the level of detail in the report based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reporting unit, When making a report, the optimal timing for reporting should be selected considering the schedule of the person in charge at the receiving end. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reporting unit, We will increase the means of reporting, including reporting via SMS and messaging apps. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, When making a proposal, we will refer to the effectiveness of past countermeasures to make the most appropriate proposal. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, Add a feature that automatically summarizes proposals and highlights important information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When making a proposal, we will select the optimal timing for the proposal, taking into consideration the schedule of the person in charge at the recipient company. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned proposal section is, We will increase our methods of making proposals, including using video calls and chatbots. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0199] 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. The collection unit collects audio data from cameras, microphones, and smartphones in real time, An analysis unit analyzes the data collected by the aforementioned collection unit and detects signs of bullying, A monitoring unit that continuously monitors the scene based on the signs of bullying detected by the aforementioned analysis unit, The aforementioned monitoring unit will notify the school administrator or the police if the abnormality persists, The system comprises a proposal unit that proposes countermeasures based on the information reported by the reporting unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects audio data from cameras, microphones, and smartphones in real time. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to detect signs of bullying. The system according to feature 1.

4. The aforementioned monitoring unit, If signs of bullying are detected, the scene will be continuously monitored. The system according to feature 1.

5. The aforementioned reporting unit, If the abnormality persists, report it to the school administrator or the police. The system according to feature 1.

6. The aforementioned proposal section is, We will propose countermeasures based on the information reported. The system according to feature 1.

7. The aforementioned analysis unit, It comprehensively analyzes diverse data such as the content of the conversation, tone of voice, facial expressions, and body movements. The system according to feature 1.

8. The aforementioned proposal section is, We will comprehensively assess the situation on-site and propose countermeasures to the reporting party. The system according to feature 1.

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