System

Abstract

An object of a system according to an embodiment is to efficiently detect a manner violation behavior in an event or a concert and quickly cope with the behavior.SOLUTION: A system according to an embodiment includes a security camera, a sensor, a AI analysis unit, and an attention calling unit. Security cameras and sensors collect data. The data-analyzing unit analyzes AI collected by the security camera and the sensor. The attention calling unit calls attention to the manner violation behavior detected by the AI analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently detect and quickly respond to rude behavior at events and concerts.

[0005] The system according to the embodiment aims to efficiently detect and quickly respond to rude behavior at events and concerts. [Means for solving the problem]

[0006] A system according to an embodiment includes a security camera, a sensor, an AI analysis unit, and a warning unit. The security camera and the sensor collect data. The AI ​​analysis unit analyzes the data collected by the security camera and the sensor. The warning unit warns against bad manners detected by the AI ​​analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently detect and quickly respond to rude behavior at events and concerts. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The manners violation prevention system according to the embodiment of the present invention is a system that automatically detects and warns against impolite behavior at concerts and events, thereby allowing participants to enjoy themselves in a comfortable and safe environment.

[0029] A manners violation prevention system according to an embodiment includes a security camera, a sensor, an AI analysis unit, and a warning unit. The security camera collects video data from within a venue. For example, a fixed camera, a pan-tilt-zoom (PTZ) camera, or an infrared camera may be used. The sensor collects audio and movement data from within the venue. For example, a motion detection sensor, an audio sensor, or a temperature sensor may be used. The AI ​​analysis unit analyzes the data collected by the security camera and the sensor. For example, it identifies manners violations using algorithms such as deep learning, image recognition, or natural language processing. The warning unit warns participants of manners violations detected by the AI ​​analysis unit. For example, it uses methods such as audio alerts, visual alerts, or message displays. This allows the manners violation prevention system to provide participants with a comfortable and safe environment.

[0030] The etiquette violation prevention system also uses drones to collect video data from the air, which is then analyzed by the AI ​​analysis unit. Drones collect video data from the air. For example, fixed-wing drones, multi-rotor drones, and drones equipped with cameras are used. The AI ​​analysis unit analyzes the video data collected by the drones. For example, a drone could patrol the entire venue and detect abnormal behavior from an aerial perspective. This allows for wider monitoring by detecting abnormal behavior from an aerial perspective.

[0031] The etiquette violation prevention system also adds environmental data such as temperature, humidity, and carbon dioxide concentration to the sensors, and the AI ​​analysis unit analyzes this data to detect signs of abnormal behavior from multiple angles. The sensors collect environmental data such as temperature, humidity, and carbon dioxide concentration. For example, thermistors, infrared thermometers, capacitive humidity sensors, resistive humidity sensors, NDIR sensors, and chemical sensors are used. The AI ​​analysis unit analyzes the environmental data collected by the sensors to detect signs of abnormal behavior from multiple angles. For example, it detects abnormal temperature increases, humidity changes, and fluctuations in carbon dioxide concentration. This enables more accurate monitoring by detecting signs of abnormal behavior from multiple angles based on environmental data.

[0032] The etiquette violation prevention system also shares data from security cameras and sensors with other event venues and public facilities in real time, and the AI ​​analysis unit identifies trends in etiquette violations over a wide area. Data from security cameras and sensors is shared with other event venues and public facilities in real time. For example, the data is uploaded to the cloud and shared with other facilities. The AI ​​analysis unit analyzes the shared data and identifies trends in etiquette violations over a wide area. For example, it analyzes trends in abnormal behavior using frequency analysis and time series analysis. This allows for an understanding of trends in etiquette violations over a wide area, enabling more effective countermeasures.

[0033] The etiquette violation prevention system further expands the types of sensors by adding a voice recognition sensor, allowing the AI ​​analysis unit to detect specific keywords and voice patterns. The sensor adds a voice recognition sensor. For example, a microphone array or voice command recognition is used. The AI ​​analysis unit analyzes the data collected by the voice recognition sensor and detects specific keywords and voice patterns. For example, it detects keywords such as "help" and "stop." By adding the voice recognition sensor, the accuracy of detecting abnormal behavior is improved.

[0034] The etiquette violation prevention system also has an AI analysis unit that accumulates a history of etiquette violations and develops an algorithm to predict the risk of recidivism. The AI ​​analysis unit accumulates the history of etiquette violations, for example, by storing it in a database. The AI ​​analysis unit then develops an algorithm to predict the risk of recidivism based on the accumulated historical data, for example, by calculating the probability of recidivism using past behavioral patterns and statistical models. This allows for more effective countermeasures by predicting the risk of recidivism.

[0035] The etiquette violation prevention system also has an AI analysis unit that analyzes participants' movement patterns to detect early signs of abnormal behavior. The AI ​​analysis unit analyzes participants' movement patterns, for example, using walking patterns and gesture recognition. Based on the movement patterns, the AI ​​analysis unit detects early signs of abnormal behavior, for example, detecting sudden or unnatural movements. This allows for early detection of abnormal behavior, enabling a swift response.

[0036] The etiquette violation prevention system also has an AI analysis unit that compares etiquette violation behavior with data from other events and facilities to extract common patterns. The AI ​​analysis unit compares etiquette violation behavior with data from other events and facilities. For example, it uses data from sporting events, concerts, exhibitions, etc. The AI ​​analysis unit extracts common patterns based on the compared data. For example, it analyzes common patterns using frequency analysis or clustering. By extracting common patterns, more effective countermeasures can be implemented.

[0037] Furthermore, the etiquette violation prevention system's AI analysis unit notifies event organizers of data in real time, enabling a swift response. The AI ​​analysis unit notifies event organizers of data in real time, for example, using a smartphone app or web application. The AI ​​analysis unit then takes swift action based on the notified data, for example by setting notification methods and response procedures. This allows for real-time notifications to be sent, enabling a swift response.

[0038] The etiquette violation prevention system further customizes the messages of the directional speakers based on the participants' behavioral history to provide more effective warnings. The directional speakers customize messages based on the participants' behavioral history. For example, they can emit specific messages to participants who have violated etiquette in the past. This allows for more effective warnings by customizing messages based on behavioral history.

[0039] The etiquette violation prevention system further equips the robot with a facial recognition function, which allows it to individually warn specific participants. The robot is equipped with a facial recognition function. The AI ​​analysis unit uses the facial recognition function to identify specific participants and individually warn them. For example, facial recognition technology can be used to identify specific participants and issue individual messages. In this way, the facial recognition function can be used to individually warn specific participants.

[0040] The manners violation prevention system also utilizes directional speakers or robots in other public facilities and transportation facilities to help prevent a wide range of manners violations. Directional speakers and robots are also utilized in other public facilities and transportation facilities. For example, they are used in stations, airports, libraries, trains, buses, airplanes, etc. This makes it possible to prevent a wide range of manners violations by utilizing them in public facilities and transportation facilities.

[0041] The etiquette violation prevention system will also add a multilingual function to the robot, allowing it to effectively warn people at international events. The robot will add a multilingual function, for example, it will be able to support multiple languages ​​such as English and Chinese. By adding this multilingual function, it will be possible to effectively warn people at international events.

[0042] Furthermore, when the AI ​​analysis unit detects bad manners, the etiquette violation prevention system notifies surrounding participants and encourages cooperation. When the AI ​​analysis unit detects bad manners, it notifies surrounding participants. For example, it can do so using a smartphone app or web application. Based on the notified data, the AI ​​analysis unit encourages cooperation from surrounding participants. For example, it can notify participants within a certain distance or in a specific area. This notifies surrounding participants and encourages cooperation, which is expected to curb bad manners.

[0043] The etiquette violation prevention system also has an AI analysis unit that analyzes data on etiquette violations and automatically generates a report suggesting areas for improvement after the event has ended. The AI ​​analysis unit analyzes data on etiquette violations, for example, using statistical analysis and machine learning algorithms. Based on the analysis results, the AI ​​analysis unit automatically generates a report suggesting areas for improvement after the event has ended, for example, by setting the report format and generation algorithm. This allows for the automatic generation of a report suggesting areas for improvement after the event has ended, which can be useful for managing the next event.

[0044] The etiquette violation prevention system also creates new guidelines for event design and management based on the data collected by the AI ​​analysis unit. The AI ​​analysis unit creates new guidelines for event design and management based on the collected data. For example, it sets areas for improvement and specific implementation procedures based on past data. By creating new guidelines, it is possible to improve the quality of event design and management.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The etiquette violation prevention system can also analyze participants' movement patterns and provide route guidance to avoid congestion. For example, the AI ​​analysis unit analyzes participants' movement patterns in real time based on data collected from security cameras and sensors. It identifies areas where congestion is expected and provides participants with route guidance to avoid congestion. This makes it possible to provide an environment in which participants can move around comfortably. It is also possible to suggest improvements to the layout and arrangement of the venue based on the movement pattern analysis.

[0047] The etiquette violation prevention system can also monitor the health of participants and notify medical staff if any abnormalities are detected. For example, sensors collect vital data such as heart rate, body temperature, and blood pressure. The AI ​​analysis unit analyzes the collected vital data and notifies medical staff if any abnormalities are detected. This allows participants' health to be monitored in real time, enabling rapid response. Health data can also be used to suggest improvements to safety measures in event management.

[0048] The etiquette violation prevention system can also generate individual warning messages based on participants' behavioral history. For example, the AI ​​analysis unit can analyze past behavioral history and generate individual warning messages for specific participants. This allows for more effective warnings based on past behavioral patterns. It can also predict the risk of recidivism and suggest preventative measures based on behavioral history.

[0049] The etiquette violation prevention system can also analyze participants' movement patterns and provide real-time guidance to avoid congestion. For example, the AI ​​analysis unit analyzes participants' movement patterns based on data collected from security cameras and sensors. It identifies areas where congestion is expected and provides real-time guidance to participants to avoid congestion. This makes it possible to provide an environment in which participants can move around comfortably. It can also suggest improvements to the layout and arrangement of the venue based on movement patterns.

[0050] The etiquette violation prevention system can also create new guidelines for event design and management based on participant behavior data. For example, the AI ​​analysis unit analyzes behavior data collected from security cameras and sensors to create new guidelines for event design and management. This makes it possible to identify areas for improvement based on past data and set specific implementation procedures. It is also possible to make suggestions based on behavior data to help improve event safety measures and comfort.

[0051] The etiquette violation prevention system can also automatically generate a report suggesting improvements after the event based on participant behavior data. For example, the AI ​​analysis unit analyzes behavior data collected from security cameras and sensors, and automatically generates a report suggesting improvements after the event ends. This makes it possible to suggest specific improvements based on past data. In addition, efficient report creation is possible by configuring the report format and generation algorithm.

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

[0053] Step 1: Security cameras collect video data within the venue. For example, fixed cameras, pan-tilt-zoom (PTZ) cameras, and infrared cameras are used. Sensors collect audio and movement data within the venue. For example, motion detection sensors, audio sensors, and temperature sensors are used. Step 2: The AI ​​analysis unit analyzes the data collected by security cameras and sensors, using algorithms such as deep learning, image recognition, and natural language processing to identify impolite behavior. Step 3: The warning unit warns the user of any bad manners detected by the AI ​​analysis unit, for example, by using audio or visual alerts or displaying a message.

[0054] (Example 2) The manners violation prevention system according to the embodiment of the present invention is a system that automatically detects and warns against impolite behavior at concerts and events, thereby allowing participants to enjoy themselves in a comfortable and safe environment.

[0055] A manners violation prevention system according to an embodiment includes a security camera, a sensor, an AI analysis unit, and a warning unit. The security camera collects video data from within a venue. For example, a fixed camera, a pan-tilt-zoom (PTZ) camera, or an infrared camera may be used. The sensor collects audio and movement data from within the venue. For example, a motion detection sensor, an audio sensor, or a temperature sensor may be used. The AI ​​analysis unit analyzes the data collected by the security camera and the sensor. For example, it identifies manners violations using algorithms such as deep learning, image recognition, or natural language processing. The warning unit warns participants of manners violations detected by the AI ​​analysis unit. For example, it uses methods such as audio alerts, visual alerts, or message displays. This allows the manners violation prevention system to provide participants with a comfortable and safe environment.

[0056] The etiquette violation prevention system also uses drones to collect video data from the air, which is then analyzed by the AI ​​analysis unit. Drones collect video data from the air. For example, fixed-wing drones, multi-rotor drones, and drones equipped with cameras are used. The AI ​​analysis unit analyzes the video data collected by the drones. For example, a drone could patrol the entire venue and detect abnormal behavior from an aerial perspective. This allows for wider monitoring by detecting abnormal behavior from an aerial perspective.

[0057] The etiquette violation prevention system also adds environmental data such as temperature, humidity, and carbon dioxide concentration to the sensors, and the AI ​​analysis unit analyzes this data to detect signs of abnormal behavior from multiple angles. The sensors collect environmental data such as temperature, humidity, and carbon dioxide concentration. For example, thermistors, infrared thermometers, capacitive humidity sensors, resistive humidity sensors, NDIR sensors, and chemical sensors are used. The AI ​​analysis unit analyzes the environmental data collected by the sensors to detect signs of abnormal behavior from multiple angles. For example, it detects abnormal temperature increases, humidity changes, and fluctuations in carbon dioxide concentration. This enables more accurate monitoring by detecting signs of abnormal behavior from multiple angles based on environmental data.

[0058] The etiquette violation prevention system also analyzes participants' facial expressions from security camera footage, and the AI ​​analysis unit detects signs of etiquette violations based on changes in emotion. Security cameras capture participants' facial expressions. The AI ​​analysis unit uses emotion estimation functionality to analyze participants' facial expressions from security camera footage and detects signs of etiquette violations based on changes in emotion. For example, facial expressions of anger or anxiety can be detected using facial recognition technology and expression recognition algorithms. This makes it possible to detect signs of etiquette violations based on changes in emotion, enabling early response.

[0059] The etiquette violation prevention system also shares data from security cameras and sensors with other event venues and public facilities in real time, and the AI ​​analysis unit identifies trends in etiquette violations over a wide area. Data from security cameras and sensors is shared with other event venues and public facilities in real time. For example, the data is uploaded to the cloud and shared with other facilities. The AI ​​analysis unit analyzes the shared data and identifies trends in etiquette violations over a wide area. For example, it analyzes trends in abnormal behavior using frequency analysis and time series analysis. This allows for an understanding of trends in etiquette violations over a wide area, enabling more effective countermeasures.

[0060] The etiquette violation prevention system further expands the types of sensors by adding a voice recognition sensor, allowing the AI ​​analysis unit to detect specific keywords and voice patterns. The sensor adds a voice recognition sensor. For example, a microphone array or voice command recognition is used. The AI ​​analysis unit analyzes the data collected by the voice recognition sensor and detects specific keywords and voice patterns. For example, it detects keywords such as "help" and "stop." By adding the voice recognition sensor, the accuracy of detecting abnormal behavior is improved.

[0061] The etiquette violation prevention system also uses an emotion estimation function to monitor the emotional state of participants in real time, and the AI ​​analysis unit adjusts the environment to bring out positive emotions. The emotion estimation function monitors the emotional state of participants in real time. The AI ​​analysis unit uses the emotion estimation function to analyze the emotional state of participants, and adjusts the environment to bring out positive emotions. For example, it adjusts the lighting and music. This makes it possible to adjust the environment to bring out positive emotions.

[0062] The etiquette violation prevention system also has an AI analysis unit that accumulates a history of etiquette violations and develops an algorithm to predict the risk of recidivism. The AI ​​analysis unit accumulates the history of etiquette violations, for example, by storing it in a database. The AI ​​analysis unit then develops an algorithm to predict the risk of recidivism based on the accumulated historical data, for example, by calculating the probability of recidivism using past behavioral patterns and statistical models. This allows for more effective countermeasures by predicting the risk of recidivism.

[0063] The etiquette violation prevention system also has an AI analysis unit that analyzes participants' movement patterns to detect early signs of abnormal behavior. The AI ​​analysis unit analyzes participants' movement patterns, for example, using walking patterns and gesture recognition. Based on the movement patterns, the AI ​​analysis unit detects early signs of abnormal behavior, for example, detecting sudden or unnatural movements. This allows for early detection of abnormal behavior, enabling a swift response.

[0064] The etiquette violation prevention system further uses an emotion estimation function to analyze changes in participants' emotions and identify sudden changes in emotion as signs of etiquette violations. The emotion estimation function analyzes changes in participants' emotions. The AI ​​analysis unit uses the emotion estimation function to identify sudden changes in emotion as signs of etiquette violations. For example, it detects sudden increases or decreases in emotion scores. This makes it possible to identify signs of etiquette violations based on sudden changes in emotion and respond quickly.

[0065] The etiquette violation prevention system also has an AI analysis unit that compares etiquette violation behavior with data from other events and facilities to extract common patterns. The AI ​​analysis unit compares etiquette violation behavior with data from other events and facilities. For example, it uses data from sporting events, concerts, exhibitions, etc. The AI ​​analysis unit extracts common patterns based on the compared data. For example, it analyzes common patterns using frequency analysis or clustering. By extracting common patterns, more effective countermeasures can be implemented.

[0066] Furthermore, the etiquette violation prevention system's AI analysis unit notifies event organizers of data in real time, enabling a swift response. The AI ​​analysis unit notifies event organizers of data in real time, for example, using a smartphone app or web application. The AI ​​analysis unit then takes swift action based on the notified data, for example by setting notification methods and response procedures. This allows for real-time notifications to be sent, enabling a swift response.

[0067] The etiquette violation prevention system further uses an emotion estimation function to collect emotional data from participants, analyze emotional trends, and propose preventative measures for etiquette violations. The emotion estimation function collects emotional data from participants. The AI ​​analysis unit uses the emotion estimation function to analyze the collected emotional data and analyze emotional trends. For example, time series analysis and frequency analysis are used. The AI ​​analysis unit proposes preventative measures for etiquette violations based on the analysis results. For example, it issues advance warnings or adjusts the environment. In this way, by proposing preventative measures based on the emotional data, it is possible to prevent etiquette violations from occurring in the first place.

[0068] The etiquette violation prevention system further customizes the messages of the directional speakers based on the participants' behavioral history to provide more effective warnings. The directional speakers customize messages based on the participants' behavioral history. For example, they can emit specific messages to participants who have violated etiquette in the past. This allows for more effective warnings by customizing messages based on behavioral history.

[0069] The etiquette violation prevention system further equips the robot with a facial recognition function, which allows it to individually warn specific participants. The robot is equipped with a facial recognition function. The AI ​​analysis unit uses the facial recognition function to identify specific participants and individually warn them. For example, facial recognition technology can be used to identify specific participants and issue individual messages. In this way, the facial recognition function can be used to individually warn specific participants.

[0070] The etiquette violation prevention system further uses an emotion estimation function to analyze participants' emotions when issuing a warning, and warns them at the optimal timing and in the optimal way. The emotion estimation function analyzes participants' emotions when issuing a warning. The AI ​​analysis unit uses the emotion estimation function to analyze participants' emotions, and warns them at the optimal timing and in the optimal way. For example, it warns participants before their feelings of anger build up. In this way, by analyzing emotions, it is possible to warn them at the optimal timing and in the optimal way.

[0071] The manners violation prevention system also utilizes directional speakers or robots in other public facilities and transportation facilities to help prevent a wide range of manners violations. Directional speakers and robots are also utilized in other public facilities and transportation facilities. For example, they are used in stations, airports, libraries, trains, buses, airplanes, etc. This makes it possible to prevent a wide range of manners violations by utilizing them in public facilities and transportation facilities.

[0072] The etiquette violation prevention system will also add a multilingual function to the robot, allowing it to effectively warn people at international events. The robot will add a multilingual function, for example, it will be able to support multiple languages ​​such as English and Chinese. By adding this multilingual function, it will be possible to effectively warn people at international events.

[0073] The etiquette violation prevention system further uses an emotion estimation function to evaluate the effectiveness of the warning in real time and make improvements based on feedback. The emotion estimation function evaluates the effectiveness of the warning in real time. The AI ​​analysis unit uses the emotion estimation function to evaluate the effectiveness of the warning and make improvements based on feedback. For example, it monitors emotional changes after the warning and makes adjustments based on feedback. This makes it possible to increase the effectiveness of the warning by evaluating in real time and making improvements based on feedback.

[0074] Furthermore, when the AI ​​analysis unit detects bad manners, the etiquette violation prevention system notifies surrounding participants and encourages cooperation. When the AI ​​analysis unit detects bad manners, it notifies surrounding participants. For example, it can do so using a smartphone app or web application. Based on the notified data, the AI ​​analysis unit encourages cooperation from surrounding participants. For example, it can notify participants within a certain distance or in a specific area. This notifies surrounding participants and encourages cooperation, which is expected to curb bad manners.

[0075] The etiquette violation prevention system also has an AI analysis unit that analyzes data on etiquette violations and automatically generates a report suggesting areas for improvement after the event has ended. The AI ​​analysis unit analyzes data on etiquette violations, for example, using statistical analysis and machine learning algorithms. Based on the analysis results, the AI ​​analysis unit automatically generates a report suggesting areas for improvement after the event has ended, for example, by setting the report format and generation algorithm. This allows for the automatic generation of a report suggesting areas for improvement after the event has ended, which can be useful for managing the next event.

[0076] The etiquette violation prevention system further uses an emotion estimation function to monitor the overall emotional state of participants and adjust the environment according to the progress of the event. The emotion estimation function monitors the overall emotional state of participants. The AI ​​analysis unit uses the emotion estimation function to analyze the emotional state of participants and adjust the environment according to the progress of the event, for example, by adjusting lighting or music. In this way, participant satisfaction can be improved by monitoring the emotional state and adjusting the environment according to the progress of the event.

[0077] The etiquette violation prevention system also creates new guidelines for event design and management based on the data collected by the AI ​​analysis unit. The AI ​​analysis unit creates new guidelines for event design and management based on the collected data. For example, it sets areas for improvement and specific implementation procedures based on past data. By creating new guidelines, it is possible to improve the quality of event design and management.

[0078] The etiquette violation prevention system further uses an emotion estimation function to analyze participants' emotional data and identify factors that contributed to the success of the event. The emotion estimation function analyzes participants' emotional data. The AI ​​analysis unit uses the emotion estimation function to analyze the collected emotional data and identify factors that contributed to the success of the event. For example, it extracts success factors based on the emotion score. In this way, by analyzing the emotional data, it is possible to identify factors that contributed to the success of the event and use this information to help manage the next event.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The etiquette violation prevention system can also analyze participants' movement patterns and provide route guidance to avoid congestion. For example, the AI ​​analysis unit analyzes participants' movement patterns in real time based on data collected from security cameras and sensors. It identifies areas where congestion is expected and provides participants with route guidance to avoid congestion. This makes it possible to provide an environment in which participants can move around comfortably. It is also possible to suggest improvements to the layout and arrangement of the venue based on the movement pattern analysis.

[0081] The etiquette violation prevention system can also monitor the health of participants and notify medical staff if any abnormalities are detected. For example, sensors collect vital data such as heart rate, body temperature, and blood pressure. The AI ​​analysis unit analyzes the collected vital data and notifies medical staff if any abnormalities are detected. This allows participants' health to be monitored in real time, enabling rapid response. Health data can also be used to suggest improvements to safety measures in event management.

[0082] The etiquette violation prevention system can also estimate participants' emotions and provide music and videos that reflect their emotions. For example, the AI ​​analysis unit estimates participants' emotions based on data collected from security cameras and sensors. It then provides relaxing music and videos based on the estimated emotions. This improves participants' emotional state and provides a comfortable environment. It can also suggest improvements to the event's production and program based on the emotional data.

[0083] The etiquette violation prevention system can also generate individual warning messages based on participants' behavioral history. For example, the AI ​​analysis unit can analyze past behavioral history and generate individual warning messages for specific participants. This allows for more effective warnings based on past behavioral patterns. It can also predict the risk of recidivism and suggest preventative measures based on behavioral history.

[0084] The etiquette violation prevention system can also use an emotion estimation function to adjust the progress of the event based on the emotional state of participants. For example, the AI ​​analysis unit can use the emotion estimation function to analyze the emotional state of participants in real time, and if emotions become heightened, make adjustments such as temporarily slowing down the progress of the event. This allows for flexible event management based on the emotional state of participants. It can also suggest improvements to the event schedule based on emotional data.

[0085] The etiquette violation prevention system can also analyze participants' movement patterns and provide real-time guidance to avoid congestion. For example, the AI ​​analysis unit analyzes participants' movement patterns based on data collected from security cameras and sensors. It identifies areas where congestion is expected and provides real-time guidance to participants to avoid congestion. This makes it possible to provide an environment in which participants can move around comfortably. It can also suggest improvements to the layout and arrangement of the venue based on movement patterns.

[0086] The etiquette violation prevention system can also use an emotion estimation function to generate individual warning messages based on participants' emotional state. For example, the AI ​​analysis unit can use the emotion estimation function to analyze participants' emotional state in real time and generate individual warning messages if emotions are high. This enables effective warnings based on participants' emotional state. Furthermore, the system can also suggest improvements to the method and timing of warnings based on emotional data.

[0087] The etiquette violation prevention system can also create new guidelines for event design and management based on participant behavior data. For example, the AI ​​analysis unit analyzes behavior data collected from security cameras and sensors to create new guidelines for event design and management. This makes it possible to identify areas for improvement based on past data and set specific implementation procedures. It is also possible to make suggestions based on behavior data to help improve event safety measures and comfort.

[0088] The etiquette violation prevention system can also use an emotion estimation function to identify factors that contributed to the success of an event based on participants' emotional data. For example, the AI ​​analysis unit can analyze the emotional data collected using the emotion estimation function to identify factors that contributed to the success of the event. This allows the system to extract factors that contributed to the success of the event based on the emotional data and use this information to help manage the next event. It can also suggest improvements to the event's production and program based on the emotional data.

[0089] The etiquette violation prevention system can also automatically generate a report suggesting improvements after the event based on participant behavior data. For example, the AI ​​analysis unit analyzes behavior data collected from security cameras and sensors, and automatically generates a report suggesting improvements after the event ends. This makes it possible to suggest specific improvements based on past data. In addition, efficient report creation is possible by configuring the report format and generation algorithm.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: Security cameras collect video data within the venue. For example, fixed cameras, pan-tilt-zoom (PTZ) cameras, and infrared cameras are used. Sensors collect audio and movement data within the venue. For example, motion detection sensors, audio sensors, and temperature sensors are used. Step 2: The AI ​​analysis unit analyzes the data collected by security cameras and sensors, using algorithms such as deep learning, image recognition, and natural language processing to identify impolite behavior. Step 3: The warning unit warns the user of any bad manners detected by the AI ​​analysis unit, for example, by using audio or visual alerts or displaying a message.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with security cameras and sensors, an AI analysis unit that analyzes data collected by the security camera and the sensor; and a warning unit that warns the user about bad manners detected by the AI ​​analysis unit. A system characterized by:

2. In addition to the security cameras, Drones are used to collect video data from the sky, and the AI ​​analysis unit analyzes the data.

2. The system of claim 1.

3. The data from the security cameras and sensors will be shared in real time with other event venues and public facilities, and the AI ​​analysis unit will be able to grasp trends in manner violations across a wide area.

2. The system of claim 1.

4. The AI ​​analysis unit Develop an algorithm that accumulates the history of such bad manners and predicts the risk of recidivism.

2. The system of claim 1.

5. Customize messages from directional speakers based on participants' behavioral history for more effective attention 2. The system of claim 1.

6. The AI ​​analysis unit analyzes participants' facial expressions from the security camera footage and detects signs of impolite behavior based on changes in emotions.

2. The system of claim 1.

Images