System

Abstract

An object of the system according to the embodiment is to analyze video data of a monitoring camera and predict and prevent a preliminary action.SOLUTION: A system according to an embodiment includes a video data storage unit, an analysis unit, a patterning unit, and a suggestion generation unit. The video data storage unit stores video data of the monitoring camera. The analysis unit analyzes the video data accumulated by the video data accumulation unit. The patterning unit learns and patterns the characteristics of the preliminary action analyzed by the analysis unit. The suggestion generator generates a prevention suggestion or a script based on the pattern of the prior action learned by the patternizer.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 does not adequately utilize video data from surveillance cameras to predict and prevent behavior in advance, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze video data from a surveillance camera, predict pre-emptive behavior, and prevent it from occurring. [Means for solving the problem]

[0006] The system according to the embodiment includes a video data storage unit, an analysis unit, a patterning unit, and a suggestion generation unit. The video data storage unit stores video data from a surveillance camera. The analysis unit analyzes the video data stored by the video data storage unit. The patterning unit learns and patterns the characteristics of the pre-actions analyzed by the analysis unit. The suggestion generation unit generates preventive suggestions and scripts based on the patterns of the pre-actions learned by the patterning unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze video data from a surveillance camera, predict pre-emptive behavior, and prevent it from happening. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The prevention system according to an embodiment of the present invention accumulates video data from surveillance cameras capturing the actions of people immediately before similar incidents have occurred in the past, such as suicides by jumping in front of trains at train stations, and uses a generation AI to learn and pattern the characteristics of the prior actions, generate prevention suggestions and scripts, and deter the actions. This allows the prevention system to prevent similar incidents, such as suicides by jumping in front of trains at train stations, before they occur.

[0029] A preventive system according to an embodiment includes a video data storage unit, an analysis unit, a pattern generation unit, and a suggestion generation unit. The video data storage unit stores video data from a surveillance camera. For example, the video data storage unit can store high-resolution video data for a long period of time. The video data storage unit can also integrate and manage video data from multiple surveillance cameras. The video data storage unit can efficiently store data using video data compression technology. The analysis unit analyzes the video data stored by the video data storage unit. For example, the analysis unit can detect specific movements or behavioral patterns from the video data using image recognition technology. The analysis unit can also analyze movements in the video data using a movement detection algorithm. The analysis unit can also analyze facial expressions of people in the video data using facial expression recognition technology. The pattern generation unit learns and patterns features of pre-behaviors analyzed by the analysis unit. For example, the pattern generation unit learns patterns of pre-behaviors using a machine learning algorithm. The pattern generation unit can also classify patterns of pre-behaviors using a clustering method. Furthermore, the patterning unit can analyze pre-action patterns along a time axis using time series analysis. The suggestion generation unit generates preventive suggestions and scripts based on the pre-action patterns learned by the patterning unit. For example, the suggestion generation unit generates a suggestion that notifies a security guard when a specific movement is detected. The suggestion generation unit can also generate a script that displays a warning message when a specific movement is observed. The suggestion generation unit can also generate a script that sounds an audio alarm when a specific movement is detected. This allows the prevention system according to the embodiment to prevent similar incidents, such as suicides in front of trains, from occurring. For example, if a security guard responds quickly when a specific movement or behavior is detected, the occurrence of an incident can be prevented. Furthermore, using a warning message or audio alarm can attract the attention of people in the vicinity and deter them from engaging in dangerous behavior.

[0030] The analysis unit can analyze specific movements, behavioral patterns, and changes in facial expressions from video data. For example, the analysis unit uses generative AI to analyze video data from surveillance cameras in real time and instantly extract specific movements and behavioral patterns. For example, a system can be built that notifies security guards on the spot when specific movements or behaviors are detected. The analysis unit can also use image recognition technology to detect specific movements and behavioral patterns from video data. For example, if a specific movement is repeated, it can recognize that pattern. The analysis unit can also use facial expression recognition technology to analyze changes in the facial expressions of people in video data. For example, it can recognize facial expressions such as smiling, anger, and sadness and analyze those changes. This allows for early detection of dangerous behavior by analyzing specific movements, behavioral patterns, and changes in facial expressions.

[0031] The suggestion generation unit can generate suggestions and scripts that notify security guards when specific movements are detected. For example, the suggestion generation unit can simultaneously collect audio data in addition to video data and analyze the correlation between the audio and video to make more accurate behavior predictions. For example, a system can be built that simultaneously collects video data and audio data and analyzes the correlation using a generation AI. For example, a system can be built that immediately notifies security guards when specific sounds and movements are detected simultaneously. The suggestion generation unit can also generate suggestions that immediately notify security guards when specific movements are detected. For example, when specific movements are detected, a script can be generated that immediately notifies security guards. This allows security guards to be notified when specific movements are detected, enabling a quick response.

[0032] The suggestion generation unit can generate suggestions and scripts that display a warning message when a specific behavior is observed. The suggestion generation unit, for example, uses an emotion estimation function to estimate a user's emotions from video data and predict risky behavior based on changes in emotions. For example, an emotion estimation function can be equipped in the generation AI to build a system that estimates a user's emotions from video data in real time. For example, a system can be built that notifies security guards on the spot when a change in emotion is detected. The suggestion generation unit can also generate suggestions that display a warning message when a specific behavior is observed. For example, a script can be generated that displays a warning message on the spot when a specific behavior is observed. This makes it possible to deter risky behavior by displaying a warning message when a specific behavior is observed.

[0033] The suggestion generation unit can generate suggestions and scripts that sound an audio alarm when a specific movement is detected. The suggestion generation unit generates, for example, a suggestion that sounds an audio alarm when a specific movement is detected. For example, when a specific movement is detected, it generates a script that sounds an audio alarm on the spot. The suggestion generation unit can also set the type and volume of the audio alarm. For example, it can set a warning sound or a warning sound and adjust the volume. In this way, by sounding an audio alarm when a specific movement is detected, it is possible to attract the attention of those around and deter dangerous behavior.

[0034] The video data storage unit simultaneously collects audio data in addition to video data, and the analysis unit can analyze the correlation between the video data and audio data. The video data storage unit, for example, simultaneously collects audio data in addition to video data. For example, a system can be built in which a microphone is installed on a surveillance camera and video and audio are simultaneously recorded. The video data storage unit can also efficiently store data using audio data compression technology. The analysis unit, for example, analyzes the correlation between video data and audio data. For example, a system can be built in which the correlation between video data and audio data is analyzed using generative AI to detect specific movement or behavior patterns. The analysis unit can also detect specific audio patterns from audio data using voice recognition technology. This enables more accurate behavior prediction by analyzing the correlation between video data and audio data.

[0035] The system can be introduced in public facilities other than train stations, such as shopping malls and airports, to prevent risky behavior over a wide area. For example, a system can be created that notifies security guards on the spot when specific movements or actions are detected. The system can also generate prevention suggestions and scripts customized for each public facility. This will prevent risky behavior in public facilities other than train stations, improving safety over a wide area.

[0036] The analysis unit can link the results of the video data analysis with a facial recognition system or an access control system to comprehensively strengthen security. The analysis unit, for example, links the results of the video data analysis with a facial recognition system. For example, if a specific movement or behavior is detected, a system is constructed in which the person is identified on the spot using a facial recognition system and a security guard is notified. The analysis unit can also link the results of the video data analysis with an access control system. For example, if a specific movement or behavior is detected, a system is constructed in which access control is performed on the spot using an access control system. In this way, by linking the results of the video data analysis with other security systems, comprehensive security can be strengthened.

[0037] The patterning unit can analyze pre-action patterns in detail along a time axis and track changes in behavior. The patterning unit can, for example, use generative AI to analyze pre-action patterns in detail along a time axis and track changes in behavior. For example, a system can be built that analyzes how specific movements and behaviors change over time. The patterning unit can also analyze pre-action patterns along a time axis using time series analysis. For example, if a specific behavior is repeated, it tracks its changes. In this way, by analyzing pre-action patterns along a time axis, it is possible to track changes in behavior.

[0038] The patterning unit classifies the patterned behavior data by different seasons or time periods, and can clarify behavior patterns under specific conditions. The patterning unit, for example, classifies the patterned behavior data by different seasons or time periods, and can clarify behavior patterns under specific conditions. For example, a system is constructed that analyzes differences in behavior patterns between summer and winter. The patterning unit can also classify the behavior data by time period, and can analyze behavior patterns in specific time periods. For example, it analyzes differences in behavior patterns between morning and night. In this way, by classifying the behavior data by season or time period, it is possible to clarify behavior patterns under specific conditions.

[0039] The patterning unit can compare pre-behavior patterns for different cultural spheres or regions to clarify region-specific behavior patterns. The patterning unit, for example, compares pre-behavior patterns for different cultural spheres or regions to clarify region-specific behavior patterns. For example, a system can be built that analyzes differences in behavior patterns between Japan and the United States. The patterning unit can also analyze behavior patterns based on the cultural characteristics of each region. For example, it can analyze behavior patterns in a specific region to clarify behavior unique to that region. This makes it possible to predict region-specific behavior by clarifying behavior patterns for different cultural spheres or regions.

[0040] The patterning unit can share the patterned behavioral data with other crime prevention systems to achieve comprehensive crime prevention. The patterning unit can share the patterned behavioral data with, for example, a theft prevention system to achieve comprehensive crime prevention. For example, a system can be built that notifies security guards on the spot when specific movements or behaviors are detected. The patterning unit can also work in conjunction with a violence prevention system to share behavioral data. For example, a system can be built that notifies security guards on the spot when specific behaviors are detected. In this way, sharing behavioral data with other crime prevention systems enables comprehensive crime prevention.

[0041] The suggestion generation unit can use generation AI to generate preventive suggestions and scripts in real time and execute them instantly. The suggestion generation unit can use generation AI to generate preventive suggestions and scripts in real time and execute them instantly. For example, a system can be built that generates a script that notifies security guards on the spot when a specific movement or behavior is detected. The suggestion generation unit can also use generation AI to automatically update the contents of preventive suggestions and scripts. For example, it can generate preventive suggestions and scripts based on the latest data and execute them instantly. This allows preventive suggestions and scripts to be generated in real time and executed immediately, enabling a rapid response.

[0042] The suggestion generation unit can refer to past success stories when generating suggestions and scripts and propose the most effective preventive measures. The suggestion generation unit, for example, refers to past success stories when generating suggestions and scripts and proposes the most effective preventive measures. For example, a system can be built that automatically generates optimal preventive measures based on past data. The suggestion generation unit can also set the content and standards of successful cases. For example, preventive measures can be proposed based on past performance and evaluation criteria. In this way, the most effective preventive measures can be proposed by referring to past success stories.

[0043] The suggestion generation unit can adapt the preventive suggestions and scripts to different languages ​​and cultures, enabling international use. The suggestion generation unit, for example, adapts the preventive suggestions and scripts to different languages, enabling international use. For example, a system that translates into multiple languages, such as English, French, and Chinese, can be built. The suggestion generation unit can also generate preventive suggestions and scripts that take into account the cultural characteristics and customs of each region in order to adapt to different cultures. This allows the preventive suggestions and scripts to adapt to different languages ​​and cultures, enabling international use.

[0044] The suggestion generation unit can link the generated results of suggestions and scripts with other security systems to implement comprehensive preventive measures. The suggestion generation unit, for example, links the generated results of suggestions and scripts with other security systems. For example, it links with a face recognition system or an access control system to build a system that implements comprehensive preventive measures. The suggestion generation unit can also set a linking method and a data integration method for each security system. For example, data is shared between multiple systems to implement comprehensive preventive measures. In this way, comprehensive preventive measures can be implemented by linking the generated results of suggestions and scripts with other security systems.

[0045] The video data storage unit can share video data from surveillance cameras on the cloud and realize data linkage between multiple cameras. The video data storage unit, for example, can share video data from surveillance cameras on the cloud and realize data linkage between multiple cameras. For example, a system can be built using cloud storage to centrally manage video data from multiple surveillance cameras. The video data storage unit can also use cloud computing to efficiently analyze and process video data. For example, a system can be built that analyzes video data on the cloud and provides feedback on the results in real time. This allows video data to be shared on the cloud and realizes data linkage between multiple cameras, making it possible to monitor a wide area.

[0046] The video data storage unit can link video data from surveillance cameras with other security systems to enable monitoring over a wide area. The video data storage unit can, for example, link video data from surveillance cameras with a drone monitoring system to enable monitoring over a wide area. For example, a system can be built in which, when a specific movement or behavior is detected, a drone is flown on the spot to monitor the site. The video data storage unit can also link with other security systems to share data. For example, a system can be built that links with a facial recognition system or an access control system to provide comprehensive monitoring. This makes it possible to monitor a wide area by linking video data with other security systems.

[0047] The analysis unit can link video data from surveillance cameras with an AI assistant and issue voice warnings and instructions. The analysis unit, for example, can link video data from surveillance cameras with an AI assistant and issue voice warnings and instructions. For example, a system can be built in which, if a specific movement or behavior is detected, the voice assistant will issue a warning on the spot. The analysis unit can also use the AI ​​assistant to issue voice instructions. For example, a system can be built in which, if a specific behavior is observed, the voice assistant will issue instructions on the spot. By linking with the AI ​​assistant, voice warnings and instructions become possible.

[0048] When the analysis unit detects risky behavior using the generation AI, it can immediately notify security guards and encourage a quick response. When the analysis unit detects risky behavior using the generation AI, it can immediately notify security guards and encourage a quick response. For example, a system can be built that notifies security guards on the spot when a specific movement or behavior is detected. The analysis unit can also use the generation AI to provide feedback on the results of risky behavior detection in real time and implement countermeasures immediately. For example, a system can be built that displays a warning message on the spot when risky behavior is detected. This makes it possible to immediately notify security guards when risky behavior is detected, enabling a quick response.

[0049] The analysis unit can optimize behavioral deterrence measures based on past success stories and select the most effective method. The analysis unit can, for example, optimize behavioral deterrence measures based on past success stories and select the most effective method. For example, a system can be built that automatically generates optimal deterrence measures based on past data. The analysis unit can also set the content and standards of successful cases. For example, deterrence measures can be selected based on past performance and evaluation criteria. This allows for more effective deterrence by selecting optimal behavioral deterrence measures based on past success stories.

[0050] The analysis unit can adapt the behavioral deterrent measures to different languages ​​and cultures, enabling international use. The analysis unit, for example, can adapt the behavioral deterrent measures to different languages, enabling international use. For example, a system can be built that translates into multiple languages, such as English, French, and Chinese. The analysis unit can also implement behavioral deterrent measures that take into account the cultural characteristics and customs of each region in order to adapt to different cultures. This allows the behavioral deterrent measures to adapt to different languages ​​and cultures, enabling international use.

[0051] The analysis unit can link behavioral deterrence measures with other security systems to implement comprehensive deterrence measures. The analysis unit, for example, links behavioral deterrence measures with other security systems. For example, it links with a face recognition system and an access control system to build a system that implements comprehensive deterrence measures. The analysis unit can also set the linking method and data integration method for each security system. For example, data can be shared between multiple systems to implement comprehensive deterrence measures. In this way, comprehensive deterrence measures can be implemented by linking behavioral deterrence measures with other security systems.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The suggestion generation unit can generate suggestions and scripts that notify security guards when specific movements are detected. For example, the suggestion generation unit can simultaneously collect audio data in addition to video data and analyze the correlation between the audio and video to make more accurate behavior predictions. For example, a system can be built that simultaneously collects video data and audio data and analyzes the correlation using a generation AI. For example, a system can be built that immediately notifies security guards when specific sounds and movements are detected simultaneously. The suggestion generation unit can also generate suggestions that immediately notify security guards when specific movements are detected. For example, when specific movements are detected, a script can be generated that immediately notifies security guards. This allows security guards to be notified when specific movements are detected, enabling a quick response.

[0054] The suggestion generation unit can generate suggestions and scripts that sound an audio alarm when a specific movement is detected. The suggestion generation unit generates, for example, a suggestion that sounds an audio alarm when a specific movement is detected. For example, when a specific movement is detected, it generates a script that sounds an audio alarm on the spot. The suggestion generation unit can also set the type and volume of the audio alarm. For example, it can set a warning sound or a warning sound and adjust the volume. In this way, by sounding an audio alarm when a specific movement is detected, it is possible to attract the attention of those around and deter dangerous behavior.

[0055] The video data storage unit simultaneously collects audio data in addition to video data, and the analysis unit can analyze the correlation between the video data and audio data. The video data storage unit, for example, simultaneously collects audio data in addition to video data. For example, a system can be built in which a microphone is installed on a surveillance camera and video and audio are simultaneously recorded. The video data storage unit can also efficiently store data using audio data compression technology. The analysis unit, for example, analyzes the correlation between video data and audio data. For example, a system can be built in which the correlation between video data and audio data is analyzed using generative AI to detect specific movement or behavior patterns. The analysis unit can also detect specific audio patterns from audio data using voice recognition technology. This enables more accurate behavior prediction by analyzing the correlation between video data and audio data.

[0056] The system can be introduced in public facilities other than train stations, such as shopping malls and airports, to prevent risky behavior over a wide area. For example, a system can be created that notifies security guards on the spot when specific movements or actions are detected. The system can also generate prevention suggestions and scripts customized for each public facility. This will prevent risky behavior in public facilities other than train stations, improving safety over a wide area.

[0057] The analysis unit can link the results of the video data analysis with a facial recognition system or an access control system to comprehensively strengthen security. The analysis unit, for example, links the results of the video data analysis with a facial recognition system. For example, if a specific movement or behavior is detected, a system is constructed in which the person is identified on the spot using a facial recognition system and a security guard is notified. The analysis unit can also link the results of the video data analysis with an access control system. For example, if a specific movement or behavior is detected, a system is constructed in which access control is performed on the spot using an access control system. In this way, by linking the results of the video data analysis with other security systems, comprehensive security can be strengthened.

[0058] The patterning unit can analyze pre-action patterns in detail along a time axis and track changes in behavior. The patterning unit can, for example, use generative AI to analyze pre-action patterns in detail along a time axis and track changes in behavior. For example, a system can be built that analyzes how specific movements and behaviors change over time. The patterning unit can also analyze pre-action patterns along a time axis using time series analysis. For example, if a specific behavior is repeated, it tracks its changes. In this way, by analyzing pre-action patterns along a time axis, it is possible to track changes in behavior.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The video data storage unit stores video data from a surveillance camera. For example, the video data storage unit can store high-resolution video data for a long period of time. The video data storage unit can also integrate and manage video data from multiple surveillance cameras. Furthermore, the video data storage unit can store data efficiently using video data compression technology. Step 2: The analysis unit analyzes the video data stored by the video data storage unit. For example, the analysis unit may use image recognition technology to detect specific movements or behavioral patterns from the video data. The analysis unit may also use a movement detection algorithm to analyze movements within the video data. Furthermore, the analysis unit may use facial expression recognition technology to analyze facial expressions of people within the video data. Step 3: The patterning unit learns the characteristics of the pre-action analyzed by the analysis unit and patterns them. For example, the patterning unit learns patterns of pre-action using a machine learning algorithm. The patterning unit can also classify patterns of pre-action using a clustering method. Furthermore, the patterning unit can analyze patterns of pre-action along a time axis using time series analysis. Step 4: The suggestion generation unit generates preventive suggestions and scripts based on the patterns of pre-actions learned by the patternization unit. For example, the suggestion generation unit generates a suggestion to notify a security guard when a specific movement is detected. The suggestion generation unit can also generate a script to display a warning message when a specific movement is observed. Furthermore, the suggestion generation unit can also generate a script to sound an audio alarm when a specific movement is detected.

[0061] (Example 2) The prevention system according to an embodiment of the present invention accumulates video data from surveillance cameras capturing the actions of people immediately before similar incidents have occurred in the past, such as suicides by jumping in front of trains at train stations, and uses a generation AI to learn and pattern the characteristics of the prior actions, generate prevention suggestions and scripts, and deter the actions. This allows the prevention system to prevent similar incidents, such as suicides by jumping in front of trains at train stations, before they occur.

[0062] A preventive system according to an embodiment includes a video data storage unit, an analysis unit, a pattern generation unit, and a suggestion generation unit. The video data storage unit stores video data from a surveillance camera. For example, the video data storage unit can store high-resolution video data for a long period of time. The video data storage unit can also integrate and manage video data from multiple surveillance cameras. The video data storage unit can efficiently store data using video data compression technology. The analysis unit analyzes the video data stored by the video data storage unit. For example, the analysis unit can detect specific movements or behavioral patterns from the video data using image recognition technology. The analysis unit can also analyze movements in the video data using a movement detection algorithm. The analysis unit can also analyze facial expressions of people in the video data using facial expression recognition technology. The pattern generation unit learns and patterns features of pre-behaviors analyzed by the analysis unit. For example, the pattern generation unit learns patterns of pre-behaviors using a machine learning algorithm. The pattern generation unit can also classify patterns of pre-behaviors using a clustering method. Furthermore, the patterning unit can analyze pre-action patterns along a time axis using time series analysis. The suggestion generation unit generates preventive suggestions and scripts based on the pre-action patterns learned by the patterning unit. For example, the suggestion generation unit generates a suggestion that notifies a security guard when a specific movement is detected. The suggestion generation unit can also generate a script that displays a warning message when a specific movement is observed. The suggestion generation unit can also generate a script that sounds an audio alarm when a specific movement is detected. This allows the prevention system according to the embodiment to prevent similar incidents, such as suicides in front of trains, from occurring. For example, if a security guard responds quickly when a specific movement or behavior is detected, the occurrence of an incident can be prevented. Furthermore, using a warning message or audio alarm can attract the attention of people in the vicinity and deter them from engaging in dangerous behavior.

[0063] The analysis unit can analyze specific movements, behavioral patterns, and changes in facial expressions from video data. For example, the analysis unit uses generative AI to analyze video data from surveillance cameras in real time and instantly extract specific movements and behavioral patterns. For example, a system can be built that notifies security guards on the spot when specific movements or behaviors are detected. The analysis unit can also use image recognition technology to detect specific movements and behavioral patterns from video data. For example, if a specific movement is repeated, it can recognize that pattern. The analysis unit can also use facial expression recognition technology to analyze changes in the facial expressions of people in video data. For example, it can recognize facial expressions such as smiling, anger, and sadness and analyze those changes. This allows for early detection of dangerous behavior by analyzing specific movements, behavioral patterns, and changes in facial expressions.

[0064] The suggestion generation unit can generate suggestions and scripts that notify security guards when specific movements are detected. For example, the suggestion generation unit can simultaneously collect audio data in addition to video data and analyze the correlation between the audio and video to make more accurate behavior predictions. For example, a system can be built that simultaneously collects video data and audio data and analyzes the correlation using a generation AI. For example, a system can be built that immediately notifies security guards when specific sounds and movements are detected simultaneously. The suggestion generation unit can also generate suggestions that immediately notify security guards when specific movements are detected. For example, when specific movements are detected, a script can be generated that immediately notifies security guards. This allows security guards to be notified when specific movements are detected, enabling a quick response.

[0065] The suggestion generation unit can generate suggestions and scripts that display a warning message when a specific behavior is observed. The suggestion generation unit, for example, uses an emotion estimation function to estimate a user's emotions from video data and predict risky behavior based on changes in emotions. For example, an emotion estimation function can be equipped in the generation AI to build a system that estimates a user's emotions from video data in real time. For example, a system can be built that notifies security guards on the spot when a change in emotion is detected. The suggestion generation unit can also generate suggestions that display a warning message when a specific behavior is observed. For example, a script can be generated that displays a warning message on the spot when a specific behavior is observed. This makes it possible to deter risky behavior by displaying a warning message when a specific behavior is observed.

[0066] The suggestion generation unit can generate suggestions and scripts that sound an audio alarm when a specific movement is detected. The suggestion generation unit generates, for example, a suggestion that sounds an audio alarm when a specific movement is detected. For example, when a specific movement is detected, it generates a script that sounds an audio alarm on the spot. The suggestion generation unit can also set the type and volume of the audio alarm. For example, it can set a warning sound or a warning sound and adjust the volume. In this way, by sounding an audio alarm when a specific movement is detected, it is possible to attract the attention of those around and deter dangerous behavior.

[0067] The video data storage unit simultaneously collects audio data in addition to video data, and the analysis unit can analyze the correlation between the video data and audio data. The video data storage unit, for example, simultaneously collects audio data in addition to video data. For example, a system can be built in which a microphone is installed on a surveillance camera and video and audio are simultaneously recorded. The video data storage unit can also efficiently store data using audio data compression technology. The analysis unit, for example, analyzes the correlation between video data and audio data. For example, a system can be built in which the correlation between video data and audio data is analyzed using generative AI to detect specific movement or behavior patterns. The analysis unit can also detect specific audio patterns from audio data using voice recognition technology. This enables more accurate behavior prediction by analyzing the correlation between video data and audio data.

[0068] The analysis unit can use the emotion estimation function to estimate the user's emotion from the video data and predict risky behavior based on changes in emotion. The analysis unit, for example, uses the emotion estimation function to estimate the user's emotion from the video data. For example, facial expression recognition technology is used to analyze the facial expressions of people in the video data and estimate the emotion. The analysis unit can also use voice analysis technology to estimate the user's emotion from audio data. For example, the tone and speed of the voice are analyzed to estimate the emotion. Furthermore, the analysis unit predicts risky behavior based on changes in emotion. For example, a system is constructed that notifies security guards on the spot when a change in emotion is detected. This allows risky behavior to be detected earlier by predicting risky behavior based on changes in emotion.

[0069] The system can be introduced in public facilities other than train stations, such as shopping malls and airports, to prevent risky behavior over a wide area. For example, a system can be created that notifies security guards on the spot when specific movements or actions are detected. The system can also generate prevention suggestions and scripts customized for each public facility. This will prevent risky behavior in public facilities other than train stations, improving safety over a wide area.

[0070] The analysis unit can link the results of the video data analysis with a facial recognition system or an access control system to comprehensively strengthen security. The analysis unit, for example, links the results of the video data analysis with a facial recognition system. For example, if a specific movement or behavior is detected, a system is constructed in which the person is identified on the spot using a facial recognition system and a security guard is notified. The analysis unit can also link the results of the video data analysis with an access control system. For example, if a specific movement or behavior is detected, a system is constructed in which access control is performed on the spot using an access control system. In this way, by linking the results of the video data analysis with other security systems, comprehensive security can be strengthened.

[0071] The patterning unit can analyze pre-action patterns in detail along a time axis and track changes in behavior. The patterning unit can, for example, use generative AI to analyze pre-action patterns in detail along a time axis and track changes in behavior. For example, a system can be built that analyzes how specific movements and behaviors change over time. The patterning unit can also analyze pre-action patterns along a time axis using time series analysis. For example, if a specific behavior is repeated, it tracks its changes. In this way, by analyzing pre-action patterns along a time axis, it is possible to track changes in behavior.

[0072] The patterning unit classifies the patterned behavior data by different seasons or time periods, and can clarify behavior patterns under specific conditions. The patterning unit, for example, classifies the patterned behavior data by different seasons or time periods, and can clarify behavior patterns under specific conditions. For example, a system is constructed that analyzes differences in behavior patterns between summer and winter. The patterning unit can also classify the behavior data by time period, and can analyze behavior patterns in specific time periods. For example, it analyzes differences in behavior patterns between morning and night. In this way, by classifying the behavior data by season or time period, it is possible to clarify behavior patterns under specific conditions.

[0073] The patterning unit can use the emotion estimation function to analyze changes in emotions included in the pattern of pre-behavior and evaluate the impact of emotional fluctuations on behavior. The patterning unit, for example, uses the emotion estimation function to analyze changes in emotions included in the pattern of pre-behavior and evaluate the impact of emotional fluctuations on behavior. For example, it builds a system that analyzes how emotional changes affect behavior. The patterning unit can also build a behavior prediction model based on emotional fluctuations. For example, it evaluates the impact of emotional fluctuations on behavior and creates a prediction model based on the results. This enables more accurate behavior prediction by evaluating the impact of emotional fluctuations on behavior.

[0074] The patterning unit can compare pre-behavior patterns for different cultural spheres or regions to clarify region-specific behavior patterns. The patterning unit, for example, compares pre-behavior patterns for different cultural spheres or regions to clarify region-specific behavior patterns. For example, a system can be built that analyzes differences in behavior patterns between Japan and the United States. The patterning unit can also analyze behavior patterns based on the cultural characteristics of each region. For example, it can analyze behavior patterns in a specific region to clarify behavior unique to that region. This makes it possible to predict region-specific behavior by clarifying behavior patterns for different cultural spheres or regions.

[0075] The patterning unit can share the patterned behavioral data with other crime prevention systems to achieve comprehensive crime prevention. The patterning unit can share the patterned behavioral data with, for example, a theft prevention system to achieve comprehensive crime prevention. For example, a system can be built that notifies security guards on the spot when specific movements or behaviors are detected. The patterning unit can also work in conjunction with a violence prevention system to share behavioral data. For example, a system can be built that notifies security guards on the spot when specific behaviors are detected. In this way, sharing behavioral data with other crime prevention systems enables comprehensive crime prevention.

[0076] The patterning unit can use the emotion estimation function to display a relaxation message to stabilize the user's emotions based on the pattern of the pre-action. The patterning unit, for example, uses the emotion estimation function to display a relaxation message to stabilize the user's emotions based on the pattern of the pre-action. For example, a system can be constructed that displays a relaxation message on the spot when a change in emotion is detected. The patterning unit can also set the content and format of the relaxation message. For example, a text message or a voice message can be set to provide the user with appropriate relaxation. In this way, the relaxation message can be displayed to stabilize the user's emotions.

[0077] The suggestion generation unit can use generation AI to generate preventive suggestions and scripts in real time and execute them instantly. The suggestion generation unit can use generation AI to generate preventive suggestions and scripts in real time and execute them instantly. For example, a system can be built that generates a script that notifies security guards on the spot when a specific movement or behavior is detected. The suggestion generation unit can also use generation AI to automatically update the contents of preventive suggestions and scripts. For example, it can generate preventive suggestions and scripts based on the latest data and execute them instantly. This allows preventive suggestions and scripts to be generated in real time and executed immediately, enabling a rapid response.

[0078] The suggestion generation unit can refer to past success stories when generating suggestions and scripts and propose the most effective preventive measures. The suggestion generation unit, for example, refers to past success stories when generating suggestions and scripts and proposes the most effective preventive measures. For example, a system can be built that automatically generates optimal preventive measures based on past data. The suggestion generation unit can also set the content and standards of successful cases. For example, preventive measures can be proposed based on past performance and evaluation criteria. In this way, the most effective preventive measures can be proposed by referring to past success stories.

[0079] The suggestion generation unit can use the emotion estimation function to generate individual preventive suggestions and scripts according to the user's emotions. The suggestion generation unit, for example, uses the emotion estimation function to generate individual preventive suggestions and scripts according to the user's emotions. For example, a system is constructed that generates individual preventive suggestions on the spot when a change in emotion is detected. The suggestion generation unit can also set the content and criteria of individual preventive suggestions. For example, customization content and suggestion methods are set for each user. This enables more effective prevention by generating individual preventive suggestions and scripts according to the user's emotions.

[0080] The suggestion generation unit can adapt the preventive suggestions and scripts to different languages ​​and cultures, enabling international use. The suggestion generation unit, for example, adapts the preventive suggestions and scripts to different languages, enabling international use. For example, a system that translates into multiple languages, such as English, French, and Chinese, can be built. The suggestion generation unit can also generate preventive suggestions and scripts that take into account the cultural characteristics and customs of each region in order to adapt to different cultures. This allows the preventive suggestions and scripts to adapt to different languages ​​and cultures, enabling international use.

[0081] The suggestion generation unit can link the generated results of suggestions and scripts with other security systems to implement comprehensive preventive measures. The suggestion generation unit, for example, links the generated results of suggestions and scripts with other security systems. For example, it links with a face recognition system or an access control system to build a system that implements comprehensive preventive measures. The suggestion generation unit can also set a linking method and a data integration method for each security system. For example, data is shared between multiple systems to implement comprehensive preventive measures. In this way, comprehensive preventive measures can be implemented by linking the generated results of suggestions and scripts with other security systems.

[0082] The suggestion generation unit can use the emotion estimation function to display a positive message to encourage a specific behavior based on the user's emotion. The suggestion generation unit, for example, uses the emotion estimation function to display a positive message to encourage a specific behavior based on the user's emotion. For example, a system can be constructed that displays a positive message on the spot when a change in emotion is detected. The suggestion generation unit can also set the content and format of the positive message. For example, a text message or a voice message can be set and an appropriate message can be provided to the user. In this way, the specific behavior can be encouraged by displaying a positive message.

[0083] The video data storage unit can share video data from surveillance cameras on the cloud and realize data linkage between multiple cameras. The video data storage unit, for example, can share video data from surveillance cameras on the cloud and realize data linkage between multiple cameras. For example, a system can be built using cloud storage to centrally manage video data from multiple surveillance cameras. The video data storage unit can also use cloud computing to efficiently analyze and process video data. For example, a system can be built that analyzes video data on the cloud and provides feedback on the results in real time. This allows video data to be shared on the cloud and realizes data linkage between multiple cameras, making it possible to monitor a wide area.

[0084] The analysis unit can use the emotion estimation function to estimate the user's emotion from surveillance camera footage and respond according to changes in emotion. The analysis unit, for example, uses the emotion estimation function to estimate the user's emotion from surveillance camera footage. For example, facial expression recognition technology is used to analyze the facial expressions of people in the video data and estimate the emotion. The analysis unit can also use voice analysis technology to estimate the user's emotion from voice data. For example, the tone and speed of voice are analyzed to estimate the emotion. Furthermore, the analysis unit responds according to changes in emotion. For example, a system can be built that notifies security guards on the spot when a change in emotion is detected. This allows for more appropriate responses by responding according to changes in emotion.

[0085] The video data storage unit can link video data from surveillance cameras with other security systems to enable monitoring over a wide area. The video data storage unit can, for example, link video data from surveillance cameras with a drone monitoring system to enable monitoring over a wide area. For example, a system can be built in which, when a specific movement or behavior is detected, a drone is flown on the spot to monitor the site. The video data storage unit can also link with other security systems to share data. For example, a system can be built that links with a facial recognition system or an access control system to provide comprehensive monitoring. This makes it possible to monitor a wide area by linking video data with other security systems.

[0086] The analysis unit can link video data from surveillance cameras with an AI assistant and issue voice warnings and instructions. The analysis unit, for example, can link video data from surveillance cameras with an AI assistant and issue voice warnings and instructions. For example, a system can be built in which, if a specific movement or behavior is detected, the voice assistant will issue a warning on the spot. The analysis unit can also use the AI ​​assistant to issue voice instructions. For example, a system can be built in which, if a specific behavior is observed, the voice assistant will issue instructions on the spot. By linking with the AI ​​assistant, voice warnings and instructions become possible.

[0087] The analysis unit can play a voice message to deter a specific behavior based on emotions estimated from surveillance camera footage using the emotion estimation function. The analysis unit, for example, uses the emotion estimation function to play a voice message to deter a specific behavior based on emotions estimated from surveillance camera footage. For example, a system can be constructed that plays a voice message on the spot when a change in emotion is detected. The analysis unit can also set the content and format of the voice message. For example, a warning message or a warning voice message can be set and an appropriate message can be provided to the user. In this way, a specific behavior can be deterred by playing a voice message based on emotions.

[0088] When the analysis unit detects risky behavior using the generation AI, it can immediately notify security guards and encourage a quick response. When the analysis unit detects risky behavior using the generation AI, it can immediately notify security guards and encourage a quick response. For example, a system can be built that notifies security guards on the spot when a specific movement or behavior is detected. The analysis unit can also use the generation AI to provide feedback on the results of risky behavior detection in real time and implement countermeasures immediately. For example, a system can be built that displays a warning message on the spot when risky behavior is detected. This makes it possible to immediately notify security guards when risky behavior is detected, enabling a quick response.

[0089] The analysis unit can optimize behavioral deterrence measures based on past success stories and select the most effective method. The analysis unit can, for example, optimize behavioral deterrence measures based on past success stories and select the most effective method. For example, a system can be built that automatically generates optimal deterrence measures based on past data. The analysis unit can also set the content and standards of successful cases. For example, deterrence measures can be selected based on past performance and evaluation criteria. This allows for more effective deterrence by selecting optimal behavioral deterrence measures based on past success stories.

[0090] The analysis unit can use the emotion estimation function to implement individual behavioral deterrence measures according to the user's emotions. The analysis unit, for example, uses the emotion estimation function to implement individual behavioral deterrence measures according to the user's emotions. For example, a system can be constructed that implements individual deterrence measures on the spot when a change in emotion is detected. The analysis unit can also set the content and standards of individual behavioral deterrence measures. For example, it can set customization content and proposal methods for each user. This enables more effective deterrence by implementing individual behavioral deterrence measures according to the user's emotions.

[0091] The analysis unit can adapt the behavioral deterrent measures to different languages ​​and cultures, enabling international use. The analysis unit, for example, can adapt the behavioral deterrent measures to different languages, enabling international use. For example, a system can be built that translates into multiple languages, such as English, French, and Chinese. The analysis unit can also implement behavioral deterrent measures that take into account the cultural characteristics and customs of each region in order to adapt to different cultures. This allows the behavioral deterrent measures to adapt to different languages ​​and cultures, enabling international use.

[0092] The analysis unit can link behavioral deterrence measures with other security systems to implement comprehensive deterrence measures. The analysis unit, for example, links behavioral deterrence measures with other security systems. For example, it links with a face recognition system and an access control system to build a system that implements comprehensive deterrence measures. The analysis unit can also set the linking method and data integration method for each security system. For example, data can be shared between multiple systems to implement comprehensive deterrence measures. In this way, comprehensive deterrence measures can be implemented by linking behavioral deterrence measures with other security systems.

[0093] The analysis unit can use the emotion estimation function to display a positive message to encourage a specific behavior based on the user's emotions. The analysis unit, for example, uses the emotion estimation function to display a positive message to encourage a specific behavior based on the user's emotions. For example, a system can be constructed that displays a positive message on the spot when a change in emotion is detected. The analysis unit can also set the content and format of the positive message. For example, a text message or a voice message can be set and an appropriate message can be provided to the user. In this way, the positive message can be displayed to encourage a specific behavior.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The analysis unit can analyze specific movements, behavioral patterns, and changes in facial expressions from video data. For example, the analysis unit uses generative AI to analyze video data from surveillance cameras in real time and instantly extract specific movements and behavioral patterns. For example, a system can be built that notifies security guards on the spot when specific movements or behaviors are detected. The analysis unit can also use image recognition technology to detect specific movements and behavioral patterns from video data. For example, if a specific movement is repeated, it can recognize that pattern. The analysis unit can also use facial expression recognition technology to analyze changes in the facial expressions of people in video data. For example, it can recognize facial expressions such as smiling, anger, and sadness and analyze those changes. This allows for early detection of dangerous behavior by analyzing specific movements, behavioral patterns, and changes in facial expressions.

[0096] The suggestion generation unit can generate suggestions and scripts that notify security guards when specific movements are detected. For example, the suggestion generation unit can simultaneously collect audio data in addition to video data and analyze the correlation between the audio and video to make more accurate behavior predictions. For example, a system can be built that simultaneously collects video data and audio data and analyzes the correlation using a generation AI. For example, a system can be built that immediately notifies security guards when specific sounds and movements are detected simultaneously. The suggestion generation unit can also generate suggestions that immediately notify security guards when specific movements are detected. For example, when specific movements are detected, a script can be generated that immediately notifies security guards. This allows security guards to be notified when specific movements are detected, enabling a quick response.

[0097] The suggestion generation unit can generate suggestions and scripts that display a warning message when a specific behavior is observed. The suggestion generation unit, for example, uses an emotion estimation function to estimate a user's emotions from video data and predict risky behavior based on changes in emotions. For example, an emotion estimation function can be equipped in the generation AI to build a system that estimates a user's emotions from video data in real time. For example, a system can be built that notifies security guards on the spot when a change in emotion is detected. The suggestion generation unit can also generate suggestions that display a warning message when a specific behavior is observed. For example, a script can be generated that displays a warning message on the spot when a specific behavior is observed. This makes it possible to deter risky behavior by displaying a warning message when a specific behavior is observed.

[0098] The suggestion generation unit can generate suggestions and scripts that sound an audio alarm when a specific movement is detected. The suggestion generation unit generates, for example, a suggestion that sounds an audio alarm when a specific movement is detected. For example, when a specific movement is detected, it generates a script that sounds an audio alarm on the spot. The suggestion generation unit can also set the type and volume of the audio alarm. For example, it can set a warning sound or a warning sound and adjust the volume. In this way, by sounding an audio alarm when a specific movement is detected, it is possible to attract the attention of those around and deter dangerous behavior.

[0099] The video data storage unit simultaneously collects audio data in addition to video data, and the analysis unit can analyze the correlation between the video data and audio data. The video data storage unit, for example, simultaneously collects audio data in addition to video data. For example, a system can be built in which a microphone is installed on a surveillance camera and video and audio are simultaneously recorded. The video data storage unit can also efficiently store data using audio data compression technology. The analysis unit, for example, analyzes the correlation between video data and audio data. For example, a system can be built in which the correlation between video data and audio data is analyzed using generative AI to detect specific movement or behavior patterns. The analysis unit can also detect specific audio patterns from audio data using voice recognition technology. This enables more accurate behavior prediction by analyzing the correlation between video data and audio data.

[0100] The analysis unit can use the emotion estimation function to estimate the user's emotion from the video data and predict risky behavior based on changes in emotion. The analysis unit, for example, uses the emotion estimation function to estimate the user's emotion from the video data. For example, facial expression recognition technology is used to analyze the facial expressions of people in the video data and estimate the emotion. The analysis unit can also use voice analysis technology to estimate the user's emotion from audio data. For example, the tone and speed of the voice are analyzed to estimate the emotion. Furthermore, the analysis unit predicts risky behavior based on changes in emotion. For example, a system is constructed that notifies security guards on the spot when a change in emotion is detected. This allows risky behavior to be detected earlier by predicting risky behavior based on changes in emotion.

[0101] The system can be introduced in public facilities other than train stations, such as shopping malls and airports, to prevent risky behavior over a wide area. For example, a system can be created that notifies security guards on the spot when specific movements or actions are detected. The system can also generate prevention suggestions and scripts customized for each public facility. This will prevent risky behavior in public facilities other than train stations, improving safety over a wide area.

[0102] The analysis unit can link the results of the video data analysis with a facial recognition system or an access control system to comprehensively strengthen security. The analysis unit, for example, links the results of the video data analysis with a facial recognition system. For example, if a specific movement or behavior is detected, a system is constructed in which the person is identified on the spot using a facial recognition system and a security guard is notified. The analysis unit can also link the results of the video data analysis with an access control system. For example, if a specific movement or behavior is detected, a system is constructed in which access control is performed on the spot using an access control system. In this way, by linking the results of the video data analysis with other security systems, comprehensive security can be strengthened.

[0103] The patterning unit can analyze pre-action patterns in detail along a time axis and track changes in behavior. The patterning unit can, for example, use generative AI to analyze pre-action patterns in detail along a time axis and track changes in behavior. For example, a system can be built that analyzes how specific movements and behaviors change over time. The patterning unit can also analyze pre-action patterns along a time axis using time series analysis. For example, if a specific behavior is repeated, it tracks its changes. In this way, by analyzing pre-action patterns along a time axis, it is possible to track changes in behavior.

[0104] The patterning unit can use the emotion estimation function to analyze changes in emotions included in the pattern of pre-behavior and evaluate the impact of emotional fluctuations on behavior. The patterning unit, for example, uses the emotion estimation function to analyze changes in emotions included in the pattern of pre-behavior and evaluate the impact of emotional fluctuations on behavior. For example, it builds a system that analyzes how emotional changes affect behavior. The patterning unit can also build a behavior prediction model based on emotional fluctuations. For example, it evaluates the impact of emotional fluctuations on behavior and creates a prediction model based on the results. This enables more accurate behavior prediction by evaluating the impact of emotional fluctuations on behavior.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The video data storage unit stores video data from a surveillance camera. For example, the video data storage unit can store high-resolution video data for a long period of time. The video data storage unit can also integrate and manage video data from multiple surveillance cameras. Furthermore, the video data storage unit can store data efficiently using video data compression technology. Step 2: The analysis unit analyzes the video data stored by the video data storage unit. For example, the analysis unit may use image recognition technology to detect specific movements or behavioral patterns from the video data. The analysis unit may also use a movement detection algorithm to analyze movements within the video data. Furthermore, the analysis unit may use facial expression recognition technology to analyze facial expressions of people within the video data. Step 3: The patterning unit learns the characteristics of the pre-action analyzed by the analysis unit and patterns them. For example, the patterning unit learns patterns of pre-action using a machine learning algorithm. The patterning unit can also classify patterns of pre-action using a clustering method. Furthermore, the patterning unit can analyze patterns of pre-action along a time axis using time series analysis. Step 4: The suggestion generation unit generates preventive suggestions and scripts based on the patterns of pre-actions learned by the patternization unit. For example, the suggestion generation unit generates a suggestion to notify a security guard when a specific movement is detected. The suggestion generation unit can also generate a script to display a warning message when a specific movement is observed. Furthermore, the suggestion generation unit can also generate a script to sound an audio alarm when a specific movement is detected.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. 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 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.

[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 (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).

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

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a video data storage unit that stores video data from a surveillance camera; an analysis unit that analyzes the video data stored by the video data storage unit; a patterning unit that learns the characteristics of the prior behavior analyzed by the analysis unit and patterns the characteristics; a suggestion generation unit that generates preventive suggestions and scripts based on the patterns of prior actions learned by the patterning unit. A system characterized by:

2. The analysis unit Estimating the user's emotions from the video data and predicting risky behavior based on changes in the emotions 2. The system of claim 1.

3. The system comprises: Similar systems will be introduced in public facilities other than stations to prevent risky behavior in a wide area.

2. The system of claim 1.

4. The patterning unit is The patterns of the above-mentioned prior behavior are analyzed in detail along the time axis, and changes in behavior are tracked.

2. The system of claim 1.

5. The suggestion generation unit The generation AI is used to generate the prevention suggestions and the scripts in real time and execute them immediately.

2. The system of claim 1.

6. The analysis unit The generation AI is used to analyze the surveillance camera footage in real time and instantly detect risky behavior.

2. The system of claim 1.

7. The analysis unit When risky behavior is detected using the generation AI, it immediately notifies security guards and prompts them to take action.

2. The system of claim 1.

8. The suggestion generation unit Displaying a counseling message to discourage specific behavior based on emotions estimated from the video data 2. The system of claim 1.

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