System

Abstract

An object of a system according to an embodiment is to efficiently detect a lost article or a lost article and promptly notify the lost article or the lost article.SOLUTION: A system according to an embodiment includes a monitoring camera, a dedicated sensor, a generation AI, and a notifier. The surveillance camera and the dedicated sensor acquire video data. The generation unit analyzes the video AI acquired by the monitoring camera and the dedicated sensor. The notifying unit notifies information on the left object detected by the generated AI in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently detect lost or forgotten items in public places, offices, commercial facilities, etc., and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently detect lost or forgotten items and to notify them promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes a surveillance camera, a dedicated sensor, a generation AI, and a notification unit. The surveillance camera and the dedicated sensor acquire video data. The generation AI analyzes the video data acquired by the surveillance camera and the dedicated sensor. The notification unit notifies information about abandoned objects detected by the generation AI in real time. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently detect lost or forgotten items and quickly notify the user. [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 abandoned property detection system according to an embodiment of the present invention uses image recognition AI to solve the problem of users forgetting or losing personal belongings in public places, offices, commercial facilities, etc. This system constantly monitors the space using surveillance cameras and dedicated sensors, and the AI ​​analyzes people's movements and belongings. This allows the abandoned property detection system to quickly find forgotten or lost items and assist in their return.

[0029] An abandoned property detection system according to an embodiment includes a surveillance camera, a dedicated sensor, a generation AI, and a notification unit. The surveillance camera constantly monitors spaces such as public places, offices, and commercial facilities. For example, it constantly monitors people's movements and the locations of their belongings within the area where the surveillance camera is installed. The dedicated sensor detects movement within the space. For example, a motion detection sensor detects people's movements and determines the location of their belongings. The generation AI analyzes video data acquired from the surveillance camera or the dedicated sensor to analyze people's movements and the locations of their belongings. For example, the generation AI analyzes video data to recognize belongings such as a bag or umbrella carried by a specific person and track their location. The generation AI also automatically detects and recognizes objects that remain stationary in the same place for a certain period of time (e.g., 30 minutes). For example, the generation AI analyzes video data to detect belongings such as a bag or umbrella that have been left in the same place for 30 minutes or more. The notification unit notifies facility managers, etc., of abandoned property detected by the generation AI in real time. For example, the AI ​​can notify the manager of the location and type of abandoned items detected by the AI, prompting a prompt response. As a result, the abandoned item detection system according to the embodiment can quickly find forgotten or lost items and help return them. For example, if a user leaves a bag behind, the AI ​​can detect the bag and notify the manager, allowing it to be quickly returned to the owner.

[0030] Surveillance cameras can improve the accuracy of abandoned object detection by collecting audio data in addition to video data and performing audio analysis. For example, surveillance cameras can collect audio data in a space at the same time as video data and perform audio analysis. For example, they can analyze the sound of objects falling or people's voices to improve the accuracy of abandoned object detection. In this way, the accuracy of abandoned object detection can be improved by using audio data.

[0031] By 3D modeling video data from surveillance cameras, it is possible to grasp the positional relationships of objects in a space in three dimensions. For example, surveillance cameras use video data to generate 3D models of objects in a space. For example, they can integrate video from multiple cameras to create a three-dimensional spatial model. This allows the positional relationships of objects to be grasped in three dimensions through 3D modeling.

[0032] When detecting an object that has been stationary for a certain period of time or more, the generation AI can detect the object's minute movements and vibrations, enabling more accurate detection. For example, the generation AI can analyze video data from a surveillance camera and detect the minute movements of a stationary object. For example, it can detect minute swaying and vibrations caused by wind. This allows for highly accurate detection by detecting minute movements and vibrations.

[0033] When the generation AI detects an abandoned object, it can speed up the administrator's response by including detailed information about the detected object in the notification content. For example, the generation AI can include detailed information about the material and shape of the detected object in the notification content. For example, it can notify information about a metal bag or an object with a specific shape. This allows the administrator to respond more quickly by notifying them of detailed information about the object.

[0034] The notification unit can add a voice notification function to enable the administrator to respond immediately. The notification unit can add a voice notification function to the notification system, for example, to enable the administrator to respond immediately. For example, the notification can be sent by voice to a smartphone or PC. This allows the administrator to respond immediately using the voice notification function.

[0035] The notification unit can cooperate with other security systems to implement a comprehensive security response. For example, the notification unit cooperates with an alarm system to implement a comprehensive security response. For example, the notification unit notifies the alarm system of information about a detected object. This enables a comprehensive security response by coordinating with other security systems.

[0036] The notification unit can be linked with a guidance system within the facility to automatically provide guidance if the user leaves their belongings behind. For example, the notification unit can link the notification system with a guidance system within the facility to automatically provide guidance if the user leaves their belongings behind. For example, the location of the belongings can be displayed on the guidance system. In this way, by linking with the guidance system, automatic guidance can be provided if the user leaves their belongings behind.

[0037] Generative AI can analyze the characteristics of lost or forgotten items in detail, improving the accuracy of identifying their owners. Generative AI can analyze the characteristics of lost or forgotten items in detail, improving the accuracy of identifying their owners. For example, it can analyze the shape, material, color, etc. of an object. This allows for a detailed analysis of the characteristics of lost or forgotten items, improving the accuracy of identifying their owners.

[0038] Generative AI can automate the return procedures for lost or forgotten items, reducing the burden on administrators. Generative AI can, for example, build a system that automates the return procedures for lost or forgotten items. For example, it can automatically recognize the characteristics of an item and notify the owner. This reduces the burden on administrators by automating the return procedures.

[0039] The return support system can be linked with other facilities to provide return support over a wide area. The return support system can link, for example, the return support system for lost or forgotten items with other facilities to provide return support over a wide area. For example, it can link with systems at stations and airports to share information about belongings. This allows for return support over a wide area by linking with other facilities.

[0040] The return support system can be linked with a guidance system within a facility and automatically provide guidance if a user leaves their belongings behind. For example, the return support system can link a lost or forgotten item return support system with a guidance system within a facility and automatically provide guidance if a user leaves their belongings behind. For example, the location of the belongings can be displayed on the guidance system. In this way, by linking with the guidance system, guidance can be automatically provided if a user leaves their belongings behind.

[0041] Generative AI can analyze the shape and material of suspicious objects in detail, improving the accuracy of identifying dangerous objects. For example, generative AI can analyze video data from surveillance cameras and analyze the shape and material of suspicious objects in detail. For example, it can detect metal objects or objects with specific shapes. This improves the accuracy of identifying dangerous objects by analyzing the shape and material of suspicious objects in detail.

[0042] The generation AI can link the results of suspicious object monitoring with other security systems to implement a comprehensive security response. For example, the generation AI can link the results of suspicious object monitoring with an alarm system to implement a comprehensive security response. For example, it can notify the alarm system of information about detected suspicious objects. This makes it possible to implement a comprehensive security response by linking with other security systems.

[0043] The generation AI can link the results of on-street parking detection with a traffic management system, making it possible to more efficiently crack down on illegal parking. For example, the generation AI can link the results of on-street parking detection with a traffic management system, making it possible to more efficiently crack down on illegal parking. For example, it can notify the traffic management system of information about detected vehicles. This makes it possible to more efficiently crack down on illegal parking by linking it with the traffic management system.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The abandoned item detection system can further include a temperature sensor. The temperature sensor, for example, detects temperature changes in the space and detects abnormal temperature increases or decreases. This allows for quick action to be taken if there is a risk of fire from abandoned items or if items that require cooling are left unattended. For example, if the temperature sensor detects an abnormal temperature increase, it can notify the manager, which can help prevent fires. In addition, if medicines or food that require cooling are left unattended, the temperature sensor can detect the abnormality and prompt appropriate action.

[0046] The abandoned item detection system can further include a humidity sensor. The humidity sensor, for example, detects humidity changes in the space and detects abnormal increases or decreases in humidity. This allows for quick action to be taken when humidity-sensitive items are left unattended. For example, if the humidity sensor detects an abnormal increase in humidity, it can notify a manager, which can help protect items that require humidity control. In addition, if the humidity drops too low, the humidity sensor can detect the abnormality and prompt appropriate action.

[0047] The abandoned item detection system can further include an odor sensor. The odor sensor can, for example, detect abnormal odors in a space and help identify abandoned items. This allows for quick action when dangerous items such as spoiled food or chemicals are left behind. For example, if the odor sensor detects an abnormal odor, it can notify a manager and prompt them to remove the spoiled food or dispose of the chemicals. In addition, the odor sensor can detect specific odors, making it easier to identify the type of abandoned item.

[0048] The abandoned item detection system can further include a vibration sensor. The vibration sensor can, for example, detect minute vibrations in a space and be useful in identifying abandoned items. This allows for quick action to be taken when items that have been moved due to an earthquake or impact are left abandoned. For example, if the vibration sensor detects abnormal vibrations, it can notify a manager and prompt them to check the items after the earthquake or to dispose of any items damaged by the impact. In addition, the vibration sensor can detect specific vibration patterns, making it easier to identify the type of abandoned item.

[0049] The abandoned item detection system may further include an optical sensor. The optical sensor, for example, may detect changes in light within a space and help identify abandoned items. This allows for quick action to be taken when light-sensitive items are left unattended. For example, if the optical sensor detects an abnormal change in light, it may notify a manager, helping to protect the light-sensitive items. Furthermore, the optical sensor may detect specific light patterns, making it easier to identify the type of abandoned item.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: Surveillance cameras constantly monitor spaces such as public places, offices, and commercial facilities. For example, they constantly monitor people's movements within the area where the cameras are installed and the locations of their belongings. Dedicated sensors detect movement within the space. For example, motion detection sensors detect people's movements and track the location of their belongings. Step 2: The generating AI analyzes video data acquired from surveillance cameras and dedicated sensors to analyze people's movements and the location of their belongings. For example, the generating AI analyzes video data to recognize belongings such as a bag or umbrella held by a specific person and track their location. The generating AI also automatically detects and recognizes objects that remain stationary in the same place for a certain period of time (e.g., 30 minutes) or more. For example, the generating AI analyzes video data to detect belongings such as a bag or umbrella that have been left in the same place for 30 minutes or more. Step 3: The notification unit notifies the facility manager or other person in real time of information about the abandoned item detected by the generation AI. For example, the AI ​​may notify the manager of the location and type of the abandoned item detected, prompting a prompt response. This allows the abandoned item detection system according to the embodiment to quickly find forgotten or lost items and assist in their return. For example, if a user leaves a bag behind, the AI ​​can detect the bag and notify the manager, allowing it to be quickly returned to the owner.

[0052] (Example 2) The abandoned property detection system according to an embodiment of the present invention uses image recognition AI to solve the problem of users forgetting or losing personal belongings in public places, offices, commercial facilities, etc. This system constantly monitors the space using surveillance cameras and dedicated sensors, and the AI ​​analyzes people's movements and belongings. This allows the abandoned property detection system to quickly find forgotten or lost items and assist in their return.

[0053] An abandoned property detection system according to an embodiment includes a surveillance camera, a dedicated sensor, a generation AI, and a notification unit. The surveillance camera constantly monitors spaces such as public places, offices, and commercial facilities. For example, it constantly monitors people's movements and the locations of their belongings within the area where the surveillance camera is installed. The dedicated sensor detects movement within the space. For example, a motion detection sensor detects people's movements and determines the location of their belongings. The generation AI analyzes video data acquired from the surveillance camera or the dedicated sensor to analyze people's movements and the locations of their belongings. For example, the generation AI analyzes video data to recognize belongings such as a bag or umbrella carried by a specific person and track their location. The generation AI also automatically detects and recognizes objects that remain stationary in the same place for a certain period of time (e.g., 30 minutes). For example, the generation AI analyzes video data to detect belongings such as a bag or umbrella that have been left in the same place for 30 minutes or more. The notification unit notifies facility managers, etc., of abandoned property detected by the generation AI in real time. For example, the AI ​​can notify the manager of the location and type of abandoned items detected by the AI, prompting a prompt response. As a result, the abandoned item detection system according to the embodiment can quickly find forgotten or lost items and help return them. For example, if a user leaves a bag behind, the AI ​​can detect the bag and notify the manager, allowing it to be quickly returned to the owner.

[0054] Surveillance cameras can improve the accuracy of abandoned object detection by collecting audio data in addition to video data and performing audio analysis. For example, surveillance cameras can collect audio data in a space at the same time as video data and perform audio analysis. For example, they can analyze the sound of objects falling or people's voices to improve the accuracy of abandoned object detection. In this way, the accuracy of abandoned object detection can be improved by using audio data.

[0055] By 3D modeling video data from surveillance cameras, it is possible to grasp the positional relationships of objects in a space in three dimensions. For example, surveillance cameras use video data to generate 3D models of objects in a space. For example, they can integrate video from multiple cameras to create a three-dimensional spatial model. This allows the positional relationships of objects to be grasped in three dimensions through 3D modeling.

[0056] When detecting an object that has been stationary for a certain period of time or more, the generation AI can detect the object's minute movements and vibrations, enabling more accurate detection. For example, the generation AI can analyze video data from a surveillance camera and detect the minute movements of a stationary object. For example, it can detect minute swaying and vibrations caused by wind. This allows for highly accurate detection by detecting minute movements and vibrations.

[0057] When the generation AI detects an abandoned object, it can speed up the administrator's response by including detailed information about the detected object in the notification content. For example, the generation AI can include detailed information about the material and shape of the detected object in the notification content. For example, it can notify information about a metal bag or an object with a specific shape. This allows the administrator to respond more quickly by notifying them of detailed information about the object.

[0058] The notification unit can add a voice notification function to enable the administrator to respond immediately. The notification unit can add a voice notification function to the notification system, for example, to enable the administrator to respond immediately. For example, the notification can be sent by voice to a smartphone or PC. This allows the administrator to respond immediately using the voice notification function.

[0059] The notification unit can analyze the emotions of the manager who received the notification and take measures to reduce stress. The notification unit, for example, analyzes the facial expression of the manager who received the notification and estimates the emotions. For example, it detects facial expressions of stress or impatience. This makes it possible to analyze the emotions of the manager and take measures to reduce stress.

[0060] The notification unit can cooperate with other security systems to implement a comprehensive security response. For example, the notification unit cooperates with an alarm system to implement a comprehensive security response. For example, the notification unit notifies the alarm system of information about a detected object. This enables a comprehensive security response by coordinating with other security systems.

[0061] The notification unit can be linked with a guidance system within the facility to automatically provide guidance if the user leaves their belongings behind. For example, the notification unit can link the notification system with a guidance system within the facility to automatically provide guidance if the user leaves their belongings behind. For example, the location of the belongings can be displayed on the guidance system. In this way, by linking with the guidance system, automatic guidance can be provided if the user leaves their belongings behind.

[0062] The notification unit can analyze the emotions of the manager who received the notification in real time and provide guidance to elicit positive emotions. The notification unit, for example, analyzes the facial expression of the manager who received the notification and estimates the emotions in real time. For example, it detects the manager's smiling or relaxed facial expression. This makes it possible to analyze the manager's emotions in real time and provide guidance to elicit positive emotions.

[0063] Generative AI can analyze the characteristics of lost or forgotten items in detail, improving the accuracy of identifying their owners. Generative AI can analyze the characteristics of lost or forgotten items in detail, improving the accuracy of identifying their owners. For example, it can analyze the shape, material, color, etc. of an object. This allows for a detailed analysis of the characteristics of lost or forgotten items, improving the accuracy of identifying their owners.

[0064] Generative AI can automate the return procedures for lost or forgotten items, reducing the burden on administrators. Generative AI can, for example, build a system that automates the return procedures for lost or forgotten items. For example, it can automatically recognize the characteristics of an item and notify the owner. This reduces the burden on administrators by automating the return procedures.

[0065] The generation AI can analyze the user's emotions during the return process and take measures to reduce stress. For example, the generation AI can analyze the user's facial expressions during the return process and estimate their emotions. For example, it can detect facial expressions of stress or impatience. This makes it possible to analyze the user's emotions during the return process and take measures to reduce stress.

[0066] The return support system can be linked with other facilities to provide return support over a wide area. The return support system can link, for example, the return support system for lost or forgotten items with other facilities to provide return support over a wide area. For example, it can link with systems at stations and airports to share information about belongings. This allows for return support over a wide area by linking with other facilities.

[0067] The return support system can be linked with a guidance system within a facility and automatically provide guidance if a user leaves their belongings behind. For example, the return support system can link a lost or forgotten item return support system with a guidance system within a facility and automatically provide guidance if a user leaves their belongings behind. For example, the location of the belongings can be displayed on the guidance system. In this way, by linking with the guidance system, guidance can be automatically provided if a user leaves their belongings behind.

[0068] The generative AI can analyze the user's emotions in real time during the return process and provide guidance to elicit positive emotions. For example, the generative AI can analyze the user's facial expressions during the return process and estimate their emotions in real time. For example, it can detect the user's smiling or relaxed facial expression. This makes it possible to analyze the user's emotions in real time during the return process and provide guidance to elicit positive emotions.

[0069] Generative AI can analyze the shape and material of suspicious objects in detail, improving the accuracy of identifying dangerous objects. For example, generative AI can analyze video data from surveillance cameras and analyze the shape and material of suspicious objects in detail. For example, it can detect metal objects or objects with specific shapes. This improves the accuracy of identifying dangerous objects by analyzing the shape and material of suspicious objects in detail.

[0070] The generation AI can analyze the user's emotions when they discover a suspicious object and encourage a quick response. For example, the generation AI can analyze the user's facial expression when they discover a suspicious object and estimate their emotions. For example, it can detect expressions of surprise or anxiety. This makes it possible to analyze the user's emotions when they discover a suspicious object and encourage a quick response.

[0071] The generation AI can link the results of suspicious object monitoring with other security systems to implement a comprehensive security response. For example, the generation AI can link the results of suspicious object monitoring with an alarm system to implement a comprehensive security response. For example, it can notify the alarm system of information about detected suspicious objects. This makes it possible to implement a comprehensive security response by linking with other security systems.

[0072] The generation AI can link the results of on-street parking detection with a traffic management system, making it possible to more efficiently crack down on illegal parking. For example, the generation AI can link the results of on-street parking detection with a traffic management system, making it possible to more efficiently crack down on illegal parking. For example, it can notify the traffic management system of information about detected vehicles. This makes it possible to more efficiently crack down on illegal parking by linking it with the traffic management system.

[0073] The generative AI can analyze the user's emotions in real time when they discover a suspicious object, and provide guidance that elicits positive emotions. For example, the generative AI can analyze the user's facial expression when they discover a suspicious object and estimate their emotions in real time. For example, it can detect the user's smiling or relaxed facial expression. This makes it possible to analyze the user's emotions in real time when they discover a suspicious object, and provide guidance that elicits positive emotions.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The abandoned item detection system can further include a temperature sensor. The temperature sensor, for example, detects temperature changes in the space and detects abnormal temperature increases or decreases. This allows for quick action to be taken if there is a risk of fire from abandoned items or if items that require cooling are left unattended. For example, if the temperature sensor detects an abnormal temperature increase, it can notify the manager, which can help prevent fires. In addition, if medicines or food that require cooling are left unattended, the temperature sensor can detect the abnormality and prompt appropriate action.

[0076] The abandoned item detection system can further include a humidity sensor. The humidity sensor, for example, detects humidity changes in the space and detects abnormal increases or decreases in humidity. This allows for quick action to be taken when humidity-sensitive items are left unattended. For example, if the humidity sensor detects an abnormal increase in humidity, it can notify a manager, which can help protect items that require humidity control. In addition, if the humidity drops too low, the humidity sensor can detect the abnormality and prompt appropriate action.

[0077] The abandoned item detection system can further include an odor sensor. The odor sensor can, for example, detect abnormal odors in a space and help identify abandoned items. This allows for quick action when dangerous items such as spoiled food or chemicals are left behind. For example, if the odor sensor detects an abnormal odor, it can notify a manager and prompt them to remove the spoiled food or dispose of the chemicals. In addition, the odor sensor can detect specific odors, making it easier to identify the type of abandoned item.

[0078] The abandoned item detection system can further include a vibration sensor. The vibration sensor can, for example, detect minute vibrations in a space and be useful in identifying abandoned items. This allows for quick action to be taken when items that have been moved due to an earthquake or impact are left abandoned. For example, if the vibration sensor detects abnormal vibrations, it can notify a manager and prompt them to check the items after the earthquake or to dispose of any items damaged by the impact. In addition, the vibration sensor can detect specific vibration patterns, making it easier to identify the type of abandoned item.

[0079] The abandoned item detection system may further include an optical sensor. The optical sensor, for example, may detect changes in light within a space and help identify abandoned items. This allows for quick action to be taken when light-sensitive items are left unattended. For example, if the optical sensor detects an abnormal change in light, it may notify a manager, helping to protect the light-sensitive items. Furthermore, the optical sensor may detect specific light patterns, making it easier to identify the type of abandoned item.

[0080] The notification unit can analyze the emotions of the manager who received the notification and suggest an appropriate response. For example, it can analyze the facial expression of the manager who received the notification and estimate their emotions. For example, if it detects an expression of stress or impatience, it can make suggestions for relaxation. This allows the manager's stress to be reduced by analyzing their emotions and suggesting appropriate responses. For example, it can suggest relaxing music or display a message encouraging them to take deep breaths.

[0081] The notification unit can analyze the emotions of the administrator who received the notification and select a notification method according to the emotions. For example, the notification unit can analyze the facial expressions of the administrator who received the notification and estimate the emotions. For example, if a stressed or anxious expression is detected, a gentler notification method can be selected. In this way, by analyzing the emotions of the administrator and selecting a notification method according to the emotion, the stress of the administrator can be reduced. For example, the volume of the audio notification can be set to a moderate level, or the notification message can be expressed in softer language.

[0082] The notification unit can analyze the emotions of the manager who received the notification and suggest a response that matches those emotions. For example, it can analyze the facial expression of the manager who received the notification and estimate their emotions. For example, if it detects an expression of joy or relief, it can suggest a positive response. This makes it possible to improve the manager's motivation by analyzing their emotions and suggesting a response that matches their emotions. For example, it can display a message congratulating them on their success or provide words of encouragement for the next step.

[0083] The notification unit can analyze the emotions of the administrator who received the notification and provide feedback according to those emotions. For example, the notification unit can analyze the facial expression of the administrator who received the notification and estimate the emotion. For example, if an expression of surprise or anxiety is detected, it can provide feedback to reassure the administrator. In this way, by analyzing the emotions of the administrator and providing feedback according to those emotions, it is possible to reduce the administrator's anxiety. For example, it can provide a detailed explanation of the situation or clearly indicate the next response steps.

[0084] The notification unit can analyze the emotions of the manager who received the notification and provide support according to those emotions. For example, the notification unit can analyze the facial expression of the manager who received the notification and estimate the emotion. For example, if an expression of fatigue or lethargy is detected, support can be provided to encourage the manager to take a break. In this way, by analyzing the emotions of the manager and providing support according to those emotions, the fatigue of the manager can be reduced. For example, the notification unit can suggest taking a short break or introduce an activity to refresh the manager.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: Surveillance cameras constantly monitor spaces such as public places, offices, and commercial facilities. For example, they constantly monitor people's movements within the area where the cameras are installed and the locations of their belongings. Dedicated sensors detect movement within the space. For example, motion detection sensors detect people's movements and track the location of their belongings. Step 2: The generating AI analyzes video data acquired from surveillance cameras and dedicated sensors to analyze people's movements and the location of their belongings. For example, the generating AI analyzes video data to recognize belongings such as a bag or umbrella held by a specific person and track their location. The generating AI also automatically detects and recognizes objects that remain stationary in the same place for a certain period of time (e.g., 30 minutes) or more. For example, the generating AI analyzes video data to detect belongings such as a bag or umbrella that have been left in the same place for 30 minutes or more. Step 3: The notification unit notifies the facility manager or other person in real time of information about the abandoned item detected by the generation AI. For example, the AI ​​may notify the manager of the location and type of the abandoned item detected, prompting a prompt response. This allows the abandoned item detection system according to the embodiment to quickly find forgotten or lost items and assist in their return. For example, if a user leaves a bag behind, the AI ​​can detect the bag and notify the manager, allowing it to be quickly returned to the owner.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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]

[0154] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with surveillance cameras and dedicated sensors, A generation AI that analyzes video data acquired by the surveillance camera and the dedicated sensor; a notification unit that notifies information about abandoned objects detected by the generation AI in real time; A system characterized by:

2. The surveillance camera is By collecting audio data in addition to the video data and performing audio analysis, the accuracy of detecting abandoned objects can be improved.

2. The system of claim 1.

3. The generated AI is When detecting an object that has been stationary for a certain period of time, the system detects minute movements and vibrations of the object, enabling more accurate detection.

2. The system of claim 1.

4. The generated AI is When an abandoned object is detected, detailed information about the detected object is included in the notification, allowing administrators to respond more quickly.

2. The system of claim 1.

5. The notification unit Analyze the emotions of administrators who receive notifications and take measures to reduce stress 2. The system of claim 1.

6. The generated AI is Detailed analysis of the characteristics of lost or forgotten items improves the accuracy of identifying their owners 2. The system of claim 1.

7. The generated AI is Improve the accuracy of identifying dangerous objects by analyzing the shape and material of suspicious objects in detail 2. The system of claim 1.

8. The generated AI is Analyzing users' emotions when they discover a suspicious object and encouraging them to take prompt action 2. The system of claim 1.

Images