system

Abstract

An object of a system according to an embodiment is to clarify a spatiotemporal behavior pattern of an object in real time.SOLUTION: A system according to an embodiment includes an IP camera, a monitoring unit, an analysis unit, and a notification unit. The IP camera acquires video data using the IP camera. The monitoring unit monitors video data acquired by the IP camera in real time. The analysis unit analyzes the video data monitored by the monitoring unit and clarifies a spatiotemporal action pattern of the object. The notification unit notifies the spatiotemporal action pattern analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not adequately revealed the spatiotemporal behavioral patterns of objects in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to reveal the spatiotemporal behavior patterns of objects in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an IP camera, a monitoring unit, an analysis unit, and a notification unit. The IP camera acquires video data using the IP camera. The monitoring unit monitors the video data acquired by the IP camera in real time. The analysis unit analyzes the video data monitored by the monitoring unit to identify spatiotemporal behavior patterns of objects. The notification unit notifies the spatiotemporal behavior patterns analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can reveal the spatiotemporal behavioral patterns of objects in real time. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A flow tracking system according to an embodiment of the present invention is a system for revealing spatiotemporal behavioral patterns of objects using IP cameras. In this system, a generative AI analyzes video data to monitor and analyze the movement of objects in real time. This allows the flow tracking system to monitor the movement of objects in real time and provide analysis results.

[0029] A flow tracking system according to an embodiment includes an IP camera, a monitoring unit, an analysis unit, and a notification unit. The IP camera acquires video data of an object. For example, the IP camera can acquire high-resolution video in real time. The IP camera can also have infrared capabilities to acquire clear video even at night. The IP camera can also have pan-tilt-zoom capabilities to cover a wide area. The monitoring unit monitors the video data acquired by the IP camera in real time. For example, the monitoring unit can display the video data in real time and detect abnormal movement. The monitoring unit can also record the video data and play it back later. The monitoring unit can also integrate and display video data from multiple IP cameras. The analysis unit analyzes the video data monitored by the monitoring unit to identify the spatiotemporal behavioral patterns of the object. For example, the analysis unit can use generative AI to track the object's movement and analyze the movement pattern. The analysis unit can also analyze the speed and direction of the object's movement. The analysis unit can also detect abnormalities in the object's movement and issue an alert. The notification unit notifies the user of the spatiotemporal behavior patterns analyzed by the analysis unit. For example, the notification unit sends the analysis results as email or an alert. The notification unit can also display the analysis results on a dashboard. The notification unit can also generate and periodically send reports on the analysis results. This allows the flow tracking system according to the embodiment to monitor the movement of objects in real time and provide the analysis results. For example, the output unit displays the analysis results to the user via a web application or a mobile application. If feedback is desired in paper form, the output unit prints the results using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0030] The monitoring unit can maximize the surveillance area by using an algorithm that automatically optimizes the installation position of IP cameras. For example, the monitoring unit develops an algorithm to automatically optimize the installation position of IP cameras to maximize the surveillance area. For example, the monitoring unit adjusts the camera position to reduce blind spots in a store. The monitoring unit also uses generative AI to build a system that optimizes the camera installation position in real time and dynamically adjusts the surveillance area. For example, the monitoring unit changes the camera position according to the flow of vehicles in traffic management. The monitoring unit also performs simulations to optimize the camera installation position and develops an algorithm that proposes the optimal placement. For example, the monitoring unit adjusts the camera position according to the congestion level in an airport. This allows the monitoring area to be maximized by automatically optimizing the installation position of IP cameras.

[0031] The analysis unit converts video data from IP cameras into 3D models, enabling the movement of objects to be grasped in three dimensions. For example, the analysis unit develops a system that converts video data from IP cameras into 3D models and grasps the movement of objects in three dimensions. For example, the movement of customers within a store is visualized using a 3D model. The analysis unit also uses generative AI to generate 3D models from video data and track the movement of objects in real time. For example, vehicle movement is displayed using a 3D model for traffic management. The analysis unit also uses the 3D model to develop algorithms that analyze the movement of objects in three dimensions and detect abnormal movement. For example, the movement of passengers within an airport is monitored using a 3D model. This allows the movement of objects to be grasped in three dimensions, enabling more detailed behavioral patterns to be analyzed.

[0032] The analysis unit can combine video data from IP cameras with audio data and perform audio analysis to clarify the relationship between object movement and sound. For example, the analysis unit can analyze customer movement and conversations in a store. The analysis unit can also use generative AI to integrate video and audio data and build a system that analyzes the relationship between object movement and sound in real time. For example, it can analyze vehicle movement and horn sounds in traffic management. The analysis unit can also combine video and audio data to analyze the relationship between object movement and sound and develop algorithms to detect abnormal movement. For example, it can analyze the relationship between passenger movement and announcements in an airport. This can clarify the relationship between object movement and sound, allowing for more detailed behavioral patterns to be analyzed.

[0033] The analysis unit captures video data from IP cameras using light of different wavelengths, enabling it to obtain information that is invisible to ordinary cameras. For example, the analysis unit will develop a system that captures video data from IP cameras using light of different wavelengths (infrared or ultraviolet) to obtain information that is invisible to ordinary cameras. For example, an infrared camera could be used to monitor changes in customers' body temperature inside a store. The analysis unit also uses generative AI to analyze video data captured with light of different wavelengths and extract information that is invisible to ordinary cameras. For example, an infrared camera could be used to monitor vehicle engine temperatures for traffic management. The analysis unit also develops an algorithm that analyzes video data captured with light of different wavelengths and obtains information related to object movement. For example, an infrared camera could be used to monitor changes in passenger body temperature inside an airport. This makes it possible to obtain information that is invisible to ordinary cameras by capturing images with light of different wavelengths.

[0034] Generative AI can analyze data on the movement of monitored objects and compare it with past data to detect abnormal patterns. For example, generative AI develops algorithms that analyze object movement data and compare it with past data to detect abnormal patterns. For example, it detects abnormal customer movement within a store. Generative AI also compares object movement data with past data to build systems that detect abnormal patterns in real time. For example, it detects abnormal vehicle movement in traffic management. Generative AI also develops algorithms that analyze object movement by comparing it with past data to detect abnormal patterns. For example, it detects abnormal passenger movement within an airport. This allows abnormal patterns to be detected by comparing it with past data, making it possible to quickly identify abnormal behavior.

[0035] Generative AI can analyze data on the movement of monitored objects and build models to predict future movements. For example, generative AI develops systems that analyze data on object movements and build models to predict future movements. For example, it predicts the future movements of customers within a store. Generative AI also analyzes data on object movements and develops algorithms to predict future movements. For example, it predicts the future movements of vehicles in traffic management. Generative AI also analyzes data on object movements and builds models to predict future movements, developing systems that provide prediction results in real time. For example, it predicts the future movements of passengers within an airport. This makes it possible to predict future movements and take measures in advance.

[0036] When analyzing data on the movement of monitored objects, generative AI can simultaneously analyze environmental data such as temperature and humidity to clarify correlations with environmental factors. For example, when analyzing data on object movement, generative AI can simultaneously analyze environmental data such as temperature and humidity to develop a system that clarifies correlations with environmental factors. For example, it can analyze the correlation between customer movement and temperature within a store. Generative AI can also integrate data on object movement and environmental data to build a system that analyzes correlations with environmental factors in real time. For example, it can analyze the correlation between vehicle movement and humidity in traffic management. Generative AI can also develop algorithms that simultaneously analyze data on object movement and environmental data to clarify correlations with environmental factors. For example, it can analyze the correlation between passenger movement and temperature within an airport. This makes it possible to simultaneously analyze environmental data and clarify correlations with environmental factors.

[0037] Generative AI can analyze data on the movement of monitored objects and reveal interactions between different objects. For example, generative AI can analyze object movement data and develop a system that reveals interactions between different objects. For example, it can analyze interactions between customers in a store. Generative AI can also analyze object movement data and build a system that analyzes interactions between different objects in real time. For example, it can analyze interactions between vehicles in traffic management. Generative AI can also analyze object movement data and develop algorithms that reveal interactions between different objects. For example, it can analyze interactions between passengers in an airport. This can reveal interactions between different objects, allowing for more detailed understanding of behavioral patterns.

[0038] Generative AI can analyze customer purchasing behavior data and propose optimal promotions to individual customers in real time. For example, generative AI analyzes customer purchasing behavior data and develops a system that proposes optimal promotions to individual customers in real time. For example, it provides personalized promotions based on the customer's past purchasing history. Generative AI also analyzes customer purchasing behavior data and develops an algorithm that proposes optimal promotions in real time. For example, it changes promotions based on the customer's current purchasing behavior. Generative AI also analyzes customer purchasing behavior data and builds a system that proposes optimal promotions to individual customers in real time. For example, it optimizes promotions based on the customer's purchasing patterns. This increases purchasing motivation by proposing optimal promotions to individual customers in real time.

[0039] Generative AI can analyze data on customer movements and optimize the paths leading up to a purchase. For example, generative AI analyzes data on customer movements and develops a system that builds an algorithm to optimize the paths leading up to a purchase. For example, it analyzes customer movements and proposes optimal product placement. Generative AI also analyzes data on customer movements and develops an algorithm that optimizes the paths leading up to a purchase in real time. For example, it changes the layout of a store based on customer movements. Generative AI also analyzes data on customer movements and builds an algorithm that optimizes the paths leading up to a purchase, developing a system that adjusts the paths in real time. For example, it changes the placement of promotions according to customer movements. This optimizes the paths leading up to a purchase, improving the customer's purchasing experience.

[0040] Generative AI can analyze customer movement data and optimize music and lighting settings in a store. For example, generative AI can analyze customer movement data and develop a system that optimizes music and lighting settings in a store. For example, it can adjust music selection and lighting brightness based on customer movement patterns. Generative AI can also analyze customer movement data and develop algorithms that optimize music and lighting settings in a store in real time. For example, it can change music and lighting in response to changes in customer emotions. Generative AI can also analyze customer movement data and build algorithms that optimize music and lighting settings in a store, developing systems that adjust them in real time. For example, it can optimize music and lighting based on customer movement patterns. In this way, optimizing music and lighting settings in a store increases customer purchasing motivation.

[0041] Generative AI can analyze customer movement data and optimize the placement of in-store displays and advertisements. For example, generative AI analyzes customer movement data and develops a system that optimizes the placement of in-store displays and advertisements. For example, it adjusts the placement of displays and advertisements based on customer movement paths. Generative AI also analyzes customer movement data and develops algorithms that optimize the placement of in-store displays and advertisements in real time. For example, it changes displays and advertisements in response to changes in customer emotions. Generative AI also analyzes customer movement data and builds algorithms that optimize the placement of in-store displays and advertisements, developing systems that make adjustments in real time. For example, it optimizes displays and advertisements based on customer movement paths. In this way, optimizing the placement of in-store displays and advertisements increases customer purchasing motivation.

[0042] Generative AI can analyze vehicle movement data and predict traffic congestion and propose mitigation measures in real time. For example, generative AI analyzes vehicle movement data and develops a system that predicts traffic congestion and proposes mitigation measures in real time. For example, it analyzes the flow of vehicles at a specific intersection and optimizes the timing of traffic lights. Generative AI can also analyze vehicle movement data and develop algorithms that predict traffic congestion and propose mitigation measures in real time. For example, it can change the timing of traffic lights according to the flow of vehicles. Generative AI can also analyze vehicle movement data and build a system that predicts traffic congestion and proposes mitigation measures in real time. For example, it can analyze the flow of vehicles at a specific intersection and optimize the timing of traffic lights. This helps to smooth traffic by predicting traffic congestion and proposing mitigation measures in real time.

[0043] Generative AI can analyze vehicle movement data and build a model that predicts the risk of accidents. For example, generative AI develops a system that analyzes vehicle movement data and builds a model that predicts the risk of accidents. For example, it analyzes the flow of vehicles at a specific intersection and predicts the risk of accidents. Generative AI can also analyze vehicle movement data and develop an algorithm that predicts the risk of accidents. For example, it predicts the risk of accidents based on the flow of vehicles. Generative AI can also analyze vehicle movement data and build a model that predicts the risk of accidents, developing a system that provides prediction results in real time. For example, it analyzes the flow of vehicles at a specific intersection and predicts the risk of accidents. This allows for the risk of accidents to be predicted and measures to be taken in advance.

[0044] Generative AI can analyze vehicle movement data and optimize public transportation schedules. For example, generative AI analyzes vehicle movement data and develops a system that optimizes public transportation schedules. For example, it adjusts bus and train schedules based on vehicle flow. Generative AI also analyzes vehicle movement data and develops algorithms that optimize public transportation schedules in real time. For example, it changes bus and train schedules according to vehicle flow. Generative AI also analyzes vehicle movement data and builds algorithms that optimize public transportation schedules and develops systems that make adjustments in real time. For example, it optimizes bus and train schedules based on vehicle flow. In this way, optimizing public transportation schedules will help smooth traffic.

[0045] Generative AI can analyze vehicle movement data, grasp parking lot usage status in real time, and optimize parking spaces. For example, generative AI analyzes vehicle movement data, grasps parking lot usage status in real time, and develops a system that optimizes parking spaces. For example, it displays parking lot availability in real time. Generative AI also analyzes vehicle movement data, grasps parking lot usage status in real time, and develops an algorithm that optimizes parking spaces. For example, it makes efficient use of available parking spaces. Generative AI also analyzes vehicle movement data, grasps parking lot usage status in real time, and builds a system that optimizes parking spaces. For example, it displays parking lot availability in real time and suggests the optimal parking space. This allows for efficient use of parking spaces by grasping parking lot usage status in real time and optimizing parking spaces.

[0046] Generative AI can analyze data on passenger movements and suggest improvements to the efficiency of boarding procedures in real time. For example, generative AI could analyze data on passenger movements and develop a system that suggests improvements to the efficiency of boarding procedures in real time. For example, it could analyze the congestion situation in front of a specific gate and suggest measures to smooth the flow of passengers. Generative AI could also analyze data on passenger movements and develop an algorithm that suggests improvements to the efficiency of boarding procedures in real time. For example, it could change the boarding procedure depending on passenger movements. Generative AI could also analyze data on passenger movements and build a system that suggests improvements to the efficiency of boarding procedures in real time. For example, it could analyze the congestion situation in front of a specific gate and suggest measures to smooth the flow of passengers. This would improve the operational efficiency of airports by suggesting improvements to the efficiency of boarding procedures in real time.

[0047] Generative AI can analyze passenger movement data and build a model that predicts congestion within an airport. For example, generative AI develops a system that analyzes passenger movement data and builds a model that predicts congestion within an airport. For example, it analyzes the congestion situation in front of a specific gate and makes a congestion prediction. Generative AI also analyzes passenger movement data and develops an algorithm that predicts congestion within an airport. For example, it predicts congestion based on passenger movement. Generative AI also analyzes passenger movement data and builds a model that predicts congestion within an airport, developing a system that provides prediction results in real time. For example, it analyzes the congestion situation in front of a specific gate and makes a congestion prediction. This makes it possible to predict congestion within an airport and take measures in advance.

[0048] Generative AI can analyze passenger movement data and optimize the placement of guide signs and signs within an airport. For example, generative AI analyzes passenger movement data and develops a system that optimizes the placement of guide signs and signs within an airport. For example, it adjusts the placement of guide signs and signs based on passenger flow. Generative AI also analyzes passenger movement data and develops algorithms that optimize the placement of guide signs and signs within an airport in real time. For example, it changes guide signs and signs according to passenger flow. Generative AI also analyzes passenger movement data and develops algorithms that optimize the placement of guide signs and signs within an airport and develops systems that make adjustments in real time. For example, it optimizes guide signs and signs based on passenger flow. In this way, optimizing the placement of guide signs and signs improves passenger convenience.

[0049] Generative AI can analyze passenger movement data and optimize the placement of stores and services within an airport. For example, generative AI analyzes passenger movement data and develops a system that optimizes the placement of stores and services within an airport. For example, it adjusts the placement of stores and services based on passenger flow. Generative AI also analyzes passenger movement data and develops an algorithm that optimizes the placement of stores and services within an airport in real time. For example, it changes the placement of stores and services according to passenger flow. Generative AI also analyzes passenger movement data and builds an algorithm that optimizes the placement of stores and services within an airport, developing a system that makes adjustments in real time. For example, it optimizes the placement of stores and services based on passenger flow. In this way, optimizing the placement of stores and services improves passenger convenience.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The analysis unit can use video data from IP cameras to analyze temperature changes accompanying object movement in real time. For example, it can monitor changes in customer body temperature in a store to detect abnormalities in their physical condition. The analysis unit can also use generative AI to extract temperature changes from video data and monitor temperature trends in specific areas in real time. For example, it can monitor changes in the body temperature of passengers in airport waiting rooms. The analysis unit can also develop algorithms based on video data to associate object movement with temperature changes and detect abnormal temperature changes. For example, it can monitor changes in the body temperature of drivers in traffic management. This allows for real-time analysis of temperature changes accompanying object movement to identify more detailed behavioral patterns.

[0052] The analysis unit can use video data from IP cameras to analyze changes in audio accompanying object movement in real time. For example, it can analyze customer conversations in a store to understand changes in purchasing intent. The analysis unit also uses generative AI to extract audio changes from video data and monitor audio trends in specific areas in real time. For example, it can monitor passenger conversations in an airport waiting room. The analysis unit can also develop algorithms based on video data to associate object movement with audio changes and detect abnormal audio changes. For example, it can monitor driver conversations in traffic management. This allows for more detailed behavioral patterns to be identified by analyzing audio changes accompanying object movement in real time.

[0053] The analysis unit can use video data from IP cameras to analyze changes in light caused by object movement in real time. For example, it monitors changes in light caused by customer movement within a store and optimizes lighting. The analysis unit also uses generative AI to extract changes in light from video data and monitor lighting trends in specific areas in real time. For example, it monitors changes in light caused by passenger movement in an airport waiting room. The analysis unit also develops algorithms based on video data to associate object movement with changes in light and detect abnormal changes in light. For example, it monitors changes in light caused by vehicle movement in traffic management. This allows for more detailed behavioral patterns to be identified by analyzing changes in light caused by object movement in real time.

[0054] The analysis unit can use video data from IP cameras to analyze changes in vibrations caused by object movement in real time. For example, it can monitor vibrations caused by customer movement in a store and detect abnormal movements. The analysis unit also uses generative AI to extract vibration changes from the video data and monitor vibration trends in specific areas in real time. For example, it can monitor vibrations caused by passenger movement in an airport waiting room. The analysis unit can also develop algorithms based on video data to associate object movement with vibration changes and detect abnormal vibrations. For example, it can monitor vibrations caused by vehicle movement in traffic management. This allows for real-time analysis of vibration changes caused by object movement, making it possible to understand more detailed behavioral patterns.

[0055] The analysis unit can use video data from IP cameras to analyze changes in pressure associated with object movement in real time. For example, it monitors pressure associated with customer movement within a store and detects abnormal movements. The analysis unit also uses generative AI to extract pressure changes from the video data and monitor pressure trends in specific areas in real time. For example, it monitors pressure associated with passenger movement in an airport waiting room. The analysis unit also develops an algorithm based on the video data to associate object movement with pressure changes and detects abnormal pressure. For example, it monitors pressure associated with vehicle movement in traffic management. This allows for real-time analysis of pressure changes associated with object movement, making it possible to understand more detailed behavioral patterns.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The IP camera captures the video data of the object. For example, the IP camera can capture high-resolution video in real time, and it can have infrared capabilities to capture clear video even at night, and pan-tilt-zoom capabilities to cover a wide area. Step 2: The monitoring unit monitors the video data captured by the IP cameras in real time. For example, the monitoring unit displays the video data in real time and detects abnormal activity. It can also record the video data and play it back later, and can integrate and display video data from multiple IP cameras. Step 3: The analysis unit analyzes the video data monitored by the monitoring unit to identify the object's spatiotemporal behavioral patterns. For example, it uses generative AI to track the object's movement and analyze its movement patterns. It can also analyze the speed and direction of the object's movement to detect abnormalities and issue an alert. Step 4: The notification unit notifies the user of the spatiotemporal behavior patterns analyzed by the analysis unit. For example, the analysis results can be sent as emails or alerts, and displayed on a dashboard. The analysis results can also be generated as reports and sent periodically.

[0058] (Example 2) A flow tracking system according to an embodiment of the present invention is a system for revealing spatiotemporal behavioral patterns of objects using IP cameras. In this system, a generative AI analyzes video data to monitor and analyze the movement of objects in real time. This allows the flow tracking system to monitor the movement of objects in real time and provide analysis results.

[0059] A flow tracking system according to an embodiment includes an IP camera, a monitoring unit, an analysis unit, and a notification unit. The IP camera acquires video data of an object. For example, the IP camera can acquire high-resolution video in real time. The IP camera can also have infrared capabilities to acquire clear video even at night. The IP camera can also have pan-tilt-zoom capabilities to cover a wide area. The monitoring unit monitors the video data acquired by the IP camera in real time. For example, the monitoring unit can display the video data in real time and detect abnormal movement. The monitoring unit can also record the video data and play it back later. The monitoring unit can also integrate and display video data from multiple IP cameras. The analysis unit analyzes the video data monitored by the monitoring unit to identify the spatiotemporal behavioral patterns of the object. For example, the analysis unit can use generative AI to track the object's movement and analyze the movement pattern. The analysis unit can also analyze the speed and direction of the object's movement. The analysis unit can also detect abnormalities in the object's movement and issue an alert. The notification unit notifies the user of the spatiotemporal behavior patterns analyzed by the analysis unit. For example, the notification unit sends the analysis results as email or an alert. The notification unit can also display the analysis results on a dashboard. The notification unit can also generate and periodically send reports on the analysis results. This allows the flow tracking system according to the embodiment to monitor the movement of objects in real time and provide the analysis results. For example, the output unit displays the analysis results to the user via a web application or a mobile application. If feedback is desired in paper form, the output unit prints the results using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0060] The analysis unit can analyze changes in emotions accompanying object movement in real time. For example, the analysis unit analyzes video data from an IP camera in real time to detect changes in emotions accompanying object movement. For example, it analyzes the facial expressions of customers in a store to understand changes in purchasing intent. The analysis unit also uses generative AI to extract changes in emotions from video data and monitor emotional trends in specific areas in real time. For example, it monitors the stress levels of passengers in airport waiting rooms. The analysis unit also develops an algorithm that associates object movement with changes in emotions based on video data to detect abnormal changes in emotions. For example, it monitors the stress levels of drivers in traffic management. This allows for real-time analysis of changes in emotions accompanying object movement to identify more detailed behavioral patterns.

[0061] The monitoring unit can maximize the surveillance area by using an algorithm that automatically optimizes the installation position of IP cameras. For example, the monitoring unit develops an algorithm to automatically optimize the installation position of IP cameras to maximize the surveillance area. For example, the monitoring unit adjusts the camera position to reduce blind spots in a store. The monitoring unit also uses generative AI to build a system that optimizes the camera installation position in real time and dynamically adjusts the surveillance area. For example, the monitoring unit changes the camera position according to the flow of vehicles in traffic management. The monitoring unit also performs simulations to optimize the camera installation position and develops an algorithm that proposes the optimal placement. For example, the monitoring unit adjusts the camera position according to the congestion level in an airport. This allows the monitoring area to be maximized by automatically optimizing the installation position of IP cameras.

[0062] The analysis unit converts video data from IP cameras into 3D models, enabling the movement of objects to be grasped in three dimensions. For example, the analysis unit develops a system that converts video data from IP cameras into 3D models and grasps the movement of objects in three dimensions. For example, the movement of customers within a store is visualized using a 3D model. The analysis unit also uses generative AI to generate 3D models from video data and track the movement of objects in real time. For example, vehicle movement is displayed using a 3D model for traffic management. The analysis unit also uses the 3D model to develop algorithms that analyze the movement of objects in three dimensions and detect abnormal movement. For example, the movement of passengers within an airport is monitored using a 3D model. This allows the movement of objects to be grasped in three dimensions, enabling more detailed behavioral patterns to be analyzed.

[0063] The analysis unit can combine video data from IP cameras with audio data and perform audio analysis to clarify the relationship between object movement and sound. For example, the analysis unit can analyze customer movement and conversations in a store. The analysis unit can also use generative AI to integrate video and audio data and build a system that analyzes the relationship between object movement and sound in real time. For example, it can analyze vehicle movement and horn sounds in traffic management. The analysis unit can also combine video and audio data to analyze the relationship between object movement and sound and develop algorithms to detect abnormal movement. For example, it can analyze the relationship between passenger movement and announcements in an airport. This can clarify the relationship between object movement and sound, allowing for more detailed behavioral patterns to be analyzed.

[0064] The analysis unit captures video data from IP cameras using light of different wavelengths, enabling it to obtain information that is invisible to ordinary cameras. For example, the analysis unit will develop a system that captures video data from IP cameras using light of different wavelengths (infrared or ultraviolet) to obtain information that is invisible to ordinary cameras. For example, an infrared camera could be used to monitor changes in customers' body temperature inside a store. The analysis unit also uses generative AI to analyze video data captured with light of different wavelengths and extract information that is invisible to ordinary cameras. For example, an infrared camera could be used to monitor vehicle engine temperatures for traffic management. The analysis unit also develops an algorithm that analyzes video data captured with light of different wavelengths and obtains information related to object movement. For example, an infrared camera could be used to monitor changes in passenger body temperature inside an airport. This makes it possible to obtain information that is invisible to ordinary cameras by capturing images with light of different wavelengths.

[0065] The analysis unit uses an IP camera equipped with an emotion estimation function to estimate the emotional state of a monitored object in real time and detect abnormal behavior. The analysis unit, for example, uses an IP camera equipped with an emotion estimation function to develop a system that estimates the emotional state of a monitored object in real time and detects abnormal behavior. For example, the emotional state of customers in a store is monitored. The analysis unit also uses generative AI to analyze video data from an IP camera equipped with an emotion estimation function and detect abnormal emotional states. For example, the emotional state of drivers is monitored in traffic management. The analysis unit also uses an IP camera equipped with an emotion estimation function to develop an algorithm that analyzes the emotional state of a monitored object in real time and detects abnormal behavior. For example, the emotional state of passengers in an airport is monitored. In this way, the emotion estimation function can be used to grasp the emotional state of a monitored object in real time and quickly detect abnormal behavior.

[0066] Generative AI can analyze data on the movement of monitored objects and analyze changes in emotions. For example, generative AI can develop a system that analyzes changes in emotions from object movement data and reveals emotional patterns. For example, it can analyze changes in customers' emotions in a store. Generative AI can also analyze data on object movement and develop algorithms that extract changes in emotions. For example, it can analyze changes in the emotions of drivers in traffic management. Generative AI can also analyze changes in emotions from object movement data in real time and reveal emotional patterns. For example, it can analyze changes in the emotions of passengers in an airport. This makes it possible to understand more detailed behavioral patterns by analyzing changes in emotions from object movement data.

[0067] Generative AI can analyze data on the movement of monitored objects and compare it with past data to detect abnormal patterns. For example, generative AI develops algorithms that analyze object movement data and compare it with past data to detect abnormal patterns. For example, it detects abnormal customer movement within a store. Generative AI also compares object movement data with past data to build systems that detect abnormal patterns in real time. For example, it detects abnormal vehicle movement in traffic management. Generative AI also develops algorithms that analyze object movement by comparing it with past data to detect abnormal patterns. For example, it detects abnormal passenger movement within an airport. This allows abnormal patterns to be detected by comparing it with past data, making it possible to quickly identify abnormal behavior.

[0068] Generative AI can analyze data on the movement of monitored objects and build models to predict future movements. For example, generative AI develops systems that analyze data on object movements and build models to predict future movements. For example, it predicts the future movements of customers within a store. Generative AI also analyzes data on object movements and develops algorithms to predict future movements. For example, it predicts the future movements of vehicles in traffic management. Generative AI also analyzes data on object movements and builds models to predict future movements, developing systems that provide prediction results in real time. For example, it predicts the future movements of passengers within an airport. This makes it possible to predict future movements and take measures in advance.

[0069] When analyzing data on the movement of monitored objects, generative AI can simultaneously analyze environmental data such as temperature and humidity to clarify correlations with environmental factors. For example, when analyzing data on object movement, generative AI can simultaneously analyze environmental data such as temperature and humidity to develop a system that clarifies correlations with environmental factors. For example, it can analyze the correlation between customer movement and temperature within a store. Generative AI can also integrate data on object movement and environmental data to build a system that analyzes correlations with environmental factors in real time. For example, it can analyze the correlation between vehicle movement and humidity in traffic management. Generative AI can also develop algorithms that simultaneously analyze data on object movement and environmental data to clarify correlations with environmental factors. For example, it can analyze the correlation between passenger movement and temperature within an airport. This makes it possible to simultaneously analyze environmental data and clarify correlations with environmental factors.

[0070] Generative AI can analyze data on the movement of monitored objects and reveal interactions between different objects. For example, generative AI can analyze object movement data and develop a system that reveals interactions between different objects. For example, it can analyze interactions between customers in a store. Generative AI can also analyze object movement data and build a system that analyzes interactions between different objects in real time. For example, it can analyze interactions between vehicles in traffic management. Generative AI can also analyze object movement data and develop algorithms that reveal interactions between different objects. For example, it can analyze interactions between passengers in an airport. This can reveal interactions between different objects, allowing for more detailed understanding of behavioral patterns.

[0071] Generative AI can use its emotion estimation function to analyze changes in emotions that accompany the movement of objects and predict behavioral patterns based on emotional patterns. For example, generative AI can use its emotion estimation function to analyze changes in emotions that accompany the movement of objects and develop a system that predicts behavioral patterns based on emotional patterns. For example, it can predict purchasing behavior based on changes in customers' emotions in a store. Generative AI can also analyze the movement of objects and changes in emotions and develop an algorithm that predicts future behavior based on emotional patterns. For example, it can predict driving behavior based on changes in the emotions of drivers in traffic management. Generative AI can also use its emotion estimation function to build a system that analyzes the movement of objects and changes in emotions in real time and predicts behavioral patterns based on emotional patterns. For example, it can predict behavior based on changes in the emotions of passengers in an airport. This makes it possible to predict behavioral patterns based on emotional patterns, making it possible to make more detailed behavioral predictions.

[0072] Generative AI can analyze changes in customer movements and emotions and optimize store layouts based on emotions. For example, generative AI can analyze changes in customer movements and emotions and develop a system that optimizes store layouts based on emotions. For example, it can adjust the placement of product shelves based on customer emotion scores. Generative AI can also analyze changes in customer movements and emotions and develop algorithms that optimize store layouts based on emotions in real time. For example, it can change in-store displays according to changes in customer emotions. Generative AI can also analyze changes in customer movements and emotions and build a system that optimizes store layouts based on emotions. For example, it can optimize in-store traffic flow based on customer emotion scores. In this way, optimizing store layouts based on emotions improves customer satisfaction.

[0073] Generative AI can analyze customer purchasing behavior data and propose optimal promotions to individual customers in real time. For example, generative AI analyzes customer purchasing behavior data and develops a system that proposes optimal promotions to individual customers in real time. For example, it provides personalized promotions based on the customer's past purchasing history. Generative AI also analyzes customer purchasing behavior data and develops an algorithm that proposes optimal promotions in real time. For example, it changes promotions based on the customer's current purchasing behavior. Generative AI also analyzes customer purchasing behavior data and builds a system that proposes optimal promotions to individual customers in real time. For example, it optimizes promotions based on the customer's purchasing patterns. This increases purchasing motivation by proposing optimal promotions to individual customers in real time.

[0074] Generative AI can analyze data on customer movements and optimize the paths leading up to a purchase. For example, generative AI analyzes data on customer movements and develops a system that builds an algorithm to optimize the paths leading up to a purchase. For example, it analyzes customer movements and proposes optimal product placement. Generative AI also analyzes data on customer movements and develops an algorithm that optimizes the paths leading up to a purchase in real time. For example, it changes the layout of a store based on customer movements. Generative AI also analyzes data on customer movements and builds an algorithm that optimizes the paths leading up to a purchase, developing a system that adjusts the paths in real time. For example, it changes the placement of promotions according to customer movements. This optimizes the paths leading up to a purchase, improving the customer's purchasing experience.

[0075] Generative AI can analyze customer movement data and optimize music and lighting settings in a store. For example, generative AI can analyze customer movement data and develop a system that optimizes music and lighting settings in a store. For example, it can adjust music selection and lighting brightness based on customer movement patterns. Generative AI can also analyze customer movement data and develop algorithms that optimize music and lighting settings in a store in real time. For example, it can change music and lighting in response to changes in customer emotions. Generative AI can also analyze customer movement data and build algorithms that optimize music and lighting settings in a store, developing systems that adjust them in real time. For example, it can optimize music and lighting based on customer movement patterns. In this way, optimizing music and lighting settings in a store increases customer purchasing motivation.

[0076] Generative AI can analyze customer movement data and optimize the placement of in-store displays and advertisements. For example, generative AI analyzes customer movement data and develops a system that optimizes the placement of in-store displays and advertisements. For example, it adjusts the placement of displays and advertisements based on customer movement paths. Generative AI also analyzes customer movement data and develops algorithms that optimize the placement of in-store displays and advertisements in real time. For example, it changes displays and advertisements in response to changes in customer emotions. Generative AI also analyzes customer movement data and builds algorithms that optimize the placement of in-store displays and advertisements, developing systems that make adjustments in real time. For example, it optimizes displays and advertisements based on customer movement paths. In this way, optimizing the placement of in-store displays and advertisements increases customer purchasing motivation.

[0077] The generative AI can use the emotion estimation function to make product suggestions based on customer emotions. For example, the generative AI uses the emotion estimation function to develop a system that makes product suggestions based on customer emotions. For example, it suggests optimal products based on the customer's emotion score. The generative AI also analyzes customer emotions in real time and develops an algorithm that makes product suggestions based on emotions. For example, it changes product suggestions according to changes in the customer's emotions. The generative AI also uses the emotion estimation function to build a system that makes product suggestions based on customer emotions and adjusts the content of suggestions in real time. For example, it optimizes product suggestions based on the customer's emotion score. In this way, product suggestions based on customer emotions can be made, thereby improving customer satisfaction.

[0078] Generative AI can analyze changes in vehicle movement and driver emotions to optimize traffic signals based on emotions. For example, generative AI can analyze changes in vehicle movement and driver emotions to develop a system that optimizes traffic signals based on emotions. For example, it can adjust the timing of signals based on the driver's emotion score. Generative AI can also analyze changes in vehicle movement and driver emotions to develop an algorithm that optimizes traffic signals based on emotions in real time. For example, it can change the timing of signals according to changes in the driver's emotion. Generative AI can also analyze changes in vehicle movement and driver emotions to build a system that optimizes traffic signals based on emotions. For example, it can optimize the timing of signals based on the driver's emotion score. In this way, optimizing traffic signals based on emotions improves traffic smoothness and safety.

[0079] Generative AI can analyze vehicle movement data and predict traffic congestion and propose mitigation measures in real time. For example, generative AI analyzes vehicle movement data and develops a system that predicts traffic congestion and proposes mitigation measures in real time. For example, it analyzes the flow of vehicles at a specific intersection and optimizes the timing of traffic lights. Generative AI can also analyze vehicle movement data and develop algorithms that predict traffic congestion and propose mitigation measures in real time. For example, it can change the timing of traffic lights according to the flow of vehicles. Generative AI can also analyze vehicle movement data and build a system that predicts traffic congestion and proposes mitigation measures in real time. For example, it can analyze the flow of vehicles at a specific intersection and optimize the timing of traffic lights. This helps to smooth traffic by predicting traffic congestion and proposing mitigation measures in real time.

[0080] Generative AI can analyze vehicle movement data and build a model that predicts the risk of accidents. For example, generative AI develops a system that analyzes vehicle movement data and builds a model that predicts the risk of accidents. For example, it analyzes the flow of vehicles at a specific intersection and predicts the risk of accidents. Generative AI can also analyze vehicle movement data and develop an algorithm that predicts the risk of accidents. For example, it predicts the risk of accidents based on the flow of vehicles. Generative AI can also analyze vehicle movement data and build a model that predicts the risk of accidents, developing a system that provides prediction results in real time. For example, it analyzes the flow of vehicles at a specific intersection and predicts the risk of accidents. This allows for the risk of accidents to be predicted and measures to be taken in advance.

[0081] Generative AI can analyze vehicle movement data and optimize public transportation schedules. For example, generative AI analyzes vehicle movement data and develops a system that optimizes public transportation schedules. For example, it adjusts bus and train schedules based on vehicle flow. Generative AI also analyzes vehicle movement data and develops algorithms that optimize public transportation schedules in real time. For example, it changes bus and train schedules according to vehicle flow. Generative AI also analyzes vehicle movement data and builds algorithms that optimize public transportation schedules and develops systems that make adjustments in real time. For example, it optimizes bus and train schedules based on vehicle flow. In this way, optimizing public transportation schedules will help smooth traffic.

[0082] Generative AI can analyze vehicle movement data, grasp parking lot usage status in real time, and optimize parking spaces. For example, generative AI analyzes vehicle movement data, grasps parking lot usage status in real time, and develops a system that optimizes parking spaces. For example, it displays parking lot availability in real time. Generative AI also analyzes vehicle movement data, grasps parking lot usage status in real time, and develops an algorithm that optimizes parking spaces. For example, it makes efficient use of available parking spaces. Generative AI also analyzes vehicle movement data, grasps parking lot usage status in real time, and builds a system that optimizes parking spaces. For example, it displays parking lot availability in real time and suggests the optimal parking space. This allows for efficient use of parking spaces by grasping parking lot usage status in real time and optimizing parking spaces.

[0083] The generative AI can use the emotion estimation function to develop a driving assistance system based on the driver's emotions. For example, the generative AI uses the emotion estimation function to develop a driving assistance system based on the driver's emotions. For example, it provides driving assistance advice based on the driver's emotion score. The generative AI also analyzes the driver's emotions in real time and develops a driving assistance system based on emotions. For example, it changes the content of driving assistance in response to changes in the driver's emotions. The generative AI also uses the emotion estimation function to build a driving assistance system based on the driver's emotions and provides driving assistance advice in real time. For example, it optimizes the content of driving assistance based on the driver's emotion score. In this way, developing a driving assistance system based on the driver's emotions improves driving safety.

[0084] Generative AI can analyze passenger movements and emotional changes to optimize security checks based on emotions. For example, generative AI can analyze passenger movements and emotional changes to develop a system that optimizes security checks based on emotions. For example, it can adjust security check procedures based on passenger emotion scores. Generative AI can also analyze passenger movements and emotional changes to develop an algorithm that optimizes security checks based on emotions in real time. For example, it can change security check procedures in response to changes in passenger emotions. Generative AI can also analyze passenger movements and emotional changes to build a system that optimizes security checks based on emotions. For example, it can optimize security check procedures based on passenger emotion scores. In this way, optimizing security checks based on emotions improves airport security.

[0085] Generative AI can analyze data on passenger movements and suggest improvements to the efficiency of boarding procedures in real time. For example, generative AI could analyze data on passenger movements and develop a system that suggests improvements to the efficiency of boarding procedures in real time. For example, it could analyze the congestion situation in front of a specific gate and suggest measures to smooth the flow of passengers. Generative AI could also analyze data on passenger movements and develop an algorithm that suggests improvements to the efficiency of boarding procedures in real time. For example, it could change the boarding procedure depending on passenger movements. Generative AI could also analyze data on passenger movements and build a system that suggests improvements to the efficiency of boarding procedures in real time. For example, it could analyze the congestion situation in front of a specific gate and suggest measures to smooth the flow of passengers. This would improve the operational efficiency of airports by suggesting improvements to the efficiency of boarding procedures in real time.

[0086] Generative AI can analyze passenger movement data and build a model that predicts congestion within an airport. For example, generative AI develops a system that analyzes passenger movement data and builds a model that predicts congestion within an airport. For example, it analyzes the congestion situation in front of a specific gate and makes a congestion prediction. Generative AI also analyzes passenger movement data and develops an algorithm that predicts congestion within an airport. For example, it predicts congestion based on passenger movement. Generative AI also analyzes passenger movement data and builds a model that predicts congestion within an airport, developing a system that provides prediction results in real time. For example, it analyzes the congestion situation in front of a specific gate and makes a congestion prediction. This makes it possible to predict congestion within an airport and take measures in advance.

[0087] Generative AI can analyze passenger movement data and optimize the placement of guide signs and signs within an airport. For example, generative AI analyzes passenger movement data and develops a system that optimizes the placement of guide signs and signs within an airport. For example, it adjusts the placement of guide signs and signs based on passenger flow. Generative AI also analyzes passenger movement data and develops algorithms that optimize the placement of guide signs and signs within an airport in real time. For example, it changes guide signs and signs according to passenger flow. Generative AI also analyzes passenger movement data and develops algorithms that optimize the placement of guide signs and signs within an airport and develops systems that make adjustments in real time. For example, it optimizes guide signs and signs based on passenger flow. In this way, optimizing the placement of guide signs and signs improves passenger convenience.

[0088] Generative AI can analyze passenger movement data and optimize the placement of stores and services within an airport. For example, generative AI analyzes passenger movement data and develops a system that optimizes the placement of stores and services within an airport. For example, it adjusts the placement of stores and services based on passenger flow. Generative AI also analyzes passenger movement data and develops an algorithm that optimizes the placement of stores and services within an airport in real time. For example, it changes the placement of stores and services according to passenger flow. Generative AI also analyzes passenger movement data and builds an algorithm that optimizes the placement of stores and services within an airport, developing a system that makes adjustments in real time. For example, it optimizes the placement of stores and services based on passenger flow. In this way, optimizing the placement of stores and services improves passenger convenience.

[0089] The generative AI can use the emotion estimation function to propose services within the airport based on passenger emotions. For example, the generative AI uses the emotion estimation function to develop a system that proposes services within the airport based on passenger emotions. For example, it proposes optimal services based on the passenger's emotion score. The generative AI also analyzes passenger emotions in real time and develops an algorithm that proposes services within the airport based on emotions. For example, it changes service proposals according to changes in passenger emotions. The generative AI also uses the emotion estimation function to build a system that proposes services within the airport based on passenger emotions and adjusts the proposal content in real time. For example, it optimizes service proposals based on the passenger's emotion score. In this way, passenger satisfaction is improved by proposing services based on passenger emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can use video data from IP cameras to analyze temperature changes accompanying object movement in real time. For example, it can monitor changes in customer body temperature in a store to detect abnormalities in their physical condition. The analysis unit can also use generative AI to extract temperature changes from video data and monitor temperature trends in specific areas in real time. For example, it can monitor changes in the body temperature of passengers in airport waiting rooms. The analysis unit can also develop algorithms based on video data to associate object movement with temperature changes and detect abnormal temperature changes. For example, it can monitor changes in the body temperature of drivers in traffic management. This allows for real-time analysis of temperature changes accompanying object movement to identify more detailed behavioral patterns.

[0092] The analysis unit can use video data from IP cameras to analyze changes in audio accompanying object movement in real time. For example, it can analyze customer conversations in a store to understand changes in purchasing intent. The analysis unit also uses generative AI to extract audio changes from video data and monitor audio trends in specific areas in real time. For example, it can monitor passenger conversations in an airport waiting room. The analysis unit can also develop algorithms based on video data to associate object movement with audio changes and detect abnormal audio changes. For example, it can monitor driver conversations in traffic management. This allows for more detailed behavioral patterns to be identified by analyzing audio changes accompanying object movement in real time.

[0093] The analysis unit can use video data from IP cameras to analyze changes in light caused by object movement in real time. For example, it monitors changes in light caused by customer movement within a store and optimizes lighting. The analysis unit also uses generative AI to extract changes in light from video data and monitor lighting trends in specific areas in real time. For example, it monitors changes in light caused by passenger movement in an airport waiting room. The analysis unit also develops algorithms based on video data to associate object movement with changes in light and detect abnormal changes in light. For example, it monitors changes in light caused by vehicle movement in traffic management. This allows for more detailed behavioral patterns to be identified by analyzing changes in light caused by object movement in real time.

[0094] The analysis unit can use video data from IP cameras to analyze changes in vibrations caused by object movement in real time. For example, it can monitor vibrations caused by customer movement in a store and detect abnormal movements. The analysis unit also uses generative AI to extract vibration changes from the video data and monitor vibration trends in specific areas in real time. For example, it can monitor vibrations caused by passenger movement in an airport waiting room. The analysis unit can also develop algorithms based on video data to associate object movement with vibration changes and detect abnormal vibrations. For example, it can monitor vibrations caused by vehicle movement in traffic management. This allows for real-time analysis of vibration changes caused by object movement, making it possible to understand more detailed behavioral patterns.

[0095] The analysis unit can use video data from IP cameras to analyze changes in pressure associated with object movement in real time. For example, it monitors pressure associated with customer movement within a store and detects abnormal movements. The analysis unit also uses generative AI to extract pressure changes from the video data and monitor pressure trends in specific areas in real time. For example, it monitors pressure associated with passenger movement in an airport waiting room. The analysis unit also develops an algorithm based on the video data to associate object movement with pressure changes and detects abnormal pressure. For example, it monitors pressure associated with vehicle movement in traffic management. This allows for real-time analysis of pressure changes associated with object movement, making it possible to understand more detailed behavioral patterns.

[0096] The analysis unit can use video data from IP cameras to analyze changes in emotions accompanying object movement in real time. For example, it can analyze the facial expressions of customers in a store to understand changes in purchasing intent. The analysis unit also uses generative AI to extract changes in emotions from video data and monitor emotional trends in specific areas in real time. For example, it can monitor the stress levels of passengers in airport waiting rooms. The analysis unit can also develop algorithms based on video data to associate object movement with changes in emotions and detect abnormal emotional changes. For example, it can monitor the stress levels of drivers in traffic management. This allows for real-time analysis of changes in emotions accompanying object movement to identify more detailed behavioral patterns.

[0097] The analysis unit uses video data from IP cameras to analyze changes in emotions accompanying object movements in real time, making it possible to predict behavioral patterns based on emotions. For example, it predicts purchasing behavior based on changes in customer emotions in a store. The analysis unit also uses generative AI to extract changes in emotions from video data and develop algorithms that predict emotion-based behavioral patterns in real time. For example, it predicts behavior based on changes in the emotions of passengers in an airport waiting room. The analysis unit also develops algorithms that associate object movements with changes in emotions based on video data, and predicts emotion-based behavioral patterns. For example, in traffic management, it predicts driving behavior based on changes in the driver's emotions. This makes it possible to predict behavioral patterns based on emotions, making it possible to make more detailed behavioral predictions.

[0098] The analysis unit uses video data from IP cameras to analyze changes in emotions accompanying object movements in real time, enabling it to detect abnormal behavior based on emotions. For example, it monitors changes in customer emotions in a store and detects abnormal behavior. The analysis unit also uses generative AI to extract changes in emotions from video data and develops algorithms to detect abnormal behavior based on emotions in real time. For example, it monitors changes in the emotions of passengers in an airport waiting room and detects abnormal behavior. The analysis unit also develops algorithms that associate object movements with changes in emotions based on video data, detecting abnormal behavior based on emotions. For example, it monitors changes in the emotions of drivers in traffic management and detects abnormal behavior. This enables rapid response by detecting abnormal behavior based on emotions.

[0099] The analysis unit uses video data from IP cameras to analyze changes in emotions accompanying object movements in real time, and can propose services based on emotions. For example, it proposes optimal services based on changes in customer emotions in a store. The analysis unit also uses generative AI to extract changes in emotions from video data and develop algorithms that propose services based on emotions in real time. For example, it proposes services based on changes in the emotions of passengers in an airport waiting room. The analysis unit also develops algorithms that associate object movements with changes in emotions based on video data, and proposes services based on emotions. For example, it proposes driving assistance services based on changes in the driver's emotions in traffic management. In this way, customer satisfaction can be improved by proposing services based on emotions.

[0100] The analysis unit uses video data from IP cameras to analyze changes in emotions that accompany the movement of objects in real time, and can adjust the environment based on emotions. For example, it adjusts lighting and music based on changes in customers' emotions in a store. The analysis unit also uses generative AI to extract changes in emotions from video data and develops algorithms that adjust the environment based on emotions in real time. For example, it adjusts the environment based on changes in the emotions of passengers in an airport waiting room. The analysis unit also develops algorithms that associate changes in object movement with changes in emotions based on video data, and adjusts the environment based on emotions. For example, in traffic management, the in-car environment is adjusted based on changes in the driver's emotions. In this way, environmental adjustments based on emotions provide a comfortable environment.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The IP camera captures the video data of the object. For example, the IP camera can capture high-resolution video in real time, and it can have infrared capabilities to capture clear video even at night, and pan-tilt-zoom capabilities to cover a wide area. Step 2: The monitoring unit monitors the video data captured by the IP cameras in real time. For example, the monitoring unit displays the video data in real time and detects abnormal activity. It can also record the video data and play it back later, and can integrate and display video data from multiple IP cameras. Step 3: The analysis unit analyzes the video data monitored by the monitoring unit to identify the object's spatiotemporal behavioral patterns. For example, it uses generative AI to track the object's movement and analyze its movement patterns. It can also analyze the speed and direction of the object's movement to detect abnormalities and issue an alert. Step 4: The notification unit notifies the user of the spatiotemporal behavior patterns analyzed by the analysis unit. For example, the analysis results can be sent as emails or alerts, and displayed on a dashboard. The analysis results can also be generated as reports and sent periodically.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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. An IP camera that acquires video data using an IP camera; a monitoring unit that monitors the video data acquired by the IP camera in real time; an analysis unit that analyzes the video data monitored by the monitoring unit and clarifies spatiotemporal behavior patterns of objects; a notification unit that notifies the spatiotemporal behavior pattern analyzed by the analysis unit. A system characterized by:

2. The analysis unit Analyze changes in emotions accompanying the movement of the object in real time 2. The system of claim 1.

3. The monitoring unit Maximize surveillance coverage with an algorithm that automatically optimizes the placement of the IP cameras.

2. The system of claim 1.

4. The analysis unit The video data from the IP camera is converted into a 3D model, and the movement of the object is grasped in three dimensions.

2. The system of claim 1.

5. The analysis unit By combining the video data from the IP camera with audio data and performing audio analysis, the relationship between the movement of the object and the sound is clarified.

2. The system of claim 1.

6. The analysis unit The IP camera captures the image data using light of different wavelengths to obtain information that cannot be seen with a normal camera.

2. The system of claim 1.

7. The analysis unit Using the IP camera equipped with emotion estimation functionality, the emotional state of the monitored subject can be estimated in real time and abnormal behavior can be detected.

2. The system of claim 1.

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