system

Abstract

The system according to this embodiment aims to reduce security risks by analyzing video footage of the area around a house and generating images that compensate for variations in lighting. [Solution] The system according to the embodiment comprises a video acquisition unit, an image generation unit, and a display unit. The video acquisition unit acquires video of the area around the house. The image generation unit analyzes the video acquired by the video acquisition unit and generates an image that compensates for the lighting conditions. The display unit displays the image generated by the image generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that parts that are vulnerable to home intrusion when absent are visible, posing a security risk.

[0005] The system according to the embodiment aims to reduce the security risk by analyzing the video around the house and generating an image that compensates for the light intensity changes.

Means for Solving the Problems

[0006] The system according to the embodiment includes a video acquisition unit, an image generation unit, and a display unit. The video acquisition unit acquires video around the house. The image generation unit analyzes the video acquired by the video acquisition unit and generates an image that compensates for the light intensity changes. The display unit displays the image generated by the image generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce security risks by analyzing video footage of the area around the house and generating images that compensate for variations in lighting. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The security system according to an embodiment of the present invention is a system for solving the problem of easily accessible areas such as doors and windows being clearly visible when the house is unoccupied. This security system is a method for preventing crime by making the house itself invisible. First, an organic electroluminescent display is attached to the exterior wall of the house. This organic electroluminescent display is installed so as to cover the entire exterior wall of the house. Cameras are also attached in each direction. This allows for real-time acquisition of images of the area around the house. Next, when the system is turned ON, a generating AI analyzes the images acquired from the cameras and generates an image that compensates for the lighting conditions. This generated image is displayed on the organic electroluminescent display. As a result, the exterior wall of the house blends into the surrounding landscape, and the house itself becomes invisible. For example, the trees, grass, and sky in front of the house are displayed on the organic electroluminescent display, making the house blend into the surrounding landscape. In this way, the exterior wall of the house becomes invisible, and easily accessible areas such as doors and windows can also be hidden. Furthermore, this system can also be applied to outdoor tents. For example, when setting up a tent at a campsite, using this system makes the tent blend into the surrounding landscape and become inconspicuous. This makes it an effective security measure during camping. By making the house itself invisible, it can deter crime. Furthermore, it can be applied to outdoor tents, making it an effective security measure during camping. This security system can prevent crime by making the exterior walls of the house invisible.

[0029] The security system according to this embodiment comprises a video acquisition unit, an image generation unit, and a display unit. The video acquisition unit acquires video of the area around the house. The video acquisition unit includes, for example, cameras installed at the four corners of the house. The cameras can acquire video in all directions, such as the front yard, back yard, and sides of the house. The video acquisition unit acquires video of the area around the house in real time, for example, using cameras installed at the four corners of the house. The video acquisition unit also has a function to automatically adjust the camera settings. For example, the video acquisition unit can adjust the camera's sensitivity and exposure according to the weather and time of day. The image generation unit uses a generation AI to analyze the video acquired by the video acquisition unit and generate an image that complements the lighting. The generation AI uses, for example, deep learning or generative opposite-agent networks (GANs) to generate natural-looking images. The image generation unit uses, for example, a generation AI to analyze the video acquired from the camera and generate an image that complements the lighting. Based on the video acquired from the camera, the generation AI adjusts the brightness and removes shadows to generate a natural-looking image. The display unit displays the generated image on an organic electroluminescent display. The display unit uses, for example, an organic electroluminescent display to provide high-quality display. By displaying the generated image on the organic electroluminescent display, the display unit makes the house blend into the surrounding landscape. The display unit displays the generated image on the organic electroluminescent display to make the exterior walls of the house invisible. As a result, the security system according to this embodiment can make the exterior walls of the house invisible and prevent crime.

[0030] The video acquisition unit captures images of the house's surroundings. Specifically, it includes cameras installed at the four corners of the house, which can capture images in all directions, including the front yard, back yard, and sides. The cameras are high-resolution, providing clear images day and night. Furthermore, the cameras are equipped with infrared sensors, enabling clear images even at night. The video acquisition unit uses these cameras to acquire images of the house's surroundings in real time and transmits them to a central database. The cameras have an automatic adjustment function, automatically adjusting sensitivity and exposure according to weather and time of day. For example, sensitivity is increased on cloudy days, and the system switches to infrared mode at night, ensuring optimal image capture at all times. The video acquisition unit also has a motion detection function, which can issue an alert if it detects abnormal movement. This allows for immediate notification of suspicious activity around the house, enabling a quick response. In addition, the video acquisition unit stores the acquired images on a cloud server, allowing past footage to be reviewed at any time. This allows for reviewing past events, making it an effective security measure.

[0031] The image generation unit uses a generation AI to analyze the video acquired by the video acquisition unit and generate an image with corrected lighting. The generation AI uses technologies such as deep learning and generative opposite-agent networks (GANs) to produce natural-looking images. Specifically, the generation AI receives video acquired from the camera as input and adjusts brightness and removes shadows. For example, it removes shadows caused by strong sunlight during the day and brightens dark images at night. The generation AI is pre-trained on a dataset of millions of images, enabling image correction under various environmental conditions. Furthermore, the generation AI can reduce noise in the video and generate clear, high-quality images. The image generation unit processes the generated images in real time and transmits them to the display unit. This ensures that the latest corrected video is always displayed. The image generation unit also has an anomaly detection algorithm and can issue a warning if it detects unusual patterns or abnormal movements. In this way, the image generation unit not only improves the quality of the video but also plays a role in improving the reliability and safety of the entire security system.

[0032] The display unit shows the generated images on an organic electroluminescent display. Specifically, the organic electroluminescent display is high-resolution and can display clear and natural images. The display unit displays the generated images in real time, making the exterior walls of the house invisible. This allows the house to blend into the surrounding landscape and hide its presence from criminals. Furthermore, the display unit can dynamically change the displayed content, for example, displaying different scenery depending on the season or time of day. This makes the appearance of the house constantly change, providing criminals with inconsistent information. The display unit also has a function to display warning messages when an anomaly is detected, providing residents with information quickly. For example, if a suspicious person approaches the house, a warning message will be displayed to alert residents. Thus, the display unit plays an important role as part of the security system and can ensure the safety of the house. In addition, the display unit is energy-efficient and has a long lifespan, which reduces maintenance costs. Thus, the display unit can provide an economical and sustainable security measure.

[0033] The video acquisition unit includes cameras installed at the four corners of the house. The video acquisition unit, for example, uses the cameras installed at the four corners of the house to acquire omnidirectional video. By using the cameras installed at the four corners of the house, the video acquisition unit can acquire omnidirectional video in real time, including the front yard, back yard, and sides of the house. The video acquisition unit, for example, uses the cameras installed at the four corners of the house to acquire video of the area around the house in real time. This allows for the acquisition of omnidirectional video by installing cameras at the four corners of the house. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, the video acquisition unit can input the video acquired from the cameras installed at the four corners of the house into a generating AI and have the generating AI perform an analysis of the omnidirectional video.

[0034] The image generation unit analyzes the video acquired from the camera using a generation AI and generates an image with corrected lighting. The image generation unit, for example, uses a generation AI to analyze the video acquired from the camera and generates an image with corrected lighting. The generation AI uses technologies such as deep learning and generative opposite networks (GANs) to generate natural-looking images. The image generation unit uses a generation AI to adjust brightness and remove shadows based on the video acquired from the camera, generating a natural-looking image. The image generation unit, for example, uses a generation AI to analyze the video acquired from the camera and generates an image with corrected lighting. This allows for the generation of natural-looking images with corrected lighting by using a generation AI. Some or all of the above-described processes in the image generation unit may be performed using AI, or not. For example, the image generation unit can input the video acquired from the camera into the generation AI and have the generation AI generate an image with corrected lighting.

[0035] The display unit includes an organic electroluminescent display. The display unit performs high-quality display using, for example, an organic electroluminescent display. The display unit enables high-quality display by using an organic electroluminescent display. The display unit displays the generated image using, for example, an organic electroluminescent display. This enables high-quality display by using an organic electroluminescent display. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or without AI. For example, when the display unit displays the generated image on the organic electroluminescent display, it may have the generation AI perform display optimization.

[0036] The display unit displays the generated image on an organic electroluminescent display. The display unit, for example, displays the generated image on an organic electroluminescent display to make the exterior walls of the house invisible. By displaying the generated image on an organic electroluminescent display, the display unit makes the house blend into the surrounding landscape. The display unit, for example, displays the generated image on an organic electroluminescent display to make the exterior walls of the house invisible. By displaying the generated image on an organic electroluminescent display, the house blends into the surrounding landscape. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, when the display unit displays the generated image on an organic electroluminescent display, it may have the generation AI perform display optimization.

[0037] The system can also be applied to outdoor tents. For example, when setting up a tent at a campsite, using this system makes the tent blend into the surrounding landscape and become less conspicuous. The system can also be applied to outdoor tents, making it effective as a security measure during camping. For example, when setting up a tent at a campsite, using this system makes the tent blend into the surrounding landscape and become less conspicuous. This makes it effective as a security measure during camping when applied to outdoor tents. Some or all of the above processing in the system may be performed using AI, for example, or without AI. For example, when setting up a tent at a campsite, the system can input video of the area around the tent into a generating AI and generate an image that makes the tent blend into the surrounding landscape.

[0038] The video acquisition unit automatically adjusts camera settings according to weather and time of day when acquiring video. For example, at night, the video acquisition unit increases the camera's sensitivity to acquire clear images even in dark places. The video acquisition unit can automatically deploy a waterproof cover to protect the camera in rainy weather. The video acquisition unit can adjust the camera's exposure to acquire appropriate images under strong sunlight during the day. In this way, by automatically adjusting the camera settings according to weather and time of day, the optimal image can always be acquired. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, the video acquisition unit can input weather and time-of-day data into a generating AI and have the generating AI perform the automatic adjustment of camera settings.

[0039] The video acquisition unit adds a function to detect surrounding movement and focus on a specific area during video acquisition. For example, if the video acquisition unit detects the movement of a person or animal, it automatically points the camera towards that area. If the video acquisition unit detects suspicious movement, it can use the zoom function to acquire detailed video. If there is little movement in the surroundings, the video acquisition unit can widen the camera's field of view to cover a wider area. This enhances surveillance of important areas by detecting surrounding movement and focusing on a specific area. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, the video acquisition unit can input data on surrounding movement into a generating AI and have the generating AI perform the task of focusing on a specific area.

[0040] The video acquisition unit integrates sensor information from inside the house and links it with external conditions when acquiring video. For example, the video acquisition unit adjusts camera settings in conjunction with external temperature based on temperature sensor information from inside the house. The video acquisition unit can adjust the camera's field of view in conjunction with external movement based on motion sensor information from inside the house. The video acquisition unit can adjust camera sensitivity in conjunction with external sound based on sound sensor information from inside the house. By integrating sensor information from inside the house, more accurate monitoring becomes possible. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, the video acquisition unit can input sensor information from inside the house into a generating AI and have the generating AI execute processing to link it with external conditions.

[0041] The video acquisition unit acquires ambient audio information simultaneously with video acquisition and uses it for analysis. For example, the video acquisition unit acquires ambient audio information and issues an alarm if it detects a suspicious sound. The video acquisition unit analyzes ambient audio information and can direct the camera in the direction of a specific sound (for example, the sound of breaking glass) if it detects such a sound. The video acquisition unit acquires ambient audio information, identifies the source of the sound, and adjusts the camera's field of view. This allows for more detailed monitoring by simultaneously acquiring ambient audio information. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, the video acquisition unit can input ambient audio data into a generating AI and have the generating AI perform audio analysis.

[0042] The image generation unit generates more natural-looking images by referencing past video data during image generation. For example, the image generation unit generates natural-looking images that are appropriate for the season and weather based on past video data. The image generation unit can generate images suitable for a specific time of day by referencing past video data. The image generation unit can use past video data to generate images that blend into the surrounding landscape. In this way, more natural-looking images can be generated by referencing past video data. Some or all of the above-described processes in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input past video data into a generation AI and have the generation AI perform the generation of natural-looking images.

[0043] The image generation unit adds a function to reflect the movements of surrounding plants and animals in real time during image generation. For example, the image generation unit can generate images that reflect the swaying of surrounding trees in real time. The image generation unit can generate images that reflect the movements of surrounding animals in real time. The image generation unit can generate images that reflect the movement of surrounding wind in real time. As a result, by reflecting the movements of surrounding plants and animals in real time, more realistic images can be generated. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without using AI. For example, the image generation unit can input data on the movements of surrounding plants and animals into a generation AI and cause the generation AI to perform the generation of images that reflect these movements in real time.

[0044] The image generation unit generates images that reflect ambient temperature and humidity information during image generation. For example, the image generation unit can generate images with warm tones based on ambient temperature information. The image generation unit can generate images that reflect fog or humidity based on ambient humidity information. The image generation unit can combine ambient temperature and humidity information to generate images that convey a sense of the season. By reflecting ambient temperature and humidity information, more natural images can be generated. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input ambient temperature and humidity data into a generation AI and have the generation AI perform the generation of natural images.

[0045] The image generation unit generates more precise images by incorporating information about surrounding buildings and structures during image generation. For example, the image generation unit generates images that reflect the shape and color of surrounding buildings. The image generation unit can generate accurate images based on the positional information of surrounding structures. The image generation unit can generate realistic images by incorporating detailed information about surrounding buildings and structures. As a result, by incorporating information about surrounding buildings and structures, more precise images can be generated. Some or all of the above-described processes in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input data about surrounding buildings and structures into a generation AI and have the generation AI perform the generation of precise images.

[0046] The display unit automatically adjusts the angle and position of the display to provide an optimal display. For example, the display unit adjusts the angle of the display to make it easier to see in strong sunlight during the day. The display unit can adjust the position of the display at night to ensure proper visibility. The display unit can automatically deploy a waterproof cover to protect the display in rainy weather. As a result, the display unit can always provide an optimal display by automatically adjusting the angle and position of the display. Some or all of the above processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data on the angle and position of the display into a generating AI and have the generating AI perform adjustments for the optimal display.

[0047] The display unit automatically adjusts the brightness of the display according to the ambient light level when displaying information. For example, the display unit increases the brightness of the display in bright sunlight during the day to make it easier to see. At night, the display unit can lower the brightness of the display to provide a display that is easy on the eyes. In rainy weather, the display unit can adjust the brightness of the display to ensure appropriate visibility. In this way, optimal visibility can always be ensured by automatically adjusting the brightness of the display according to the ambient light level. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input ambient light data into a generating AI and have the generating AI perform automatic brightness adjustment.

[0048] The display unit provides a more durable display unit by changing the display material during display. For example, the display unit uses a weather-resistant material considering outdoor use. The display unit can select a highly durable material to withstand long-term use. The display unit can use a material that is difficult to destroy as a security measure. In this way, by changing the display material, durability is improved and long-term use is possible. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data on the display material into a generating AI and have the generating AI select a highly durable material.

[0049] The display unit applies an anti-fouling coating to the surface of the display during display to facilitate maintenance. The display unit applies an anti-fouling coating to the surface of the display to make it difficult for dirt to adhere. The display unit can prevent dirt from rain and dust with the anti-fouling coating. The display unit makes regular maintenance easier by applying an anti-fouling coating. As a result, by applying an anti-fouling coating to the surface of the display, dirt adheres less easily and maintenance becomes easier. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input anti-fouling coating data into a generating AI and have the generating AI perform the coating application.

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

[0051] The security system can detect ambient temperature and humidity in its video acquisition unit and automatically adjust camera settings based on this information. For example, if the temperature is high, the camera's sensitivity can be reduced to minimize thermal noise. If the humidity is high, the waterproof cover can be automatically deployed to protect the camera. If the temperature is low, the camera's exposure can be adjusted to acquire clearer images. This allows for the acquisition of optimal images at all times by automatically adjusting camera settings according to ambient environmental conditions. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, temperature and humidity data can be input into a generating AI, which can then perform the automatic adjustment of camera settings.

[0052] The security system can acquire ambient audio information simultaneously in its video acquisition unit and use it for analysis. For example, it can acquire ambient audio information and issue an alarm if it detects a suspicious sound. By analyzing the ambient audio information, it can detect a specific sound (for example, the sound of breaking glass) and direct the camera in that direction. It can also acquire ambient audio information to identify the source of the sound and adjust the camera's field of view. This allows for more detailed surveillance by simultaneously acquiring ambient audio information. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, ambient audio data can be input into a generating AI, and the generating AI can be made to perform audio analysis.

[0053] The security system's image generation unit can generate more natural-looking images by referencing past video data. For example, it can generate natural-looking images that are appropriate for the season and weather based on past video data. It can also generate images suitable for a specific time of day by referencing past video data. Furthermore, it can use past video data to generate images that blend into the surrounding landscape. In this way, more natural-looking images can be generated by referencing past video data. Some or all of the above-described processes in the image generation unit may be performed using AI, for example, or without AI. For example, past video data can be input into a generation AI, and the generation AI can be made to generate natural-looking images.

[0054] The security system can automatically adjust the angle and position of the display unit to provide an optimal display. For example, the display angle can be adjusted for better visibility in bright sunlight during the day. At night, the display position can be adjusted to ensure proper visibility. In addition, a waterproof cover can be automatically deployed to protect the display in rainy weather. As a result, the display angle and position can be automatically adjusted to ensure an optimal display at all times. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, data on the display angle and position can be input into a generating AI, and the generating AI can be made to perform adjustments for the optimal display.

[0055] The security system can have a function added to its video acquisition unit that detects surrounding movement and focuses on a specific area. For example, if it detects the movement of a person or animal, it can automatically point the camera towards that area. If suspicious movement is detected, it can use the zoom function to acquire detailed footage. Also, if there is little movement in the surroundings, the camera's field of view can be widened to cover a wider area. This enhances surveillance of important areas by detecting surrounding movement and focusing on a specific area. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, data on surrounding movement can be input into a generating AI, and the generating AI can be made to focus on a specific area.

[0056] The security system's display unit can be made easier to maintain by applying an anti-fouling coating to the surface of the display. For example, applying an anti-fouling coating to the surface of the display makes it harder for dirt to adhere. The anti-fouling coating prevents dirt from accumulating due to rain and dust. Furthermore, applying the anti-fouling coating makes regular maintenance easier. Thus, by applying an anti-fouling coating to the surface of the display, dirt adheres less easily and maintenance becomes easier. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, data on the anti-fouling coating can be input into a generating AI, and the generating AI can be made to perform the application of the coating.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The video acquisition unit acquires images of the house's surroundings. The video acquisition unit includes cameras installed at the four corners of the house, for example, to acquire images in all directions, including the front yard, back yard, and sides of the house, in real time. The video acquisition unit also has a function to automatically adjust the camera's sensitivity and exposure according to the weather and time of day. Step 2: The image generation unit uses a generation AI to analyze the video acquired by the video acquisition unit and generate an image with corrected lighting. The generation AI uses technologies such as deep learning and generative opposing networks (GANs) to adjust brightness and remove shadows, generating a natural-looking image. Step 3: The display unit displays the generated image on an organic electroluminescent display. This makes the house blend into the surrounding landscape and enhances security by making the exterior walls of the house invisible.

[0059] (Example of form 2) The security system according to an embodiment of the present invention is a system for solving the problem of easily accessible areas such as doors and windows being clearly visible when the house is unoccupied. This security system is a method for preventing crime by making the house itself invisible. First, an organic electroluminescent display is attached to the exterior wall of the house. This organic electroluminescent display is installed so as to cover the entire exterior wall of the house. Cameras are also attached in each direction. This allows for real-time acquisition of images of the area around the house. Next, when the system is turned ON, a generating AI analyzes the images acquired from the cameras and generates an image that compensates for the lighting conditions. This generated image is displayed on the organic electroluminescent display. As a result, the exterior wall of the house blends into the surrounding landscape, and the house itself becomes invisible. For example, the trees, grass, and sky in front of the house are displayed on the organic electroluminescent display, making the house blend into the surrounding landscape. In this way, the exterior wall of the house becomes invisible, and easily accessible areas such as doors and windows can also be hidden. Furthermore, this system can also be applied to outdoor tents. For example, when setting up a tent at a campsite, using this system makes the tent blend into the surrounding landscape and become inconspicuous. This makes it an effective security measure during camping. By making the house itself invisible, it can deter crime. Furthermore, it can be applied to outdoor tents, making it an effective security measure during camping. This security system can prevent crime by making the exterior walls of the house invisible.

[0060] The security system according to this embodiment comprises a video acquisition unit, an image generation unit, and a display unit. The video acquisition unit acquires video of the area around the house. The video acquisition unit includes, for example, cameras installed at the four corners of the house. The cameras can acquire video in all directions, such as the front yard, back yard, and sides of the house. The video acquisition unit acquires video of the area around the house in real time, for example, using cameras installed at the four corners of the house. The video acquisition unit also has a function to automatically adjust the camera settings. For example, the video acquisition unit can adjust the camera's sensitivity and exposure according to the weather and time of day. The image generation unit uses a generation AI to analyze the video acquired by the video acquisition unit and generate an image that complements the lighting. The generation AI uses, for example, deep learning or generative opposite-agent networks (GANs) to generate natural-looking images. The image generation unit uses, for example, a generation AI to analyze the video acquired from the camera and generate an image that complements the lighting. Based on the video acquired from the camera, the generation AI adjusts the brightness and removes shadows to generate a natural-looking image. The display unit displays the generated image on an organic electroluminescent display. The display unit uses, for example, an organic electroluminescent display to provide high-quality display. By displaying the generated image on the organic electroluminescent display, the display unit makes the house blend into the surrounding landscape. The display unit displays the generated image on the organic electroluminescent display to make the exterior walls of the house invisible. As a result, the security system according to this embodiment can make the exterior walls of the house invisible and prevent crime.

[0061] The video acquisition unit captures images of the house's surroundings. Specifically, it includes cameras installed at the four corners of the house, which can capture images in all directions, including the front yard, back yard, and sides. The cameras are high-resolution, providing clear images day and night. Furthermore, the cameras are equipped with infrared sensors, enabling clear images even at night. The video acquisition unit uses these cameras to acquire images of the house's surroundings in real time and transmits them to a central database. The cameras have an automatic adjustment function, automatically adjusting sensitivity and exposure according to weather and time of day. For example, sensitivity is increased on cloudy days, and the system switches to infrared mode at night, ensuring optimal image capture at all times. The video acquisition unit also has a motion detection function, which can issue an alert if it detects abnormal movement. This allows for immediate notification of suspicious activity around the house, enabling a quick response. In addition, the video acquisition unit stores the acquired images on a cloud server, allowing past footage to be reviewed at any time. This allows for reviewing past events, making it an effective security measure.

[0062] The image generation unit uses a generation AI to analyze the video acquired by the video acquisition unit and generate an image with corrected lighting. The generation AI uses technologies such as deep learning and generative opposite-agent networks (GANs) to produce natural-looking images. Specifically, the generation AI receives video acquired from the camera as input and adjusts brightness and removes shadows. For example, it removes shadows caused by strong sunlight during the day and brightens dark images at night. The generation AI is pre-trained on a dataset of millions of images, enabling image correction under various environmental conditions. Furthermore, the generation AI can reduce noise in the video and generate clear, high-quality images. The image generation unit processes the generated images in real time and transmits them to the display unit. This ensures that the latest corrected video is always displayed. The image generation unit also has an anomaly detection algorithm and can issue a warning if it detects unusual patterns or abnormal movements. In this way, the image generation unit not only improves the quality of the video but also plays a role in improving the reliability and safety of the entire security system.

[0063] The display unit shows the generated images on an organic electroluminescent display. Specifically, the organic electroluminescent display is high-resolution and can display clear and natural images. The display unit displays the generated images in real time, making the exterior walls of the house invisible. This allows the house to blend into the surrounding landscape and hide its presence from criminals. Furthermore, the display unit can dynamically change the displayed content, for example, displaying different scenery depending on the season or time of day. This makes the appearance of the house constantly change, providing criminals with inconsistent information. The display unit also has a function to display warning messages when an anomaly is detected, providing residents with information quickly. For example, if a suspicious person approaches the house, a warning message will be displayed to alert residents. Thus, the display unit plays an important role as part of the security system and can ensure the safety of the house. In addition, the display unit is energy-efficient and has a long lifespan, which reduces maintenance costs. Thus, the display unit can provide an economical and sustainable security measure.

[0064] The video acquisition unit includes cameras installed at the four corners of the house. The video acquisition unit, for example, uses the cameras installed at the four corners of the house to acquire omnidirectional video. By using the cameras installed at the four corners of the house, the video acquisition unit can acquire omnidirectional video in real time, including the front yard, back yard, and sides of the house. The video acquisition unit, for example, uses the cameras installed at the four corners of the house to acquire video of the area around the house in real time. This allows for the acquisition of omnidirectional video by installing cameras at the four corners of the house. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, the video acquisition unit can input the video acquired from the cameras installed at the four corners of the house into a generating AI and have the generating AI perform an analysis of the omnidirectional video.

[0065] The image generation unit analyzes the video acquired from the camera using a generation AI and generates an image with corrected lighting. The image generation unit, for example, uses a generation AI to analyze the video acquired from the camera and generates an image with corrected lighting. The generation AI uses technologies such as deep learning and generative opposite networks (GANs) to generate natural-looking images. The image generation unit uses a generation AI to adjust brightness and remove shadows based on the video acquired from the camera, generating a natural-looking image. The image generation unit, for example, uses a generation AI to analyze the video acquired from the camera and generates an image with corrected lighting. This allows for the generation of natural-looking images with corrected lighting by using a generation AI. Some or all of the above-described processes in the image generation unit may be performed using AI, or not. For example, the image generation unit can input the video acquired from the camera into the generation AI and have the generation AI generate an image with corrected lighting.

[0066] The display unit includes an organic electroluminescent display. The display unit performs high-quality display using, for example, an organic electroluminescent display. The display unit enables high-quality display by using an organic electroluminescent display. The display unit displays the generated image using, for example, an organic electroluminescent display. This enables high-quality display by using an organic electroluminescent display. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or without AI. For example, when the display unit displays the generated image on the organic electroluminescent display, it may have the generation AI perform display optimization.

[0067] The display unit displays the generated image on an organic electroluminescent display. The display unit, for example, displays the generated image on an organic electroluminescent display to make the exterior walls of the house invisible. By displaying the generated image on an organic electroluminescent display, the display unit makes the house blend into the surrounding landscape. The display unit, for example, displays the generated image on an organic electroluminescent display to make the exterior walls of the house invisible. By displaying the generated image on an organic electroluminescent display, the house blends into the surrounding landscape. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, when the display unit displays the generated image on an organic electroluminescent display, it may have the generation AI perform display optimization.

[0068] The system can also be applied to outdoor tents. For example, when setting up a tent at a campsite, using this system makes the tent blend into the surrounding landscape and become less conspicuous. The system can also be applied to outdoor tents, making it effective as a security measure during camping. For example, when setting up a tent at a campsite, using this system makes the tent blend into the surrounding landscape and become less conspicuous. This makes it effective as a security measure during camping when applied to outdoor tents. Some or all of the above processing in the system may be performed using AI, for example, or without AI. For example, when setting up a tent at a campsite, the system can input video of the area around the tent into a generating AI and generate an image that makes the tent blend into the surrounding landscape.

[0069] The video acquisition unit estimates the user's emotions and adjusts the timing of video acquisition based on the estimated emotions. For example, if the user is feeling anxious, the video acquisition unit acquires video more frequently to enhance real-time monitoring. If the user is relaxed, the video acquisition unit can reduce the frequency of video acquisition to alleviate the system load. If the user is away from home, the video acquisition unit can acquire video at specific times to provide necessary information. This allows for more appropriate monitoring by adjusting the timing of video acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the video acquisition unit may be performed using AI or not using AI. For example, the video acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The video acquisition unit automatically adjusts camera settings according to weather and time of day when acquiring video. For example, at night, the video acquisition unit increases the camera's sensitivity to acquire clear images even in dark places. The video acquisition unit can automatically deploy a waterproof cover to protect the camera in rainy weather. The video acquisition unit can adjust the camera's exposure to acquire appropriate images under strong sunlight during the day. In this way, by automatically adjusting the camera settings according to weather and time of day, the optimal image can always be acquired. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, the video acquisition unit can input weather and time-of-day data into a generating AI and have the generating AI perform the automatic adjustment of camera settings.

[0071] The video acquisition unit adds a function to detect surrounding movement and focus on a specific area during video acquisition. For example, if the video acquisition unit detects the movement of a person or animal, it automatically points the camera towards that area. If the video acquisition unit detects suspicious movement, it can use the zoom function to acquire detailed video. If there is little movement in the surroundings, the video acquisition unit can widen the camera's field of view to cover a wider area. This enhances surveillance of important areas by detecting surrounding movement and focusing on a specific area. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, the video acquisition unit can input data on surrounding movement into a generating AI and have the generating AI perform the task of focusing on a specific area.

[0072] The video acquisition unit estimates the user's emotions and determines the priority of video to acquire based on the estimated user emotions. For example, if the user is feeling anxious, the video acquisition unit will prioritize acquiring video of easily intruded areas. If the user is relaxed, the video acquisition unit can acquire a balanced overall video. If the user is out, the video acquisition unit can prioritize acquiring video of important areas such as the entrance and windows. This enhances surveillance of important areas by prioritizing video according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, the video acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The video acquisition unit integrates sensor information from inside the house and links it with external conditions when acquiring video. For example, the video acquisition unit adjusts camera settings in conjunction with external temperature based on temperature sensor information from inside the house. The video acquisition unit can adjust the camera's field of view in conjunction with external movement based on motion sensor information from inside the house. The video acquisition unit can adjust camera sensitivity in conjunction with external sound based on sound sensor information from inside the house. By integrating sensor information from inside the house, more accurate monitoring becomes possible. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, the video acquisition unit can input sensor information from inside the house into a generating AI and have the generating AI execute processing to link it with external conditions.

[0074] The video acquisition unit acquires ambient audio information simultaneously with video acquisition and uses it for analysis. For example, the video acquisition unit acquires ambient audio information and issues an alarm if it detects a suspicious sound. The video acquisition unit analyzes ambient audio information and can direct the camera in the direction of a specific sound (for example, the sound of breaking glass) if it detects such a sound. The video acquisition unit acquires ambient audio information, identifies the source of the sound, and adjusts the camera's field of view. This allows for more detailed monitoring by simultaneously acquiring ambient audio information. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, the video acquisition unit can input ambient audio data into a generating AI and have the generating AI perform audio analysis.

[0075] The image generation unit estimates the user's emotions and adjusts the color tone and brightness of the generated image based on the estimated emotions. For example, if the user is feeling anxious, the image generation unit will avoid dark tones and generate a bright image. If the user is relaxed, the image generation unit can generate an image while maintaining natural tones. If the user is excited, the image generation unit can generate an image using vivid tones. This allows for a more natural display by adjusting the color tone and brightness of the image according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0076] The image generation unit generates more natural-looking images by referencing past video data during image generation. For example, the image generation unit generates natural-looking images that are appropriate for the season and weather based on past video data. The image generation unit can generate images suitable for a specific time of day by referencing past video data. The image generation unit can use past video data to generate images that blend into the surrounding landscape. In this way, more natural-looking images can be generated by referencing past video data. Some or all of the above-described processes in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input past video data into a generation AI and have the generation AI perform the generation of natural-looking images.

[0077] The image generation unit adds a function to reflect the movements of surrounding plants and animals in real time during image generation. For example, the image generation unit can generate images that reflect the swaying of surrounding trees in real time. The image generation unit can generate images that reflect the movements of surrounding animals in real time. The image generation unit can generate images that reflect the movement of surrounding wind in real time. As a result, by reflecting the movements of surrounding plants and animals in real time, more realistic images can be generated. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without using AI. For example, the image generation unit can input data on the movements of surrounding plants and animals into a generation AI and cause the generation AI to perform the generation of images that reflect these movements in real time.

[0078] The image generation unit estimates the user's emotions and adjusts the level of detail of the generated image based on the estimated emotions. For example, if the user is feeling anxious, the image generation unit increases the level of detail to produce a sharp image. If the user is relaxed, the image generation unit can produce a natural image with a moderate level of detail. If the user is excited, the image generation unit can adjust the level of detail to produce a visually stimulating image. This allows for a more appropriate display by adjusting the level of detail of the image according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0079] The image generation unit generates images that reflect ambient temperature and humidity information during image generation. For example, the image generation unit can generate images with warm tones based on ambient temperature information. The image generation unit can generate images that reflect fog or humidity based on ambient humidity information. The image generation unit can combine ambient temperature and humidity information to generate images that convey a sense of the season. By reflecting ambient temperature and humidity information, more natural images can be generated. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input ambient temperature and humidity data into a generation AI and have the generation AI perform the generation of natural images.

[0080] The image generation unit generates more precise images by incorporating information about surrounding buildings and structures during image generation. For example, the image generation unit generates images that reflect the shape and color of surrounding buildings. The image generation unit can generate accurate images based on the positional information of surrounding structures. The image generation unit can generate realistic images by incorporating detailed information about surrounding buildings and structures. As a result, by incorporating information about surrounding buildings and structures, more precise images can be generated. Some or all of the above-described processes in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input data about surrounding buildings and structures into a generation AI and have the generation AI perform the generation of precise images.

[0081] The display unit estimates the user's emotions and adjusts the frequency of image updates based on the estimated emotions. For example, if the user is feeling anxious, the display unit frequently updates images to provide real-time information. If the user is relaxed, the display unit can reduce the image update frequency to lessen the system load. If the user is out, the display unit can update images at specific times to provide necessary information. This allows for more appropriate display by adjusting the image update frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, or not using AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The display unit automatically adjusts the angle and position of the display to provide an optimal display. For example, the display unit adjusts the angle of the display to make it easier to see in strong sunlight during the day. The display unit can adjust the position of the display at night to ensure proper visibility. The display unit can automatically deploy a waterproof cover to protect the display in rainy weather. As a result, the display unit can always provide an optimal display by automatically adjusting the angle and position of the display. Some or all of the above processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data on the angle and position of the display into a generating AI and have the generating AI perform adjustments for the optimal display.

[0083] The display unit automatically adjusts the brightness of the display according to the ambient light level when displaying information. For example, the display unit increases the brightness of the display in bright sunlight during the day to make it easier to see. At night, the display unit can lower the brightness of the display to provide a display that is easy on the eyes. In rainy weather, the display unit can adjust the brightness of the display to ensure appropriate visibility. In this way, optimal visibility can always be ensured by automatically adjusting the brightness of the display according to the ambient light level. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input ambient light data into a generating AI and have the generating AI perform automatic brightness adjustment.

[0084] The display unit estimates the user's emotions and adjusts the size of the displayed image based on the estimated emotions. For example, if the user is feeling anxious, the display unit increases the image size to provide more detailed information. If the user is relaxed, the display unit can provide a natural display at an appropriate size. If the user is excited, the display unit can adjust the image size to provide a visually stimulating display. By adjusting the image size according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The display unit provides a more durable display unit by changing the display material during display. For example, the display unit uses a weather-resistant material considering outdoor use. The display unit can select a highly durable material to withstand long-term use. The display unit can use a material that is difficult to destroy as a security measure. In this way, by changing the display material, durability is improved and long-term use is possible. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data on the display material into a generating AI and have the generating AI select a highly durable material.

[0086] The display unit applies an anti-fouling coating to the surface of the display during display to facilitate maintenance. The display unit applies an anti-fouling coating to the surface of the display to make it difficult for dirt to adhere. The display unit can prevent dirt from rain and dust with the anti-fouling coating. The display unit makes regular maintenance easier by applying an anti-fouling coating. As a result, by applying an anti-fouling coating to the surface of the display, dirt adheres less easily and maintenance becomes easier. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input anti-fouling coating data into a generating AI and have the generating AI perform the coating application.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The security system can estimate the user's emotions and adjust the type of alarm based on those emotions. For example, if the user is feeling anxious, the alarm volume can be increased and the visual warnings strengthened. If the user is relaxed, the alarm volume can be reduced and the visual warnings gentler. Furthermore, if the user is away from home, remote monitoring can be enhanced by sending notifications to their smartphone. This allows for more appropriate security measures by adjusting the type of alarm according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Adjustment of the type of alarm may be performed using AI or not. For example, alarm type data can be input into a generative AI, and the generative AI can perform emotion-based adjustments.

[0089] The security system can detect ambient temperature and humidity in its video acquisition unit and automatically adjust camera settings based on this information. For example, if the temperature is high, the camera's sensitivity can be reduced to minimize thermal noise. If the humidity is high, the waterproof cover can be automatically deployed to protect the camera. If the temperature is low, the camera's exposure can be adjusted to acquire clearer images. This allows for the acquisition of optimal images at all times by automatically adjusting camera settings according to ambient environmental conditions. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, temperature and humidity data can be input into a generating AI, which can then perform the automatic adjustment of camera settings.

[0090] The security system's image generation unit can estimate the user's emotions and adjust the movement of the generated images based on those emotions. For example, if the user is feeling anxious, the image movement can be reduced to provide a sense of stability. If the user is relaxed, the image can be generated with natural movement. If the user is excited, the movement can be increased to generate a visually stimulating image. By adjusting the image movement according to the user's emotions, a more appropriate display becomes possible. Emotion estimation can be achieved using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The adjustment of image movement may be performed using AI or without AI. For example, user emotion data can be input into the generation AI, and the generation AI can be made to perform emotion-based movement adjustments.

[0091] The security system can estimate the user's emotions on its display and adjust the color temperature of the displayed image based on those emotions. For example, if the user is feeling anxious, a warm color temperature can be used to provide a sense of security. If the user is relaxed, the image can be displayed while maintaining a natural color temperature. If the user is excited, a cool color temperature can be used to encourage calming. By adjusting the color temperature of the image according to the user's emotions, a more appropriate display becomes possible. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Color temperature adjustment may be performed using AI or not using AI. For example, user emotion data can be input into a generative AI, and the generative AI can be made to perform emotion-based color temperature adjustments.

[0092] The security system can acquire ambient audio information simultaneously in its video acquisition unit and use it for analysis. For example, it can acquire ambient audio information and issue an alarm if it detects a suspicious sound. By analyzing the ambient audio information, it can detect a specific sound (for example, the sound of breaking glass) and direct the camera in that direction. It can also acquire ambient audio information to identify the source of the sound and adjust the camera's field of view. This allows for more detailed surveillance by simultaneously acquiring ambient audio information. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, ambient audio data can be input into a generating AI, and the generating AI can be made to perform audio analysis.

[0093] The security system's image generation unit can generate more natural-looking images by referencing past video data. For example, it can generate natural-looking images that are appropriate for the season and weather based on past video data. It can also generate images suitable for a specific time of day by referencing past video data. Furthermore, it can use past video data to generate images that blend into the surrounding landscape. In this way, more natural-looking images can be generated by referencing past video data. Some or all of the above-described processes in the image generation unit may be performed using AI, for example, or without AI. For example, past video data can be input into a generation AI, and the generation AI can be made to generate natural-looking images.

[0094] The security system can automatically adjust the angle and position of the display unit to provide an optimal display. For example, the display angle can be adjusted for better visibility in bright sunlight during the day. At night, the display position can be adjusted to ensure proper visibility. In addition, a waterproof cover can be automatically deployed to protect the display in rainy weather. As a result, the display angle and position can be automatically adjusted to ensure an optimal display at all times. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, data on the display angle and position can be input into a generating AI, and the generating AI can be made to perform adjustments for the optimal display.

[0095] The security system can have a function added to its video acquisition unit that detects surrounding movement and focuses on a specific area. For example, if it detects the movement of a person or animal, it can automatically point the camera towards that area. If suspicious movement is detected, it can use the zoom function to acquire detailed footage. Also, if there is little movement in the surroundings, the camera's field of view can be widened to cover a wider area. This enhances surveillance of important areas by detecting surrounding movement and focusing on a specific area. Some or all of the above processing in the video acquisition unit may be performed using AI, for example, or without AI. For example, data on surrounding movement can be input into a generating AI, and the generating AI can be made to focus on a specific area.

[0096] The security system's image generation unit can estimate the user's emotions and adjust the level of detail of the generated images based on the estimated emotions. For example, if the user is feeling anxious, the level of detail can be increased to generate a sharp image. If the user is relaxed, a natural image with appropriate detail can be generated. If the user is excited, the level of detail can be adjusted to generate a visually stimulating image. By adjusting the level of detail of the image according to the user's emotions, a more appropriate display becomes possible. Emotion estimation can be achieved using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The adjustment of detail may be performed using, for example, AI or not. For example, user emotion data can be input into the generation AI, and the generation AI can be made to perform detail adjustments based on emotions.

[0097] The security system's display unit can be made easier to maintain by applying an anti-fouling coating to the surface of the display. For example, applying an anti-fouling coating to the surface of the display makes it harder for dirt to adhere. The anti-fouling coating prevents dirt from accumulating due to rain and dust. Furthermore, applying the anti-fouling coating makes regular maintenance easier. Thus, by applying an anti-fouling coating to the surface of the display, dirt adheres less easily and maintenance becomes easier. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, data on the anti-fouling coating can be input into a generating AI, and the generating AI can be made to perform the application of the coating.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The video acquisition unit acquires images of the house's surroundings. The video acquisition unit includes cameras installed at the four corners of the house, for example, to acquire images in all directions, including the front yard, back yard, and sides of the house, in real time. The video acquisition unit also has a function to automatically adjust the camera's sensitivity and exposure according to the weather and time of day. Step 2: The image generation unit uses a generation AI to analyze the video acquired by the video acquisition unit and generate an image with corrected lighting. The generation AI uses technologies such as deep learning and generative opposing networks (GANs) to adjust brightness and remove shadows, generating a natural-looking image. Step 3: The display unit displays the generated image on an organic electroluminescent display. This makes the house blend into the surrounding landscape and enhances security by making the exterior walls of the house invisible.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the video acquisition unit, image generation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the video acquisition unit acquires video of the surroundings of the house using the camera 42 of the smart device 14. The image generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the video using generation AI and generates an image that compensates for the lighting conditions. The display unit displays the generated image on an organic electroluminescent display using the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the video acquisition unit, image generation unit, and display unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the video acquisition unit acquires images of the surroundings of the house using the camera 42 of the smart glasses 214. The image generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the images using generation AI and generates an image that complements the lighting conditions. The display unit displays the generated image on an organic electroluminescent display using the output device 40 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the video acquisition unit, image generation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the video acquisition unit acquires video of the surroundings of the house using the camera 42 of the headset terminal 314. The image generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the video using generation AI and generates an image that compensates for the lighting conditions. The display unit displays the generated image on an organic electroluminescent display using the output device 40 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

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

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the video acquisition unit, image generation unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the video acquisition unit acquires video of the house's surroundings using the camera 42 of the robot 414. The image generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the video using generation AI and generates an image that compensates for the lighting conditions. The display unit displays the generated image on an organic electroluminescent display using the output device 40 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) A video acquisition unit that acquires images of the area around the house, An image generation unit analyzes the video acquired by the aforementioned video acquisition unit and generates an image with the lighting conditions corrected, The system includes a display unit that displays the image generated by the image generation unit. A system characterized by the following features. (Note 2) The aforementioned video acquisition unit, Including cameras installed in the four corners of the house The system described in Appendix 1, characterized by the features described herein. (Note 3) The image generation unit, The AI ​​generates images by analyzing video footage acquired from the camera and supplementing it with images that compensate for variations in lighting. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is Includes organic electroluminescent displays The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is The generated image is displayed on an organic electroluminescent display. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned system, It can also be applied to outdoor tents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned video acquisition unit, The system estimates the user's emotions and adjusts the timing of video acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned video acquisition unit, When acquiring video footage, the camera settings are automatically adjusted according to the weather and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned video acquisition unit, Add a feature that detects surrounding movement during video acquisition and focuses on a specific area. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned video acquisition unit, It estimates the user's emotions and determines the priority of the videos to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned video acquisition unit, When acquiring video footage, the system integrates sensor information from inside the house and links it with the external situation. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned video acquisition unit, When acquiring video footage, ambient audio information is simultaneously acquired and used for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The image generation unit, It estimates the user's emotions and adjusts the color tone and brightness of the generated images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The image generation unit, When generating images, past video data is referenced to create more natural-looking images. The system described in Appendix 1, characterized by the features described herein. (Note 15) The image generation unit, Add a feature that reflects the movement of surrounding plants and animals in real time when generating images. The system described in Appendix 1, characterized by the features described herein. (Note 16) The image generation unit, It estimates the user's emotions and adjusts the level of detail in the generated images based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The image generation unit, When generating an image, the system generates an image that reflects ambient temperature and humidity information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The image generation unit, During image generation, information about surrounding buildings and structures is incorporated to produce more precise images. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is It estimates the user's emotions and adjusts the update frequency of the displayed images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displaying content, the screen angle and position are automatically adjusted to provide the optimal display. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying content, the display brightness is automatically adjusted according to the ambient light level. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is It estimates the user's emotions and adjusts the size of the images displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is During display, the display material is changed to provide a more durable display unit. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying, an anti-fouling coating is applied to the surface of the display to facilitate maintenance. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A video acquisition unit that acquires images of the area around the house, An image generation unit analyzes the video acquired by the aforementioned video acquisition unit and generates an image with the lighting conditions corrected, The system includes a display unit that displays the image generated by the image generation unit. A system characterized by the following features.

2. The aforementioned video acquisition unit, Including cameras installed in the four corners of the house The system according to feature 1.

3. The image generation unit, The AI ​​generates images by analyzing video footage acquired from the camera and supplementing it with images that compensate for variations in lighting. The system according to feature 1.

4. The aforementioned display unit is Includes organic electroluminescent displays The system according to feature 1.

5. The aforementioned display unit is The generated image is displayed on an organic electroluminescent display. The system according to feature 1.

6. The aforementioned system, It can also be applied to outdoor tents. The system according to feature 1.

7. The aforementioned video acquisition unit, The system estimates the user's emotions and adjusts the timing of video acquisition based on those emotions. The system according to feature 1.

8. The aforementioned video acquisition unit, When acquiring video footage, the camera settings are automatically adjusted according to the weather and time of day. The system according to feature 1.

9. The aforementioned video acquisition unit, Add a feature that detects surrounding movement during video acquisition and focuses on a specific area. The system according to feature 1.

10. The aforementioned video acquisition unit, It estimates the user's emotions and determines the priority of the videos to acquire based on the estimated user emotions. The system according to feature 1.

Images