system

Abstract

The system according to this embodiment aims to allow the user to blend naturally into their surroundings. [Solution] The system according to the embodiment comprises an acquisition unit, a generation unit, and a display unit. The acquisition unit acquires images of the surroundings. The generation unit analyzes the images acquired by the acquisition unit and stitches them together in a natural manner. The display unit displays the images generated by the 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 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, it is difficult to completely blend into the surroundings even when wearing camouflage clothing, and there is a risk of being discovered.

[0005] The system according to the embodiment aims for the user to blend naturally into the surroundings.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, a generation unit, and a display unit. The acquisition unit acquires an image of the surroundings. The generation unit analyzes the image acquired by the acquisition unit and stitches it together in a natural form. The display unit displays the image generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment allows the user to blend naturally into their surroundings. [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 AI ​​stealth suit according to an embodiment of the present invention is a system that solves the conventional problem of being detected even when wearing camouflage clothing. This system acquires and analyzes images of the surroundings, stitches them together in a natural way, and displays them, allowing the user to enter a stealth state and become difficult to detect. For example, the user attaches a 360-degree camera to their head. This camera acquires images of the surroundings in real time. For example, if the user is in a forest, the camera acquires images of the surrounding trees and ground. These images are input to a generating AI. Next, the generating AI analyzes the acquired images and stitches them together in a natural way. The generating AI analyzes each part of the image and identifies the location of each part. For example, it identifies the positions of tree trunks and leaves and stitches them together in a natural way. Through this process, the acquired images are displayed as a single continuous landscape. The generated image is displayed on clothing made of an organic EL display. For example, if the user is in a forest, the clothing displays images of the surrounding trees and ground. This allows the user to blend into the surrounding landscape. This mechanism allows the user to enter a stealth state and become difficult to detect in survival games or hunting. For example, in survival games, it becomes more difficult for members of the opposing team to spot the user. Similarly, in hunting, it improves the success rate of hunts by making it harder for prey to detect the user. Furthermore, this technology can be applied to other fields. For instance, it can be used in military applications and police investigations where inconspicuousness is required. In the entertainment field, it can be combined with virtual reality and augmented reality technologies to provide new experiences. Thus, AI stealth suits solve the traditional problem of being easily detected even when wearing camouflage, making them not only harder to spot in survival games and hunting, but also applicable to other fields. This allows the user to enter a stealthy state, making them difficult to detect.

[0029] The AI ​​stealth suit according to this embodiment comprises an acquisition unit, a generation unit, and a display unit. The acquisition unit acquires images of the surroundings. The acquisition unit acquires images of the surroundings using, for example, a 360-degree camera. The acquisition unit can acquire images of the surroundings in real time by, for example, having the user wear a 360-degree camera on their head. The generation unit analyzes the images acquired by the acquisition unit and stitches them together in a natural way. The generation unit analyzes the acquired images using, for example, a generation AI and stitches them together in a natural way. The generation unit analyzes each part of the image and identifies the location of each part. The generation unit identifies the locations of, for example, tree trunks and leaves and stitches them together in a natural way. The display unit displays the images generated by the generation unit. The display unit displays the generated images using, for example, an organic EL display. The display unit can, for example, display the generated images in real time. As a result, the AI ​​stealth suit according to this embodiment allows the user to enter a stealth state and become difficult to detect.

[0030] The acquisition unit acquires images of the surroundings. For example, the acquisition unit uses a 360-degree camera to acquire images of the surroundings. Specifically, the 360-degree camera is mounted on the user's head and captures omnidirectional video in real time. This camera is equipped with a high-resolution lens and sensor, allowing it to capture even fine details clearly. Furthermore, the camera is equipped with an infrared sensor and a high-sensitivity sensor to acquire high-quality images even in low-light environments. This allows for accurate understanding of the surroundings even at night or in dark places. The acquired image data is transmitted to the generation unit via wireless communication. High-speed and stable communication protocols such as Wi-Fi and Bluetooth® are used for wireless communication. This allows the acquisition unit to continuously acquire the latest surrounding images while following the user's movements. The acquisition unit can also simultaneously acquire environmental data such as ambient sound, temperature, and humidity. This is used as auxiliary information for the generation unit to generate more natural and realistic images. The acquisition unit has a lightweight and compact design, and can withstand long-term use without hindering the user's movements.

[0031] The generation unit analyzes the images acquired by the acquisition unit and stitches them together in a natural way. For example, the generation unit uses a generation AI to analyze the acquired images and stitch them together naturally. Specifically, the generation AI uses deep learning technology to analyze each part of the image and identify the location of each part. The generation AI is pre-trained on a large amount of natural environment image data and can recognize features such as tree trunks and leaves, building walls and windows with high accuracy. After analyzing each part of the acquired image and identifying their relative positions, the generation AI applies an algorithm to stitch them together naturally. For example, it identifies the positions of tree trunks and leaves and adjusts the image's color tone, brightness, and contrast to stitch them together naturally. Furthermore, the generation AI uses interpolation techniques to smoothly stitch together image boundaries, generating a natural image without any unnaturalness. In addition, the generation unit utilizes high-performance GPUs and dedicated hardware acceleration to rapidly process image data acquired in real time. This allows the generation unit to continuously generate the latest natural images while following the user's movements.

[0032] The display unit displays images generated by the generation unit. The display unit displays images generated using, for example, an organic EL display. Specifically, organic EL displays can reproduce vivid colors with high resolution and display the generated images in real time. The display unit is incorporated into the user's clothing or equipment and achieves a stealth effect by displaying the generated images around the user. For example, an organic EL display is placed on the surface of the jacket or pants worn by the user, and the generated images are displayed. This makes the user's appearance blend in with the surrounding environment, making them difficult to spot. The display unit has a thin and flexible design and can be freely deformed to match the user's movements. In addition, the display unit has low power consumption and can withstand long periods of use. Furthermore, the display unit is equipped with an ambient light sensor and can automatically adjust the brightness of the display according to the ambient light. This ensures that optimal display quality is always maintained, day or night. The display unit works in conjunction with the generation unit to smoothly display images that are updated in real time. As a result, the AI ​​stealth suit according to the embodiment allows the user to enter a stealth state and become difficult to spot.

[0033] The acquisition unit can acquire images of the surroundings using a 360-degree camera. The acquisition unit can, for example, acquire images of the surroundings using a 360-degree camera. The acquisition unit can, for example, acquire images of the surroundings in real time by having a user attach a 360-degree camera to their head. This makes it possible to acquire images in all directions by using a 360-degree camera. 360-degree cameras include, for example, high-resolution cameras and high-frame-rate cameras, but are not limited to such examples. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input image data acquired by the 360-degree camera into a generating AI and have the generating AI perform analysis of the image data.

[0034] The generation unit can analyze the images acquired by the acquisition unit and stitch them together in a natural way. For example, the generation unit can use a generation AI to analyze the acquired images and stitch them together in a natural way. For example, the generation unit can analyze each part of the image and identify the location of each part. For example, the generation unit can identify the locations of tree trunks and leaves and stitch them together in a natural way. In this way, by analyzing the acquired images and stitching them together in a natural way, a continuous landscape can be displayed. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the acquired image data into a generation AI and have the generation AI perform the analysis and stitching of the image data.

[0035] The display unit can display images generated by the generation unit using an organic EL display. The display unit can, for example, display images generated using an organic EL display. The display unit can, for example, display generated images in real time. This allows for the display of sharp images by using an organic EL display. Organic EL displays include, but are not limited to, high-resolution displays and high-contrast ratio displays. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input generated image data into a generation AI and cause the generation AI to display the image data.

[0036] The generation unit can analyze each part of an image and identify the location of each part. For example, the generation unit can analyze each part of an image and identify the location of each part. For example, the generation unit can identify the locations of tree trunks and leaves and connect them in a natural way. In this way, by analyzing each part of an image and identifying its location, the image can be connected in a natural way. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input image data into a generation AI and have the generation AI perform location identification of each part of the image.

[0037] The display unit can display the generated images in real time. The display unit can, for example, display the generated images in real time. The display unit can, for example, display the generated images in real time. This allows the user to always maintain the latest stealth state by displaying images in real time. Real-time display includes, but is not limited to, low-latency displays or high-frame-rate displays. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input the generated image data into a generation AI and have the generation AI perform real-time display.

[0038] The acquisition unit can simultaneously acquire ambient audio information and determine the optimal timing for image acquisition based on the audio information. For example, if ambient noise suddenly increases, the acquisition unit can immediately acquire an image to check the situation. For example, if the surroundings are quiet, the acquisition unit can periodically acquire images to monitor the situation. For example, if the acquisition unit detects a specific sound pattern (e.g., animal noise), it can concentrate on acquiring images in that direction. By determining the timing of image acquisition based on audio information, a more effective stealth state can be maintained. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input ambient audio data into a generating AI and have the generating AI perform the determination of the image acquisition timing based on the audio information.

[0039] The acquisition unit also acquires environmental data such as temperature and humidity, and can adjust the image acquisition method based on this data. For example, the acquisition unit can use a specific filter to correct thermal image distortion when the temperature is high. For example, the acquisition unit can adjust the frequency of image acquisition to prevent lens fogging when the humidity is high. For example, the acquisition unit can reduce the frequency of image acquisition to conserve battery power when the temperature is low. By adjusting the image acquisition method based on environmental data, a more effective stealth state can be maintained. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input temperature and humidity data into a generating AI and have the generating AI perform adjustments to the image acquisition method based on the environmental data.

[0040] The acquisition unit can automatically adjust the camera angle according to the user's movement to acquire the optimal image. For example, the acquisition unit automatically adjusts the camera angle according to the user's movement to acquire the optimal image. For example, if the user is moving forward, the acquisition unit adjusts the camera angle forward. For example, if the user is moving backward, the acquisition unit adjusts the camera angle backward. For example, if the user is moving left or right, the acquisition unit adjusts the camera angle left or right. In this way, by adjusting the camera angle according to the user's movement, the optimal image can always be acquired. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input user movement data into a generating AI and have the generating AI perform the camera angle adjustment.

[0041] The acquisition unit can prioritize acquiring images of a specific area based on the user's location information. For example, the acquisition unit prioritizes acquiring images of a specific area based on the user's location information. For example, when the user approaches a specific area, the acquisition unit prioritizes acquiring images of that area. For example, when the user moves away from a specific area, the acquisition unit reduces the acquisition of images of that area. For example, when the user is staying in a specific area, the acquisition unit acquires detailed images of that area. This enhances the stealth effect in a specific area by acquiring images based on the user's location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's location information into a generating AI and have the generating AI perform image acquisition based on the location information.

[0042] The generation unit can automatically adjust the color tone and brightness of acquired images and stitch them together in a more natural way. For example, the generation unit can automatically adjust the color tone and brightness of acquired images and stitch them together in a more natural way. For example, the generation unit can automatically adjust the color tone of acquired images to match the surrounding environment. For example, the generation unit can automatically adjust the brightness of acquired images to match the surrounding light environment. For example, the generation unit can automatically adjust the contrast of acquired images to match the surrounding environment. By adjusting the color tone and brightness of the images, a more natural stealth effect can be achieved. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input acquired image data into a generation AI and have the generation AI perform automatic adjustment of color tone and brightness.

[0043] The generation unit can predict the movement and changes of images and achieve natural stitching even in dynamic environments. For example, the generation unit can predict the movement and changes of images and achieve natural stitching even in dynamic environments. For example, the generation unit can predict the movement of trees swaying in the wind and stitch them together in a natural way. For example, the generation unit can predict the movement of animals and stitch them together in a natural way even in dynamic environments. For example, the generation unit can predict the movement of people and stitch them together in a natural way even in dynamic environments. By achieving natural stitching even in dynamic environments, the stealth effect can be maintained. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the generation unit can input movement and change data into a generation AI and have the generation AI perform the prediction of movement and change and stitching.

[0044] The generation unit can integrate multiple images acquired from different viewpoints to enable three-dimensional display. For example, the generation unit integrates multiple images acquired from different viewpoints to achieve three-dimensional display. For example, the generation unit integrates images acquired from different heights to achieve three-dimensional display. For example, the generation unit integrates images acquired from different angles to achieve three-dimensional display. By integrating images from different viewpoints, three-dimensional display becomes possible, and the stealth effect can be enhanced. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input image data acquired from different viewpoints into a generation AI and have the generation AI perform image integration and three-dimensional display.

[0045] The generation unit can refer to past image data and compare it with the current image to select the optimal stitching method. For example, the generation unit can refer to past image data and compare it with the current image to select the optimal stitching method. For example, the generation unit can refer to past image data and select the optimal stitching method for the current environment. For example, the generation unit can refer to past image data and select the optimal stitching method for the current situation. In this way, by referring to past image data, the optimal stitching method for the current situation can be selected. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input past image data into a generation AI and have the generation AI perform a comparison with the current image and select a stitching method.

[0046] The display unit can track the user's gaze and dynamically change the displayed content according to the direction of the gaze. For example, the display unit tracks the user's gaze and dynamically changes the displayed content according to the direction of the gaze. For example, if the user is looking straight ahead, the display unit will prioritize displaying images of the front. For example, if the user is looking left or right, the display unit will prioritize displaying images of the left or right. For example, if the user is looking upwards, the display unit will prioritize displaying images of the upwards. By changing the displayed content according to the user's gaze, a more effective stealth effect can be achieved. 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 the user's gaze data into a generating AI and have the generating AI perform gaze tracking and dynamic changes to the displayed content.

[0047] The display unit can automatically adjust the brightness and contrast of the display according to the ambient light environment. For example, the display unit automatically adjusts the brightness and contrast of the display according to the ambient light environment. For example, in a bright environment, the display unit increases the brightness of the display to ensure visibility. For example, in a dark environment, the display unit decreases the brightness of the display to reduce eye strain. For example, the display unit automatically adjusts the contrast to match the ambient light environment. In this way, visibility can be ensured by adjusting the brightness and contrast of the display according to the ambient light environment. 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 environment data into a generating AI and cause the generating AI to perform brightness and contrast adjustments according to the light environment.

[0048] The display unit can update its display content in real time according to the user's movements. For example, the display unit updates its display content in real time according to the user's movements. For example, if the user is moving forward, the display unit prioritizes displaying images in front. For example, if the user is moving backward, the display unit prioritizes displaying images behind. For example, if the user is moving left or right, the display unit prioritizes displaying images to the left or right. By updating the display content according to the user's movements, the optimal stealth effect can always be maintained. 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 movement data into a generating AI and cause the generating AI to perform updates to the display content according to the movement.

[0049] The display unit can prioritize displaying images of a specific area based on the user's location information. For example, the display unit prioritizes displaying images of a specific area based on the user's location information. For example, when the user approaches a specific area, the display unit prioritizes displaying images of that area. For example, when the user moves away from a specific area, the display unit reduces the display of images of that area. For example, when the user is staying in a specific area, the display unit displays detailed images of that area. This enhances the stealth effect in a specific area by displaying images based on the user's location information. 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 the user's location information into a generating AI and have the generating AI perform image display based on the location information.

[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 image acquisition unit monitors the user's heart rate and can adjust the frequency of image acquisition based on heart rate fluctuations. For example, if the user's heart rate increases, the unit increases the frequency of image acquisition to quickly respond to changes in the surroundings. Conversely, if the heart rate is stable, the frequency of image acquisition can be reduced to conserve battery power. Furthermore, if a rapid change in heart rate is detected, the unit can concentrate on acquiring images in that direction. By adjusting the frequency of image acquisition according to the user's physiological state, a more effective stealth state can be maintained.

[0052] The generation unit can apply a noise reduction filter to the acquired image to produce a sharper image. For example, applying a noise reduction filter to an image acquired in a low-light environment improves image quality. It can also apply a filter to correct motion blur to images acquired in environments with movement. Furthermore, it can apply a filter to correct image degradation due to weather conditions (e.g., rain or fog). This allows for the generation of high-quality images under various environmental conditions, enhancing the stealth effect.

[0053] The acquisition unit can detect ambient temperature changes and adjust the timing of image acquisition based on these changes. For example, if a rapid temperature increase is detected, the acquisition unit can immediately acquire an image to check the situation. If the temperature is stable, it can periodically acquire images to monitor the situation. Furthermore, if a specific temperature pattern (e.g., animal body temperature) is detected, it can concentrate image acquisition in that direction. By adjusting the timing of image acquisition based on temperature changes, a more effective stealth state can be maintained.

[0054] The display unit can track the user's gaze and dynamically change the displayed content according to the direction of their gaze. For example, if the user is looking straight ahead, the display unit will prioritize displaying images of what is in front of them. Conversely, if the user is looking left or right, the display unit will prioritize displaying images of what is to the left or right. Also, if the user is looking upwards, the display unit can prioritize displaying images of what is above them. By changing the displayed content according to the user's gaze, a more effective stealth effect can be achieved.

[0055] The generation unit can apply edge detection filters to acquired images to emphasize important parts. For example, emphasizing the edges of tree trunks and leaves improves the accuracy of natural image stitching. Furthermore, emphasizing animal and human silhouettes enables natural stitching even in dynamic environments. Additionally, emphasizing the edges of buildings and terrain provides an effective stealth effect even in urban environments. Thus, applying edge detection filters enhances stealth effects in a variety of environments.

[0056] The acquisition unit simultaneously acquires ambient audio information and can determine the optimal timing for image acquisition based on this audio information. For example, if ambient noise suddenly increases, the acquisition unit immediately acquires an image to check the situation. Conversely, if the surroundings are quiet, it can periodically acquire images to monitor the situation. Furthermore, if a specific audio pattern (e.g., animal sounds) is detected, it can concentrate on acquiring images in that direction. By determining the timing of image acquisition based on audio information, a more effective stealth state can be maintained.

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

[0058] Step 1: The acquisition unit acquires images of the surroundings. The acquisition unit acquires images of the surroundings using, for example, a 360-degree camera. By attaching a 360-degree camera to the user's head, images of the surroundings can be acquired in real time. Step 2: The generation unit analyzes the images acquired by the acquisition unit and stitches them together in a natural way. The generation unit, for example, uses generation AI to analyze the acquired images and stitches them together in a natural way. It analyzes each part of the image, identifies the location of each part, identifies the positions of tree trunks and leaves, and stitches them together in a natural way. Step 3: The display unit displays the image generated by the generation unit. The display unit displays the image generated using, for example, an organic EL display. The generated image can be displayed in real time.

[0059] (Example of form 2) The AI ​​stealth suit according to an embodiment of the present invention is a system that solves the conventional problem of being detected even when wearing camouflage clothing. This system acquires and analyzes images of the surroundings, stitches them together in a natural way, and displays them, allowing the user to enter a stealth state and become difficult to detect. For example, the user attaches a 360-degree camera to their head. This camera acquires images of the surroundings in real time. For example, if the user is in a forest, the camera acquires images of the surrounding trees and ground. These images are input to a generating AI. Next, the generating AI analyzes the acquired images and stitches them together in a natural way. The generating AI analyzes each part of the image and identifies the location of each part. For example, it identifies the positions of tree trunks and leaves and stitches them together in a natural way. Through this process, the acquired images are displayed as a single continuous landscape. The generated image is displayed on clothing made of an organic EL display. For example, if the user is in a forest, the clothing displays images of the surrounding trees and ground. This allows the user to blend into the surrounding landscape. This mechanism allows the user to enter a stealth state and become difficult to detect in survival games or hunting. For example, in survival games, it becomes more difficult for members of the opposing team to spot the user. Similarly, in hunting, it improves the success rate of hunts by making it harder for prey to detect the user. Furthermore, this technology can be applied to other fields. For instance, it can be used in military applications and police investigations where inconspicuousness is required. In the entertainment field, it can be combined with virtual reality and augmented reality technologies to provide new experiences. Thus, AI stealth suits solve the traditional problem of being easily detected even when wearing camouflage, making them not only harder to spot in survival games and hunting, but also applicable to other fields. This allows the user to enter a stealthy state, making them difficult to detect.

[0060] The AI ​​stealth suit according to this embodiment comprises an acquisition unit, a generation unit, and a display unit. The acquisition unit acquires images of the surroundings. The acquisition unit acquires images of the surroundings using, for example, a 360-degree camera. The acquisition unit can acquire images of the surroundings in real time by, for example, having the user wear a 360-degree camera on their head. The generation unit analyzes the images acquired by the acquisition unit and stitches them together in a natural way. The generation unit analyzes the acquired images using, for example, a generation AI and stitches them together in a natural way. The generation unit analyzes each part of the image and identifies the location of each part. The generation unit identifies the locations of, for example, tree trunks and leaves and stitches them together in a natural way. The display unit displays the images generated by the generation unit. The display unit displays the generated images using, for example, an organic EL display. The display unit can, for example, display the generated images in real time. As a result, the AI ​​stealth suit according to this embodiment allows the user to enter a stealth state and become difficult to detect.

[0061] The acquisition unit acquires images of the surroundings. For example, the acquisition unit uses a 360-degree camera to acquire images of the surroundings. Specifically, the 360-degree camera is mounted on the user's head and captures omnidirectional video in real time. This camera is equipped with a high-resolution lens and sensor, allowing it to capture even fine details clearly. Furthermore, the camera is equipped with an infrared sensor and a high-sensitivity sensor to acquire high-quality images even in low-light environments. This allows for accurate perception of the surroundings even at night or in dark places. The acquired image data is transmitted to the generation unit via wireless communication. High-speed and stable communication protocols such as Wi-Fi and Bluetooth are used for wireless communication. This allows the acquisition unit to continuously acquire the latest surrounding images while following the user's movements. The acquisition unit can also simultaneously acquire environmental data such as ambient sound, temperature, and humidity. This is used as auxiliary information for the generation unit to generate more natural and realistic images. The acquisition unit has a lightweight and compact design, and can withstand long-term use without hindering the user's movements.

[0062] The generation unit analyzes the images acquired by the acquisition unit and stitches them together in a natural way. For example, the generation unit uses a generation AI to analyze the acquired images and stitch them together naturally. Specifically, the generation AI uses deep learning technology to analyze each part of the image and identify the location of each part. The generation AI is pre-trained on a large amount of natural environment image data and can recognize features such as tree trunks and leaves, building walls and windows with high accuracy. After analyzing each part of the acquired image and identifying their relative positions, the generation AI applies an algorithm to stitch them together naturally. For example, it identifies the positions of tree trunks and leaves and adjusts the image's color tone, brightness, and contrast to stitch them together naturally. Furthermore, the generation AI uses interpolation techniques to smoothly stitch together image boundaries, generating a natural image without any unnaturalness. In addition, the generation unit utilizes high-performance GPUs and dedicated hardware acceleration to rapidly process image data acquired in real time. This allows the generation unit to continuously generate the latest natural images while following the user's movements.

[0063] The display unit displays images generated by the generation unit. The display unit displays images generated using, for example, an organic EL display. Specifically, organic EL displays can reproduce vivid colors with high resolution and display the generated images in real time. The display unit is incorporated into the user's clothing or equipment and achieves a stealth effect by displaying the generated images around the user. For example, an organic EL display is placed on the surface of the jacket or pants worn by the user, and the generated images are displayed. This makes the user's appearance blend in with the surrounding environment, making them difficult to spot. The display unit has a thin and flexible design and can be freely deformed to match the user's movements. In addition, the display unit has low power consumption and can withstand long periods of use. Furthermore, the display unit is equipped with an ambient light sensor and can automatically adjust the brightness of the display according to the ambient light. This ensures that optimal display quality is always maintained, day or night. The display unit works in conjunction with the generation unit to smoothly display images that are updated in real time. As a result, the AI ​​stealth suit according to the embodiment allows the user to enter a stealth state and become difficult to spot.

[0064] The acquisition unit can acquire images of the surroundings using a 360-degree camera. The acquisition unit can, for example, acquire images of the surroundings using a 360-degree camera. The acquisition unit can, for example, acquire images of the surroundings in real time by having a user attach a 360-degree camera to their head. This makes it possible to acquire images in all directions by using a 360-degree camera. 360-degree cameras include, for example, high-resolution cameras and high-frame-rate cameras, but are not limited to such examples. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input image data acquired by the 360-degree camera into a generating AI and have the generating AI perform analysis of the image data.

[0065] The generation unit can analyze the images acquired by the acquisition unit and stitch them together in a natural way. For example, the generation unit can use a generation AI to analyze the acquired images and stitch them together in a natural way. For example, the generation unit can analyze each part of the image and identify the location of each part. For example, the generation unit can identify the locations of tree trunks and leaves and stitch them together in a natural way. In this way, by analyzing the acquired images and stitching them together in a natural way, a continuous landscape can be displayed. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the acquired image data into a generation AI and have the generation AI perform the analysis and stitching of the image data.

[0066] The display unit can display images generated by the generation unit using an organic EL display. The display unit can, for example, display images generated using an organic EL display. The display unit can, for example, display generated images in real time. This allows for the display of sharp images by using an organic EL display. Organic EL displays include, but are not limited to, high-resolution displays and high-contrast ratio displays. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input generated image data into a generation AI and cause the generation AI to display the image data.

[0067] The generation unit can analyze each part of an image and identify the location of each part. For example, the generation unit can analyze each part of an image and identify the location of each part. For example, the generation unit can identify the locations of tree trunks and leaves and connect them in a natural way. In this way, by analyzing each part of an image and identifying its location, the image can be connected in a natural way. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input image data into a generation AI and have the generation AI perform location identification of each part of the image.

[0068] The display unit can display the generated images in real time. The display unit can, for example, display the generated images in real time. The display unit can, for example, display the generated images in real time. This allows the user to always maintain the latest stealth state by displaying images in real time. Real-time display includes, but is not limited to, low-latency displays or high-frame-rate displays. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input the generated image data into a generation AI and have the generation AI perform real-time display.

[0069] The acquisition unit can estimate the user's emotions and adjust the timing of image acquisition based on the estimated emotions. For example, the acquisition unit estimates the user's emotions and adjusts the timing of image acquisition based on the estimated emotions. For example, if the user is nervous, the acquisition unit increases the frequency of image acquisition to respond quickly to changes in the surroundings. For example, if the user is relaxed, the acquisition unit decreases the frequency of image acquisition to conserve battery power. For example, if the user is excited, the acquisition unit focuses on acquiring images in a specific direction. By adjusting the timing of image acquisition according to the user's emotions, a more effective stealth state can be maintained. 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 acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of image acquisition timing.

[0070] The acquisition unit can simultaneously acquire ambient audio information and determine the optimal timing for image acquisition based on the audio information. For example, if ambient noise suddenly increases, the acquisition unit can immediately acquire an image to check the situation. For example, if the surroundings are quiet, the acquisition unit can periodically acquire images to monitor the situation. For example, if the acquisition unit detects a specific sound pattern (e.g., animal noise), it can concentrate on acquiring images in that direction. By determining the timing of image acquisition based on audio information, a more effective stealth state can be maintained. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input ambient audio data into a generating AI and have the generating AI perform the determination of the image acquisition timing based on the audio information.

[0071] The acquisition unit also acquires environmental data such as temperature and humidity, and can adjust the image acquisition method based on this data. For example, the acquisition unit can use a specific filter to correct thermal image distortion when the temperature is high. For example, the acquisition unit can adjust the frequency of image acquisition to prevent lens fogging when the humidity is high. For example, the acquisition unit can reduce the frequency of image acquisition to conserve battery power when the temperature is low. By adjusting the image acquisition method based on environmental data, a more effective stealth state can be maintained. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input temperature and humidity data into a generating AI and have the generating AI perform adjustments to the image acquisition method based on the environmental data.

[0072] The acquisition unit can estimate the user's emotions and determine the priority of images to acquire based on the estimated user emotions. For example, if the user is tense, the acquisition unit prioritizes acquiring images in front of them. If the user is relaxed, the acquisition unit acquires images of the entire surroundings evenly. If the user is excited, the acquisition unit focuses on acquiring images in a specific direction. This allows for a more effective stealth state to be maintained by prioritizing images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 acquisition unit may be performed using AI or not. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and image prioritization.

[0073] The acquisition unit can automatically adjust the camera angle according to the user's movement to acquire the optimal image. For example, the acquisition unit automatically adjusts the camera angle according to the user's movement to acquire the optimal image. For example, if the user is moving forward, the acquisition unit adjusts the camera angle forward. For example, if the user is moving backward, the acquisition unit adjusts the camera angle backward. For example, if the user is moving left or right, the acquisition unit adjusts the camera angle left or right. In this way, by adjusting the camera angle according to the user's movement, the optimal image can always be acquired. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input user movement data into a generating AI and have the generating AI perform the camera angle adjustment.

[0074] The acquisition unit can prioritize acquiring images of a specific area based on the user's location information. For example, the acquisition unit prioritizes acquiring images of a specific area based on the user's location information. For example, when the user approaches a specific area, the acquisition unit prioritizes acquiring images of that area. For example, when the user moves away from a specific area, the acquisition unit reduces the acquisition of images of that area. For example, when the user is staying in a specific area, the acquisition unit acquires detailed images of that area. This enhances the stealth effect in a specific area by acquiring images based on the user's location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's location information into a generating AI and have the generating AI perform image acquisition based on the location information.

[0075] The generation unit can estimate the user's emotions and adjust the image stitching method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates images that progress at a leisurely pace. If the user is in a hurry, the generation unit generates images that emphasize the shortest route. If the user is excited, the generation unit generates images with visually stimulating effects. By adjusting the image stitching method according to the user's emotions, a more natural stealth effect can be achieved. 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 generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation and adjust the image stitching method.

[0076] The generation unit can automatically adjust the color tone and brightness of acquired images and stitch them together in a more natural way. For example, the generation unit can automatically adjust the color tone and brightness of acquired images and stitch them together in a more natural way. For example, the generation unit can automatically adjust the color tone of acquired images to match the surrounding environment. For example, the generation unit can automatically adjust the brightness of acquired images to match the surrounding light environment. For example, the generation unit can automatically adjust the contrast of acquired images to match the surrounding environment. By adjusting the color tone and brightness of the images, a more natural stealth effect can be achieved. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input acquired image data into a generation AI and have the generation AI perform automatic adjustment of color tone and brightness.

[0077] The generation unit can predict the movement and changes of images and achieve natural stitching even in dynamic environments. For example, the generation unit can predict the movement and changes of images and achieve natural stitching even in dynamic environments. For example, the generation unit can predict the movement of trees swaying in the wind and stitch them together in a natural way. For example, the generation unit can predict the movement of animals and stitch them together in a natural way even in dynamic environments. For example, the generation unit can predict the movement of people and stitch them together in a natural way even in dynamic environments. By achieving natural stitching even in dynamic environments, the stealth effect can be maintained. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the generation unit can input movement and change data into a generation AI and have the generation AI perform the prediction of movement and change and stitching.

[0078] The 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, the generation unit estimates the user's emotions and adjusts the level of detail of the generated images based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a detailed image. For example, if the user is in a hurry, the generation unit generates a simplified image. For example, if the user is excited, the generation unit generates an image with a visually stimulating effect. By adjusting the level of detail of the image according to the user's emotions, a more effective stealth effect can be achieved. 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 generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation and image detail adjustment.

[0079] The generation unit can integrate multiple images acquired from different viewpoints to enable three-dimensional display. For example, the generation unit integrates multiple images acquired from different viewpoints to achieve three-dimensional display. For example, the generation unit integrates images acquired from different heights to achieve three-dimensional display. For example, the generation unit integrates images acquired from different angles to achieve three-dimensional display. By integrating images from different viewpoints, three-dimensional display becomes possible, and the stealth effect can be enhanced. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input image data acquired from different viewpoints into a generation AI and have the generation AI perform image integration and three-dimensional display.

[0080] The generation unit can refer to past image data and compare it with the current image to select the optimal stitching method. For example, the generation unit can refer to past image data and compare it with the current image to select the optimal stitching method. For example, the generation unit can refer to past image data and select the optimal stitching method for the current environment. For example, the generation unit can refer to past image data and select the optimal stitching method for the current situation. In this way, by referring to past image data, the optimal stitching method for the current situation can be selected. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input past image data into a generation AI and have the generation AI perform a comparison with the current image and select a stitching method.

[0081] The display unit can estimate the user's emotions and adjust the color tone and brightness of the displayed image based on the estimated emotions. For example, the display unit can estimate the user's emotions and adjust the color tone and brightness of the displayed image based on the estimated emotions. For example, if the user is tense, the display unit can display an image with calm colors. For example, if the user is relaxed, the display unit can display an image with bright colors. For example, if the user is excited, the display unit can display an image with visually stimulating colors. By adjusting the color tone and brightness of the image according to the user's emotions, a more effective stealth effect can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 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 and adjustment of the color tone and brightness of the image.

[0082] The display unit can track the user's gaze and dynamically change the displayed content according to the direction of the gaze. For example, the display unit tracks the user's gaze and dynamically changes the displayed content according to the direction of the gaze. For example, if the user is looking straight ahead, the display unit will prioritize displaying images of the front. For example, if the user is looking left or right, the display unit will prioritize displaying images of the left or right. For example, if the user is looking upwards, the display unit will prioritize displaying images of the upwards. By changing the displayed content according to the user's gaze, a more effective stealth effect can be achieved. 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 the user's gaze data into a generating AI and have the generating AI perform gaze tracking and dynamic changes to the displayed content.

[0083] The display unit can automatically adjust the brightness and contrast of the display according to the ambient light environment. For example, the display unit automatically adjusts the brightness and contrast of the display according to the ambient light environment. For example, in a bright environment, the display unit increases the brightness of the display to ensure visibility. For example, in a dark environment, the display unit decreases the brightness of the display to reduce eye strain. For example, the display unit automatically adjusts the contrast to match the ambient light environment. In this way, visibility can be ensured by adjusting the brightness and contrast of the display according to the ambient light environment. 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 environment data into a generating AI and cause the generating AI to perform brightness and contrast adjustments according to the light environment.

[0084] The display unit can estimate the user's emotions and determine the priority of images to display based on the estimated emotions. For example, if the user is tense, the display unit will prioritize displaying images in front of them. If the user is relaxed, the display unit will display images of the entire surroundings evenly. If the user is excited, the display unit will concentrate images in a specific direction. This allows for a more effective stealth effect by prioritizing images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 display unit may be performed using AI, or not. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and image priority determination.

[0085] The display unit can update its display content in real time according to the user's movements. For example, the display unit updates its display content in real time according to the user's movements. For example, if the user is moving forward, the display unit prioritizes displaying images in front. For example, if the user is moving backward, the display unit prioritizes displaying images behind. For example, if the user is moving left or right, the display unit prioritizes displaying images to the left or right. By updating the display content according to the user's movements, the optimal stealth effect can always be maintained. 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 movement data into a generating AI and cause the generating AI to perform updates to the display content according to the movement.

[0086] The display unit can prioritize displaying images of a specific area based on the user's location information. For example, the display unit prioritizes displaying images of a specific area based on the user's location information. For example, when the user approaches a specific area, the display unit prioritizes displaying images of that area. For example, when the user moves away from a specific area, the display unit reduces the display of images of that area. For example, when the user is staying in a specific area, the display unit displays detailed images of that area. This enhances the stealth effect in a specific area by displaying images based on the user's location information. 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 the user's location information into a generating AI and have the generating AI perform image display based on the location information.

[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 image acquisition unit monitors the user's heart rate and can adjust the frequency of image acquisition based on heart rate fluctuations. For example, if the user's heart rate increases, the unit increases the frequency of image acquisition to quickly respond to changes in the surroundings. Conversely, if the heart rate is stable, the frequency of image acquisition can be reduced to conserve battery power. Furthermore, if a rapid change in heart rate is detected, the unit can concentrate on acquiring images in that direction. By adjusting the frequency of image acquisition according to the user's physiological state, a more effective stealth state can be maintained.

[0089] The generation unit can apply a noise reduction filter to the acquired image to produce a sharper image. For example, applying a noise reduction filter to an image acquired in a low-light environment improves image quality. It can also apply a filter to correct motion blur to images acquired in environments with movement. Furthermore, it can apply a filter to correct image degradation due to weather conditions (e.g., rain or fog). This allows for the generation of high-quality images under various environmental conditions, enhancing the stealth effect.

[0090] The display unit can estimate the user's emotions and adjust the movement of the displayed images based on those emotions. For example, if the user is tense, the display unit will suppress the image movement for a stable display. Conversely, if the user is relaxed, the display unit will smooth the image movement for a natural display. Also, if the user is excited, the display unit can speed up the image movement to provide visual stimulation. By adjusting the image movement according to the user's emotions, a more effective stealth effect can be achieved.

[0091] The acquisition unit can detect ambient temperature changes and adjust the timing of image acquisition based on these changes. For example, if a rapid temperature increase is detected, the acquisition unit can immediately acquire an image to check the situation. If the temperature is stable, it can periodically acquire images to monitor the situation. Furthermore, if a specific temperature pattern (e.g., animal body temperature) is detected, it can concentrate image acquisition in that direction. By adjusting the timing of image acquisition based on temperature changes, a more effective stealth state can be maintained.

[0092] The generation unit can estimate the user's emotions and adjust the image's colors based on those emotions. For example, if the user is tense, the generation unit adjusts the image's colors to a calmer tone. Conversely, if the user is relaxed, the generation unit adjusts the image's colors to a brighter, more vivid tone. Furthermore, if the user is excited, the generation unit can adjust the image's colors to a visually stimulating tone. This allows for a more effective stealth effect by adjusting the image's colors according to the user's emotions.

[0093] The display unit can track the user's gaze and dynamically change the displayed content according to the direction of their gaze. For example, if the user is looking straight ahead, the display unit will prioritize displaying images of what is in front of them. Conversely, if the user is looking left or right, the display unit will prioritize displaying images of what is to the left or right. Also, if the user is looking upwards, the display unit can prioritize displaying images of what is above them. By changing the displayed content according to the user's gaze, a more effective stealth effect can be achieved.

[0094] The image acquisition unit can estimate the user's emotions and adjust the resolution of the images it acquires based on those emotions. For example, if the user is tense, the unit acquires high-resolution images to provide detailed information. Conversely, if the user is relaxed, the unit acquires low-resolution images to conserve battery power. Also, if the user is excited, it can focus on a specific direction to acquire high-resolution images. By adjusting the image resolution according to the user's emotions, a more effective stealth effect can be achieved.

[0095] The generation unit can apply edge detection filters to acquired images to emphasize important parts. For example, emphasizing the edges of tree trunks and leaves improves the accuracy of natural image stitching. Furthermore, emphasizing animal and human silhouettes enables natural stitching even in dynamic environments. Additionally, emphasizing the edges of buildings and terrain provides an effective stealth effect even in urban environments. Thus, applying edge detection filters enhances stealth effects in a variety of environments.

[0096] The display unit can estimate the user's emotions and adjust the contrast of the displayed image based on those emotions. For example, if the user is tense, the display unit increases the contrast to improve visibility. Conversely, if the user is relaxed, the display unit decreases the contrast to reduce eye strain. Furthermore, if the user is excited, the display unit can dynamically adjust the contrast to provide visual stimulation. This allows for a more effective stealth effect by adjusting the image contrast according to the user's emotions.

[0097] The acquisition unit simultaneously acquires ambient audio information and can determine the optimal timing for image acquisition based on this audio information. For example, if ambient noise suddenly increases, the acquisition unit immediately acquires an image to check the situation. Conversely, if the surroundings are quiet, it can periodically acquire images to monitor the situation. Furthermore, if a specific audio pattern (e.g., animal sounds) is detected, it can concentrate on acquiring images in that direction. By determining the timing of image acquisition based on audio information, a more effective stealth state can be maintained.

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

[0099] Step 1: The acquisition unit acquires images of the surroundings. The acquisition unit acquires images of the surroundings using, for example, a 360-degree camera. By attaching a 360-degree camera to the user's head, images of the surroundings can be acquired in real time. Step 2: The generation unit analyzes the images acquired by the acquisition unit and stitches them together in a natural way. The generation unit, for example, uses generation AI to analyze the acquired images and stitches them together in a natural way. It analyzes each part of the image, identifies the location of each part, identifies the positions of tree trunks and leaves, and stitches them together in a natural way. Step 3: The display unit displays the image generated by the generation unit. The display unit displays the image generated using, for example, an organic EL display. The generated image can be displayed in real time.

[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 acquisition unit, 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 acquisition unit acquires images of the surroundings using the camera 42 of the smart device 14. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the acquired images and stitches them together in a natural manner. The display unit displays the generated images using, for example, the display 40A 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, including the acquisition unit, generation unit, and display unit described above, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires images of the surroundings using the camera 42 of the smart glasses 214. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the acquired images and stitches them together in a natural way. The display unit displays the generated images using, for example, the display 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, including the acquisition unit, generation unit, and display unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires images of the surroundings using the camera 42 of the headset terminal 314. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the acquired images and stitches them together in a natural manner. The display unit displays the generated images using, for example, the display 343 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 acquisition unit, generation unit, and display unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires images of the surroundings using the camera 42 of the robot 414. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the acquired images and stitches them together in a natural manner. The display unit displays the generated images using, for example, the display of the robot 414. The correspondence between each unit and the device or control unit 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) An acquisition unit that acquires images of the surroundings, A generation unit analyzes the images acquired by the acquisition unit and stitches them together in a natural manner, The system includes a display unit that displays the image generated by the generation unit. A system characterized by the following features. (Note 2) The acquisition unit is, A 360-degree camera is used to acquire images of the surroundings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The image acquisition unit analyzes the acquired images and stitches them together in a natural way. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is The image generated by the generation unit is displayed using an organic EL display. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Analyze each part of the image to determine its location. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is Display the generated images in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of image acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, The system simultaneously acquires ambient audio information and determines the optimal timing for image acquisition based on that information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, Environmental data such as temperature and humidity are also acquired, and the image acquisition method is adjusted based on this data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of images to retrieve based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, The camera angle automatically adjusts according to the user's movements to acquire the optimal image. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, Based on the user's location information, images from a specific area are prioritized for acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the image stitching method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system automatically adjusts the color tone and brightness of the acquired images to create a more natural stitching effect. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It predicts image movement and changes, enabling natural stitching even in dynamic environments. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is 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 generating unit is It integrates multiple images acquired from different viewpoints to enable three-dimensional display. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is Referencing past image data and comparing it to the current image, select the optimal stitching method. 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 color tone and brightness of the displayed images based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is Track the user's gaze and dynamically change the displayed content according to the direction of their gaze. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is The display brightness and contrast are automatically adjusted according to the surrounding light environment. 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 determines the priority of images to display based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is The displayed content is updated in real time according to the user's movements. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is Based on the user's location, images from a specific area will be displayed preferentially. 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. An acquisition unit that acquires images of the surroundings, A generation unit analyzes the images acquired by the acquisition unit and stitches them together in a natural manner, The system includes a display unit that displays the image generated by the generation unit. A system characterized by the following features.

2. The acquisition unit is, A 360-degree camera is used to acquire images of the surroundings. The system according to feature 1.

3. The generating unit is The images acquired by the aforementioned acquisition unit are analyzed and stitched together in a natural manner. The system according to feature 1.

4. The aforementioned display unit is The image generated by the generation unit is displayed using an organic EL display. The system according to feature 1.

5. The generating unit is Analyze each part of the image to determine its location. The system according to feature 1.

6. The aforementioned display unit is Display the generated images in real time. The system according to feature 1.

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

8. The acquisition unit is, The system simultaneously acquires ambient audio information and determines the optimal timing for image acquisition based on that information. The system according to feature 1.

Images