Electronic apparatus and screen display method thereof

Abstract

An electronic device and a method for displaying the same screen are disclosed. The electronic device includes a first main body having a first screen area and a second main body pivotally connected to the first main body. The screen display method includes: detecting whether another user has entered within a preset range when a user is using the electronic device; and in response to detecting that another user has entered within the preset range, displaying at least a portion of the screen currently displayed in the first screen area on the second screen area, wherein the second screen area is located at a position different from the first screen area.

Description

Technical Field

[0001] The present invention relates to an electronic device and a display method thereof, and in particular to an electronic device and a screen display method thereof. Prior Art

[0002] Due to their single screen, commercially available laptops can lead to users experiencing reduced multitasking efficiency. Furthermore, limited workspaces, especially in professional fields requiring a large screen, can be inadequate, requiring frequent switching between applications or windows, which can affect operational fluidity and create a less-than-perfect user experience. For example, the single screen of a commercially available laptop means that sharing content with a second party requires flipping the laptop over. Alternatively, when multiple users are viewing content, all viewers must be positioned directly in front of the screen, a highly inconvenient requirement. Summary of the Invention

[0003] The present invention provides an electronic device and a screen display method thereof, which can create a diversified user experience.

[0004] The present invention provides a screen display method for an electronic device, wherein the electronic device includes a detection module, a first body provided with a first screen area, and a second body pivotally connected to the first body. The screen display method includes executing the following steps via a processor, including: when a user is using the electronic device, the detection module detecting whether other users have entered within a preset range of the electronic device; and in response to detecting that other users have entered within the preset range, displaying at least a portion of the display screen displayed in the first screen area in a second screen area, wherein the second screen area is provided on the electronic device and is located at a position different from the first screen area.

[0005] The electronic device of the present invention includes: a first body; a second body; a pivot structure for pivotally connecting the first body and the second body; a detection module; a first display module for controlling the display of a first screen area, wherein the first screen area is arranged on a first side of the first body; a second display module for controlling the display of a second screen area, wherein the second screen area is arranged at a position different from the first screen area; a processor, communicatively connected to the detection module, the first display module and the second display module, wherein the processor is configured to: when a user is using the electronic device, detect whether other users enter within a preset range through the detection module; and in response to detecting that other users have entered within the preset range, display at least a portion of the display image displayed in the first screen area in the second screen area.

[0006] Based on the above, the present invention adopts a dual-screen area architecture and combines generative AI technology to create a diversified user experience. Simple diagram description

[0007] FIG1 is a schematic diagram of an electronic device according to an embodiment of the present invention. FIG. 2 is a schematic diagram of an electronic device with a flexible screen according to an embodiment of the present invention. FIG. 3 is a schematic diagram of an electronic device with dual screens according to an embodiment of the present invention. FIG. 4 is a schematic diagram of an electronic device having a projection module according to an embodiment of the present invention. FIG. 5 is a schematic diagram of an electronic device equipped with a logic display according to an embodiment of the present invention. FIG6 is a flowchart of a screen display method according to an embodiment of the present invention. FIG. 7 is a schematic diagram of an instant sharing mode according to an embodiment of the present invention. FIG8 is a schematic diagram of a local sharing mode according to an embodiment of the present invention. FIG. 9 is a flowchart of a method for displaying images in a presentation mode according to an embodiment of the present invention. FIG. 10 is a schematic diagram of a sharing mode in a presentation mode according to an embodiment of the present invention. FIG. 11 is a flowchart of a screen display method in a sharing mode according to an embodiment of the present invention. FIG. 12 is a flowchart of a method for displaying images in a multimedia playback mode according to an embodiment of the present invention. FIG. 13 is a schematic diagram of a sharing mode in a multimedia playback mode according to an embodiment of the present invention. Implementation Method

[0008] FIG1 is a schematic diagram of an electronic device according to an embodiment of the present invention. Referring to FIG1 , electronic device 100 includes a first body 100A, a second body 100B, and a pivot structure 100C for pivotally connecting first body 100A and second body 100B. First body 100A is, for example, a top cover, and second body 100B is, for example, a base with a keyboard. Rotation of pivot structure 100C allows first body 100A to move relative to second body 100B. Electronic device 100 also includes at least a processor 110, a first display module 120, a second display module 130, a first screen area 140, a second screen area 150, and a detection module 160.

[0009] The processor 110 is in communication with the first display module 120, the second display module 130, and the detection module 160. The first display module 120 is used to control the display of the first screen area 140. The second display module 130 is used to control the display of the second screen area 150. The detection module 160 is used to detect whether other users enter within a preset range.

[0010] The processor 110 is, for example, a central processing unit (CPU), a physical processing unit (PPU), a programmable microprocessor, an embedded control chip, a digital signal processor (DSP), an application specific integrated circuit (ASIC), or other similar devices.

[0011] The first display module 120 and the second display module 130 are, for example, display drivers. The second display module 130 is used to control the display of the second screen area.

[0012] In one embodiment, the first screen area 140 is disposed on a first side of the first body 100A, and the second screen area 150 is disposed on the electronic device 100 at a location different from the first screen area 140. The first screen area 140 and the second screen area 150 can be configured in one of the following configurations: the first screen area 140 and the second screen area 150 are disposed on the same flexible screen; the first screen area 140 and the second screen area 150 are independent screens, with the second screen area 150 disposed on the second side of the first body 100A; the hinge structure 100C is used as the second screen area 150 in conjunction with a projection module; and the second screen area 150 is disposed on the top surface of the second body 100B.

[0013] The detection module 160 is, for example, a human body sensor. For example, a pyroelectric infrared detector (PIR) can be used as the detection module 160 to determine whether an object enters the PIR's sensing range (set to a preset range).

[0014] Figure 2 is a schematic diagram of an electronic device with a flexible screen according to an embodiment of the present invention. Referring to Figure 2 , the flexible screen 200 includes a first screen area 140 and a second screen area 150. A bend line L divides the flexible screen 200 into a larger first screen area 140 and a smaller second screen area 150. In this embodiment, the hinge structure 100C includes a light-transmitting area. Light from the second screen area 150 can project light through the light-transmitting area of ​​the hinge structure 100C. Therefore, a user on the back side of the first body 100A of the electronic device 100 (i.e., the side of the first body 100A opposite the first screen area 140) can see the content displayed in the second screen area 150 through the hinge structure 100C. The hinge structure 100C can be a transparent plate, a semi-transparent plate, a gradient transparent plate, or a dimming transparent plate.

[0015] For example, the pivot structure 100C can be constructed using a metal frame surrounding a fully transparent plastic panel, thereby creating a fully transparent effect. Alternatively, the pivot structure 100C can be constructed using a translucent plastic panel, thereby creating a semi-transparent effect. Alternatively, the pivot structure 100C can be constructed using fully transparent plastic or acrylic sprayed with a gradient paint, thereby creating a gradient transparency effect.

[0016] Alternatively, the hinge structure 100C can be constructed from a fully transparent plastic, such as a vacuum metallization (VM) film or a non-conductive vacuum metallization (NCVM) film, thus achieving dimming and transparency effects. When the second screen area 150 is not illuminated, the VM or NCVM film appears opaque, maintaining a consistent product appearance. When the second screen area 150 is illuminated, light can penetrate the VM or NCVM film, resulting in a translucent state.

[0017] In the embodiment shown in FIG. 2 , the first display module 120 and the second display module 130 may also be integrated into a display driver to control the first screen area 140 and the second screen area 150 .

[0018] Figure 3 is a schematic diagram of an electronic device with dual screens according to an embodiment of the present invention. In Figure 3, a dual-screen configuration is employed, with a larger screen positioned on a first side of a first body 100A as a first screen area 140, and a smaller screen positioned on a second side (opposite the first side) of the first body 100A as a second screen area 150.

[0019] In this embodiment, a mirror member 310 is further provided to reflect the image of the second screen area 150, allowing a user on the back side of the first body 100A to see the image reflected by the mirror member 310 through the pivot structure 100C. In this embodiment, the pivot structure 100C is light-transmissive, and the bright light reflected by the mirror member 310 can pass through the pivot structure 100C.

[0020] Figure 4 is a schematic diagram of an electronic device with a projection module according to one embodiment of the present invention. In Figure 4 , a larger screen is mounted on a first side of a first body 100A as a first screen area 140 . A projection module 410 is used to project an image toward a pivot structure 100C, thereby utilizing the pivot structure 100C as a second screen area 150 .

[0021] The projection module 410 is used to receive image data from the processor 110 and project the image data onto the pivot structure 100C to form a projected image. The projection module 410 includes components such as an imaging element (e.g., a light valve), a projection lens, a light source, and optical elements for transmitting light beams (e.g., including reflectors, beam splitters, and lenses). The light source may include, for example, a light emitting diode (LED), a laser diode (LD), or a combination thereof. The imaging element (e.g., a light valve) may be a reflective light modulator such as a liquid crystal on silicon (LCoS) panel or a digital micro-mirror device (DMD).

[0022] Figure 5 is a schematic diagram of an electronic device equipped with a logic display according to an embodiment of the present invention. In Figure 5, a larger screen is mounted on the first side of the first body 100A as the first screen area 140, while a logic display 510 is used as the second screen area 150. The logic display 510 is mounted on the top surface of the second body 100B. In this embodiment, the hinge structure 100C is light-transmissive, allowing light from the logic display 510 to pass through the hinge structure 100C.

[0023] FIG6 is a flowchart of a screen display method according to an embodiment of the present invention. In this embodiment, the method is described in conjunction with electronic device 100. Electronic device 100 further includes a storage device. The storage device may be, for example, any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, secure digital card, hard drive, or other similar device, or a combination of these devices. The storage device stores at least one code segment. Once installed, the processor 110 executes the following steps. This embodiment is described using the case where the first body 100A is unfolded relative to the second body 100B by rotating the pivot structure 100C.

[0024] Referring to Figures 1 and 6 , in step S605, while the user is using the electronic device 100, it is detected whether another user has entered within a preset range. For example, the detection module 160 determines whether an object (person) has entered the sensing range (set as the preset range). In step S610, in response to detecting another user has entered within the preset range, at least a portion of the display image displayed in the first screen area 140 is displayed in the second screen area 150. For example, depending on the content of the displayed image, the entire display image may be proportionally reduced to obtain a reduced image, which is then displayed in the second screen area 150; alternatively, at least a portion of the display image (a selected image) may be proportionally displayed in the second screen area 150. Alternatively, both the reduced image and the partial image may be displayed in the second screen area 150.

[0025] Figure 7 is a schematic diagram illustrating the instant sharing mode according to an embodiment of the present invention. Referring to Figure 7 , in this embodiment, user U1 is using electronic device 100. Electronic device 100 includes a human body sensor to detect whether another user enters a predetermined range R1. For example, a PIR sensor can be used as the human body sensor to determine whether an object enters the sensing range (set to predetermined range R1). In the embodiment of Figure 7 , instant sharing mode is entered when user U2 is detected entering predetermined range R1.

[0026] In the instant sharing mode, a display image 710 is displayed in the first screen area 140. The user U1 can perform a frame selection action on the display image 710 in the first screen area 140 using an input device such as a mouse or touch panel. The processor 110 captures a framed image F2 from the display image 710 based on the framed area 720 corresponding to the frame selection action and displays the framed image F2 in the second screen area 150.

[0027] In this embodiment, the second screen area 150 is further divided into a first block B1 and a second block B2. The first block B1 is used to synchronously present a scaled-down image F1 of the display image 710, and the second block B2 is used to maximize the presentation of the framed image F2.

[0028] Figure 8 is a schematic diagram of a local sharing mode according to an embodiment of the present invention. Referring to Figure 8 , in this embodiment, user U1 is using electronic device 100. When the human body sensor in electronic device 100 detects user U2 entering a predetermined range R1, local sharing mode is entered. In this embodiment, the first screen area 140 includes a first interactive area 810 and a second interactive area 820. User U1 can use an input device such as a mouse or touch panel to drag the displayed content in the first interactive area 810 of the first screen area 140. For example, drag "Display Content B" from the first interactive area 810 to the second interactive area 820. The processor 110 then displays the displayed content in the second interactive area 820 (i.e., "Display Content B") on the second screen area 150.

[0029] In addition, when it is detected that the “display content B” is dragged from the first interactive area 810 to the second interactive area 820 , the processor 110 can translate the “display content B” to obtain corresponding translation content, and display the translation content in the second screen area 150 .

[0030] In one embodiment, the content to be presented in the second screen area 150 can be determined based on the application currently running on the electronic device 100 or the content displayed in the first screen area 140. The following uses the execution of a presentation application as an example. FIG. 9 is a flow chart of a screen display method in presentation mode according to one embodiment of the present invention. FIG. 10 is a schematic diagram of a sharing mode in presentation mode according to one embodiment of the present invention.

[0031] In the embodiments of Figures 9 and 10 , a first image sensor C1 and a second image sensor C2 are respectively disposed on the first and second sides of the first body 100A of the electronic device 100. However, this is for ease of illustration only. In other embodiments, the placement of the first image sensor C1 and the second image sensor C2 can be determined as long as the following requirements are met: namely, the first image sensor C1 is disposed on the first side where the first screen area 140 is located, and the second image sensor C2 is disposed on the second side where the second screen area 150 is located.

[0032] When the processor 110 detects that the display screen in the first screen area 140 is a presentation screen, in step S905, in response to detecting the other user entering the predetermined range, the processor 110 activates the first image sensor C1 and the second image sensor C2 to obtain a first image sequence and a second image sequence, respectively. Next, in step S910, the processor analyzes the first image sequence and the second image sequence to determine whether the user U1 has moved from the first side to the second side.

[0033] Specifically, in response to determining based on the analysis of the first image sequence that user U1 has left the first sensing range of the first image sensor C1, a determination is made based on the analysis of the second image sequence whether a new object has entered the second sensing range of the second image sensor C2. Next, when the analysis of the second image sequence determines that a new object has entered the second sensing range of the second image sensor C2, a determination is made as to whether the new object is user U1. In response to determining that the new object is user U1, it is determined that user U1 has moved from the first side to the second side. Alternatively, in response to determining that the new object is not user U1, a warning message is issued. For example, user U1 may have moved from the first side to the second side on their own, or may have rotated the electronic device 100.

[0034] The following three methods are used to detect whether the user U1 moves from the front side to the back side (from the first side to the second side) of the first screen area 140. (1) Synchronous detection method emphasizes the coordinated operation of the first image sensor C1 and the second image sensor C2 to track the continuous movement of the user U1 in real time, without focusing on detailed feature comparison or time difference calculation. (2) Image matching and continuous tracking method focuses on accurate identity confirmation and continuous tracking, emphasizing feature matching of the user U1 to ensure identity consistency and system security. (3) Time difference and direction detection method mainly infers the movement of the user U1 through time difference and movement direction. It has lower requirements for identity confirmation and feature recognition and is suitable for general motion detection.

[0035] Regarding the synchronous detection method (1), the first image sensor C1 and the second image sensor C2 need to be highly synchronized, and the processor 110 needs to quickly switch the detection focus to ensure that the first image sensor C1 and the second image sensor C2 can be seamlessly connected. Method (1) emphasizes the synchronous operation of the first image sensor C1 and the second image sensor C2. That is, the first image sensor C1 and the second image sensor C2 set on the front and rear sides need to detect at the same time. When the user U1 is detected to leave one side, the appearance of the user U1 needs to be detected immediately on the other side. The synchronous detection method is suitable for continuous motion tracking, which requires real-time judgment of the movement of the user U1 from the front to the rear. Method (1) is suitable for use in scenarios that require rapid response, such as operation switching of smart home devices or real-time tracking of personnel movement in a monitoring system. Based on synchronous detection, feature comparison or time difference is not emphasized, and the focus is on the collaborative work and real-time tracking of the first image sensor C1 and the second image sensor C2.

[0036] The method (1) includes the following steps. The first image sensor C1 on the front side and the second image sensor C2 on the rear side are activated to ensure that both begin detecting the environment and objects simultaneously. The first image sensor C1 detects the facial features or other identity authentication information (such as eye movements, facial expressions, etc.) of the user U1. Once it is confirmed that the user U1 has been registered, the processor 110 begins tracking the position of the user U1 in the first image sequence. Thereafter, the first image sequence of the first image sensor C1 is continuously analyzed to continuously track the activities of the registered user U1 and determine whether the user U1 is about to leave the first sensing range of the first image sensor C1.

[0037] When the face or other features of user U1 gradually disappear from the first image sequence detected by the first image sensor C1 (for example, if the face leaves the sensing range or no detection occurs for a long period of time), the processor 110 records this state and assumes that user U1 may be moving to the rear. Simultaneously, the second image sensor C2 continues to monitor the rear environment, and the processor 110 continuously analyzes the second image sequence to detect any new objects within the second sensing range. When a new object (possibly a face or other biometric feature) is detected within the second sensing range, the processor 110 matches the data of the new object with the previously recorded data of the registered user U1. For example, using facial recognition technology or other biometric identification methods, the processor 110 compares the features (such as facial structure and eye details) of the new object detected from the rear with those of user U1 who left the front. If a match is successful, it confirms that user U1 has moved from the front to the rear. The processor 110 ultimately confirms the change in user U1's movement direction and position and outputs the result (for example, sending a message notifying user U1 of the move to the rear, or triggering a subsequent action, such as locking or unlocking the device). If the user U1 is no longer detected in the second image sequence, the processor 110 may end the current synchronous detection, or continue to monitor the first image sequence to wait for the next movement of the user U1 (or other registered users).

[0038] Regarding the method (2) of using image matching and continuous tracking, the core lies in accurate image matching and continuous tracking of user U1. The processor 110 not only detects the movement of user U1, but also needs to confirm whether the object after movement is consistent with user U1 through biometric features (such as facial recognition). Method (2) is suitable for scenarios where identity confirmation is crucial, especially in places with high security requirements, such as financial institutions or access control systems. This method ensures that the user's identity will not be misused. Method (2) requires facial recognition technology or other biometric recognition technology, and the processor 110 needs to process a large amount of image data for accurate comparison, which has high requirements for computing power. Since method (2) emphasizes the identification and matching of user features, that is, feature comparison accuracy and continuous tracking are the key points of this method, accordingly, it has high security requirements.

[0039] Method (2) includes the following steps. The first image sensor C1 and the second image sensor C2 are simultaneously activated and enter the tracking mode, and the first image sequence and the second image sequence are analyzed to track the user U1 and the surrounding environment. Specifically, the processor 110 analyzes the first image sequence of the first image sensor C1, and then recognizes the face or other biometric features of the user U1 (such as facial key points, eye movements, skin texture, etc.), and extracts the biometric features of the user U1, such as facial contour, pupil position, eye distance, nose shape, etc., and stores these feature data as identification data of the user U1. The first image sequence obtained by the first image sensor C1 is continuously analyzed to track the movement and position of the user U1, and the identification data of the user U1 is updated in real time.

[0040] When the analysis of the first image sequence determines that user U1's facial features or other biometric features gradually disappear (e.g., the face moves outside the first sensing range of the first image sensor C1 or moves laterally away from the first sensing range of the first image sensor C1), user U1 is determined to have left the first sensing range. When user U1 leaves the first sensing range, processor 110 records and stores the final facial features or other biometric data as user U1's identification data for subsequent matching. Next, the second image sequence of the second image sensor C2 is analyzed to detect whether a new object has entered the second sensing range of the second image sensor C2. Upon detecting a new object entering the second sensing range, processor 110 analyzes the second image sequence to extract features of the new object, thereby generating a set of data identical to the identification data recorded by the first image sensor C1. Processor 110 then compares user U1's identification data with the identification data of the new object to determine whether the new object is user U1. For example, a facial recognition algorithm (such as a neural network or deep learning model) is used for detailed analysis. When it is determined that the identification information of user U1 and the identification information of the new object reach a certain similarity (usually determined by setting a threshold, such as a similarity of more than 80%), the new object is confirmed to be user U1.

[0041] After determining that the new object's identification information successfully matches user U1's identification information, the processor 110 continuously analyzes the second image sequence obtained by the second image sensor C2 to track user U1's movements and position changes behind the object, continuously updating its feature data and ensuring that subsequent operations align with user U1's movements. Anomaly Detection: If the new object's identification information is determined to not match user U1's identification information, the processor 110 may further trigger an anomaly alarm, indicating a possible misidentification or unidentified object.

[0042] Processor 110 ultimately confirms that user U1 has moved from the front to the back and feeds this confirmation back to relevant systems (e.g., triggering subsequent actions, unlocking the device, etc.). If no further action is required, tracking can be terminated. If necessary, processor 110 can continue to monitor the back environment. Furthermore, processor 110 archives each image matching and tracking record to facilitate subsequent optimization of the recognition model and improve detection accuracy. Based on the matching results, processor 110 can use the collected data for self-learning, continuously improving the accuracy of future image matching and ongoing tracking.

[0043] Regarding the (3) time difference and direction detection method, the time difference and movement direction are used as the judgment criteria. When the user U1 moves from the front to the back, the time difference and movement direction detected by the first image sensor C1 and the second image sensor C2 are used to infer the user U1's movement. Method (3) is suitable for simple direction and movement detection, where identity confirmation is not particularly emphasized, such as some basic user movement tracking or location switching systems. It is suitable for use in scenarios where it is necessary to understand the user's approximate location changes. Method (3) mainly relies on timestamp recording and judgment of spatial position changes. It does not require highly precise image recognition technology and has relatively low requirements for computing power. Method (3) focuses on judging the movement of user U1 based on the time interval and spatial position changes from the front to the back, ignoring the confirmation of specific identity.

[0044] Method (3) includes the following steps. The first image sensor C1 and the second image sensor C2 are activated and enter a monitoring mode to prepare to detect the movements and position changes of the user U1. Specifically, the processor 110 analyzes the first image sequence of the first image sensor C1 and then recognizes the presence of the user U1. The processor 110 records the facial features, position and current timestamp of the user U1, and continuously analyzes the first image sequence of the first image sensor C1 to track whether the user U1 is within the first sensing range. When the user U1 is detected to enter or stay within the first sensing range, the processor 110 records the current time point (T1) as the time point when the user U1 enters the first sensing range. When the user U1 is detected to leave the first sensing range, the processor 110 records the current time point (T2) as the time point when the user U1 leaves the first sensing range. Then, the user U1's stay time from time point T1 to time point T2 is calculated (T2-T1) so as to perform time difference comparison in the subsequent process.

[0045] Next, the second image sequence from the second image sensor (rear) C2 is analyzed to detect whether a new object has entered the second sensing range. When a new object is detected entering the second sensing range, user U1 records the current time (T3) as the time the new object entered the second sensing range, along with the new object's location information. The processor 110 then calculates the time difference and determines the direction of movement. Specifically, the processor 110 calculates the time difference ΔT = T3 - T2 between time T2 when the new object left the first sensing range and time T3 when the new object entered the second sensing range. If the time difference ΔT falls within a reasonable range (based on a pre-set threshold, such as a time difference of 1 to several seconds), it can be inferred that user U1 moved from the front to the back. If the time difference ΔT is too large or too small, it indicates that the new object is not user U1, or that user U1's movement path is abnormal. Furthermore, the processor 110 determines whether the user U1's movement direction is from front to back based on the user U1's last detected position P1 before leaving the first sensing range (the user U1's last detected position within the first sensing range at time T2) and the new object's initial detected position P2 within the second sensing range (the new object's initial position within the second sensing range at time T3). If the positional changes of both the last detected position P1 and the initial detected position P2 are consistent with the spatial layout of the electronic device 100 (from front to back), the user U1's movement direction is confirmed to be correct.

[0046] Afterward, the user's identity is confirmed. Specifically, after the time difference and movement direction match expectations, the processor 110 can perform further facial recognition or biometric comparison to confirm whether the new object detected on the rear side is the same person as user U1 on the front side. After determining that the new object's identification information successfully matches user U1's, the processor 110 confirms the new object is user U1 and confirms that the object has moved from the front to the back. The processor 110 then outputs the result. For example, a message notifying user U1 that the object has moved to the back side may be sent, potentially triggering subsequent actions (such as switching control permissions, unlocking the device, etc.). The processor 110 can also continue monitoring activity on the rear side or terminate the detection as needed. Furthermore, if the time difference ΔT does not fall within a reasonable range, the processor 110 determines that an anomaly has occurred, possibly indicating that user U1 did not move from the front to the back side, or that another object has entered the second sensing range on the rear side. Furthermore, if the movement direction does not conform to the front-to-back movement path, this may indicate that another unidentified object has entered the rear side. In this case, the processor 110 may trigger an alarm or conduct further inspections.

[0047] Returning to FIG. 9 , in response to determining that user U1 has moved from the first side to the second side, in step S915 , the display screen 1010 is presented in full screen mode in the first screen area 140 , and the size of the display screen 1010 is reduced to obtain a reduced screen F1 . The reduced screen F1 and the screen operation interface 1020 are then presented in the second screen area 150 . In this embodiment, the second screen area 150 is further divided into a first block B1 and a second block B2 . The first block B1 is used to synchronously present the reduced screen F1, which is a proportional reduction of the display screen 710 , while the second block B2 is used to present the screen operation interface 1020 .

[0048] In response to determining that user U1 is located at the first side, in step S920, the display image 1010 is presented in the first screen area 140, and at least a portion of the display image 1010 displayed in the first screen area 140 is displayed in the second screen area 150. For example, as shown in FIG. 7 or FIG. 8 .

[0049] Furthermore, user U1's sharing intent can be further determined through gestures, voice, facial expressions, environmental factors, and other factors, thereby determining the content to be presented in the second screen area 150. Figure 11 is a flow chart of a screen display method in sharing mode according to one embodiment of the present invention. Referring to Figure 11 , in step S1105 , in response to detecting another user entering within a predetermined range, the sensor is activated to obtain sensing data from the user. The processor 110 then analyzes the sensing data to determine whether to enter either the instant sharing mode or the local sharing mode.

[0050] In response to determining that the live sharing mode has been entered, in step S1115, the display image is presented in the first screen area 140, and a reduced image of the display image and at least a portion of the display image are presented in the second screen area 150. For example, in FIG7 , in live sharing mode, the processor 110 reduces the size of the display image 710 to obtain a reduced image F1. Based on an operation (e.g., a frame selection action) performed on the display image 51 displayed in the first screen area 140, the processor 110 captures at least a portion of the display image 710 (the image corresponding to the frame selection area 720), and presents the reduced image F1 and the frame selection image F2 in the second screen area 150.

[0051] In response to determining that partial sharing mode has been entered, in step S1120, the display image is presented in first screen area 140, and at least a portion of the display image is presented in second screen area 150. For example, in FIG. 8 , in partial sharing mode, processor 110 displays only the display content in second interactive area 820 in second screen area 150.

[0052] There are five ways to determine a user's sharing intent: gesture detection, voice commands, facial expression recognition, environmental awareness, and gaze tracking.

[0053] Regarding the first embodiment (gesture detection), the sensor is an image sensor (for example, located on the first side of the first screen area 140), and the sensing data is an image sequence. The processor 110 detects the hand area in the image sequence and obtains the hand area's motion trajectory and / or gestures. Based on the motion trajectory and / or gestures, the processor determines whether to enter either instant sharing mode or partial sharing mode. Specifically, the image sensor captures the user's hand movements and motion trajectory. The analyzed data primarily focuses on hand position and movement changes, and gestures are recognized using deep learning or computer vision techniques. The analysis focuses on the user's body movements, particularly hand movements, and is suitable for determining when the user needs to perform specific operations or indicate an action. For example, gestures such as waving, pinching to zoom, or swiping can directly express the intention to share the screen.

[0054] When users perform specific gestures, they typically clearly convey their intent to share immediately or partially, resulting in higher accuracy. However, gesture recognition accuracy can be affected by factors such as image quality and lighting conditions, and effective recognition requires explicit user action. Gesture detection is ideal for non-voice-based operations or situations requiring quick and easy control. For example, in meetings, where voice commands are inconvenient, gestures can trigger sharing.

[0055] During the initialization phase, the image sensor is activated to prepare for monitoring the user's gestures, and the image sensor's angle, focus, and lighting are calibrated to ensure accurate gesture capture. The gesture detection model is also loaded to ensure that the processor 110 can recognize different gestures in real time.

[0056] During the data acquisition phase, the image sensor continuously captures the hand movements of the user in front of it, generating an image sequence (e.g., a series of real-time images or a video stream). This image sequence is then pre-processed with techniques such as image noise reduction, background removal, and skin color segmentation to ensure that the hand area can be effectively isolated.

[0057] During the feature extraction stage, computer vision techniques (such as convolutional neural networks (CNNs)) are used to locate the hand in the image. Key hand features, such as finger joints, palms, and fingertips, are extracted, and a hand skeleton model is generated as the basis for subsequent gesture recognition. Motion trajectory analysis is performed to analyze the gesture's trajectory, such as the finger movement path and palm rotation angle, recording continuous hand movement changes.

[0058] During the gesture classification phase, the extracted gesture features are compared with those in a pre-established gesture database. Common gestures such as "waving," "pinch to zoom," and "swiping" should be pre-trained and annotated. Next, deep learning algorithms (such as long short-term memory (LSTM) or hidden Markov models (HMM)) are used to classify and recognize gestures, determining whether the gesture meets the criteria for immediate or partial sharing.

[0059] During the intent inference stage, processor 110 further infers the gesture's intent based on the current application context. For example, it determines whether the user is in a meeting or has selected at least a portion of the screen, thereby determining whether the gesture is for instant sharing or partial sharing. Furthermore, if other signals are present (such as voice commands or facial expressions), processor 110 can integrate these multiple perceptions to improve the accuracy of instant sharing intent.

[0060] During the System Response phase, when a clear sharing gesture is detected, the system automatically activates the screen sharing function. Furthermore, before triggering the sharing function, the system may request further user confirmation, such as by displaying a prompt message or playing a voice reminder. During the Feedback & Learning phase, the processor 110 allows users to provide feedback on the accuracy of gesture detection, allowing for further adjustments. Furthermore, the processor 110 continuously updates the gesture detection model based on feedback from actual usage scenarios, improving detection accuracy for different users and scenarios.

[0061] In the second embodiment (voice command), the sensor is a voice sensor, and the sensed data is a voice signal. Processor 110 analyzes the voice signal to determine whether to enter instant sharing mode or local sharing mode. Specifically, a microphone or voice sensor can be used to capture the user's voice signal, and natural language processing (NLP) technology can be used to transcribe and recognize voice commands from the voice signal. The data analyzed is the user's voice content and acoustic characteristics, and the language information is analyzed to understand the user's intent through voice commands. Voice commands are suitable for users who are accustomed to operating the device through speech or issuing explicit commands, such as "share screen" or "show screen." Voice commands are highly specific, and processor 110 can almost immediately infer intent when the user directly speaks the relevant command. However, background noise, speech misunderstandings, or different accents may affect accuracy. Voice commands are also suitable for scenarios where users can freely issue voice commands, such as in a personal work environment or remote meetings, where voice commands are more convenient for quick operations.

[0062] During the initialization phase, the voice sensor is activated to prepare for capturing and processing voice commands. The voice sensor can be a microphone or a built-in voice assistant module. Furthermore, the voice recognition system (such as an NLP engine) is activated to analyze and recognize voice commands in real time. During the data acquisition phase, the voice sensor captures ambient voice signals in real time and converts them into digitized voice data. Next, processor 110 performs pre-processing on the voice signal, such as removing background noise and improving voice clarity, to ensure accurate voice recognition.

[0063] During the speech command recognition phase, processor 110 uses speech recognition technology (such as Automatic Speech Recognition (ASR)) to transcribe the voice signal into text. Furthermore, processor 110 performs keyword matching, identifying keywords or phrases related to screen sharing (such as "share screen," "start presenting," or "screen sharing"). Various voice command variations can be configured, such as "show everyone," "show this screen," or "share it with them."

[0064] During the contextual analysis of voice commands, processor 110 not only identifies keywords in the voice command but also infers the user's intention to share their screen based on the context of the command. For example, when a user says, "Let me share this," processor 110 not only recognizes the word "share" but also analyzes the entire sentence to determine whether the intention is related to screen sharing. Furthermore, processor 110 analyzes whether the voice command appears in a context related to the screen. For example, a voice command spoken in a context such as using a conference application, displaying a document, or presenting a presentation may be related to screen sharing. Processor 110 can analyze whether the speaker mentions screen content, such as "this file," "this page," or "this slide," to determine whether the voice command refers to specific content on the screen.

[0065] During the multimodal integration analysis phase, other sensor data can be combined, such as voice commands combined with other sensors (such as gaze tracking, gesture detection, and environmental awareness) to make a judgment. If the user simultaneously gazes at the screen or performs a gesture while issuing a voice command, processor 110 further confirms their sharing intent. Furthermore, the voice command can be combined with the current environment or application context for a more in-depth judgment. For example, if a meeting mode or the proximity of another person is detected, the voice command may be more likely to represent an immediate sharing intent.

[0066] During the intent inference phase, the voice command's tone, pitch, and pronunciation speed are analyzed. If the user's voice tone rises, expressing emphasis or instruction, it can be inferred that the command is likely imperative, indicating an immediate need to share. Furthermore, intent can be further inferred based on context. This involves integrating the voice command, the surrounding context, and user actions (such as gestures and gaze) to determine whether the user truly intends to share their screen.

[0067] During the system response phase, if the voice command is clear and unambiguous, processor 110 can automatically activate the corresponding sharing function, immediately fulfilling the user's request. If processor 110 cannot completely determine whether the voice command is intended for immediate or partial sharing, it can display a confirmation prompt to allow the user to further confirm the desired sharing mode. For example, a prompt might appear asking "Do you want to start sharing your screen?" for the user to select. Furthermore, if the voice command does not involve sharing, but others are nearby, processor 110 can also prompt the user to enable privacy protection mode. During the feedback and learning phase, the voice command recognition and intent judgment models can be adjusted based on user feedback. If a user repeatedly rejects the system's sharing prompts, processor 110 will record these instances to reduce unnecessary prompts. Processor 110 learns from the voice command patterns of different users. For example, some users use specific phrases or intonations to express sharing requests, and processor 110 can further improve judgment accuracy by learning these patterns.

[0068] In the third embodiment (expression recognition), the sensor is an image sensor, and the sensor data is an image sequence. Processor 110 detects the facial area in the image sequence, analyzes the facial expression, and based on the facial expression, determines whether to enter instant sharing mode or partial sharing mode. Specifically, the image sensor analyzes the user's facial expression, including changes in features such as the corners of the mouth, eyes, and eyebrows. Processor 110 infers the user's emotion or intention based on these changes in facial features. This approach focuses on analyzing the user's emotional state and facial expressions and is suitable for understanding non-verbal expressions in certain situations, which may reflect the user's mental state or interactive intention. For example, a smile or nod may indicate an intention to share instantly. Facial expressions can reflect a user's emotions or attitudes, but the diversity and individual differences in expressions may lead to inaccurate inferences. Processor 110 needs to combine other behaviors or context to make a comprehensive judgment. Expression recognition is suitable for situations where it is necessary to capture the user's emotions or attitudes to make inferences. For example, a user may indicate an intention to share instantly or partially through facial expressions without explicitly speaking or gesturing.

[0069] During the initialization phase, the front-facing image sensor is activated to prepare for facial expression detection. Furthermore, based on factors such as lighting and distance, the processor 110 automatically adjusts the image sensor's focus and exposure to ensure a clear and visible image of the user's face. During the data capture phase, the image sensor continuously captures images of the user's face, generating an image sequence (e.g., a series of live images or a video stream). These image sequences are then pre-processed, for example, by image enhancement, noise removal, and background removal to ensure that only the facial area is recognized. This step may include skin color segmentation to more clearly isolate the contours of facial features.

[0070] During the feature extraction phase, computer vision algorithms (such as OpenCV or deep learning models) are used to identify 68 key points on the face. These key points include the locations of important features such as the eyes, nose, mouth, and eyebrows. A skeletal model of the face is then constructed based on the locations of these key points, enabling more accurate motion and expression analysis. Processor 110 continuously tracks the movement of these facial key points, recording changes in expression, such as the upward movement of the corners of the mouth or the raising of the eyebrows.

[0071] During the emotion classification stage, detected facial features are compared against a pre-trained database of facial expressions. Common expressions include happiness, surprise, disgust, sadness, anger, and so on. Machine learning or deep learning algorithms (such as CNNs or LSTMs) can be used to classify facial expressions and identify the current emotional state. These algorithms can infer specific expressions based on changes in feature points.

[0072] During the emotion and intent inference stage, the recognized expression is combined with the current context (e.g., in a meeting, viewing the screen, or interacting with others) to infer whether the expression indicates an intention to share the screen. For example, a smile or nod may indicate a willingness to share, while a frown may indicate a need for further assistance. If the electronic device 100 also detects other signals (such as gestures or voice), the expression recognition results can be combined with these signals to further determine the user's intention.

[0073] During the system response phase, when a user's expression is detected to meet the conditions for a sharing intent, the screen sharing function will be activated or a prompt will pop up requesting confirmation. Before the automatic trigger, the user can also be prompted to confirm whether to execute the screen sharing function to avoid misjudgment. During the feedback and learning phase, users provide feedback on expression detection to confirm whether processor 110 has accurately interpreted their expressions. Furthermore, based on this feedback, processor 110 can continuously update and fine-tune the expression recognition model, thereby improving the accuracy of recognition of different user expressions.

[0074] In practical applications, gesture detection can be further configured to be combined with facial expression recognition. For example, pushing an open hand toward the screen indicates sharing everything. However, if the user's facial expression is further detected, the processor 110 may detect that the user's current mood does not indicate a desire to share, or that the user is focused on the content rather than the new object. In this case, the processor 110 will block the gesture appropriately. Even if the gesture indicates sharing intent, the combination with the facial expression may indicate that the gesture was unintentional and does not indicate intentional sharing.

[0075] In the fourth embodiment (environmental sensing), the sensor is an environmental sensor, and the sensing data is environmental sensing data. For example, a conference room, a crowded environment, or an office environment—these different environments allow the system to learn whether it is appropriate to share. Specifically, environmental sensors can be implemented using a combination of image sensors, proximity sensors, and sound sensors, primarily monitoring environmental changes around the electronic device 100, such as changes in light and the approach of people. The data analyzed comes from the surrounding environment, not directly from user behavior. The processor 110 focuses on changes in the external environment, such as the proximity of other people and changes in light. Environmental sensing is suitable for inferring the intention of real-time sharing or partial sharing in scenarios involving user interaction or multi-party participation. For example, in a multi-person meeting, the proximity of other people may increase the likelihood of screen sharing. Environmental sensing is suitable for use in multi-person interaction and can help infer the user's sharing needs. However, it is difficult to directly determine the intention of real-time sharing or partial sharing based on environmental changes alone, and it usually needs to be combined with user behavior. For example, context sensing can be combined with gesture detection to blur content that shouldn't be shared before it's shared, even if the user isn't intending to. It can also be used in situations where attention to external environmental changes is crucial, such as in a multi-person meeting, where the user's desire to share can be inferred by sensing the proximity of nearby people.

[0076] During the initialization phase, sensors such as the image sensor, proximity sensor, and sound sensor are activated, and confirmation is made that another person is approaching the electronic device 100 before proceeding to the next step. In one embodiment, the image sensor, sound sensor, and distance sensor are combined to determine if another person is within close proximity (e.g., within 1-2 meters). During the user behavior analysis phase, the gaze direction of the (registered) user is detected to confirm whether the user is continuously looking at the electronic device screen. If the user frequently looks at nearby other people or the screen, they may be preparing to share their screen with others. Furthermore, the user's gestures can be monitored to see if they perform sharing-related actions, such as pinching to zoom, swiping, or pressing a share button, which may indicate the user's intention to share their screen. Furthermore, the user's facial expressions, such as a smile or nod, can be detected, which may indicate the user's intention to communicate or interact with the nearby person, suggesting the possibility of screen sharing.

[0077] During the environmental change and situational assessment phase, the system analyzes ambient sounds. If it detects that the user is engaged in a conversation with a nearby person, involving screen content (e.g., "Look at this" or "Let me share"), it can be inferred that the user intends to share their screen. Furthermore, it can detect changes in ambient light or the device's surroundings (e.g., whether the electronic device 100 is connected to an external display or in conference mode), which may indicate that the user is about to present or share screen content. Furthermore, if it detects multiple people approaching and surrounding the electronic device 100, this may indicate that the user is preparing to discuss the content on the screen with others, a situation often accompanied by a desire to share.

[0078] During the intent inference phase, user behavior data (such as gaze, gestures, and expressions) is combined with the context of nearby people for comprehensive analysis. For example, if a user gazes at the screen and initiates gestures while multiple people are nearby, the user is inferred to have a strong intention to share. Furthermore, the current application context (such as using a conferencing app or presentation software) can be combined to make more detailed inferences based on the user's behavior and the number and location of nearby people.

[0079] During the system's response and prompting phase, if the system determines that a user may have the intention to share, a prompt will automatically pop up asking them to "Start sharing screen," allowing them to quickly confirm. Furthermore, if similar behavior is detected in multiple situations (e.g., a user repeatedly initiates sharing when near others), screen sharing can be automatically initiated, improving user convenience. Furthermore, if someone is detected approaching and the user doesn't immediately indicate an intention to share, privacy prompts can be provided, such as blurring or automatically locking the screen, to protect user data.

[0080] During the user feedback and learning phase, the accuracy of predictions can be verified based on user behavior and operational feedback. If a user fails to take action when prompted to share, this situation is recorded and used to adjust the model. Furthermore, learning can be conducted based on user behavior to optimize the accuracy of predictions about specific users' sharing behavior. For example, if certain users frequently share with specific expressions or gestures, these patterns can be used to make predictions in advance.

[0081] In the fifth embodiment (gaze tracking), the sensor is an image sensor, and the sensed data is an image sequence. Processor 110 detects the eye area in the image sequence, analyzes the gaze position, and based on the gaze position, determines whether to enter instant sharing mode or partial sharing mode. Specifically, image sensing technology is used to track the user's eye movement and gaze direction. The analyzed data includes the user's eye position and head movement, and this data is used to determine whether the user is paying attention to specific content on the screen. This primarily analyzes the user's visual attention and is suitable for use in situations where the user's gaze focus clearly indicates their intention. For example, when a user gazes at a button or a specific content area on the screen, it can be inferred that the user is preparing to share this content with others. When the user's gaze direction is clear and relevant to the on-screen content, accuracy is high. However, if the user's gaze changes frequently or the usage scenario is complex, misjudgment may occur. Gaze tracking is suitable for scenarios where screen operation or content selection clearly reflects the user's intention. For example, when a user gazes at a button or a piece of content, it can be inferred that the user is interested in sharing this content. Gaze tracking is also convenient for users with ALS.

[0082] During the initialization phase, the front-facing image sensor is activated to capture the registered user's face, specifically their gaze. The sensor is then calibrated based on ambient lighting, autofocus, and user distance to ensure accurate capture of the user's eyes and facial features.

[0083] During the data acquisition phase, facial data, specifically eye and head posture and movement, is continuously captured from the image sensor's image sequence. Furthermore, the image data is pre-processed, including noise reduction, background removal, and image detail enhancement, to ensure accurate recognition of the user's eyes and key facial features.

[0084] During the facial feature extraction and gaze direction analysis phase, computer vision techniques (such as OpenCV or deep learning models) are used to extract facial key points, such as the eyes, nose, and mouth, with a particular focus on the eye area. Furthermore, based on the locations of these key points, the user's gaze direction (e.g., left-right, up-down) and gaze focus are calculated. Processor 110 not only tracks eye movement but also analyzes head rotation to more accurately infer whether the user's gaze is directed at a specific location on the screen or at another object.

[0085] During the gaze behavior pattern analysis phase, monitoring whether the user's gaze is focused on a specific area of ​​the screen (such as a window or content) for an extended period of time may indicate that the user is preparing to share this content. Furthermore, if the user's gaze frequently switches between the screen and nearby people, this may indicate that the user is considering showing their screen to others. Furthermore, if the user's gaze is detected to remain on a sharing-related button or function (such as the "Share Screen" button) for an extended period of time, this may be a clear signal that the user has immediate sharing intent.

[0086] During the contextual assessment and multimodal fusion phase, gaze behavior is combined with other sensor data (such as the presence of others or changes in ambient light) for contextual analysis. When gaze behavior coincides with the presence of others nearby, a determination is made as to whether the screen is likely to be displayed. Furthermore, gaze tracking data can be further combined with gesture, voice, or expression detection. If the user makes sharing gestures or issues voice commands while looking at the screen, processor 110 further confirms the intention to share immediately.

[0087] During the intent inference phase, gaze patterns are combined with the current context (e.g., a meeting, close proximity of multiple people), to infer whether the user intends to share their screen. For example, if the user is detected to be in a meeting and looking at a document or presentation on the screen, the likelihood of screen sharing is high. Furthermore, the user's current application context (e.g., using conferencing software, a browser, or an editor) can be analyzed, and combined with the user's prolonged gaze on specific parts of these applications, a real-time sharing intent can be determined.

[0088] During the system response and action prompt phase, if the user is clearly intent to share their screen, a prompt will automatically pop up asking them to "Begin screen sharing," allowing them to quickly confirm the sharing action. If a sharing request is highly likely and the user has set automatic sharing, screen sharing can proceed directly, improving operational efficiency. Furthermore, if the user is detected to be looking at the screen for an extended period without displaying or sharing their screen, and if others are nearby, privacy protection options such as blurring the screen or automatically locking the screen can be automatically prompted.

[0089] During the user feedback and behavior learning phase, feedback learning is conducted based on user responses to confirm whether the real-time sharing intention is correctly identified in the correct context. For example, if no user action is taken when a sharing prompt pops up, processor 110 will record this situation to optimize future judgments. Furthermore, learning can be performed based on the gaze behavior patterns of different users to further optimize the accuracy of identifying different users' gaze behaviors and real-time sharing intentions.

[0090] The first through fifth embodiments described above each have their own advantages and limitations, and a single approach may not cover all scenarios. Combining multiple technologies (such as gesture detection and gaze tracking, or voice commands and expression recognition) can more accurately infer user intent and adapt to different usage scenarios. For example, in some scenarios, a user may simultaneously perform gestures and speak commands. In these cases, processor 110 can perform multimodal fusion to improve accuracy.

[0091] In other embodiments, the electronic device 100 can also provide an immersive experience by running a multimedia player and displaying multimedia images in the first screen area 140. FIG12 is a flow chart of a method for displaying images in multimedia playback mode according to one embodiment of the present invention. FIG13 is a schematic diagram of a sharing mode in multimedia playback mode according to one embodiment of the present invention.

[0092] Referring to FIG. 12 , in step S1205 , a multimedia player is executed. The multimedia player may be, for example, a Blu-ray player, a set-top box, or a game console. Next, in response to the electronic device 100 executing the multimedia player and presenting a multimedia screen (game screen or movie screen) in the first screen area 140 , in step S1210 , the multimedia screen is presented in full screen in the first screen area 140 , and a corresponding color gamut distribution is displayed in the second screen area 150 based on the bottom color gamut distribution of the first screen area 140 . For example, as shown in FIG. 13 , the regional luminous display in the second screen area 150 is based on the real-time color gamut below the first screen area 140 . This allows the color tone of the video or game screen to be continued throughout the environment, enhancing user immersion. For example, if the bottom of the display screen in the first screen area 140 has blue, purple, and green colors, the corresponding blue, purple, and green light sources are extended in the second screen area 150. These light sources will be projected onto surrounding devices such as the desktop and walls, creating an immersive feeling that the visual effects of the first screen area 140 are blurred into the surrounding environment.

[0093] In summary, the present invention utilizes a dual-screen architecture and combines sensor technology to create a diverse user experience. When the electronic device is activated, dual screens are displayed to display information. Furthermore, using sensor technology, when multiple users are using the device, the second screen area can simultaneously share the image from the first screen area, facilitating simultaneous viewing by multiple users.

[0094] 100: Electronic devices 100: Electronic devices 100A: First Body 100B: Second subject 100C: Pivot structure 110: Processor 120: First display module 130: Second display module 140: First screen area 150: Second screen area 160:Detection module 200: Flexible screen 310: Mirror component 410: Projection module 510:Logical Display 710, 1010: Display screen 720: Select area 810: First interactive area 820: Second interactive area 1020: Screen operation interface B1: The first block B2: The second block C1: First image sensor C2: Second image sensor F1: Zoom out F2: Frame selection L: bending line R1: Preset range U1, U2: users S605~S610, S905~S920, S1105~S1120, S1205~S1210: Steps

Claims

1. A screen display method for an electronic device, wherein the electronic device includes a detection module, a first main body having a first screen area, and a second main body pivotally connected to the first main body, the screen display method comprising executing the following steps via a processor: When a user uses the electronic device, the detection module detects whether another user enters within a preset range of the electronic device; and in response to detecting that another user has entered within the preset range, at least a portion of a display screen that is displayed in the first screen area is displayed in a second screen area, wherein the second screen area is located on the electronic device in a position different from the first screen area, and the second screen area is located on a plane different from the plane on which the first screen area is located.

2. The screen display method of the electronic device as described in claim 1, wherein in response to detecting the entry of another user within a preset range, it further includes: A smaller image is obtained by reducing the size of the displayed image. And at least a portion of the displayed image and the reduced image are displayed together on the second screen area.

3. The screen display method of the electronic device as described in claim 1, wherein responding to the detection of another user entering within a preset range includes: In response to performing a selection action on the display screen displayed in the first screen area, a selection screen is captured from the display screen based on a selection area corresponding to the selection action, and the selection screen is displayed in the second screen area.

4. The screen display method of the electronic device as described in claim 1, wherein the first screen area includes a first interactive area and a second interactive area, and in response to detecting that another user has entered within a preset range, includes: In response to the detection that a display content in the first interactive area is dragged to the second interactive area, the display content is displayed in the second screen area.

5. The screen display method of the electronic device as described in claim 1, wherein the first screen area includes a first interactive area and a second interactive area, and in response to detecting that another user has entered within a preset range, includes: In response to the detection that a display content in the first interactive area is dragged to the second interactive area, the display content is translated to obtain a translated content, and the translated content is displayed in the second screen area.

6. The screen display method of the electronic device as described in claim 1, wherein in response to detecting the entry of another user within a preset range, it further includes: Drive a first image sensor and a second image sensor to obtain a first image sequence and a second image sequence respectively; and determine whether the user has moved from a first side to a second side by analyzing the first image sequence and the second image sequence.

7. The screen display method of the electronic device as claimed in claim 6, wherein the first image sensor is disposed on the first side where the first screen area is located, and the second image sensor is disposed on the second side where the second screen area is located.

8. The screen display method of the electronic device as described in claim 6, wherein after the step of determining whether the user has moved from the first side to the second side, it further includes: In response to determining that the user has moved from the first side to the second side, the display screen is presented in the first screen area, and the size of the display screen is reduced to obtain a reduced screen, and the reduced screen and a screen operation interface are presented in the second screen area.

9. The screen display method of the electronic device as described in claim 6, wherein after the step of determining whether the user has moved from the first side to the second side, it further includes: In response to determining that the user is located on the first side, at least a portion of the display screen that will be displayed in the first screen area will be displayed in the second screen area.

10. The screen display method of the electronic device as claimed in claim 6, wherein determining whether the user has moved from the first side to the second side by analyzing the first image sequence and the second image sequence includes: In response to determining, based on the analysis of the first image sequence, that the user has left a first sensing range of the first image sensor, and based on the analysis of the second image sequence, determining whether a new object has entered a second sensing range of the second image sensor; and if, based on the analysis of the second image sequence, it is determined that a new object has entered the second sensing range of the second image sensor, determining whether the new object is the user.

11. The screen display method of the electronic device as described in claim 10, wherein after the step of determining whether the new object is the user, it further includes: In response to the new object being the user, it is determined that the user has moved from the first side to the second side.

12. The screen display method of the electronic device as described in claim 10, wherein after the step of determining whether the new object is the user, it further includes: A warning message is issued in response to the fact that the new object is not the user.

13. The screen display method of the electronic device as described in claim 1, wherein in response to detecting the entry of another user within a preset range, the method further includes: Drive a sensor to obtain sensing data from the user; The system analyzes the sensing data to determine whether to enter an instant sharing mode or a partial sharing mode; in response to determining that the partial sharing mode has been entered, at least a portion of the display screen in the first screen area is displayed in the second screen area; and in response to determining that the instant sharing mode has been entered, the size of the display screen is reduced to obtain a reduced image, and based on an operation performed on the display screen in the first screen area, the at least a portion of the image is captured from the display screen and presented in the second screen area as the reduced image and the at least a portion of the image.

14. A screen display method for an electronic device as described in claim 13, wherein the sensor is an image sensor, the sensing data is an image sequence, and determining whether to enter the real-time sharing mode or the partial sharing mode by analyzing the sensing data includes: Detect a hand region in the image sequence and obtain at least one of a motion trajectory and a gesture of the hand region, and determine whether to enter the instant sharing mode or the local sharing mode based on the motion trajectory and the gesture of the hand region.

15. A screen display method for an electronic device as described in claim 13, wherein the sensor is a voice sensor, the sensing data is a voice signal, and determining whether to enter the real-time sharing mode or the partial sharing mode by analyzing the sensing data includes: The voice signal is analyzed to determine whether the instant sharing mode or the partial sharing mode has been entered.

16. A screen display method for an electronic device as described in claim 13, wherein the sensor is an image sensor, the sensing data is an image sequence, and determining whether to enter the real-time sharing mode or the partial sharing mode by analyzing the sensing data includes: Detect a facial region in the image sequence, analyze a facial expression, and determine whether to enter the instant sharing mode or the local sharing mode based on the facial expression.

17. A screen display method for an electronic device as described in claim 13, wherein the sensor is an image sensor, the sensing data is an image sequence, and determining whether to enter the real-time sharing mode or the partial sharing mode by analyzing the sensing data includes: The system detects an eye region in the image sequence, analyzes a gaze position, and determines whether to enter the instant sharing mode or the local sharing mode based on the gaze position.

18. The screen display method of the electronic device as claimed in claim 13, wherein the sensor is an environmental sensor and the sensing data is environmental sensing data.

19. The screen display method of the electronic device as described in claim 1, further comprising: In response to the electronic device running a multimedia player and displaying a multimedia image in the first screen area, the multimedia image is displayed in a full-screen display mode in the first screen area, and based on the bottom color gamut distribution of the first screen area, the same color gamut distribution is displayed in the second screen area.

20. The screen display method of the electronic device as described in claim 19, wherein the multimedia screen is a game screen or a video screen.

21. An electronic device comprising: The primary subject; A second subject; A pivot structure for pivotally connecting the first body and the second body; a detection module; A first display module for controlling the display of a first screen area, wherein the first screen area is disposed on a first side of the first main body; a second display module for controlling the display of a second screen area, wherein the second screen area is disposed at a position different from the first screen area and is disposed on a plane different from the plane on which the first screen area is located; and a processor communicatively connected to the detection module, the first display module and the second display module, wherein the processor is configured to: detect, when a user uses the electronic device, whether another user enters within a preset range through the detection module; and in response to detecting that another user has entered within the preset range, display at least a portion of a display screen currently displayed in the first screen area on the second screen area.

Images