DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
2. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
3. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
4. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
5. Claim(s) 1-11 and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drozdov et al. (US 2024/0121377 A1) in view of Tsang (US 2012/0314021 A1).
6. With reference to claim 1, Drozdov teaches A computer-implemented method performed by a predictive display system (PDS), the method comprising: capturing, by a set of one or more cameras of the PDS, images of a physical object from different angles; (“a method includes a method for enabling projection of images from a digital display, the method comprising: a) obtaining face image data and eye region image data for one or more viewers within a field of view of at least one camera in proximity to a 3D-enabled digital display;” [0008-0009] “FIG. 1 depicts a system environment in which a single 3D display and associated cameras may predict gaze or point-of-regard for multiple viewers for personalized rendering of a 3D projected image of an object.” [0030] “FIG. 1 shows a plurality of cameras 110 (shown above the 3D display 100), and three viewers represented in the figure by the glasses shown looking at a 3D-rendered object being displayed (the soccer ball). It is noted that based on where the viewers are located relative to the display, the cameras may receive image data at different angles and distances for the different viewers.” [0049]) Drozdov also teaches generating, by a holographic image generator of the PDS based on the images of the physical object, a 3-D image of the physical object; (“FIG. 1 depicts a system environment in which a single 3D display and associated cameras may predict gaze or point-of-regard for multiple viewers for personalized rendering of a 3D projected image of an object.” [0030] “A 3D-enabled digital display, or simply a 3D display 100, refers to a display that generates three-dimensional (3D) output for a viewer, for example, one that uses lenticular lenses.” [0046] “Infrared laser displays focus light on a point in space, generating a plasma that emits visible light. Holographic displays implement a multi-directional backlight that enable a wide parallax angle view to display 3D images. Integral imaging displays implement an array of microlenses in front of an image and reproduces a 3D light field that exhibit parallax as the viewer moves. Compressive light field displays implement layered panels that are algorithm-driven to generate 3D content for the viewer. The 3D display 100 may implement any of these and/or a wide variety of technologies now known or later developed.” [0048]) Drozdov further teaches computing, by a positional frame set computation component of the PDS based on the 3-D image of the physical object, sets of image frames, (“FIG. 1 depicts a system environment in which a single 3D display and associated cameras may predict gaze or point-of-regard for multiple viewers for personalized rendering of a 3D projected image of an object.” [0030] “FIG. 8 is a flowchart that shows a method for projecting multi-viewer-specific 3D object perspectives, according to some embodiments of the present disclosure. At 800, the method may include obtaining face image data and eye region image data for one or more viewers within a field of view of at least one camera in proximity to a 3D-enabled digital display. The camera may be integrated into with the 3D display or provided separately. More than one camera may be implemented, for example, to combine input data from multiple vantage points. At 802, the method may include detecting face and eye landmarks for the one or more viewers in one or more image frames based on the face image data.” [0068] “For each frame, the facial landmark detector may compute additional frames. For example, frames may be computed where the face bounding box is slightly moved in a random direction, in order to prevent the model from being limited to facial landmarks that are in the middle of a frame.” [0094] “The face detection algorithm may be further used to compute head pose in six degrees of freedom (DOF). Some exemplary methods for estimating head pose localization and angular orientation can be a detector array method (DAM), in which a series of head detectors are trained, each configured to classify a specific pose and assign a discrete pose to the detector with the greatest support, a technique using machine learning and neural networks. This method can be supplanted or replaced by Nonlinear Regression Methods (NRM), which estimates head pose by learning a nonlinear functional mapping from the image space to one or more pose directions, normally using regression tools and neural networks. Additional methods can be, for example: a flexible algorithm, in which a non-rigid model is fit to the facial structure of the user in the image and wherein head pose is estimated from feature-level comparisons or from the instantiation of the parameters, using the location of extracted features such as the eyes, mouth, and nose tip to determine pose from their relative configuration, recovering the global pose change of the head from the observed movement between video frames then using weighted least squares on particle filtering to discern the head pose.” [0113]) Drozdov teaches determining, by a viewer vantage point and eye position monitor of the PDS, a vantage point of a viewer with respect to a display device of the PDS; (“FIG. 1 depicts a system environment in which a single 3D display and associated cameras may predict gaze or point-of-regard for multiple viewers for personalized rendering of a 3D projected image of an object.” [0030] “FIG. 8 is a flowchart that shows a method for projecting multi-viewer-specific 3D object perspectives, according to some embodiments of the present disclosure. At 800, the method may include obtaining face image data and eye region image data for one or more viewers within a field of view of at least one camera in proximity to a 3D-enabled digital display. The camera may be integrated into with the 3D display or provided separately. More than one camera may be implemented, for example, to combine input data from multiple vantage points. …the 3D eye position may include the distance of the viewer's eye from the 3D display, or the location of the viewer's eye ball(s) in an x, y, z coordinate reference grid including the 3D display.” [0068-0069] “the method may include acquiring eye region image data of a plurality of viewers within a field of view of at least one camera associated with a 3D-enabled digital display. The field of view may be defined in two-dimensional or three-dimensional space, such as from side-to-side, top-to-bottom, and far or near. The method may include analyzing the eye region image data to determine at least one 3D eye position, at least one gaze angle, and at least one point-of-regard for at least one viewer relative to at least one camera associated with the 3D-enabled digital display, from which to estimate gaze direction or PoR. Input from more than one source (e.g., multiple cameras) may be received. In some embodiments, the method may include analyzing the eye region image data for at least one of engagement with the 3D-enabled digital display, fixation, or saccade.” [0079]) Drozdov also teaches selecting a set of image frames from among the sets of image frames to be displayed on the display device of the PDS based on the vantage point of the viewer with respect to the display device; and displaying the set of image frames on the display device of the PDS. (“FIG. 1 depicts a system environment in which a single 3D display and associated cameras may predict gaze or point-of-regard for multiple viewers for personalized rendering of a 3D projected image of an object.” [0030] “FIG. 8 is a flowchart that shows a method for projecting multi-viewer-specific 3D object perspectives, according to some embodiments of the present disclosure. At 800, the method may include obtaining face image data and eye region image data for one or more viewers within a field of view of at least one camera in proximity to a 3D-enabled digital display. The camera may be integrated into with the 3D display or provided separately. More than one camera may be implemented, for example, to combine input data from multiple vantage points. At 802, the method may include detecting face and eye landmarks for the one or more viewers in one or more image frames based on the face image data.” [0068] “FIG. 9 is a flowchart that shows a method for selecting image data to be used in 3D image projection, according to some embodiments of the present disclosure. At 900, the method may include determining, based on image data from one or more cameras in proximity to the 3D-enabled digital display a) one or more facial landmarks of each of the one or more viewers of the 3D-enabled digital display 902; b) a point of regard (PoR) of each eye of each of one or more viewers of a 3D-enabled digital display 904; and c) a position of each eye of each of the one or more viewers relative to the 3D-enabled digital display 906.” [0083] “the method may include selecting, based on the analyzing, image data for each eye of each of the one or more viewers of the 3D-enabled digital display.” [0085] “the method may include rendering the plurality of image projections for respective viewers on the single 3D display.” [0089])
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Drozdov does not explicitly teach each set of image frames represents the physical object from different vantage points; This is what Tsang teaches (“a viewer observing the 3-D holographic scene 812 can experience the depth and the parallax or disparity, for example, when changing viewing positions with respect to the 3-D holographic scene 812 (e.g., as the observer views the 3-D holographic image(s) from different vantage points as the observer walks around the display area or otherwise shifts his viewing position in relation to the display area).” [0096] “the four HPMs 1002, 1004, 1006 and 1008 can project four respective holographic 3-D scene (e.g., front view image portion, left-side view image portion, back view image portion, and right-side view image portion of the 3-D holographic reproduction of the original 3-D object scene) to the front direction, left-side direction, back direction, and right-side direction. The respective portions of the holographic 3-D scene (e.g., partial images from the respective display components that when combined form a whole 3-D holographic image that is part of the 3-D holographic scene), that is, the respective optical waves of the 3-D holographic images, that are projected from (e.g., emerging from) the respective display components 1010, 1012, 1014 and 1016 can be reflected, at least partially, off of the respective reflector components 1018, 1020, 1022 and 1024 so that the respective holographic 3-D image portions can be displayed together as a whole 3-D image of the 3-D holographic scene in a display area 1026 (e.g., 3-D chamber), which can be in an area located outside of the four HPMs 1002, 1004, 1006 and 1008, and which can comprise, for example, a front display section, left-side display section, back display section and right-side display section that respectively can display the respective 3-D holographic image portions projected from the four HPMs 1002, 1004, 1006 and 1008, to facilitate presenting the 3-D holographic image representing a portion (e.g., a frame, a moment in time, a section, a segment) of the original 3-D object scene as a whole 3-D holographic image to viewers. The reflector components 1018, 1020, 1022 and 1024 can be angled at a desired angle in relation to the display components 1010, 1012, 1014 and 1016 to facilitate accurate reflection of the respective holographic 3-D image portions to the desired positions (e.g., front, left side, back, right side) in the display area 1026.” [0102]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Tsang into Drozdov, in order to improve quality of the display of the 3-D integrated image.
7. With reference to claim 2, Drozdov teaches selecting the set of image frames from among the sets of image frames to be displayed on the display device of the PDS based on the vantage point of the viewer with respect to the display device comprises: selecting a first set of image frames from among the sets of image frames when it is determined that the viewer is at a first vantage point that is toward a left side of the display device; selecting a second set of image frames from among the sets of image frames when it is determined that the viewer is at a second vantage point that is directly in front of a center of the display device; and selecting a third set of image frames from among the sets of image frames when it is determined that the viewer is at a third vantage point that is toward a right side of the display device. (“FIG. 1 depicts a system environment in which a single 3D display and associated cameras may predict gaze or point-of-regard for multiple viewers for personalized rendering of a 3D projected image of an object.” [0030] “projecting multi-viewer-object 3D image perspectives from a single 3D display is achieved by acquiring eye region image data of a plurality of viewers within a field of view of at least one camera associated with a 3D-enabled digital display. Trained neural networks are then used to calculate point-of-regard for each viewer, and projections can then be calculated and rendered based on each viewer's position and point-of-regard with respect to the 3D-enabled digital display.” [0045] “FIG. 8 is a flowchart that shows a method for projecting multi-viewer-specific 3D object perspectives, according to some embodiments of the present disclosure. At 800, the method may include obtaining face image data and eye region image data for one or more viewers within a field of view of at least one camera in proximity to a 3D-enabled digital display. The camera may be integrated into with the 3D display or provided separately. More than one camera may be implemented, for example, to combine input data from multiple vantage points. At 802, the method may include detecting face and eye landmarks for the one or more viewers in one or more image frames based on the face image data.” [0068] “the method may include acquiring eye region image data of a plurality of viewers within a field of view of at least one camera associated with a 3D-enabled digital display. The field of view may be defined in two-dimensional or three-dimensional space, such as from side-to-side, top-to-bottom, and far or near. The method may include analyzing the eye region image data to determine at least one 3D eye position, at least one gaze angle, and at least one point-of-regard for at least one viewer relative to at least one camera associated with the 3D-enabled digital display, from which to estimate gaze direction or PoR. Input from more than one source (e.g., multiple cameras) may be received.” [0079] “FIG. 9 is a flowchart that shows a method for selecting image data to be used in 3D image projection, according to some embodiments of the present disclosure. At 900, the method may include determining, based on image data from one or more cameras in proximity to the 3D-enabled digital display a) one or more facial landmarks of each of the one or more viewers of the 3D-enabled digital display 902; b) a point of regard (PoR) of each eye of each of one or more viewers of a 3D-enabled digital display 904; and c) a position of each eye of each of the one or more viewers relative to the 3D-enabled digital display 906.” [0083] “the method may include selecting, based on the analyzing, image data for each eye of each of the one or more viewers of the 3D-enabled digital display.” [0085])
8. With reference to claim 3, Drozdov teaches the viewer vantage point and eye position monitor of the PDS includes sensors that track the vantage point of the viewer as the viewer moves about in front of the display device. (“FIG. 1 depicts a system environment in which a single 3D display and associated cameras may predict gaze or point-of-regard for multiple viewers for personalized rendering of a 3D projected image of an object.” [0030] “FIG. 8 is a flowchart that shows a method for projecting multi-viewer-specific 3D object perspectives, according to some embodiments of the present disclosure. At 800, the method may include obtaining face image data and eye region image data for one or more viewers within a field of view of at least one camera in proximity to a 3D-enabled digital display. The camera may be integrated into with the 3D display or provided separately. More than one camera may be implemented, for example, to combine input data from multiple vantage points. …the 3D eye position may include the distance of the viewer's eye from the 3D display, or the location of the viewer's eye ball(s) in an x, y, z coordinate reference grid including the 3D display.” [0068-0069] “the method may include determining a number of projections and a distribution of projections for each eye of each of the one or more viewers based on the eye tracking information. In some embodiments, at least one of the plurality of projections may be calculated to be appropriate for each respective viewer's position and point-of-regard relative to the 3D-enabled digital display. … a viewer may move or turn at an angle to the camera, reducing the quality of image data captured by a particular camera. In some embodiments, the method may include switching to a different camera based on the degradation in the eye region image data. For example, another camera may have a better view of the viewer as the viewer turns his or her head or walks toward or away from the camera.” [0076-0077] “the method may include acquiring eye region image data of a plurality of viewers within a field of view of at least one camera associated with a 3D-enabled digital display. The field of view may be defined in two-dimensional or three-dimensional space, such as from side-to-side, top-to-bottom, and far or near. The method may include analyzing the eye region image data to determine at least one 3D eye position, at least one gaze angle, and at least one point-of-regard for at least one viewer relative to at least one camera associated with the 3D-enabled digital display, from which to estimate gaze direction or PoR. Input from more than one source (e.g., multiple cameras) may be received. In some embodiments, the method may include analyzing the eye region image data for at least one of engagement with the 3D-enabled digital display, fixation, or saccade.” [0079])
9. Claims 4-6 are similar in scope to claims 1-3, and they are rejected under similar rationale.
10. With reference to claim 7, Drozdov teaches the sensors detect one or more of body position, body orientation, body posture, eye position, or eye orientation of the viewer. (“FIG. 8 is a flowchart that shows a method for projecting multi-viewer-specific 3D object perspectives, according to some embodiments of the present disclosure. At 800, the method may include obtaining face image data and eye region image data for one or more viewers within a field of view of at least one camera in proximity to a 3D-enabled digital display. The camera may be integrated into with the 3D display or provided separately. More than one camera may be implemented, for example, to combine input data from multiple vantage points. …the 3D eye position may include the distance of the viewer's eye from the 3D display, or the location of the viewer's eye ball(s) in an x, y, z coordinate reference grid including the 3D display.” [0068-0069] “the method may include determining a number of projections and a distribution of projections for each eye of each of the one or more viewers based on the eye tracking information. In some embodiments, at least one of the plurality of projections may be calculated to be appropriate for each respective viewer's position and point-of-regard relative to the 3D-enabled digital display.” [0076])
11. With reference to claim 8, Drozdov teaches determining the vantage point of the viewer with respect to the display device is based on output of a vantage point predictive engine (VPPE) that uses a predictive model and parameters for the viewer to compute likelihoods for next possible vantage points of the viewer with respect to the display device. (“FIG. 1 depicts a system environment in which a single 3D display and associated cameras may predict gaze or point-of-regard for multiple viewers for personalized rendering of a 3D projected image of an object.” [0030] “the system may use deep learning to model the whole left eye and whole right eye, as well as the position of the eyes relative to the display, and eye state, gaze angle, and point of regard. Once these models are built for each viewer, giving position of the eyes in space relative to the display, and a good point of regard estimate, dynamic facial landmark detection is used to maintain a stable modeling of both eyes over time so that 3D projections are as uninterrupted as possible.” [0056-0057] “Additional parameters that the camera selector algorithm can evaluate include viewer distance and angle relative to the display screen.” [0063] “The display may be configured for object rendering, left/right view projection to the user, and next view estimation … the next view prediction involves the rendering engine preparing a 3D object or portion of a 3D object ahead of time, to be cached for later projection and viewing. FIG. 8 is a flowchart that shows a method for projecting multi-viewer-specific 3D object perspectives, according to some embodiments of the present disclosure. At 800, the method may include obtaining face image data and eye region image data for one or more viewers within a field of view of at least one camera in proximity to a 3D-enabled digital display. The camera may be integrated into with the 3D display or provided separately. More than one camera may be implemented, for example, to combine input data from multiple vantage points.” [0067-0068] “the method may include acquiring eye region image data of a plurality of viewers within a field of view of at least one camera associated with a 3D-enabled digital display. The field of view may be defined in two-dimensional or three-dimensional space, such as from side-to-side, top-to-bottom, and far or near. The method may include analyzing the eye region image data to determine at least one 3D eye position, at least one gaze angle, and at least one point-of-regard for at least one viewer relative to at least one camera associated with the 3D-enabled digital display, from which to estimate gaze direction or PoR. Input from more than one source (e.g., multiple cameras) may be received. In some embodiments, the method may include analyzing the eye region image data for at least one of engagement with the 3D-enabled digital display, fixation, or saccade.” [0079])
12. With reference to claim 9, Drozdov teaches the output of the VPPE of one or more sets of possible vantage point parameters is provided as input to a positional frame set computation module, which uses the input to compute the sets of image frames. (“FIG. 1 depicts a system environment in which a single 3D display and associated cameras may predict gaze or point-of-regard for multiple viewers for personalized rendering of a 3D projected image of an object.” [0030] “the system may use deep learning to model the whole left eye and whole right eye, as well as the position of the eyes relative to the display, and eye state, gaze angle, and point of regard. Once these models are built for each viewer, giving position of the eyes in space relative to the display, and a good point of regard estimate, dynamic facial landmark detection is used to maintain a stable modeling of both eyes over time so that 3D projections are as uninterrupted as possible.” [0056-0057] “Additional parameters that the camera selector algorithm can evaluate include viewer distance and angle relative to the display screen.” [0063] “The display may be configured for object rendering, left/right view projection to the user, and next view estimation … the next view prediction involves the rendering engine preparing a 3D object or portion of a 3D object ahead of time, to be cached for later projection and viewing. FIG. 8 is a flowchart that shows a method for projecting multi-viewer-specific 3D object perspectives, according to some embodiments of the present disclosure. At 800, the method may include obtaining face image data and eye region image data for one or more viewers within a field of view of at least one camera in proximity to a 3D-enabled digital display. The camera may be integrated into with the 3D display or provided separately. More than one camera may be implemented, for example, to combine input data from multiple vantage points.” [0067-0068] “The method may include analyzing the eye region image data to determine at least one 3D eye position, at least one gaze angle, and at least one point-of-regard for at least one viewer relative to at least one camera associated with the 3D-enabled digital display, from which to estimate gaze direction or PoR. Input from more than one source (e.g., multiple cameras) may be received. In some embodiments, the method may include analyzing the eye region image data for at least one of engagement with the 3D-enabled digital display, fixation, or saccade.” [0079] “For each frame, the facial landmark detector may compute additional frames. For example, frames may be computed where the face bounding box is slightly moved in a random direction, in order to prevent the model from being limited to facial landmarks that are in the middle of a frame.” [0094] “The face detection algorithm may be further used to compute head pose in six degrees of freedom (DOF). Some exemplary methods for estimating head pose localization and angular orientation can be a detector array method (DAM), in which a series of head detectors are trained, each configured to classify a specific pose and assign a discrete pose to the detector with the greatest support, a technique using machine learning and neural networks. This method can be supplanted or replaced by Nonlinear Regression Methods (NRM), which estimates head pose by learning a nonlinear functional mapping from the image space to one or more pose directions, normally using regression tools and neural networks. Additional methods can be, for example: a flexible algorithm, in which a non-rigid model is fit to the facial structure of the user in the image and wherein head pose is estimated from feature-level comparisons or from the instantiation of the parameters, using the location of extracted features such as the eyes, mouth, and nose tip to determine pose from their relative configuration, recovering the global pose change of the head from the observed movement between video frames then using weighted least squares on particle filtering to discern the head pose.” [0113])
13. With reference to claim 10, Drozdov teaches authenticating the viewer based on physical features of the viewer. (“Additional deep learning blocks use each bounding box/face patch, in the image plane, to perform facial analysis to generate a set of facial landmarks for each viewer. Additional deep learning blocks then use eye region data and head pose data: X, Y, Z, yaw, pitch, and roll, which are the six degrees of freedom (6DOF) of the head (assumed to be a rigid body), to perform dynamic facial analysis to generate eye localization, eye state, point of regard, gaze direction, and eye patch illumination information.” [0052-0053])
14. With reference to claim 11, Drozdov teaches the physical features include one or more of a fingerprint, a palmprint, a facial feature, or a retinal pattern. (“Additional deep learning blocks use each bounding box/face patch, in the image plane, to perform facial analysis to generate a set of facial landmarks for each viewer. Additional deep learning blocks then use eye region data and head pose data: X, Y, Z, yaw, pitch, and roll, which are the six degrees of freedom (6DOF) of the head (assumed to be a rigid body), to perform dynamic facial analysis to generate eye localization, eye state, point of regard, gaze direction, and eye patch illumination information.” [0052-0053])
15. Claim 15 is similar in scope to claim 4, and thus is rejected under similar rationale. Drozdov additionally teaches A computing system comprising: one or more processors; and memory storing instructions that, upon execution by the one or more processors, cause the computing system to (“Computing platform(s) 402 may include non-transitory electronic storage 430 operable to store any of machine readable instructions 406-426, one or more processors 432, and/or other components.” [0135] “Those having ordinary skill in the art will recognize that a data processing system generally includes one or more of a system unit housing, a video display device, memory such as volatile or non-volatile memory, processors such as microprocessors or digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interaction devices (e.g., a touch pad, a touch screen, an antenna, etc.), and/or control systems including feedback loops and control motors (e.g., feedback for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities).” [0174])
16. Claims 16-20 are similar in scope to claims 5-9, and they are rejected under similar rationale.
17. Claim(s) 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drozdov et al. (US 2024/0121377 A1) and Tsang (US 2012/0314021 A1), as applied to claim 4 above, and further in view of Chan et al. (US 2020/0137460 A1).
18. With reference to claim 12, the combination of Drozdov and Tsang does not explicitly teach encrypting the sets of image frames and decrypting the set of image frames prior to displaying the set of image frames on the display device. This is what Chan teaches (“the demultiplexer and authentication process is illustrated in which a multimedia container file is received and portions of which are identified or separated (101). If encryption data is identified, cryptographic packets or material are generated (102) and stored in a temporary buffer (103). However, if video data is identified (104), the cryptographic material stored in the temporary buffer is combined with the video data (105) and then transmitted to a video decoder (106). If audio data is identified (107), the audio data is transmitted (108) to the audio decoder. It should be appreciated that audio or other types of data may also include encryption data and thus associated cryptographic material is generated and combined with the associated data and transmitted to the respective decoder. … a decoder and decipher process is illustrated in which the decoder receives video and/or audio data sent from the demultiplexer (201). The decoder deciphers the cryptographic material supplied with the associated data (202). Utilizing the deciphered material, the encrypted data is decrypted (203) and decoded (204) by the decoder for playback.” [0056-0057] “The frame payload indicates that the current frame is encrypted and includes key information and a reference to at least a portion of the encoded frame that is encrypted. The frame payload can be used to decrypt the video frame. The synchronization payload is the first packet sent to synchronize the authentication engine of the demultiplexer to the decrypt engine of the decoder. This synchronization ensures that data transmitted from the demultiplexer to the decoder is not being intercepted. The wrap key includes information to unwrap or decipher the transmitted data from the demultiplexer.” [0062] “identifying the encrypted portion of the partially encrypted frame using the block reference; decrypting the encrypted portion of the partially encrypted frame using the frame key and the video decoder; and decoding the decrypted portion of the frame for rendering on a display device using the video decoder.” claim 1) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Chan into the combination of Drozdov and Tsang, in order to protect digital visual data from unauthorized access.
19. With reference to claim 13, Drozdov teaches authenticating the viewer. (“Additional deep learning blocks use each bounding box/face patch, in the image plane, to perform facial analysis to generate a set of facial landmarks for each viewer. Additional deep learning blocks then use eye region data and head pose data: X, Y, Z, yaw, pitch, and roll, which are the six degrees of freedom (6DOF) of the head (assumed to be a rigid body), to perform dynamic facial analysis to generate eye localization, eye state, point of regard, gaze direction, and eye patch illumination information.” [0052-0053])
The combination of Drozdov and Tsang does not explicitly teach authenticating prior to decrypting the set of image frames. This is what Chan teaches (“the demultiplexer and authentication process is illustrated in which a multimedia container file is received and portions of which are identified or separated (101). If encryption data is identified, cryptographic packets or material are generated (102) and stored in a temporary buffer (103). However, if video data is identified (104), the cryptographic material stored in the temporary buffer is combined with the video data (105) and then transmitted to a video decoder (106). If audio data is identified (107), the audio data is transmitted (108) to the audio decoder. It should be appreciated that audio or other types of data may also include encryption data and thus associated cryptographic material is generated and combined with the associated data and transmitted to the respective decoder. … a decoder and decipher process is illustrated in which the decoder receives video and/or audio data sent from the demultiplexer (201). The decoder deciphers the cryptographic material supplied with the associated data (202). Utilizing the deciphered material, the encrypted data is decrypted (203) and decoded (204) by the decoder for playback.” [0056-0057] “The frame payload indicates that the current frame is encrypted and includes key information and a reference to at least a portion of the encoded frame that is encrypted. The frame payload can be used to decrypt the video frame. The synchronization payload is the first packet sent to synchronize the authentication engine of the demultiplexer to the decrypt engine of the decoder. This synchronization ensures that data transmitted from the demultiplexer to the decoder is not being intercepted. The wrap key includes information to unwrap or decipher the transmitted data from the demultiplexer.” [0062]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Chan into the combination of Drozdov and Tsang, in order to protect digital visual data from unauthorized access.
20. Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drozdov et al. (US 2024/0121377 A1) and Tsang (US 2012/0314021 A1), as applied to claim 4 above, and further in view of Taylor (US 2020/0374504 A1).
21. With reference to claim 14, the combination of Drozdov and Tsang does not explicitly teach image frames of the image frame set are layered with respect to one another, such that one of the image frames partially hides another one of the image frames on the display device, thereby effecting a 3-D topology on the display device that mimics 3-D surface features of the physical object. This is what Taylor teaches (“a method for displaying a three dimensional (“3D”) image in a blended mode includes rendering a frame of 3D image data. The method also includes analyzing the frame of 3D image data to generate depth data. The method further includes using the depth data to segment the 3D image data into a plurality of frames of two dimensional (“2D”) image data. Moreover, the method includes displaying the plurality of frames. In addition, the plurality of frames includes a left near frame of 2D image data corresponding to a near depth, a left far frame of 2D image data corresponding to a far depth that is farther than the near depth from a point of view, a right near frame of 2D image data corresponding to the near depth, and a right far frame of 2D image data corresponding to the far depth. The left near frame and the left far frame are displayed simultaneously. The right near frame and the right far frame are displayed simultaneously.” [0016] “the left near frame and the right near frame are displayed to a user at a first depth from the user. The left far frame and the right far frame may be displayed to the user at a second depth from the user, the second depth being greater than the first depth. The first and second depths correspond to about 1.96 and about 0.67 diopters respectively. When the left near frame, the left far frame, the right near frame, and the right far frame are displayed to a user, the user may perceive a 3D image. The 3D image may correspond to the frame of 3D image data.” [0018]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Taylor into the combination of Drozdov and Tsang, in order to render and display 3D images to viewers/users while minimize vergence-accommodation conflict.
Conclusion
22. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle Chin whose telephone number is (571)270-3697. The examiner can normally be reached on Monday-Friday 8:00 AM-4:30 PM.
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/MICHELLE CHIN/
Primary Examiner, Art Unit 2614
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