Notice of Pre-AIA or AIA Status
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) do not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to CRM, the spec [0091] and [0097] uses the term “maybe” and “not limited to” examples. By broadest reasonable interpretation, it will include signal.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) do not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to computer program per se.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 7, 9, 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Ali (US-20220277489-A1) in view of Shen (US-20190332182-A1) and in further view of Zohar (US-20230090645-A1).
Regarding claim 1, Ali teaches An image display method, applied to a head-mounted device on which a camera module is provided ( Para 49, 52: teaches an image processing system containing an image sensor, image processing engine, modeling engine , and rendering engine. The system may be part of an XR device, including a head mounted display or smart glasses), wherein the method comprises:determining, in response to receiving a realistic scene image which is acquired by the camera module and comprises a hand object ( Para 65 and 89: teaches receiving a monocular image captured by an image sensor, detecting a hand in the image and determining the hand real world coordinates and depth information);, wherein the target mesh information is used to represent three-dimensional spatial information corresponding to the hand object (Para 72 and 90-94: teaches generating mesh parameters and feature embeddings for a detected hand from the captured hand image. The mesh information includes 3d key points, rotations, translation, pose, shape, and texture parameters and is used to generate a 3d mesh model of the hand in real world coordinates);cropping a target image corresponding to the hand object from the realistic scene image ( Para 89-92: teaches detecting the hand in the monocular image and cropping the image to include the hand while excluding other portions. The cropped hand image is then processed to generate mesh parameters and a 3D hand model).
Shen teaches whether the hand object overlaps with a virtual screen at a preset spatial position ( Para 137-140, 214 and 223-230: teaches a virtual 2d interface/menu panel positioned within a vr environment. The panel moves to a predetermined target position and then remains at a fixed position. Shen further determines a virtual hand gesture position based on the motion of a real hand and detects whether the gesture position coordinates overlap the virtual article/ menu position coordinates). selecting target mesh information corresponding to the hand object from three-dimensional mesh information corresponding to the realistic scene image when the hand object overlaps with the virtual screen ( Para 80-81 and 137-142: teaches that its gesture object represents a virtual hand generated from a 3d network model. When the virtual hand position overlaps the virtual article position, the system switches to and displays a selected hand gesture object.). and displaying the virtual screen and the three-dimensional hand image on a display interface of the head-mounted device, based on the preset spatial position corresponding to the virtual screen and the three-dimensional hand image corresponding to the hand object( Para 56-61, 137-142: teaches an hmd that displays a 3d virtual environment, a virtual hand representation and a virtual 2d menu panel. The menu is placed at a predetermined fixed position while the virtual hand is displayed according to a position corresponding to the user’s real world hand/ handle position. It would have been obvious to incorporate Shen’s virtual screen positioning and hand overlap detection into Ali XR had modeling system to permit camera-derived 3d hand to interact accurately with spatially positioned virtual interface content.).
Zohar teaches combining the target image with the target mesh information to obtain a three-dimensional hand image corresponding to the hand object ( Para 37, 40-41 and 64: teaches generating a mesh using 3d body mesh and texture corresponding to the person depicted in the image. It obtains a uv map representing pixels of the person and projects the 2d image pixels onto the 3d mesh surface to generate its texture. Zohar further teaches blending the image pixels into the mesh based AR representation and expressly identifies a hand as a body part to which the technique may be applied. It would have been obvious to apply Zohar uv texture mapping technique to Ali in view of Shen Cropped hand image system to produce a more realistic textured 3d representation of the user’s hand).
Regarding claim 7, teaches The method according to claim 1, wherein cropping the target image corresponding to the hand object from the realistic scene image comprises:recognizing the hand object in the realistic scene image through an artificial neural network model (Ali, Para 71, teaches network 210 implemented using one or more artificial neural networks, including cnn, resNet, or other neural network architecture. Para 89 specifically detects the hand in the monocular scene image), to obtain a recognition result corresponding to the hand object (Ali, Para.69: teaches determining an object area/bounding box, including its shape, size, and location. That bounding box/location information constitutes the hand recognition result when applied to the hand embodiment ); and cropping the target image corresponding to the hand object from the realistic scene image based on the recognition result corresponding to the hand object ( Ali, Para 89. Expressly crops the monocular image after detecting the hand so that the cropped image contains the hand , excludes other portions, and positions the hand near the center).
Regarding claim 9, Ali in view of Shen and in further view of Zohar teaches The method according to claim 1, further comprising:determining a plurality of trace points corresponding to the hand object (Ali, Para 17, and 95-96: teaches determine multiple 3d hand key points corresponding to joints, bones, or other points of interest and determine their 3D locations and visibility), and performing trajectory tracking on the plurality of trace points to obtain an operation instruction of the hand object for a virtual object ( Ali, Para 43, identifies motion tracking as an operation performed using its 3D models. Shen, Para 54 teaches tracking a user’s hand and finger movement. Para 57-78: determines hand posture, movement direction, movement distance, rotation, and finger position, and 95-99 use the resulting motion input to determine gesture and ray positions relative to a virtual article. Applying Shen’s tracking to Ali’s plurality of hand key points would track their successive positions and translate the resulting trajectory into a gesture operation instruction for the virtual object); and controlling the virtual object to execute an operation corresponding to the operation instruction(Shen’, Para 168-172: detect user operations, instruct a corresponding virtual object to move to a target menu object, and perform the processing operation associated with the menu object. Para 179-180 and 198: further explain that the virtual object or virtual hand is controlled according to the detected real world movement direction and distance).
Regarding claim 12, Ali teaches An image display apparatus, applied to a head-mounted device on which a camera module is provided, wherein the apparatus comprises:a receiving module ( Para 49, 52: teaches an image processing system containing an image sensor, image processing engine, modeling engine , and rendering engine. The system may be part of an XR device, including a head mounted display or smart glasses), configured to determine, in response to receiving a realistic scene image which is acquired by the camera module and comprises a hand object ( Para 65 and 89: teaches receiving a monocular image captured by an image sensor, detecting a hand in the image and determining the hand real world coordinates and depth information);, wherein the target mesh information is used to represent three-dimensional spatial information corresponding to the hand object (Para 72 and 90-94: teaches generating mesh parameters and feature embeddings for a detected hand from the captured hand image. The mesh information includes 3d key points, rotations, translation, pose, shape, and texture parameters and is used to generate a 3d mesh model of the hand in real world coordinates);cropping a target image corresponding to the hand object from the realistic scene image ( Para 89-92: teaches detecting the hand in the monocular image and cropping the image to include the hand while excluding other portions. The cropped hand image is then processed to generate mesh parameters and a 3D hand model).
Shen teaches whether the hand object overlaps with a virtual screen at a preset spatial position ( Para 137-140, 214 and 223-230: teaches a virtual 2d interface/menu panel positioned within a vr environment. The panel moves to a predetermined target position and then remains at a fixed position. Shen further determines a virtual hand gesture position based on the motion of a real hand and detects whether the gesture position coordinates overlap the virtual article/ menu position coordinates). selecting target mesh information corresponding to the hand object from three-dimensional mesh information corresponding to the realistic scene image when the hand object overlaps with the virtual screen ( Para 80-81 and 137-142: teaches that its gesture object represents a virtual hand generated from a 3d network model. When the virtual hand position overlaps the virtual article position, the system switches to and displays a selected hand gesture object.). and displaying the virtual screen and the three-dimensional hand image on a display interface of the head-mounted device, based on the preset spatial position corresponding to the virtual screen and the three-dimensional hand image corresponding to the hand object( Para 56-61, 137-142: teaches an hmd that displays a 3d virtual environment, a virtual hand representation and a virtual 2d menu panel. The menu is placed at a predetermined fixed position while the virtual hand is displayed according to a position corresponding to the user’s real world hand/ handle position. It would have been obvious to incorporate Shen’s virtual screen positioning and hand overlap detection into Ali XR had modeling system to permit camera-derived 3d hand to interact accurately with spatially positioned virtual interface content.).
Zohar teaches combining the target image with the target mesh information to obtain a three-dimensional hand image corresponding to the hand object ( Para 37, 40-41 and 64: teaches generating a mesh using 3d body mesh and texture corresponding to the person depicted in the image. It obtains a uv map representing pixels of the person and projects the 2d image pixels onto the 3d mesh surface to generate its texture. Zohar further teaches blending the image pixels into the mesh based AR representation and expressly identifies a hand as a body part to which the technique may be applied. It would have been obvious to apply Zohar uv texture mapping technique to Ali in view of Shen Cropped hand image system to produce a more realistic textured 3d representation of the user’s hand).
Regarding claim 13, Ali teaches An electronic device, comprising a processor and a memory,wherein the memory is in communication with the processor,the memory stores computer-executable instructions, andthe computer-executable instructions, when executed by the processor, cause the processor to implement an image display method applied to a head-mounted device on which a camera module is provided, which comprises ( Para 49, 52: teaches an image processing system containing an image sensor, image processing engine, modeling engine , and rendering engine. The system may be part of an XR device, including a head mounted display or smart glasses):determining, in response to receiving a realistic scene image which is acquired by the camera module and comprises a hand object( Para 65 and 89: teaches receiving a monocular image captured by an image sensor, detecting a hand in the image and determining the hand real world coordinates and depth information);, wherein the target mesh information is used to represent three-dimensional spatial information corresponding to the hand object (Para 72 and 90-94: teaches generating mesh parameters and feature embeddings for a detected hand from the captured hand image. The mesh information includes 3d key points, rotations, translation, pose, shape, and texture parameters and is used to generate a 3d mesh model of the hand in real world coordinates);cropping a target image corresponding to the hand object from the realistic scene image ( Para 89-92: teaches detecting the hand in the monocular image and cropping the image to include the hand while excluding other portions. The cropped hand image is then processed to generate mesh parameters and a 3D hand model).
Shen teaches whether the hand object overlaps with a virtual screen at a preset spatial position ( Para 137-140, 214 and 223-230: teaches a virtual 2d interface/menu panel positioned within a vr environment. The panel moves to a predetermined target position and then remains at a fixed position. Shen further determines a virtual hand gesture position based on the motion of a real hand and detects whether the gesture position coordinates overlap the virtual article/ menu position coordinates). selecting target mesh information corresponding to the hand object from three-dimensional mesh information corresponding to the realistic scene image when the hand object overlaps with the virtual screen ( Para 80-81 and 137-142: teaches that its gesture object represents a virtual hand generated from a 3d network model. When the virtual hand position overlaps the virtual article position, the system switches to and displays a selected hand gesture object.). and displaying the virtual screen and the three-dimensional hand image on a display interface of the head-mounted device, based on the preset spatial position corresponding to the virtual screen and the three-dimensional hand image corresponding to the hand object( Para 56-61, 137-142: teaches an hmd that displays a 3d virtual environment, a virtual hand representation and a virtual 2d menu panel. The menu is placed at a predetermined fixed position while the virtual hand is displayed according to a position corresponding to the user’s real world hand/ handle position. It would have been obvious to incorporate Shen’s virtual screen positioning and hand overlap detection into Ali XR had modeling system to permit camera-derived 3d hand to interact accurately with spatially positioned virtual interface content.).
Zohar teaches combining the target image with the target mesh information to obtain a three-dimensional hand image corresponding to the hand object ( Para 37, 40-41 and 64: teaches generating a mesh using 3d body mesh and texture corresponding to the person depicted in the image. It obtains a uv map representing pixels of the person and projects the 2d image pixels onto the 3d mesh surface to generate its texture. Zohar further teaches blending the image pixels into the mesh based AR representation and expressly identifies a hand as a body part to which the technique may be applied. It would have been obvious to apply Zohar uv texture mapping technique to Ali in view of Shen Cropped hand image system to produce a more realistic textured 3d representation of the user’s hand).
Regarding claim 14, Ali in view of Shen and in further view of Zohar teaches A computer-readable storage medium, wherein computer-executable instructions are stored on the computer-readable storage medium, and the computer-executable instructions, when executed by a processor, cause the processor to implement the image display method according to claim 1 ( Ali, Para 5 teaches a computer readable medium).
Regarding claim 15, Ali in view of Shen and in further view of Zohar teaches A computer program product, comprising a computer program,wherein the computer program, when executed by a processor, causes the processor to implement the image display method according to claim 1 ( Ali, Para 49, 52: teaches an image processing system containing an image sensor, image processing engine, modeling engine , and rendering engine. The system may be part of an XR device, including a head mounted display or smart glasses).
Claim(s) 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ali (US-20220277489-A1) in view of Shen (US-20190332182-A1) and in further view of Zohar (US-20230090645-A1) and Clohset (US-20230148279-A1).
Regarding claim 2, Ali in view of Shen and in further view of Zohar teaches The method according to claim 1, wherein displaying the virtual screen and the three- dimensional hand image on the display interface of the head-mounted device based on the preset spatial position corresponding to the virtual screen and the three-dimensional hand image corresponding to the hand object comprises ( Ali para 49, 50, 63, 65: teaches an image processing rendering system that generates and renders a 3d model using real world coordinates, location depth information and a rendering engine. It also contemplates smart glasses. Shen, para 215-221 and 226: describes a 3d game scene and a 3d or 2d scene with a menu functioning as a 2d interface. Also explain integrating mapping that interface into the 3d virtual world. Para 219-221: further discloses a 3d object menu surrounding a virtual hand, where the hand exists in virtual 3d space). But fails to teach determining an occlusion relationship between the virtual screen and the three-dimensional hand image, based on the preset spatial position corresponding to the virtual screen and the three- dimensional hand image corresponding to the hand object; and rendering, based on the occlusion relationship between the virtual screen and the three- dimensional hand image, the virtual screen and the three-dimensional hand image to obtain a composite image, and displaying the composite image on the display interface of the head- mounted device.
Clohset teaches determining an occlusion relationship between the virtual screen and the three-dimensional hand image ( Para 20: determines distances from the user’s hand to the viewpoint. Para 22: compares those distances with the known distances of virtual objects. Para. 26: performs a per pixel height/depth test to decide whether the hand is in front of or behind a virtual object), based on the preset spatial position corresponding to the virtual screen and the three- dimensional hand image corresponding to the hand object ( Para 71: places virtual objects at positions within the coordinate system of the virtual environment. Para 23: generates a three dimensional representation of the hand based on its detected pose. Para 50-52: describe the 3d hand mesh 502, height grid 504 and surface representations 508); and rendering, based on the occlusion relationship between the virtual screen and the three- dimensional hand image ( Para.21: explains that hand portions closer than the virtual objects are made visible while portions behind virtual objects are excluded . Para 53 and 57 determine, for each viewing ray, whether the ray first intersects the hand surface or a virtual object surface.), the virtual screen and the three-dimensional hand image to obtain a composite image and displaying the composite image on the display interface of the head- mounted device ( Para 30-33, 42 and 53-59: teaches combining /rendering the hand with virtual content according to the determined visibility occlusion relationship to produce the resulting image and displaying that image through the hmd display system. It would have been obvious to modify the combination of Ali in view of Shen and in further view of Zohar in view of Clohset to determine and render the proper occlusion relationship between the virtual content and the user’s, thereby providing a more realistic and visually accurate representation of the user’s interaction with virtual objects in the virtual environment).
Claim(s) 8 are rejected under 35 U.S.C. 103 as being unpatentable over Ali (US-20220277489-A1) in view of Shen (US-20190332182-A1) and in further view of Zohar (US-20230090645-A1) Woo (US-20240153133-A1).
Regarding claim 8, Ali in view of Shen and in further view of Zohar teaches The method according to claim 7, wherein cropping the target image corresponding to the hand object from the realistic scene image comprises: but fails to teach selecting, from the realistic scene image, a local scene image comprising the hand object, and cropping the target image corresponding to the hand object from the local scene image, wherein a display scale of the local scene image is greater than a display scale of the realistic scene image.
Woo teaches selecting, from the realistic scene image, a local scene image comprising the hand object ( Para 31: processes full depth and IR scene images estimates the hand center of mass establishes a 3d bounding box centered on that location and selects/resizes the image patch within the bounding box), and cropping the target image corresponding to the hand object from the local scene image ( para 32: applies grabcut within selected bounding box region, identifies foreground/background pixels using the segmented depth image, and then crops the segmented IR hand image, and then crops the segmented IR hand image from that same bounding box), wherein a display scale of the local scene image is greater than a display scale of the realistic scene image (Para.31: resizes the bounded local hand patch to 128x128. It would have been obvious to modify Ali in view of Shen and in further view of Zohar system of hand recognition and cropping process to use Park’s hand centered bounding box preprocessing and foreground segmentation. This technique more accurately isolates the hand for subsequent mesh generation. ).
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Ali (US-20220277489-A1) in view of Shen (US-20190332182-A1) and in further view of Zohar (US-20230090645-A1) and Shamir (US-20180189556-A1).
Regarding claim 10, Ali in view of Shen and in further view of Zohar teaches The method according to claim 9, wherein performing trajectory tracking on the plurality of trace points to obtain the operation instruction of the hand object for the virtual object comprises: but fails to teach performing trajectory tracking on the plurality of trace points to obtain position information respectively corresponding to the plurality of trace points within a preset duration, wherein the position information comprises first position information of the trace points before movement of the trace points, and second position information of the trace points after movement of the trace points; obtaining a movement gesture of the hand object based on the position information respectively corresponding to the plurality of trace points; and determining an operation instruction corresponding to the movement gesture as the operation instruction of the hand object for the virtual object.
Shamir teaches performing trajectory tracking on the plurality of trace points to obtain position information respectively corresponding to the plurality of trace points within a preset duration ( Para 29, 43-44 and 54-55: track a plurality of hand feature points using optical flow across consecutive frames. Para 36-37: stores the last n or k frames, with K selected according to the time required to perform gesture, thereby teaching the preset duration), wherein the position information comprises first position information of the trace points before movement of the trace points, and second position information of the trace points after movement of the trace points ( Para 24: determines corresponding points in a current frame and a next frame. Para 54: finds each feature point in a previous image and its corresponding point in a current image, with the vector between them representing movement); obtaining a movement gesture of the hand object based on the position information respectively corresponding to the plurality of trace points ( Para 29-31: derives motion vectors from the tracked hand points. Para 36-37 combines their movement over multiple frames into a descriptor. Para 39: classifies that descriptor as a particular gesture and outputs a corresponding gesture event); and determining an operation instruction corresponding to the movement gesture as the operation instruction of the hand object for the virtual object ( Para 39: outputs the gesture event after recognizing the movement gesture. Para 12: further teaches using the calculated hand movement to apply movement to a rendered VR/AR presentation. It would have been obvious to incorporate Shamir’s frame to frame feature point tracking and gesture classification technique into the system of Ali in view of Shen and in further view of Zohar to reliably distinguish dynamic hand gestures from tracked hand movement and convert those gestures into control instructions for virtual objects in a extended reality environment).
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Ali (US-20220277489-A1) in view of Shen (US-20190332182-A1) and in further view of Zohar (US-20230090645-A1) and Cok (US-5185808-A).
Regarding claim 11, Ali in view of Shen and in further view of Zohar teaches The method according to claim 1, further comprising:but fails to teach performing feathering processing on an edge region of the three-dimensional hand image, wherein a distance between the edge region and an edge of the three-dimensional hand image is less than a first preset distance; and/or in case of the three-dimensional hand image comprising a hand and an arm, performing transparency processing on a target arm region of the arm, wherein a distance between the target arm region and the hand is greater than a second preset distance.
Cok teaches wherein a distance between the edge region, and an edge of the three-dimensional hand image is less than a first preset distance ( Figs 2.4, claims 1-3 and column 3-5: teaches gradually blending an overlay/paste image with a base image along the paste image’s border to eliminate boundary artifacts. Cok defines a predetermined feathering distance “f”. Only pixels within “f” pixels of the paste-image border are feathered; pixels beyond that distance remain unchanged. Lastly the transparency/weight applied to each pixel varies according to its distance from the border. It would have been obvious to apply Cok’s distance controlled edge feathering technique to the three dimensional hand images of Ali in view of Shen and in further view of Zohar to reduce visibly boundary artifacts and provide a smoother, more realistic transition between the hand image and the surrounding virtual scene. ).
Allowable Subject Matter
Claim 3 is objected to as allowable subject matter. The following is a statement of reasons for the indication of allowable subject matter: None of the prior art teaches such limitations “for each first vertex of the first vertices in the three-dimensional hand image, selecting a target vertex with an identical position as the first vertex in the three-dimensional hand image from the second vertices corresponding to the virtual screen, wherein the identical position indicates identical coordinate values in an X-axis direction and identical coordinate values in a Y-axis direction, wherein when a coordinate value of the first vertex in a Z-axis direction is less than a coordinate value of the target vertex in the Z-axis direction.”.
Clohset is the closest prior at. Clohset performs depth/height comparisons at corresponding projected locations to determine whether the hand or virtual object is occluded. However, it does not select, for each hand mesh vertex, a virtual screen vertex having identical x and y coordinate values and then compare their z coordinates.
Claim 4 and its dependents 5-6 are objected to as allowable subject matter. The following is a statement of reasons for the indication of allowable subject matter: None of the prior art teaches such limitations: determining the three-dimensional mesh information corresponding to the realistic scene image, wherein the three-dimensional mesh information comprises mesh information respectively corresponding to the plurality of pixel regions; and selecting, based on a target pixel region corresponding to the hand object, target mesh information corresponding to the target pixel region from the mesh information respectively corresponding to the plurality of pixel regions.
Ali is the closest prior art. Ali detects and crops the hand region and generates corresponding 3D hand mesh information. However, Ali does not determine separate mesh information for a plurality of object pixel regions and then select the hand mesh from that plurality based on the hand’s target pixel region.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tang (X. Tang, T. Wang and C. -W. Fu, "Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstruction," 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 11678-11687, doi: 10.1109/ICCV48922.2021.01149.): discloses the concept of inputting a image of a hand object and segmenting it from a larger picture.
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/LATRELL ANTHONY CREARY/Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
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