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 § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 4, 6-9, 11, 13-16, 18 and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vinayak et al. (US 9,383,895), hereinafter as Vinayak.
As for claim 1, Vinayak teaches a computer-implemented method comprising:
providing, by one or more processors, a user interface of an eXtended Reality (XR) application to a user, the user interface (3D virtual interaction space) including one or more virtual objects (virtual shape; Abstract);
capturing, using one or more cameras of an XR system (data capture unit), video frame tracking data of a pose of a hand of the user (hand locations and postures) while the user interacts with a virtual object of the one or more virtual objects (steps 71-72, fig. 29 captures hand locations and postures; col 18, lines 39-48 discuss using a depth camera to capture the position and movement of hands; steps 80-82, fig. 29 discuss interaction with a virtual object; col 26, lines 22-30; user interaction may result in the shape of the virtual object(s) being modified by either sub-module 34 or 35; fig. 2);
generating skeletal model data of the hand of the user based on the video frame tracking data (col 18, lines 52-61 discuss gesture module 20 extracting specifically skeleton hand 3D data; fig. 2);
generating grasp detection data (gesture and context of the user’s hand(s); col 26, lines 27-30) based on the skeletal model data (3D position data of the user’s hand(s); col 25, lines 56-59) and virtual object data of the virtual object (virtual object as a matrix of 3D data points; col 25, lines 56-59);
determining a grasp type of the pose based on the grasp detection data (gestures such as grab, hold and pinch are determined; fig. 8, lines 31-45);
determining a user intent based on the determined grasp type (when context module 25 determines user’s intent to alter the existing virtual object or shape, the hand and posture data is fed to the shape module 32 to modify the shape of the virtual object; col 26, lines 22-30; fig. 2 and 29); and
performing an operation by the XR application based on the determined user intent (the grasp type hand gesture determined is then used to alter the virtual object; col 28, line 65 through col 29, line 29; fig. 8 and 34(a-d)).
Machine claim 8 is similarly rejected as for claim 1 with system 10, data processing units 16 and inherent memory (flowchart of fig. 29 needs to run on a program) correspond to the machine, processors and memory as claimed.
Computer-readable storage medium claim 15 is similarly rejected as for claim 1 with inherent memory (flowchart of fig. 29 needs to run on a program) correspond to the storage medium.
As for claims 2, 9 and 16, Vinayak teaches
wherein the skeletal model data further comprises three-dimensional locations of hand joints (col 18, lines 52-61 discuss gesture module 20 extracting specifically skeleton hand 3D data; fig. 2) and the virtual object data comprises three-dimensional vertices on the virtual object’s mesh surface (virtual object as a matrix of 3D data points; col 25, lines 56-59; the virtual object behaves as a deformable mesh; col 28, line 65-67).
As for claims 4, 11 and 18, Vinayak teaches
wherein the grasp detection data includes a binary indication of the user grasping the virtual object (the use of 1s and 0s in order to indicate contact between user’s hand/fingers and the virtual object is inherent in order to determine the extent of interaction).
As for claims 6 and 13, Vinayak teaches
wherein the grasp detection data includes an object configuration of the virtual object (the virtual object as a matrix of 3D data points uniquely defines a configuration of the object).
As for claims 7, 14 and 20, Vinayak teaches
wherein the XR system comprises a head-wearable apparatus (system 10 includes visualization/feedback unit 18 can be a head-mounted display; col 7, lines 52-62; fig. 1).
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) 3, 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vinayak as applied to claim 1 above, and further in view of Wei et al. (US 2015/0097811), hereinafter as Wei.
As for claims 3, 10 and 17, Vinayak does not teach
wherein the grasp detection data includes a radius of curvature of a grasp being made by the user.
On the other hand, Wei teaches gesture detection and specifically on detecting a grab gesture according to reference radius of the gesture (Abstract and [0034]; fig. 3). One or ordinary skill in the art would recognize the advantage of incorporating Wei’s use of radius determination of a grab gesture to enhance differentiation in the determination of the types of grasp/grab. Therefore, it would have been obvious to further incorporate Wei’s radius determination for the advantage above.
Claim(s) 5, 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vinayak as applied to claim 1 above, and further in view of Velez et al. (US 2022/0165013), hereinafter as Velez.
As for claims 5, 12 and 19, Vinayak does not teach
wherein the grasp detection data includes a grasp confidence level of the user grasping the virtual object.
On the other hand, Velez teaches selecting potential gesture target by the confidence value of the gesture ([0075]). One of ordinary skill in the art would recognize the advantage of incorporating Velez’s use of confidence value/level to further enhance accuracy in determining the type use of user gesture. Therefore, it would have been obvious to further incorporate Velez’s use of confidence value/level for the advantage above.
Conclusion
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/TOM V SHENG/ Primary Examiner, Art Unit 2628