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 .
Response to Arguments
Applicant’s arguments, see page 1, filed 4/9/2026, with respect to objection to the drawings have been fully considered and are persuasive. The objection to the drawings has been withdrawn.
Applicant's arguments filed 4/9/2026 with respect to 35 U.S.C. 102 have been fully considered but they are not persuasive.
Applicants arguments with respect the Powers reference and 35 U.S.C. 102 are rendered moot as the Powers reference is not applied in the rejection below.
Applicant's arguments filed 4/9/2026 with respect to 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant argues Fei do not disclose the features argued with respect to Powers. The examiner nots that the Fei reference is now cited as disclosing some of those features. Applicant arguments however are unpersuasive as those augments are mere allegation and do not address the new grounds of rejection below. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the reference.
Applicants arguments with respect to Emami Kashu and Han rely on the arguments with respect to Powers and are moot in view of the new grounds of rejection below.
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) 1,2, 4 8-10, 12 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fei US 2018/0285636 in view of Lee US 20210248358 A1 in further view of Mierle US 20170243324 A1.
Re claim 1
Fei discloses
An Extended Reality (XR) device adapted for tracking an object for XR applications, the XR device comprising:
an imaging module configured to capture image data of an environment containing the object (see abstract note that images are captured using a camera see also figure 9 for example);
a processor configured to (see paragraph 24):
analyze the image data using a machine learning algorithm (see paragraph 25 “To this end, the hand tracking camera module 102 and/or the HMD 103 may implement various algorithms (e.g., 3D computer vision and machine learning-based algorithms). The computer vision and machine learning-based algorithms can be stored anywhere in the hand tracking system (e.g., in the HMD 103, in the hand tracking camera module 102, in a cloud device coupled to the HMD 103 or the hand tracking camera module 102, etc.), and can process camera-captured images, interpret hand movement from the images” note that machine learning may be used in part ) to estimate a pose of the object, generating pose estimation data (see figure 8 steps 3 note that the hand and object pose is determined see paragraph 53 note that a robust object pose recognition algorithm is used) including location, rotation, and movement of the object ( see figure 8 and paragraph 53 note that the “the 3D Hand Skeleton Joints And Robust Object Pose Recognition algorithm” is used to determine the hand and object pose see paragraph 48 “ e.g., the 3D Hand Skeleton Joints And Robust Object Pose Recognition algorithm) to output more accurate pose (3D rotation and 3D position) of the object held in the hand and output hand 3D skeleton information”: note that the location and rotation is determined see paragraph 55 see also paragraph 58 note the data also includes motion data of the controller which is subsequently compared to data of the IMU);
obtain inertial data corresponding to movements and/or orientations of the object from an Inertial Measurement Unit (IMU) affixed to the object (see paragraph 52 note that controller may have an IMU sensor as part of the controller);
fuse the pose estimation data and the inertial data by combining the location, rotation, and movement of the object with the inertial data to generate combined tracking data for the object (see paragraph 53 and figure 8 note that the 3d object pose is combined with the IMU data to generate a refined object pose);
and render one or more of position, movement, and orientation within the XR application based on the combined tracking data (see paragraph 25 “The HMD 103 can render a virtual environment. The user 101 may view and perceive the rendered virtual environment as if seeing a real physical environment. The user 101 can use her hand 104 for performing various interactions with (1) virtual objects in the rendered virtual environment and/or (2) real physical objects in the real physical environment. ” see also paragraph 57 “obtaining an output of the Hand And Controller Fusion Detection algorithm and the IMU data as inputs to a 3D Hand Pose And 3D Object Pose And IMU Fusion algorithm to refine the determined 3D skeleton joints and to determine a 3D object pose; (step 11) rendering the virtual hand based at least on the refined 3D skeleton joints” note that a virtual hand is rendered in the virtual environment );
and a display module for projecting the rendered position, movement, and orientation (see paragraph 27 “The head mounted display may be configured to render a virtual hand resembling the physical hand in a virtual environment for viewing by the user based at least on the images”).
Fei does not expressly disclose that the IMU data is high frequency acceleration and rotational velocity. Lee discloses he IMU data is high frequency acceleration and rotational velocity (see paragraph 170 “the system may be configured to integrate the angular velocity information to get rotational information (e.g., the integral of angular velocity (change in rotational position over change in time) is angular position (change in angular position)): likewise for translational information (in other words, by doing a double integral of the translational acceleration, the system will get position data). With such calculation the system can be configured to get 6 degree-of-freedom (DOF) pose information”). The motivation to combine is to get 6DOF pose information at high frequency from the IMU device (see paragraph 170). One of ordinary skill in the art could have easily used the IMU sensor of Lee as the IMU sensor of Fei and the results would be the same estimates of the pose. Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Fei and Lee to reach the aforementioned advantage.
Fei discloses generating tracking data for both the hand and the object [i.e. controller] but does not expressly disclose rendering and displaying the object [controller] but rather only explicitly discloses rendering the hand. Therefore, Lee and Fei do not disclose render one or more of position, movement, and orientation of the object within the XR application. Mierle discloses render one or more of position, movement, and orientation of the object within the XR application (see paragraph 53 “VR controller 424A (e.g., rendering, depicting or displaying the VR controller 424A, the user's hand(s), or an object, e.g., bat, sword, golf club, etc., controlled by the VR controller 424A, for example) based on current pose information (e.g., location and/or orientation) of VR controller 424A“ note that a controller or object controlled by the controller is rendered into the scene based on the position of the controller.) The motivation to combine is “depicting…. an object, e.g., bat, sword, golf club, etc., controlled by the VR controller 424A, for example) based on current pose information (e.g., location and/or orientation) of VR controller”. One of ordinary skill in the art could have easily modified Fei and Lee to further display a rendered objected based on the location and orientation of the controller. 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 Fee Lee and Mierle to reach the aforementioned advantage.
Re claim 2 Fei discloses
Fei further disclose wherein the object is a handheld controller for use with the XR device ( see paragraph 52 note that the hand may be holding a controller ) wherein the XR device further comprises a proximity sensor configured to detect the presence of a user's hand relative to the handheld controller (see paragraph 30 note that a touch sensor is provided to determine if the hand is touching the controller) wherein the processor is further configured to: determine an initial position of the handheld controller based on the combined tracking data therefor; and utilizing the initial position as a reference point for subsequent tracking of the handheld controller (see paragraph 53 “and least one of determined skeleton joints or a determined 3D object pose in a previous frame as inputs to execute the Hand And Object Detection algorithm;” note that the pose of the hand and controller are determined based on the previous positions of the hand and controller).
Re claim 4 Fei discloses Fei discloses wherein the imaging module is further configured to capture additional image data related to a user's hand, and wherein the processor is configured to: analyze the additional image data, using a hand-tracking algorithm, to determine a position and/or an orientation of the user's hand, generating hand tracking data (see abstract note a hand tracking camera module captures images to track the and hand and the system tracks the hand ); and integrate the hand tracking data with the combined tracking data, for implementation in tracking of the object (see paragraph 58 “Fusion algorithm can (1) determine 6DOF object pose (3D position+3D rotation) by fusing 3D position given by marker tracking with 3D rotation given by IMU data, and (2) fuse 6DOF controller pose with 6DOF hand pose to get more accurate 6DOF pose for both the controller and the hand.” See also paragraph 53 “inputting the determined 3D skeleton joints, the determined 3D object pose, and the IMU date to a 3D Hand Pose And 3D Object Pose And IMU Fusion algorithm to refine the determined 3D skeleton joints and the determined 3D object pose,” Note that hand tracking data and controller tracking data are fused to get a more accurate pose).
Re claim 8 wherein the imaging module employs multiple cameras for capturing the image data (see paragraph 29 note that multiple cameras may be used).
Re claim 9
Fei discloses
A method for tracking an object for Extended Reality (XR) applications, the method comprising:
an imaging module configured to capture image data of an environment containing the object (see abstract note that images are captured using a camera see also figure 9 for example);
a processor configured to (see paragraph 24):
analyzing the image data using a machine learning algorithm (see paragraph 25 “To this end, the hand tracking camera module 102 and/or the HMD 103 may implement various algorithms (e.g., 3D computer vision and machine learning-based algorithms). The computer vision and machine learning-based algorithms can be stored anywhere in the hand tracking system (e.g., in the HMD 103, in the hand tracking camera module 102, in a cloud device coupled to the HMD 103 or the hand tracking camera module 102, etc.), and can process camera-captured images, interpret hand movement from the images” note that machine learning may be used in part ) to estimating a pose of the object, generating pose estimation data (see figure 8 steps 3 note that the hand and object pose is determined see paragraph 53 note that a robust object pose recognition algorithm is used) including location, rotation, and movement of the object ( see figure 8 and paragraph 53 note that the “the 3D Hand Skeleton Joints And Robust Object Pose Recognition algorithm” is used to determine the hand and object pose see paragraph 48 “ e.g., the 3D Hand Skeleton Joints And Robust Object Pose Recognition algorithm) to output more accurate pose (3D rotation and 3D position) of the object held in the hand and output hand 3D skeleton information”: note that the location and rotation is determined see paragraph 55 see also paragraph 58 note the data also includes motion data of the controller which is subsequently compared to data of the IMU);
obtaining inertial data corresponding to movements and/or orientations of the object from an Inertial Measurement Unit (IMU) affixed to the object (see paragraph 52 note that controller may have an IMU sensor as part of the controller);
fusing the pose estimation data and the inertial data by combining the location, rotation, and movement of the object with the inertial data to generate combined tracking data for the object (see paragraph 53 and figure 8 note that the 3d object pose is combined with the IMU data to generate a refined object pose);
and rendering one or more of position, movement, and orientation within the XR application based on the combined tracking data (see paragraph 25 “The HMD 103 can render a virtual environment. The user 101 may view and perceive the rendered virtual environment as if seeing a real physical environment. The user 101 can use her hand 104 for performing various interactions with (1) virtual objects in the rendered virtual environment and/or (2) real physical objects in the real physical environment. ” see also paragraph 57 “obtaining an output of the Hand And Controller Fusion Detection algorithm and the IMU data as inputs to a 3D Hand Pose And 3D Object Pose And IMU Fusion algorithm to refine the determined 3D skeleton joints and to determine a 3D object pose; (step 11) rendering the virtual hand based at least on the refined 3D skeleton joints” note that a virtual hand is rendered in the virtual environment );
Fei does not expressly disclose that the IMU data is high frequency acceleration and rotational velocity. Lee discloses he IMU data is high frequency acceleration and rotational velocity (see paragraph 170 “the system may be configured to integrate the angular velocity information to get rotational information (e.g., the integral of angular velocity (change in rotational position over change in time) is angular position (change in angular position)): likewise for translational information (in other words, by doing a double integral of the translational acceleration, the system will get position data). With such calculation the system can be configured to get 6 degree-of-freedom (DOF) pose information”). The motivation to combine is to get 6DOF pose information at high frequency from the IMU device (see paragraph 170). One of ordinary skill in the art could have easily used the IMU sensor of Lee as the IMU sensor of Fei and the results would be the same estimates of the pose. Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Fei and Lee to reach the aforementioned advantage.
Fei discloses generating tracking data for both the hand and the object [i.e. controller] but does not expressly disclose rendering and displaying the object [controller] but rather only explicitly discloses rendering the hand. Therefore, Lee and Fei do not disclose render one or more of position, movement, and orientation of the object within the XR application. Mierle discloses render one or more of position, movement, and orientation of the object within the XR application (see paragraph 53 “VR controller 424A (e.g., rendering, depicting or displaying the VR controller 424A, the user's hand(s), or an object, e.g., bat, sword, golf club, etc., controlled by the VR controller 424A, for example) based on current pose information (e.g., location and/or orientation) of VR controller 424A“ note that a controller or object controlled by the controller is rendered into the scene based on the position of the controller.) The motivation to combine is “depicting…. an object, e.g., bat, sword, golf club, etc., controlled by the VR controller 424A, for example) based on current pose information (e.g., location and/or orientation) of VR controller”. One of ordinary skill in the art could have easily modified Fei and Lee to further display a rendered objected based on the location and orientation of the controller. 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 Fei Lee and Mierle to reach the aforementioned advantage.
Re claim 10 Fei discloses
Fei further disclose wherein the object is a handheld controller for use in the XR applications ( see paragraph 52 note that the hand may be holding a controller ) w wherein the method further comprises: detecting a presence of a user's hand in proximity to the handheld controller (see paragraph 30 note that a touch sensor is provided to determine if the hand is touching the controller) determining an initial position of the handheld controller based on the combined tracking data therefor; and utilizing the initial position as a reference point for subsequent tracking of the handheld controller (see paragraph 53 “and least one of determined skeleton joints or a determined 3D object pose in a previous frame as inputs to execute the Hand And Object Detection algorithm;” note that the pose of the hand and controller are determined based on the previous positions of the hand and controller).
Re claim 12 Fei discloses Fei discloses capturing additional image data related to a user's hand; analyzing the additional image data, using a hand-tracking algorithm, to determine a position and/or an orientation of the user's hand, generating hand tracking data (see abstract note a hand tracking camera module captures images to track the and hand and the system tracks the hand ); and integrating the hand tracking data with the combined tracking data, for implementation in tracking of the object (see paragraph 58 “Fusion algorithm can (1) determine 6DOF object pose (3D position+3D rotation) by fusing 3D position given by marker tracking with 3D rotation given by IMU data, and (2) fuse 6DOF controller pose with 6DOF hand pose to get more accurate 6DOF pose for both the controller and the hand.” See also paragraph 53 “inputting the determined 3D skeleton joints, the determined 3D object pose, and the IMU date to a 3D Hand Pose And 3D Object Pose And IMU Fusion algorithm to refine the determined 3D skeleton joints and the determined 3D object pose,” Note that hand tracking data and controller tracking data are fused to get a more accurate pose).
Re claim 15 projecting the rendered position, movement, and orientation onto a display of an XR device (see paragraph 27 “The head mounted display may be configured to render a virtual hand resembling the physical hand in a virtual environment for viewing by the user based at least on the images”).
Fei discloses generating tracking data for both the hand and the object [i.e. controller] but does not expressly disclose rendering and displaying the object [controller] but rather only explicitly discloses rendering the hand. Therefore, Lee and Fei do not disclose render one or more of position, movement, and orientation of the object within the XR application. Mierle discloses render one or more of position, movement, and orientation of the object within the XR application (see paragraph 53 “VR controller 424A (e.g., rendering, depicting or displaying the VR controller 424A, the user's hand(s), or an object, e.g., bat, sword, golf club, etc., controlled by the VR controller 424A, for example) based on current pose information (e.g., location and/or orientation) of VR controller 424A“ note that a controller or object controlled by the controller is rendered into the scene based on the position of the controller.) The motivation to combine is “depicting…. an object, e.g., bat, sword, golf club, etc., controlled by the VR controller 424A, for example) based on current pose information (e.g., location and/or orientation) of VR controller”. One of ordinary skill in the art could have easily modified Fei and Lee to further display a rendered objected based on the location and orientation of the controller. 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 Fei Lee and Mierle to reach the aforementioned advantage.
Claim(s) 3 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fei US 2018/0285636 A1 in view of Lee US 20210248358 A1 and Mierle US 20170243324 A1 in further view of Kashu US 20250377724 A1.
Re claim 3 Fei and Lee do not expressly discloses wherein the handheld controller has a predetermined shape and a predetermined button configuration, and wherein the processor is further configured to: correlate one or more of the predetermined shape and the predetermined button configuration of the handheld controller with detected finger positions of the user's hand thereon, generating hand position data; and integrate the hand position data with the combined tracking data, for implementation in tracking of the object.
Kashu discloses wherein the handheld controller has a predetermined shape (see paragraph 39 note that the controller may have a ring shape) and a predetermined button configuration (se paragraph 39 4 and figure 1 note that the button configuration is shows in the image), and wherein the processor is further configured to: correlate one or more of the predetermined shape and the predetermined button configuration of the handheld controller with detected finger positions of the user's hand thereon, generating hand position data (see paragraph 34 note that finger tracking of the hand is used to determine if the a figure is close to the position of a button on the controller see also paragraph 102 “To determine whether the user is about to perform the gesture for pressing the button, it is determined whether a first joint point 911 that is a point representing a fingertip of the finger with which the button press is performed is present in a neighboring area 910 while a position of the controller 120 is set as a center. In FIGS. 9, the finger with which the button operation is performed is assumed as the thumb.” ); and integrate the hand position data with the combined tracking data, for implementation in tracking of the object (see paragraph 112 note that the position orientation of the controller used in combination with the position orientation information of the finger “in S1001, the control unit 211 determines whether the change in at least one of the position or the orientation of the controller is a change while the wearing finger is set as a central axis. A motion set as a target of this determination will be described by using FIG. 9A. In the determination in S1001, an example of the determination target includes a sliding and rotating motion of the controller on the finger, that is, such a motion for the controller 120 to rotate while the controller wearing finger 313 is set as the central axis. Such an unintended rotating motion includes a case where the thumb 314 erroneously comes into contact with the controller 120 and a case where the thumb 314 slips on a surface of the controller 120 at the time of the button operation. In this case, for the fluctuation of the hand, that is, the motion of the finger on which the controller 120 is worn, it is conceivable that the controller 120 indicates a fluctuation that is independent of the fluctuation of the wearing finger. For example, a case where the controller fluctuates in a rotation direction along the finger even though the finger is not rotated is exemplified. The control unit 211 receives fluctuation information of at least one of the position or the orientation of the controller 120 which is output from the communication unit 223 of the controller 120 and estimation information of at least one of the position or the orientation of the hand finger which is output from the control unit 201 of the HMD 100.” ). The motivation to combine is to obtain accurate information (see paragraph 112). 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 Fei Mierle and Lee with Kashu to reach the aforementioned advantage.
Re claim 11 Fei and Lee do not expressly discloses correlating one or more of a predetermined shape and a predetermined button configuration of the handheld controller with detected finger positions of the user's hand thereon, generating hand position data; and integrating the hand position data with the combined tracking data, for implementation in tracking of the object
Kashu discloses correlating one or more of a predetermined shape and a predetermined button configuration of the handheld controller with detected finger positions of the user's hand thereon, generating hand position data (see paragraph 34 note that finger tracking of the hand is used to determine if the a figure is close to the position of a button on the controller see also paragraph 102 “To determine whether the user is about to perform the gesture for pressing the button, it is determined whether a first joint point 911 that is a point representing a fingertip of the finger with which the button press is performed is present in a neighboring area 910 while a position of the controller 120 is set as a center. In FIGS. 9, the finger with which the button operation is performed is assumed as the thumb.” ); integrating the hand position data with the combined tracking data, for implementation in tracking of the object (see paragraph 112 note that the position orientation of the controller used in combination with the position orientation information of the finger “in S1001, the control unit 211 determines whether the change in at least one of the position or the orientation of the controller is a change while the wearing finger is set as a central axis. A motion set as a target of this determination will be described by using FIG. 9A. In the determination in S1001, an example of the determination target includes a sliding and rotating motion of the controller on the finger, that is, such a motion for the controller 120 to rotate while the controller wearing finger 313 is set as the central axis. Such an unintended rotating motion includes a case where the thumb 314 erroneously comes into contact with the controller 120 and a case where the thumb 314 slips on a surface of the controller 120 at the time of the button operation. In this case, for the fluctuation of the hand, that is, the motion of the finger on which the controller 120 is worn, it is conceivable that the controller 120 indicates a fluctuation that is independent of the fluctuation of the wearing finger. For example, a case where the controller fluctuates in a rotation direction along the finger even though the finger is not rotated is exemplified. The control unit 211 receives fluctuation information of at least one of the position or the orientation of the controller 120 which is output from the communication unit 223 of the controller 120 and estimation information of at least one of the position or the orientation of the hand finger which is output from the control unit 201 of the HMD 100.” ). The motivation to combine is to obtain accurate information (see paragraph 112). 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 Fei Mierle and Lee with Kashu to reach the aforementioned advantage.
Claim(s) 5, 6, 13 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fei US 2018/0285636 in view of Lee US 20210248358 A1 and Mierle US 20170243324 A1 in further view of Han et al US 20240104749 A1.
Re claim 5 Fei and Lee disclose all the features of claim 1. Fei and Lee does not expressly disclose wherein the processor is further configured to segment the image data to define region of interest containing the object in the environment, for analysis and feature extraction by the machine learning algorithm. Han discloses herein the processor is further configured to segment the image data to define region of interest containing the object in the environment, for analysis and feature extraction by the machine learning algorithm. (see paragraph 86 17 and 19 note that a neural network may include a region proposal network and a refinement model for object tracking) The motivation to combine is “a method of object tracking that reduces performance speed reduction and reduces computational burden” (see paragraph 12). 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 Fei Meirle and Lee with Han to reach the aforementioned advantage.
Re claim 6 Fei and Lee do not expressly disclose the machine learning algorithm utilizes a convolutional neural network and/or pooling operations to analyze pixel intensities in the image data and extract feature vectors from the image data, for estimating the pose of the object. Fei does not expressly disclose feature vectors. Han discloses features vectors (see paragraph 81 or 94 note that 5d vectors are produced by the network). The motivation to combine is “a method of object tracking that reduces performance speed reduction and reduces computational burden” (see paragraph 12). 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 Fei Meirle and Lee with Han to reach the aforementioned advantage.
Re claim 13 Fei and Lee disclose all the features of claim 1. Fei and Lee does not expressly disclose segmenting the image data to define region of interest containing the object in the environment, for analysis and feature extraction by the machine learning algorithm. Han discloses herein the processor is further configured to segment the image data to define region of interest containing the object in the environment, for analysis and feature extraction by the machine learning algorithm. (see paragraph 86 17 and 19 note that a neural network may include a region proposal network and a refinement model for object tracking) The motivation to combine is “a method of object tracking that reduces performance speed reduction and reduces computational burden” (see paragraph 12). 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 Fei Meirle and Lee with Han to reach the aforementioned advantage.
Re claim 14 Fei and Lee do not expressly disclose the machine learning algorithm utilizes a convolutional neural network and/or pooling operations to analyze pixel intensities in the image data and extract feature vectors from the image data, for estimating the pose of the object. Fei does not expressly disclose feature vectors. Han discloses features vectors (see paragraph 81 or 94 note that 5d vectors are produced by the network). The motivation to combine is “a method of object tracking that reduces performance speed reduction and reduces computational burden” (see paragraph 12). 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 Fei Meirle and Lee with Han to reach the aforementioned advantage.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fei US 2018/0285636 in view of Lee US 20210248358 A1 and Mierle US 20170243324 A1 in view of Emami US 20240281071 A1.
Re claim 7 Fei does not expressly disclose wherein the proximity sensor employs one or more of capacitive sensing. Emami discloses wherein the proximity sensor employs one or more of capacitive sensing (see abstract note that capacitive sensing is used.), infrared sensing, or ultrasonic sensing techniques to detect the presence of the user's hand relative to the handheld controller. The motivation to combine is to allow that finger to provide hand input, without the user having to put down the controller. (see abstract “Thus, if the user has a controller in her hand and want to touch a UI element, she can also use her finger, where the system identifies the finger not touching the controller (e.g., via capacitance sensors) and that a hand pose is present (e.g., finger extended—from camera) to allow that finger to provide hand input, without the user having to put down the controller.”). One of ordinary skill in the art could have easily added a capacitive sensor to the controller of Fei to reach the aforementioned advantage. 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 Emami with Mierle Lee and Fei to reach the aforementioned advantage.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN T MOTSINGER whose telephone number is (571)270-1237. The examiner can normally be reached 9AM-5PM.
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/SEAN T MOTSINGER/Primary Examiner, Art Unit 2673
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