DETAILED ACTION
This action is in response to communications: Preliminary-Amendment filed November 1, 2024.
Claims 1-4, 6-12, 14-22 are pending in this case. Claims 1-4, 6-8, 10, 11, 14, 17-19, 21, and 22 have been newly amended. Claims 5, 13, 23-25 have been newly cancelled. No claims have been newly added. This action is made Non-Final.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on November 1, 2024 and November 8, 2024 were filed on/after the filing date of the application on November 1, 2024. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings were received on November 1, 2024. These drawings are accepted.
Claim Objections
Claims 1, 14, and 15 are objected to because of the following informalities:
Claims 1, 14, and 15 comprise a semi-colon punctuation mark (;) after the terms “configured to” but should be a colon punctuation mark (:)
Claim 1 further recites, “…generate an image…based on a rendered image associated with the at least one camera pose…” but should recite, “…generate an image…based on the rendered image associated with the at least one camera pose…”
Claim 14 further recites, “…a display for displaying the rendered image…” but should recite, “…a display for displaying a rendered image…”
Claim 15 further recites, “…generating an image for displaying on a first device…determine a pose of a first device having a display…” but should recite, “…determine a pose of the first device having a display…”
Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
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) 1-4, 8, 14-16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Williams et al. (US 2015/0029218).
As to claim 1, Williams et al. disclose a computing device (Figure 1, networked computing environment 100 with computing devices, e.g. mobile device 11, 12, 19 and server 15, where [0035] notes mobile device 19 comprises a head-mounted display (HMD) that provides an augmented reality or mixed reality environment to an end user of the HMD) for generating an image configured to be captured by a camera (e.g. camera 148, e.g. outward and inward facing cameras, where [0031] notes mobile device 19 may comprise a camera 148 for capturing color images and/or depth images of an environment, e.g. outward facing cameras that capture images of the environment and inward facing cameras that capture images of the end user of the mobile device, where Figure 2B, [0041] further notes mobile device 19, e.g. as HMD, may comprise a capture device 213 including one or more cameras for recording digital images and/or videos and may transmit the visual recordings to processing unit 236 and capturing color information, IR information, and/or depth information, e.g. used to determine a current and predicted pose of the HMD), the computing device (e.g. mobile device 19 as an HMD with components illustrated in Figure 3A) being configured to (perform the method as outlined in Figure 4A): determine an image to be rendered associated with at least one camera pose ([0048] notes pose estimation module 312 may determine a current pose of the HMD based on camera-based pose tracking information and low-latency IMU motion information, and may also predict a future pose of the HMD by extrapolating previous movement of the HMD (e.g. the movement of the HMD 5ms or 10ms prior to determining the current pose), e.g. as an initial pose further noted below) and then render said image (e.g. render a pre-rendered image 412, where [0047] notes rendering module 302 may generate a pre-rendered image corresponding with a particular pose of the HMD, the particular pose may be provided by the pose estimation module 312, where [0056] further notes the pre-rendered image 412 may be rendered based on an initial pose estimate for the HMD (e.g. a predicted pose of the HMD 8ms or 16ms into the future), the initial pose estimate may be determined based on a current position and orientation of the HMD and an acceleration and a velocity of the HMD immediately prior to determining the initial pose estimate), receive information on a current camera pose (e.g. as noted above, the pose estimation module 312 may also determine a current pose of the HMD, e.g. as an updated pose, where [0056] further notes the updated pose estimate may be determined based on updated pose information that is acquired at a point in time subsequent to the determination of the initial pose estimate, e.g. the updated pose information may be generated based on camera-based pose tracking information and low-latency IMU motion information corresponding to the HMD), and generate an image associated with the current camera pose based on a rendered image associated with the at least one camera pose (e.g. generate an updated, adjusted image 414, [0049] notes late stage reprojection (LSR) module 308 may perform late stage graphical adjustments to pre-rendered images generated by the rendering module 302 based on updated pose estimation information provided by the pose estimation module 312, e.g. as an updated, adjusted image, where [0057], [0058] notes the updated image may be generated by applying an image transformation to the pre-rendered 412 based on the pose difference between the updated pose estimate and the initial pose estimate).
As noted above, Williams et al. describe generating a “pre-rendered image” corresponding with a particular pose of the HMD, where it would have been obvious to one of ordinary skill in the art that the “pre-rendered image” is still a “rendered image” as it is rendered by rendering module 302 as described, yielding predictable results, without changing the scope of the invention.
As to claim 2, Williams et al. disclose a perspective of the image associated with the current camera pose is different to the perspective of the rendered image of the at least one camera pose (e.g. as noted in claim 1, the pre-rendered image 412 may be generated based on an initial pose estimate for the HMD, e.g. a predicted pose, and the updated, adjusted image 414 may be generated by applying an image transformation to the pre-rendered 412 based on the pose difference between the updated pose estimate and the initial pose estimate, thus from different perspectives).
As to claim 3, Williams et al. disclose the computing device is configured to generate the image associated with the current camera pose based on depth information and/or geometric information of the rendered image associated with the at least one camera pose (e.g. Figure 2B further illustrates mobile device 19, e.g. HMD, comprising a capture device 213 including one or more cameras for recording digital images and/or videos and may transmit the visual recordings to processing unit 236 and capturing color information, IR information, and/or depth information, e.g. used to determine a current and predicted pose of the HMD, where [0057], [0058] notes the updated, adjusted image 414 may be generated via various methods, e.g. based on various characteristics of the pre-rendered image 412: 1) by applying an image transformation to the pre-rendered image 412 based on a pose difference between the updated pose estimate and the initial pose estimate, e.g. the image transformation may comprise an image rotation, translation, resizing (e.g., stretching or shrinking), shifting, or tilting of at least a portion of the pre-rendered image 412; 2) via a homographic transformation of the pre-rendered image 412, where the homographic transformation may comprise a multi-plane homography, a single plane homography, and/or an affine homography; 3) by applying a pixel offset adjustment to the pre-rendered image 402, where the degree of the pixel offset adjustment may depend on a difference between the updated pose estimate and the initial pose estimate; 4) using a pixel offset adjustment or a combination of homographic transformations and pixel offset adjustments, where the homographic transformations and/or pixel offset adjustments may be generated using a controller or processor integrated with a display, thus the updated, adjusted image generated based on depth and/or geometric information of the pre-rendered image).
As to claim 4, Williams et al. disclose the at least one camera pose is a predicted at least one camera pose or a previous camera pose (e.g. as noted in claim 1, the pose estimation module may predict a future pose of the HMD by extrapolating previous movement of the HMD, where the initial pose estimate used to generate pre-rendered image 412 may be a predicted pose of the HMD, e.g. 8ms or 16ms into the future).
As to claim 8, Williams et al. disclose the at least one camera pose comprises a plurality of camera poses (e.g. plurality of predicted, future poses), the computing device being configured to render a plurality of images each of which is associated with a corresponding camera pose (e.g. rendering module generates plurality of pre-rendered images for each of the plurality of predicted, future poses), based on the received information on the current camera pose, select the camera pose of the plurality of camera poses closest in matching the current camera pose (e.g. select the closest pose, e.g. best predicted pose, for generating the updated, adjusted image), and generate an image associated with the current camera pose based on the selected camera pose and at least one further image associated with the plurality of camera poses (e.g. generate an updated, adjusted image based on the selected predicted, future pose and its associated pre-rendered image)([0047] notes the pose estimation module 312 may predict more than one future pose of the HMD (e.g., three possible future poses for the HMD) and the rendering module 302 may generate a plurality of pre-rendered images corresponding with the more than one future poses, and when updated pose information becomes available, the closest pose (e.g. the best predicted pose) of the more than one future poses and the corresponding pre-rendered images for the closest pose may be used for generating updated images by applying late stage graphical adjustments to the corresponding pre-rendered images for the closest pose, where [0049] further notes LSR module 308 performs late stage graphical adjustments to the pre-rendered image, e.g. selected for the closest pose, generated by the rendering module 302 based on updated pose estimation information provided by the pose estimation module 312, e.g. to generate the update image).
As to claim 14, Williams et al. disclose a system (Figure 1, networked computing environment 100) comprising a computing device (e.g. with computing devices, e.g. mobile device 11, 12, 19 and server 15, where [0035] notes mobile device 19 comprises a head-mounted display (HMD) that provides an augmented reality or mixed reality environment to an end user of the HMD) for generating an image (e.g. to generate an image, further noted below), a display for displaying the rendered image (e.g. display 150, where [0031] notes mobile device 19 may comprise display 150 may display digital images and/or video, further illustrated as display 310 of Figure 3A, where [0049] notes updated, adjusted images generated by late stage reprojection (LSR) module 308 may be displayed on display 310), and a camera configured to capture the rendered image displayed on the display (e.g. camera 148, e.g. outward and inward facing cameras, where [0031] notes mobile device 19 may comprise a camera 148 for capturing color images and/or depth images of an environment, e.g. outward facing cameras that capture images of the environment and inward facing cameras that capture images of the end user of the mobile device, where Figure 2B further illustrates mobile device 19, e.g. as an HMD, may comprise a capture device 213 including one or more cameras for recording digital images and/or videos and may transmit the visual recordings to processing unit 236 and capturing color information, IR information, and/or depth information, e.g. used to determine a current and predicted pose of the HMD); the computing device being configured to determine a plurality of camera poses and render an image for each camera pose ([0048] notes pose estimation module 312 may determine a current pose of the HMD based on camera-based pose tracking information and low-latency IMU motion information, and may also predict a future pose of the HMD by extrapolating previous movement of the HMD (e.g. the movement of the HMD 5ms or 10ms prior to determining the current pose), e.g. as an initial pose further noted below, where [0047] notes the pose estimation module 312 may predict more than one future pose of the HMD (e.g., three possible future poses for the HMD), and the rendering module 302 may generate a plurality of pre-rendered images corresponding with the more than one future poses), the camera comprising at least one sensor for detecting a current camera pose ([0031] notes camera 148 may capture depth images of an environment, and sensors 149 may generate motion and/or orientation information associated with the mobile device, where Figure 2B further illustrates mobile device, e.g. as an HMD, may comprise a capture device 213 including one or more cameras for recording digital images and/or videos and may transmit the visual recordings to processing unit 236 and capturing color information, IR information, and/or depth information, e.g. used to determine a current and predicted pose of the HMD, e.g. by pose estimation module 312 as noted above, where the capture device may be considered a “camera” as a whole), and the camera being configured to transmit its current pose to the computing device (e.g. as noted above, pose estimation module 312 receives camera-based pose tracking information and low-latency motion information to determine current pose of the HMD), the computing device (e.g. mobile device 19 as an HMD with components illustrated in Figure 3A) being further configured to (e.g. to perform the method as outlined in Figure 4A): select a determined camera pose of the plurality of camera poses closest in matching the current camera pose (e.g. select the closest pose, e.g. best predicted pose, for generating an updated, adjusted image, where [0047] further notes when updated pose information becomes available, the closest pose (e.g. the best predicted pose) of the more than one future poses and the corresponding pre-rendered images for the closest pose may be used for generating updated images by applying late stage graphical adjustments to the corresponding pre-rendered images for the closest pose, where [0056] further notes the pre-rendered image 412 may be rendered based on an initial pose estimate for the HMD (e.g. a predicted pose of the HMD 8ms or 16ms into the future), the initial pose estimate may be determined based on a current position and orientation of the HMD and an acceleration and a velocity of the HMD immediately prior to determining the initial pose estimate), and generate an image associated with the current camera pose based on at least one image of the plurality of determined camera poses (e.g. generate an updated, adjusted image 414 based on the closest pose, e.g. best predicted pose, [0049] notes late stage reprojection (LSR) module 308 may perform late stage graphical adjustments to pre-rendered images generated by the rendering module 302, e.g. the pre-rendered image 412 generated based on the closest pose (e.g. best predicted pose) noted above, based on updated pose estimation information provided by the pose estimation module 312, e.g. as an updated, adjusted image, where [0057], [0058] notes the updated image may be generated by applying an image transformation to the pre-rendered 412 based on the pose difference between the updated pose estimate and the initial pose estimate), and the display being configured to display the generated image whilst the camera is in its current pose and captures the generated image ([0049] notes the updated, adjusted images generated by the LSR module 308 may be displayed on display 310).
As noted above, Williams et al. describe generating a “pre-rendered image” corresponding with a particular pose of the HMD, where it would have been obvious to one of ordinary skill in the art that the “pre-rendered image” is still a “rendered image” as it is rendered by rendering module 302 as described, yielding predictable results, without changing the scope of the invention.
As to claim 15, Williams et al. disclose a computing device for generating an image for displaying on a first device (Figure 1, e.g. computing devices, e.g. mobile device 19, further illustrated in Figure 2A as mobile device 19, where [0035] notes mobile device 19 comprises a head-mounted display (HMD) that provides an augmented reality or mixed reality environment to an end user of the HMD), the computing device being configured to: determine a pose of a first device having a display (e.g. display 150, further illustrated as display 310 of Figure 3A, where [0048] notes pose estimation module 312 may determine a current pose of the HMD based on camera-based pose tracking information and low-latency IMU motion information, and may also predict a future pose of the HMD by extrapolating previous movement of the HMD (e.g. the movement of the HMD 5ms or 10ms prior to determining the current pose)), identify at least one second device having a display (e.g. one other of computing devices, e.g. mobile device 11, 12, further illustrated as mobile device 5 illustrated with display, e.g. as phone or tablet of Figure 2A), wherein the at least one second device has a pose different to the first device (e.g. [0040] notes mobile device 5 may provide motion and/or orientation information associated with mobile device 5 to mobile device 19), render an image associated with the pose of the at least one second device (e.g. render a pre-rendered image 412, where [0047] notes rendering module 302 may generate a pre-rendered image corresponding with a particular pose of the HMD, the particular pose may be provided by the pose estimation module 312, where [0056] further notes the pre-rendered image 412 may be rendered based on an initial pose estimate for the HMD (e.g. a predicted pose of the HMD 8ms or 16ms into the future), the initial pose estimate may be determined based on a current position and orientation of the HMD and an acceleration and a velocity of the HMD immediately prior to determining the initial pose estimate, NOTE: considering mobile device 5 may provide motion and/or orientation information associated with mobile device 5 to mobile device 19, it may be considered that pre-rendered image may be based on that information provided from mobile device 5), generate an image associated with the pose of the first device based on the image associated with the pose of the at least one second device (e.g. generate an updated, adjusted image 414, [0049] notes late stage reprojection (LSR) module 308 may perform late stage graphical adjustments to pre-rendered images generated by the rendering module 302 based on updated pose estimation information provided by the pose estimation module 312, e.g. as an updated, adjusted image, where [0057], [0058] notes the updated image may be generated by applying an image transformation to the pre-rendered 412 based on the pose difference between the updated pose estimate and the initial pose estimate).
As noted above, Williams et al. describe a plurality of computing devices, e.g. multiple devices 5, 11, 12, 19 as illustrated in Figures 1 and 2A. Although Williams et al. use the example of mobile device 19 as an HMD comprising components, e.g. rendering module 302, pose estimation module 312, and late stage reprojection (LSR) module 308, it would have been obvious to one of ordinary skill in the art to recognize that each of these computing devices may comprise similar components for operating as described, yielding predictable results, without changing the scope of the invention. Additionally, as described regarding Figure 2A, multiple computing devices may be in communication with each other, transmit information associated with its device to another device, e.g. position and/or orientation, e.g. pose, associated with the respective device, and render images as described, further yielding predictable results, without changing the scope of the invention.
As to claim 16, Williams et al. disclose the computing device is configured to generate the image associated with the pose of the first device by using depth information and/or geometric information of the image associated with the pose of the at least one second device (e.g. Figure 2B further illustrates mobile device 19, e.g. HMD, comprising a capture device 213 including one or more cameras for recording digital images and/or videos and may transmit the visual recordings to processing unit 236 and capturing color information, IR information, and/or depth information, e.g. used to determine a current and predicted pose of the HMD, where [0057], [0058] notes the updated image 414 may be generated via various methods, e.g. based on various characteristics of the pre-rendered image 412: 1) by applying an image transformation to the pre-rendered image 412 based on a pose difference between the updated pose estimate and the initial pose estimate, e.g. the image transformation may comprise an image rotation, translation, resizing (e.g., stretching or shrinking), shifting, or tilting of at least a portion of the pre-rendered image 412; 2) via a homographic transformation of the pre-rendered image 412, where the homographic transformation may comprise a multi-plane homography, a single plane homography, and/or an affine homography; 3) by applying a pixel offset adjustment to the pre-rendered image 402, where the degree of the pixel offset adjustment may depend on a difference between the updated pose estimate and the initial pose estimate; 4) using a pixel offset adjustment or a combination of homographic transformations and pixel offset adjustments, where the homographic transformations and/or pixel offset adjustments may be generated using a controller or processor integrated with a display, thus the updated, adjusted image generated based on depth and/or geometric information of the pre-rendered image).
As to claim 19, Williams et al. disclose the at least one second device comprises a plurality of second devices differing in poses (e.g. Figure 1 illustrates additional computing devices, e.g. mobile device 11, 12, where [0028] notes networked computing environment 100 may comprise more than depicted), and the computing device is configured to render an image associated with each second device such that a plurality of images is rendered (e.g. via rendering module 302, [0047] notes rendering module 302 may generate a pre-rendered image corresponding with a particular pose of the HMD), determine the pose of each plurality of second devices (e.g. via pose estimation module 312, [0048] notes pose estimation module 312 may determine a current pose of the HMD based on camera-based pose tracking information and low-latency IMU motion information, and may also predict a future pose of the HMD by extrapolating previous movement of the HMD (e.g. the movement of the HMD 5ms or 10ms prior to determining the current pose)), and select the second device closest in matching the pose of the first device (e.g. select the closest pose, e.g. best predicted pose, for generating the updated, adjusted image), and generate an image associated with the first device based on the image of the selected second device and at least one further image associated with another second device (e.g. generate an updated, adjusted image based on the selected predicted, future pose and its associated pre-rendered image)([0047] notes the pose estimation module 312 may predict more than one future pose of the HMD (e.g., three possible future poses for the HMD) and the rendering module 302 may generate a plurality of pre-rendered images corresponding with the more than one future poses, and when updated pose information becomes available, the closest pose (e.g. the best predicted pose) of the more than one future poses and the corresponding pre-rendered images for the closest pose may be used for generating updated images by applying late stage graphical adjustments to the corresponding pre-rendered images for the closest pose, where [0049] further notes LSR module 308 performs late stage graphical adjustments to the pre-rendered image, e.g. selected for the closest pose, generated by the rendering module 302 based on updated pose estimation information provided by the pose estimation module 312, e.g. to generate the update image). NOTE: As noted in claim 15, each of the computing devices may comprise similar components, thus capable of performing similar operations, e.g. as described for mobile device 19, and may be in communication with each other, transmit information to each other, e.g. position and/orientation, e.g. pose, associated with the respective device, and further render images based on the transmitted information of the other device as described.
Claim(s) 11, 12, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Williams et al. (US 2015/0029218) as applied to claims 1 and 15 above, and further in view of SHOTTON et al. (US 2013/0156297).
As to claim 11, Williams et al. disclose the generated image associated with the current camera pose (e.g. updated, adjusted image) and an image associated with the at least one camera pose (e.g. pre-rendered image), but do not disclose, but SHOTTON et al. disclose further configured to perform inpainting for filling in a disoccluded area in the generated image associated with the current camera pose, wherein the inpainting is based on an image associated with the at least one camera pose (Figure 4, [0037] notes empirical image data is available when each empirical image of a scene has an associated known camera pose of a camera which captured the image (which may encompass a current camera pose and/or other camera poses), [0038] notes for each selected empirical image the associated camera pose is known and an image may be rendered 402 from the scene reconstruction 112 according to that known pose, ground truth values are then established 404, then in the case of image inpainting, the process of establishing the ground truth values may comprise using multiple views from the scene reconstruction to determine occlusion boundaries and/or visible points in the transformed views, where [0027] further notes image inpainting comprises filling in missing image elements of an image, e.g. pixels, groups of pixels, voxels, groups of voxels, blobs, patches or other components of an image).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Williams et al.’s system and method of generating images with SHOTTON et al.’s method of inpainting to fill in missing elements of images, thus generating complete reconstructed images while improving visual quality of images (see [0027], [0038] [0039] of SHOTTON et al.).
As to claim 12, Williams et al. modified with SHOTTON et al. disclose the inpainting is based on surface normal information of an object in the image associated with the at least one camera pose (modified with SHOTTON, [0027] notes transformations of images include image inpainting which comprises filling in missing image elements of an image as well as surface normal detection which identifies surface normals of surfaces depicted by empirical image elements, where image elements may be e.g. pixels, groups of pixels, voxels, groups of voxels, blobs, patches or other components of an image (e.g. objects), see also [0116]-[0118]).
As to claim 22, Williams et al. disclose the generated image associated with the first device, an image associated with at least one second device (e.g. see claim 15), but do not disclose, but SHOTTON et al. disclose further configured to perform inpainting information for filling in a disoccluded area in the generated image associated with the first device, wherein the inpainting is based on an image associated with at least one second device (Figure 4, [0037] notes empirical image data is available when each empirical image of a scene has an associated known camera pose of a camera which captured the image (which may encompass a current camera pose and/or other camera poses), where the image data may be obtained captured using a handheld camera (e.g. “a first device”) or any other suitable image capture system (e.g. “a second device”), [0038] notes for each selected empirical image the associated camera pose is known and an image may be rendered 402 from the scene reconstruction 112 according to that known pose, ground truth values are then established 404, then in the case of image inpainting, the process of establishing the ground truth values may comprise using multiple views from the scene reconstruction to determine occlusion boundaries and/or visible points in the transformed views, where [0027] further notes image inpainting comprises filling in missing image elements of an image, e.g. pixels, groups of pixels, voxels, groups of voxels, blobs, patches or other components of an image).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Williams et al.’s system and method of generating images with SHOTTON et al.’s method of inpainting to fill in missing elements of images, thus generating complete reconstructed images while improving visual quality of images (see [0027], [0038] [0039] of SHOTTON et al.).
Allowable Subject Matter
Claims 6, 7, 9, 10, 17, 18, 20, and 21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding each of dependent claims 6, 9, 17, and 20, the prior art of record fails to teach or suggest, singly or combined, the limitations of the claims as recited.
Dependent claim 7 is indicated allowable for depending upon indicated allowable claim 6; dependent claim 10 is indicated allowable for depending upon indicated allowable claim 9; dependent claim 18 is indicated allowable for depending upon indicated allowable claim 17; and dependent claim 21 is indicated allowable for depending upon indicated allowable claim 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Margolis et al. (US 2017/0213388) disclose a system and method generating pre-rendered scenes incorporating motion vector estimations, the system comprising a head-mounted display (HMD) device for determining a predicted pose associated with a future position and orientation of the HMD, rendering a current frame based on the predicted pose, determining a set of motion vectors based on the current frame and a previous frame, generating an updated image based on the set of motion vectors and the current frame, and displaying the updated image on the HMD.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACINTA M CRAWFORD whose telephone number is (571)270-1539. The examiner can normally be reached 8:30a.m. to 4:30p.m.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, King Y. Poon can be reached at (571)272-7440. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JACINTA M CRAWFORD/Primary Examiner, Art Unit 2617
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