DETAILED ACTIONS
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 .
Status of Claims
Claims 1-22 are pending.
Terminal Disclaimer
The terminal disclaimer filed on 07/22/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US Patent No. 12,106,509 B2 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Amendment
The amendment filed 07/22/2026 has been entered in full.
Response to Arguments
Applicant’s arguments, see pages 7-9 of Remarks, filed 07/22/2026, with respect to the rejections of claims 1 and 15 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of further search and finding a closer prior art.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7-10, 15-16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Milovanovic et al., (US 2020/0226762 A1, published 07/16/2020, cited in an IDS filed by the applicant), hereinafter referred to as Milovanovic, in view of Vino et al., "Revisiting Harris Corner Detector Algorithm: A Gradual Thresholding Approach " (2013), hereinafter referred to as Vino.
Claim 1
Milovanovic discloses an apparatus (Milovanovic, Fig. 1A) configured for head-worn by a user (Milovanovic, [0022], “the computing device(s) 102 may be a virtual reality system, and the system may include a headset, a computer, and/or be in communication with one or more server(s) over the network(s) 128”), comprising:
a screen configured to present graphics for the user (Milovanovic, [0089], “An NUI may implement any combination of speech recognition , stylus recognition , facial recognition , biometric recognition , gesture recognition both on screen and adjacent to the screen , air gestures , head and eye tracking , and touch recognition ( as described in more detail below ) associated with a display of the computing device 600.”);
a camera system (Milovanovic, Fig. 1A, camera 120) configured to view an environment in which the user is located (Milovanovic, [0001], “detect and identify the fiducial markers in a scene or environment”); and
a processing unit (Milovanovic, Fig. 6, CPU(s) 606) comprises of a processor is coupled to the camera system (Milovanovic, Fig. 1A, camera 120), the processing unit (Milovanovic, Fig. 6, CPU(s) 606) configured to:
obtain a feature detection response for a first image (Milovanovic, Fig. 4, step B404, detect pixels that represent candidate corner points),
obtain a first threshold value associated with the feature detection response (Milovanovic, Fig. 4, step B410, determine a set of the candidate corner points that are within a threshold distance to a boundary).
Milovanovic does not explicitly disclose to obtain a second threshold value, the second threshold value being an absolute threshold, obtain a first criterion value that is the larger of the first threshold value and the second threshold value, and identify a first set of one or more features for a first region of the first image based on the first criterion value.
However, Vino teaches to obtain a first threshold value associated with the feature detection response (Vino, Section 3, “The first step works by using two threshold values; note that these values do not need a fine tuning like in Harris algorithm, just two coarsely tuned values are enough”), obtain a second threshold value, the second threshold value being an absolute threshold (Vino, Section 3, “The first step works by using two threshold values; note that these values do not need a fine tuning like in Harris algorithm, just two coarsely tuned values are enough”, Section 3, “In the current work we empirically define the threshold values as 3 × 106 and 12× 106, lower and higher respectively”), obtain a first criterion value that is the larger of the first threshold value and the second threshold value (Vino, Section 3, “If the RF value is smaller than the lower threshold, the point is not considered, while, if the value is bigger than the higher threshold the point is directly considered as a corner.”, the higher threshold which is analogous to the second threshold is used as a criterion to determine the corner, under BRI the higher threshold is the larger threshold of the two), and identify a first set of one or more features for a first region of the first image based on the first criterion value (Vino, Section 3, “If the RF value is smaller than the lower threshold, the point is not considered, while, if the value is bigger than the higher threshold the point is directly considered as a corner. Actually, these points are strong corners. The points with a RF value in between the two thresholds could be noisy data or weak corners. In the current work we empirically define the threshold values as 3 × 106 and 12× 106, lower and higher respectively, expressing the image in a [0, 255] brightness range.”).
Milovanovic and Vino are both considered to be analogous to the claimed invention because they are in the same field of corner detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by Milovanovic to incorporate the teachings of Vino to obtain a second threshold value, the second threshold value being an absolute threshold, obtain a first criterion value that is the larger of the first threshold value and the second threshold value, and identify a first set of one or more features for a first region of the first image based on the first criterion value. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been because the use of a single threshold does not allow to discern weak corner from strong noisy points since their RF value is similar (Vino, Section 3).
Claim 2
The combination of Milovanovic in view of Vino discloses the apparatus of claim 1 (Milovanovic, Fig. 1A), wherein the first set of one or more features comprises a corner, an edge, or a user-defined shape (Milovanovic, Fig. 4, step B404, detect pixels that represent candidate corner points).
Claim 3
The combination of Milovanovic in view of Vino discloses the apparatus of claim 1 (Milovanovic, Fig. 1A), wherein the processing unit (Milovanovic, Fig. 6, CPU(s) 606) is configured to identify a response value in the feature detection response that is larger than the first criterion value to identify the first set of one or more features for the first region of the first image (Vino, Section 3, “If the RF value is smaller than the lower threshold, the point is not considered, while, if the value is bigger than the higher threshold the point is directly considered as a corner. Actually, these points are strong corners. The points with a RF value in between the two thresholds could be noisy data or weak corners. In the current work we empirically define the threshold values as 3 × 106 and 12× 106, lower and higher respectively, expressing the image in a [0, 255] brightness range.”).
Claim 9
The combination of Milovanovic in view of Vino discloses the apparatus of claim 1 (Milovanovic, Fig. 1A), wherein the processing unit is configured to obtain the feature detection response by performing feature detection on the first image (Milovanovic, Fig. 2B), or by receiving the feature detection response from a device that is configured to perform feature detection on the first image (Milovanovic, Fig. 4).
Claim 10
The combination of Milovanovic in view of Vino discloses the apparatus of claim 1 (Milovanovic, Fig. 1A), wherein the processing unit (Milovanovic, Fig. 6, CPU(s) 606) comprises a feature detector (Milovanovic, Fig. 2B, Corner Detector 110) configured to perform feature detection to obtain the feature detection response (Milovanovic, Fig. 4).
Claims 15-16 and 18 are rejected for similar reasons as those described in claims 1, 3, and 9, respectively. The additional elements in Claims 15-16 and 18 (the combination of Milovanovic in view of Vino) discloses includes: a method (Milovanovic, Fig. 4) performed by a head-worn image display device (Milovanovic, [0022], “the computing device(s) 102 may be a virtual reality system, and the system may include a headset, a computer, and/or be in communication with one or more server(s) over the network(s) 128”). The proposed combination as well as the motivation for combining the Milovanovic and Vino references presented in the rejection of Claim 1, apply to Claims 15-16 and 18 and are incorporated herein by reference. Thus, the method recited in Claims 15-16 and 18 is met by Milovanovic and Vino.
Claim 21
The combination of Milovanovic in view of Vino discloses the apparatus of claim 1 (Milovanovic, Fig. 1A), wherein the processing unit is also configured to obtain a relative factor (Vino, Section 4, “a parameter (p), which have a value in between 0.005 and 0.015”) and a detection response value (Vino, Section 4, “the maximum value of the response function”), and multiple the detection response value by the relative factor to obtain the first threshold value (Vino, Section 4, “evaluate the local threshold multiplying the maximum value of the response function in a sub-image by a parameter (p), which have a value in between 0.005 and 0.015. In Fig.4 the results obtained using these limits are depicted. In these cases, the amount of detected corners are 5231 and 3330, respectively. Note that in this case, even if the algorithm works fine, it is necessary to select the right value of the p parameter, although it gets lower effects than the selection of the threshold in the original Harris algorithm”).
The proposed combination as well as the motivation for combining the Milovanovic and Vino references presented in the rejection of Claim 1, apply to Claim 21 and are incorporated herein by reference. Thus, the apparatus recited in Claim 21 is met by Milovanovic and Vino.
Claim 22
The combination of Milovanovic in view of Vino discloses the method of claim 15 (Milovanovic, Fig. 4), further comprising obtaining a relative factor (Vino, Section 4, “a parameter (p), which have a value in between 0.005 and 0.015”) and a detection response value (Vino, Section 4, “the maximum value of the response function”), and multiplying the detection response value by the relative factor to obtain the first threshold value (Vino, Section 4, “evaluate the local threshold multiplying the maximum value of the response function in a sub-image by a parameter (p), which have a value in between 0.005 and 0.015. In Fig.4 the results obtained using these limits are depicted. In these cases, the amount of detected corners are 5231 and 3330, respectively. Note that in this case, even if the algorithm works fine, it is necessary to select the right value of the p parameter, although it gets lower effects than the selection of the threshold in the original Harris algorithm”).
The proposed combination as well as the motivation for combining the Milovanovic and Vino references presented in the rejection of Claim 15, apply to Claim 22 and are incorporated herein by reference. Thus, the method recited in Claim 21 is met by Milovanovic and Vino.
Claims 5-6, 11, 13, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Milovanovic in view of Vino in further view of Rosten et al., “Faster and Better: A Machine Learning Approach to Corner Detection” (November 2008, cited in an IDS filed by the applicant), hereinafter referred to as Rosten.
Claim 4
The combination of Milovanovic in view of Vino discloses the apparatus of claim 1 (Milovanovic, Fig. 1A),
The combination of Milovanovic in view of Vino does not explicitly disclose wherein the processing unit is configured to identify the first set of first set of one or more features for the first region of the first image also based on a second criterion having a mask size for non-maxima suppression.
However, Rosten teaches wherein the processing unit is configured to identify the first set of first set of one or more features for the first region of the first image also based on a second criterion having a mask size for non-maxima suppression (Rostem, page 16, “Since the segment test does not compute a corner response function, non maximal suppression cannot be applied directly to the resulting features. For a given n, as t is increased, the number of detected corners will decrease. Since n = 9 produces the best repeatability results (see Section VI), variations in n will not be considered. The corner strength is therefore defined to be the maximum value of t for which a point is detected as a corner”, “Non maximal suppression is performed in a 3 ×3 mask”).
Rosten is considered to be analogous to the claimed invention because they are in the same field of corner detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by the combination of Milovanovic in view of Vino to incorporate the teachings of Rosten wherein the processing unit is configured to identify the first set of first set of one or more features for the first region of the first image also based on a second criterion having a mask size for non-maxima suppression. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to make the algorithm more efficient (Rosten, Section 3.3).
Claim 5
The combination of Milovanovic in view of Vino in view of Rosten discloses the apparatus of claim 4 (Milovanovic, Fig. 1A), wherein the mask size for the non-maxima suppression is 3x3 (Section 3.3 (Nonmaximal Suppression), “Nonmaximal suppression is performed in a 3×3 mask.”, Rosten also teaches having a mask size for non-maxima suppression).
Claim 6
The combination of Milovanovic in view of Vino in view of Rosten discloses the apparatus of claim 4 (Milovanovic, Fig. 1A), wherein the non-maxima suppression (Section 3.3 (Nonmaximal Suppression), “Nonmaximal suppression is performed in a 3×3 mask.”, Rosten also teaches having a mask size for non-maxima suppression) is for eliminating duplicate detected features within a moving mask having the mask size (Section 3.3, Fig. 2, repeatability is tested).
Rosten is considered to be analogous to the claimed invention because they are in the same field of corner detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by the combination of Milovanovic in view of Vino to incorporate the teachings of Rosten wherein the non-maxima suppression is for eliminating duplicate detected features within a moving mask having the mask size. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to make the algorithm more efficient (Rosten, Section 3.3).
Claim 7
The combination of Milovanovic in view of Vino in view of Rosten discloses the apparatus of claim 4 (Milovanovic, Fig. 1A), wherein the processing unit (Milovanovic, Fig. 6, CPU(s) 606) is configured to identify one of the first set of one or more features (1) if a response value in the feature detection response for a pixel is larger than a criterion value obtained based on a first maximum value (Milovanovic, [0031], “Once the grayscale input image is generated , adaptive thresholding 136 may be executed in order to find minimum and maximum values in a region around each pixel of the grayscale input image . However , in some examples , instead of computing exact extrema ( e.g. , maxi mum and minimum values ) around each pixel of the gray scale input image , the grayscale input image may be divided into tiles of pixels ( e.g. , 4x4 pixels , in some examples ) . The extrema ( e.g. , maximum and minimum ) within each of the tiles may then computed . To prevent artifacts from arising between tile boundaries that have large differences in extrema values , the extrema in a group of surrounding tiles ( e.g. , a 3x3 neighborhood of tiles , in some examples ) may be used when computing extrema for adjacent pixels in the input grayscale image ( e.g. , to ensure that at least one tile overlap is factored into the calculating of the extrema”), and (2) if the response value for the pixel is larger than all surrounding values in a mask with the mask size (Milovanovic, [0031], “To prevent artifacts from arising between tile boundaries that have large differences in extrema values , the extrema in a group of surrounding tiles ( e.g. , a 3x3 neighborhood of tiles , in some examples ) may be used when computing extrema for adjacent pixels in the input grayscale image ( e.g. , to ensure that at least one tile overlap is factored into the calculating of the extrema ) . As a result , each pixel may take the maximum and minimum from the surrounding tiles as its maximum and minimum . If the maximum and minimum values are too close in a given tile , the tile may be identified as not having a high enough contrast , and pixels within the tile may be assigned a pixel value associated with gray ( or another color ( s ) and omitted from further processing”).
Claim 8
The combination of Milovanovic in view of Vino in view of Rosten discloses the apparatus of claim 7 (Milovanovic, Fig. 1A), wherein the processing unit (Milovanovic, Fig. 6, CPU(s) 606) is also configured to: determine a second maximum value in the feature detection response, wherein the processing unit is configured to determine the second maximum value for a second region of the first image (Milovanovic teaches determining a second maximum value for a second region of the input image which is the first image, para. 0031, “the grayscale input image may be divided into tiles of pixels (e.g., 4×4 pixels, in some examples). The extrema (e.g., maximum and minimum) within each of the tiles may then computed.”, the input image is divided into tiles and the maximum value are computed for each tiles or regions), and identify a second set of one or more features in the second region of the first image based on a second criterion that relates to the second maximum value (Milovanovic teaches identifying features or black and white pixels to find the edges using the threshold, where there is a threshold for each tile or region that is based on the maximum value, para. 0031, “When a tile has sufficient contrast, each pixel may be assigned a value of white or black using the mean value as the threshold (e.g., (maximum+minimum)/2)”).
Claim 11
The combination of Milovanovic in view of Vino discloses the apparatus of claim 10 (Milovanovic, Fig. 1A).
The combination of Milovanovic in view of Vino does not explicitly disclose wherein the feature detector is configured to perform the feature detection based on a Scale-Invariant Feature Transform (SIFT) algorithm, a Features from Accelerated Segment Test (FAST) algorithm, or a Shi-Thomasi algorithm.
However, Rosten teaches wherein the feature detector is configured to perform the feature detection based on a Scale-Invariant Feature Transform (SIFT) algorithm, a Features from Accelerated Segment Test (FAST) algorithm, or a Shi-Thomasi algorithm (Section 3.1, Rosten corner detection is based on FAST algorithm).
Rosten is considered to be analogous to the claimed invention because they are in the same field of corner detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by the combination of Milovanovic in view of Vino to incorporate the teachings of Rosten wherein the feature detector is configured to perform the feature detection based on a Scale-Invariant Feature Transform (SIFT) algorithm, a Features from Accelerated Segment Test (FAST) algorithm, or a Shi-Thomasi algorithm. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been because the FAST algorithm has unmatched processing speed (Rosten, Section 7).
Claim 13
The combination of Milovanovic in view of Br Vino ick discloses the apparatus of claim 1 (Milovanovic, Fig. 1A), wherein the processing unit is configured to identify one of the one or more features (Vino, Section 3, “If the RF value is smaller than the lower threshold, the point is not considered, while, if the value is bigger than the higher threshold the point is directly considered as a corner. Actually, these points are strong corners. The points with a RF value in between the two thresholds could be noisy data or weak corners. In the current work we empirically define the threshold values as 3 × 106 and 12× 106, lower and higher respectively, expressing the image in a [0, 255] brightness range.”) (1) if a response value in the feature detection response for a pixel is larger than a criterion value obtained based on the first maximum value (Milovanovic, [0031], “Once the grayscale input image is generated , adaptive thresholding 136 may be executed in order to find minimum and maximum values in a region around each pixel of the grayscale input image . However , in some examples , instead of computing exact extrema ( e.g. , maxi mum and minimum values ) around each pixel of the gray scale input image , the grayscale input image may be divided into tiles of pixels ( e.g. , 4x4 pixels , in some examples ) . The extrema ( e.g. , maximum and minimum ) within each of the tiles may then computed . To prevent artifacts from arising between tile boundaries that have large differences in extrema values , the extrema in a group of surrounding tiles ( e.g. , a 3x3 neighborhood of tiles , in some examples ) may be used when computing extrema for adjacent pixels in the input grayscale image ( e.g. , to ensure that at least one tile overlap is factored into the calculating of the extrema”).
The combination of Milovanovic in view of Vino does not expressly disclose if the response value for the first pixel is larger than all surrounding values in a non-maxima suppression mask.
However, Rosten teaches if the response value for the first pixel is larger than all surrounding values in a non-maxima suppression mask (Rosten, Section 3.3, the nonmaximal suppression mask size is 3x3 and “The corner strength is, therefore, defined to be the maximum value of t for which a point is detected as a corner.”, Rosten also teaches using threshold as a criterion to identify the corners in Section 3.2).
Rosten is considered to be analogous to the claimed invention because they are in the same field of corner detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by the combination of Milovanovic in view of Vino to incorporate the teachings of Rosten if the response value for the first pixel is larger than all surrounding values in a non-maxima suppression mask. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to make the algorithm more efficient (Rosten, Section 3.3).
Claims 17-19 are rejected for similar reasons as those described in claims 4 and 13, respectively. The additional elements in Claims 17 and 19 (the combination of Milovanovic in view of Vino in further view of Rosten) discloses includes: a method (Milovanovic, Fig. 4) performed by a head-worn image display device (Milovanovic, [0022], “the computing device(s) 102 may be a virtual reality system, and the system may include a headset, a computer, and/or be in communication with one or more server(s) over the network(s) 128”). The proposed combination as well as the motivation for combining the Milovanovic, Vino, and Rosten references presented in the rejection of Claims 4 and 13, apply to Claims 17 and 19 and are incorporated herein by reference. Thus, the method recited in Claims 17 and 19 is met by Milovanovic, Vino, and Rosten.
Claims 12, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Milovanovic in view of Vino in further view of Sinha et al., (US 2014/0010407 A1, published 01/09/2014, cited in an IDS filed by the applicant), hereinafter referred to as Sinha.
Claim 12
The combination of Milovanovic in view of Vino discloses the apparatus of claim 1 (Milovanovic, Fig. 1A).
The combination of Milovanovic in view of Vino does not explicitly disclose wherein the processing unit comprises a divider configured to divide the feature detection response into a plurality of patches, and a selector configured to select a response value from a plurality of response values in the feature detection response that has the highest value as the first maximum value.
However, Sinha teaches wherein the processing unit (Sinha, Fig. 13, GPUs 14) comprises a divider configured to divide the feature detection response into the plurality of patches (Sinha, [0043], “the adaptive threshold is set to .gamma.{tilde over (r)}. where {tilde over (r)} is the maximum cornerness of keypoints in the previous frame and .gamma.=0.001. Next, for a .mu..times..mu. pixel, square patch around each keypoint, a 256-bit Binary Robust Independent Elementary Feature (BRIEF) descriptor is computed. BRIEF descriptors lack rotational and scale invariance, but can be computed very fast. The tracked keypoints from the prior frame are compared to all the keypoint in the current frame, within a .rho..times..rho. search window around its respective positions in the prior frame.”, the search window is the divider), and a selector configured to select a response value from a plurality of response values in the feature detection response that has the highest value as the first maximum value (Sinha, [0043], “the adaptive threshold is set to .gamma.{tilde over (r)}. where {tilde over (r)} is the maximum cornerness of keypoints in the previous frame and .gamma.=0.001”, the maximum value is selected to be used for the threshold).
Sinha is considered to be analogous to the claimed invention because they are in the same field of corner detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by the combination of Milovanovic in view of Vino to incorporate the teachings of Sinha wherein the processing unit comprises a divider configured to divide the feature detection response into a plurality of patches, and a selector configured to select a response value from a plurality of response values in the feature detection response that has the highest value as the first maximum value. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been because BRIEF descriptors can be computed very fast (Sinha, [0043]).
Claim 14
The combination of Milovanovic in view of Vino discloses the apparatus of claim 1 (Milovanovic, Fig. 1A).
The combination of Milovanovic in view of Vino does not explicitly disclose wherein the processing unit is configured to create a map based at least in part on the first set of the one or more features, and to perform localization of the user based on the created map.
However, Sinha teaches wherein the processing unit (Sinha, Fig. 13, GPUs 14) is configured to create a map (Sinha, [0046], “Once the tracked keypoints in the current image frame have been identified, a current version of a tracking table is generated”, para. 0037, “a mapping between each database descriptors and its corresponding 3D point in the 3DR is saved in a lookup table”), based at least in part on the first set of the one or more features (Sinha, [0046, “Once the tracked keypoints in the current image frame have been identified, a current version of a tracking table is generated”, the tracked keypoints are the identified features), and to perform localization of the user based on the created map (Sinha, [0043], “this procedure localizes frame f, given the 3DR map M, and a track table T”).
Sinha is considered to be analogous to the claimed invention because they are in the same field of corner detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by the combination of Milovanovic in view of Vino to incorporate the teachings of Sinha wherein the processing unit is configured to create a map based at least in part on the first set of the one or more features, and to perform localization of the user based on the created map. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been because of the advantage of achieving real-time or near real-time performance by avoiding the need for extracting scale-invariant keypoints at runtime (Sinha, [0025]).
Claim 20 is rejected for similar reasons as those described in claim 14. The additional elements in Claim 20 (the combination of Milovanovic in view of Vino in further view of Sinha) discloses includes: a method (Milovanovic, Fig. 4) performed by a head-worn image display device (Milovanovic, [0022], “the computing device(s) 102 may be a virtual reality system, and the system may include a headset, a computer, and/or be in communication with one or more server(s) over the network(s) 128”). The proposed combination as well as the motivation for combining the Milovanovic, Vino, and Sinha references presented in the rejection of Claim 14, apply to Claim 20 and are incorporated herein by reference. Thus, the method recited in Claim 20 is met by Milovanovic, Vino, and Sinha.
Conclusion
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/DENISE G ALFONSO/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
