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.
Claims 1, 4, 14, 16, 18, and 20 have been amended. Claims 1-20 are currently pending and are being considered.
Response to Arguments
Applicant's arguments filed 7/22/2026 have been fully considered but they are not persuasive. Regarding claim 1, applicant argues that the prior art reference Hong et. al. fails to disclose “extracting the initial keypoints in combination with a keypoint acquisition condition in a previous video image, so as to acquire the keypoints of the block, wherein the keypoint acquisition condition comprises setting a preset number of keypoints in the current video image, and wherein the preset number of keypoints of the current video image has been set according to a total number of keypoints finally acquired from the previous video image”. Examiner disagrees. Hong et. al. discloses in paragraphs [0152]-[0153], In some embodiments, a fixed number of detected key-points may be extracted for each dataset [0181] in many visual SLAM systems, key-points are first detected and then their descriptors are extracted for each frame captured by the input cameras. Thus, the prior art of reference Hong et. al. is still effective in rejecting the amended claims.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hong et. al. (United States Patent Application Publication US 2020/0250807 A1).
Regarding claim 1, Hong et. al. discloses a detection method for keypoints of a video image, comprising: acquiring a current video image (Hong et. al., abstract, Fig. 5); dividing the current video image into a plurality of blocks (Hong et. al. [0138]); and acquiring keypoints from each block in sequence; wherein acquiring keypoints from each block in sequence comprises: generating initial keypoints of the block; and extracting the initial keypoints in combination with a keypoint acquisition condition in a previous video image, so as to acquire the keypoints of the block, wherein the keypoint acquisition condition comprises setting a preset number of keypoints in the current video image, and wherein the preset number of keypoints of the current video image has been set according to a total number of keypoints finally acquired from the previous video image, wherein the method is performed by at least one processor (Hong et. al. [0152]-[0153], In some embodiments, a fixed number of detected key-points may be extracted for each dataset [0181] in many visual SLAM systems, key-points are first detected and then their descriptors are extracted for each frame captured by the input cameras.).
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Regarding claim 2, Hong et. al. discloses the detection method for keypoints of a video image according to claim 1, wherein in a case of the dividing the current video image into a plurality of blocks, a size of each block divided is set according to a size of the current video image (Hong et. al. [0169] each image block for sparse coding can have three dimensions, for example dimensions=nxnx3 where 3 is the number of color channels, and the dimensions are not nxn as when grayscale images are processed. Therefore, an RGB image block can be reshaped into a vector of nxnx3 by 1. (Hong et. al. [0139], the size of the image block is predetermined, for certain image processing applications. For example, the image block size can be 11x11 or 17x17, as this was seen to generate better performance for the tests described herein. However, depending on application, other block sizes may deliver better key-point detection performance which can be determined through testing, [0169]).
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Regarding claim 19, which is a device, comprising at least one memory and at least one processor, wherein the at least one memory stores one or more computer instructions that, when executed by the at least one processor, cause the at least one processor to implement the detection method for keypoints of a video image according to claim 1, which the rejection analysis is incorporated herein.
Regarding claim 20, which is a non-transitory storage medium storing one or more computer instructions, wherein the one or more computer instructions are configured to cause at least one processor to implement the detection method for keypoints of a video image according to claim 1, which the rejection analysis is incorporated herein.
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.
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.
Claim(s) 3-18 are rejected under 35 U.S.C. 103 as being unpatentable over Hong et. al. (United States Patent Application Publication US 2020/0250807 A1) in view of Wang et. al. (International Patent Application Publication WO 2013/056311 A1).
Regarding claim 3, Hong et. al. discloses the detection method for keypoints of a video image according to claim 1. However, Hong et. al. fails to disclose comprising: generating the initial keypoints of the block through a Features from Accelerated Segment Test (FAST) algorithm, wherein a detection threshold in the FAST algorithm comprises a high threshold and a low threshold
Wang et. al. teaches comprising: generating the initial keypoints of the block through a Features from Accelerated Segment Test (FAST) algorithm, wherein a detection threshold in the FAST algorithm comprises a high threshold and a low threshold (Wang et. al. col 27, lines 20-25, pg. 28, KBKS-fast; col 11, lines 20-32, coverage threshold that each keyframe must satisfy, claims 14-16).
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This is important to the claimed invention because the FAST detector threshold adaptation allows for capturing corners in both strong and weak texture areas, creating candidate points even in low-texture blocks. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Hong et. al. and Wang et. al. so that the FAST algorithm is used in the solution for keypoints detection.
Regarding claim 4, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 3, and Wang et. al. further discloses wherein the generating the initial keypoints of the block through an FAST algorithm comprises: performing simultaneous detection on the block based on the high threshold and the low threshold; at a first time: stopping, in a case that the initial keypoints are detected based on the high threshold, detection based on the low threshold, and acquiring the initial keypoints detected based on the high threshold; at a second time: acquiring, in a case that no initial keypoints are detected based on the high threshold and the initial keypoints are detected based on the low threshold, the initial keypoints detected based on the low threshold; and at a third time: skipping the acquiring keypoints from the block in a case that no initial keypoints are detected based on the high threshold and the low threshold (Wang et. al. col 27, lines 20-25, pg. 28, KBKS-fast; col 11, lines 20-32, coverage threshold that each keyframe must satisfy, claims 14-16, Fig. 1).
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Regarding claim 5, Hong et. al. discloses the detection method for keypoints of a video image according to claim 1. However, Hong et. al. fails to disclose wherein the extracting the initial keypoints in combination with a keypoint acquisition condition in a previous video image, so as to acquire the keypoints of the block comprises: acquiring, according to keypoint acquisition information of a block in the previous video image corresponding to a current block, an estimated number of keypoints of the current block; and extracting a corresponding number of the initial keypoints according to the estimated number of keypoints of the current block as the keypoints of the block
Wang teaches wherein the extracting the initial keypoints in combination with a keypoint acquisition condition in a previous video image, so as to acquire the keypoints of the block comprises: acquiring, according to keypoint acquisition information of a block in the previous video image corresponding to a current block, an estimated number of keypoints of the current block; and extracting a corresponding number of the initial keypoints according to the estimated number of keypoints of the current block as the keypoints of the block (Wang et. al. col. 4, lines 7-11, pg.5).
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This is important to the claimed invention because this enables localized control of keypoints based on adjacent blocks within a video sequence. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Hong et. al. and Wang et. al. so that this feature is included in the solution.
Regarding claim 6, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 5, and Wang et. al. further discloses wherein the acquiring, according to keypoint acquisition information of a block in the previous video image corresponding to a current block, an estimated number of keypoints of the current block comprises: acquiring the block in the previous video image corresponding to the current block as a reference block; acquiring a block in a neighborhood of the reference block from the previous video image as a surrounding block; and acquiring the estimated number of keypoints of the current block in combination with keypoint acquisition information of the reference block and keypoint acquisition information of the surrounding block (Wang et. al., Fig. 1, col.8, lines 25-31, pg. 9, the unique keypoints are identified from a window of a video sequence, which defines a global keypoint pool for the window).
Regarding claim 7, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 6, and Wang et. al. further discloses wherein in a case of the acquiring a block in a neighborhood of the reference block from the previous video image as a surrounding block, a neighborhood range of the reference block is set according to a global motion vector of the previous video image, wherein a size of the global motion vector is directly proportional to a size of the neighborhood range (Wang et. al. col. 16, lines 17-26, pg. 17, a scale-invariant feature transform (SIFT) is implemented at step 110 of Fig. 1 to detect and describe keypoints in frames of a video sequence; col 17, lines 1-8, pg. 18, continuity among adjacent frames, a matching strategy is used that considers only those candidate keypoints within a certain radius R of the target keypoint).
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Regarding claim 8, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 6, and Wang et. al. further discloses wherein the acquiring the estimated number of keypoints of the current block in combination with keypoint acquisition information of the reference block and keypoint acquisition information of the surrounding block comprises: grading the block of the current video image in combination with the keypoint acquisition information of the reference block and the keypoint acquisition information of the surrounding block, so as to obtain a grade corresponding to each block, wherein each grade has a corresponding estimated number; and acquiring an estimated number of keypoints of each block according to the grade corresponding to each block (Wang et. al. col. 5, lines 1-8, here the term ‘grade’ is construed as a selection value for each frame outside said set of keyframes; F-score is a combination of both the precision and recall indicating the overall quality).
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Regarding claim 9, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 6, and Wang et. al. further discloses comprising: generating the initial keypoints of the block through a Features From Accelerated Segment Test (FAST) algorithm, wherein a detection threshold in the FAST algorithm comprises a high threshold and a low threshold; and wherein the acquiring the estimated number of keypoints of the current block in combination with keypoint acquisition information of the reference block and keypoint acquisition information of the surrounding block comprises: acquiring a condition that whether the reference block has keypoints as first information; acquiring a condition that whether the detection threshold is the high threshold or the low threshold in a case that initial keypoints of the reference block are generated through the FAST algorithm as second information; acquiring a condition that whether a number of the surrounding block having keypoints is greater than or equal to a set threshold as third information; and acquiring the estimated number of keypoints of the current block in combination with the first information, the second information, and the third information (Wang et. al. col 27, lines 20-25, pg. 28, KBKS-fast; col 11, lines 20-32, coverage threshold that each keyframe must satisfy, claims 14-16).
Regarding claim 10, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 9, and Wang et. al. further discloses wherein in a case of the acquiring a condition that whether a number of the surrounding block having keypoints is greater than or equal to a set threshold, the set threshold is half of a total number of the surrounding block (Wang et. al. col. 12, lines 17-22, pg.13, the quality criteria include a predefined number or percentage of frames that are to be selected).
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Regarding claim 11, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 5, and Wang et. al. further discloses wherein the extracting a corresponding number of the initial keypoints according to the estimated number of keypoints of the current block as the keypoints of the block comprises: acquiring a keypoint number adjustment value of the current block according to a number of keypoints of all blocks before the current block and the estimated number of keypoints; acquiring a target number of keypoints of the current block in combination with the keypoint number adjustment value and the estimated number of keypoints of the current block; and extracting the initial keypoints as the keypoints of the block according to the target number (Wang et. al. col 27, lines 20-25, pg. 28, KBKS-fast; col 11, lines 20-32, coverage threshold that each keyframe must satisfy, claims 14-16; the user is able to adjust the number of frames, the contribution threshold, and the redundancy threshold in the quality criteria to achieve the required level of summarization or compression and quality for the particular application. If a user sets the quality criteria to include a coverage threshold that selects as keyframes those frames having a minimum coverage value of 40% and a maximum redundancy value of 70%, that quality criteria produces a set of keyframes consisting of frame 1 1410 and frame 4 1440. In that particular example, the set of keyframes includes 2 frames, which is 50% less than the number of frames in the original video sequence, yet the set of keyframes includes all 11 - keypoints in the global keypoint pool 1450).
Regarding claim 12, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 11, and Wang et. al. further discloses wherein the acquiring a keypoint number adjustment value of the current block according to a number of keypoints of all blocks before the current block and the estimated number of keypoints comprises: setting a selection mode for acquiring the keypoint number adjustment value of the current block according to a keypoint distribution condition of the previous video image, wherein the selection mode comprises an even allocation mode or a greedy acquisition mode; and acquiring the keypoint number adjustment value of the current block according to the selection mode (col 18-19, lines 26-32, 1-10; In accordance with the method and system of the present disclosure, keyframes are selected to cover as many keypoints in the global keypoint pool as possible. Since this can be formulated as a variation of the well-known Set Cover Problem, which has been proven to be nondeterministic polynomial time (NP)-complete, one arrangement implements a greedy algorithm to approximately tackle this issue).
Regarding claim 13, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 12, and Wang et. al. further discloses wherein the setting a selection mode for acquiring the keypoint number adjustment value of the current block according to a keypoint distribution condition of the previous video image comprises: determining whether keypoints of the previous video image are distributed evenly; setting the selection mode for acquiring the keypoint number adjustment value of the current block as the even allocation mode in a case that the keypoints of the previous video image are distributed evenly; and setting the selection mode for acquiring the keypoint number adjustment value of the current block as the greedy acquisition mode otherwise (Wang et. al. col 12, lines 7-9, pg. 13, The user is able to adjust the number of frames, the contribution threshold, and the redundancy threshold in the quality criteria to achieve the required level of summarisation or compression and quality for the particular application).
Regarding claim 14, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 13, and Wang et. al. further discloses wherein the determining whether keypoints of the previous video image are distributed evenly comprises: dividing the previous video image one or more times, and acquiring two sub-regions having the same area during each division; acquiring a variance of numbers of keypoints in a plurality of sub-regions acquired during all divisions respectively; acquiring the sum of the numbers of keypoints in the plurality of sub-regions acquired during all the divisions; acquiring a ratio of the variance to the sum; at a first time: determining that the keypoints of the previous video image are distributed evenly in a case that the ratio of the variance to the sum is less than or equal to an evenness threshold; and at a second time: determining that the keypoints of the previous video image are distributed unevenly in a case that the ratio of the variance to the sum is greater than an evenness threshold (Wang et. al. col 16, lines17-31, pg. 17, keypoint matching process based on SIFT descriptors, ratio test criterion).
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Regarding claim 15, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 13, and Wang et. al. further discloses wherein in a case of setting the selection mode for acquiring the keypoint number adjustment value of the current block as the even allocation mode, the acquiring the keypoint number adjustment value of the current block according to the selection mode comprises: acquiring a difference between the sum of estimated numbers of the keypoints of all the blocks before the current block and the sum of the keypoints as a deviation value; acquiring a number of the current block and a block number of all blocks after the current block; and taking a ratio of the deviation value to the block number as the keypoint number adjustment value of the current block (Wang et. al. col. 19, lines4-25, pg. 20, the global keypoint pool is separated into two sets).
Regarding claim 16, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 13, and Wang et. al. further discloses wherein in a case of the acquiring a current video image, the video image is set to have a preset number of keypoints; and in a case of setting the selection mode for acquiring the keypoint number adjustment value of the current block as the greedy acquisition mode, the acquiring the keypoint number adjustment value of the current block according to the selection mode comprises: acquiring a difference between the sum of estimated numbers of keypoints of all the blocks before the current block and the sum of the keypoints as a deviation value; at a first time: taking a ratio of the preset number to a block number of the current video image as a number threshold, and acquiring a difference between the number threshold and the estimated number of keypoints of the current block as the keypoint number adjustment value of the current block in a case that the estimated number of keypoints of the current block is less than the number threshold and the estimated number of keypoints of the current block is less than a number of the initial keypoints; and at a second time: acquiring the keypoint number adjustment value of the current block as 0 in a case that the estimated number of keypoints of the current block is greater than or equal to the number threshold, and alternatively, the estimated number of keypoints of the current block is less than the number threshold and the estimated number of keypoints of the current block is greater than or equal to a number of the initial keypoints (Wang et. al. col. 19, lines4-25, pg. 20, the global keypoint pool is separated into two sets).
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Regarding claim 17, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 11, and Wang et. al. further discloses wherein the acquiring a target number of keypoints of the current block in combination with the keypoint number adjustment value and the estimated number of keypoints of the current block comprises: acquiring the sum of the keypoint number adjustment value and the estimated number of keypoints of the current block as the target number of keypoints of the current block (Wang et. al. col 12, lines 7-9, pg. 13, The user is able to adjust the number of frames, the contribution threshold, and the redundancy threshold in the quality criteria to achieve the required level of summarisation or compression and quality for the particular application col. 19, lines4-25, pg. 20, the global keypoint pool is separated into two sets).
Regarding claim 18, Hong et. al. and Wang et. al. discloses the detection method for keypoints of a video image according to claim 11, and Wang et. al. further discloses wherein the extracting the initial keypoints as the keypoints of the block according to the target number comprises: at a first time: extracting the initial keypoints in a number equal to the target number as the keypoints of the block in a case that the target number of keypoints of the current block is less than or equal to a number of the initial keypoints; and at a second time: extracting all initial keypoints as the keypoints of the block in a case that the target number of keypoints of the current block is greater than a number of the initial keypoints (Wang et. al. col. 11, lines 20-30, pg. 12, the quality criteria includes a minimum number, a maximum number, or an exact number of frames that are to be returned by the keyframe selection process).
Conclusion
Response to Amendment
Examiner has carefully considered the amendments to the claims and conducted an updated search. However, the prior art reference Hong et. al. is still effective in rejecting the amended independent claim 1. The prior art references used in the previous non-final rejection are still effective in rejecting all claims 1-20.
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 JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 August 7, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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