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 .
DETAILED ACTION
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement(s) submitted on 11/4/2024 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
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 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, 6, 9-11, 15, 17 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al (US 20210035346 A1) or, in the alternative, under 35 U.S.C. 103 as obvious over Zheng et al (CN-107341804-B).
Regarding claim 1, Chen teaches An effect video generation method, comprising:
in a case that it is detected that a current video frame to be processed comprises point cloud data to be processed, determining distance information between the point cloud data to be processed and at least one historically estimated plane, wherein the at least one historically estimated plane is determined based on historical point cloud data to be processed (paras. 0052-0058; “three-dimensional coordinates of the feature points are obtained by using a triangulation technique. If a majority of the extracted feature points are located in one plane, planes in the real environment are estimated based on extracted FAST corner points by using a RANSAC algorithm”; “the video frame image is transformed into a three-dimensional point cloud… a plane pose is extracted from each single-frame three-dimensional point cloud by using an RANSAC algorithm”; RANSAC algorithm determining distance information between the point cloud data to be processed and at least one historically estimated plane); and
in a case that the distance information and a data volume of the point cloud data to be processed satisfy a plane estimation condition, performing plane estimation on the point cloud data to be processed based on a random sample consensus algorithm to generate a target estimated plane, so as to display, based on each estimated plane, a target effect corresponding to the current video frame to be processed and obtain a target effect video frame (Figs. 1-2; paras. 0059-0061; “a virtual object corresponding to the model is arranged on one of the multiple real planes”).
Or, in the alternative, under 35 U.S.C. 103 as obvious over Zheng:
In the same field of endeavor Zheng teaches
determining distance information between the point cloud data to be processed and at least one historically estimated plane, wherein the at least one historically estimated plane is determined based on historical point cloud data to be processed (page 8, line 26 to page 9, line 53; “RANSAC) algorithm to perform plane detection,”; “S23: calculating the distance from all data points in the sub-data set to the first fitting plane; setting the data point with the distance less than the first fitting plane to the first set threshold as the inner point; Specifically, after obtaining the plane equation of the first fitting plane by S22, it can calculate all data points of the subdata set to the distance from the first fitting plane, taking a point Pj (Xj, Yj, Zj) in the sub-data set as an example, Pj to the first fitting plane distance is: then setting the data point with the distance from the first fitting plane less than the first set threshold value as the inner point; setting the data point with the distance greater than or equal to the first set threshold value with the first fitting plane as the free point; the free point will be processed in the subsequent process; in this step, it is not processed.”).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Zheng in Chen to have determining distance information between the point cloud data to be processed and at least one historically estimated plane, wherein the at least one historically estimated plane is determined based on historical point cloud data to be processed for utilizing RANSAC algorithm optimizing fitting plane according to inner points yielding a predicted result.
Regarding claim 2, Chen teaches the method according to claim 1, wherein determining the distance information between the point cloud data to be processed and the at least one historically estimated plane comprises: determining the distance information between the point cloud data to be processed and the at least one historically estimated plane based on coordinate information of the point cloud data to be processed and plane information of the at least one historically estimated plane (paras. 0052-0058; RANSAC algorithm determining distance information between the point cloud data to be processed and at least one historically estimated plane using coordinate information of the point cloud data).
Or, in the alternative, under 35 U.S.C. 103 as obvious over Zheng:
In the same field of endeavor Zheng teaches
wherein determining the distance information between the point cloud data to be processed and the at least one historically estimated plane comprises: determining the distance information between the point cloud data to be processed and the at least one historically estimated plane based on coordinate information of the point cloud data to be processed and plane information of the at least one historically estimated plane (page 8, line 26 to page 9, line 53; “RANSAC) algorithm to perform plane detection,”; “S23: calculating the distance from all data points in the sub-data set to the first fitting plane; setting the data point with the distance less than the first fitting plane to the first set threshold as the inner point; Specifically, after obtaining the plane equation of the first fitting plane by S22, it can calculate all data points of the subdata set to the distance from the first fitting plane, taking a point Pj (Xj, Yj, Zj) in the sub-data set as an example, Pj to the first fitting plane distance is: then setting the data point with the distance from the first fitting plane less than the first set threshold value as the inner point; setting the data point with the distance greater than or equal to the first set threshold value with the first fitting plane as the free point; the free point will be processed in the subsequent process; in this step, it is not processed.”)
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Zheng in Chen to have wherein determining the distance information between the point cloud data to be processed and the at least one historically estimated plane comprises: determining the distance information between the point cloud data to be processed and the at least one historically estimated plane based on coordinate information of the point cloud data to be processed and plane information of the at least one historically estimated plane for utilizing RANSAC algorithm optimizing fitting plane according to inner points yielding a predicted result.
Regarding claim 6, Chen teaches the method according to claim 1, further comprising:
in a case that it is detected again that the video frame to be processed comprises new point cloud data to be processed, adding the target estimated plane into the at least one historically estimated plane, and determining distance information between the point cloud data to be processed and the at least one historically estimated plane (para. 0052-0054, 0057).
Or, in the alternative, Zheng teaches
in a case that it is detected again that the video frame to be processed comprises new point cloud data to be processed, adding the target estimated plane into the at least one historically estimated plane, and determining distance information between the point cloud data to be processed and the at least one historically estimated plane (page 8, line 26 to page 9, line 53; page 13).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Zheng in Chen to have in a case that it is detected again that the video frame to be processed comprises new point cloud data to be processed, adding the target estimated plane into the at least one historically estimated plane, and determining distance information between the point cloud data to be processed and the at least one historically estimated plane for utilizing RANSAC algorithm optimizing detection of multiple planes yielding a predicted result.
Regarding claim 9, Chen teaches An electronic device (Fig. 6), comprising:
one or more processors (controller 67); and
a storage apparatus, configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method (Fig. 6; paras. 0111-0112) comprising: (corresponding features as presented in claim 1).
Regarding claim 10, Chen teaches A non-transitory computer readable storage medium comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor (Fig. 6; paras. 0111-0112), are configured to: (corresponding features as presented in claim 1).
Regarding claim 11, claim 11 reciting features corresponding to claim 2 is also rejected for the same reason above.
Regarding claim 15, claim 15 reciting features corresponding to claim 6 is also rejected for the same reason above.
Regarding claim 17, claim 17 reciting features corresponding to claim 2 is also rejected for the same reason above.
Regarding claim 20, claim 20 reciting features corresponding to claim 6 is also rejected for the same reason above.
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 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) 3-5, 12-14 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (US 20210035346 A1) in views of Zheng et al (CN-107341804-B) and Poelman et al (US 20150125071 A1).
Regarding claim 3, Chen teaches everything as claimed in claim 1, but fails to teach
wherein in a case that the distance information and the data volume of the point cloud data to be processed satisfy the plane estimation condition, performing plane estimation on the point cloud data to be processed based on the random sample consensus algorithm to generate the target estimated plane comprises:
in a case that the distance information is greater than a preset distance threshold in the plane estimation condition, accumulating the data volume of the point cloud data to be processed to obtain a current data volume; and
in a case that the current data volume reaches a data volume threshold in the plane estimation condition, performing, based on the random sample consensus algorithm, plane estimation processing on each piece of currently stored point cloud data to be processed, to generate the target estimated plane.
However, in the same field of endeavor Zheng teaches
wherein in a case that the distance information and the data volume of the point cloud data to be processed satisfy the plane estimation condition, performing plane estimation on the point cloud data to be processed based on the random sample consensus algorithm to generate the target estimated plane comprises:
in a case that the distance information is greater than a preset distance threshold in the plane estimation condition, accumulating the data volume of the point cloud data to be processed to obtain a current data volume (page 8, line 26 to page 9, line 53; “RANSAC) algorithm to perform plane detection,”; “S23: calculating the distance from all data points in the sub-data set to the first fitting plane; setting the data point with the distance less than the first fitting plane to the first set threshold as the inner point; Specifically, after obtaining the plane equation of the first fitting plane by S22, it can calculate all data points of the subdata set to the distance from the first fitting plane, taking a point Pj (Xj, Yj, Zj) in the sub-data set as an example, Pj to the first fitting plane distance is: then setting the data point with the distance from the first fitting plane less than the first set threshold value as the inner point; setting the data point with the distance greater than or equal to the first set threshold value with the first fitting plane as the free point; the free point will be processed in the subsequent process; in this step, it is not processed.”); and
distance between the free data point and the first plane of the global plane is less than the second set threshold value, adding the free data point into the first plane”).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Zheng in Chen to have wherein in a case that the distance information and the data volume of the point cloud data to be processed satisfy the plane estimation condition, performing plane estimation on the point cloud data to be processed based on the random sample consensus algorithm to generate the target estimated plane comprises: in a case that the distance information is greater than a preset distance threshold in the plane estimation condition, accumulating the data volume of the point cloud data to be processed to obtain a current data volume; performing, based on the random sample consensus algorithm, plane estimation processing on each piece of currently stored point cloud data to be processed, to generate the target estimated plane for utilizing RANSAC algorithm optimizing fitting plane according to inner point yielding a predicted result.
Moreover, in the same field of endeavor Poelman teaches
in a case that the current data volume reaches a data volume threshold in the plane estimation condition, performing, based on the random sample consensus algorithm, plane estimation processing on each piece of currently stored point cloud data to be processed, to generate the target estimated plane (paras. 0043-0044; “a minimum number of points may be required in order to perform plane fitting (e.g., one or two points may not comply with the minimum threshold number of points while ten points may be sufficient). Similarly, too many points may be overly burdensome to perform the plane fitting (e.g., consumes a disproportionate amount of processing and/or time)”).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Poelman in the combination to have in a case that the current data volume reaches a data volume threshold in the plane estimation condition, performing, based on the random sample consensus algorithm, plane estimation processing on each piece of currently stored point cloud data to be processed, to generate the target estimated plane for ensuring sufficient number of points being available for plane detection so that plane detection performance can be optimized yielding a predicted result.
Regarding claim 4, Chen teaches everything as claimed in claim 1, but fails to teach
further comprising: in a case that the distance information is less than a preset distance threshold in the plane estimation condition, determining a target historically estimated plane corresponding to the point cloud data to be processed, and fusing the point cloud data to be processed to the target historically estimated plane.
However, in the same field of endeavor Zheng teaches
further comprising: in a case that the distance information is less than a preset distance threshold in the plane estimation condition, determining a target historically estimated plane corresponding to the point cloud data to be processed, and fusing the point cloud data to be processed to the target historically estimated plane (page 8, line 26 to page 9, line 53; “RANSAC) algorithm to perform plane detection,”; “S23: calculating the distance from all data points in the sub-data set to the first fitting plane; setting the data point with the distance less than the first fitting plane to the first set threshold as the inner point”; page 13: “S3: calculating the distance from all data points in the sub-data set to the first fitting plane; setting the data point with the distance less than the first fitting plane to the first set threshold as the inner point; S4: re-performing plane detection according to the inner point; obtaining the second fitting plane;… the plane with the most inner point number in N second fitting plane is determined as a local plane”; the inner points are fused/collected into the inner point collection).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Zheng in Chen to have further comprising: in a case that the distance information is less than a preset distance threshold in the plane estimation condition, determining a target historically estimated plane corresponding to the point cloud data to be processed, and fusing the point cloud data to be processed to the target historically estimated plane for utilizing RANSAC algorithm optimizing fitting plane according to inner points yielding a predicted result.
Regarding claim 5, the combination of Chen, Zheng and Poelman teaches everything as claimed in claim 3. In addition, Poelman teaches further comprising: in a case that the current data volume does not reach the data volume threshold, accumulating the current data volume, and storing the point cloud data to be processed, so as to perform, in a case that the current data volume reaches the data volume threshold, plane estimation processing on the stored point cloud data to be processed to obtain the target estimated plane (paras. 0043-0044; “a minimum number of points may be required in order to perform plane fitting (e.g., one or two points may not comply with the minimum threshold number of points while ten points may be sufficient). Similarly, too many points may be overly burdensome to perform the plane fitting (e.g., consumes a disproportionate amount of processing and/or time)”).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Poelman in the combination to have further comprising: in a case that the current data volume does not reach the data volume threshold, accumulating the current data volume, and storing the point cloud data to be processed, so as to perform, in a case that the current data volume reaches the data volume threshold, plane estimation processing on the stored point cloud data to be processed to obtain the target estimated plane for ensuring sufficient number of points being available for plane detection so that plane detection performance can be optimized when needed yielding a predicted result.
Regarding claims 12-14, claims 12-14 reciting features corresponding to claims 3-5 are also rejected for the same reasons above, respectively.
Regarding claim 18, claim 18 reciting features corresponding to claim 3 is also rejected for the same reason above.
Claim(s) 7, 16 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (US 20210035346 A1) in views of Wei et al (US 20200250852 A1).
Regarding claim 7, Chen teaches everything as claimed in claim 1, but fails to teach
further comprising: in a case that it is detected that the target effect is to be added to the current video frame to be processed, determining a target display position of the target effect based on touch point position information on a display interface and the at least one historically estimated plane; and displaying the target effect at the target display position to obtain a target effect video frame corresponding to the current video frame to be processed.
However, in the same field of endeavor Wei teaches
further comprising: in a case that it is detected that the target effect is to be added to the current video frame to be processed, determining a target display position of the target effect based on touch point position information on a display interface and the at least one historically estimated plane; and displaying the target effect at the target display position to obtain a target effect video frame corresponding to the current video frame to be processed (para. 0024: “the user can simply touch the viewfinder to place fun, three-dimensional virtual objects on static or moving surfaces (e.g. horizontal surfaces such as tables, floors, sidewalks, hands, etc.), allowing the user to seamlessly interact with a dynamic real-world environment”).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Wei in Chen to have further comprising: in a case that it is detected that the target effect is to be added to the current video frame to be processed, determining a target display position of the target effect based on touch point position information on a display interface and the at least one historically estimated plane; and displaying the target effect at the target display position to obtain a target effect video frame corresponding to the current video frame to be processed for allowing the user to seamlessly interact with a dynamic real-world environment yielding a predicted result.
Regarding claim 16, claim 16 reciting features corresponding to claim 7 is also rejected for the same reason above.
Regarding claim 21, claim 21 reciting features corresponding to claim 7 is also rejected for the same reason above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Quan Pham whose telephone number is (571)272-4438. The examiner can normally be reached Mon-Fri 9am-7pm.
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/Quan Pham/Primary Examiner, Art Unit 2637
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