DETAILED ACTION
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
2. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without being integrated into a practical application and do not include additional elements that amount to significantly more than the judicial exception.
Utilizing the two step process adopted by the Supreme Court (Alice Corp vs CLS Bank Int'l, US Supreme Court, 110 USPQ2d 1976 (2014) and the recent 101 guideline, Federal Register Vol. 84, No., Jan 2019)), determination of the subject matter eligibility under the 35 USC 101 is as follows: Specifically, the Step 1 requires claim belongs to one of the four statutory categories (process, machine, manufacture, or composition of matter). If Step 1 is satisfied, then in the first part of Step 2A (Prong one), identification of any judicial recognized exceptions in the claim is made. If any limitation in the claim is identified as judicial recognized exception, then proceeding to the second part of Step 2A (Prong two), determination is made whether the identified judicial exception is being integrated into practical application. If the identified judicial exception is not integrated into a practical application, then in Step 2B, the claim is further evaluated to see if the additional elements, individually and in combination, provide “inventive concept” that would amount to significantly more than the judicial exception. If the element and combination of elements do not amount to significantly more than the judicial recognized exception itself, then the claim is ineligible under the 35 USC 101.
Looking at the claims, the claims satisfy the first part of the test 1A, namely the claims are directed to two of the four statutory classes, apparatus and method. In Step 2A Prong one, we next identify any judicial exceptions in the claims. In Claim 1 (as a representative example), we recognize that the limitations “estimating a ground height of the current environment based on the three-dimensional point clouds of the current environment by using a ground height estimation model based on a convolutional neural network, determining, based on the ground height, non-ground point clouds not belonging to the ground in the three-dimensional point clouds, performing obstacle detection on the non-ground point clouds to obtain one or more obstacles, and
determining a drivable area in the driving environment of the vehicle based on the position of the obstacles, wherein the estimating a ground height of the current environment based on the three-dimensional point clouds of the current environment by using a ground height estimation model based on a convolutional neural network comprises at least the following steps: griding the point clouds space of the three-dimensional point clouds by using the ground height estimation model, to form multiple three-dimensional point cloud grids, extracting three-dimensional convolution features of multiple downsampling scales and two-dimensional convolution features of multiple downsampling scales from three-dimensional point clouds in each of the point cloud grids, performing feature fusion on the three-dimensional convolution features and the two- dimensional convolution features to obtain point cloud grid features, and estimating, based on point cloud grid features of each of the point cloud grids, a ground height of a ground area in each of the point cloud grids,” are abstract ideas, as they recite a combination of mental process and usage of mathematical concept. Similar rejections are made for other independent and dependent claims. With the identification of abstract ideas, we proceed to Step 2A, Prong two, where with additional elements and taken as a whole, we evaluate whether the identified abstract idea is being integrated into a practical application.
In Step 2A, Prong two, the claims additionally recite “obtaining three-dimensional point clouds of a driving environment of a vehicle,” but said limitation is merely directed to insignificant data collection activity, recited at high level of generality. The claims additionally recite “a computer device,” “processor” and vehicle, but said limitations are merely directed to general-purpose computer and vehicle to be used in the implementation of the abstract idea. The claims do not improve the functioning of any machines and do not improve other technology. Rather, at most, the claim is an improvement in the abstract idea of estimating the ground height and drivable area. However, new or improved abstract ideas are still abstract ideas and not eligible under the 101. In short, the claims do not provide sufficient evidence to show that they are more than a drafting effort to monopolize the abstract idea. As such, the abstract idea is not integrated into a practical application. Consequently, with the identified abstract idea not being integrated into a practical application, we proceed to Step 2B and evaluate whether the additional elements provide “inventive concept” that would amount to significantly more than the abstract idea.
In Step 2B, the claims additionally recite “obtaining three-dimensional point clouds of a driving environment of a vehicle,” but said limitation is merely directed to insignificant data collection activity, recited at high level of generality, that are well-understood, routine and conventional. The claims additionally recite “a computer device,” “processor” and vehicle, but said limitations are merely directed to general-purpose computer and vehicle to be used in the implementation of the abstract idea, that are also well-understood, routine and conventional. As such, the claims do not recite additional elements that would amount to significantly more than the abstract idea.
In Summary, the claims recite abstract idea without being integrated into a practical application, and do not provide additional elements that would amount to significantly more than the abstract idea. As such, taken as a whole, the claims are ineligible under the 35 USC 101.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 8-9 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Zou et al., US-PGPUB 2018/0260636 (hereinafter Zou)
Regarding Claims 1, 8 and 15. Zou discloses a drivable area detection (Abstract), comprising:
obtaining three-dimensional point clouds of a driving environment of a vehicle (Fig. 1, 101);
estimating a ground height of the current environment based on the three-dimensional point clouds of the current environment by using a ground height estimation model based on a convolutional neural network (Fig. 1, Paragraph [0039]-[0044], convolutional neural network; Paragraph [0062], ground surface height),
determining, based on the ground height, non-ground point clouds not belonging to the ground in the three-dimensional point clouds, performing obstacle detection on the non-ground point clouds to obtain one or more obstacles (Paragraphs [0062]-[0063], portion located above the ground surface and below the fault as an obstacle); and
determining a drivable area in the driving environment of the vehicle based on the position of the obstacles (Paragraph [0005], perform obstacle avoiding operation, which obviously includes determining a drivable area in the driving environment), wherein the estimating a ground height of the current environment based on the three-dimensional point clouds of the current environment by using a ground height estimation model based on a convolutional neural network (Paragraph [0039]-[0044], convolutional neural network) comprises at least the following steps: griding the point clouds space of the three-dimensional point clouds by using the ground height estimation model, to form multiple three-dimensional point cloud grids (Paragraph [0041], obtain output of the training sample through an obstacle 3D block by manual marking, and grids), extracting three-dimensional convolution features of multiple downsampling scales (Paragraph [0042], convolution neural network) and two-dimensional convolution features of multiple downsampling scales from three-dimensional point clouds in each of the point cloud grids (Fig. 1, 3D to 2D grid projection and convolution neural network, Paragraphs [0032]-[0041]), performing feature fusion on the three-dimensional convolution features and the two- dimensional convolution features to obtain point cloud grid features, and estimating, based on point cloud grid features of each of the point cloud grids, a ground height of a ground area in each of the point cloud grids (Fig. 1, 102, Paragraphs [0016]; [0043]-[0058])
Regarding Claims 2, 9 and 16. Zou discloses the step of extracting three-dimensional convolution features of multiple downsampling scales and two-dimensional convolution features of multiple downsampling scales from a three-dimensional point cloud in each of the point cloud grids” specifically comprises: performing a convolution operation on the three-dimensional point clouds in each of the point cloud grids by using three-dimensional convolutions of multiple downsampling scales, to obtain the three-dimensional convolution features of the multiple downsampling scales of each of the point cloud grids (Paragraphs [0041]-[0042], obstacle 3D block from manual marking and convolution neural network); and converting the three-dimensional convolution features into initial two- dimensional convolution features, and performing a convolution operation on each of the initial two-dimensional convolution features by using two-dimensional convolutions of multiple downsampling scales, to obtain the final two-dimensional convolution features of the multiple downsampling scales of each of the point cloud grids (Fig. 1, 3D to 2D grid projection and convolution neural network, Paragraphs [0032]-[0041])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kumar et al., US-PGPUB 2021/0150720, object detection using point clouds
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/HYUN D PARK/Primary Examiner, Art Unit 2857