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 .
Claim Objections
Claims 1-20 are objected to because of the following informalities:
In claim 1, line 7, “revised LiDAR voxel memory” should be --a revised LiDAR voxel memory-- to correct a grammatical error.
In claim 8, line 8, “the missing transformed memory voxel” should be --the missing
In claim 9, lines 4-5, “the missing transformed memory voxel” should be --the missing
In claim 12, line 13, “revised LiDAR voxel memory” should be --a revised LiDAR voxel memory-- to correct a grammatical error.
In claim 19, line 8, “the missing transformed memory voxel” should be --the missing
In claim 20, line 8, “revised LiDAR voxel memory” should be --a revised LiDAR voxel memory-- to correct a grammatical error.
The other claim(s) not discussed above, or depending on the above claim(s), are objected to for inheriting the issue(s) from their linking claim(s).
Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities:
In [0034], “a LiDAR point cloud (4)” should be --a LiDAR point cloud 304-- to be consistent with FIG. 3.
In [0093], “the encoder (616)” should be -- the encoder (602)-- to be consistent with FIG. 6.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 6-10 and 17-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 6, it recites “transforming, in position, the plurality of LiDAR memory voxels to obtain a plurality of transformed memory voxels; padding the plurality of LiDAR memory voxels with the plurality of encoded voxels to obtain a plurality of padded memory voxels” in lines 3-6. It appears that the transforming step and the padding step are independent from each other.
However, according to specification [0072], the missing encoded voxel “is added to the memory voxels in the transformed LiDAR voxel memory.” That is, the padding step depends on the transforming step. Therefore, the claim limitation at issue is inconsistent with the specification.
As an example, the limitation at issue should be “transforming, in position, the plurality of LiDAR memory voxels to obtain a plurality of transformed memory voxels; padding the plurality of transformed memory voxels with the plurality of encoded voxels to obtain a plurality of padded memory voxels” to be consistent with the specification.
Claim 17 is rejected by analogy to claim 6.
The other claim(s) not discussed above, or depending on the above claim(s), are rejected for inheriting the issue(s) from their linking claim(s).
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.
MPEP 2106 outlines a two-part analysis for Subject Matter Eligibility as shown in the chart below.
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645
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Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter.
Step 2, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception.
Step 2A is a two-prong inquiry, as shown in the chart below.
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681
881
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Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. If the claim does not recite a judicial exception (a law of nature, natural phenomenon, or abstract idea), then the claim cannot be directed to a judicial exception (Step 2A: NO), and thus the claim is eligible at Pathway B without further analysis. Abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes.
Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B.
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 significantly more.
Regarding claim 1, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below).
Prong one: Whether the claim recites a judicial exception? (Yes). The claim is directed to an abstract idea because it recites the limitations beginning from “voxelizing the plurality of LiDAR points to obtain a plurality of LiDAR voxels” to the end of the claim. These limitations are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper).
Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites additional elements of “obtaining a LiDAR point cloud comprising a plurality of LiDAR points from a LiDAR sensor.” However, this is recited at a high level of generality to collect the data for the abstract idea, which is an insignificant extra-solution activity. See MPEP 2106.05(g). Accordingly, the additional elements are insufficient to integrate the abstract idea into a practical application of the abstract idea.
Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below).
The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two above, the additional element(s) in the claim are an insignificant extra-solution activity. Considered as a whole, the claim does not amount to significantly more than the abstract idea.
Claims 12 and 20 are similarly rejected by analogy to claim 1. Note that computer processors and computer readable medium are to invoke a generic computer for its computing power to facilitate the application of the abstract idea. They are insufficient to make the claim(s) a practical application of, or significantly more than, the abstract idea. See MPEP 2106.05(f).
Dependent claims 2-11 and 13-19 when analyzed as a whole respectively are held to be patent ineligible under 35 U.S.C. 101 because they either extend (or add more details to) the abstract idea or the additional recited limitation(s) (if any) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as discussed below: there is no additional element(s) in the dependent claims that sufficiently integrates the abstract idea into a practical application of, or makes the claims significantly more than, the judicial exception (abstract idea). The additional element(s) (if any) are mere instructions to apply an except, field of use, and/or insignificant extra-solution activities (applied to Step 2A_Prong Two and Step 2B; see MPEP 2016.05(f)-(h)) and/or well-understood, routine, or conventional (applied to Step 2B; see MPEP 2106.05(d)) to facilitate the application of the abstract idea.
Notes
Claims 1, 12, and 20 distinguish over the closest prior art of record as discussed below.
Regarding clams 1, 12, and 20, the closest prior art of record fails to teach the feature of claim 1 (as the representative): “revising a LiDAR voxel memory using the plurality of encoded voxels to obtain revised LiDAR voxel memory; decoding the revised LiDAR voxel memory to obtain a plurality of decoded LiDAR voxel memory features; and segmenting the plurality of LiDAR points using the plurality of decoded LiDAR voxel memory features to generate a segmented LiDAR point cloud,” in combination with the rest of the claim limitations as claimed and defined by the Applicant.
ROY (US 20210042557 A1) teaches a method of classifying objects as static objects or dynamic objects based on point cloud data, involving receiving point cloud data (i.e., the obtaining step); computing voxel sequences from the point cloud data (i.e., voxelizing); extracting voxel-wise semantic features from the voxel sequences (i.e., encoding); modeling voxel-wise temporal changes based on the voxel-wise semantic features; and classifying objects in the environment as dynamic objects or static objects based on the modeled voxel-wise temporal changes. ROY does not teach or suggest the feature at issue.
Englard et al. (US 20190179024 A1) teaches a method of classifying an object, involving a segmentation module that partitions lidar or radar point clouds into subsets of points that correspond to probable objects; a classification module that determines labels/classes for the subsets of points (segmented objects); and a tracking module that tracks segmented and/or classified objects over time (i.e., across subsequent point cloud frames). Englard does not teach or suggest the feature at issue.
ZHAO (CN 115371663 A) teaches a method of laser mapping, involving obtaining the laser point cloud data of the current frame, and performing feature extraction to the laser point cloud data of the current frame, obtaining the characteristic point data of the current frame; obtaining a voxel map, and matching the characteristic point data of the current frame with the voxel map, each voxel in the voxel map respectively corresponding to the same characteristic; updating the voxel map according to the matching result, obtaining the updated voxel map; and based on the updated voxel map, using the preset optimization algorithm to optimize the to-be-optimized pose data, to obtain the laser point cloud map according to the optimized pose data. One of the differences is that the voxel map is updated based on matching the characteristic point data of the current frame with the voxel map, different from “revising a LiDAR voxel memory using the plurality of encoded voxels” as claimed (emphasis added). ZHAO also fails to teach or suggest the rest of the feature at issue.
Yuan et al. (US 11468690 B2) teaches a method of road identification from LiDAR point cloud, involving updating an 3D occupancy grid map based on a subsequent frame of point cloud. However, this is different from “revising a LiDAR voxel memory using the plurality of encoded voxels” as claimed (emphasis added). Essentially, Yuan does not teach or suggest the feature at issue.
None of the closest prior art of record, singly or in combination, teaches or suggests the indicated feature as claimed.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Noh et al. ("HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection" CVPR 2021) teaches a method of 3D object detection from point cloud, involving a CNN that integrates both voxel-based and point-based features into a single 3D representation, by augmenting the point-based features with a memory module to reduce the computational cost. The memory items are updated by encouraging the aggregated prototypical and point-based features to be similar.
Wu et al. ("Deep 3D Object Detection Networks Using LiDAR Data: A Review" IEEE SENSORS JOURNAL, VOL. 21, NO. 2, JANUARY 15, 2021) provides a review of 3D object detection by LiDAR point cloud.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN C KUAN whose telephone number is (571)270-7066. The examiner can normally be reached M-F: 9:00AM-5:30PM.
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/JOHN C KUAN/Primary Examiner, Art Unit 2857
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