DETAILED ACTION
This Office action is in response to amendment filed on 11/24/2025.
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. Applicant’s amendments filed 11/24/2025 to the claims are accepted and entered. In this amendment:
Claims 1-3, 5, 7-12, 14-16, and 20 have been amended.
Claims 1-20 are examined.
Claim 13 is examined because the prior art was found.
Response to Argument
3. Applicant’s arguments filed on 11/24/2025 have been fully considered.
The argument regarding the 112 rejection to claim 8 is persuasive and thus, the
112 rejection of claim 8 is withdrawn.
The arguments regarding the 101 rejection are not persuasive.
Applicant argues that the claims should be found to integrate any alleged
judicial exception into a practical application. More specifically, the pending claims should be found to reflect an improved 3D surface estimation technique that improves surface estimation and machine control and therefore amounts to an improvement in the fields of perception and control for autonomous and semi-autonomous machines. Initially, the pending claims should not be found to recite a mental or mathematical process because the claims recite a number of elements that cannot reasonably be interpreted as covering mere mental steps or mathematical processes. Claim 1 recites "generating and iteratively smoothing a projected representation of an estimated three-dimensional (3D) surface structure based at least on the depth data and ego-motion data." This is not something that can be practically performed in the human mind, so claim 1 should not be found to recite a mental process. Even if the pending claims were found to recite an abstract idea, they should be found to integrate any abstract idea into a practical application. One consideration indicative of an element (or combination of elements) that may have integrated an exception into a practical application is whether the element improves the functioning of a computer or some other technology or technical field. Remark, p. 14.
In response, the Examiner respectfully disagrees. Claims 1, 11, and 20 recite a mathematical concept of “generating and iteratively smoothing a projected
representation of an estimated 3D surface structure” and a mental process of “controlling one or more operations of the ego-machine based at least on the estimated 3D surface structure”. The additional element of claim 1 and 20 “generating depth data” using generic depth sensor of ego-machine is mere data gathering which is a form of insignificantly extra-solution activity. The claims do not recite any additional element that amount significantly more than the abstract idea. Thus, the claims are not eligible.
(ii) Further Applicant refers to the Specification that, "an autonomous vehicle may be better Equipped to navigate through a detected navigable space, avoid obstacles, adapt the vehicle's suspension system to match the current road surface (e.g., by compensating for bumps in the road), to navigate the vehicle to avoid protuberances (e.g., dips, holes) in the road, and/ or to apply an early acceleration or deceleration based on an approaching surface slope in the road. Any of these functions may serve to enhance safety, improve the longevity of the vehicle, improve energy efficiency, and/or provide a smooth driving experience." Id These should all be considered bona fide technological benefits. Remark, p.15.
In response to applicant’s argument that the references fail to show certain features of applicant’s invention, it is noted that the features upon which applicant relies as stated above in (ii) are not recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Claim Objections
4. Claim 6 is objected to because of the following informalities:
Claim 6 is a non-compliant amendment claim. It is unclear whether the claim was intended or not to be amended. It is interpreted the claim has been amended.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a method, machine, system (claims 1, 11, and 20) which are statutory categories.
However, evaluating claims 1, 11, and 20, under Step 2A, Prong One, the claims are directed to the judicial exception of an abstract idea using the groupings of mathematical concepts including “generating and iteratively smoothing a projected representation of an estimated 3D surface structure” and mental processes of “controlling one or more operations of the ego-machine based at least on the estimated 3D surface structure”.
Next, Step 2A, Prong Two evaluates whether additional elements of the claims “integrate the abstract idea into a practical application” in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the exception. The additional element of claims 1 and 20 “generating depth data” using generic depth sensor of ego-machine is mere data gathering which is a form of insignificantly extra-solution activity. In addition, “depth sensors of ego-machine”, i.e., ego-machine equipped with LiDAR sensor, see spec. [0030] are considered insignificant extra-solution (e.g., selecting a particular data source or type to be manipulated). The claims do not recite any additional element that can integrate the judicial exception into a practical application. The additional elements do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
At Step 2B, consideration is given to additional element that may make the abstract idea significantly more. Under Step 2B, there are no additional elements that make the claims significantly more than the abstract idea.
The additional limitation as recited above in step 2A - Prong Two, is considered insignificant extra-solution activity, and not sufficient to integrate the claims into a particular practical application under Step 2B. The claims are not eligible.
Dependent claims 2-10 and 12-19 are also not eligible because they merely add details to the algorithm which forms the abstract idea and/or include additional elements that are insignificant extra-solution activities, and mere computer implementation using a generic computer element such as “processor/processing units”, which do not integrate the claims into a practical application or amount to "significant more". Thus, the dependent claims are ineligible.
Claim Rejections - 35 USC § 102
9. 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)(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.
10. Claims 1, 3-4, 6-12, and 15-20 are rejected under AIA 35 U.S.C. 102(a)(2) as being anticipated over Mahata et al., hereinafter “Mahata” (US 2023/0186647- of record).
As per Claim 1, Mahata teaches a method comprising:
generating, using one or more depth sensors of an ego-machine, depth data corresponding to one or more sensory fields of the one or more depth sensors ( LiDAR sensor considered “depth sensor” generates LiDAR data considered “depth data” which includes multiple point clouds, see Abstract, [0022], the car moving direction considered “generating ego-motion data” [0369] ); and
generating and iteratively smoothing a projected representation of an estimated three-dimensional (3D) surface structure based at least on the depth data and ego-motion data ( generate individual “supervoxels” is considered “a 3D representation of surface structure”, see [0141], [0022], car moving direction considered “generating ego-motion data” [0369], LiDAR scan trajectory on road surface is smooth [0347], repeatedly extracted point cloud, i.e., size is larger, considered in a form of iteratively smoothing, see [0117], [0139], [0149], [0156]); and
controlling one or more operations of the ego-machine based at least on the estimated 3D surface structure (define road segment, filtering the point cloud data and removing all data below a predetermined height above a road plane, see [0031]-[0035], filtering the input cloud points and extracting [0238], filtering and removing many false road candidates [0339]).
As per Claim 3, Mahata teaches the method of claim 1, wherein the iteratively smoothing of the projected representation of the estimated 3D surface structure is based at least on deviations between measured points represented in the depth data and corresponding points of the estimated 3D surface structure (standard deviation of detected road, see [0367], [0297]. The deviation between measured points standard deviation is called “standard deviation”).
As per Claim 4, Mahata teaches the method of claim 3, wherein the smoothing comprises at individual iterations of the one or more iterations, updating one or more weights associated with one or more points of one or more point clouds corresponding to the depth data (LiDAR generates “point clouds/depth data”, see Abstract, each point having a weight [0144]-[0145]. Mapping image data to point cloud data considered “update weight” [0179], [0318]).
As per Claim 6, Mahata teaches the method of claim 4, further comprising detecting one or more obstacles using projected representation of the estimated 3D surface structure based on determining a subset of the one or more points having associated heights that are greater than a threshold height above the estimated 3D surface structure (identify roadside poles on road surface from LiDAR point cloud data, see Abstract. Detect side poles in point cloud data, see Claim 6 in page 20. Pole height calculation [0080]. Indicate if the pole larger than a threshold [0273]-[0275]).
As per Claim 7, Mahata teaches the method of claim 6, wherein the associated heights correspond to relative heights with respect to the estimated 3D surface structure (height information of n-th pole considered “associated height of n-th pole”, see [0273], [0272]).
As per Claim 8, Mahata teaches the method of claim 3, wherein at a first set of iterations the smoothing is applied at a first spatial resolution and at a second set of iterations the smoothing is applied at a second spatial resolution that is greater than the first spatial resolution (image data is captured sequentially a known distance after the previous image, thus, “the previous image” considered “a first spatial resolution” and the image captured after “the first image” considered “a second spatial resolution”, see [0043], [0108], [0182]. Down sampling the point cloud to form a voxelized grid, or reducing the size of the point cloud data set considered “smoothing the spatial resolution because this operation decreases/ reduces spatial resolution, i.e., after down sampling, a first spatial resolution will be smaller than a second, higher spatial resolution, see [0044], [0046], [0113]).
As per Claim 9, Mahata teaches the method of claim 1, wherein the projected representation of the estimated 3D surface structure a grid of equally sized cells (down sampling the point cloud data to form “a voxelized grid” considered using equally sized cells [0023], i.e., “3D voxelized grid” meaning having equally sized cells [0113] ).
As per Claim 10, Mahata teaches the method of claim 1, further comprising:
generating, using a deep neural network (DNN), data representative of a semantic segmentation map corresponding to the estimated 3D surface structure (using deep learning fast RCNN for road marking and boundary detection and classification considered “generating data labelling” that results in a semantic segmentation map [0160]-[0161]);
identifying one or more points of one or more point clouds represented by the depth data that are not part of the estimated 3D surface structure based at least on comparing the one or more points to corresponding locations in the semantic segmentation map (identify lane marking of point cloud and identify road separating curb and road boundary lines, see [0337]-[0338], identify trees/ detect poles in the point cloud data, see [0035]-[0036], [0001] ); and
excluding the one or more points from the projected representation of the estimate 3D surface structure (exclude surrounding terrain as road segment, see Claim 3, remove many false road candidates from input point cloud [0338], [0071], Fig 16: Step-1).
As per Claim 11, Mahata teaches a processor comprising: one or more processing units (machine learning algorithm is trained to adapt is required a physical processor to compute, see [0159] ) to generate and iteratively smooth a projected representation of an estimated 3D surface structure based at least on one or more point clouds and motion of an ego-machine over time ( generate individual “supervoxels” is considered “a 3D representation of surface structure”, see [0141], [0022], car moving direction considered “generating ego-motion data” [0369], LiDAR scan trajectory on road surface is smooth [0347], repeatedly extracted point cloud, i.e., size is larger, considered in a form of iteratively smoothing, see [0117], [0139], [0149], [0156]); and
execute one or more operations that control motion of the ego-machine based at least on the estimated 3D surface structure (define road segment, filtering the point cloud data and removing all data below a predetermined height above a road plane, see [0031]-[0035], filtering the input cloud points and extracting [0238], filtering and removing many false road candidates [0339] ).
As per Claim 12, Mahata teaches the processor of claim 11, where the one or more processing units are further to generate the projected represented of the estimated 3D surface structure over a plurality of iterations, at least, by smoothing the projected represented at individual iterations of the plurality of iterations (projecting the image based road detection result on point cloud considered generating a projected represented of the estimated 3D surface structure, see [0109], filtering the output of point cloud considered a process that generates projected represented of the estimated 3D surface structure, see [0073]-[0074] ).
As per Claim 15, Mahata teaches the processor of claim 11, wherein the
projected representation of the estimated 3D surface structure models the estimated surface as a grid of cells (a 3D voxelized grid” considered a grid composed of volumetric cells called “voxels” or a grid of voxels, see [0113], [0141]).
As per Claim 16, Mahata teaches the processor of claim 15, wherein the one or more processing units are further to perform object detection using the estimated 3D surface structure, at least, by determining one or more points of the one or more point clouds associated with height values greater than a threshold height relative to the estimated 3D surface structure (identify roadside poles on road surface from LiDAR point cloud data, see Abstract. Detect side poles in point cloud data, see Claim 6. Pole height calculation [0080]. Indicate if the pole larger than a threshold [0273]-[0275]).
As per Claim 17, Mahata teaches the processor of claim 11, wherein the one or more processing units are further to use calibration data corresponding to one or more depth sensors that generate data representative of the one or more points clouds to convert the one or more point clouds from a coordinate system of the one or more depth sensors to a coordinate system of the ego-machine (for every given camera snapshot location, rotate the point cloud around the z-axis so that azimuth angle of the point cloud is aligned with car azimuth angle” considered “coordinate conversion from the camera or LiDAR frame into a consistent vehicle-centric coordinate frame,” i.e., slice the point cloud up to 4 meter in y-direction to starting correct vehicle position considered “calibrate data based on the depth data” [0114], correct camera locations at the time of camera snapshot and its azimuth al angle θ considered calibrate data or correct car azimuth angles [0191]).
As per Claim 18, Mahata teaches the processor of claim 11, wherein the one or more point clouds include a first point cloud corresponding to a first time and a second point cloud corresponding to a second time prior to the first time, wherein the one or more processing units are further to convert the second point cloud to a coordinate system of the first point cloud using the motion of the ego-machine between the second time and the first time (camera image data including successive series of images taken at regular distance intervals “i.e., first/second time” from a vehicle, the image data includes the azimuth angle of the vehicle position “coordinate” relative to the y-axis of the point cloud data for each image [0015], for the i-th camera image, convert the image data to a point cloud domain to produce a first point cloud “step a” [0016], rotate associated cloud points of the car position “step b” to form a first point cloud data “step c” [0017]-[0018], repeated the steps a-c to generate a second point cloud data [0019], combine the first point cloud data with the second point cloud data [0021]. It is note combine or merge two point clouds involves transforming into the coordinate system of the other, i.e., merging/align a second point cloud to the first coordinate of the first point cloud, see [0206], [0387], [0402]-[0404]).
As per Claim 19, Mahata teaches the processor of claim 11, wherein the processor is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations (using deep learning fast RCNN [0160], applying deep learning [0268] ); a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.
As per Claim 20, Mahata teaches a system comprising: one or more processing units to:
generate, using one or more depth sensors of an ego-machine, depth data corresponding to one or more sensory fields of the one or more depth sensors ( LiDAR sensor considered “depth sensor” generates LiDAR data considered “depth data” which includes multiple point clouds, see Abstract, [0022], the car moving direction considered “generating ego-motion data” [0369] );
generate and iteratively smooth a projected representation of an estimated three-dimensional (3D) surface structure based at least on the depth data and ego-motion data (generate individual “supervoxels” is considered “a 3D representation of surface structure”, see [0141], [0022], car moving direction considered “generating ego-motion data” [0369], LiDAR scan trajectory on road surface is smooth [0347], repeatedly extracted point cloud, i.e., size is larger, considered in a form of iteratively smoothing, see [0117], [0139], [0149], [0156] ); and
execute one or more control operations of the ego-machine based at least on the projected representation of the estimated 3D surface structure (define road segment, filtering the point cloud data and removing all data below a predetermined height above a road plane, see [0031]-[0035], filtering the input cloud points and extracting [0238], filtering and removing many false road candidates [0339] ).
Claim Rejections - 35 USC § 103
9. The following is a quotation under AIA of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action.
A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
10. Claim 2 is rejected under 35 U.S.C. 103 as being obvious over Mahata in view of US patent 7777669 of Tokoro et al., hereinafter Tokoro.
As per Claim 2, Mahata teaches the method of claim 1, but does not teach wherein the controlling of the one or more operations of the ego-machine comprises at least one of adapting a suspension system, generating a path that avoids a protuberance or detected object, or applying an acceleration or deceleration based at least on the estimated 3D surface structure. Tokoro teaches the controlling of the one or more operations of the ego-machine comprises at least one of adapting a suspension system, generating a path that avoids a protuberance or detected object (Fig 1 shows suspension control 8 adapting to detect object, or generate an image target 23 from “travelling direction” or “path way”, see col 7 lines 9-11, col 13 lines 47-58 ), or applying an acceleration or deceleration based at least on the estimated 3D surface structure. It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teaching of Mahata having suspension control to detect object as taught by Tokoro that would generate multiple positions direction results of the objects have been detected (Tokoro, Abstract).
11. Claim 5 is rejected under 35 U.S.C. 103 as being obvious over Mahata in view of Yang et al, hereinafter Yang (CN111798397A – of record).
As per Claim 5, Mahata teaches the method of claim 4, Mahata does not teach wherein the generating of the projected representation of the estimated surface structure (as addressed in claim 1 above) but does not teach further comprises initializing the one or more weights. Yang teaches initializing the one or more weights (initialization values of the model training in deep learning convolutional neural network considered “weight initialization” in deep learning, see page 7, para 3 para 2, point cloud image of a “laser radar” is “LiDAR”, see Abstract lines 1-2). It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teaching of Mahata having initialization in deep learning as taught by Yang that would facilitate initializing values in deep training for model training to provide the learning efficiency of a deep learning convolution neural network is improved, and the training time is saved (Yang, page 2, para 1).
12. Claims 13-14 are rejected under 35 U.S.C. 103 as being obvious over Mahata in view of US 2022/0319043 of Chandler et al., hereinafter Chandler).
As per Claim 13, Mahata teaches the processor of claim 12, but does not teach wherein the smoothing comprises evaluating a global cost function that penalizes deviations in height or range between points of the one or more point clouds and corresponding points of the estimated surface. Chandler teaches the smoothing comprises evaluating a global cost function that penalizes deviations in height or range between points of the one or more point clouds and corresponding points of the estimated surface (see [0102], [0113] – It is noted cross-entropy loss function is considered global cost function in machine learning ). It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teaching of Mahata evaluating a global cost function in point cloud as taught by Chandler that would minimize the overall error for the training set to the extent it can be minimized without overfitting (Chandler, [0103]).
As per Claim 14, Mahata teaches the processor of claim 12, but does not explicitly teach wherein the one or more processing units are further to apply smoothing of the projected representation of the estimated 3D surface structure at a first spatial resolution during a first set of iterations of the plurality of iterations. Chandler teaches the one or more processing units ( [0291]-[0292] ) are further to apply smoothing of the projected representation of the estimated 3D surface structure at a first spatial resolution during a first set of iterations of the plurality of iterations (noise filtering is to filter out “noise points”, i.e., apply K-NN filtering to remove points considered smoothing spatial resolution in point cloud, see [0198], [0023] ); and
at a second spatial resolution during a second set of iterations of the plurality of iterations, the first spatial resolution being different from the second spatial resolution (as stated above, iterations considered to include both first and second spatial resolution, see [0198]. Those pixels in the left and right images exhibit a relative "disparity", considered the first and second spatial resolutions are different [0150], the disparity assigned to pixels (i,j), see [0153], [0155] ).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teaching of Mahata smoothing first and second spatial resolutions as taught by Chandler that would output the depth extraction component disparity space and transforms that output into distance space to provide the depth map D in units of distance [0155], to minimize a measure of distance between the extracted points and a reconstructed 3D surface (Chandler, [0206] ).
Conclusion
13. Applicant's amendment necessitated the new ground 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 extension fee 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.
14. Any inquiry concerning this communication or earlier communications from the
examiner should be directed to LYNDA DINH whose telephone number is (571) 270-
7150. The examiner can normally be reached on M-F 10 AM-6 PM ET.
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/LYNDA DINH/Examiner, Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857