DETAILED ACTION
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 .
Status of Claims
This Office Action is in response to the application filed on December 19, 2024. Claims 1-20 are pending. Claims 1, 12 and 16 are independent.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/19/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
“road surface profile 650” is referenced in paragraph [0086] in the specification but is not included in Figure 6.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
In paragraph [0080], line 7, “in in bird’s eye view” should read as “in a bird’s eye view”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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.
Claims 1-4, 6-9, and 11-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kumar (US-20210150720-A1).
Regarding claim 1, Kumar teaches one or more processors comprising processing circuitry (see Kumar, figures 3A, 3C and 6-7, paragraphs 29, 98 and 103, regarding vehicle computing device 604 with CPU(s) 708 (one or more processors comprising processor circuitry) comprising vehicle control system 348) to:
generate an estimated three-dimensional (3D) representation of a surface in an environment of an ego-machine based at least on fitting one or more height values to one or more sets of LiDAR detections sampled in one or more local neighborhoods along one or more predicted trajectories of the ego-machine (see Kumar, figures 8-9, paragraphs 111-113 and 119-121, regarding ground surface estimation instructions 810 module generating “a piece-wise local ground representation” (3D representation) at step 925 (Estimate local ground at each spatial point) by performing ground surface segmentation from detected Read LiDAR point cloud 905 to include store maximum height for each valid special point 920 (fitting one or more height values from detected LiDAR sampled in an autonomous vehicle’s (ego-machine) travelling environment)); and
control one or more operations of the ego-machine based at least on the estimated 3D representation of the surface (see Kumar, figures 1, 3A and 3C, paragraphs 44 and 71, regarding vehicle 100 (ego-machine) operating autonomously in the environment based at least in part on sensor information, such as LiDAR data providing estimated 3D representation of the surface).
Regarding claim 2, Kumar teaches the one or more processors of claim 1, including wherein the processing circuitry is further to refine one or more ego-motion transforms aligning the LiDAR detections based at least on registering segmented LiDAR points clouds representing one or more static reference surfaces in the environment (see Kumar, figures 1, 3A, 3C and 8, paragraphs 108-109, regarding performing “real-time ground surface segmentation for LiDAR point clouds by computing a local piece-wise ground representation of the scenes' surface making the representation more accurate (refining)”, wherein the “the local neighbor search likely returns (registers) a ground point (a static surface reference in the environment) or at least a point very close to the ground”).
Regarding claim 3, Kumar teaches the one or more processors of claim 1, including wherein the processing circuitry is further to refine one or more ego-motion transforms aligning the LiDAR detections based at least on registering LiDAR points clouds segmented based at least on height above an estimated ground surface (see Kumar, figures 8-9, paragraphs 119-122, regarding a (refining) process for ground surface segmentation resulting with classify(ing) (registering) each spatial point as ground or non-ground 930, that “can be based on the maximum height value for each spatial point and (above) the estimated local ground (surface) value for each spatial point”, wherein the “maximum height map can be constructed by the navigation system 302 from the three-dimensional representation of each of the one or more objects in the physical surroundings detected by the LiDAR sensor 320”).
Regarding claim 4, Kumar teaches the one or more processors of claim 1, including wherein the processing circuitry is further to refine one or more ego-motion transforms aligning the LiDAR detections based at least on registering segmented LiDAR points clouds that remove one or more points in a band of heights above an estimated ground surface (see Kumar, figures 8-9, paragraphs 114-122, regarding “each spatial point can then be classified 930 by the navigation system 302 as a ground point or a non-ground point based on the minimum filtering (by height slicing) of each spatial point”, wherein minimum filtering is discarding (removing) one of more points in a band of heights (height slice/range) above an estimated ground surface).
Regarding claim 6, Kumar teaches the one or more processors of claim 1, including wherein the processing circuitry is further to sample a set of trajectory points along the one or more predicted trajectories, and sample the one or more sets of LiDAR detections within one or more designated 3D radii of at least one individual trajectory point of the set of trajectory points (see Kumar, figure 2, paragraph 25, regarding “ranging and imaging system 112 (LiDAR) may be configured to generate changing 360-degree views of the environment 200 in real-time, for instance, as the vehicle 100 drives. In some cases, the ranging and imaging system 112 may have an effective detection limit 204 that is some distance from the center of the vehicle 100 outward over 360 degrees. The effective detection limit 204 of the ranging and imaging system 112 defines a view zone 208 (e.g., an area and/or volume, etc.) surrounding the vehicle 100”, wherein the detection limit 204 is a designated 3D radii (distance)).
.
Regarding claim 7, Kumar teaches the one or more processors of claim 1, including wherein the processing circuitry is further to sample the one or more sets of LiDAR detections within a direction-dependent 3D radius of at least one individual trajectory point of the one or more predicted trajectories (see Kumar, figure 2, paragraph 25, regarding “ranging and imaging system 112 (LiDAR) may be configured to generate changing 360-degree views of the environment 200 in real-time, for instance, as the vehicle 100 drives. In some cases, the ranging and imaging system 112 may have an effective detection limit 204 that is some distance from the center of the vehicle 100 outward over 360 degrees. The effective detection limit 204 of the ranging and imaging system 112 defines a view zone 208 (e.g., an area and/or volume, etc.) surrounding the vehicle 100”, wherein the detection limit 204 is a 3D radii(distance) at a given direction-dependent angle in a 360-degree view).
Regarding claim 8, Kumar teaches the one or more processors of claim 1, including wherein the fitting of the one or more height values applies a nonlinear optimization to observed height values of the one or more sets of LiDAR detections sampled in at least one individual local neighborhood of the one or more local neighborhoods (see Kumar, figures 8-9, paragraphs 114-122, regarding “each spatial point can then be classified 930 by the navigation system 302 as a ground point or a non-ground point based on the minimum filtering (by height slicing) of each spatial point”, wherein minimum filtering (by applying a non-linear optimization and searching local neighborhood spatial points) is discarding (removing) one of more points in a band of heights (height slice/range) above an estimated ground surface, exemplary of fitting one or more height values from detected LiDAR sampled in an autonomous vehicle’s (ego-machine) travelling environment).
Regarding claim 9, Kumar teaches the one or more processors of claim 1, including wherein the fitting of the one or more height values applies a one-dimensional (1D) nonlinear optimization to observed height values of the one or more sets of LiDAR detections sampled along the one or more predicted trajectories (see Kumar, figures 8-9, paragraphs 114-122, regarding “each spatial point can then be classified 930 by the navigation system 302 as a ground point or a non-ground point based on the minimum filtering (by height slicing) of each spatial point”, wherein minimum filtering (by applying a 1D (one-dimensional) non-linear optimization and searching local neighborhood spatial points) is discarding (removing) one of more points in a band of heights (height slice/range) above an estimated ground surface, exemplary of fitting one or more (one-dimensional) height values from detected LiDAR sampled in an autonomous vehicle’s (ego-machine) travelling environment).
Regarding claim 11, Kumar teaches the one or more processors of claim 1, including wherein the one or more processors are comprised in at least one of:
a control system for an autonomous or semi-autonomous machine (see Kumar, figures 1 and 3A, paragraphs 19 and 29, regarding vehicle control system 348 for autonomous or semi-autonomous vehicle 100);
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;
a system for performing remote operations;
a system for performing real-time streaming;
a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing conversational AI operations;
a system implementing one or more language models;
a system implementing one or more large language models (LLMs);
a system implementing one or more vision language models (VLMs);
a system implementing one or more multi-modal language models;
a system for generating synthetic data;
a system for generating synthetic data using AI;
a system for performing one or more generative AI operations;
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.
Regarding claims 12-15, independent claim 12 is a method performing the identical function of the one or more processors comprising processor circuitry of independent claim 1, and similarly, dependent claims 13-15 of independent claim 12 are also performing identical functions corresponding to dependent claims 6, 8 and 11 of independent claim 1, respectively, therefore, claims 12-15 are also rejected under 35 USC § 102 for the same rationale as claims 1, 6, 8 and 11, respectively.
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.
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 5 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar (US-20210150720-A1) in view of Peterson (US-20220180578-A1).
this.
Regarding claim 5, Kumar teaches the one or more processors of claim 1, excluding wherein the processing circuitry is further to refine one or more ego-motion transforms aligning the LiDAR detections based at least on estimating pitch relative to an estimated ground surface.
However, Peterson remedies this shortfall with a teaching of ground segmentation that addresses and corrects LiDAR cloud point height errors due to pitch angle of the LiDAR beam, especially at long ranges (see Peterson, Abstract, paragraph 18).
wherein the processing circuitry is further to refine one or more ego-motion transforms aligning the LiDAR detections based at least on estimating pitch relative to an estimated ground surface.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar (US-20210150720-A1) in view of Anderson (US-20170137023-A1).
Regarding claim 10, Kumar teaches the one or more processors of claim 1, excluding wherein the one or more operations of the ego-machine comprise at least one of adapting a suspension system, generating a path that avoids a protuberance, or applying an acceleration or deceleration based at least on the estimated 3D representation of the surface.
However, Anderson remedies this shortfall with a teaching of an active safety suspension system for an autonomous vehicle in response to road surface data in the vehicle’s operating environment, such as, for example, detected LiDAR data (see Anderson, Abstract, figure 7A, paragraphs 8-9, 151, 136, 151 and 161).
It would have been obvious to one of ordinary skill at the time of applicant’s effective filing date to modify the one or more processors comprising processing circuitry of Kumar to further comprise the active safety suspension system of Anderson, because by incorporating this addition feature improves upon the operational autonomous driving safety and passenger comfort experience, therefore, modified Kumar enables wherein the one or more operations of the ego-machine comprise at least one of adapting a suspension system, generating a path that avoids a protuberance, or applying an acceleration or deceleration based at least on the estimated 3D representation of the surface.
Claims 16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (US-20210150720-A1) in view of Peake (US-20200074266-A1).
Regarding claim 16, independent claim 16 is a system comprising: one or more processors to control, within a simulation rendered using one or more light transport simulation algorithms, one or more operations of an ego-machine that simulates the identical function of the one or more processors comprising processing circuitry of independent claim 1 as taught by Kumar.
Peake teaches autonomous vehicle control simulation in virtual space (environment) with simulated sensor data to include LiDAR data (see Peake, Abstract, figure 2B, paragraph 7, 39, 69 and 71)
It would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to modify claim 1 as taught by Kumar to further comprise the autonomous vehicle control simulator of Peake because this integration improves upon development of autonomous driving by mitigating real-world testing costs and accidents, therefore, modified Kumar enables a system comprising: one or more processors to control, within a simulation rendered using one or more light transport simulation algorithms, one or more operations of an ego-machine in a simulated environment based at least on an estimated three-dimensional (3D) representation of a road surface in the simulated environment, the 3D representation of the road surface generated based at least on fitting one or more height values to one or more sets of simulated LiDAR detections sampled in one or more local neighborhoods along one or more predicted trajectories of the ego-machine.
Regarding claim 19, modified Kumar teaches the system of claim 16, including wherein the fitting of the one or more height values applies a nonlinear optimization to simulated height values of the one or more sets of simulated LiDAR detections sampled in at least one individual local neighborhood of the one or more local neighborhoods (see Kumar, figures 8-9, paragraphs 114-122, regarding “each spatial point can then be classified 930 by the navigation system 302 as a ground point or a non-ground point based on the minimum filtering (by height slicing) of each spatial point”, wherein minimum filtering (by applying a non-linear optimization and searching local neighborhood spatial points) is discarding (removing) one of more points in a band of heights (height slice/range) above an estimated ground surface, exemplary of fitting one or more height values from detected LiDAR sampled in an autonomous vehicle’s (ego-machine) travelling environment).
Regarding claim 20, modified Kumar teaches the system of claim 16, including wherein at least one of the processors is implemented in at least one processing node of a plurality of processing nodes of a data center and accessible to one or more remote clients via at least one of an application programming interface (API), or an application plug-in (see Peake, paragraphs 52-53, regarding client-server platform technology (data center) using API’s).
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (US-20210150720-A1) in view of Peake (US-20200074266-A1) and further in view of Zhang (CN-118069277-A).
Regarding claim 17, modified Kumar teaches the system of claim 16, excluding wherein the simulation is generated, at least in part, using one or more content creation applications of a three-dimensional (3D) content collaboration platform for 3D assets.
However, Zhang remedies this shortfall with a teaching of utilizing an Omniverse (collaboration) platform utilizing OpenUSD, an open-source, high-performance 3D scene description and file framework developed by Pixar for content creation, interchange, and simulation. It acts as a universal language for 3D data, enabling seamless collaboration, nondestructive editing, and interoperability between different 3D software tools across industries like VFX, animation, architecture, and robotics (see Zhang, Abstract, paragraphs n0049 and n0068).
It would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to modify the system of modified Kumar to further comprise the OpenUSD of Wang because utilizing this opensource modelling facilitates collaboration amongst developers with a common platform, therefore further modified Kumar enables wherein the simulation is generated, at least in part, using one or more content creation applications of a three-dimensional (3D) content collaboration platform for 3D assets.
Regarding claim 18, further modified Kumar teaches the system of claim 17, including wherein the simulated environment is represented in at least one content creation application of the one or more content creation applications using an OpenUSD format (see Zhang, Abstract, paragraphs n0049 and n0068).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached form PTO-892.
Conclusion
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/P.Y.N./Examiner, Art Unit 3661
April 15, 2026
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661
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