DETAILED ACTION
This is a response to applicant’s submissions filed on 02/23/2026. Claims 1-20 are presently pending and are presented for examination.
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 .
Election/Restrictions
In response to the Restriction/Election requirement dated 02/23/2026, Applicant has elected, without traverse, Group 1, currently including claims 1-20.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/05/2024 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 because FIG.’s 1-3 (e.g., image data 101 and lidar data 121) and FIG.'s 5-8 are blurry and/o. 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. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. 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.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to one or more processors comprising processing circuitry (i.e., a machine). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
One or more processors comprising processing circuitry to:
detect, based at least on one or more neural networks (NNs) comprising one or more transformers processing a representation of image data and LiDAR data corresponding to an environment of an ego-machine, one or more features of a surface in the environment [mental process/step]; and
control one or more operations of the ego-machine based at least on the one or more features of the surface.
The examiner submits that the foregoing bolded limitation(s) constitute “mental processes” because under its broadest reasonable interpretation, the claims cover detecting, within one’s mind, features of a surface in an environment based upon collected data including camera data.
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
One or more processors [applying the abstract idea using generic computing module] comprising processing circuitry to:
detect, based at least on one or more neural networks (NNs) comprising one or more transformers processing a representation of image data and LiDAR data corresponding to an environment of an ego-machine [applying the abstract idea using generic computing module], one or more features of a surface in the environment [mental process/step]; and
control one or more operations of the ego-machine based at least on the one or more features of the surface [extra-solution activity (data gathering)].
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “[o]ne or more processors” and “based at least on one or more neural networks…” the examiner submits that these are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Regarding the additional limitation of “controlling one or more operations of the ego-machine…” the examiner submits that given the breadth of this limitation, this may merely comprise storing or transmitting data, which would include extra-solution activities. Alternatively, the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor and neural network to perform the detection step amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And, as discussed above, in regards to the additional limitations of “control one or more operations of the ego-machine…” the examiner submits that, due to the breadth of this limitation, this may merely comprise storing or transmitting data, which would include extra-solution activities. In addition, these additional limitations (and the combination, thereof) amount to no more than what is well-understood, routine and conventional activity. Hence, the claim is not patent eligible.
Dependent claims 2-10, 12 and 14-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-10, 12 and 14-20 are not patent eligible under the same rationale as provided for in the rejection of claim 1. Independent claims 11 and 13 recite substantially similar technical features compared to claim 1 and are therefore are also not patent eligible under the same rationale as provided for in the rejection of claim 1.
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 (i.e., changing from AIA to pre-AIA ) 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)(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) 1-5, 10-17 and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li et al. "Li" (US 2025/0022143 A1).
Regarding claim 1 and similarly with respect to claim 11 and 13, Li teaches
One or more processors see [0098] comprising processing circuitry to:
detect, based at least on one or more neural networks (NNs) comprising one or more transformers processing a representation of image data and LiDAR data corresponding to an environment of an ego-machine see at least Figs. 1 and 3, and paras 0030-0050, wherein an autonomous vehicle (AV) (i.e. ego-machine) is provided including a sensing system 110 having LiDAR 112 and cameras 118 transmitting data (LiDAR and image data) to a perception system 130 including an object tracking pipeline (OTP) 132, wherein the OTP includes machine learning models 220-240 comprising deep neural networks having various neural networks and transformers) , one or more features of a surface in the environment see at least Fig. 4, para 0047, wherein the AV 402, via LiDAR sensor 404 and camera 406, captures features of a surface in an environment such as other vehicles 410, 420 and street signs 414, 424; and
control one or more operations of the ego-machine based at least on the one or more features of the surface see at least para 0039, wherein based upon the monitoring and prediction component the AV is controlled by the AV control system 140).
Regarding claim 2 and similarly with respect to claim 14, Li teaches
The one or more processors of claim 1, wherein the circuitry is further to generate a plurality of three-dimensional transformer queries based at least on one or more sampled points on the surface and one or more ego-motion compensated transformer predictions see Fig. 4 and paras 0019 and 0047-0050, wherein bounding boxes 412, 422 of two-dimensional camera images are converted, via a lifting transform (i.e. one or more NNs), to three-dimension representations (i.e. three dimensional transformer queries) of the driving environment) and wherein the transformation, based upon camera and LiDAR data, is based upon the formation of bounding boxes 412, 422). Further, see Fig. 4, wherein position dots are identified by LiDAR 404 and see at least see para 0038, wherein the AV includes prediction component 134.
Regarding claim 3 and similarly with respect to claim 15, Li teaches
The one or more processors of claim 1, wherein the circuitry is further to generate one or more three-dimensional transformer queries representing one or more three- dimensional locations based at least on one or more trajectories of the ego-machine see Fig. 4 and paras 0019 and 0047-0050, wherein bounding boxes 412, 422 of two-dimensional camera images are converted, via a lifting transform (i.e. one or more NNs), to three-dimension representations (i.e. three dimensional transformer queries) of the driving environment. As disclosed in para 0047, the images used to form the three-dimensional shapes are based upon cropped images formed by a cropping module 330, which utilizes both the camera and LiDAR images. Further, see at least [0039] where the AVCS 140 can also include a driving path selection system for selecting a particular path through the immediate driving environment while avoiding obstacles detected within the driving environment.
Regarding claim 4 and similarly with respect to claim 16, Li teaches
The one or more processors of claim 1, wherein the circuitry is further to generate one or more three-dimensional transformer queries representing one or more three-dimensional locations based at least on logarithmically sampling one or more trajectories of the ego-machine see at least [0026]-[0028] where a driving system may use the road profile and/or perturbation map to accurately navigate a vehicle through an environment. For example, the driving system may control the vehicle according to the three-dimensional road profile (e.g., according to hills which the road traverses). Objects detected in the environment may be detected based on the road profile and the system may constrain object detection to objects that are on (or close to) the road as defined by the road profile. By navigating the vehicle based on point-cloud-based three-dimensional features, it is implied that the vehicle is controlled to focus computing power on the immediate surroundings (dense sampling close by) while still observing the distant horizon (sparse sampling far away).
Regarding claim 5 and similarly with respect to claim 17, Li teaches
The one or more processors of claim 1, wherein the processing of the representation of the image data and the LiDAR data comprises refining one or more initial heights of the surface represented by one or more initial three-dimensional transformer queries based at least on fusing one or more sampled two-dimensional image features and one or more sampled two-dimensional LiDAR features in one or more cross-attention layers of the one or more transformers see Figs. 3 and 6, wherein camera and LiDAR data are combined (i.e. fused), which would include two-dimensional image features of both since three-dimensional aspects could not be rendered in two-dimensional form, using models (i.e. transformers, which include cross-attention layers). Further, see at least [0047] where determination of depths of objects can be further assisted by radar data, e.g., using a combination of camera images and the radar data.
Regarding claim 10 and similarly with respect to claim 12 and 20, Li teaches
The one or more processors of claim 1, wherein the one or more processors are comprised in at least one of:
a control system for an autonomous or semi-autonomous machine see at least Li [0042];
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 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.
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 (i.e., changing from AIA to pre-AIA ) 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) 6-9 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Yogamani et al. "Yogamani" (US 2024/0412534 A1).
Regarding claim 6 and similarly with respect to claim 18, Li teaches
The one or more processors of claim 1, wherein the circuitry is further to project one or more keypoints associated with one or more reference three-dimensional positions corresponding to one or more transformer queries representing [one or more initial heights of] the surface into extracted image features and extracted LiDAR features see Fig. 4 and [0047], wherein the transformation, based upon camera and LiDAR data, is based upon keypoints, shown as dots on objects detected by the LiDAR 404.
The examiner notes that [brackets] have been added around claim limitations not expressly disclosed by Li.
Li teaches all of the elements of the current invention as stated above except wherein the circuitry is further to project one or more keypoints associated with one or more reference three-dimensional positions corresponding to one or more transformer queries representing one or more initial heights of the surface into extracted image features and extracted LiDAR features.
Yogamani teaches that it is known to provide the method wherein the circuitry is further to project one or more keypoints associated with one or more reference three-dimensional positions corresponding to one or more transformer queries representing one or more initial heights of the surface into extracted image features and extracted LiDAR features. See at least [0023]-[0024] where a point-cloud-based three-dimensional features may be extracted from one or more point clouds (e.g., LIDAR point clouds) using a machine-learning encoder. The information from the point map can include elevation or height information (e.g., road elevation/ height). Further, see at least [0026] where the system of Yogamani comprises combining the image-based three-dimensional features, the point-cloud based three-dimensional features, and/or the map-based three-dimensional features to generate combined three-dimensional features. This may be done by using a volumetric voxel-attention transformer. The combined three-dimensional features are then used to generate a road profile and/or a perturbation map.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have modified Li to incorporate the teachings of Yogamani and provide the system wherein the circuitry is further to project one or more keypoints associated with one or more reference three-dimensional positions corresponding to one or more transformer queries representing one or more initial heights of the surface into extracted image features and extracted LiDAR features. In doing so, the driving system may use the road profile to accurately navigate a vehicle through an environment by making intelligent motion-planning and/or trajectory-planning decisions [0027].
Regarding claim 7 and similarly with respect to claim 19, Li in view of Yogamani teaches
The one or more processors of claim 1, wherein the circuitry is further to detect the one or more features of the surface based at least on the one or more transformers:
regressing a representation of one or more height values of one or more sampled points of the surface corresponding to each transformer query of one or more transformer queries see at least Yogamani [0031]-[0040] where road profile 114 may represent a globally smooth polynomial surface and perturbation map 116 may represent subtle variations in the surface (e.g., potholes, etc.). “For example, it may be easier to regress a smooth polynomial surface which is globally consistent using the road profile 114 and perturbation map 116 together. In one example, perturbation map 116 may represent subtle variations in the surface (e.g., to account for speed bumps and/or potholes).
Regarding claim 8, Li in view of Yoganami teaches
The one or more processors of claim 1, wherein the one or more NNs form a multitask network comprising a first transformer output head that regresses one or more surface profiles of the surface see at least Yoganami [0031]-[0040] and a second transformer output head that regresses one or more bounding shapes of detected road debris on the surface see Li Fig. 4, the Office further notes that while vehicle and sign objects are identified and included within bounding shapes, given the object identification and classification other objects would also be identified and bounded within a shape.
Regarding claim 9, Li in view of Yoganami teaches
The one or more processors of claim 1, wherein the one or more operations comprise at least one of:
avoiding one or more detected protuberances represented by the one or more features of the surface, adapting a suspension of the ego-machine based at least on a surface profile represented by the one or more features of the surface see at least Yoganami [0038] where perturbation map 116 may include a map of deviations from the road profile 114 (for example, potholes or bumps in the road) and further see at least [0027] where the driving system may be controlled to avoid potholes or bumps using the perturbation map, or applying an early acceleration or deceleration based at least on an approaching surface slope represented by the one or more features of the surface.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li et al. (“End-to-end Contextual Perception and Prediction with Interaction Transformer”) discloses a recurrent neural network comprising Transformer architecture configured to detect objects in 3D and forecast their future motion in the context of self-driving.
Cserna et al. (US 12,319,271 B2) discloses road surface condition guided decision making and prediction.
Li et al. (US 2025/0022143 A1) discloses object tracking across a wide range of distances for driving applications.
Moskowitz et al. (US 2025/0115240 A1) discloses using deep learning to identify road geometry from point clouds.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brittany Renee Peko whose telephone number is (408)918-7506. The examiner can normally be reached Monday - Thursday 8:30-6:30 PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Bishop can be reached at 571-270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B.R.P./06/21/2026Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
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