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 application number 18/947,351 filed on 11/14/2024, in which claims 1-25 are presented for examination.
Priority
Acknowledgment is made of applicant’s claim this application to be CIP of PCT/CN2024/099225, filed on 06/14/2024.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on 01/16/2025 has/have been received and considered.
Examiner Notes
Examiner cites particular paragraphs (or columns and lines) in the references as applied to Applicant’s claims for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP §2163.06. Applicant is reminded that the Examiner is entitled to give the Broadest Reasonable Interpretation (BRI) to the language of the claims. Furthermore, the Examiner is not limited to Applicant’s definition which is not specifically set forth in the claims. See MPEP §2111.01.
Claim Objections
Claim(s) 19 is/ are objected to because of the following informalities:
Claim 19 recites “neural networks having been trained” in lines 3 & 7. It should be “neural networks have been trained”.
Appropriate correction is required.
Claim Rejections – 35 USC §101
35 USC §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.
Claim(s) 19 & 21-25 is/are rejected under 35 USC §101 because the claimed invention is directed to an abstract idea without significantly more. See MPEP 2106 (III)
The determination of whether a claim recites patent ineligible subject matter is a two-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
Claim 19, A computer-implemented method comprising:
using, by a computer system [applying the abstract idea using generic computing module], one or more first neural networks to [particular technological environment or field of use without telling you how it is accomplished] generate a set of candidate trajectories [mental process/step], the one or more first neural networks having been trained using at least one real-world observation [particular technological environment or field of use without telling you how it is accomplished];
using, by the computer system [applying the abstract idea using generic computing module], one or more second neural networks to [particular technological environment or field of use without telling you how it is accomplished] generate a set of predicted scores for each candidate trajectory in the set of candidate trajectories [mental process/step], the one or more second neural networks having been trained using simulation results obtained by performing at least one simulation [particular technological environment or field of use without telling you how it is accomplished]; and
selecting, by the computer system [applying the abstract idea using generic computing module], at least one of the set of candidate trajectories as at least one predicted trajectory based at least in part on the set of predicted scores generated for each candidate trajectory in the set of candidate trajectories [mental process/step].
101 Analysis - Step 1: Statutory category – Yes
The claim recites a computer-implemented method comprising at least one step. The claim falls within one of the four statutory categories. See MPEP 2106.03
Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
The claim recites the limitation of generate a set of candidate trajectories; generate a set of predicted scores for each candidate trajectory in the set of candidate trajectories; and selecting at least one of the set of candidate trajectories as at least one predicted trajectory based at least in part on the set of predicted scores generated for each candidate trajectory in the set of candidate trajectories.
These limitation, as drafted, are simple processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of “by a/the computer system”. That is, other than reciting a “computer system”, nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the “computer system” language, the claim encompasses a person looking at data collected and forming a simple trajectory evaluation judgement(s), i.e., selection. The mere nominal recitation of by a controller does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process.
Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), 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, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: 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.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional elements or steps of using, by a computer system, one or more first neural networks … been trained using at least one real-world observation; using, by the computer system, one or more second neural networks … been trained using simulation results obtained by performing at least one simulation. The using of previously trained neural networks step(s)/element(s) is/are recited at a high level of generality, and amounts to mere linking use of a judicial exception to a particular technological environment or field of use without telling how it is accomplished. The computer system element is merely describes how to generally and merely automates the generate/selecting steps, therefore acting as a generic computer to perform the abstract idea and/ or “apply” the otherwise mental judgements using a generic or general-purpose processor, i.e. a computer. The computer system is recited at a high level of generality and is merely automates the generate/selecting steps. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(f).
Under the 2019 PEG, a conclusion that an additional element is insignificant extra- solution activity in Step 2A should be re-evaluated in Step 2B. Here, the using neural networks, and/or computer system element(s) were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field.
The background recites that the claimed neural network is using a conventional oneCCL that is a library that implements various applications for deep learning and machine learning workloads, and the Specification does not provide any indication that the neural network(s) is/are anything other than a conventional neural network and/or machine learning general-purpose library, See ¶¶78-781.
The Specification recites that the said computer system is a general-purpose processor, See ¶324, and does not provide any indication that the claimed computer system is anything other than a conventional computers, See ¶¶ 324 & 794.
Accordingly, a conclusion that the using neural networks steps, and the computer system element(s) elements is/are well-understood, routine, conventional activity that is/are supported under Berkheimer. Thus, the claim is ineligible.
Dependent claims 21-25 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 and amounts to mere input and/or output data manipulation.
Therefore, dependent claims 21-25 are not patent eligible under the same rationale as provided for in the rejection of claim 19.
Thus, claims 19 & 21-25 are ineligible under 35 USC §101.
Examiner suggests, in order to overcome the rejections under §101 outlined above, amending the base claim 19 to recite the subject matter of claim 20, i.e., “causing at least one device to move in accordance with the at least one predicted trajectory”.
Claim Rejections - 35 USC §102
In the event the determination of the status of the application as subject to AIA 35 USC §102 and §103 (or as subject to pre-AIA 35 USC §102 and §103) is incorrect, any correction of the statutory basis 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 USC §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.
Claim(s) 1-25 is/are rejected under 35 USC §102(a)(1) as being clearly anticipated by Publication No. 2406.06978v1 by Li et al. (hereinafter “Li”), which is found in the IDS submitted on 01/16/2025
As per claim 1, Li discloses a processor comprising:
one or more circuits to:
use one or more first machine learning processes trained to imitate real-world observations, and one or more second machine learning processes trained to imitate results obtained by performing at least one simulation to predict a trajectory (Li, in at least Abstract, Fig. 2 [reproduced here for convenience], and §§1-3, discloses Multimodal Planning with Multi-target Hydra-distillation (Hydra-MDP) consists of two networks: a Perception Network and a Trajectory Decoder, wherein the Perception Network builds upon the official challenge baseline Transfuser, which consists of an image backbone, a LiDAR backbone, and perception heads for 3D object detection and BEV segmentation, wherein multiple transformer layers connect features from stages of both backbones, extracting meaningful information from different modalities, such that the final output of the perception network comprises environmental tokens Fenv, which encode abundant semantic information derived from both images and LiDAR point clouds [i.e., one or more first machine learning processes trained to imitate real-world observations]. Li further discloses Trajectory Decoder, following Vadv2 [4], by constructing a fixed planning vocabulary to discretize the continuous action space, wherein to build the vocabulary 700K trajectories have been first randomly sampled from the original nuPlan database [2], wherein the planning vocabulary Vk is formed as K-means clustering centers of the 700K trajectories, where k denotes the size of the vocabulary. Vk is then embedded as k latent queries with an MLP, sent into layers of transformer encoders [19], and added to the ego status E, wherein the intuition behind this imitation target is to reward trajectory proposals that are close to human driving behaviors. Li also discloses that though the imitation target provides certain clues for the planner, it is insufficient for the model to associate the planning decision with the driving environment under the closed-loop setting, leading to failures such as collisions and leaving drivable areas [14]. Therefore, to boost the closed-loop performance of our end-to-end planner, Multi-target Hydra-Distillation is proposed, a learning strategy that aligns the planner with simulation-based metrics in this challenge [i.e., one or more second machine learning processes trained to imitate results obtained by performing at least one simulation to predict a trajectory]), and
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Li’s Fig. 2
cause at least one device to move in accordance with the predicted trajectory (Li, in at least Abstract, Fig. 2, and §§1-3, discloses an end-to-end autonomous driving framework called Hydra-MDP (Multimodal Planning with Multi-target Hydra-distillation), which is based on a novel teacher-student knowledge distillation (KD) architecture, wherein the student model learns diverse trajectory candidates tailored to various evaluation metrics through KD from both human and rule-based teachers. Li further discloses models are trained on the Navtrain split using 8 NVIDIA A100 GPUs, with a total batch size of 256 across 20 epochs. Li also discloses Perception outputs P are explicitly used to postprocess suitable trajectories via a cost function f(Ti, P), wherein the trajectory with the lowest cost is selected, wherein End-to-end autonomous driving, which involves learning a neural planner, the method achieves the 1st place in the Navsim challenge, demonstrating significant improvements in generalization across diverse driving environments and conditions, wherein rule-based driving knowledge has been distilled into the end-to-end planner, and the trajectory with the lowest overall cost is chosen [i.e., at least one device to move in accordance with the predicted trajectory]).
As per claim 2, Li discloses the processor of claim 1, accordingly, the rejection of claim 1 above is incorporated. Li further discloses wherein the one or more first machine learning processes comprise at least one first neural network to generate a set of candidate trajectories (Li, in at least Abstract, Fig. 2, and §§1-3, discloses Multimodal Planning with Multi-target Hydra-distillation (Hydra-MDP) consists of two networks: a Perception Network and a Trajectory Decoder, wherein the Perception Network builds upon the official challenge baseline Transfuser, which consists of an image backbone, a LiDAR backbone, and perception heads for 3D object detection and BEV segmentation, wherein multiple transformer layers connect features from stages of both backbones, extracting meaningful information from different modalities, such that the final output of the perception network comprises environmental tokens Fenv, which encode abundant semantic information derived from both images and LiDAR point clouds. Li further discloses Perception outputs P are explicitly used to postprocess suitable trajectories via a cost function f(Ti, P)),
the one or more second machine learning processes comprise at least one second neural network to generate a set of scores for each candidate trajectory in the set of candidate trajectories (Li, in at least Abstract, Fig. 2, and §§1-3, discloses Trajectory Decoder, following Vadv2 [4], by constructing a fixed planning vocabulary to discretize the continuous action space, wherein to build the vocabulary 700K trajectories have been first randomly sampled from the original nuPlan database [2], wherein the planning vocabulary Vk is formed as K-means clustering centers of the 700K trajectories, where k denotes the size of the vocabulary. Vk is then embedded as k latent queries with an MLP, sent into layers of transformer encoders [19], and added to the ego status E, wherein the intuition behind this imitation target is to reward trajectory proposals that are close to human driving behaviors. Li further discloses that though the imitation target provides certain clues for the planner, it is insufficient for the model to associate the planning decision with the driving environment under the closed-loop setting, leading to failures such as collisions and leaving drivable areas [14]. Therefore, to boost the closed-loop performance of our end-to-end planner, Multi-target Hydra-Distillation is proposed, a learning strategy that aligns the planner with simulation-based metrics in this challenge, wherein models are trained on the Navtrain split using 8 NVIDIA A100 GPUs, with a total batch size of 256 across 20 epochs),
and the one or more circuits are to select the predicted trajectory from the set of candidate trajectories based at least in part on the set of scores generated for each candidate trajectory in the set of candidate trajectories (Li, in at least Abstract, Fig. 2, and §§1-3, discloses models are trained on the Navtrain split using 8 NVIDIA A100 GPUs, with a total batch size of 256 across 20 epochs. Li further discloses Perception outputs P are explicitly used to postprocess suitable trajectories via a cost function f(Ti, P), wherein the trajectory with the lowest cost is selected, wherein End-to-end autonomous driving, which involves learning a neural planner, the method achieves the 1st place in the Navsim challenge, demonstrating significant improvements in generalization across diverse driving environments and conditions, wherein rule-based driving knowledge has been distilled into the end-to-end planner, and the trajectory with the lowest overall cost is chosen).
As per claim 3, Li discloses the processor of claim 1, accordingly, the rejection of claim 1 above is incorporated. Li further discloses wherein the one or more first machine learning processes are to generate a set of candidate trajectories and at least one first score corresponding to each candidate trajectory in the set of candidate trajectories (Li, in at least Abstract, Fig. 2, and §§1-3, discloses the Perception Network builds upon the official challenge baseline Transfuser, which consists of an image backbone, a LiDAR backbone, and perception heads for 3D object detection and BEV segmentation, wherein multiple transformer layers connect features from stages of both backbones, extracting meaningful information from different modalities, such that the final output of the perception network comprises environmental tokens Fenv, which encode abundant semantic information derived from both images and LiDAR point clouds, wherein the Perception outputs P are explicitly used to postprocess suitable trajectories via a cost function f(Ti, P)),
the one or more second machine learning processes are to generate at least one second score corresponding to each candidate trajectory in the set of candidate trajectories (Li, in at least Abstract, Fig. 2, and §§1-3, discloses imitation score Sim of the Trajectory Decoder by constructing a fixed planning vocabulary to discretize the continuous action space, wherein to build the vocabulary 700K trajectories have been first randomly sampled from the original nuPlan database [2], wherein the planning vocabulary Vk is formed as K-means clustering centers of the 700K trajectories, where k denotes the size of the vocabulary. Vk is then embedded as k latent queries with an MLP, sent into layers of transformer encoders [19], and added to the ego status E, wherein the intuition behind this imitation target is to reward trajectory proposals that are close to human driving behaviors. Li further discloses a learning strategy that aligns the planner with simulation-based metrics in this challenge, wherein models are trained on the Navtrain split using 8 NVIDIA A100 GPUs, with a total batch size of 256 across 20 epochs), and
the one or more circuits are to select the predicted trajectory from the set of candidate trajectories based at least in part on the at least one first score and the at least one second score corresponding to each candidate trajectory in the set of candidate trajectories (Li, in at least Abstract, Fig. 2, and §§1-3, discloses Perception outputs P are explicitly used to postprocess suitable trajectories via a cost function f(Ti, P), wherein the trajectory with the lowest cost is selected, wherein rule-based driving knowledge has been distilled into the end-to-end planner, and the trajectory with the lowest overall cost is chosen).
As per claim 4, Li discloses the processor of claim 1, accordingly, the rejection of claim 1 above is incorporated. Li further discloses wherein during training, the one or more circuits are to generate a set of simulation scores corresponding to each candidate trajectory in at least one set of candidate trajectories,
the one or more second machine learning processes are to generate a set of predicted scores for each candidate trajectory in at least one set of candidate trajectories, and
the one or more circuits are to determine at least one weight to be used by the one or more second machine learning processes based at least in part on the set of simulation scores and the set of predicted scores (Li, in at least Abstract, Fig. 2, and §§1-3, discloses imitation score Sim of the Trajectory Decoder by constructing a fixed planning vocabulary to discretize the continuous action space, wherein to build the vocabulary 700K trajectories have been first randomly sampled from the original nuPlan database [2], wherein the planning vocabulary Vk is formed as K-means clustering centers of the 700K trajectories, where k denotes the size of the vocabulary. Vk is then embedded as k latent queries with an MLP, sent into layers of transformer encoders [19], and added to the ego status E, wherein the intuition behind this imitation target is to reward trajectory proposals that are close to human driving behaviors. Li further discloses a learning strategy that aligns the planner with simulation-based metrics in this challenge, wherein models are trained on the Navtrain split using 8 NVIDIA A100 GPUs, with a total batch size of 256 across 20 epochs).
As per claim 5, Li discloses the processor of claim 4, accordingly, the rejection of claim 4 above is incorporated. Li further discloses wherein during training, the one or more first machine learning processes are to generate the at least one set of candidate trajectories for at least one training dataset based at least in part on a planning vocabulary comprising a set of planning trajectories, and the at least one simulation is to use the planning vocabulary to generate the set of simulation scores (Li, in at least Abstract, Fig. 2, and §§1-3, discloses constructing a fixed planning vocabulary to discretize the continuous action space, wherein to build the vocabulary 700K trajectories have been first randomly sampled from the original nuPlan database, wherein the intuition behind this imitation target is to reward trajectory proposals that are close to human driving behaviors).
As per claim 6, Li discloses the processor of claim 1, accordingly, the rejection of claim 1 above is incorporated. Li further discloses wherein during training, the one or more first machine learning processes are to generate a set of candidate trajectories for each of at least one training dataset, and
the one or more circuits are to determine at least one weight to be used by the one or more first machine learning processes based at least in part on a distance between each candidate trajectory in the set of candidate trajectories determined for each of the at least one training dataset and a corresponding ground truth trajectory (Li, in at least Abstract, Fig. 2, and §§1-3, discloses implement a distance based cross-entropy loss to imitate human drivers. Li further discloses student model uses environmental observations during training, while the teacher models use ground truth (GT) data, wherein this setup allows the teacher models to generate better planning predictions, helping the student model to learn effectively, wherein by training the student model with environmental observations, it becomes adept at handling realistic conditions where GT perception is not accessible during testing).
As per claim 7, Li discloses the processor of claim 1, accordingly, the rejection of claim 1 above is incorporated. Li further discloses wherein the real-world observations comprise image data and LIDAR information (Li, in at least Abstract, Fig. 2, and §§1-3, discloses the Perception Network builds upon the official challenge baseline Transfuser, which consists of an image backbone, a LiDAR backbone, and perception heads for 3D object detection and BEV segmentation, wherein multiple transformer layers connect features from stages of both backbones, extracting meaningful information from different modalities, such that the final output of the perception network comprises environmental tokens Fenv, which encode abundant semantic information derived from both images and LiDAR point clouds. Li further discloses student model uses environmental observations during training, while the teacher models use ground truth (GT) data, wherein this setup allows the teacher models to generate better planning predictions, helping the student model to learn effectively, wherein by training the student model with environmental observations, it becomes adept at handling realistic conditions where GT perception is not accessible during testing).
As per claim 8, Li discloses the processor of claim 1, accordingly, the rejection of claim 1 above is incorporated. Li further discloses wherein the real-world observations were captured as at least one human user operated at least one vehicle, and
the at least one device comprises at least one autonomous or semi-autonomous vehicle (Li, in at least Abstract, Fig. 2, and §§1-3, discloses an end-to-end autonomous driving framework called Hydra-MDP (Multimodal Planning with Multi-target Hydra-distillation), which is based on a novel teacher-student knowledge distillation (KD) architecture, wherein the student model learns diverse trajectory candidates tailored to various evaluation metrics through KD from both human and rule-based teachers. Li further the end-to-end autonomous driving involves learning a neural planner with raw sensor inputs, and more effectively evaluate end-to-end autonomous driving by ensuring that the machine-learned planner meets essential criteria beyond merely mimicking human drivers. Li also discloses allowing the model to learn from both rule-based planners and human drivers in a scalable manner, wherein the intuition behind this imitation target is to reward trajectory proposals that are close to human driving behaviors).
As per claim(s) 9-18, the claim(s) is/are directed towards system(s), but recite(s) similar limitations performed by the processor(s) of claim(s) 1-8. The cited portions of Li used in the rejection(s) of claim(s) 1-8 disclose/ teach the same system limitations of claim(s) 9-18. Therefore, claim(s) 9-18 is/are rejected under the same rationales used in the rejection(s) of claim(s) 1-8 as outlined above.
As per claim 19, Li discloses a computer-implemented method comprising:
using, by a computer system, one or more first neural networks to generate a set of candidate trajectories, the one or more first neural networks having been trained using at least one real-world observation (Li, in at least Abstract, Fig. 2, and §§1-3, discloses Multimodal Planning with Multi-target Hydra-distillation (Hydra-MDP) consists of two networks: a Perception Network and a Trajectory Decoder, wherein the Perception Network builds upon the official challenge baseline Transfuser, which consists of an image backbone, a LiDAR backbone, and perception heads for 3D object detection and BEV segmentation, wherein multiple transformer layers connect features from stages of both backbones, extracting meaningful information from different modalities, such that the final output of the perception network comprises environmental tokens Fenv, which encode abundant semantic information derived from both images and LiDAR point clouds. Li further discloses Perception outputs P are explicitly used to postprocess suitable trajectories via a cost function f(Ti, P));
using, by the computer system, one or more second neural networks to generate a set of predicted scores for each candidate trajectory in the set of candidate trajectories, the one or more second neural networks having been trained using simulation results obtained by performing at least one simulation (Li, in at least Abstract, Fig. 2, and §§1-3, discloses Trajectory Decoder, following Vadv2 [4], by constructing a fixed planning vocabulary to discretize the continuous action space, wherein to build the vocabulary 700K trajectories have been first randomly sampled from the original nuPlan database [2], wherein the planning vocabulary Vk is formed as K-means clustering centers of the 700K trajectories, where k denotes the size of the vocabulary. Vk is then embedded as k latent queries with an MLP, sent into layers of transformer encoders [19], and added to the ego status E, wherein the intuition behind this imitation target is to reward trajectory proposals that are close to human driving behaviors. Li further discloses that though the imitation target provides certain clues for the planner, it is insufficient for the model to associate the planning decision with the driving environment under the closed-loop setting, leading to failures such as collisions and leaving drivable areas [14]. Therefore, to boost the closed-loop performance of our end-to-end planner, Multi-target Hydra-Distillation is proposed, a learning strategy that aligns the planner with simulation-based metrics in this challenge, wherein models are trained on the Navtrain split using 8 NVIDIA A100 GPUs, with a total batch size of 256 across 20 epochs); and
selecting, by the computer system, at least one of the set of candidate trajectories as at least one predicted trajectory based at least in part on the set of predicted scores generated for each candidate trajectory in the set of candidate trajectories (Li, in at least Abstract, Fig. 2, and §§1-3, discloses models are trained on the Navtrain split using 8 NVIDIA A100 GPUs, with a total batch size of 256 across 20 epochs. Li further discloses Perception outputs P are explicitly used to postprocess suitable trajectories via a cost function f(Ti, P), wherein the trajectory with the lowest cost is selected, wherein End-to-end autonomous driving, which involves learning a neural planner, the method achieves the 1st place in the Navsim challenge, demonstrating significant improvements in generalization across diverse driving environments and conditions, wherein rule-based driving knowledge has been distilled into the end-to-end planner, and the trajectory with the lowest overall cost is chosen).
As per claim 20, Li discloses the computer-implemented method of claim 19, accordingly, the rejection of claim 19 above is incorporated. Li discloses further comprising:
causing at least one device to move in accordance with the at least one predicted trajectory (Li, in at least Abstract, Fig. 2, and §§1-3, discloses an end-to-end autonomous driving framework called Hydra-MDP (Multimodal Planning with Multi-target Hydra-distillation), which is based on a novel teacher-student knowledge distillation (KD) architecture, wherein the student model learns diverse trajectory candidates tailored to various evaluation metrics through KD from both human and rule-based teachers. Li further discloses models are trained on the Navtrain split using 8 NVIDIA A100 GPUs, with a total batch size of 256 across 20 epochs. Li also discloses Perception outputs P are explicitly used to postprocess suitable trajectories via a cost function f(Ti, P), wherein the trajectory with the lowest cost is selected, wherein End-to-end autonomous driving, which involves learning a neural planner, the method achieves the 1st place in the Navsim challenge, demonstrating significant improvements in generalization across diverse driving environments and conditions, wherein rule-based driving knowledge has been distilled into the end-to-end planner, and the trajectory with the lowest overall cost is chosen [i.e., at least one device to move in accordance with the predicted trajectory]).
As per claim 21, Li discloses the computer-implemented method of claim 19, accordingly, the rejection of claim 19 above is incorporated. Li discloses further comprising:
using, by the computer system, the one or more first neural networks to generate an imitation score for each candidate trajectory in the set of candidate trajectories (Li, in at least Abstract, Fig. 2, and §§1-3, discloses the Perception Network builds upon the official challenge baseline Transfuser, which consists of an image backbone, a LiDAR backbone, and perception heads for 3D object detection and BEV segmentation, wherein multiple transformer layers connect features from stages of both backbones, extracting meaningful information from different modalities, such that the final output of the perception network comprises environmental tokens Fenv, which encode abundant semantic information derived from both images and LiDAR point clouds, wherein the Perception outputs P are explicitly used to postprocess suitable trajectories via a cost function f(Ti, P)), wherein the at least one predicted trajectory is selected based at least in part on the imitation score and the set of predicted scores generated for each candidate trajectory in the set of candidate trajectories (Li, in at least Abstract, Fig. 2, and §§1-3, discloses Perception outputs P are explicitly used to postprocess suitable trajectories via a cost function f(Ti, P), wherein the trajectory with the lowest cost is selected, wherein rule-based driving knowledge has been distilled into the end-to-end planner, and the trajectory with the lowest overall cost is chosen).
As per claim(s) 22-24, the claim(s) is/are directed towards computer-implemented method(s), but recite(s) similar limitations performed by the processor(s) of claim(s) 1-8. The cited portions of Li used in the rejection(s) of claim(s) 1-8 disclose/ teach the same system limitations of claim(s) 22-24. Therefore, claim(s) 22-24 is/are rejected under the same rationales used in the rejection(s) of claim(s) 1-8 as outlined above.
As per claim 25, Li discloses the computer-implemented method of claim 19, accordingly, the rejection of claim 19 above is incorporated. Li discloses further comprising:
obtaining the simulation results by performing the at least one simulation, the at least one simulation to comprise an agent operating in an environment based at least in part on ground truth data; and
training the one or more second neural networks using the simulation results (Li, in at least Abstract, Fig. 2, and §§1-3, discloses a learning strategy of the proposed Multi-target Hydra-Distillation that aligns the planner with simulation-based metrics, and to implement a distance based cross-entropy loss to imitate human drivers. Li further discloses student model uses environmental observations during training, while the teacher models use ground truth (GT) data, wherein this setup allows the teacher models to generate better planning predictions, helping the student model to learn effectively, wherein by training the student model with environmental observations, it becomes adept at handling realistic conditions where GT perception is not accessible during testing).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tarek Elarabi whose telephone number is (313)446-4911. The examiner can normally be reached on Monday thru Thursday; 6:00 AM - 4:00 PM EST.
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/Tarek Elarabi/Primary Examiner, Art Unit 3661
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