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 .
In response to amendment filed 01/30/2026, claims 1, 8-20 have been amended. No claims are new. Previous claim objection has been withdrawn.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (US 20230035995 A1) in view of NPL: ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst, hereinafter “Chauffeurnet”
In regards to claim 1, Sun teaches, A method, comprising:
generating, with at least one processor, a perturbed dataset from a real dataset; (See paragraphs 26, 32, 41, 46, 51, binary class attribute data acquired from set of images, which are used for pre-training)
pre-training, with the at least one processor, at least one component of a machine leaning model to perform an alternative task, wherein the machine learning model performs a primary task; (See fig. 2, S201-S202, figs. 3A-3B, pre-train model using binary class attribute classifier and train model using multi class attribute classifier (e.g. different task providing different outcomes. See paragraph 61, 63, the infrastructure of the training model may be basically consistent with that of the pre-trained model, including, for example, a convolutional neural network model and a following multi-class fully connected layer. Here, the convolutional neural network model can be the same as the model contained in the pre-trained model, and the multi-class fully connected layer corresponds to the multi-class label data, can be different from the connection layer of the pre-trained model. Also see paragraph 34, the binary class attribute data includes data indicating whether the to-be-classified attribute is “Yes” or “No” for each of at least one class label; and in step S202 (referred to as a pre-training step), pre-training of a model for object attribute classification is performed based on the binary class attribute data…paragraph 53, 57-58, In the pre-training stage, the pre-trained model can adopt Backbone+FC, and the last layer corresponds to a plurality of binary class attribute classifiers, which may be somewhat distinct from the final eyebrow classification model. It is well known that neural network models are part of machine learning models).
inserting, with the at least one processor, the pre-trained at least one component into the machine learning model that performs the primary task; and training, with the at least one processor, the machine learning model comprising the pre- trained at least one component to perform the primary task. (See fig. 2, S203, fig. 3B, and paragraphs 59, 61, 63, As shown in FIG. 3B, firstly, the pre-trained model Backbone and the corresponding fully connected layer are loaded, and the multiple binary class attribute classifiers in the last layer of the model is replaced by a multi-class FC layer…adopting the cross-entropy loss to carry out final model training or model fine-tuning. In this way, compared with a way of not using the pre-trained model and a way of using ImageNet pre-trained model, the final result can obtain a further improved classification model, which has higher classification accuracy and achieve better classification effect)
Sun does not expressly teach, wherein the perturbed dataset is generated by augmenting the real dataset to include collisions or trajectories not present in the real dataset;
Chauffeurnet further discloses wherein the perturbed dataset is generated by augmenting the real dataset to include collisions or trajectories not present in the real dataset; (See abstract, “We propose exposing the learner to synthesized data in the form of perturbations to the expert’s driving, which creates interesting situations such as collisions and/or going off the road.”…page 9-10, “we train the model by adding some examples with realistic perturbations to the agent trajectories…we do allow the perturbed trajectories to collide with other agents or drive off-road, because the network can then experience and avoid such behaviors even though real examples of these cases are not present in the training data… Since our training data does not have any real collisions, the idea of avoiding collisions is implicit and will not generalize well.”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the time the invention was effectively filed to modify pre-training framework of Sun to further comprise Chauffeurnet’s augmentation because Chauffeurnet expressly discloses that without such augmentation, collision avoidance “will not generalize well,” and that adding synthesized collision and trajectory examples to the real dataset allows the model to experience and avoid safety-critical behaviors absent from real-world data (Chauffeurnet page 10). Both Sun and Chauffeurnet are directed to the same underlying principle that augmenting a real dataset with synthesized examples covering scenarios absent from real-world data improves model robustness, and a person of ordinary skilled in the art would have recognized that this principle applies equally to Sun’s pre-training framework.
In regards to claim 2, Sun-Chauffeurnet teaches the method of claim 1, wherein generating the perturbed dataset comprises modifying a past trajectory of an autonomous vehicle, a future trajectory of the autonomous vehicle, trajectories of actors, adding or removing lanes, adding or removing actors, or any combinations thereof. (See Chauffeurnet page 9, we train the model by adding some examples with realistic perturbations to the agent trajectories. The start and end of a trajectory are kept constant, while a perturbation is applied around the midpoint and smoothed across the other points. This falls under “future trajectory of the AV”)
In regards to claim 3, Sun-Chauffeurnet teaches the method of claim 1, wherein the real dataset is a real world driving log. (See Chauffeurnet page 12, The training data to train our model was obtained by randomly sampling segments of real world expert driving)
In regards to claim 4, Sun- Chauffeurnet teaches the method of claim 1, wherein the pre- trained at least one component is stored as an intermediate model that is iteratively updated when new data is available. (See paragraphs 57-58, pre-training stage, the pre-trained model can adopt Backbone+FC, and the last layer corresponds to a plurality of binary class attribute classifiers,…The input is a training sample set, which comprises images containing object attributes and corresponding binary class attribute data. In this way, the collected binary class attributes can be utilized for model pre-training…each picture in the model training data set, the binary class data for respective attributes in image regions in each picture containing the to-be-classified attribute can be labeled or acquired, and then serve as the input for the model training. Also see paragraphs 53-54, the object attribute features can be extracted from each training sample/training picture in the training sample set, and can be used for per-training of model in conjunction with the binary class attribute data of the attributes acquired for each training sample… the training can be performed based on the extracted features and the binary class attribute data by using a loss function, so as to optimize the weights for parameters of the model. Backbone and fully connected layers are stored as intermediate model)
In regards to claim 5, Sun-Chauffeurnet teaches the method of claim 1, wherein the at least one component captures interactions between actor features and map features. (See Chauffeurnet fig. 1f and page 4, (f) Dynamic objects in the environment: a temporal sequence of images showing all the potential dynamic objects (vehicles, cyclists, pedestrians) rendered as oriented boxes…fig. 1a-1d, roadmaps, lanes, stop signs, crosswalks, traffic lights, etc. Also see page 6, a convolutional feature network (FeatureNet) that consumes the input data to create a digested contextual feature repre sentation that is shared by the other networks...page 7, The rendered inputs shown in Fig. 1 are fed to a large-receptive field convolutional FeatureNet)
In regards to claim 6, Sun- Chauffeurnet teaches the method of claim 1, wherein the alternative task is a classification task. (See fig. 3A and associated paragraphs, binary classification)
In regards to claim 7, Sun-Chauffeurnet teaches the method of claim 1, wherein the primary task is a planning task or a prediction task. (See fig. 3B and associated paragraphs, multi-class classifier task also falls under “prediction task” since the model outputs data that has the most confidence/probability value. Examiner recommends further clarifying “planning” and “prediction” task into claim language)
Claims 8-14 and 15-20 are similar in scope to claims 1-7. Therefore, they are rejected under similar rationale as set forth above.
Response to Arguments
Applicants’ arguments have been fully considered but are moot in view of the new grounds of rejection presented above necessitated by applicant’s amendment.
Conclusion
Applicant's amendment necessitated the new ground(s) 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN S LEE whose telephone number is (571)272-2674. The examiner can normally be reached Monday - Friday 8-5.
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/JUSTIN S LEE/Primary Examiner, Art Unit 3668