DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 12/11/2025. Claims 1-2,4-12 and 14-22 are pending in this Office 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 .
Response to Arguments
Applicant’s arguments filed 12/11/25 with respect to the 103 rejection have been considered and are moot in view of new ground of rejection.
Claim Objections
Claims 2 and 12 are objected to because of the following informalities: Claims 2 and 12 recite the limitation of “wherein the representation of one or more objects..” . There is insufficient antecedent basis for this limitation because the “representation” is not previously mentioned anywhere in the independent claims . Appropriate correction is required.
Allowable Subject Matter
Claims 9-10 and 18-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
Claims 1-2,4-5, 11-12,14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kang et al.( US Patent Application Publication 2017/0083829 A1, hereinafter “Kang”) in view of Fukuda et al.(US Patent Application Publication 2019/0378006 A1, hereinafter “Fukuda”) , in view of Iscen et al.(US Patent Application Publication 2024/0320493 A1, hereinafter “Iscen”)
As to claim 1, Kang teaches a method comprising: obtaining a plurality of target outputs (bold emphasis added. Kang par [0073] teaches selecting 2 teacher models to train student model),
[each of the plurality of target outputs comprising a classification of a training input among a respective set of classes of a plurality of sets of classes obtained using a respective teacher machine learning model (MLM) of a plurality of teacher MLMs], ; and
using the training input, a combined loss function and the plurality of target outputs to train a student MLM to classify the one or more objects among each of the plurality of sets of classes, (Kang par [0076] teaches the student model 320 is trained based on output data of the selected teacher models. Kang par [0073] teaches the model training apparatus may trin the student model 320 to reduce the loss calculated through equation 3)
wherein the combined loss function characterizes similarity of (i) classifications generated by the student MLM among said each of the plurality of sets of classes and (ii) classifications obtained using corresponding teacher MLMs among said each of the plurality of sets of classes; (Kang par [0077] teaches the model training may calculate a loss based on output data of at least one teacher model and output data of student model and train the student model to reduce the loss) and
[causing the trained student model to be configured to control a driving path of a vehicle modified in view of one or more additional objects identified, by the trained student model, in an environment of the vehicle].
Kang fails to expressly teach each of the plurality of target outputs comprising a classification of a training input among a respective set of classes of a plurality of sets of classes obtained using a respective teacher machine learning model (MLM) of a plurality of teacher MLMs.
However, in an analogous art directed to machine learning, Fukuda teaches each of the plurality of target outputs comprising a classification of a training input among a respective set of classes of a plurality of sets of classes obtained using a respective teacher machine learning model (MLM) of a plurality of teacher MLMs.(Fukuda par [006] teaches plurality of teacher models, each of which is specialized for different one of the plurality of domains. Fukuda par [0013] teaches plurality of teacher models and student model are image processing models and the plurality of domains has difference in color modes of an input image signal)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kang and Fukuda to achieve the claimed invention. One would have been motivated to make such combination to reduce computational resources (Fukuda par [0007])
Kang and Fukuda fail to expressly teach causing the trained student model to be configured to control a driving path of a vehicle modified in view of one or more additional objects identified, by the trained student model, in an environment of the vehicle.
However, Iscen teaches causing the trained student model to be configured to control a driving path of a vehicle modified in view of one or more additional objects identified, by the trained student model, in an environment of the vehicle.(Iscen par [0075] teaches a robotic agent of autonomous vehicle may use the output of a computer vision task as to determine a suitable trajectory to follow or an alteration to make its current trajectory and adapt it motion accordingly)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kang , Fukuda and Iscen to achieve the claimed invention. One would have been motivated to make such combination to enhance recognition capabilities.(Iscen par [0026])
As to claim 2, Kang , Fukuda and Iscen teach the method of claim 1, wherein the representation of the one or more objects comprises at least one of a camera image of the one or more objects, a lidar image of the one or more objects, or a radar image of the one or more objects.(Iscen par [0011] teaches one or more objects recognized in the image)
As to claim 4, Kang, Fukuda and Iscen teach the method of claim 1 wherein the classification of the training input among the respective set of classes comprises a set of values, wherein each value of the set of values characterizes a likelihood of the one or more objects belonging to a corresponding class of the respective set of classes.(Iscen par [0073] teaches image processing task can be image classification where the output is a set of scores , each score corresponding to a different object class representing the likelihood that the one or more images depict an object belong to the object class)
As to claim 5, Kang , Fukuda and Iscen teach the method of claim 4, wherein the training input comprises a plurality of frames, wherein each of the plurality of frames depicts the one or more objects at a respective one of a plurality of times. (Iscen par [0061] teaches training dataset include image data , video data)
Claims 11-12 and 14 merely recite a system to perform the method of claims 1-2 and 4 respectively. Accordingly, Kang , Fukuda and Iscen teach every limitation of claims 11-12 and 14 as indicates in the above rejection of claims 1-2 and 4 respectively.
Claim 20 merely recites a non-transitory computer readable medium storing instructions when executed by a processor, performs the method of claim 1. Accordingly, Kang , Fukuda and Iscen teach every limitation of claim 20 as indicates in the above rejection of claim 1.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kang , Fukuda and Iscen and further in view of Pu Wang et al.(US Patent Application Publication 2023/0305136 A1, hereinafter “Pu”)
As to claim 6, Kang , Fukuda and Iscen teach the method of claim 5 but fail to teach wherein using the training input to train the student MLM comprises: using a first neural network (NN) to obtain a plurality of sets of object embeddings, wherein each of the plurality of sets of object embeddings is associated with a respective time of the plurality of times ; and using a second NN to perform a temporal processing of the plurality of sets of the object embeddings,
wherein performing the temporal processing comprises at least one of: , or performing a sequential memory-based processing of the plurality of sets of the object embeddings.
However, in an analogous art directed to machine learning, Pu teaches using a first neural network (NN) to obtain a plurality of sets of object embeddings, wherein each of the plurality of sets of object embeddings is associated with a respective time of the plurality of times (Pu par [0014] teaches collecting a sequence of radar image frames indicative of radar measurements of a scene at different consecutive instances of time and processing each permuted sequence of radar image frames with a first neural network trained to extract features of a frame at the dominant position in a temporal correlation with features of one or multiple frames in a subordinate position to produce temporally enhanced features for each of the frames in the sequence of radar image frames) and using a second NN to perform a temporal processing of the plurality of sets of the object embeddings (Pu par [0014] teaches processing a list of feature vectors from each feature map of the sequence of feature maps with a second neural network ), wherein performing the temporal processing comprises at least one of: performing a concurrent attention-based processing of the plurality of sets of the object embeddings, or performing a sequential memory-based processing of the plurality of sets of the object embeddings (Pu par [0014] teaches processing a list of feature vectors from each feature map of the sequence of feature maps with a second neural network ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Pu with the teachings of Kang , Fukuda and Iscen to achieve the claimed invention. One would have been motivated to make such combination to enhance capacity of the automotive radar.(Pu par [0006])
As to claim 15, see the above rejection of claim 6.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kang , Fukuda and Iscen, in view of Wang et al.(US Patent Application Publication 2023/0078218 A1, hereinafter “Wang”)
As to claim 7, Kang , Fukuda and Iscen teach the method of claim 1 but fail to teach wherein one or more of the plurality of target outputs further comprise one or more manual annotations for at least one of the one or more objects.
However, Wang teaches wherein one or more of the plurality of target outputs further comprise one or more manual annotations for at least one of the one or more objects. (Wang par [0166] teaches human annotated)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kang , Fukuda, Iscen and Wang to achieve the claimed invention. One would have been motivated to make such combination to improve accuracy of the classification.
As to claim 16, see the above rejection of claim 7.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kang , Fukuda and Iscen and further in view of Telpaz et al.(US Patent Application Publication 2023/0177840 A1, hereinafter “Telpaz”)
As to claim 8, Kang , Fukuda and Iscen teach the method of claim 1 but fail to teach wherein the student MLM comprises a common backbone of neural layers and a plurality of classification heads, each classification head of the plurality of classification heads outputting a classification of the one or more objects among a respective set of the plurality of sets of classes.
However, in an analogous art directed to machine learning, Telpaz teaches the MLM comprises a common backbone of neural layers and a plurality of classification heads, each classification head of the plurality of classification heads outputting a classification of the one or more objects among a respective set of the plurality of sets of classes. (Telpaz par [0049] teaches common backbone and multiple task heads to perform image classification tasks)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kang , Fukuda , Iscen and Telpaz to achieve the claimed invention. One would have been motivated to make such combination to helps the machine learning training process to focus on features that contain identifiably “strong visual” information instead of simply overfitting the model to the training dataset.(Telpaz par [0049])
As to claim 17, see the above rejection of claim 8.
Claims 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Kang , Fukuda and Iscen, in view of Haidar et al.(US Patent Application Publication 2022/0335303 A1, hereinafter “Haidar” )
As to claim 21, Kang , Fukuda and Iscen teach the method of claim 1 but fail to teach wherein the combined loss function further characterizes similarity of one or more intermediate embeddings outputted by at least one hidden layer of the respective teacher MLM and one or more intermediate embeddings outputted by the student MLM.
However, Haidar teaches wherein the combined loss function further characterizes similarity of one or more intermediate embeddings outputted by at least one hidden layer of the respective teacher MLM and one or more intermediate embeddings outputted by the student MLM. ( Haidar par [0076]-[0077] teaches at 512, the intermediate representation loss module 222 processes the teacher intermediate representations and the student intermediate representations for the training batch 302 to compute an intermediate representation loss 316)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kang , Fukuda, Iscen and Haidar to achieve the claimed invention. One would have been motivated to make such combination to improve the performance of the trained student at inference and provide better generalization capability and /or training student efficiently.(Haidar par [0056])
As to claim 22, see the above rejection of claim 21
Conclusion
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/HIEN L DUONG/Primary Examiner, Art Unit 2147