DETAILED ACTION
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choe ( US 20210309248) in view of Satazoda ( US 20180012082)
Regarding claim 1, Choe teaches a method comprising:
obtaining a data recording including one or more frames, the one or more frames including corresponding frame data; ( B902 in Figure 9)
comparing the frame data of the one or more frames against an dataset ( B908 in Figure 9) ;
identifying one or more features in the one or more frames based at least on the comparison between the frame data and the dataset(B910 in Figure 9);
determining a subset of the one or more frames including the one or more features associated with one or more operational domains ( B912 in Figure 9); and
providing the subset of frames as training data to a detection model( B914 in Figure 9).
Choe does not expressly teach
annotated dataset, the annotated dataset including known features and annotations corresponding to the known features.
However, the Satazoda teaches
annotated dataset, the annotated dataset including known features and annotations corresponding to the known features ( [0016], annotating the image data based on the object… verifying the annotation within the image data S400; training a module based on the verified image data );
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Choe and Satazoda, by annotating the synthesized image in Choe following the method taught by Satazoda, with motivation “ to train computational models (e.g., to train a network) that can be used to detect, identify, classify, and/or otherwise recognize objects in images and/or image sequences” ( Satazoda , [0017])
Regarding claim 2, Choe in view of Satazoda teaches the method of claim 1, further comprising: after determining the subset of frames, filtering the subset of frames including operational domains including errors (Choe, [0073]-[0074], Because bounding shape 502 and virtual instance 208 in visualization 500 meet both distance threshold and overlap constraints, real-world image 302 augmented with virtual instance 208 may be used to train machine learning model(s); [0076], those that match (or closely match) the randomly sampled criteria may be selected to insert into a real-world image)
Regarding claim 3, Choe in view of Satazoda teaches the method of claim 2, wherein the errors include one or more of intrinsic errors, extrinsic errors(Choe, [0073], meet both distance threshold and overlap constraints), or random errors.
Regarding claim 4, Choe in view of Satazoda teaches the method of claim 1, wherein the one or more operational domains include scenarios present in the one or more frames ( Choe, [0078], the virtual instance of the object is within a threshold distance to a driving surface depicted).
Regarding claim 5, Choe in view of Satazoda teaches the method of claim 1, wherein the data recording is obtained using one or more sensors ( Choe, 1072, 1070, 1062, 1060 in figure 10A).
Regarding claim 6, Choe in view of Satazoda teaches the method of claim 1, wherein the annotations include at least one of bounding shapes or polylines corresponding to the known features corresponding to the annotations( Choe, [0073]-[0081], bounding shapes).
Regarding claim 7, Choe in view of Satazoda teaches the method of claim 1, wherein the one or more operational domains are described using corresponding operational domain definitions( Choe,[0079], instance of the object is within a threshold distance to a driving surface depicted) .
Regarding claim 8, Choe in view of Satazoda teaches the method of claim 1, wherein the annotated dataset corresponds to a road map including a plurality of road segments( Choe, [0062], real-world images augmented with simulated objects (e.g., road debris, road signs) ).
Regarding claim 9, Choe in view of Satazoda teaches the method of claim 8, wherein individual road segments of the plurality of road segments include ground truth labels corresponding to the one or more features associated with the individual road segments( Choe, [0062], training a machine learning model to detect road debris).
Regarding claim 10, Choe in view of Satazoda teaches the method of claim 1, further comprising: after identifying one or more features in the one or more frames, performing additional operations to identify additional features not included in the frame data ( Choe, [0062], training image data 702 may also include images that contain objects that the machine learning model was not trained with).
Claims 11-20 recite the system for the method in claims 1-10. Since Choe also teaches a system ( Figure 10A), those claims are also rejected.
Conclusion
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JIANGENG SUN
Examiner
Art Unit 2661
/Jiangeng Sun/Examiner, Art Unit 2671
