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 .
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over A Traffic Surveillance System for Obtaining Comprehensive Information of the Passing Vehicles Based on Instance Segmentation, by Zhang et al (IDS document).
With respect to claim 1, Zhang discloses A method (see Abstract), comprising:
receiving, by a computing system, a first series of frames of image data comprising a representation of a vehicle (see figure 2, and section III, Instance segmentation based on MaskR-CNN);
for each frame of the first series of frames of the image data:
identifying, by the computing system, a wheel based at least in part on at least a portion of the frame of the image data, (see figure 7, for identifying the wheel on each frame); and
determining, by the computing system, a set of coordinates indicating a position of the wheel within the frame of the image data, (see figure 7 and section IV. Identification of number of axles, where the position of the two objects are consider to evaluate the MaskIoU, wheel position is one of it);
generating, by the computing system,
determining, by the computing system, whether the wheel is associated with the vehicle, (see section IV. Identification of Number of Axles, wherein …For a detected wheel, it is necessary to judge which vehicle it belongs to when there is more than one vehicle in a frame); and generating, by the computing system, an axle count of the vehicle, (see section IV. Identification of number of Axles, and Algorithm 1).
However, Zhang fails to explicitly disclose generating, by the computing system, a graph based at least in part on the set of coordinates indicating the position, (emphasis added) as claimed.
But as described in the figure 16, the tracking process and vehicle parameters obtained from different frames are graphed to attain the result i.e. number of axles, this obviates the graph “a graph” based on the coordinates, as claimed.
Therefore, it would have been obvious to one ordinary skilled in the art at the effective date of invention to simply utilize the learning/teaching of getting tracking information as a graph representation of the parameters, this yields the predicted results of counting the axles of the vehicles, as claimed.
With respect to claim 2, Zhang further discloses receiving, by the computing system, a second series of frames of image data comprising a representation of a portion of the vehicle; determining, by the computing system, that the portion of the vehicle represented in the second series of frames is associated with the vehicle; and associating, by the computing system, the portion of the vehicle with the vehicle represented in the first series of frames, (see section VII. The tracking method and pseudocode of the proposed system, subsection A. Multiple object tracking based on SORT, where series of images are processed for the tracking), as claimed.
With respect to claims 3 and 4, Zhang further discloses generating, by the computing system, a bounding box about a portion of each frame of the first series of frames; identifying, by the computing system, a plurality of wheels in each of the first series of frames of image data; determining, by the computing system, a first subset of the plurality of wheels comprising one or more wheels inside the bounding box and a second subset of the plurality of wheels outside the bounding box; and retaining, by the computing system, the first subset of the plurality of wheels for further processing; and wherein a second subset of the plurality of wheels is identified outside of the bounding box and is excluded from further processing based at least in part on a position of each of the second subset of the plurality of wheels, (see section IV. Identification of number of axles, and figure 8 and 9 for each frame bounding boxes are calculated and then wheels are identified and using MaskIoU and IoU the intersection or overlapping is calculated), as claimed.
With respect to claim 5, Zhang further discloses wherein the computing system determines the vehicle is within a region of interest, (see section III. Instance segmentation based on MASK R-CNN, virtual detection region is read as region of interest), as claimed.
With respect to claim 6, Zhang discloses all the elements as claimed in claim 1 above. However, Zhang fails to explicitly disclose the data indicating the axle count is used to verify a historical axle count associated with the vehicle, as claimed.
But it is well-known “official notice” in the art to have vehicle classification depending upon the historical axle count data (see cited reference Sasongko).
Therefore, it would have been obvious to one ordinary skilled in the art at the effective date of invention to simply combining the conventional knowledge of classification of vehicles based on axle count in to the Zhang system to yield the predicted results of classification of vehicles, as claimed.
With respect to claim 7, Zhang further discloses the wheel is identified using a machine learning model trained exclusively on wheel data, (see figure 1 for Mask R-CNN), as claimed.
With respect to claim 8, Zhang further discloses providing, by the computing system, at least a portion of the first series of frames to a machine learning model; determining, by the computing system and using the machine learning model, abounding box about the vehicle; determining, by the computing system and using the machine learning model, a vehicle-type of the vehicle and a confidence score associated with the vehicle type; and determining, by the computing system, the axle count of the vehicle based at least in part on the vehicle-type of the vehicle, (see figure 1 and figure 15, for the architecture of the process), as claimed.
Claim 9 and 11-14 are rejected for the same reasons as set forth in the rejections of claims 1 and 4-7, because claims 9 and 11-14 are claiming subject matter of similar scope as claimed in claims 1 and 4-7 respectively.
With respect to claim 10, Zhang further discloses a first detector comprising a first machine learning model configured to identify the vehicle in the first series of frames of image data, determine a vehicle-type of the vehicle, and a confidence score associated with the vehicle-type; a second detector comprising a second machine learning model configured to identify the wheel within a bounding box generated within the first series of frame of image data; and a post-processing module configured to generate the axle count of the vehicle based at least in part on the wheel identified within the bounding box, (see figure 1, vehicle tracking module where a bounding box is for the object i.e. car and number of axles is calculated within the bounding box, also see the section VII. Tracking method and pseudocode of the proposed system figures 7 and 15), as claimed.
Claim 15-20 are rejected for the same reasons as set forth in the rejections of claims 1-6, because claims 15-20 are claiming subject matter of similar scope as claimed in claims 1-6 respectively.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663
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