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 the Claims
Claims 1-9, as originally filed, are currently pending and have been considered below.
Drawings
Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Note that the requirement for three sets of color drawings under 37 C.F.R. § 1.84(a)(2)(ii) is not applicable to color drawings submitted via the USPTO patent electronic filing system. Therefore, only one set of such color drawings is necessary when filing via the USPTO patent electronic filing system.
The drawings are objected to because Figures 1-3 and 5-8 include color, but no petition under 37 C.F.R. § 1.84(a)(2) has been filed.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the collected image data" in line 4 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the model algorithm" in lines 7-8 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation "the image" in lines 3-4 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 3 recites the limitation "the YOLO block" in line 3 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 3 recites the limitation "the YOLO model" in line 5 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the Faster R-CNN block" in line 3 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the RoI pooled images” in line 8 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the Fully Connected CNN" in lines 8-9 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the output" in line 10 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 5 recites the limitation "the collected image data" in line 8 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the image" in lines 6 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the YOLO block" in line 3 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the YOLO model" in line 5 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation "the Faster R-CNN block" in line 4 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation "the RoI pooled images” in line 8 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation "the Fully Connected CNN" in lines 8-9 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation "the output" in line 10 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "the collected image data" in line 5 of the claim. There is insufficient antecedent basis for this limitation in the claim.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 5-7 and-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fan, Jiayi, et al. "Improvement of object detection based on faster R-CNN and YOLO." 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). IEEE, 2021, hereinafter, “Fan”, and further in view of Mittal, Usha, Priyanka Chawla, and Rajeev Tiwari. "EnsembleNet: A hybrid approach for vehicle detection and estimation of traffic density based on faster R-CNN and YOLO models." Neural Computing and Applications 35.6 (2023): 4755-4774, hereinafter, “Mittal”.
As per claim 1, Fan discloses a method for object detection by using a hybrid deep learning object detection model (Fan, page 1,1. Introduction, In this paper, the fusion of object detectors Faster R-CNN and YOLO v2 for vehicle detection is proposed), comprising:
(a) collecting captured images in real time (Fan, page 3, 4. Experimental Results and Discussion, An annotated driving dataset is used, including frames collected from cameras while driving in cities during daylight conditions);
(b) performing preprocessing on the collected image data (Fan, page 3, 4. Experimental Results and Discussion, An annotated driving dataset is used, including frames collected from cameras while driving in cities during daylight conditions. The 1920x1200 resolution images are first resized to 359x224 to fit the input size of YOLO v2 and Faster R-CNN);
(c) inputting the preprocessed image data into the hybrid deep learning object detection model to execute the model algorithm (Fan, page 3, 4. Experimental Results and Discussion, A pretrained ResNet-50 network is used for feature extraction to build the YOLO v2 and Faster RCNN detection network. After obtaining the coordinates and sizes of the bounding boxes from each algorithm, the results are then fed to Kalman Filter. The coordinates predicted from Kalman Filter are compared with those obtained from YOLO v2 and Faster R-CNN; the results are shown in Fig. 4 ... The IOU comparison of the two algorithms and the fusion is shown in Fig. 5); and
(d) outputting from the hybrid deep learning object detection model (Fan, pages 3-4, 4. Experimental Results and Discussion, After obtaining the coordinates and sizes of the bounding boxes from each algorithm, the results are then fed to Kalman Filter. The coordinates predicted from Kalman Filter are compared with those obtained from YOLO v2 and Faster R-CNN; the results are shown in Fig. 4 ... The IOU comparison of the two algorithms and the fusion is shown in Fig. 5 ... After the fusion of the two methods in Kalman Filter, the IOU achieves the highest, as shown in Fig. 5(c), which is 0.78547).
Fan does not explicitly disclose the following limitations as further recited however Mittal discloses
(d) outputting classification or segmentation from the hybrid deep learning object detection model (Mittal, page 4755, Abstract, a hybrid model of Faster R-CNN and YOLO using majority voting classifier has been trained on processed data; Mittal, page 4761, 3 Proposed methodology, In this paper, Faster R-CNN and YOLOv5 models are investigated and an ensemble of these two architectures named as EnsembleNet is implemented for vehicle detection ... data pre-processing is also applied ... Over C + 1 categories, a multi-class classification model is trained, where C refers to actual classes plus one background class; Mittal, page 4765, 3.6 Traffic density estimation, In this study, vehicles are divided into six classes ... total of each model’s predictions is calculated by multiplying each vehicle by the associated units [Equation 3] where predicted_categoryi denotes the vehicle class detected by the model; Mittal, page 4769, 4.2 Results of density estimation, Each identified vehicle in the image is depicted by a bounding box that includes the vehicle’s label and confidence value).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Mittal with Fan because they are in the same field of endeavor. One skilled in the art would have been motivated to include the classification of the detected object as taught by Mittal in the system of Fan in order to enhance the detection and classification of vehicles for road traffic management (Mittal, Abstract).
As per claim 2, Fan and Mittal disclose the method of claim 1, wherein the step (c) includes:
(c1) extracting a bounding box for feature extraction from the preprocessed image data (Fan, pages 3-4, 4. Experimental Results and Discussion, A pretrained ResNet-50 network is used for feature extraction to build the YOLO v2 and Faster RCNN detection network. After obtaining the coordinates and sizes of the bounding boxes from each algorithm; Fan, page 4, Figure 5; Mittal, page 4761, 3.2 Data pre-processing, Before feeding images to the model, images are pre-processed to improve the quality; Mittal, page 4764, 3.5 Detection of vehicles using the Ensemble method, The confidence scores of both model predictions are compared to retain the single bounding box); and
(c2) outputting classification or segmentation from the image in which the bounding box has been extracted (Mittal, page 4769, 4.2 Results of density estimation, Each identified vehicle in the image is depicted by a bounding box that includes the vehicle’s label and confidence value).
As per claim 3, Fan and Mittal disclose the method of claim 2, wherein the extraction of the bounding box in step (c1) includes inputting the preprocessed image data into the YOLO block of the hybrid deep learning object detection model, wherein the bounding box is extracted by the YOLO model (Fan, pages 3-4, 4. Experimental Results and Discussion, A pretrained ResNet-50 network is used for feature extraction to build the YOLO v2 and Faster RCNN detection network. After obtaining the coordinates and sizes of the bounding boxes from each algorithm, the results are then fed to Kalman Filter; Mittal, page 4764, 3.5 Detection of vehicles using the Ensemble method, Predictions made by base estimators
Faster R-CNN and YOLOv5 are stored in two variables, Pfaster and Pyolo ... Based on bounding box coordinates, predictions from the two models are contrasted).
As per claim 5, Fan discloses a system for object detection by using a hybrid deep learning model (Fan, page 1,1. Introduction, In this paper, the fusion of object detectors Faster R-CNN and YOLO v2 for vehicle detection is proposed), comprising:
at least one processor; and at least one memory storing computer-executable instructions that, when executed by the at least one processor, cause the system to:
(a) collect captured images in real time (Fan, page 3, 4. Experimental Results and Discussion, An annotated driving dataset is used, including frames collected from cameras while driving in cities during daylight conditions);
(b) perform preprocessing on the collected image data (Fan, page 3, 4. Experimental Results and Discussion, An annotated driving dataset is used, including frames collected from cameras while driving in cities during daylight conditions. The 1920x1200 resolution images are first resized to 359x224 to fit the input size of YOLO v2 and Faster R-CNN);
(c) input the preprocessed image data into the hybrid deep learning model (Fan, page 3, 4. Experimental Results and Discussion, A pretrained ResNet-50 network is used for feature extraction to build the YOLO v2 and Faster RCNN detection network. After obtaining the coordinates and sizes of the bounding boxes from each algorithm, the results are then fed to Kalman Filter. The coordinates predicted from Kalman Filter are compared with those obtained from YOLO v2 and Faster R-CNN; the results are shown in Fig. 4 ... The IOU comparison of the two algorithms and the fusion is shown in Fig. 5); and
(d) output from the hybrid deep learning model (Fan, pages 3-4, 4. Experimental Results and Discussion, After obtaining the coordinates and sizes of the bounding boxes from each algorithm, the results are then fed to Kalman Filter. The coordinates predicted from Kalman Filter are compared with those obtained from YOLO v2 and Faster R-CNN; the results are shown in Fig. 4 ... The IOU comparison of the two algorithms and the fusion is shown in Fig. 5 ... After the fusion of the two methods in Kalman Filter, the IOU achieves the highest, as shown in Fig. 5(c), which is 0.78547).
Fan does not explicitly disclose the following limitations as further recited however Mittal discloses
(d) output classification or segmentation from the hybrid deep learning model (Mittal, page 4755, Abstract, a hybrid model of Faster R-CNN and YOLO using majority voting classifier has been trained on processed data; Mittal, page 4761, 3 Proposed methodology, In this paper, Faster R-CNN and YOLOv5 models are investigated and an ensemble of these two architectures named as EnsembleNet is implemented for vehicle detection ... data pre-processing is also applied ... Over C + 1 categories, a multi-class classification model is trained, where C refers to actual classes plus one background class; Mittal, page 4765, 3.6 Traffic density estimation, In this study, vehicles are divided into six classes ... total of each model’s predictions is calculated by multiplying each vehicle by the associated units [Equation 3] where predicted_categoryi denotes the vehicle class detected by the model; Mittal, page 4769, 4.2 Results of density estimation, Each identified vehicle in the image is depicted by a bounding box that includes the vehicle’s label and confidence value).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Mittal with Fan because they are in the same field of endeavor. One skilled in the art would have been motivated to include the classification of the detected object as taught by Mittal in the system of Fan in order to enhance the detection and classification of vehicles for road traffic management (Mittal, Abstract).
As per claim 6, Fan and Mittal disclose the system of claim 5, wherein the step (c) includes:
(c1) extracting a bounding box for feature extraction from the preprocessed image data (Fan, pages 3-4, 4. Experimental Results and Discussion, A pretrained ResNet-50 network is used for feature extraction to build the YOLO v2 and Faster RCNN detection network. After obtaining the coordinates and sizes of the bounding boxes from each algorithm; Fan, page 4, Figure 5; Mittal, page 4761, 3.2 Data pre-processing, Before feeding images to the model, images are pre-processed to improve the quality; Mittal, page 4764, 3.5 Detection of vehicles using the Ensemble method, The confidence scores of both model predictions are compared to retain the single bounding box); and
(c2) outputting classification or segmentation from the image in which the bounding box has been extracted (Mittal, page 4769, 4.2 Results of density estimation, Each identified vehicle in the image is depicted by a bounding box that includes the vehicle’s label and confidence value).
As per claim 7, Fan and Mittal disclose the system of claim 6, wherein the extraction of the bounding box in step (c1) includes inputting the preprocessed image data into the YOLO block of the hybrid deep learning object detection model, wherein the bounding box is extracted by the YOLO model (Fan, pages 3-4, 4. Experimental Results and Discussion, A pretrained ResNet-50 network is used for feature extraction to build the YOLO v2 and Faster RCNN detection network. After obtaining the coordinates and sizes of the bounding boxes from each algorithm, the results are then fed to Kalman Filter; Mittal, page 4764, 3.5 Detection of vehicles using the Ensemble method, Predictions made by base estimators
Faster R-CNN and YOLOv5 are stored in two variables, Pfaster and Pyolo ... Based on bounding box coordinates, predictions from the two models are contrasted).
As per claim 9, Fan discloses a computer-readable non-transitory storage medium storing instructions that, when executed by a processor, cause the processor to:
(a) collect captured images in real time (Fan, page 3, 4. Experimental Results and Discussion, An annotated driving dataset is used, including frames collected from cameras while driving in cities during daylight conditions);
(b) perform preprocessing on the collected image data (Fan, page 3, 4. Experimental Results and Discussion, An annotated driving dataset is used, including frames collected from cameras while driving in cities during daylight conditions. The 1920x1200 resolution images are first resized to 359x224 to fit the input size of YOLO v2 and Faster R-CNN);
(c) input the preprocessed image data into a hybrid deep learning model (Fan, page 3, 4. Experimental Results and Discussion, A pretrained ResNet-50 network is used for feature extraction to build the YOLO v2 and Faster RCNN detection network. After obtaining the coordinates and sizes of the bounding boxes from each algorithm, the results are then fed to Kalman Filter. The coordinates predicted from Kalman Filter are compared with those obtained from YOLO v2 and Faster R-CNN; the results are shown in Fig. 4 ... The IOU comparison of the two algorithms and the fusion is shown in Fig. 5); and
(d) output from the hybrid deep learning model (Fan, pages 3-4, 4. Experimental Results and Discussion, After obtaining the coordinates and sizes of the bounding boxes from each algorithm, the results are then fed to Kalman Filter. The coordinates predicted from Kalman Filter are compared with those obtained from YOLO v2 and Faster R-CNN; the results are shown in Fig. 4 ... The IOU comparison of the two algorithms and the fusion is shown in Fig. 5 ... After the fusion of the two methods in Kalman Filter, the IOU achieves the highest, as shown in Fig. 5(c), which is 0.78547).
Fan does not explicitly disclose the following limitations as further recited however Mittal discloses
(d) output classification or segmentation from the hybrid deep learning model (Mittal, page 4755, Abstract, a hybrid model of Faster R-CNN and YOLO using majority voting classifier has been trained on processed data; Mittal, page 4761, 3 Proposed methodology, In this paper, Faster R-CNN and YOLOv5 models are investigated and an ensemble of these two architectures named as EnsembleNet is implemented for vehicle detection ... data pre-processing is also applied ... Over C + 1 categories, a multi-class classification model is trained, where C refers to actual classes plus one background class; Mittal, page 4765, 3.6 Traffic density estimation, In this study, vehicles are divided into six classes ... total of each model’s predictions is calculated by multiplying each vehicle by the associated units [Equation 3] where predicted_categoryi denotes the vehicle class detected by the model; Mittal, page 4769, 4.2 Results of density estimation, Each identified vehicle in the image is depicted by a bounding box that includes the vehicle’s label and confidence value).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Mittal with Fan because they are in the same field of endeavor. One skilled in the art would have been motivated to include the classification of the detected object as taught by Mittal in the system of Fan in order to enhance the detection and classification of vehicles for road traffic management (Mittal, Abstract).
Claim(s) 4 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fan, Jiayi, et al. "Improvement of object detection based on faster R-CNN and YOLO." 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). IEEE, 2021, hereinafter, “Fan”, in view of Mittal, Usha, Priyanka Chawla, and Rajeev Tiwari. "EnsembleNet: A hybrid approach for vehicle detection and estimation of traffic density based on faster R-CNN and YOLO models." Neural Computing and Applications 35.6 (2023): 4755-4774, hereinafter, “Mittal” as applied to claims 3 and 7 above, and further in view of Zarei N, Moallem P, Shams M. Fast-Yolo-rec: Incorporating Yolo-base detection and recurrent-base prediction networks for fast vehicle detection in consecutive images. IEEE access. 2022 Nov 14;10:120592-605, hereinafter, “Zarei”.
As per claim 4, Fan and Mittal disclose the method of claim 3, wherein the step (c2) includes:
(c21) inputting the image, from which the bounding box has been extracted, into the Faster R-CNN block of the hybrid deep learning object detection model (Fan, Figure 1, Structure of Faster R-CNN; Fan, page 2, 2.1 Faster R-CNN Algorithm, After obtaining region proposals with different sizes from RPN, it is applied to the ROI Pooling layer);
(c22) performing Region of Interest (RoI) pooling on the input image in the Faster R-CNN block (Fan, Figure 1, Structure of Faster R-CNN, Input image, Feature Map, ROI Pooling; Fan, page 2, 2.1 Faster R-CNN Algorithm, After obtaining region proposals with different sizes from RPN, it is applied to the ROI Pooling layer); and
(c23) inputting the RoI pooled images into the Fully Connected CNN of the Faster R-CNN block (Fan, page 2, 2.1 Faster R-CNN Algorithm, The ROI Pooling splits the input feature maps into a fixed number of roughly equal regions followed by Max Pooling to obtain the fixed-length output. After obtaining proposal feature maps, it is applied to the fully connected layer).
Fan and Mittal do not explicitly disclose the following limitation as further recited however Zarei discloses
wherein the output of the Fully Connected CNN in step (c23) is processed by a softmax classifier to output classification or segmentation in step (d) (Zarei, pages 120599-120600, C. Position Prediction and Trajectory Classification Blocks, Trajectory classification is carried out by sampling various trajectories learned for each trajectory. It consists of two trajectory feature extraction (TFE) blocks with different receptive fields, a fully connected layer, and a soft-max layer ... The obtained properties are transferred through a fully connected layer fc_MRF to the Softmax layer. This layer determines the probability of each of the two trajectory classes).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Zarei with Fan and Mittal because they are in the same field of endeavor. One skilled in the art would have been motivated to substitute the classification algorithm as taught by Fan and Mittal for the classification algorithm as taught by Zarei as an alternate means for vehicle detection and classification in real time (Zarei, Abstract).
As per claim 8, Fan and Mittal disclose the system of claim 7, wherein the step (c2) includes:
(c21) inputting the image, from which the bounding box has been extracted, into the Faster R-CNN block of the hybrid deep learning object detection model (Fan, Figure 1, Structure of Faster R-CNN; Fan, page 2, 2.1 Faster R-CNN Algorithm, After obtaining region proposals with different sizes from RPN, it is applied to the ROI Pooling layer);
(c22) performing Region of Interest (RoI) pooling on the input image in the Faster R-CNN block (Fan, Figure 1, Structure of Faster R-CNN, Input image, Feature Map, ROI Pooling; Fan, page 2, 2.1 Faster R-CNN Algorithm, After obtaining region proposals with different sizes from RPN, it is applied to the ROI Pooling layer); and
(c23) inputting the RoI pooled images into the Fully Connected CNN of the Faster R-CNN block (Fan, Figure 1, Structure of Faster R-CNN, Input image, Feature Map, ROI Pooling; Fan, page 2, 2.1 Faster R-CNN Algorithm, After obtaining region proposals with different sizes from RPN, it is applied to the ROI Pooling layer).
Fan and Mittal do not explicitly disclose the following limitation as further recited however Zarei discloses
wherein the output of the Fully Connected CNN in step (c23) is processed by a softmax classifier to output classification or segmentation in step (d) (Zarei, pages 120599-120600, C. Position Prediction and Trajectory Classification Blocks, Trajectory classification is carried out by sampling various trajectories learned for each trajectory. It consists of two trajectory feature extraction (TFE) blocks with different receptive fields, a fully connected layer, and a soft-max layer ... The obtained properties are transferred through a fully connected layer fc_MRF to the Softmax layer. This layer determines the probability of each of the two trajectory classes).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Zarei with Fan and Mittal because they are in the same field of endeavor. One skilled in the art would have been motivated to substitute the classification algorithm as taught by Fan and Mittal for the classification algorithm as taught by Zarei as an alternate means for vehicle detection and classification in real time (Zarei, Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRACY MANGIALASCHI whose telephone number is (571)270-5189. The examiner can normally be reached M-F, 9:30AM TO 6:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at (571) 272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TRACY MANGIALASCHI/Primary Examiner, Art Unit 2668
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