DETAILED ACTION
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.
Claim(s) 1, 4-6, 8, 9, 12-13, 15-16 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over He (“Truck and Trailer Classification with Deep Learning Based Geometric Features”)
Regarding claim 1, He discloses a method to perform vehicle classification, comprising: (He teaches a truck classification system using deep learning techniques, see Abstract)
obtaining, via at least one computing device, a plurality of images of at least one vehicle; and (Pg. 7784, left column, ¶ 2 teaches the truck detection for flagging a truck in a video camera stream, also see pg. 7789, left column, second paragraph from bottom.)
performing object detection and instance segmentation on the plurality of images of the at least one vehicle resulting in wheel instance identification and vehicle classification of at least one motorcycle, at least one passenger car, at least one pickup or van, at least one bus, or at least one truck, or a combination thereof. (Pg. 7786, left column, ¶ 1-2 teach embodiments for wheel detection and segmentation. Also see Pg. 7787, left column, ¶ 3. Truck vehicle classification is taught at pg. 7786, right column, ¶ 2.)
He does not expressly disclose that all of its above-cited teachings on truck classification are expressly disclosed as occurring in the same embodiment. That is, despite the reference being clear that these functions are disclosed, there is no express disclosure that the details are all found in the same embodiment, for example the different object detection embodiments and different embodiments in the Experiments Section IV. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the various teachings to provide a single system capable of the variety of tasks which are disclosed. In view of these teachings, this cannot be considered a non-obvious improvement over the prior art. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 4, the above combination discloses the method of claim 1, wherein object detection and instance segmentation are performed using view geometry. (pg. 7786, right column, ¶ 2, teaches using view geometry features such as aspect ratio for the truck classifier. Also see rejection of claim 1 above.)
Regarding claim 5, the above combination discloses the method of claim 1, wherein vehicle classification is verified using a length and a height of the vehicle. (Pg. 7786, right column, ¶ 2, teaches using view geometry features such as length and height aspect ratio for the truck classifier.)
Regarding claim 6, the above combination discloses the method of claim 1, wherein the vehicle classification comprises at least five categories. (See pg. 7789, right column, last paragraph as well as Figs. 4-5.)
Regarding claim 8, the above combination discloses the method of claim 1, wherein vehicle classification occurs while at least one vehicle is on-road. (See rejection of claim 1.)
Claims 9, 12, 13 and 15 are the system claims corresponding to the method of claims 1, 4, 6, and 8, absent the list of vehicle options. Claims 16 and 19-20 are non-transitory, computer-readable medium corresponding to claims 1, 4 and 8. Cai teaches an imaging device and computational processing, pg. 7789, left column, second paragraph from bottom and pg. 7784, right column, second paragraph from bottom. Remaining limitations are rejected similarly. See detailed analysis above. The above combination does not expressly disclose a computer and computer readable medium, but does disclose a state of the art technique in computer vision. Examiner notes that both the concept and advantage of using a computer readable medium and a computer to perform computational steps is well known and practiced in the art, and therefore would have been obvious to incorporate with predictable result and without undue experimentation. Official Notice is applied here. Please see above for detailed analysis.
Claim(s) 2, 3, 10, 11 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over He (“Truck and Trailer Classification with Deep Learning Based Geometric Features”) in view of Steinberg (“CART: Classification and Regression Trees”)
Regarding claim 2, the above combination discloses the method of claim 1, further comprising: obtaining a truck axle configuration from the plurality of images of the at least one truck; identifying the truck axle configuration of the at least one truck; and comparing the truck axle configuration of the at least one truck to the truck axle configuration dictionary resulting in a vehicle truck classification of the at least one truck (pg. 7786, right column, ¶ 2, teaches using the geometric features including the axel detections for a CART decision tree learned classifier for the truck classifier.)
In the field of learned CART decision trees Steinberg teaches what the above combination does not expressly disclose, namely, preparing the configuration dictionary (Pg. 181, ¶ 1-2 teach the process of preparing the CART decision tree via tree growing and pruning.)
It would have been obvious to one of ordinary skill in the art to have combined He’s decision tree classification with Seinberg’s decision tree classification. He does not expressly teach steps of preparing the tree which it discloses is learned and used for classification. Steinberg teaches the process of preparing the CART decision tree via tree growing and pruning. Simply applying Steinberg’s teachings cannot be considered a non-obvious improvement over the prior art. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 3, the above combination discloses the method of claim 2, wherein the vehicle truck classification comprises at least nine categories. (See He pg. 7789, right column, last paragraph as well as Figs. 4-5.)
Claims 10-11 are the system claims corresponding to the method of claims 2-3, absent the list of vehicle options. Claim 17 is the non-transitory computer-readable medium corresponding to claim 2. Cai teaches an imaging device and computational processing, pg. 7789, left column, second paragraph from bottom and pg. 7784, right column, second paragraph from bottom. Remaining limitations are rejected similarly. See detailed analysis above. The above combination does not expressly disclose a computer and computer readable medium, but does disclose a state of the art technique in computer vision. Examiner notes that both the concept and advantage of using a computer readable medium and a computer to perform computational steps is well known and practiced in the art, and therefore would have been obvious to incorporate with predictable result and without undue experimentation. Official Notice is applied here. Please see above for detailed analysis.
Claim(s) 7, 14 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over He (“Truck and Trailer Classification with Deep Learning Based Geometric Features”) in view of Cai (“Cascade R-CNN: High Quality Object Detection and Instance Segmentation”).
Regarding claim 7, the above combination discloses the method of claim 1, wherein object detection and instance segmentation are performed using a convolutional neural network. (See pg. 7784, right column, second paragraph from bottom and the YOLOv3 CNN at pg. 7784, left column, second paragraph from bottom.)
In the field of object detection Cai teaches what the above combination does not expressly disclose, namely, a cascade mask region-based convolutional neural network. (Abstract and Fig. 6 teach the Cascade mask R-CNN framework as an improved object detector and segmentation tool.)
It would have been obvious to one of ordinary skill in the art to have combined He’s CNN object detection with Cai’s Cascade mask R-CNN object detection. Cai’s Cascade mask R-CNN framework is an improved object detector and segmentation tool, for higher quality object detection. Simply substituting this detector here cannot be considered a non-obvious improvement over the prior art. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Claim 14 is the system claim corresponding to the method of claim 7, absent the list of vehicle options. Claim 18 is the non-transitory computer-readable medium corresponding to claim 7, absent the list of vehicle options. Cai teaches an imaging device and computational processing, pg. 7789, left column, second paragraph from bottom and pg. 7784, right column, second paragraph from bottom. Remaining limitations are rejected similarly. See detailed analysis above. The above combination does not expressly disclose a computer and computer readable medium, but does disclose a state of the art technique in computer vision. Examiner notes that both the concept and advantage of using a computer readable medium and a computer to perform computational steps is well known and practiced in the art, and therefore would have been obvious to incorporate with predictable result and without undue experimentation. Official Notice is applied here. Please see above for detailed analysis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Raphael Schwartz whose telephone number is (571)270-3822. The examiner can normally be reached Monday to Friday 9am-5pm CT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vincent Rudolph can be reached at (571) 272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RAPHAEL SCHWARTZ/ Examiner, Art Unit 2671
Prosecution Insights