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 .
This office action is in response to communication filed on 6/30/2026. Claims 2-20 have been added. Claims 1-20 are pending on this application.
Election/Restrictions
Newly submitted claims directed to an invention that is independent or distinct from the invention originally claimed for the following reasons: Independent claims 10 and 16 comprise transforming images to grayscale, identifying animals from the grayscale images, simplifying noise raw detections by clustering, and determining a count of the animals based on the simplifying. These steps are different than independent claim 1 which performs counting based on different criteria.
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claims 10-20 are withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
To preserve a right to petition, the reply to this action must distinctly and specifically point out supposed errors in the restriction requirement. Otherwise, the election shall be treated as a final election without traverse. Traversal must be timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are subsequently added, applicant must indicate which of the subsequently added claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3, 4, and 6-9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Xu et al ("Automated cattle counting using Mask R-CNN in quadcopter vision system” Computers and Electronics in Agriculture 171 (04/2020): 105300, pages 1-12, retrieved from the Internet on 7/22/2026).
Regarding claim 1, Xu discloses a system comprising:
at least one processor (section 2, the processing pipeline would require a processor); and
memory, the memory containing instructions to control any number of the at least one processor (it would be necessary to have memory coupled to the processor) to:
receive video from an image capture device (fig. 3), the image capture device being positioned over a livestock path (fig. 2; section 3.2, The cattle datasets were captured at a height of 8–25 m with angles of inclination in the pastures and at a downward angle vertically in the feedlot to simulate the aerial detection), the video containing images of livestock walking along the livestock path (fig. 2; section 3.2, different key frames during cattle movement), the image capture device including any number of cameras (fig. 3; section 3.2, Fifteen flight campaigns were conducted by the MAVIC PRO drone which is equipped with an integrated PTZ camera shown in Fig. 3);
select one or more images from the video (fig. 1; section 3.1, The RGB image acquired by the drone);
apply one or more models to the one or more images (Region proposal network in fig. 1) to generate a plurality of regions of interest (fig. 1; section 3.1, then the obtained feature map is sent to the Region Proposal Network (RPN) to generate Region of Interests);
generate segmentation masks for at least a subset of the plurality of regions of interest (section 3.3, Mask R-CNN additionally provides a mask prediction branch composed of a small Fully Convolutional Network for segmenting each Region of Interest);
classify objects within the plurality of regions of interest to generate classification (section 3.3, Mask R-CNN additionally produces a binary mask besides the class label and bounding-box for each ROI);
identify individual animals within the objects based on the classifications and the segmentation masks (Cattle detection and counting in fig. 1 which is the output of Mask and Classification blocks);
count individual animals based on the identification (Cattle detection and counting in fig. 1; section 4.2; Tables 3 and 4); and
provide the count to a digital device for display (Cattle detection and counting in fig. 1. It would be obvious to display the count).
Regarding claim 3, Xu discloses a system wherein the system receives multiple images of a herd of livestock as the herd of livestock travels the livestock path (frames of the video in fig. 1), the memory containing instructions to count at least a subset of the animals of the herd using the multiple images and generate a total of the counted animals (Cattle detection and counting in fig. 1; section 4.2; Tables 3 and 4).
Regarding claim 4, Xu discloses a system wherein the one or more models include a Region Proposal Network configured to generate the plurality of regions of interest (fig. 1; section 3.1, then the obtained feature map is sent to the Region Proposal Network (RPN) to generate Region of Interests).
Regarding claim 6, Xu discloses a system wherein the system applies a fully convolutional network to the plurality of regions of interest to generate segmentation masks in a pixel-to-pixel manner (section 2; Mask R-CNN (an extension of Faster R-CNN) which also allows for instance segmentation (associating specific image pixels to the detected object); section 3.3, Mask R-CNN additionally provides a mask prediction branch composed of a small Fully Convolutional Network for segmenting each Region of Interest).
Regarding claim 7, Xu discloses a system wherein counting occurs in real time as the livestock walks along the livestock path (Cattle detection and counting in fig. 1; section 5, Mask R-CNN can also be used for key point detection, which can be used for real-time detection of behaviours of the animals).
Regarding claim 8, Xu discloses a system wherein the one or more models include a RoIAlign configured to extract features from the plurality of regions of interest (fig. 1; section 3.1, The RoiAlign layer selects the feature corresponding to each ROI on the feature map according to the output of the RPN, and send them to the fully connected layer for classification prediction, mask prediction and bounding-box prediction).
Regarding claim 9, Xu fails to explicitly disclose a system wherein the video and the segmentation masks are stored in a livestock datastore. However it would be necessary to store the video and the segmentation masks to be used in the image processing shown in fig. 1.
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) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al ("Automated cattle counting using Mask R-CNN in quadcopter vision system” Computers and Electronics in Agriculture 171 (04/2020): 105300, pages 1-12, retrieved from the Internet on 7/22/2026) in view of Huber et al (US20210267172).
Regarding claim 2, Xu fails to teach a system wherein the image capture device comprises a LiDAR device.
However Huber teaches a system for counting animals (para. [0055]) wherein an image capture device comprises a LiDAR device (para. [0052], [0066]).
Therefore taking the combined teachings of Xu and Huber as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Huber into the system of Xu. The motivation to combine Huber and Xu would be to provide efficient classification (para. [0066] of Huber).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al ("Automated cattle counting using Mask R-CNN in quadcopter vision system” Computers and Electronics in Agriculture 171 (04/2020): 105300, pages 1-12, retrieved from the Internet on 7/22/2026) in view of Mindel et al (US20210153479).
Regarding claim 1, Xu fails to teach a system wherein the plurality of regions of interest are non-rectangular, polygonal shapes with defined locations and scales according to the one or more images.
However Mindel teaches wherein a plurality of regions of interest are non-rectangular, polygonal shapes with defined locations and scales according to one or more images (para. [0078]).
Therefore taking the combined teachings of Xu and Mindel as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Mindel into the system of Xu. The motivation to combine Mindel and Xu would be to improve the accuracy of animal identification (para. [0103] of Mindel).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEON VIET Q NGUYEN whose telephone number is (571)270-1185. The examiner can normally be reached Mon-Fri 11AM-7PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LEON VIET Q NGUYEN/ Primary Examiner, Art Unit 2663
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