DETAILED ACTION
Status of the Claims
Original claims 1-18 filed March 3, 2025, are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 26, 2026, is being considered by the examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 62/689,251, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. For example, the disclosure of the ‘251 application does not describe evaluating a suitability of one or more animals for further processing by determining whether a bounding box thereof has a specific shape as recited in the independent claims.
The disclosure of the prior-filed application, Application No. 62/827,203, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. For example, the disclosure of the ‘203 application does not describe evaluating a suitability of one or more animals for further processing by determining whether a bounding box thereof has a specific shape as recited in the independent claims.
Double Patenting
Claim(s) 1-18 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 1-14 of U.S. Patent No. 12,266,142 in view of ‘Banhazi’ (“Improved image analysis based system to reliably predict the live weight of pigs on farm: Preliminary results,” 2011).
The claims of the patent disclose all elements of the corresponding claims of the instant application (see summary table below), except that the claims of the patent do not recite processing said at least one image to: (1) reduce image distortion.
However, Banhazi does teach processing at least one image to: (1) reduce image distortion (e.g., Page 110, Section 2.3, image is processed with median filter to reduce distortion from background noise in the image).
Both Banhazi and the claims of the patent use segmentation (Patent: definition of an enveloping bounding box; Banhazi: Sec. 2.3 describes segmentation) to determine the weight of an animal from an image (Patent: e.g., Claim 1, lines 31-33 and 45-46 in column 20; Banhazi: Secs. 2.4 et seq. describe how the segmentation is used to determine pig weight). Reducing distortion due to background noise, as taught by Banhazi, is advantageous at least because it can allow for a less-noisy and more-accurate segmentation, and thus a more-accurate animal weight estimate.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the inventions of the claims of the Patent with the median filtering image distortion reduction of Banhazi in order to improve the inventions with the reasonable expectation that this would result in inventions that used higher-quality, lower-noise images and thus could obtain more accurate segmentations and animal weight estimates. This technique for improving the inventions of the claims of the Patent was within the ordinary ability of one of ordinary skill in the art based on the teachings Banhazi.
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of the claims of the Patent and Banhazi to obtain the inventions as specified in the claims of the instant application.
Examiner notes that in some instances dependent claims of the instant application recite features that are substantially the same as features recited in corresponding dependent claims of the Patent, except that they depend upon claims to a different category of invention or a different combination of dependent claims. For example, claim 11 of the instant application recites limitations that are substantially the same as limitations of claim 2 in the Patent, but claim 2 in the Patent further limits a method (Patent: claim 1) rather than a system (Patent: claim 14). See the summary table below for more examples. At least the facts that all of the independent claims of the Patent have substantially the same functionality, and that all of the dependent claims specify further details of that same general functionality, would have suggested to one of ordinary skill in the art that further limitations of different claims in the Patent could be combined with a reasonable expectation of success.
Summary of Double Patenting Rejection
Claim of Instant Application
Corresponding Claim(s) of U.S. Patent No. 12,266,142
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
8, 9
10
14
11
2, 14
12
2, 3, 14
13
4, 14
14
5, 14
15
6, 14
16
7, 14
17
8, 14
18
8, 9, 14
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) 1-5, 7, 10-14, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over ‘Kamiya’ (WO 2019/044001 A1; copy provided in “parent” application no. 17/256,153) in view of ‘Banhazi’ (“Improved image analysis based system to reliably predict the live weight of pigs on farm: Preliminary results,” 2011).
Regarding claim 1, Kamiya teaches a method of estimating weight of one or more livestock animals (e.g., Fig. 7, [0055] et seq.), the method comprising:
receiving at least one image depicting a scene comprising one or more livestock animals ([0055], [0022], [0030], Fig. 7, S101, image is acquired by camera; e.g., Figs. 1 and 4, the image may depict a scene with one or more livestock animals, such as pigs);
processing said at least one target image to: (i) reduce image distortion (see Note Regarding Distortion below); and (ii) define, for each of one or more depicted animals, a bounding box enveloping a respective depicted animal of said one or more depicted animals (e.g., [0055], [0032]-[0036], Figs. 4 and 6, Fig. 7, S102, a bounding box B is defined to envelop each depicted animal in order to allow calculation of aspect ratio);
evaluating a suitability of each of said one or more depicted animals for further processing, by determining whether the bounding box thereof has a specific shape ([0056], Fig. 7, S203, it is determined whether the aspect ratio matches an optimal ratio; This is determining whether the bounding box has a specific shape – i.e., whether the rectangular box is as narrow, elongated, etc. as desired; [0056], [0058], it may be an exact match or a match within a tolerance; [0057], Fig. 7, if the aspect ratio matches the optimal ratio, then it is determined to be suitable and passed to further processing at S204-206; Otherwise, further processing is not performed on that animal image), said further processing being performed to determine weight of said one or more depicted animals ([0060], Fig. 7, further processing includes weight determination at S205); and
further processing said at least one target image to determine weight of those of said one or more depicted animals that are evaluated suitable for further processing ([0060], Fig. 7, S205, weight determination).
Note Regarding Distortion. Kamiya does not explicitly teach performing any enhancement processing on its received target images, such as processing to reduce image distortion.
However, Banhazi does teach processing at least one target image to: (1) reduce image distortion (e.g., Page 110, Section 2.3, image is processed with median filter to reduce distortion from background noise in the image; I.e., the noise is/causes distortion and the filtering reduces the distortion).
Both Banhazi and Kamiya use segmentation (Kamiya: e.g., Fig. 4, pig region Q is segmented by a bounding box; Banhazi: Sec. 2.3 describes segmentation) to determine the weight of an animal from an image (Kamiya: e.g., [0060], Fig. 7, step S205; Banhazi: Secs. 2.4 et seq. describe how the segmentation is used to determine pig weight). Reducing distortion due to background noise, as taught by Banhazi, is advantageous at least because it can allow for a less-noisy and more-accurate segmentation, and thus a more-accurate animal weight estimate.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Kamiya with the median filtering image distortion reduction of Banhazi in order to improve the inventions with the reasonable expectation that this would result in a method that used higher-quality, lower-noise images and thus could obtain more accurate segmentations and animal weight estimates. This technique for improving the inventions of the method of Kamiya was within the ordinary ability of one of ordinary skill in the art based on the teachings Banhazi.
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Kamiya and Banhazi to obtain the invention as specified in claim 1.
Regarding claim 2, Kamiya in view of Banhazi teaches the method of claim 1, and Kamiya further teaches that said scene comprises a livestock group housing environment (e.g., [0003], may be used in a farm environment housing a group of hundreds of livestock animals).
Regarding claim 3, Kamiya in view of Banhazi teaches the method of claim 1, and Kamiya further teaches that said at least one image is captured from an overhead perspective in relation to said one or more livestock animals (e.g., Figs. 1 and 4, [0013]).
Regarding claim 4, Kamiya in view of Banhazi teaches the method of claim 3, and Kamiya further teaches that said evaluating the suitability is further made based on a predetermined set of parameters with respect to each of said one or more depicted animals, said set of parameters comprising at least one of:
an angle of said overhead perspective in relation to a bounding box in said at least one image ([0051], optimal aspect ratio may vary depending on whether image is captured from directly above, or at a diagonal angle from above; As noted above, the suitability is evaluated based on the optimal aspect ratio – see, e.g., Fig. 7, S203),
a location of a bounding box in said at least one image relative to an acquisition point of said at least one image,
visibility of said one or more depicted animals in said scene,
location of said one or more animals in said scene,
occlusion of said one or more animals in said scene, and
a bodily posture of said one or more depicted animals in said scene.
Regarding claim 5, Kamiya in view of Banhazi teaches the method of claim 1, and Kamiya further teaches that said evaluating the suitability comprises assigning a suitability score to the bounding box of each of the one or more depicted animals ([0056], [0024], aspect ratio is assigned as a suitability score to the bounding box), and wherein the one or more depicted animals are evaluated suitable for further processing when said suitability score assigned to the bounding box exceeds a specified threshold ([0058], aspect ratio score of a box must fall within a predetermined range from the optimal ratio; The lower end of the predetermined range acts as a specified threshold that must be exceeded by the aspect ratio suitability score in order for an animal to be considered suitable for further processing).
Regarding claim 7, Kamiya in view of Banhazi teaches the method of claim 1, and Kamiya further teaches that said further processing comprises for each of the depicted animals suitable for the further processing:
segmenting a respective bounding box to determine boundaries of a segment associated with a bodily trunk of a respective depicted animal ([0059], Fig. 7, S204, area of region representing pig is determined; As illustrated in Figs. 4 and 6, the segments Q or Q’, respectively, are associated with a bodily trunk of a respective depicted animal and are segmented within a bounding box); and
determining weight of the respective depicted animal, based on said boundaries of the segment ([0060], Fig. 7, S205, the weight is determined based on the area within the boundaries of the pig segment).
Regarding claim 10, Examiner notes that the claim recites a system comprising: at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to: perform a method that is substantially the same as the method of claim 1.
Kamiya in view of Banhazi teaches the method of claim 1 (see above).
Kamiya further teaches implementing its method as a system (Fig. 1) comprising at least one hardware processor ([0015], computer CPU) and a non-transitory computer-readable storage medium ([0015], hard disk HDD, solid state drive SSD, etc.), where stored program instructions are executable by the at least one hardware processor to: perform the method ([0015], program installed in computer is executed to perform weight output processing unit functions).
While Kamiya teaches that the program is installed on a computer ([0014]-[0015]), it does not explicitly teach that the program is stored on the non-transitory computer-readable storage medium.
However, Examiner takes Official Notice that it is old and well-known in the art of image analysis to install a program on a computer by storing the program in a non-transitory computer-readable storage medium of that computer. Such storage advantageously preserves the program over time in a manner that allows its execution by the computer.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to install the program of Kamiya by storing it in the non-transitory computer-readable storage medium of Kamiya in order to improve the system with the reasonable expectation that this would result in a system that advantageously preserved its program over time in a manner that allowed its execution by the computer.
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Kamiya and Banhazi to obtain the invention as specified in claim 10.
Regarding claim 11, Examiner notes that the claim recites limitations that are substantially the same as limitations recited in claim 2. Kamiya in view of Banhazi teaches the invention of claim 2 (see above). Accordingly, claim 11 is also rejected under 35 U.S.C. 103 as being unpatentable over Kamiya in view of Banhazi for substantially the same reasons as claim 2.
Regarding claim 12, Examiner notes that the claim recites limitations that are substantially the same as limitations recited in claims 2 and 3. Kamiya in view of Banhazi teaches the inventions of claim 2 and 3 (see above). Accordingly, claim 12 is also rejected under 35 U.S.C. 103 as being unpatentable over Kamiya in view of Banhazi for substantially the same reasons as claims 2 and 3.
Regarding claim 13, Examiner notes that the claim recites limitations that are substantially the same as limitations recited in claims 2 and 4. Kamiya in view of Banhazi teaches the inventions of claim 2 and 4 (see above). Accordingly, claim 13 is also rejected under 35 U.S.C. 103 as being unpatentable over Kamiya in view of Banhazi for substantially the same reasons as claims 2 and 4.
Regarding claim 14, Examiner notes that the claim recites limitations that are substantially the same as limitations recited in claim 5. Kamiya in view of Banhazi teaches the invention of claim 5 (see above). Accordingly, claim 14 is also rejected under 35 U.S.C. 103 as being unpatentable over Kamiya in view of Banhazi for substantially the same reasons as claim 5.
Regarding claim 16, Examiner notes that the claim recites limitations that are substantially the same as limitations recited in claim 7. Kamiya in view of Banhazi teaches the invention of claim 7 (see above). Accordingly, claim 16 is also rejected under 35 U.S.C. 103 as being unpatentable over Kamiya in view of Banhazi for substantially the same reasons as claim 7.
Claim(s) 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kamiya in view of Banhazi as applied above, and further in view of ‘Andrew’ (“Visual Localisation and Individual Identification of Holstein Friesian Cattle via Deep Learning,” 2017; cited and copy provided in “parent” application no. 17/256,153).
Regarding claim 6, Kamiya in view of Banhazi teaches the method of claim 1.
Kamiya teaches detecting a livestock animal (e.g., [0010], pig, cow, etc.) region in an image and defining a bounding box around the animal (e.g., [0035], Figs. 4 and 6), but does not explicitly teach how the animal is detected. In particular, Kamiya does not explicitly teach applying a machine learning detection algorithm, said machine learning detection algorithm being trained to: (i) classify images to ones depicting animals and ones not depicting animals; and (ii) in the images depicting animals, separate between depicted animals by creating a bounding box around each depicted animal in said images. Banhazi also does not teach these features.
However, Andrew does teach techniques for detecting livestock animal regions in images by applying a machine learning detection algorithm (Sec. 3 and Fig. 3, R-CNN is the machine learning (ML) detection algorithm), said machine learning detection algorithm being trained to: (i) classify images to ones depicting animals and ones not depicting animals (Sec. 3 and Fig. 3, R-CNN detects any bounding boxes classified with confidence over a threshold to contain a class of interest, such as a cow livestock animal class; If the image includes such bounding boxes, then it has been classified as depicting animals, and vice versa); and (ii) in the images depicting animals, separate between depicted animals by creating a bounding box around each depicted animal in said images (e.g., Fig. 3, animals are separated with different bounding box predictions).
Andrew teaches that the livestock detection task is “well suited” to the R-CNN ML detection algorithm and that the R-CNN “produces near perfect results of correctly localising cows” (Sec. 6.1, par. spanning pages).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Kamiya in view of Banhazi as applied above with the R-CNN ML detection algorithm of Andrew in order to improve the method with the reasonable expectation that this would result in a method that could detect livestock animals with high, near-perfect performance. This technique for improving the method of Kamiya in view of Banhazi was within the ordinary ability of one of ordinary skill in the art based on the teachings of Andrew.
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Kamiya, Banhazi and Andrew to obtain the invention as specified in claim 6.
Regarding claim 15, Examiner notes that the claim recites limitations that are substantially the same as limitations recited in claim 6. Kamiya in view of Banhazi and Andrew teaches the invention of claim 6 (see above). Accordingly, claim 15 is also rejected under 35 U.S.C. 103 as being unpatentable over Kamiya in view of Banhazi and Andrew for substantially the same reasons as claim 6.
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kamiya in view of Banhazi as applied above, and further in view of ‘Wang’ (“Semantic Part Segmentation using Compositional Model combining Shape and Appearance,” 2015; cited and copy provided in “parent” application no. 17/256,153).
Regarding claim 8, Kamiya in view of Banhazi teaches the method of claim 7.
Kamiya teaches determining boundaries of a segment associated with a bodily trunk of a depicted animal ([0059], Fig. 7, S204, area of region representing pig is determined; As illustrated in Figs. 4 and 6, the segments Q or Q’, respectively, are segments with boundary of a bodily trunk of a livestock animal), but does not explicitly teach details of how the segmentation is performed. In particular, Kamiya does not explicitly teach applying a trained machine learning segmentation algorithm, and wherein said machine learning segmentation algorithm is trained using a training set comprising: (i) a plurality of images of livestock animals, wherein said images are captured from an overhead perspective; and (ii) labels associated with boundaries of segments of at least some of a bodily trunk, a head, a tail, and one or more limbs of each of said animals in said plurality of images. Banhazi also does not teach these features.
However, Wang does teach a machine learning detection algorithm that can be used to identify regions of animals in images, including trunk regions (e.g., Fig. 1, d-f, trunk is distinguished as a brown region in color version of the reference). The machine learning detection algorithm is trained using a training set comprising:
(i) a plurality of images of animals (Section 7, Dataset, images are of horse and cow animals), wherein said images are captured from an overhead perspective (see Note Regarding Perspective below); and
(ii) labels associated with boundaries of segments associated with (a) a bodily trunk, (b) a head, (b) a tail, and (d) one or more limbs of each of said animals in said plurality of images (Sec. 7, Dataset, 1st par., “pixelwise semantic part annotations for each object instance”; As seen in, e.g., Fig. 1(e), such pixelwise labels are associated with trunk, head, tail, and leg/limb segments of an image [note that they different segments are color-coded in the Figure]; Also see, e.g., Sec. 7, Setup, where the parts found using the training data include head, torso/trunk and leg, and note that a trunk segment is associated with a boundary of a tail because a boundary of the trunk is shared with a boundary of the tail at their interface).
Wang teaches that its machine learning segmentation technique provides better performance than other baseline methods (Sec. 7.3).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Kamiya in view of Banhazi as applied above with the learning-based animal segmentation of Wang in order to improve the method with the reasonable expectation that this would result in a method that enjoyed improved segmentation performance. This technique for improving the method of Kamiya in view of Banhazi was within the ordinary ability of one of ordinary skill in the art based on the teachings of Wang.
Note Regarding Perspective. Wang uses a dataset including training images captured from a variety of perspectives (e.g., Sec. 3.2; Fig. 5), but does not explicitly teach an overhead perspective.
However, Kamiya’s method does process images captured from an overhead perspective (e.g., Fig. 1, camera 20 is overhead of animal P).
Furthermore, Examiner takes Official Notice that it is old and well-known in the art of image analysis that a machine learning model should be trained using images of the same type it will be used to evaluate in order to provide good performance.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Kamiya in view of Banhazi and Wang as applied above to include overhead training images in its training dataset in order to improve the method with the reasonable expectation that this would result in a method whose machine learning model could achieve good performance. This technique for improving the method of Kamiya in view of Banhazi and Wang was within the ordinary ability of one of ordinary skill in the art based on the teachings of Wang and Kamiya.
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Kamiya, Banhazi and Wang to obtain the invention as specified in claim 8.
Regarding claim 17, Examiner notes that the claim recites limitations that are substantially the same as limitations recited in claim 8. Kamiya in view of Banhazi and Wang teaches the invention of claim 8 (see above). Accordingly, claim 17 is also rejected under 35 U.S.C. 103 as being unpatentable over Kamiya in view of Banhazi and Wang for substantially the same reasons as claim 8.
Allowable Subject Matter
Claims 9 and 18 are not rejected under 35 U.S.C. § 102 or § 103. However, these claims are rejected for double patenting and thus are not in condition for allowance at this time.
A reply (a) overcoming the double-patenting rejection (e.g., by filing a terminal disclaimer) and (b) rewriting claims 9 and 18 in independent form including all of the limitations of the base claim and any intervening claims would place these claims into condition for allowance.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEOFFREY E SUMMERS whose telephone number is (571)272-9915. The examiner can normally be reached Monday-Friday, 7:00 AM to 3:30 PM ET.
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, Chan Park can be reached at (571) 272-7409. 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.
/GEOFFREY E SUMMERS/Examiner, Art Unit 2669
Prosecution Insights