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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/11/2025 has been entered.
Response to Amendment
The amendments, filed 11/11/2025, have been entered and made of record. Claim 1 has been amended. Claims 2 and 7 have been cancelled. Claims 21 and 22 have been added. Claims 1, 3-6, 8-22 are pending.
Response to Arguments
Applicant’s arguments in the Remarks filed on 11/11/2025 have been considered but are moot in view of the new ground(s) of rejection.
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.
Kumar in view of Benjamin
Claims 1, 3-6, 8, and 15-22 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al.(USPubN 2024/0057892; hereinafter Kumar) in view of Benjamin et al.(USPubN 2023/0276773; Benjamin).
As per claim 1, Kumar teaches a method for diagnosing a disorder in an Equidae animal, said method comprising the step of using an augmented visual assessment (AVA) system comprising a first camera and a neural network system wherein the system is configured for motion capture and real-time analysis of an animal in diagnosis of the disorder in the animal(“The system 100 may include an image capture device 101, a device 102 and one or more systems 150 connected across one or more networks 199. The image capture device 101 may be part of, included in, or connected to another device (e.g., device 1600), and may be a camera, a high speed video camera, or other types of devices capable of capturing images and videos” in Para.[0041], “The image capture device 101 may capture video (or one or more images) of one or more subjects on whom the formalin assay is performed, and may send video data 104 representing the video to the system(s) 150 for processing” in Para.[0042], “The point tracker component 110 may include one or more machine learning models configured to process the video data 104. In some embodiments, the one or more machine learning models may be a neural network such as, a deep neural network, a deep convolutional neural network, a recurrent neural network, etc. … The point tracker component 110 may be configured to determine the point data 112 in near real-time speeds and may run a high processing capacity GPU” in Para.[0047], “Some aspects of the invention include use of gait and posture analysis methods with a subject. As used herein, a the term “subject” may refer to … horse” in Para.[0085], “a subject may be monitored using a gait level determining method or system of the invention and the presence or absence of an activity disorder or condition can be detected. In certain embodiments of the invention, a test subject that is an animal model of an activity and/or movement condition may be used to assess the test subject's response to the condition. In addition, a test subject that is an animal model of a movement and/or activity condition may be administered a candidate therapeutic agent or method, monitored using a gait monitoring method and/or system of the invention and results can be used to determine an efficacy of the candidate therapeutic agent to treat the condition” in Para.[0089], Fig. 1), and
Wherein the animal is an Equidae family animal(“Some aspects of the invention include use of gait and posture analysis methods with a subject. As used herein, a the term “subject” may refer to … horse” in Para.[0085]).
Kumar is silent about real-time motion capture and wherein the disorder is lameness.
Benjamin teaches real-time motion capture and wherein the disorder is lameness(“the monitoring devices 708 can provide real time predictions/assessments of animal health and predictors of animal productivity. … In one embodiment, an assessment is made of the size, weight, shape, topology, and movement of a gilt as it moves from (or is ready to move from) a growing zone. For example, a size of the animal may be determined from a depth camera, IR, camera or other similar sensor” in Para.[0086], “the neural network could be a single network that simultaneously detects both gait/lameness abnormalities as well as body composition abnormalities” in Para.[0052])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Kumar with the above teachings of Benjamin in order to improve both animal welfare and farmer productivity as well as ease the burden on caregivers.
As per claim 3, Kumar and Benjamin teach all of limitation of claim 1.
Kumar teaches wherein the disorder further comprises abnormal joint position of the animal(Para.[0038]).
As per claim 4, Kumar and Benjamin teach all of limitation of claim 1.
Kumar teaches wherein the disorder further comprises abnormal gait of the animal(Para.[0089], [0094]).
As per claim 5, Kumar and Benjamin teach all of limitation of claim 1.
Kumar teaches wherein the disorder further comprises a gait-related disorder of the animal(Para.[0089], [0094]).
As per claim 6, Kumar and Benjamin teach all of limitation of claim 1.
Kumar teaches wherein the method is performed in a field(Para.[0041], [0084]).
As per claim 8, Kumar and Benjamin teach all of limitation of claim 1.
Kumar teaches wherein the method is performed on a mobile device(Para.[0154]).
As per claim 15, Kumar and Benjamin teach all of limitation of claim 1.
Kumar teaches wherein the method comprises detection of one or more body parts of the animal, wherein the animal is performing a gait cycle(Para.[0038]).
As per claim 16, Kumar and Benjamin teach all of limitation of claim 15.
Kumar teaches wherein the one or more body parts are selected from a group consisting of hip, poll, limb, torso, and any combination thereof(Para.[0038]).
As per claim 17, Kumar and Benjamin teach all of limitation of claim 1.
Kumar teaches wherein the method comprises tracking of one or more body parts of the animal, wherein the animal is performing a gait cycle(Para.[0123]).
As per claim 18, Kumar and Benjamin teach all of limitation of claim 1.
Kumar teaches wherein the method comprises quantifying detection of one or more body parts of the animal, quantifying tracking of one or more body parts of the animal, wherein the animal is performing a gait cycle, or both(Para.[0123]).
As per claim 19, Kumar and Benjamin teach all of limitation of claim 1.
Kumar teaches wherein the method comprises recording detection of one or more body parts of the animal, recording tracking of one or more body parts of the animal, wherein the animal is performing a gait cycle, or both(Para.[0123]).
As per claim 20, Kumar and Benjamin teach all of limitation of claim 1.
Butler teaches wherein the method evaluates one or more of vertical displacement, velocity, acceleration, position, and joint angle of the animal(Para.[0038]).
As per claim 21, Kumar teaches a method for diagnosing a disorder in an animal, said method comprising the step of using an augmented visual assessment (AVA) system comprising a first camera and a neural network system wherein the system is configured for motion capture and analysis of an animal in diagnosis of the disorder in the animal(“The system 100 may include an image capture device 101, a device 102 and one or more systems 150 connected across one or more networks 199. The image capture device 101 may be part of, included in, or connected to another device (e.g., device 1600), and may be a camera, a high speed video camera, or other types of devices capable of capturing images and videos” in Para.[0041], “The image capture device 101 may capture video (or one or more images) of one or more subjects on whom the formalin assay is performed, and may send video data 104 representing the video to the system(s) 150 for processing” in Para.[0042], “The point tracker component 110 may include one or more machine learning models configured to process the video data 104. In some embodiments, the one or more machine learning models may be a neural network such as, a deep neural network, a deep convolutional neural network, a recurrent neural network, etc. … The point tracker component 110 may be configured to determine the point data 112 in near real-time speeds and may run a high processing capacity GPU” in Para.[0047], “Some aspects of the invention include use of gait and posture analysis methods with a subject. As used herein, a the term “subject” may refer to … horse” in Para.[0085], “a subject may be monitored using a gait level determining method or system of the invention and the presence or absence of an activity disorder or condition can be detected. In certain embodiments of the invention, a test subject that is an animal model of an activity and/or movement condition may be used to assess the test subject's response to the condition. In addition, a test subject that is an animal model of a movement and/or activity condition may be administered a candidate therapeutic agent or method, monitored using a gait monitoring method and/or system of the invention and results can be used to determine an efficacy of the candidate therapeutic agent to treat the condition” in Para.[0089], Fig. 1),
wherein the animal is an Equidae family animal(“Some aspects of the invention include use of gait and posture analysis methods with a subject. As used herein, a the term “subject” may refer to … horse” in Para.[0085]).
Kumar is silent about wherein the first camera utilizes three-dimensional (3D) position estimates of key body parts data of the animal, wherein the neural network system utilizes confirmation of feature track and discards false positives, and wherein the disorder is lameness.
Benjamin teaches wherein the first camera utilizes three-dimensional (3D) position estimates of key body parts data of the animal, wherein the neural network system utilizes confirmation of feature track and discards false positives(“the process 300 can begin acquiring video data upon detecting animal motion, such as acquiring three dimensional (3D) video data” in Para.[0045], “identify a set of structural locations of an animal's body throughout each frame of a video clip. In some embodiments, the process 300 can provide each video frame included in the video clips to a trained model, such as a neural network, which can accurately identify skeletal structure locations of the animal in a given video frame” in Para.[0056], “The trained model can output a score or a classification indication indicative of whether or not the motion exhibited by the animal is abnormal or not (a classification) or can provide merely percentage likelihoods or similar indications that an animal may exhibit a certain characteristic in the future (e.g., poor productivity, poor growth, health issue, etc.). In some embodiments, the score or the classification indication can be a categorical level of abnormality (e.g., abnormal or not abnormal) and/or may be selected from a continuous range of values (e.g., a number ranging from zero to one, inclusive, with zero representing “not abnormal”, and one representing “abnormal.”” in Para.[0059]), and
wherein the disorder is lameness(“the neural network could be a single network that simultaneously detects both gait/lameness abnormalities as well as body composition abnormalities” in Para.[0052]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Kumar with the above teachings of Benjamin in order to improve both animal welfare and farmer productivity as well as ease the burden on caregivers.
As per claim 22, Kumar teaches a method for diagnosing lameness in an Equidae family animal, said method comprising the step of using an augmented visual assessment (AVA) system comprising a first camera and a neural network system wherein the system is configured for motion capture and analysis of an animal in diagnosis of disorder in the Equidae family animal(“The system 100 may include an image capture device 101, a device 102 and one or more systems 150 connected across one or more networks 199. The image capture device 101 may be part of, included in, or connected to another device (e.g., device 1600), and may be a camera, a high speed video camera, or other types of devices capable of capturing images and videos” in Para.[0041], “The image capture device 101 may capture video (or one or more images) of one or more subjects on whom the formalin assay is performed, and may send video data 104 representing the video to the system(s) 150 for processing” in Para.[0042], “The point tracker component 110 may include one or more machine learning models configured to process the video data 104. In some embodiments, the one or more machine learning models may be a neural network such as, a deep neural network, a deep convolutional neural network, a recurrent neural network, etc. … The point tracker component 110 may be configured to determine the point data 112 in near real-time speeds and may run a high processing capacity GPU” in Para.[0047], “Some aspects of the invention include use of gait and posture analysis methods with a subject. As used herein, a the term “subject” may refer to … horse” in Para.[0085], “a subject may be monitored using a gait level determining method or system of the invention and the presence or absence of an activity disorder or condition can be detected. In certain embodiments of the invention, a test subject that is an animal model of an activity and/or movement condition may be used to assess the test subject's response to the condition. In addition, a test subject that is an animal model of a movement and/or activity condition may be administered a candidate therapeutic agent or method, monitored using a gait monitoring method and/or system of the invention and results can be used to determine an efficacy of the candidate therapeutic agent to treat the condition” in Para.[0089], “Some aspects of the invention include use of gait and posture analysis methods with a subject. As used herein, a the term “subject” may refer to … horse” in Para.[0085], Fig. 1),
Kumar is silent about wherein the AVA system automatically combines a measured vertical displacement data from head and pelvis of the Equidae family animal with measured joint positions of the Equidae family animal to diagnose lameness in the Equidae family animal.
Benjamin teaches wherein the AVA system automatically combines a measured vertical displacement data from head and pelvis of the Equidae family animal with measured joint positions of the Equidae family animal to diagnose lameness in the Equidae family animal (“the process 300 can begin acquiring video data upon detecting animal motion, such as acquiring three dimensional (3D) video data” in Para.[0045], “identify a set of structural locations of an animal's body throughout each frame of a video clip. In some embodiments, the process 300 can provide each video frame included in the video clips to a trained model, such as a neural network, which can accurately identify skeletal structure locations of the animal in a given video frame” in Para.[0056], “The trained model can output a score or a classification indication indicative of whether or not the motion exhibited by the animal is abnormal or not (a classification) or can provide merely percentage likelihoods or similar indications that an animal may exhibit a certain characteristic in the future (e.g., poor productivity, poor growth, health issue, etc.). In some embodiments, the score or the classification indication can be a categorical level of abnormality (e.g., abnormal or not abnormal) and/or may be selected from a continuous range of values (e.g., a number ranging from zero to one, inclusive, with zero representing “not abnormal”, and one representing “abnormal.”” in Para.[0059], “the neural network could be a single network that simultaneously detects both gait/lameness abnormalities as well as body composition abnormalities” in Para.[0052]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Kumar with the above teachings of Benjamin in order to improve both animal welfare and farmer productivity as well as ease the burden on caregivers.
Kumar in view of Benjamin and Lindner
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al.(USPubN 2024/0057892; hereinafter Kumar) in view of Benjamin et al.(USPubN 2023/0276773; Benjamin) further in view of Lindner(USPubN 2017/0322479).
As per claim 9, Kumar and Benjamin teach all of limitation of claim 1.
Kumar and Benjamin are silent about wherein the first camera comprises a parfocal lens of at least a 4-250 mm focal length.
Lindner teaches wherein the first camera comprises a parfocal lens of at least a 4-250 mm focal length (Para.[0072]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Kumar and Benjamin with the above teachings of Lindner in order to improve both animal welfare and farmer productivity as well as ease the burden on caregivers.
Kumar in view of Benjamin and Zhao
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al.(USPubN 2024/0057892; hereinafter Kumar) in view of Benjamin et al.(USPubN 2023/0276773; Benjamin) further in view of Zhao et al.(USPubN 2022/0301351; hereinafter Zhao).
As per claim 11, Kumar and Benjamin teach all of limitation of claim 1.
Kumar and Benjamin are silent about wherein the system captures slow-motion performance of the animal.
Zhao teaches wherein the system captures slow-motion performance of the animal (Para.[0178]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Kumar and Benjamin with the above teachings of Zhao in order to improve both animal welfare and farmer productivity as well as ease the burden on caregivers.
As per claim 12, Kumar and Benjamin teach all of limitation of claim 1.
Kumar and Benjamin are silent about wherein the system captures video at 60 frames or greater per second.
Zhao teaches wherein the system captures video at 60 frames or greater per second (Para.[0178]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Kumar and Benjamin with the above teachings of Zhao in order to improve both animal welfare and farmer productivity as well as ease the burden on caregivers.
As per claim 13, Kumar and Benjamin teach all of limitation of claim 1.
Kumar and Benjamin are silent about wherein the system captures video at 120 frames or greater per second.
Zhao teaches wherein the system captures video at 120 frames or greater per second (Para.[0178]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Kumar and Benjamin with the above teachings of Zhao in order to improve both animal welfare and farmer productivity as well as ease the burden on caregivers.
Kumar in view of Benjamin and Mao
Claims 10 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al.(USPubN 2024/0057892; hereinafter Kumar) in view of Benjamin et al.(USPubN 2023/0276773; Benjamin) further in view of Mao et al.(USPubN 2021/0365707; hereinafter Mao).
As per claim 10, Kumar and Benjamin teach all of limitation of claim 1.
Kumar and Benjamin are silent about wherein the second camera comprises a motorized parfocal lens.
Mao teaches wherein the second camera comprises a motorized parfocal lens (Para.[0088]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Kumar and Benjamin with the above teachings of Mao in order to improve both animal welfare and farmer productivity as well as ease the burden on caregivers.
As per claim 14, Kumar and Benjamin teach all of limitation of claim 1.
Kumar and Benjamin are silent about wherein the system comprises a steady automatic zoom feature.
Mao teaches wherein the system comprises a steady automatic zoom feature (Para.[0088]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Kumar and Benjamin with the above teachings of Mao in order to improve both animal welfare and farmer productivity as well as ease the burden on caregivers.
Conclusion
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/SUNGHYOUN PARK/Examiner, Art Unit 2484