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 .
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.
Claim 20 is 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 20 recites “The non-transitory computer-readable storage medium of Claim 16”. A lack of clarity arises as claim 16 is drawn to a method not a non-transitory computer-readable storage medium. For the purposes of examination, it is interpreted that claim 20 depends from claim 19 which is drawn to non-transitory computer-readable storage medium.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are all within at least one of the four categories.
The independent claim 1 recites:
identify with specificity an animal within the received image data;
identify at least one biomechanic marker on the identified animal;
track the position of each identified biomechanic marker;
determine a biomechanic metric based on each identified biomechanic marker; and
apply, to a biomechanic evaluation model, i) the tracked position of each biomechanic marker, and ii) the determined biomechanic metric, to generate an output including at least one biomechanic assessment metric for use in assessing the performance of the identified animal.
The independent claim 13 recites:
identify with specificity an animal within the received image data;
identify at least one biomechanic marker on the identified animal;
track the position of each identified biomechanic marker;
determine a biomechanic metric based on each identified biomechanic marker; and
apply, to a biomechanic evaluation model, i) the tracked position of each biomechanic marker, and ii) the determined biomechanic metric, to generate an output including at least one biomechanic assessment metric for use in assessing the performance of the identified animal.
The independent claim 19 recites:
identify with specificity an animal within the received image data;
identify at least one biomechanic marker on the identified animal;
track the position of each identified biomechanic marker;
determine a biomechanic metric based on each identified biomechanic marker; and
apply, to a biomechanic evaluation model, i) the tracked position of each biomechanic marker, and ii) the determined biomechanic metric, to generate an output including at least one biomechanic assessment metric for use in assessing the performance of the identified animal.
The above claim limitations constitute an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019. See footnotes 14 and 15.
“A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018) (performing a resampled statistical analysis to generate a resampled distribution).
The claimed steps of identifying; tracking; determining; applying; and generating can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
Examples of ineligible claims that recite mental processes include:
a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.;
claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics Corp.
a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
With respect to the pending claims, for example, an experienced clinician can perform the claimed step of identifying and tracking by mentally noting information on an image and collected data. The experienced clinician can then determine a metric mathematically and apply the tracked information and determined metric to generate a result mathematically to determine an assessment of a performance. Thus, the claims can be readily interpreted as being a mere application of a mental process on a computer.
Regarding the dependent claims, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry. For example, dependent claims 2-12, 14-18 and 20 recite steps (e.g. retrieving; identifying; determining; generating; and training) that can be performed in the mind. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
This judicial exception (abstract idea) in claims 1-20 is not integrated into a practical application because:
The abstract idea amounts to simply implementing the abstract idea on a computer. For example, the recitations regarding the generic computing components for receiving; identifying; tracking; determining; applying; generating; assessing; transmitting; retrieving; training and displaying merely invoke a computer as a tool.
The data-gathering step (receiving and retrieving) and the data-output step (transmitting and displaying) do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computer that is used as a tool for receiving; identifying; tracking; determining; applying; generating; assessing; transmitting; retrieving; training and displaying.
The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to determine a relationship among data to provide information about the performance of an animal.
The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computer for receiving; identifying; tracking; determining; applying; generating; assessing; transmitting; retrieving; training and displaying. The claims do not apply the obtained data to a particular machine. Rather, the data is merely output in an post-solution step.
The additional elements are identified as follows: at least one processor; at least one memory device; a computer device; and a non-transitory computer readable storage medium.
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by:
Applicant' s specification (para [0070]-[0078] and Fig. 9) which discloses that the processor and memory comprise generic computer components that are configured to perform the generic computer functions (e.g. receiving; identifying; tracking; determining; applying; generating; assessing; transmitting; retrieving; training and displaying) that are well-understood, routine, and conventional activities previously known to the pertinent industry.
the patent publication cited by applicant:
Grisel (US 20170262599 A1; cited by applicant).
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3.
Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional elements as those claimed. See option III. A. 2. in the Berkheimer memorandum.
When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 102
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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Grisel (US 20170262599 A1; cited by applicant).
With respect to claim 1, Grisel discloses a computer system for assessing the biomechanical performance of an animal (see Fig. 1: #10; and see paragraph 0016: individual can record a video of a horse walking, trotting or running using a computer device and that video can be transferred over a computer network to another computing environment for analysis with an evaluation engine using image processing techniques), said computer system comprising at least one processor in communication with at least one memory device (see paragraph 0026-0027: processing engine #130, the evaluation module #132 and the evaluation engine #140 with data store #120), wherein said processor is programmed to:
receive current image data from at least one camera of at least one animal (see paragraph 0048: image frames are received; and see paragraph 0023: computing device #160 includes a capture device #162 which can be embodied as a camera);
identify with specificity an animal within the received image data (see paragraph 0050: the feature identifier #142 can identify a species of a subject in the content #121-#123 as part of the identification process with reference to data stored in reference data #125);
identify at least one biomechanic marker on the identified animal (see paragraph 0050: one or more anatomical features associated with the species of the subject #200; and see paragraph 0049: feature identifier #142 has identified features #201-#204 which correspond to the withers, croup, right fetlock and right hook of the subject #200 respectively);
track the position of each identified biomechanic marker (see paragraph 0054: once identified by feature identifier #142, the motion tracker #144 is configured to track one or more of the features #201-#204 of the subject as they move, frame to frame, between the image frames #210A-#210N of the video file #210);
determine a biomechanic metric based on each identified biomechanic marker (see paragraph 0055, Motion data for the gait cycle pattern 300 can also be stored as one or more functions (or combinations of functions) representative of the manner in which the feature 201 of the subject 200 moves over time. Such functions can define various amplitudes, periodicities, frequencies, and other characteristics of the gait cycle pattern 300 over time);
apply, to a biomechanic evaluation model (see Fig.1: #146 motion evaluator; and see paragraph 0046: the motion evaluator 146 is configured to analyze the motion data generated by the motion tracker 144. Thus, the motion evaluator 146 can compare and evaluate various gait signatures, gait cycle patterns, gait distance differentials, gait cycle periods, gait cycle frequencies, etc., associated with one or more subjects. By evaluating the motion data from the motion tracker 144, the motion evaluator 146 can provide data to the evaluation module 132 for evaluation. As one example, the motion evaluator 146 can identify asymmetry in one or more gait cycle patterns of a subject, which may be an indicator of lameness to the evaluation module 132), i) the tracked position of each biomechanic marker (see paragraph 0055, Motion data for the gait cycle pattern 300 can also be stored as one or more functions (or combinations of functions) representative of the manner in which the feature 201 of the subject 200 moves over time. Such functions can define various amplitudes, periodicities, frequencies, and other characteristics of the gait cycle pattern 300 over time), and ii) the determined biomechanic metric, to generate an output including at least one biomechanic assessment metric for use in assessing the performance of the identified animal (see paragraph 0069 and see Fig. 1: #146); and
transmit one or more notification messages to at least one computing device, wherein each notification message includes at least one generated output biomechanic assessment metric (see Fig. 7: #712; and see paragraph 0099: Overall, based on information identified, tracked, calculated, evaluated, and stored over steps 704, 706, 708, and 710, the evaluation module 132 can provide an evaluation of a subject, possibly including the identification of likely sources of lameness or other health conditions of the subject at step 712. The sources of lameness can be identified as being associated with certain anatomical features of the subject as described herein, and those sources can be identified in one or more reports generated by the evaluation module 132).
With respect to claim 2, all limitations of claim 1 apply in which Grisel further discloses wherein the processor is further configured to:
retrieve historic image data (see paragraph 0045 and 0055: retrieve image data over time);
identify at least one animal within the retrieved historic image data (see paragraph 0050: the feature identifier #142 can identify a species of a subject in the content #121-#123 as part of the identification process with reference to data stored in reference data #125);
identify at least one historic biomechanic marker on each animal identified in the retrieved historic image data (see paragraph 0050: one or more anatomical features associated with the species of the subject #200; and see paragraph 0049: feature identifier #142 has identified features #201-#204 which correspond to the withers, croup, right fetlock and right hook of the subject #200 respectively);
determine at least one historic biomechanic metric based on the tracked position of each biomechanic marker (see paragraph 0055, Motion data for the gait cycle pattern 300 can also be stored as one or more functions (or combinations of functions) representative of the manner in which the feature 201 of the subject 200 moves over time. Such functions can define various amplitudes, periodicities, frequencies, and other characteristics of the gait cycle pattern 300 over time); and
generate a training dataset including at least one of i) the historic tracked position, ii) the determined historic biomechanic metric, and iii) one or more biomechanic risk factors (see paragraph 0045-0055).
With respect to claim 3, all limitations of claim 2 apply in which Grisel further discloses wherein the processor is further configured: train, using the generated training dataset, the biomechanic evaluation model for receiving an input including at least one of i) a current biomechanic marker position and ii) a determined biomechanic metric, and output one or more of the biometric assessment metrics (see paragraph 0049: feature identifier #142 has identified features #201-#204 which correspond to the withers, croup, right fetlock and right hook of the subject #200 respectively is utilized for training the dataset).
With respect to claim 4, all limitations of claim 1 apply in which Grisel further discloses wherein determining the biomechanic metric includes determining at least one of a velocity of a biomechanic marker, an acceleration of a biomechanic marker, a type of gait, a stride length, an average speed, a top speed, a center of mass of an anatomical segment, a velocity of an anatomical segment, an acceleration of an anatomical segment, a relative position of two or more biomechanic markers and a vertical displacement of at least one biomechanic marker, associated with the animal being assessed (see paragraph 0049: feature identifier #142 has identified features #201-#204 which correspond to the withers, croup, right fetlock and right hook of the subject #200 respectively).
With respect to claim 5, all limitations of claim 1 apply in which Grisel further discloses wherein identifying the at least one biomechanic marker on the identified animal includes at least one of identifying a joint anatomical marker and identifying a body segment marker (see paragraph 0049: feature identifier #142 has identified features #201-#204 which correspond to the withers, croup, right fetlock and right hook of the subject #200 respectively).
With respect to claim 6, all limitations of claim 1 apply in which Grisel further discloses wherein receiving image data includes receiving at least first image data from at least one first camera oriented at a first perspective relative to the animal (see paragraph 0048: image frames are received; and see paragraph 0023: computing device #160 includes a capture device #162 which can be embodied as a camera; and see paragraph 0028: different perspectives can be recorded such as front, back, top or other) and receiving at least second image data from at least one second camera oriented at a second perspective that is vertically displaced from the first perspective (see paragraph 0048: image frames are received; and see paragraph 0023: computing device #160 includes a capture device #162 which can be embodied as a camera; and see paragraph 0028: different perspectives can be recorded such as front, back, top or other).
With respect to claim 7, all limitations of claim 1 apply in which Grisel further discloses wherein generating the output including at least one biomechanic assessment metric includes at least one of a risk score, a lameness score, and an asymmetry score (see paragraph 0035: metrics can include motions tracked as healthy or lame).
With respect to claim 8, all limitations of claim 1 apply in which Grisel further discloses wherein generating the training dataset including one or more biomechanic risk factors includes at least one threshold vertical displacement of an anatomical marker located in proximity to the head of the animal (see paragraph 0055-0056: motion data points include vertical or horizontal dimensions).
With respect to claim 9, all limitations of claim 1 apply in which Grisel further discloses wherein assessing the biomechanical performance of an animal includes assessing the biomechanical performance of a horse (see paragraph 0016: horse).
With respect to claim 10, all limitations of claim 1 apply in which Grisel further discloses wherein transmitting one or more notification messages to one or more computing devices includes transmitting one or more notification messages to a computing device associated with a veterinarian (see paragraph 0041: video and reports of animal can be transmitted to veterinarian).
With respect to claim 11, all limitations of claim 1 apply in which Grisel further discloses wherein transmitting the one or more notification messages includes transmitting one or more determined biomechanic metrics (see paragraph 0041: reports generated by evaluation module can be sent to veterinarian).
With respect to claim 12, all limitations of claim 1 apply in which Grisel further discloses wherein the processor is further programmed to: display on a computer device one or more images of an identified animal overlayed with one or more identified biomechanic markers (see paragraph 0041: reports generated by evaluation module can be presented on a display).
With respect to claim 13, Grisel discloses A method for assessing the biomechanics of an animal (see Fig. 1: #10; and see paragraph 0016 and 0018: individual can record a video of a horse walking, trotting or running using a computer device and that video can be transferred over a computer network to another computing environment for analysis with an evaluation engine using image processing techniques), the method implemented using a computer device including a processor in communication with a memory device (see paragraph 0026-0027: processing engine #130, the evaluation module #132 and the evaluation engine #140 with data store #120), the method includes:
receive current image data from at least one camera of at least one animal (see paragraph 0048: image frames are received; and see paragraph 0023: computing device #160 includes a capture device #162 which can be embodied as a camera);
identify with specificity an animal within the received image data (see paragraph 0050: the feature identifier #142 can identify a species of a subject in the content #121-#123 as part of the identification process with reference to data stored in reference data #125);
identify at least one biomechanic marker on the identified animal (see paragraph 0050: one or more anatomical features associated with the species of the subject #200; and see paragraph 0049: feature identifier #142 has identified features #201-#204 which correspond to the withers, croup, right fetlock and right hook of the subject #200 respectively);
track the position of each identified biomechanic marker (see paragraph 0054: once identified by feature identifier #142, the motion tracker #144 is configured to track one or more of the features #201-#204 of the subject as they move, frame to frame, between the image frames #210A-#210N of the video file #210);
determine a biomechanic metric based on each identified biomechanic marker (see paragraph 0055, Motion data for the gait cycle pattern 300 can also be stored as one or more functions (or combinations of functions) representative of the manner in which the feature 201 of the subject 200 moves over time. Such functions can define various amplitudes, periodicities, frequencies, and other characteristics of the gait cycle pattern 300 over time);
apply, to a biomechanic evaluation model (see Fig.1: #146 motion evaluator; and see paragraph 0046: the motion evaluator 146 is configured to analyze the motion data generated by the motion tracker 144. Thus, the motion evaluator 146 can compare and evaluate various gait signatures, gait cycle patterns, gait distance differentials, gait cycle periods, gait cycle frequencies, etc., associated with one or more subjects. By evaluating the motion data from the motion tracker 144, the motion evaluator 146 can provide data to the evaluation module 132 for evaluation. As one example, the motion evaluator 146 can identify asymmetry in one or more gait cycle patterns of a subject, which may be an indicator of lameness to the evaluation module 132), i) the tracked position of each biomechanic marker (see paragraph 0055, Motion data for the gait cycle pattern 300 can also be stored as one or more functions (or combinations of functions) representative of the manner in which the feature 201 of the subject 200 moves over time. Such functions can define various amplitudes, periodicities, frequencies, and other characteristics of the gait cycle pattern 300 over time), and ii) the determined biomechanic metric, to generate an output including at least one biomechanic assessment metric for use in assessing the performance of the identified animal (see paragraph 0069 and see Fig. 1: #146); and
transmit one or more notification messages to at least one computing device, wherein each notification message includes at least one generated output biomechanic assessment metric (see Fig. 7: #712; and see paragraph 0099: Overall, based on information identified, tracked, calculated, evaluated, and stored over steps 704, 706, 708, and 710, the evaluation module 132 can provide an evaluation of a subject, possibly including the identification of likely sources of lameness or other health conditions of the subject at step 712. The sources of lameness can be identified as being associated with certain anatomical features of the subject as described herein, and those sources can be identified in one or more reports generated by the evaluation module 132).
With respect to claim 14, all limitations of claim 13 apply in which Grisel further discloses wherein the method further includes:
retrieve historic image data (see paragraph 0045 and 0055: retrieve image data over time);
identify at least one animal within the retrieved historic image data (see paragraph 0050: the feature identifier #142 can identify a species of a subject in the content #121-#123 as part of the identification process with reference to data stored in reference data #125);
identify at least one historic biomechanic marker on each animal identified in the retrieved historic image data (see paragraph 0050: one or more anatomical features associated with the species of the subject #200; and see paragraph 0049: feature identifier #142 has identified features #201-#204 which correspond to the withers, croup, right fetlock and right hook of the subject #200 respectively);
determine at least one historic biomechanic metric based on the tracked position of each biomechanic marker (see paragraph 0055, Motion data for the gait cycle pattern 300 can also be stored as one or more functions (or combinations of functions) representative of the manner in which the feature 201 of the subject 200 moves over time. Such functions can define various amplitudes, periodicities, frequencies, and other characteristics of the gait cycle pattern 300 over time); and
generate a training dataset including at least one of i) the historic tracked position, ii) the determined historic biomechanic metric, and iii) one or more biomechanic risk factors (see paragraph 0045-0055).
With respect to claim 15, all limitations of claim 13 apply in which Grisel further discloses further comprises: training, using the generated training dataset, the biomechanic evaluation model for receiving an input including at least one of i) a current biomechanic marker position and ii) a determined biomechanic metric, and output one or more of the biometric assessment metrics (see paragraph 0049: feature identifier #142 has identified features #201-#204 which correspond to the withers, croup, right fetlock and right hook of the subject #200 respectively is utilized for training the dataset).
With respect to claim 16, all limitations of claim 13 apply in which Grisel further discloses wherein identifying the at least one biomechanic marker on the identified animal includes at least one of identifying a joint anatomical marker and identifying a body segment marker (see paragraph 0049: feature identifier #142 has identified features #201-#204 which correspond to the withers, croup, right fetlock and right hook of the subject #200 respectively).
With respect to claim 17, all limitations of claim 13 apply in which Grisel further discloses wherein receiving image data includes receiving at least first image data from at least one first camera oriented at a first perspective relative to the animal (see paragraph 0048: image frames are received; and see paragraph 0023: computing device #160 includes a capture device #162 which can be embodied as a camera; and see paragraph 0028: different perspectives can be recorded such as front, back, top or other) and receiving at least second image data from at least one second camera oriented at a second perspective that is vertically displaced from the first perspective (see paragraph 0048: image frames are received; and see paragraph 0023: computing device #160 includes a capture device #162 which can be embodied as a camera; and see paragraph 0028: different perspectives can be recorded such as front, back, top or other).
With respect to claim 18, all limitations of claim 13 apply in which Grisel further discloses wherein generating the output including at least one biomechanic assessment metric includes at least one of a risk score, a lameness score, and an asymmetry score (see paragraph 0035: metrics can include motions tracked as healthy or lame).
With respect to claim 19, Grisel discloses A non-transitory computer-readable storage medium including computer-executable instructions embodied thereon for assessing the biomechanics of an animal (see Fig. 1: #10; and see paragraph 0016 and 0018: individual can record a video of a horse walking, trotting or running using a computer device and that video can be transferred over a computer network to another computing environment for analysis with an evaluation engine using image processing techniques with computer readable storage medium), wherein when executed by at least one processor (see paragraph 0026-0027: processing engine #130, the evaluation module #132 and the evaluation engine #140 with data store #120), the computer executable instructions cause the at least one processor to
receive current image data from at least one camera of at least one animal (see paragraph 0048: image frames are received; and see paragraph 0023: computing device #160 includes a capture device #162 which can be embodied as a camera);
identify with specificity an animal within the received image data (see paragraph 0050: the feature identifier #142 can identify a species of a subject in the content #121-#123 as part of the identification process with reference to data stored in reference data #125);
identify at least one biomechanic marker on the identified animal (see paragraph 0050: one or more anatomical features associated with the species of the subject #200; and see paragraph 0049: feature identifier #142 has identified features #201-#204 which correspond to the withers, croup, right fetlock and right hook of the subject #200 respectively);
track the position of each identified biomechanic marker (see paragraph 0054: once identified by feature identifier #142, the motion tracker #144 is configured to track one or more of the features #201-#204 of the subject as they move, frame to frame, between the image frames #210A-#210N of the video file #210);
determine a biomechanic metric based on each identified biomechanic marker (see paragraph 0055, Motion data for the gait cycle pattern 300 can also be stored as one or more functions (or combinations of functions) representative of the manner in which the feature 201 of the subject 200 moves over time. Such functions can define various amplitudes, periodicities, frequencies, and other characteristics of the gait cycle pattern 300 over time);
apply, to a biomechanic evaluation model (see Fig.1: #146 motion evaluator; and see paragraph 0046: the motion evaluator 146 is configured to analyze the motion data generated by the motion tracker 144. Thus, the motion evaluator 146 can compare and evaluate various gait signatures, gait cycle patterns, gait distance differentials, gait cycle periods, gait cycle frequencies, etc., associated with one or more subjects. By evaluating the motion data from the motion tracker 144, the motion evaluator 146 can provide data to the evaluation module 132 for evaluation. As one example, the motion evaluator 146 can identify asymmetry in one or more gait cycle patterns of a subject, which may be an indicator of lameness to the evaluation module 132), i) the tracked position of each biomechanic marker (see paragraph 0055, Motion data for the gait cycle pattern 300 can also be stored as one or more functions (or combinations of functions) representative of the manner in which the feature 201 of the subject 200 moves over time. Such functions can define various amplitudes, periodicities, frequencies, and other characteristics of the gait cycle pattern 300 over time), and ii) the determined biomechanic metric, to generate an output including at least one biomechanic assessment metric for use in assessing the performance of the identified animal (see paragraph 0069 and see Fig. 1: #146); and
transmit one or more notification messages to at least one computing device, wherein each notification message includes at least one generated output biomechanic assessment metric (see Fig. 7: #712; and see paragraph 0099: Overall, based on information identified, tracked, calculated, evaluated, and stored over steps 704, 706, 708, and 710, the evaluation module 132 can provide an evaluation of a subject, possibly including the identification of likely sources of lameness or other health conditions of the subject at step 712. The sources of lameness can be identified as being associated with certain anatomical features of the subject as described herein, and those sources can be identified in one or more reports generated by the evaluation module 132).
With respect to claim 20, all limitations of claim 19 apply in which Grisel further discloses wherein the processor is further configured to:
retrieve historic image data (see paragraph 0045 and 0055: retrieve image data over time);
identify at least one animal within the retrieved historic image data (see paragraph 0050: the feature identifier #142 can identify a species of a subject in the content #121-#123 as part of the identification process with reference to data stored in reference data #125);
identify at least one historic biomechanic marker on each animal identified in the retrieved historic image data (see paragraph 0050: one or more anatomical features associated with the species of the subject #200; and see paragraph 0049: feature identifier #142 has identified features #201-#204 which correspond to the withers, croup, right fetlock and right hook of the subject #200 respectively);
determine at least one historic biomechanic metric based on the tracked position of each biomechanic marker (see paragraph 0055, Motion data for the gait cycle pattern 300 can also be stored as one or more functions (or combinations of functions) representative of the manner in which the feature 201 of the subject 200 moves over time. Such functions can define various amplitudes, periodicities, frequencies, and other characteristics of the gait cycle pattern 300 over time); and
generate a training dataset including at least one of i) the historic tracked position, ii) the determined historic biomechanic metric, and iii) one or more biomechanic risk factors (see paragraph 0045-0055).
Conclusion
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/N.N.P./Examiner, Art Unit 3791
/JENNIFER ROBERTSON/Supervisory Patent Examiner, Art Unit 3791
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