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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 3, 14-15, and 47 are objected to because of the following informalities:
in claim 3, line 2: “image frame form” should be “image frame from”;
in claim 14, line 2: “motion or” should be “motion, or”;
in claim 15, line 6: “the health treatment” should be “the medical treatment”; and
in claim 47, line 14: “the camera and” should be inserted before “the horizontally-mounted camera”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-6, 8-12, 14-15, 18-21, 23-24, and 47 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “identifying a set of footfall events in the set of image frames” in line 5, claim 23 recites “identifying the set of footfall events in the set of image frames by an image processing server” in lines 6-7, and claim 47 recites “identify a set of footfall events in the set of image frames” in line 26. These are clearly computer-implemented recitations (see specification ¶[0039]-[0040], ¶[0059]-[0061], ¶[0080], ¶[0087], ¶[00108], and ¶[00170]).
Under the current guidelines of 35 USC 112, the specification fails to support a claim that defines the invention in functional language specifying a desired result when the specification does not sufficiently identify how the invention achieves the claimed function. For there to be sufficient disclosure for a computer-implemented claim limitation, it is not enough that one skilled in the art could write a program to achieve the claimed function. Rather, the specification must disclose the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that performs the claimed function in sufficient detail such that one of ordinary skill can reasonably
conclude that the inventor invented the claimed subject matter. See Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, Fed. Reg. Vol. 76, No. 27, February 9, 2011, p. 7162-7175 (“the Supplementary Examination Guidelines”). With respect to claims 1 and 23, these claims are rejected under §112, first paragraph, based on lack of written description because the specification fails to provide the algorithm (e.g., the necessary steps and/or flowcharts) that performs the claimed function. In particular, no specificity is provided with respect to the footfall identification. The disclosure provides no algorithm, flow chart, or other detailed description of the footfall identification itself, but only refers to the footfall identification in a “black box” description, meaning that the footfall identification is referred to in a general sense but the specifics of the footfall identification itself is not elaborated upon such that one of ordinary skill in the art would not have understood that the Applicant was in possession of the claimed invention at the time the application was effectively filed.
Claims 2-6, 8-12, 14-15, 18-21, and 24 are rejected by virtue of their dependence from claim 1.
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.
Claims 4, 6, 11, 24, and 47 are 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 4 recites the limitation “the presence or absence” in lines 1-2. There is insufficient antecedent basis for this limitation in the claim. Amending the recitation to “a presence or an absence” would overcome the present rejection. The claim is being read as such for the purpose of examination.
Claim 6 recites the limitation "the opposite" in line 7. There is insufficient antecedent basis for this limitation in the claim. Amending the recitation to “an opposite” would overcome the present rejection. The claim is being read as such for the purpose of examination.
Claim 6 recites “the end of the crossing event is determined based on identifying that the animal occupies 20% of the opposite of the left or right portion of the image frame from the beginning of the crossing event” in lines 5-8. This recitation appears to indicate that, the crossing event is determined based on the same image frame as the beginning event detection. However, the image frames represent discrete points in time (i.e., frames in a video, etc.), and thus cannot be the same image frame as the beginning event detection, as the animal has moved over time. This is further supported the claim, lines 1-3, which indicate that the beginning/end detection events are detected from a continuous subset of image frames in the set of image frames. Amending this recitation to “the end of the crossing event is determined based on identifying that the animal occupies 20% of the opposite of the left or right portion of a second image frame from the subset”; and amending the recitation “an image frame” in line 5 to “a first image frame from the subset” would overcome the present rejection. The claim is being read as such for the purpose of examination.
Claim 11 recites “approximating the stride length further comprises calculating the distance between two of the set of footfall events” in lines 1-2, which represents a mathematical determination of the stride length (see specification ¶[0027]-[0028] and ¶[0079]). Claim 1 recites “approximating a stride length for the animal based on the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events”, which represents an extracted stride length, such as from a fully convolutional neural network (see specification ¶[0083]-[0084] and ¶[00102]). However, it is not clear how the stride length may be further approximated by the distance between events, if the stride length has already been extracted. Is the first stride length modified by the second stride length, are two different stride lengths independently calculated, etc.? These inconsistencies render claim 11 indefinite. Appropriate clarification is required.
Claim 11 recites the limitation “the distance” in line 2. There is insufficient antecedent basis for this limitation in the claim. Amending the recitation to “a distance” would overcome the present rejection. The claim is being read as such for the purpose of examination.
Claim 24 recites “an image processing server” in line 7, but it is not clear if this recitation is the same as, related to, or different from the recitation “an image processing server” in line 4. The similar phraseology suggests that they are the same, but the indefinite article “a” suggests that they are different. If the recitations are the same, the present recitation should be “the image processing server”. If the recitations are different, the relationship between these recitations should be made clear and they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements). For the purposes of examination, the recitations are being interpreted as the same.
Claim 47 recites “an animal” in line 3, but it is not clear if this recitation is the same as, related to, or different from the recitation “an animal” in line 1. The similar phraseology suggests that they are the same, but the indefinite article “a” suggests that they are different. If the recitations are the same, the present recitation should be “the animal”. If the recitations are different, the relationship between these recitations should be made clear and they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements). For the purposes of examination, the recitations are being interpreted as the same.
Claim 47 recites the limitation “the gait pattern” in line 30. There is insufficient antecedent basis for this limitation in the claim. Amending the recitation to “a gait pattern” would overcome the present rejection. The claim is being read as such for the purpose of examination.
Claim 47 recites the limitation “the top-down image” in line 49. There is insufficient antecedent basis for this limitation in the claim. It is not clear if this recitation is the same as and/or related to any of the prior image recitations. Appropriate clarification is required.
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 1-6, 8-12, 14-15, 18-21, 23-24, and 47 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards abstract ideas without significantly more.
Claim 1 interpretation: Under the broadest reasonable interpretation (BRI), the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111. Based on the specification’s identification, the recitation “determining a location of the animal for each image frame in the set of image frames” (see specification ¶[0049], ¶[0080]-[0082], ¶[0088]-[0089], ¶[0095], ¶[00104], ¶[00111]-[00112], and ¶[00145]) is observations, judgements, and/or opinions. The recitation “identifying a set of anatomical landmarks in the set of image frames” (see specification ¶[0049], ¶[0080]-[0082], ¶[0088]-[0089], ¶[0095], ¶[00104], ¶[00111]-[00112], and ¶[00145]) is observations, judgements, and/or opinions. The recitation “identifying a set of footfall events in the set of image frames” (see specification ¶[0026], ¶[0039]-[0040], ¶[0059]-[0061], and ¶[00137]) is observations, judgements, and/or opinions. The recitation “approximating a stride length for the animal based on the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events” (see specification ¶[0027]-[0028], ¶[0079], ¶[0083]-[0084], and ¶[00102]) is observations, judgements, opinions, and/or mathematical calculations/evaluations. The recitation “deriving the gait pattern based in part on the stride length, the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events” (see specification ¶[0079]-[0084], ¶[0095], and ¶[0099]-[00102]) is observations, judgements, opinions, and/or mathematical calculations/evaluations. The recitations are computer implemented as indicated in the specification (see ¶[0039]-[0040], ¶[0059]-[0061], ¶[0079]-[0084], ¶[0087], ¶[0090], ¶[00108], and ¶[00170]-[00171]).
Claim 47 interpretation: Under the broadest reasonable interpretation (BRI), the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111. Based on the specification’s identification, the recitation “determine a location of the animal for each image frame in the set of image frames” (see specification ¶[0049], ¶[0080]-[0082], ¶[0088]-[0089], ¶[0095], ¶[00104], ¶[00111]-[00112], and ¶[00145]) is observations, judgements, and/or opinions. The recitation “identify a set of anatomical landmarks in the set of image frames” (see specification ¶[0049], ¶[0080]-[0082], ¶[0088]-[0089], ¶[0095], ¶[00104], ¶[00111]-[00112], and ¶[00145]) is observations, judgements, and/or opinions. The recitation “identify a set of footfall events in the set of image frames” (see specification ¶[0026], ¶[0039]-[0040], ¶[0059]-[0061], and ¶[00137]) is observations, judgements, and/or opinions. The recitation “approximate a stride length for the animal based on the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events” (see specification ¶[0027]-[0028], ¶[0079], ¶[0083]-[0084], and ¶[00102]) is observations, judgements, opinions, and/or mathematical calculations/evaluations. The recitation “derive the gait pattern based in part on the stride length, the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events” (see specification ¶[0079]-[0084], ¶[0095], and ¶[0099]-[00102]) is observations, judgements, opinions, and/or mathematical calculations/evaluations. The recitation “bound and isolate a central portion of the image, the central portion comprising a least distorted portion of the image; identify a center of a torso of the animal; crop the central portion of the image at a set distance from the center of the torso of the animal; segment the animal into at least head, shoulder, and torso segments; concatenate the at least head, shoulder, and torso segments onto the top-down image of the animal to form a concatenated image” (see specification ¶[00111] and ¶[00170]) is observations, judgements, and/or opinions. The recitation “predict a weight of the animal based on the concatenated image” (see specification ¶[0088] and ¶[00111]-[00113]) is observations, judgements, and/or opinions. The recitation “a predicted phenotype for the animal is derived from the predicted weight and the gait pattern” (see specification ¶[0062], ¶[0095], and ¶[00115]-[00117]) is observations, judgements, and/or opinions. The recitations are computer implemented as indicated in the claim lines 18-22, and the specification (see ¶[0039]-[0040], ¶[0059]-[0061], ¶[0079]-[0084], ¶[0087], ¶[0090], ¶[00108], and ¶[00170]-[00171]).
Step 1: This part of eligibility analysis evaluates whether the claim falls within any statutory category. MPEP 2106.03. Claim 1 recites a method, which is directed towards a process (a statutory category of invention). Claim 47 recites a system, which is directed towards a machine and/or a manufacture (a statutory category of invention). Step 1: YES.
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04(a)(2)(III). The courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). The “mental processes” abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions. As discussed in the claim interpretation section, the limitations include, under the BRI, observations, judgements, opinions, and mathematical calculations/evaluations. No limitations are provided that would force the complexity of any of the identified evaluation steps to be non-performable by pen-and-paper practice. Accordingly, the limitations as seen in claims 1 and 47 recite judicial exceptions (abstract ideas that fall within the mental process grouping).
Furthermore, as explained in MPEP 2106.04(a)(2)(I). The courts consider mathematical calculations, when the claim is given its BRI in light of the specification, as falling within the “mathematical concept” grouping of abstract ideas. A claim does not have to recite “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using a mathematical method, or “performing” a mathematical operation, may also be considered a mathematical calculation when the BRI of the claim in light of the specification encompasses a mathematical calculation. As discussed in the claim interpretation section, the limitations include, under the BRI, mathematical calculations/evaluations. Accordingly, the limitations as seen in claims 1 and 47 recite judicial exceptions (abstract ideas that fall within the mathematical calculations grouping of mathematical concepts).
Alternatively or additionally, these steps describe the concept of using implicit mathematical formulas (i.e., calculations to extract and cross-correlate the received brain activity measurements) to derive a conclusion based on input of data, which corresponds to concepts identified as abstract ideas by the courts (Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989)). The concept of the recited limitations identified as mathematical concepts above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas.
Claim 1 recites the following elements, which are part of the abstract idea (i.e., the algorithm):
a method for deriving a gait pattern in an animal, the method comprising:
capturing a set of image frames of the animal, wherein the animal is in motion;
determining a location of the animal for each image frame in the set of image frames;
identifying a set of anatomical landmarks in the set of image frames;
identifying a set of footfall events in the set of image frames;
approximating a stride length for the animal based on the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events; and
deriving the gait pattern based in part on the stride length, the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events.
Claim 47 recites the following elements, which are part of the abstract idea (i.e., the algorithm):
for determining a phenotypic trait of an animal based on a set of captured image data, comprising:
capture and transmit an image of an animal;
capture and transmit a set of image frames of the animal, wherein the animal is in motion;
read a tag associated with the animal and to transmit a set of identification information read from the tag;
receive the image transmitted from the camera;
receive the set of image frames transmitted from the horizontally-mounted camera; and
store the set of image frames and the image on the storage media;
request and receive the set of image frames from the network video recorder;
determine a location of the animal for each image frame in the set of image frames;
identify a set of anatomical landmarks in the set of image frames;
identify a set of footfall events in the set of image frames;
approximate a stride length for the animal based on the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events;
derive the gait pattern based in part on the stride length, the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events; and
store the gait pattern, the stride length, the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events in a first database,
wherein each of the gait pattern, the stride length, the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events are associated with the set of identification information read from the tag;
automatically:
request and retrieve the image from the network video recorder;
bound and isolate a central portion of the image, the central portion comprising a least distorted portion of the image;
identify a center of a torso of the animal;
crop the central portion of the image at a set distance from the center of the torso of the animal;
segment the animal into at least head, shoulder, and torso segments;
concatenate the at least head, shoulder, and torso segments onto the top-down image of the animal to form a concatenated image;
predict a weight of the animal based on the concatenated image; and
store the predicted weight of the animal in a second database; and
wherein a predicted phenotype for the animal is derived from the predicted weight and the gait pattern.
Step 2A Prong One: YES.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the judicial exceptions into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exceptions, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exceptions into a practical application. Claim 1 recites no additional element such that claim 1 recites no element that integrates the abstract ideas into a practical application, alone or as an ordered combination. Claim 47 recites additional element related to generic computers (i.e., the network video recorder, the image processing server, and the first/second databases). Thus, the system merely uses generic computers as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). Claim 47 also recites cameras and a tag reader, but the use of these cameras and the tag reader are merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a high level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g).
Step 2A Prong Two: NO.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05. As explained with Step 2A Prong Two, claim 1 recites no additional element such that claim 1 recites no element that amounts to significantly more than the recited exceptions. Claim 47 recites additional elements which are directed towards the performance of the abstract ideas utilizing a generic computer, and are at best the equivalent of merely adding the words “apply it” to the judicial exceptions. Mere instructions to apply an exception cannot provide an inventive concept. These elements/steps can be seen as well-understood, routine, and conventional individually and in combination. Thus, the generic computers do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well- understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Claim 47 further recites additional elements related to the collection of data utilizing a camera, whose use is merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements. For example, Kashiha et al. (“Automatic weight estimation of individual pigs using image analysis”, Computers and Electronics in Agriculture, 2014, pp 38-44, 107 – cited by Applicant) teaches the estimation of the weight of pigs utilizing images and image analysis (see abstract), in which a commercial camera, the Panasonic WV-BP330 camera, was utilized to image the pigs (see § 2. Materials and methods, § 2.2. Equipment and data collection). Therefore, the cameras cannot be seen as significantly more.
Claim 47 further recites an additional element related to the collection of data utilizing a tag reader, whose use is merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements. For example, Valley Vet (“EID & RFID Tags”, Valley Vet Supply, accessed at https://web.archive.org/web/20201130023705/https://www.valleyvet.com/c/livestock-supplies/ear-tags/rfid.html from 11/30/2020, with regard to https://www.valleyvet.com/c/livestock-supplies/ear-tags/rfid.html, accessed on 04/25/2026) teaches the use of commercially sold EID and RFID tags and readers for livestock, including swine (see generally pg. 1-15). Therefore, the tag reader cannot be seen as significantly more.
Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Step 2B: NO.
Claims 1 and 47 are NOT eligible.
Claims 2-6, 8-12, 14-15, 19-21, and 23-24 depend from claim 1 and merely further define the abstract ideas of claim 1 with no further element that integrates the abstract ideas into a practical application or that qualifies as being significantly more. These elements are directed towards generic computers and the nature of data gathered, pre-solution activity. The generic computers are merely a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). These elements/steps can be seen as well-understood, routine, and conventional individually and in combination. Thus, the generic computers do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well- understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, 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, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome.
Claim 15 depends from claim 1, and further includes the element of “subjecting the animal to a medical treatment based on the phenotype”. However, such an element is not required in the claim as it is part of an “or” option. A claim that recites a particular treatment or prophylaxis “meaningfully limits the claim by going beyond generally linking the use of the judicial exception to a particular technological environment, and thus transforms a claim into patent-eligible subject matter. See MPEP § 2106.04(d)(2). In order to qualify as a “treatment” or “prophylaxis", the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a "pharmaceutical composition" or that a "feed dispenser is operable to dispense a mineral supplement" are not affirmative limitations because they are merely indicating how the claimed invention might be used. Furthermore, the treatment or prophylaxis limitation must be "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s). In this case, the medical treatment is not required in the event that selecting or identifying options are chosen. Therefore, this claimed element cannot be seen as integration into a practical application.
Claim 18 depends from claim 1, and further includes the element of a tag reader, whose use is merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements. For example, Valley Vet (“EID & RFID Tags”, Valley Vet Supply, accessed at https://web.archive.org/web/20201130023705/https://www.valleyvet.com/c/livestock-supplies/ear-tags/rfid.html from 11/30/2020, with regard to https://www.valleyvet.com/c/livestock-supplies/ear-tags/rfid.html, accessed on 04/25/2026) teaches the use of commercially sold EID and RFID tags and readers for livestock, including swine (see generally pg. 1-15). Therefore, the tag reader cannot be seen as integration into a practical application or as significantly more.
Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
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.
The succeeding art rejections to the claims under 35 U.S.C. § 102 and 103 below are made with the claims as best understood and interpreted in light of the preceding rejections under 35 U.S.C. § 112 above.
Claims 1-5, 10, 12, 14, and 23-24 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Grisel (US Patent Application Publication 2017/0262599), hereinafter Grisel.
Regarding Claim 1, Grisel teaches tracking anatomical features of a subject of a number of frames to identify gait cycle patterns, such as for identifying limb lameness in the subject (see abstract and Figs. 1-6), in which the animal may include a horse, human, cattle, sheep, pig, goat, dog, cat, and other livestock/animals (see ¶[0017]). Grisel teaches a method for deriving a gait pattern in an animal (see abstract and Figs. 1-6), the method comprising:
capturing a set of image frames of the animal, wherein the animal is in motion (¶[0023]-[0024] the capture device 162, such as a camera, to capture images, video, and/or audio of one or more subjects standing, walking, trotting, or running, the capture device 162 may either be stationary or panned/moved/rotated to maintain the subject within the field of view; Fig. 1);
determining a location of the animal for each image frame in the set of image frames (¶[0048]-[0052] prior to identification of the anatomical features of the subject, the feature identifier 142 may identify the type of subject, and the boundaries of said subject, such as with silhouette compared to reference data 125, identifying the boundary of the subject would necessarily identify the location of the animal within each image frame; Fig. 2);
identifying a set of anatomical landmarks in the set of image frames (¶[0051]-[0053] the features of the subject and their locations are identified and saved in the data store 120; Fig. 2);
identifying a set of footfall events in the set of image frames (¶[0054] and ¶[0076]-[0077] the motion tracker 144 to track specific features from frame to frame, ¶[0079]-[0080] such tracked features may include foot contact (i.e., footfall) events, for determining stride height/length; Fig. 5);
approximating a stride length for the animal based on the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events (¶[0076]-[0077] and ¶[0080] the foot contact events for determining the stride length, the foot contact events are based on the animal location and identified features in each frame; Fig. 5); and
deriving the gait pattern based in part on the stride length, the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events (¶[0080]-[0081] the stride length may be utilized to evaluate lameness, comparisons are made between left/right limbs (i.e., symmetry, see generally ¶[0037] and ¶[0063]-[0067], Fig. 4, see also ¶[0083]-[0087] and ¶[0098] the features/patterns identified may be compared between sides and read/front of limbs so as to assess symmetry/asymmetry in the gait, Figs. 5-6), in which the symmetry/asymmetry would be considered a gait pattern, the stride length is based off of the foot contact events, which are based off of the animal location and identified features in each frame; Fig. 5).
Regarding Claim 2, Grisel teaches the method of claim 1 as stated above. Grisel further teaches the animal is a swine (¶[0017] the animal may include a pig).
Regarding Claim 3, Grisel teaches the method of claim 1 as stated above. Grisel further teaches the motion is from a left side to a right side or from the right side to the left side in an image frame form the set of image frames, and wherein the motion is in a direction perpendicular to an image sensor (¶[0024] the capture device 162 may be stationary while the subject moves past, see generally Figs. 3A-3B for the subject moving through the frames).
Regarding Claim 4, Grisel teaches the method of claim 1 as stated above. Grisel further teaches determining the presence or absence of the animal in an image frame from the set of image frames (¶[0048]-[0052] prior to identification of the anatomical features of the subject, the feature identifier 142 may identify the type of subject, which would include the presence or absence of a particular subject; Fig. 2).
Regarding Claim 5, Grisel teaches the method of claim 1 as stated above. Grisel further teaches updating a current location of the animal to the location of the animal in an image frame from the set of image frames (¶[0048]-[0052] prior to identification of the anatomical features of the subject, the feature identifier 142 may identify the type of subject, and the boundaries of said subject, such as with silhouette compared to reference data 125, identifying the boundary of the subject would necessarily identify the location of the animal within each image frame, ¶[0054] the identified features are tracked frame to frame by the motion tracker 144; Fig. 2).
Regarding Claim 10, Grisel teaches the method of claim 1 as stated above. Grisel further teaches each footfall event in the set of footfall events comprises a subset of image frames wherein a foot of the animal contacts a ground surface (¶[0054] and ¶[0076]-[0077] the motion tracker 144 to track specific features from frame to frame, ¶[0079]-[0080] such tracked features may include foot contact (i.e., footfall) events, as contact with the ground; Fig. 5).
Regarding Claim 12, Grisel teaches the method of claim 1 as stated above. Grisel further teaches computing a delay between a footfall event associated with a front leg of the animal and a footfall event associated with a rear leg of the animal; and deriving a stride symmetry based in part on the delay (¶[0054] and ¶[0076]-[0077] the motion tracker 144 to track specific features from frame to frame, ¶[0079]-[0081] such tracked features may include foot contact (i.e., footfall) events, as contact with the ground, for comparisons between limbs for lameness, ¶[0083]-[0087] and ¶[0098] the features/patterns identified may be compared between sides and read/front of limbs so as to assess symmetry/asymmetry in the gait; Figs. 5-6), and
wherein deriving the gait pattern is based in part on the stride symmetry (¶[0079]-[0081], ¶[0083]-[0087], and ¶[0098] the lameness determination may further be influenced by the symmetry/asymmetry determination; Figs. 5-6).
Regarding Claim 14, Grisel teaches the method of claim 1 as stated above. Grisel further teaches deriving the gait pattern is based in part on a head position of the animal in a walking motion or on a set of leg angles (¶[0023]-[0024] the capture device 162, such as a camera, to capture images, video, and/or audio of one or more subjects standing, walking, trotting, or running, the capture device 162 may either be stationary or panned/moved/rotated to maintain the subject within the field of view, ¶[0050] the identified features may include the head of the subject).
Regarding Claim 23, Grisel teaches the method of claim 1 as stated above. Grisel further teaches transmitting the set of image frames to a network video recorder; storing the set of images on the network video recorder; identifying the set of anatomical landmarks in the set of image frames by an image processing server; and identifying the set of footfall events in the set of image frames by the image processing server (¶[0018]-[0022] and ¶[0104]-[0109] the computer system for implementing the method as claimed in claim 1 may be a networked server environment, the data store attached to the network falls under the BRI of the network video recorder, and the computer server would correspond to the image processing server; Fig. 1).
Regarding Claim 24, Grisel teaches the method of claim 1 as stated above. Grisel further teaches approximating the stride length for the animal based on the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events by an image processing server; and deriving the gait pattern based in part on the stride length, the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events by an image processing server (¶[0018]-[0022] and ¶[0104]-[0109] the computer system for implementing the method as claimed in claim 1 may be a networked server environment, the computer server would correspond to the image processing server; 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The succeeding art rejections to the claims under 35 U.S.C. § 103 below are made with the claims as best understood and interpreted in light of the preceding rejections under 35 U.S.C. § 112 above.
Claims 6, 9, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Grisel as applied to claim 1 above, and in view of Spears et al. (US Patent Application Publication 2022/0104463), hereinafter Spears.
Regarding Claim 6, Grisel teaches the method of claim 1 as stated above. Grisel is silent regarding determining a beginning and an end of a crossing event comprising a continuous set of detections of the animal in a subset of the set of image frames; and wherein the beginning of the crossing event is determined based in part on identifying that the animal occupies 20% of a left or right portion of an image frame, and wherein the end of the crossing event is determined based on identifying that the animal occupies 20% of the opposite of the left or right portion of the image frame from the beginning of the crossing event.
Spears teaches a system configured to receive video and/or images from an image capture device over a livestock path, analyze the images with application of a convolution neural network (CNN) so as to identify animals within the classification process, and then count the animals (see abstract and Fig. 1), in which the CNN may be a fully convolutional network (FCN) (see ¶[0085]), in which the livestock may include cattle and pigs (see ¶[0040]), in which counting pigs may involve watching a livestock path 108 within a field of view with three zones, a registration zone on one end, a movement tracking zone in the middle, and a deregistration zone the pigs leave therethrough on the opposite end (see ¶[0094] and ¶[0104]-[0108]; Fig. 11), in which counting may be updated once the animal enters/leaves the deregistration zone (see ¶[0117]-[0118]; Fig. 11).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the three zone counting (i.e., crossing event) of the livestock, such as pigs, with the method of Grisel because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results; and/or (2) the addition of automatic counting would reduce errors in manual counting and help keep livestock handlers warm as they would not need to go outside (see Spears ¶[0075]); and/or (3) the registration and deregistration zones may help to minimize count errors from animals grouping together, etc. (see Spears ¶[0105]-[0107]).
The modified Grisel does not specifically teach what percentage portion of an image frame the registration and deregistration zones, and thus the animals within those zones, occupy.
However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize 20% of the image frame as each of the registration and deregistration zones because it is obvious to try. There are only a finite number of possible percentages to use for the registration and deregistration zones, and there would be a reasonable expectation of success with such a size. Furthermore, such a size does not differ substantially from the apparent 25% zone size as depicted in Spears Fig. 11.
Regarding Claim 9, Grisel teaches the method of claim 1 as stated above. Grisel further teaches interpolating an additional set of anatomical landmarks using interpolation where at least one of the set of anatomical landmarks could not be identified (¶[0040] and ¶[0094] the processing may include interpolating frames, which would include the features, in the video content, and that such interpolation would necessarily include filling in identified missing data so as to form a continuous set of data, as that’s merely the definition of interpolating within the context of video processing).
Grisel is silent regarding what type of interpolation that is utilized.
Spears teaches a system configured to receive video and/or images from an image capture device over a livestock path, analyze the images with application of a convolution neural network (CNN) so as to identify animals within the classification process, and then count the animals (see abstract and Fig. 1), in which the CNN may be a fully convolutional network (FCN) (see ¶[0085]), in which the livestock may include cattle and pigs (see ¶[0040]), in which binary/bilinear interpolation may be utilized to preserve spatial information at each pixel (see ¶[0004] and ¶[0049]). Note that binary/bilinear interpolation comprises performing linear interpolation twice, once for each axis (i.e., X and Y).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the binary/bilinear interpolation of Spears as the interpolation of Grisel because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results; and/or (2) Grisel requires an interpolation and Spears teaches one such known interpolation modality; and/or (3) the binary/bilinear interpolation would preserve spatial information (see Spears ¶[0004]).
Regarding Claim 20, Grisel teaches the method of claim 1 as stated above. Grisel further teaches the identifying the set of anatomical landmarks in the set of image frames further comprises interpolating an additional set of anatomical landmarks, the interpolating comprising: identifying a frame from the set of image frames where at least one anatomical landmark from the set of anatomical landmarks is not detected; and interpolating a position of the at least one anatomical landmark by interpretation between a last known location and a next known location of the at least one anatomical landmark in the set of image frames to generate a continuous set of data points for the at least one anatomical landmark for each image frame in the set of image frames (¶[0040] and ¶[0094] the processing may include interpolating frames, which would include the features, in the video content, and that such interpolation would necessarily include filling in identified missing data so as to form a continuous set of data, as that’s merely the definition of interpolating within the context of video processing).
Grisel is silent regarding what type of interpolation that is utilized.
Spears teaches a system configured to receive video and/or images from an image capture device over a livestock path, analyze the images with application of a convolution neural network (CNN) so as to identify animals within the classification process, and then count the animals (see abstract and Fig. 1), in which the CNN may be a fully convolutional network (FCN) (see ¶[0085]), in which the livestock may include cattle and pigs (see ¶[0040]), in which binary/bilinear interpolation may be utilized to preserve spatial information at each pixel (see ¶[0004] and ¶[0049]). Note that binary/bilinear interpolation comprises performing linear interpolation twice, once for each axis (i.e., X and Y).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the binary/bilinear interpolation of Spears as the interpolation of Grisel because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results; and/or (2) Grisel requires an interpolation and Spears teaches one such known interpolation modality; and/or (3) the binary/bilinear interpolation would preserve spatial information (see Spears ¶[0004]).
Claims 8 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Grisel as applied to claim 1 above, and in view of Shmigelsky et al. (US Patent Application Publication 2023/0337636), hereinafter Shmigelsky.
Regarding Claim 8, Grisel teaches the method of claim 1 as stated above. Grisel further teaches that the set of anatomical landmarks comprise a snout, a shoulder, and a set of leg joints (¶[0049] the various features that may be utilized for the animal, including the head, nose, back, and limb joints, etc.).
Grisel is silent regarding that the anatomical landmarks comprise a tail.
Shmigelsky teaches an animal management system utilizing one or more imaging devices and an AI pipeline for identifying the animals with key points identified in sections of the animals (see abstract; Figs. 1A-1B and 7-14), in which the identified sections may include a tail section and tail points (see ¶[0047] and ¶[0261]; Fig. 14).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the tail section as an anatomical landmark of the method of Grisel because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results and/or (2) Grisel contemplates using various landmarks (see ¶[0049]) and Shmigelsky teaches one such additional landmark.
Regarding Claim 21, Grisel teaches the method of claim 1 as stated above. Grisel further teaches that the set of anatomical landmarks comprise a nose, a mid-section, and a set of joints (¶[0049] the various features that may be utilized for the animal, including the head, nose, back, and limb joints, etc.),
a delay between footfall events in the set of footfall events (¶[0054] and ¶[0076]-[0077] the motion tracker 144 to track specific features from frame to frame, ¶[0079]-[0081] such tracked features may include foot contact (i.e., footfall) events, as contact with the ground, for comparisons between limbs for lameness, ¶[0083]-[0087] and ¶[0098] the features/patterns identified may be compared between sides and read/front of limbs so as to assess symmetry/asymmetry in the gait; Figs. 5-6),
a set of leg angles (¶[0077], ¶[0082], and ¶[0098] the evaluation module 132 can identify certain angles or other geometric metrics between two or more of the gait locus points 530-532 as other indicators of lameness),
a head posture (¶[0032]-[0035] and ¶[0082] the posture of the animal may be monitored for the lameness determination and abnormalities),
a speed of the animal in motion (¶[0040] and ¶[0094] the image processing takes into account the speed of the animal),
and that the gait analyzed based on such features (¶[0080]-[0081] the stride length may be utilized to evaluate lameness, comparisons are made between left/right limbs (i.e., symmetry, see generally ¶[0037] and ¶[0063]-[0067], Fig. 4, see also ¶[0083]-[0087] and ¶[0098] the features/patterns identified may be compared between sides and read/front of limbs so as to assess symmetry/asymmetry in the gait, Figs. 5-6), in which the symmetry/asymmetry would be considered a gait pattern, the stride length is based off of the foot contact events, which are based off of the animal location and identified features in each frame; Fig. 5).
Grisel teaches that reference data may be utilized (see ¶[0048]-[0050]), but does not specifically teach the usage of a trained classification network, or that a body length of the animal is utilized.
Shmigelsky teaches an animal management system utilizing one or more imaging devices and an AI pipeline for identifying the animals with key points identified in sections of the animals (see abstract; Figs. 1A-1B and 7-14), in which the identified sections may include a tail section and tail points (see ¶[0047] and ¶[0261]; Fig. 14), in which the points may extend along a body length of the animal (see key points 422 in Fig. 12), in which a gait AI model may be utilized to determine a characteristics of the animal’s gait, along with an accuracy score of the classification, via a trained LSTM model, via input features including key points 422 and leg angles (see ¶[0277]-[0280]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the gait AI model of Shmigelsky with the method of Grisel because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results; and/or (2) the gait AI model would provide gait insights not perceivable through traditional, non-AI, algorithms; and/or (3) the gait AI model would ensure accuracy through the provided accuracy gait score (see Shmigelsky ¶[0279]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Grisel as applied to claim 1 above, and in view of Yagi et al. (US Patent Application Publication 2019/0150405), hereinafter Yagi.
Regarding Claim 11, Grisel teaches the method of claim 1 as stated above. Grisel further teaches approximating the stride length further comprises calculating the distance between two of the set of footfall events (¶[0076]-[0077] and ¶[0080] the foot contact events for determining the stride length based on distance therebetween, the foot contact events are based on the animal location and identified features in each frame; Fig. 5).
Grisel is silent regarding that the stride length is normalized by a body length of the animal.
Yagi teaches a health condition estimation device capable of accurately estimating the health condition of a cow (see abstract and Figs. 1-2), in which the step length may be calculated, by further involving by normalizing the shortest distance between the fore leg and the hind leg during walking by the distance between the groins 25A and 25B of the fore leg and the hind leg (see ¶[0119]-[0120]; Fig. 2). Here, the distance between the groins 25A and 25B is interpreted to fall within the BRI of body length.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the body length normalization of Yagi with the stride length determination of the method of Grisel because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results and/or (2) the normalization would help to fit the stride length calculation to that particular animal, thus yielding more accurate results.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Grisel as applied to claim 1 above, and in view of Benjamin et al. (US Patent Application Publication 2023/0276773), hereinafter Benjamin.
Regarding Claim 15, Grisel teaches the method of claim 1 as stated above. Grisel further teaches predicting a phenotype associated with the animal based on the derived gait pattern (¶[0080]-[0081] the stride length may be utilized to evaluate lameness, comparisons are made between left/right limbs (i.e., symmetry, see generally ¶[0037] and ¶[0063]-[0067], Fig. 4, see also ¶[0083]-[0087] and ¶[0098] the features/patterns identified may be compared between sides and read/front of limbs so as to assess symmetry/asymmetry in the gait, Figs. 5-6), in which the symmetry/asymmetry may be utilized to evaluate lameness, the lameness would be considered the phenotype; Fig. 5);
selecting the animal for a future breeding event based on the phenotype, identifying the animal as unsuitable for breeding based on the phenotype, or subjecting the animal to a medical treatment based on the phenotype (¶[0041], ¶[0089]-[0090], and ¶[0101]-[0102] the animal may be assisted (i.e., have treatment applied) based on the evaluated lameness).
Grisel is silent regarding that the health treatment is removal from a general animal population or culling the animal.
Benjamin teaches systems and methods for automatically and noninvasively analyzing livestock health, in which a neural network is utilized to predict an animal outcome for the particular animal (see abstract and Figs. 1-6A), in which the livestock may be pigs (see ¶[0039]), in which the system may output flags that indicate whether the sow should be kept, culled and sent to market, or culled without being sent to market (see ¶[0060]-[0064; Figs. 1 and 3).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the flags (i.e., health treatments) of Benjamin with the lameness determination of the method of Grisel because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results; and/or (2) Grisel requires health treatments and Benjamin teaches known health treatments for livestock; and/or (3) such a flag would help the veterinarian determine the appropriate action for the livestock; and/or (4) the automatic determination would help caregivers reduce human error (see Benjamin ¶[0009]-[0011]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Grisel as applied to claim 1 above, and in view of Tasch et al. (US Patent Application Publication 2021/0145314), hereinafter Tasch.
Regarding Claim 18, Grisel teaches the method of claim 1 as stated above. Grisel is silent regarding reading an identification tag associated with the animal, and wherein the capturing the set of image frames is triggered by the reading of the identification tag.
Tasch teaches a gait analysis apparatus for an individual (see abstract and Fig. 1), in which the individual may be livestock, such as cattle and swine (see ¶[0005] and ¶[0026]), in which the livestock may have identification tags complimentary to an identification antenna, so that the gait analysis apparatus starts recording measurements via the identified tag, and the recorded measurements are accurately linked to the test subject (see ¶[0028]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the identification tag start/linking of data of Tasch with the method of Grisel because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results and/or (2) the identification tags provide an automatic modality to start measurements and accurately link the recorded data to the test subject (see Tasch ¶[0028]).
Claim 47 is rejected under 35 U.S.C. 103 as being unpatentable over Grisel in view of Tasch, and in view of Kashiha et al. (“Automatic weight estimation of individual pigs using image analysis”, Computers and Electronics in Agriculture, 2014, pp 38-44, 107 – cited by Applicant), hereinafter Kashiha, and in view of Psota (US Patent Application Publication 2019/0138801), hereinafter Psota.
Regarding Claim 47, Grisel teaches tracking anatomical features of a subject of a number of frames to identify gait cycle patterns, such as for identifying limb lameness in the subject (see abstract and Figs. 1-6), in which the animal may include a horse, human, cattle, sheep, pig, goat, dog, cat, and other livestock/animals (see ¶[0017]). Grisel teaches a system for determining a phenotypic trait of an animal based on a set of captured image data (see abstract and Figs. 1-6), the system comprising:
a horizontally-mounted camera disposed at a height aligned with a shoulder height of the animal and at an angle perpendicular to a viewing window, the horizontally-mounted camera adapted to capture and transmit a set of image frames of the animal, wherein the animal is in motion (¶[0023]-[0024] the capture device 162, such as a camera, to capture images, video, and/or audio of one or more subjects standing, walking, trotting, or running, the capture device 162 may either be stationary or panned/moved/rotated to maintain the subject within the field of view; Fig. 1, see generally Figs. 2-6 for horizontally mounted camera);
a network video recorder comprising a storage media, the network video recorder in electronic communication with the horizontally-mounted camera and adapted to: receive the image transmitted from the camera; receive the set of image frames transmitted from the horizontally-mounted camera; and store the set of image frames and the image on the storage media (¶[0018]-[0022] and ¶[0104]-[0109] the computer system for implementing the method as claimed in claim 1 may be a networked server environment, the data store attached to the network falls under the BRI of the network video recorder, the data stores to receive the data; Fig. 1);
an image processing server comprising a processor and a memory, the image processing server in electronic communication with the network video recorder, and the memory comprising a first set of computer-executable instructions (¶[0018]-[0022] and ¶[0104]-[0109] the computer system for implementing the method as claimed may be a networked server environment, the computer server would correspond to the image processing server, the corresponding processor and memory of such servers; Fig. 1) that when executed by the processor are adapted to cause the image processing server to automatically:
request and receive the set of image frames from the network video recorder (¶[0092] the receiving of content for evaluation; Fig. 7);
determine a location of the animal for each image frame in the set of image frames (¶[0048]-[0052] prior to identification of the anatomical features of the subject, the feature identifier 142 may identify the type of subject, and the boundaries of said subject, such as with silhouette compared to reference data 125, identifying the boundary of the subject would necessarily identify the location of the animal within each image frame; Fig. 2);
identify a set of anatomical landmarks in the set of image frames (¶[0051]-[0053] the features of the subject and their locations are identified and saved in the data store 120; Fig. 2);
the set of anatomical landmarks comprise a snout, a shoulder, and a set of leg joints (¶[0049] the various features that may be utilized for the animal, including the head, nose, back, and limb joints, etc.);
identify a set of footfall events in the set of image frames (¶[0054] and ¶[0076]-[0077] the motion tracker 144 to track specific features from frame to frame, ¶[0079]-[0080] such tracked features may include foot contact (i.e., footfall) events, for determining stride height/length; Fig. 5);
approximate a stride length for the animal based on the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events (¶[0076]-[0077] and ¶[0080] the foot contact events for determining the stride length, the foot contact events are based on the animal location and identified features in each frame; Fig. 5);
derive the gait pattern based in part on the stride length, the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events (¶[0080]-[0081] the stride length may be utilized to evaluate lameness, comparisons are made between left/right limbs (i.e., symmetry, see generally ¶[0037] and ¶[0063]-[0067], Fig. 4, see also ¶[0083]-[0087] and ¶[0098] the features/patterns identified may be compared between sides and read/front of limbs so as to assess symmetry/asymmetry in the gait, Figs. 5-6), in which the symmetry/asymmetry would be considered a gait pattern, the stride length is based off of the foot contact events, which are based off of the animal location and identified features in each frame; Fig. 5); and
store the gait pattern, the stride length, the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events in a first database (¶[0099] the information identified, tracked, calculated, evaluated over the steps is stored; Fig. 7).
Grisel is silent regarding a tag reader disposed proximate to the animal retaining space, the tag reader adapted to read a tag associated with the animal and to transmit a set of identification information read from the tag; wherein each of the gait pattern, the stride length, the location of the animal in each image frame of the set of image frames, the set of anatomical landmarks, and the set of footfall events are associated with the set of identification information read from the tag.
Tasch teaches a gait analysis apparatus for an individual (see abstract and Fig. 1), in which the individual may be livestock, such as cattle and swine (see ¶[0005] and ¶[0026]), in which the livestock may have identification tags complimentary to an identification antenna, so that the gait analysis apparatus starts recording measurements via the identified tag, and the recorded measurements are accurately linked to the test subject (see ¶[0028]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the identification tag start/linking of data of Tasch with the system of Grisel because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) the identification tags provide an automatic modality to start measurements and accurately link the recorded data to the test subject (see Tasch ¶[0028]).
The modified Grisel does not specifically teach an above mounted camera so as to measure weight.
Kashiha teaches automatic weight estimation of individual pigs by a top-view camera (see abstract),
a camera mounted above an animal retaining space and disposed at a fixed height above a central location in the animal retaining space, the camera adapted to capture and transmit an image of an animal (see § 2.2. Equipment and data collection, the top-view mounted camera; Figs. 1a-1b);
bound and isolate a central portion of the image, the central portion comprising a least distorted portion of the image; identify a center of a torso of the animal; crop the central portion of the image at a set distance from the center of the torso of the animal; segment the animal into at least head and torso segments, concatenate the at least head and torso segments onto the top-down image of the animal to form a concatenated image (see § 2.3. Image segmentation, the central portion of the image containing the pig is isolated, the central portion is necessarily the least distorted portion, into head and torse segments, at a set distance from the pig based on the mounted camera, the synced image of the ellipses would be the concatenated image; Figs. 1a-1b and 3);
predict a weight of the animal based on the concatenated image (see § 2.5. Weight estimation using the TF model, the weight of the pig is estimated using the body area in pixels as input; Fig. 5).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the weight estimation via top-down camera of Kashiha with the anatomical features and the device of the modified Grisel because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) weight is an important factor to monitor the health of the pig that would further help veterinarians monitor the pigs with the gait for the lameness measure (see Kashiha abstract and § 5. Conclusion).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Grisel as applied to claim 1 above, and in view of Shmigelsky, and in view of Spears, and in view of Hampali et al. (“HandsFormer: Keypoint Transformer for Monocular 3D Pose Estimation of Hands and Object in Interaction”, arXiv, 29 April 2021), hereinafter Hampali.
Regarding Claim 19, Grisel teaches the method of claim 1 as stated above. Grisel further teaches that the set of anatomical landmarks comprise a nose, a mid-section, and a set of joints (¶[0049] the various features that may be utilized for the animal, including the head, nose, back, and limb joints, etc.).
Grisel is silent regarding that the anatomical landmarks comprise a tail.
Shmigelsky teaches an animal management system utilizing one or more imaging devices and an AI pipeline for identifying the animals with key points identified in sections of the animals (see abstract; Figs. 1A-1B and 7-14), in which the identified sections may include a tail section and tail points (see ¶[0047] and ¶[0261]; Fig. 14).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the tail section as an anatomical landmark of the method of Grisel because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results and/or (2) Grisel contemplates using various landmarks (see ¶[0049]) and Shmigelsky teaches one such additional landmark.
Grisel teaches the use of an evaluation modules and processing engine (see ¶[0038]-[0040]); however, the modified Grisel is silent regarding that the identifying the set of anatomical landmarks in the set of image frames further comprises: processing each image frame in the set of image frames using a fully convolutional neural network; identifying a nose, a mid-section, a tail, and a set of joints of interest using the fully convolutional neural network; producing a set of Gaussian kernels centered at each of the nose, the mid-section, the tail, and the set of joints of interest by the fully convolutional neural network; and extracting the set of anatomical landmarks as feature point locations from the set of Gaussian kernels produced by the fully convolutional neural network using peak detection with non-max suppression.
Spears teaches a system configured to receive video and/or images from an image capture device over a livestock path, analyze the images with application of a convolution neural network (CNN) so as to identify animals within the classification process, and then count the animals (see abstract and Fig. 1), in which the livestock may include cattle and pigs (see ¶[0040]), in which the CNN may be a fully convolutional network (FCN) (see ¶[0085]), in which the features are embodied by kernels (see ¶[0049]-[0051]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the FCN feature extraction of Spears with the method of the modified Grisel because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results and/or (2) the FCN works better for pixel to pixel manner (i.e., for usage between frames) and may significantly improve segmentation mask accuracy (see Spears ¶[0085]).
The modified Grisel does not specifically teach that the feature points are Gaussian kernels or how they are extracted.
Hampali teaches a method for estimating 3D poses of two hands in close interaction from a single image (see abstract), in which keypoints for 2D hand joint locations (i.e., features) are predicted with a heatmap of local maximums by applying a 2D Gaussian kernel at each joint location (see § 3.1 Keypoint Detection and Encoding, ¶1-2), in which the images are concatenated and then locations are extracted via a non-maximum suppression operation (see § 3.1 Keypoint Detection and Encoding, ¶1-4).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the keypoint detection and extraction of the keypoints (i.e., features) of Hampali with the method of the modified Grisel because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results and/or (2) the modified Grisel requires keypoint (i.e., feature) detection and extraction and Hampali teaches one such modality for keypoint (i.e., feature) detection and extraction.
Conclusion
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/J.D.M./ Examiner, Art Unit 3791
/JENNIFER ROBERTSON/ Supervisory Patent Examiner, Art Unit 3791
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