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 .
Response to Amendment
The following Office action in response to communications received November 24, 2025. Claim 12-13, 21, 27-28 and 32-38 is canceled. Therefore, claims 1-11, 14-20, 22-26, 22-30 and 39 are pending and addressed below.
Applicant’s amendments to the claims are not sufficient to overcome the rejections set forth in the previous office action dated May 28, 2025.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-11, 14-20, 22-26, 22-30 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No.: US 20170049392 A1 to BRATTAIN in view of Pub. No.: US 20190216350 A1 to Sullivan et al.
As per Claim 1, BRATTAIN teaches a system for determining a health status of an animal upon arrival at a location (see BRATTAIN paragraphs 29-35 and 51-52), comprising: a computer processor for receiving and storing biometric data (see BRATTAIN paragraphs 29-35 and 51-52); at least one recording device for capturing and recording biometric data from the animal (see BRATTAIN paragraphs 29-35 and 51-52), said device communicating with said computer processor, said device including at least an audio recorder that records the biometric data obtained by auscultated heart and lung sounds of the animal (see BRATTAIN paragraphs 29-35 and 51-52); computer coded instructions executed by said computer processor including at least one algorithm for determining whether the animal should receive treatment (see BRATTAIN paragraphs 15, 29, 34, 73-74 and 77); a user interface associated with the computer processor for displaying information obtained by execution of said computer processor (see BRATTAIN paragraph 79); and wherein said algorithm includes an algorithm comprising input variables obtained from the recorded biometric data (see BRATTAIN paragraphs 29-34), and corresponding to the recorded heart and lung sounds (see BRATTAIN Abstract); and said algorithm incorporates the biometric data recorded as converted to numerical form for inputs to the algorithm; and wherein the inputs include arithmetic expressions of the recorded biometric data, said expressions including at least one of a cardiac breath gap, a cardiac index, wherein the cardiac index is calculated as (hr percentile/bw percentile) said ci index representing a relationship between heart performance and size of the animal, and a cardiac max (see BRATTAIN paragraphs 29-35 and 51-52).
BRATTAIN fails to explicitly teach:
-- a gradient boosted tree; and
-- said algorithm incorporates the biometric data recorded as converted to numerical form for inputs to the algorithm; and wherein the inputs include arithmetic expressions of the recorded biometric data, said expressions including at least one of a cardiac breath gap, a cardiac index, and a cardiac max.
Sullivan et al. teaches a machine learning tool can include but is not limited to classification and regression tree decision models, such as random forest and gradient boosting, (e.g., implemented using R or any other statistical/mathematical programming language) (see Sullivan et al. paragraphs 226, 311-312 and 367)
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by reference Sullivan et al. within the systems/methods as taught by reference BRATTAIN with the motivation of providing an individual and/or a medical professional time to prepare for a medical event, for example, to prepare for a potentially adverse or fatal degradation in the medical condition of a subject or patient, to potentially mitigate or avoid the adverse effects of the degradation, or even potentially completely avoid the degradation or event by providing timely, appropriate treatment (see Sullivan et al. paragraph 226).
As per Claim 2, BRATTAIN and Sullivan et al. teach the system of claim 1, wherein: said input variables further include at least one of a body weight of the animal and a rectal temperature of the animal (see BRATTAIN paragraphs 7 and 33).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 3, BRATTAIN and Sullivan et al. teach the system of claim 1, wherein: said algorithm incorporates the biometric data recorded as converted to numerical form for inputs to the algorithm (see BRATTAIN paragraphs 20, 29 and 38), and in which raw signal dated recorded of the auscultated heart and lung sounds undergo bandpass filtering (see BRATTAIN paragraphs 20, 29 and 38).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 4, BRATTAIN and Sullivan et al. teach the system of claim 3, wherein: said input variables include a heart rate (see BRATTAIN Abstract).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 5. A system, as claimed in claim 3, wherein said input variables include a respiration rate (see BRATTAIN Abstract).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 6, BRATTAIN and Sullivan et al. teach the system of claim 3, wherein said arithmetic expressions further include a cardiopulmonary ratio (see BRATTAIN Abstract).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 7, BRATTAIN and Sullivan et al. teach the system of claim 1, further including: another user interface associated with the computer processor for displaying a health status of the animal, said health status including a likelihood the animal may develop BRD (see BRATTAIN Abstract).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 8, BRATTAIN and Sullivan et al. teach the system of claim 1, wherein: said displayed information includes a user treatment decision for the animal (see BRATTAIN paragraph 34).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 9, BRATTAIN and Sullivan et al. teach the system of claim 8, wherein: said at least one algorithm is a gradient boosted tree algorithm expressed by a function F0 (x) =arg min yZ1L(yi,y) (see Sullivan et al. paragraphs 311-313, 321 and 372); wherein the function classifies the treatment decision to treat or not treat an animal (see Sullivan et al. paragraph 226); wherein y represents a specific combination of inputs that arrive at the treatment decision (see Sullivan et al. paragraphs 312, 321 and 372); and wherein Σ L represents a misclassification error (see Sullivan et al. paragraphs 313 and 372) or number of falsely estimated instances i in a training set when compared to a true label yi (see Sullivan et al. paragraphs 313 and 372).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 10, BRATTAIN and Sullivan et al. teach the system of claim 9, wherein: said specific combination of inputs y includes at least one of: (a) the cardiac breath gap (cbg), calculated as (heart rate - respiration rate); (b) the cardiac index (ci); and (c) a cardiac max (cm), calculated as (hr/(75th percentile hr (based on bw and rt)), said cm representing an estimation of how efficiently the animal is consuming oxygen upon arrival at the location. Sullivan et al. further teaches percentile, see paragraphs 371).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 11, BRATTAIN and Sullivan et al. teach the system of claim 9, wherein: execution of said at least one algorithm by said computer processor includes a plurality of decision stumps and a generated outcome resulting in a treatment decision (see BRATTAIN paragraphs 29 and 34. Sullivan et al. further teaches stumps, see paragraphs 312 and 392).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 14, BRATTAIN and Sullivan et al. teach the system of claim 9, wherein: execution of said at least one algorithm includes conducting iterative computations to arrive at the treatment decision (see BRATTAIN paragraph 34).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 15, BRATTAIN teaches a method (non-transitory computer-readable medium containing computer executable instructions, wherein, when executed by a computer processor, the instructions cause the computer processor to execute a method (see BRATTAIN paragraph 30)) for determining a health status of the livestock animal upon arrival at a location, comprising:
--providing a computer processor for receiving and storing biometric data (see BRATTAIN paragraphs 29-35 and 51-52);
-- providing at least one recording device (see BRATTAIN paragraphs 29-35 and 51-52);
-- recording raw biometric data from the livestock animal by use of at least an audio recorder that records heart and lung sounds (see BRATTAIN paragraphs 29-35 and 51-52);
-- converting the raw biometric data recorded to manipulated heart and lung sound data (see BRATTAIN paragraphs 5, 20, 29-30, 38 and 79);
-- providing computer coded instructions executed by the computer processor including at least one algorithm for determining a health status of the livestock animal (see BRATTAIN paragraphs 29-35 and 51-52);
-- executing the algorithm with input variables corresponding to the manipulated heart and lung sound data (see BRATTAIN paragraphs 29-35 and 51-52);
-- generating a user interface associated with the computer processor for displaying to a user a health status of the livestock animal (see BRATTAIN paragraph 79); and
--wherein said algorithm includes an algorithm comprising input variables corresponding to the manipulated heart and lung sound data of the livestock animal (see BRATTAIN paragraphs 29-34); and
-- wherein said algorithm includes additional inputs including arithmetic expressions of recorded heart and lung sound data, said expressions including at least one of a cardiopulmonary ratio, a cardiac breath gap, a cardiac index, and a cardiac max, and further wherein said cardiac index is determined by a calculation including a heart rate percentile and a body weight percentile (see BRATTAIN Abstract and paragraph 17).
BRATTAIN fails to explicitly teach a gradient boosted tree.
Sullivan et al. teaches a machine learning tool can include but is not limited to classification and regression tree decision models, such as random forest and gradient boosting, (e.g., implemented using R or any other statistical/mathematical programming language) (see Sullivan et al. paragraphs 226, 311-312 and 367)
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by reference Sullivan et al. within the systems/methods as taught by reference BRATTAIN with the motivation of providing an individual and/or a medical professional time to prepare for a medical event, for example, to prepare for a potentially adverse or fatal degradation in the medical condition of a subject or patient, to potentially mitigate or avoid the adverse effects of the degradation, or even potentially completely avoid the degradation or event by providing timely, appropriate treatment (see Sullivan et al. paragraph 226).
As per Claim 16, BRATTAIN and Sullivan et al. teach the method of claim 15, wherein: said at least one algorithm further includes computer coded instructions for determining whether the livestock animal should receive treatment (see BRATTAIN paragraphs 15, 29, 34, 73-74 and 77); and said user interface further includes a treatment decision for the animal (see BRATTAIN paragraphs 15, 29, 34, 73-74 and 77).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 17, BRATTAIN and Sullivan et al. teach the method of claim 15, wherein: said input variables further include at least one of a body weight of the livestock animal and a rectal temperature of the livestock animal (see BRATTAIN paragraphs 7 and 33).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 18, BRATTAIN and Sullivan et al. teach the method of claim 15, wherein: said manipulated heart and lung sound data includes the raw biometric data that undergoes bandpass filtering (see BRATTAIN paragraphs 5, 20, 29-30, 38 and 79).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 19, BRATTAIN and Sullivan et al. teach the method of claim 15, wherein: said manipulated heart and lung sound data includes a recorded heart rate (see BRATTAIN Abstract).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 20, BRATTAIN and Sullivan et al. teach the method of claim 15, wherein: said manipulated heart and lung sound data includes a respiration rate and selected recorded frequencies and amplitudes of recorded lung sounds (see BRATTAIN paragraph Abstract).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 22, BRATTAIN and Sullivan et al. teach the method of claim15, further including: generating a user interface associated with the computer processor wherein said health status including a likelihood the livestock animal may develop BRD (see BRATTAIN Abstract).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 23, BRATTAIN and Sullivan et al. teach the method of claim 15, wherein: said health status includes a user treatment decision for the livestock animal (see BRATTAIN paragraph 34).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 24, BRATTAIN and Sullivan et al. teach the method of claim 15, wherein: said at least one algorithm is a gradient boosted tree algorithm expressed by a function F0 (x) =arg min yZ1L(yi,y) (see Sullivan et al. paragraphs 311-313, 321 and 372); wherein the function classifies the treatment decision to treat or not treat a livestock animal (see Sullivan et al. paragraph 226); wherein y represents a specific combination of inputs that arrive at the treatment decision (see Sullivan et al. paragraphs 312, 321 and 372); and wherein Σ L represents a misclassification error (see Sullivan et al. paragraphs 313 and 372) or number of falsely estimated instances i in a training set when compared to a true label yi (see Sullivan et al. paragraphs 313 and 372).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 25, BRATTAIN and Sullivan et al. teach the method of claim 24, wherein: said specific combination of inputs y includes at least one of: (a) a cardio-pulmonary ratio (cpr), calculated as (hr percentile/rr percentile), said cpr ratio representing a magnitude of stress experienced by the livestock animal upon arrival at the location (see BRATTAIN paragraph 16-17 and 35); (b) a cardiac breath gap (cbg), calculated as (heart rate - respiration rate); (c) a cardiac index (ci), calculated as (hr percentile/rr percentile), said ci index representing a relationship between heart performance and size of the livestock animal; and (d) a cardiac max (cm), calculated as (hr/(75th percentile hr (based on bw and rt)), said cm representing an estimation of how efficiently the livestock animal is consuming oxygen upon arrival at the location. Sullivan et al. further teaches percentile, see paragraphs 371).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 26, BRATTAIN and Sullivan et al. teach the method of claim 24, wherein: execution of said at least one algorithm by said computer processor includes a plurality of decision stumps and a generated outcome resulting in a treatment decision (see BRATTAIN paragraphs 29 and 34. Sullivan et al. further teaches stumps, see paragraphs 312 and 392).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 29, BRATTAIN and Sullivan et al. teach the method of claim 24, wherein: execution of said at least one algorithm includes conducting iterative computations to arrive at the treatment decision (see BRATTAIN paragraph 34).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 30, Claim 30 is directed to a non-transitory computer-readable medium containing computer executable instructions, wherein, when executed by a computer processor, the instructions cause the computer processor to execute a method to determine a health status of livestock animal the computer-readable instructions. Claim 30 recites the same or substantially similar limitations as those addressed above for Claim 15 as taught by BRATTAIN and Sullivan et al. Claim 30 is therefore rejected for the same reasons as set forth above for Claim 15 respectively.
As per Claim 31, BRATTAIN and Sullivan et al. teach the non-transitory computer-readable medium of claim 30 wherein: said instructions to execute the algorithm further include instructions to determine whether the livestock animal should receive treatment (see BRATTAIN paragraphs 15, 29, 34, 73-74 and 77); and said instructions to generate the user interface further include instructions to display to the user a treatment decision for the animal (see BRATTAIN paragraphs 15, 29, 34, 73-74 and 77).
The obviousness of combining the teachings of BRATTAIN and Sullivan et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 39, Claim 39 is directed to a system for determining a health status of an animal upon arrival at a location. Claim 39 recites the same or substantially similar limitations as those addressed above for Claim 1 as taught by BRATTAIN and Sullivan et al. Claim 39 is therefore rejected for the same reasons as set forth above for Claim 1 respectively.
Response to Arguments
Applicant’s arguments filed November 24, 2025 have been fully considered but they are not persuasive. In the remarks applicant argues:
(1) Claims 1-20 and 22-39 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No.: US 20170049392 Al to BRATTAIN in view of Pub. No.: US 20190216350 Al to Sullivan et al. Applicant respectfully disagrees and traverses the rejection at last because the prior art does not make obvious at least one of the claimed arithmetic expressions of claim 1: cardiac breath gap, cardiac index, or cardiac max.
The specification defines these three arithmetic expressions in paragraphs 21-22. The cardiac breath gap is defined as heat rate minus respiratory rate. The cardiac index is defined as heart rate percentile divided by body weight percentile.
And cardiac max is defined as:
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The Office Action acknowledges that "Brattain fails to explicitly teach ... at least one of a cardiac breath gap, a cardiac index and a cardiac max." See Office Action at page 3. And these arithmetic expressions are not mentioned, nor suggested, in Sullivan nor Geissler. The cited prior art does disclose some of the basic elements of these expressions (such as heart rate or respiratory rate). But it does not disclose all of the elements (for example the claimed "75th percentile hear rate" is not disclosed), and it does not disclose the claimed combinations of the cardiac breath gap, cardiac index, or cardiac max. Nor is there any suggestion or teaching in the cited prior art to create these new arithmetic expressions. For example, there is no citation in the Office Action to prior art that would teach a person of ordinary skill to create theses arithmetic expressions and use them as "inputs" in the claimed algorithm, as claimed in claim 1. The claimed arithmetic expression also have advantages over the prior art . For example, in paragraph 23 of the published application, the claimed inputs to the algorithm are described as advantageous over the prior art at least because, in the prior art, "one input factor or variable may overly influence the outcome of the algorithm." At least because the prior art does not disclose or render obvious the arithmetic expressions in claim 1, Applicant respectfully requests that these claim rejections be withdrawn.
Claims 1-38 are rejected under 35 U.S.C. §103 as allegedly obvious over 20170049392 (hereinafter referred to as Brattain) in view of U.S. Patent Publication No. 20190216350 (hereinafter referred to as Sullivan) in view of U.S. Patent Publication No. 20190336041 (hereinafter referred to as Geissler). Applicant respectfully disagrees and traverses the rejection. At least because of the arguments outlined above regarding the arithmetic expression of claim 1, Applicant respectfully requests that these claim rejections be withdrawn.
The Office Action also appears to cite paragraphs 29-35 and 51-52 of Brattain as related to these arithmetic expressions; but as explained below, Brattain does not disclose, nor even suggest, these expressions in these paragraphs nor anywhere in its disclosure.
In response to argument (1), Examiner respectfully disagrees. As stated in arguments, “The cited prior art does disclose some of the basic elements of these expressions (such as heart rate or respiratory rate).” At least having the heart rate and BRATTAIN also teaching respiratory rate (see paragraph 21) the cardiac breath gap can be calculated. Claim 10 claims, “said specific combination of inputs y includes at least one of…,” therefore, meeting at least the at least one required, the cardiac max equation requiring said 75th percentile is not required. In addition, this equation is merely using collected measurements well known in the art and manually plugging in numbers. The Office believes that the prior art of record used in the 35 U.S.C. 103(a) rejection teaches each and every limitation of the claimed invention and that proper motivation exists for combining the prior art references, therefore, a prima facie case for obviousness has been set forth in the previous Office Action and the rejection in maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Patent No.: US 10372967 B1; Many parasites live within or on animals. One class of parasites lives within the digestive tracts of animals, such as sheep. These parasites can cause a wide range of problems, including disease and even death if left untreated. In agricultural settings, parasitic infections can have a serious impact on productivity of livestock. Furthermore, in some instances, such infections may have detrimental effects on humans who come into contact with the livestock. In another set of embodiments, the classifier is a machine-learned classifier, such as a neural network, a gradient boosted decision tree, a support vector machine, or the like. In these embodiments, a set of training data is collected and the training data is labelled by human operators as either including or not including a parasite. In one such embodiment, the human operators may identify the set of pixels that correspond to the worm (e.g., by tracing its outline on the image using a touchscreen). The training data may be, for example, a set of patches that either do or do not contain a parasite. The classifier is then trained (e.g., using backpropagation) to correctly sort the training data into those that are labelled as containing a parasite and those that are not. The training of the classifier may also be validated by applying it to a set of validation data that has also been human-labelled but which was not used for the training. Once trained (and validated, if validation is used), the classifier may then be applied in place of (or in conjunction with) a gradient of histograms approach to classify candidate patches as either including or not including a parasite.
Int. Pub. No.: WO2015/168341 A1; The evaluation method involves determining a group of first values for ratios indicating respiratory compensating responses (CPR-R), determining a group of second values for ratios indicating cardiac compensating responses (CPR-C) and then determining a group of third values for ratios indicating normal compensating responses (CPR-N). The likelihood that animal will develop a disease is determined by taking into account the ratios within the first, second, or third groups of values. A treatment is provided to the animal corresponding to the likelihood the animal will develop the disease.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD B WINSTON III whose telephone number is (571)270-7780. The examiner can normally be reached M-F 1030 to 1830.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.B.W/ Examiner, Art Unit 3683
/ROBERT W MORGAN/ Supervisory Patent Examiner, Art Unit 3683