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 .
Priority
Acknowledgment is made of applicant’s claim for priority. Application claims benefit of In Provisional Application No. 63/033,154. As such, the effective filing date of claims 1-32 is 6/1/2020.
Claim Status
Claims 1-32 are pending.
Claims 1-32 are rejected.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/1/2022, 12/18/2023, 3/17/2026, and 3/31/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: Item 6320 in line 32, page 43, and Item 1902 in line 23, page 53. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections – 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 19 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 3 recites the limitation "the customized recommendation" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation "the customized recommendation" in line 2. There is insufficient antecedent basis for this limitation in the claim.
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 1-32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a system and method for assessing risk of development of chronic kidney disease in a dog. The judicial exception is not integrated into a practical application because while claim 14 attempts to integrated the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and merely implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of stator subject matter (a process, machine, manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically a system (Claims 1-16) and a method (Claims 17-32).
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas, specifically mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claims 1 and 17: Processing the biomarkers or demographic information, and determining a probability risk score are processes of comparing/contrasting, selecting, and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claims 2 and 18: Determining a customized recommendation based on the probability is a process of comparing/contrasting, selecting, and calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claims 4 and 20: The recommendation comprising at least one of those specified is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 5 and 21: The renal sparing strategies and one or more tests comprising those specified is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 8 and 24: The biomarkers comprising information relating to an amylase is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 9 and 25: The training dataset comprising the one or more biomarkers and demographic information is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 11 and 27: The decision threshold being from about 0 to 1 is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 12 and 28: The decision threshold being about 0.5 is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 13 and 29: Imputing one or missing values from the one or more biomarkers or demographic information is a process of comparing/contrasting, selecting, and calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claims 14 and 30: The imputation being a linear regression is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mathematical concept.
Claims 15 and 31: The imputation being based on the age of the dog is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 16 and 32: The imputation being based on the number of missing values is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [see MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of nonabstract elements:
Claims 1 and 17: A computer system, processor, memory, and code are generic and nonspecific computer elements that do not improve the functioning of any computer or technology described herein [See MEPE 2106.04(d)(1) and MPEP 2106.05(d)]. Receiving biomarkers or demographic information is an insignificant extra solution activity, specifically, mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. V. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP 2106.05(g)].
Claims 3 and 19: Transmitting the customized recommendation to a user equipment is an insignificant extra solution activity, specifically, necessary data outputting (See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission), and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)) [See MPEP 2106.05(g)]
Claims 6 and 22: The network comprising hidden layer architecture with three layers, and five nodes, three nodes, and three nodes for the respective layers is mere instructions to apply the judicial exception (See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone), and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit)) [See MPEP 2106.05(f)].
Claims 7 and 23: The neural network undergoing ten-fold cross-validation and training over eight or eighteen epochs is an insignificant extra solution activity, specifically, mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. V. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP 2106.05(g)].
Claims 9 and 25: The neural network being trained using a training dataset is an insignificant extra solution activity, specifically, mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. V. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP 2106.05(g)].
Claims 10 and 26: The prediction model comprising long short-term memory is mere instructions to apply the judicial exception (See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone), and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit)) [See MPEP 2106.05(f)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional or nonspecific. These additional elements include:
The additional elements of a computer system, processor, memory, and code are generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. V. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.05(d)(II)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of receiving biomarkers or demographic information (Conventional: Determining the level of a biomarker in blood by any means, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017)), transmitting the customized recommendation to a user equipment (Conventional: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)), the neural network undergoing ten-fold cross-validation and training over eight or eighteen epochs (Conventional: Conventional: Basheer et al. 2000 – Page 7, Subsection 2.6), and the neural network being trained using a training dataset (Conventional: Basheer et al. 2000 – Page 7, Subsection 2.6), are insignificant extra solution activities, specifically necessary data gathering and outputting (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering), Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data Claims derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-32, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
Claims 1-7, 9-13, 16-23, 25-29, and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Bradley et al. (Journal of veterinary internal medicine (2019) 2644-2656) and Cobrin et al. (Journal of Small Animal Practice (2013) 647-655).
Claim 1 is directed to a system for the prediction of chronic kidney disease in dogs using biomarker/demographic information and a recurrent neural network.
Claim 17 is directed to a method for the prediction of chronic kidney disease in dogs using biomarker/demographic information and a recurrent neural network.
Bradley et al. teaches in the abstract “a model to predict the risk of cats developing chronic kidney disease (CKD) using data from electronic health records (EHRs) collected during routine veterinary practice…The final model was a recurrent neural network (RNN) with 4 features (creatinine, blood urea nitrogen, urine specific gravity, and age)”, and on page 2646, column 2, paragraph 2 “The RNN was implemented with a tanh activation function in the hidden layers and softmax for transforming the output layer into a CKD probability score”, reading on a processor; and a memory that stores code that, when executed by the processor, causes the computer system to: (a) receive at least one of: (i) one or more biomarkers, wherein the one or more biomarkers comprises information relating to at least one of a urine specific gravity, a creatinine, a urine protein, a blood urea nitrogen (BUN); or (ii) demographic information, wherein the demographic information includes at least one of age or weight; (b) process at least one of the one or more biomarkers or demographic information using a prediction model, wherein the prediction model comprises a recurrent neural network; and (c) determine a probability risk score of the dog for developing CKD based on the processed one or more biomarkers or demographic information.
Bradley et al. does not teach the method for the use on dogs.
Cobrin et al. teaches in the abstract “The main parameters used to diagnose acute and chronic kidney disease include circulating creatinine and urea concentrations, and urine-specific gravity. However, these parameters can be insensitive. Therefore, there is a need for better methods to diagnose and monitor patients with renal disease. The use of renal biomarkers is increasing in human and veterinary medicine for the diagnosis and monitoring of acute and chronic kidney diseases. An ideal biomarker would identify site and severity of injury, and correlate with renal function, among other qualities. This article will review the advantages and limitations of renal biomarkers that have been used in dogs and cats, as well as some markers used in humans that may be adapted for veterinary use. In the future, measuring a combination of biomarkers will likely be a useful approach in the diagnosis of kidney disorders”.
It would have been obvious at the time of first filing to have modified the teachings of Bradley et al. for the system and method of claims 1 and 17 with the teachings of Cobrin et al. for the application of similar methods and biomarkers across species as the latter teaches the same biomarkers across species suggesting a similar disease etiology. One would have had a reasonable expectation of success given that the method and biomarkers are already proven, the only question is regarding its use on canine data which Crobin et al. suggests through similar biomarkers and etiologies, should be effective. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 2 is directed to the system of claim 1 but further specifies determining a customized recommendation.
Claim 18 is directed to the method of claim 17 but further specifies determining a customized recommendation.
Bradley et al. teaches on 2645, columns 1-2, paragraphs 3-1 “In human health care, machine learning models have been used to assess risk and inform practice management and predict individual outcomes, length of stay, recommend treatments, and personalized medicine”, and on page 2654, column 2, paragraph 3 “The algorithm predicts current/future risk of CKD, as opposed to IRIS staging that guides the clinician toward appropriate treatment decisions based on disease progression, a step that occurs following diagnosis of the disease”, reading on wherein the computer system is caused to: determine a customized recommendation based on the probability risk of the dog for developing CKD.
Claim 3 is directed to the system of claim 1 but further specifies transmitting the recommendation to user equipment of a vet, owner, or caregiver.
Claim 19 is directed to the method of claim 17 but further specifies transmitting the recommendation to user equipment of a vet, owner, or caregiver.
Bradley et al. teaches on 2645, columns 1-2, paragraphs 3-1 “In human health care, machine learning models have been used to assess risk and inform practice management and predict individual outcomes, length of stay, recommend treatments, and personalized medicine”, and on page 2654, column 2, paragraph 3 “The algorithm predicts current/future risk of CKD, as opposed to IRIS staging that guides the clinician toward appropriate treatment decisions based on disease progression, a step that occurs following diagnosis of the disease”, while not explicity teaching the sending of the treatment information to a vet, it would be obvious if treatment decisions were to be guiding the clinician based on the output of the algorithm that said information would need to be given to the clinician and would thereby read on wherein the computer system is caused to: transmit the customized recommendation to a user equipment of a veterinarian, owner, or caregiver of the dog.
Claim 4 is directed to the system of claim 2 and thus claim 1, but further specifies the recommendation comprise at least one of the specified recommendations.
Claim 20 is directed to the method of claim 18 and thus claim 17, but further specifies the recommendation comprise at least one of the specified recommendations.
Bradley et al. teaches on page 2645, column 1, paragraph 1 “Early detection of CKD allows the implementation of care pathways that can slow the progression of the disease, improving clinical outlook and quality of life, as well as the avoidance of situations that might cause worsening of kidney function and acute kidney injury, such as administration of NSAIDs”, reading on wherein the customized recommendation comprises at least one of:(a) one or more therapeutic interventions; (b) one or more dietary recommendations; (c) one or more renal sparing strategies; or (d) one or more tests for disease progression.
Claim 5 is directed to the system of claim 4 and thus claim 1, but further specifies the renal sparing strategies or tests for disease progression.
Claim 21 is directed to the method of claim 20 and thus claim 17, but further specifies the renal sparing strategies or tests for disease progression.
Bradley et al. teaches on page 2645, column 1, paragraph 1 “Early detection of CKD allows the implementation of care pathways that can slow the progression of the disease, improving clinical outlook and quality of life, as well as the avoidance of situations that might cause worsening of kidney function and acute kidney injury, such as administration of NSAIDs”, reading on wherein: (i) the one or more renal sparing strategies comprise avoidance of non-steroidal anti-inflammatories, aminoglycosides, or any combination thereof, and/or (ii) the one or more tests for disease progression comprise testing of serum parathyroid hormone levels.
Claim 6 is directed to the system of claim 1 but further specifies the number of layers and nodes within the neural network.
Claim 22 is directed to the method of claim 17 but further specifies the number of layers and nodes within the neural network.
Bradley et al. teaches on page 2649, column 2, paragraph 2 “we then determined the best structure for the hidden layers—number of layers and nodes per layer—for a standard RNN and a LSTM variant. Results in terms of cross-entropy score (Figure 6) and the notion that higher cross-entropy scores are better, demonstrated that RNN models were slightly superior to LSTM models. For the RNN, the simpler models with a small number of nodes were better than the complex ones. A 2-layer RNN with a 3-7 structure was best. Optimizing this model for training time by testing different numbers of epochs resulted in a final RNN model with a 3-7 structure trained over 16 epochs”. While the reference does not teach the exact number of layers and nodes, as previously suggested by the reference this is obvious to optimize using either cross-entropy or, as taught later, the F1 scores, thereby reading on wherein the recurrent neural network comprises a hidden layer architecture with three layers, the three layers comprising a first layer with five nodes, a second layer with three nodes, and a third layer with three nodes.
Claim 7 is directed to the system of claim 1 but further specifies that the network undergoes ten-fold cross validation and trained over eight or eighteen epochs.
Claim 23 is directed to the method of claim 17 but further specifies that the network undergoes ten-fold cross validation and trained over eight or eighteen epochs.
Bradley et al. teaches on page 2646, column 2, paragraph 3 “Evaluation was based on the F1 score in a 10-fold cross-validation setup”, and on page 2651, column 1, paragraph 1 “Optimizing this model for training time by testing different numbers of epochs resulted in a final RNN model with a 3-7 structure trained over 16 epochs”. While the reference does not teach the exact number of training epochs this would be obvious to optimize based upon the F1 score previously described and thereby reads on wherein the recurrent neural network undergoes a ten-fold cross-validation process and is trained over eight or eighteen epochs.
Claim 9 is directed to the system of claim 1 but further specifies training the neural network using a dataset of the biomarkers or demographic information.
Claim 25 is directed to the method of claim 17 but further specifies training the neural network using a dataset of the biomarkers or demographic information.
Bradley et al. teaches on page 2649, column 1, paragraph 2 “We used a standard RNN with a 3-5-3 hidden layer structure as a starting point for a prediction model for CKD that acknowledges both the multifactorial and temporal aspects of CKD diagnosis. Using this type of model with 35 candidate factors or features was impractical both for training the model as well as for using it in practice later. Therefore, we first set out to select the most important features using a top-down and bottom-up feature selection strategy on the training data set”, reading on wherein the recurrent neural network is trained using a training dataset, wherein the training dataset comprises the one or more biomarkers and the demographic information for a plurality of other dogs.
Claim 10 is directed to the system of claim 1 but further specifies the recurrent neural network with long short-term memory.
Claim 26 is directed to the method of claim 17 but further specifies the recurrent neural network with long short-term memory.
Bradley et al. teaches on page 2649, column 2, paragraph 2 “With these 4 features, we then determined the best structure for the hidden layers—number of layers and nodes per layer—for a standard RNN and a LSTM variant”, reading on wherein the prediction model further comprises the recurrent neural network with long short-term memory (LSTM).
Claim 11 is directed to the system of claim 1 but further specifies that the decision threshold for developing CKD be about 0 to about 1.
Claim 27 is directed to the method of claim 17 but further specifies that the decision threshold for developing CKD be about 0 to about 1.
Bradley et al. teaches on page 2646. Column 2, paragraph 2 “The RNN was implemented with a tanh activation function in the hidden layers and softmax for transforming the output layer into a CKD probability score”, reading on wherein the decision threshold for developing the CKD using the recurrent neural network is about 0 to about 1.
Claim 12 is directed to the system of claim 1 but further specifies that the decision threshold for developing CKD be about 0.5.
Claim 28 is directed to the method of claim 17 but further specifies that the decision threshold for developing CKD be about 0.5.
Bradley et al. teaches on page 2646. Column 2, paragraph 2 “The RNN was implemented with a tanh activation function in the hidden layers and softmax for transforming the output layer into a CKD probability score”, reading on wherein the decision threshold for developing the CKD using the recurrent neural network is about .5.
Claim 13 is directed to the system of claim 1 but further specifies imputing missing values from the canine data.
Claim 29 is directed to the method of claim 17 but further specifies imputing missing values from the canine data.
Bradley et al. teaches on page 2646, column 1, paragraph 3 “Prior to use, missing information in the blood and urine test data was imputed using all available blood and urine data but not the CKD status information. This is needed because neural networks require complete data and was done separately for model building and test data sets to avoid any flow of information between the 2 data sets. Only records with at least some blood or urine data were imputed to fill in missing data”, and on 2647, column 1, paragraph 3 “General data management, statistical analyses, and plots were performed using R version 3.4.329 and imputation was done with the MissForest package version 1.4”, reading on wherein the compute system is caused to: impute one or more missing values from the one or more biomarkers of the dog or the demographic information of the dog.
Claim 16 is directed to the system of claim 13 and thus claim 1, but further specifies imputing based on the number of missing values.
Claim 32 is directed to the method of claim 17 but further specifies imputing based on the number of missing values.
Bradley et al. teaches on page 2646, column 1, paragraph 3 “Prior to use, missing information in the blood and urine test data was imputed using all available blood and urine data but not the CKD status information. This is needed because neural networks require complete data and was done separately for model building and test data sets to avoid any flow of information between the 2 data sets. Only records with at least some blood or urine data were imputed to fill in missing data”, reading on wherein the imputation is based on the number of missing values.
Bradley et al. and Cobrin et al. do not teach the specified renal sparing strategies or tests for disease progression.
Claims 8 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Bradley et al. (Journal of veterinary internal medicine (2019) 2644-2656) and Cobrin et al. (Journal of Small Animal Practice (2013) 647-655) as applied to claims 1-7, 9-13, 16-23, 25-29, and 32 above, and further in view of Polzin et al. (American journal of veterinary research (1983) 404-410).
Claim 8 is directed to the system of claim 1 but further specifies the one or more biomarkers comprise information relating to amylase.
Claim 24 is directed to the system of claim 17 but further specifies the one or more biomarkers comprise information relating to amylase.
Bradley et al. and Cobrin et al. teach the system of claim 1 and method of claim 17 as previously described.
Bradley et al. and Cobrin et al. do not teach the one or more biomarkers comprise information relating to amylase.
Polzin et al. teaches in the summary “Concurrent hyperamylasemia and azotemia may occur in dogs with acute pancreatitis or primary renal failure. In a survey of 34 dogs with acute pancreatitis, 65% of the dogs examined had concurrent azotemia and hyperamylasemia. 2 Serum amylase activity was examined in 28 dogs with primary renal failure and found to be approximately 2.5 times normal”, and on page 407, column 2, paragraph 3 “most dogs with primary renal failure have hyperamylasemia of this magnitude”, reading on wherein the one or more biomarker comprises information relating to an amylase.
It would have been obvious at the time of first filing to have modified the teachings of Bradley et al. and Cobrin et al. for the system and method of claims 1 and 17, with the teachings of Polzin et al. for the inclusion of amylase levels in assessing kidney disease, as the latter shows a link between the two with dogs having kidney disease also showing hyperamylasemia. One would have had a reasonable expectation of success given that this would mere be an additional feature to include in the data set and would not change the method overall. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claims 14-15 and 30-31 are rejected under 35 U.S.C. 103 as being unpatentable over Bradley et al. (Journal of veterinary internal medicine (2019) 2644-2656) and Cobrin et al. (Journal of Small Animal Practice (2013) 647-655) as applied to claims 1-7, 9-13, 16-23, 25-29, and 32 above, and further in view of Van Buuren et al. (Journal of statistical software (2011) 1-67).
Claim 14 is directed to the system of claim 13 and thus claim 1, but further specifies the imputation using linear regression.
Claim 30 is directed to the method of claim 17, but further specifies the imputation using linear regression.
Bradley et al. and Cobrin et al. teach the system of claim 1 and method of claim 17 as previously described.
Bradley et al. and Cobrin et al. do not teach the imputation using linear regression.
Van Buuren et al. teaches on page 16, in Table 1, “Built-in univariate imputation techniques. The techniques are coded as functions named mice.impute.pmm(), and so on”, of which 3 are linear regression methods, reading on wherein the imputation is a linear regression.
It would have been obvious at the time of first filing to have modified the teachings of Bradley et al. and Cobrin et al. for the system and method of claims 1 and 17, with the teachings of Van Buuren et al. for the use of a linear regression imputation as the latter states on page 1, paragraph 1 “Multiple imputation is the method of choice for complex incomplete data problems” and the specification specifically cites the method on page 20, paragraph 2 “In other non-limiting embodiments, imputation can be calculated using machine learning. For example, the imputed value, amount, level, or demographic information can be determined using one of more of the following machine learning methods: k- nearest neighbor (KNN) imputation, such as missingpy KNN or fancyimpute KNN, multiple or multivariant imputation by chained equations (MICE) imputation, such as linear regression, ridge regression, or gradient boost, and/or random forest algorithms or related algorithms, such as missingpy missForest, sciblox MICE random forest, or any other variant of missing forest”. One would have had a reasonable expectation of success given that Van Buuren et al. provides hands on stepwise guidance on the implementation of MICE. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 15 is directed to the system of claim 13 and thus claim 1, but further specifies basing the imputation on the age of the dog.
Claim 31 is directed to the method of claim 17 and thus claim 1, but further specifies basing the imputation on the age of the dog.
Bradley et al. and Cobrin et al. teach the system of claim 1 and method of claim 17 as previously described.
Bradley et al. and Cobrin et al. do not teach the imputation being based on the age of the dog.
Van Buuren et al. teaches on page 7, under section 2.4 “Simple Example” the imputation of multiple features within the dataset including age as shown on page 8, paragraph 2, and page 12, thereby reading on wherein the imputation is based on an age of the dog.
Conclusion
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/K.N.A./ Examiner, Art Unit 1687
/LARRY D RIGGS II/ Supervisory Patent Examiner, Art Unit 1686
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