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 .
The response was received 1/22/2026. Claims 1-19 are pending where claims 1-19 were previously presented and claim 20 is cancelled.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/22/2026 has been entered.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With regard to claim 1:
Step 2A, Prong One:
The claim recites the following limitations which are drawn towards an abstract idea:
A method, comprising: …
accessing a machine learning model configured to generate risk score output indicating a chronic kidney disease (CKD) progression, … applying an input dataset associated with a new patient
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
accessing a machine learning model configured to generate risk score output indicating chronic kidney disease (CKD) progression, wherein the machine learning model is generated by applying a training dataset to an untrained machine learning model to configure model parameters of the machine learning model for processing input data to generate the risk score output (recites merely apply it limitation using generic computer element including machine learning to perform the judicial exception including the initial training of the machine learning model at a high-level of generality, see MPEP 2106.05(f)),
wherein the training dataset comprises (i) a first set of medical laboratory data associated with a plurality of patients, (ii) an age of each patient included in the plurality of patients, (iii) a sex of each patient included in the plurality of patients, and (iv) CKD clinical outcomes associated with the plurality of patients, the first set of medical laboratory data indicating, for at least a combination of patients included in the plurality of patients: estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (ACR), urea, serum sodium, serum chloride, serum hemoglobin, serum potassium, glucose, serum albumin, alkaline phosphatase, serum phosphate, serum bicarbonate, serum magnesium, serum calcium, aspartate aminotransferase (AST), alanine transaminase (ALT), bilirubin, gamma-glutamyl transferase (GGT), hematocrit, and platelet count (recites field of use limitations describing the particular/preferred data and its respective meaning in order to perform the judicial exception, see MPEP 2106.05(h));
to the machine learning model to cause the machine learning model to process the input dataset via the model parameters of the machine learning model tool, such as using a machine learning model, to implement the abstract idea, see MPEP 2106.05(f)),
the input dataset comprising an age of the new patient, a sex of the new patient, and a second set of medical laboratory data indicating for the new patient: eGFR, urine ACR, urea, serum sodium, serum chloride, serum hemoglobin, serum potassium, glucose, serum albumin, alkaline phosphatase (ALKP), serum phosphate, serum bicarbonate, serum magnesium, serum calcium, AST, ALT, bilirubin, GGT, hematocrit, and platelet count (recites field of use limitations describing the particular data and its respective meaning, see MPEP 2106.05(h)).
This judicial exception is not integrated into a practical application because, as seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). The additional elements recite field of use limitations describing the particular data and its respective meaning as well as the usage of a generic computer machine learning model to implement the abstract idea.
Step 2B:
Below is the analysis of the claims:
accessing a machine learning model configured to generate risk score output indicating chronic kidney disease (CKD) progression, wherein the machine learning model is generated by applying a training dataset to an untrained machine learning model to configure model parameters of the machine learning model for processing input data to generate the risk score output (recites merely apply it limitation using generic computer element including machine learning to perform the judicial exception including the initial training of the machine learning model at a high-level of generality, see MPEP 2106.05(f)),
wherein the training dataset comprises (i) a first set of medical laboratory data associated with a plurality of patients, (ii) an age of each patient included in the plurality of patients, (iii) a sex of each patient included in the plurality of patients, and (iv) CKD clinical outcomes associated with the plurality of patients, the first set of medical laboratory data indicating, for at least a combination of patients included in the plurality of patients: estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (ACR), urea, serum sodium, serum chloride, serum hemoglobin, serum potassium, glucose, serum albumin, alkaline phosphatase, serum phosphate, serum bicarbonate, serum magnesium, serum calcium, aspartate aminotransferase (AST), alanine transaminase (ALT), bilirubin, gamma-glutamyl transferase (GGT), hematocrit, and platelet count (recites field of use limitations describing the particular/preferred data and its respective meaning in order to perform the judicial exception, see MPEP 2106.05(h));
to the machine learning model to cause the machine learning model to process the input dataset via the model parameters of the machine learning model
the input dataset comprising an age of the new patient, a sex of the new patient, and a second set of medical laboratory data indicating for the new patient: eGFR, urine ACR, urea, serum sodium, serum chloride, serum hemoglobin, serum potassium, glucose, serum albumin, alkaline phosphatase (ALKP), serum phosphate, serum bicarbonate, serum magnesium, serum calcium, AST, ALT, bilirubin, GGT, hematocrit, and platelet count (recites field of use limitations describing the particular data and its respective meaning, see MPEP 2106.05(h)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite field of use limitations describing the particular data and its respective meaning as well as the usage of a generic computer machine learning model to implement the abstract idea.
With regard to claim 2, this claim recites wherein the new patient is not associated with a CKD stage of G3 or later (recites field of use limitations describing particular preferred conditions of a user, see MPEP 2106.05(h)).
With regard to claim 3, this claim recites wherein the machine learning model comprises a random survival forest model (recites merely using generic machine learning models as a computer tool to perform the abstract idea, see MPEP 2106.05(f)).
With regard to claim 4, this claim recites wherein the risk score output indicating CKD progression for the new patient indicates a risk of experiencing CKD progression within a particular amount of time from a time period associated with the input dataset for the new patient (recites field of use limitations describing what the respective prediction/output value is meant to represent, see MPEP 2106.05(h)).
With regard to claim 5, this claim recites wherein the particular amount of time is provided as input to the machine learning model for generating the risk score output indicating CKD progression for the new patient (recites insignificant extrasolution activity of transmitting/receiving information over a network which amounts to well-understood, routine, and conventional activity of transmitting/receiving information, see MPEP 2106.05(d)).
With regard to claim 6, this claim recites wherein the particular amount of time comprises 2 years or 5 years (recites field of use limitations describing preferred data values to be used, see MPEP 2106.05(f)).
With regard to claim 7, this claim recites wherein the urine ACR for one or more of the plurality of patients or the new patient is converted from a urine protein-to-creatinine test or a urine dipstick test (recites field of use limitations describing particular manipulation/usage of particular tests).
With regard to claim 8, this claim recites wherein the risk score output indicating CKD progression for the new patient indicates a risk of the new patient experiencing kidney failure or about a 40% or greater decline of the eGFR for the new patient (recites mental process steps of evaluating the health of the employee and the respective meaning of the prediction).
With regard to claim 9, this claim recites wherein the risk of the new patient experiencing kidney failure comprises an indication that the new patient is at risk of (i) requiring chronic dialysis, (ii) requiring a kidney transplant, or (iii) experiencing a glomerular filtration rate of less than 10 ml/min/1.73m2 (recites field of use limitations describing a potential outcome associated with the prediction, see MPEP 2106.05(h)).
With regard to claim 10, this claim recites determining that the risk score output indicating CKD progression for the new patient indicates a predicted risk of the new patient experiencing CKD within a particular time period that satisfies one or more predicted risk threshold values (recites mental process steps of evaluating patient/user information);
and (i) generating a notification that the new patient may need an interventive kidney treatment; (ii) generating a recommendation of an interventive kidney treatment for the new patient based on the risk score indicating CKD progression for the new patient; (iii) generating a recommendation of a frequency of monitoring of CKD progression for the new patient based on the risk score output indicating CKD progression for the new patient; or (iv) administering an interventive kidney treatment to the new patient (recites mental process steps of generating/deciding information for the user/patient including recommendations of best options/treatments).
With regard to claim 11, this claim recites wherein the one or more predicted risk threshold values are based upon the particular time period associated with the risk score output indicating CKD progression for the new patient (recites mental process steps of using particular information as means to perform evaluation and judgments of the input data).
With regard to claim 12, this claim recites wherein the recommendation of the interventive kidney treatment or the recommendation of the frequency of monitoring of CKD progression is further based upon at least some of the second set of medical laboratory data associated with the new patient (recites mental process steps of evaluating particular information in order to make mental decisions/determinations/judgments).
With regard to claim 13, this claim recites wherein the interventive kidney treatment comprises one or more of: renin-angiotensin-aldosterone system (RAAS) inhibition, blood pressure control, sodium-glucose cotransporter-2 (SGLT2) inhibitor medication, mineralocorticoid receptor antagonists (MRAs) therapy, or preparation for nephrology consultation, home dialysis, dialysis access, or kidney transplant (recites field of use limitations describing particular treatment options, see MPEP 2106.05(h)).
With regard to claim 14, this claim recites wherein the first set of medical laboratory data comprises one or more imputed values in place of missing values (recites mental process steps of assuming/inferring a value).
With regard to claim 15, this claim recites wherein the first set of medical laboratory data indicates, with a degree of value imputation of 30% or less, eGFR, urine ACR, urea, potassium, hemoglobin, platelet count, albumin, calcium, glucose, bilirubin, sodium, bicarbonate, and GGT (recites field of use limitations describing the particular data that is being used, see MPEP 2106.05(h)).
With regard to claims 16 and 18, these claims are substantially similar to claim 1 and are rejected for similar reasons as discussed above.
With regard to claim 17, this claim is substantially similar to claim 3 and is rejected for similar reasons as discussed above.
With regard to claim 19, this is substantially similar to claim 3 and is rejected for similar reasons as discussed above.
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.
Claims 1-6, 8-13, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Cha et al [WO 2020/006571 A1] (from IDS) in view of Naylor et al [US 2010/0076787 A1].
With regard to claim 1, Cha teaches a method, comprising: accessing a machine learning model configured to generate risk score output indicating chronic kidney disease (CKD) progression (see page 6, lines 26-28; page 8, lines 8-13; the system can employ/access machine learning models to make predictions associated with CKD including calculation of risk scores),
wherein the machine learning model is generated by applying a training dataset to an untrained machine learning model to configure model parameters of the machine learning model for processing put data to generate the risk output (see page 8, lines 8-13; page 21, lines 13-19; page 52, lines 4-6 & 13-15; training data based on features from patient information can be used to train the machine learning model and generate a CKD progression prediction/risk),
wherein the training data set comprises: (i) a first set of medical laboratory data associated with a plurality of patients, (ii) an age of each patient included in the plurality of patients, (iii) a sex of each patient included in the plurality of patients (see page 17, lines 9-13; page 18, line 22 through col 19, line 2; demographic information and lab test information can be used as training data set),
and (iv) CKD clinical outcomes associated with the plurality of patients (see page 47, lines 8-12; page 21, lines 13 through page 24, line 25; and page 31, lines 1-18; the system has means to utilize CKD clinical outcomes as means part of the training/validation process for the machine learning model),
the first set of medical laboratory data indicating, for at least a combination of patients included in the plurality of patients: estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (ACR), urea, serum sodium, serum chloride,
applying an input dataset associated with a new patient to the machine learning model to cause the machine learning model to process the input dataset via the model parameters of the machine learning model to generate risk score output indicating CKD progression for the new patient, the input dataset comprising an age of the new patient, a sex of the new patient, and a second set of medical laboratory data indicating for the new patient (see page 25, lines 3-9; page 6, lines 21-25; page 2, lines 23-31; see Figure 1, box 145; the system can utilize the machine learning model with input data for new patients where various pieces of data can be used including patient demographics and various other diagnostic tests).
Cha teaches various laboratory data but does not appear to explicitly teach:
the first set of medical laboratory data indicating, for at least a combination of patients included in the plurality of patients: serum hemoglobin, glucose, alkaline phosphatase, aspartate aminotransferase (AST), alanine transaminase (ALT), bilirubin, gamma-glutamyl transferase (GGT), hematocrit, and platelet count;
the input dataset comprising: eGFR, urine ACR, urea, serum sodium, serum chloride, serum hemoglobin, serum potassium, glucose, serum albumin, alkaline phosphatase (ALKP), serum phosphate, serum bicarbonate, serum magnesium, serum calcium, AST, ALT, bilirubin, GGT, hematocrit, and platelet count.
Naylor teaches the first set of medical laboratory data indicating (see paragraph [0012]; the medical data can have various individual readings; see paragraphs [0194]-[0365] for entire list of examples and individual readings that can be measured and used), for at least a combination of patients included in the plurality of patients: serum hemoglobin (paragraphs [0231]-[0233]), glucose (see paragraphs [0336]-[0338]), alkaline phosphatase (see paragraphs [0204]-[0206]), aspartate aminotransferase (AST) (see paragraphs [0213]-[0215]), alanine transaminase (ALT) (see paragraphs [0195]-[0197]), bilirubin (see paragraphs [0216]-[0218]), gamma-glutamyl transferase (GGT) (see paragraphs [0330]-[0332]), hematocrit (see paragraphs [0234]-[0236]), and platelet count (see paragraphs [0243]-[0245]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the feature data that is used for training and making predictions as taught by Cha by including other feature data from various medical lab and test results as taught by Naylor in order to provide more relevant data so that the models can have more information that help identify amount of risk of the disease so that the trained model can be more accurate when being used since accuracy of medical diagnoses is a high-priority for both doctors (and other medical personnel) and the respective patients.
Cha in view of Naylor teach the input dataset comprising: eGFR, urine ACR, urea, serum sodium, serum chloride, serum hemoglobin, serum potassium, glucose, serum albumin, alkaline phosphatase (ALKP), serum phosphate, serum bicarbonate, serum magnesium, serum calcium, AST, ALT, bilirubin, GGT, hematocrit, and platelet count (see Cha, page 25, lines 3-9; page 6, lines 21-25; page 2, lines 23-31; see Figure 1, box 145; page 36, lines 1-16; the system can utilize the machine learning model with input data for new patients where various pieces of data can be used from the patient’s records to determine their risk score; see Cha page 4, lines 1-7; page 18, line 22 through col 19, line 2; for examples of the input data that can be utilized to make a prediction or determine a risk score and also see Naylor, paragraphs [0194]-[0365] for entire list of examples and individual readings that can be measured and used including serum hemoglobin (paragraphs [0231]-[0233]), glucose (see paragraphs [0336]-[0338]), alkaline phosphatase (see paragraphs [0204]-[0206]), aspartate aminotransferase (AST) (see paragraphs [0213]-[0215]), alanine transaminase (ALT) (see paragraphs [0195]-[0197]), bilirubin (see paragraphs [0216]-[0218]), gamma-glutamyl transferase (GGT) (see paragraphs [0330]-[0332]), hematocrit (see paragraphs [0234]-[0236]), and platelet count (see paragraphs [0243]-[0245]).
With regard to claim 2, Cha in view of Naylor teach wherein the new patient is not associated with a CKD stage of G3 or later (see Cha, page 51, lines 7-12; the patients did not have any previous kidney disease diagnoses).
With regard to claim 3, Cha in view of Naylor teach wherein the machine learning model comprises a random survival forest model (see Cha, page 51, lines 7-12; random forest ML models were employed to predict risk).
With regard to claim 4, Cha in view of Naylor teach wherein the risk score output indicating CKD progression for the new patient indicates a risk of experiencing CKD progression within a particular amount of time from a time period associated with the input dataset for the new patient (see Cha, page 51, lines 17-18; the system can make a prediction of patients who would experience, i.e. have a risk of experiencing, a decline over a particular time period).
With regard to claim 5, Cha in view of Naylor teach wherein the particular amount of time is provided as input to the machine learning model for generating the risk score output indicating CKD progression for the new patient (see Cha, page 51, line 17-18; page 21, lines 21-31; the system can make predictions based on a desired time period).
With regard to claim 6, Cha in view of Naylor teach wherein the particular amount of time comprises 2 years or 5 years (see Cha, page 51, line 17-18; the system can make predictions based on a desired time period).
With regard to claim 8, Cha in view of Naylor teach wherein the risk score output indicating CKD progression for the new patient indicates a risk of the new patient experiencing kidney failure or a 40% or greater decline of the eGFR for the new patient (see Cha, page 31, lines 9-15; the system can determine CKD progression based on a 40% decline).
With regard to claim 9, Cha in view of Naylor teach wherein the risk of the new patient experiencing kidney failure comprises an indication that the new patient is at risk of (i) requiring chronic dialysis, (ii) requiring a kidney transplant, or (iii) experiencing a glomerular filtration rate of less than 10 ml/min/1.73m2 (see Cha, page 8, lines 19-23; page 49, lines 2-9; the system can evaluate and predict risk of a patient of various events including kidney failure).
With regard to claim 10, Cha in view of Naylor teach determining that the risk score output indicating CKD progression for the new patient indicates a predicted risk of the new patient experiencing CKD within a particular time period that satisfies one or more predicted risk threshold values; and (i) generating a notification that the new patient may need an interventive kidney treatment; (ii) generating a recommendation of an interventive kidney treatment for the new patient based on the risk score output indicating CKD progression for the new patient; (iii) generating a recommendation of a frequency of monitoring of CKD progression for the new patient based on the risk score output indicating CKD progression for the new patient; or (iv) administering an interventive kidney treatment to the new patient (see Cha, page 4, lines 8-24; see page 6, lines 26-28; the system can make a risk score for the patient over a time period and be able to determine a treatment recommendation and transmit a notification of the treatment recommendation).
With regard to claim 11, Cha in view of Naylor teach wherein the one or more predicted risk threshold values are based upon the particular time period associated with the risk score output indicating CKD progression for the new patient (see Cha, page 25, line 30 through page 26, line 2; the threshold values can be based or associated with a time period).
With regard to claim 12, Cha in view of Naylor teach wherein the recommendation of the interventive kidney treatment or the recommendation of the frequency of monitoring of CKD progression is further based upon at least some of the second set of medical laboratory data associated with the new patient (see Cha, page 25, lines 3-12; the patient information or second data records are utilized to determine the risk score for the patient and what workflow actions should be performed including providing any notifications of recommendations).
With regard to claim 13, Cha in view of Naylor teach wherein the interventive kidney treatment comprises one or more of: renin-angiotensin-aldosterone system (RAAS) inhibition, blood pressure control, sodium-glucose cotransporter-2 (SGLT2) inhibitor medication, mineralocorticoid receptor antagonists (MRAs) therapy, or preparation for nephrology consultation, home dialysis, dialysis access, or kidney transplant (see Cha, page 26, lines 18-21; Figure 2; various treatment options are available).
With regard to claim 16, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above.
With regard to claim 17, this claim is substantially similar to claim 3 and is rejected for similar reasons as discussed above.
With regard to claim 18, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above.
With regard to claim 19, this claim is substantially similar to claim 3 and is rejected for similar reasons as discussed above.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Cha et al [WO 2020/006571 A1] (from IDS) in view of Naylor et al [US 2010/0076787 A1] in further view of Barasch et al [US 2010/0233740 A1].
With regard to claim 7, Cha in view of Naylor teach all the claim limitations of claim 1.
Cha in view of Naylor do not appear to explicitly teach wherein the urine ACR for one or more of the plurality of patients or the new patient is converted from a urine protein-to-creatinine test or a urine dipstick test.
Barasch teaches wherein the urine ACR for one or more of the plurality of patients or the new patient is converted from a urine protein-to-creatinine test or a urine dipstick test (see paragraph [0074]; standard testing methods including usage of a dipstick can be used to determine particular feature’s values such as urine albumin-to-creatinine ratio).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the testing and data collection process as taught by Cha in view of Naylor by utilizing standard laboratory tests and urine dipstick methods to measure/acquire feature values as taught by Barasch in order to utilize standard and widely-used methods of data acquisition that ensues that the acquired samples are retrieved for analysis.
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Cha et al [WO 2020/006571 A1] (from IDS) in view of Naylor et al [US 2010/0076787 A1] in further view of Hsich et al, Identifying Important Risk Factors for Survival in Systolic Heart Failure Patients Using Random Survival Forests (from IDS).
With regard to claim 14, Cha in view of Naylor teach all the claim limitations of claim 1.
Cha in view of Naylor do not appear to explicitly teach wherein the first set of medical laboratory data comprises one or more imputed values in place of missing values.
Hsich teaches wherein the first set of medical laboratory data comprises one or more imputed values in place of missing values (see page 2, last paragraph through the top paragraph on page 3; the system could use informed imputation to fill in missing values).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the testing and data collection process as taught by Cha in view of Naylor by incorporating imputation as taught by Hsich in order to ensure that data sets are complete and provide reasonable data values so that missing data from datasets doesn’t skew the analysis via having too few datasets with values to consider for initial training and potentially giving more undue weight to the non-missing features thus helping the prediction process of the machine learning model to be as informed and accurate as possible so that the most accurate predictions can be given to a patient.
With regard to claim 15, Cha in view of Naylor in further view of Hsich teach wherein the first set of medical laboratory data indicates, with a degree of value imputation of 30% or less, eGFR, urine ACR, urea, potassium, hemoglobin, platelet count, albumin, calcium, glucose, bilirubin, sodium, bicarbonate, and GGT (see Hsich, see page 2, last paragraph through the top paragraph on page 3; the system could use informed imputation to fill in missing values, e.g. 10%; see Naylor, paragraphs [0194]-[0365] for entire list of examples and individual readings that can be measured and used, or have their respective values be imputed).
Claims 1-13 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Cha et al [WO 2020/006571 A1] (from IDS) in view of Jain et al [US 2018/0055885 A1].
With regard to claim 1, Cha teaches a method, comprising: accessing a machine learning model configured to generate risk score output indicating chronic kidney disease (CKD) progression (see page 6, lines 26-28; page 8, lines 8-13; the system can employ/access machine learning models to make predictions associated with CKD including calculation of risk scores),
wherein the machine learning model is generated by applying a training dataset to an untrained machine learning model to configure model parameters of the machine learning model for processing put data to generate the risk output (see page 8, lines 8-13; page 21, lines 13-19; page 52, lines 4-6 & 13-15; training data based on features from patient information can be used to train the machine learning model and generate a CKD progression prediction/risk),
wherein the training data set comprises: (i) a first set of medical laboratory data associated with a plurality of patients, (ii) an age of each patient included in the plurality of patients, (iii) a sex of each patient included in the plurality of patients (see page 17, lines 9-13; page 18, line 22 through col 19, line 2; demographic information and lab test information can be used as training data set),
and (iv) CKD clinical outcomes associated with the plurality of patients (see page 47, lines 8-12; page 21, lines 13 through page 24, line 25; and page 31, lines 1-18; the system has means to utilize CKD clinical outcomes as means part of the training/validation process for the machine learning model),
the first set of medical laboratory data indicating, for at least a combination of patients included in the plurality of patients: estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (ACR), urea, serum sodium, serum chloride,
applying an input dataset associated with a new patient to the machine learning model to cause the machine learning model to process the input dataset via the model parameters of the machine learning model to generate risk score output indicating CKD progression for the new patient, the input dataset comprising an age of the new patient, a sex of the new patient, and a second set of medical laboratory data indicating for the new patient (see page 25, lines 3-9; page 6, lines 21-25; page 2, lines 23-31; see Figure 1, box 145; the system can utilize the machine learning model with input data for new patients where various pieces of data can be used including patient demographics and various other diagnostic tests).
Cha teaches various laboratory data but does not appear to explicitly teach:
the first set of medical laboratory data indicating, for at least a combination of patients included in the plurality of patients: serum hemoglobin, glucose, alkaline phosphatase, aspartate aminotransferase (AST), alanine transaminase (ALT), bilirubin, gamma-glutamyl transferase (GGT), hematocrit, and platelet count;
the input dataset comprising: eGFR, urine ACR, urea, serum sodium, serum hemoglobin, serum potassium, glucose, serum albumin, alkaline phosphatase (ALKP), serum phosphate, serum bicarbonate, serum magnesium, serum calcium, AST, ALT, bilirubin, GGT, hematocrit, and platelet count.
Jain teaches the first set of medical laboratory data indicating, for at least a combination of patients included in the plurality of patients: serum hemoglobin, glucose, alkaline phosphatase, aspartate aminotransferase (AST), alanine transaminase (ALT), bilirubin, gamma-glutamyl transferase (GGT), hematocrit, and platelet count (see Table 8 in paragraphs [0420]-[0427] and [0134]; various parameters/variables that are considered and collected/measured from laboratory assessments/tests).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the feature data that is used for training and making predictions as taught by Cha by including other feature data from various medical lab and test results as taught by Jain in order to provide more relevant data so that the models can have more information that help identify amount of risk of the disease so that the trained model can be more accurate when being used since accuracy of medical diagnoses is a high-priority for both doctors (and other medical personnel) and the respective patients.
Cha in view of Jain teach the input dataset comprising: eGFR, urine ACR, urea, serum sodium, serum chloride, serum hemoglobin, serum potassium, glucose, serum albumin, alkaline phosphatase (ALKP), serum phosphate, serum bicarbonate, serum magnesium, serum calcium, AST, ALT, bilirubin, GGT, hematocrit, and platelet count (see Cha, page 25, lines 3-9; page 6, lines 21-25; page 2, lines 23-31; see Figure 1, box 145; page 36, lines 1-16; the system can utilize the machine learning model with input data for new patients where various pieces of data can be used from the patient’s records to determine their risk score; see Cha page 4, lines 1-7; page 18, line 22 through col 19, line 2; for examples of the input data that can be utilized to make a prediction or determine a risk score and also see Jain, paragraphs [0420]-[0427] and [0134]).
With regard to claim 2, Cha in view of Jain teach wherein the new patient is not associated with a CKD stage of G3 or later (see Cha, page 51, lines 7-12; the patients did not have any previous kidney disease diagnoses).
With regard to claim 3, Cha in view of Jain teach wherein the machine learning model comprises a random survival forest model (see Cha, page 51, lines 7-12; random forest ML models were employed to predict risk).
With regard to claim 4, Cha in view of Jain teach wherein the risk score output indicating CKD progression for the new patient indicates a risk of experiencing CKD progression within a particular amount of time from a time period associated with the input dataset for the new patient (see Cha, page 51, lines 17-18; the system can make a prediction of patients who would experience, i.e. have a risk of experiencing, a decline over a particular time period).
With regard to claim 5, Cha in view of Jain teach wherein the particular amount of time is provided as input to the machine learning model for generating the risk score output indicating CKD progression for the new patient (see Cha, page 51, line 17-18; page 21, lines 21-31; the system can make predictions based on a desired time period).
With regard to claim 6, Cha in view of Jain teach wherein the particular amount of time comprises 2 years or 5 years (see Cha, page 51, line 17-18; the system can make predictions based on a desired time period).
With regard to claim 7, Cha in view of Jain teach wherein the urine ACR for one or more of the plurality of patients or the new patient is converted from a urine protein-to -creatinine test or a urine dipstick test (see Jain, paragraph [0244]; urine dipstick test can be used as means to collect the measurements).
With regard to claim 8, Cha in view of Jain teach wherein the risk score output indicating CKD progression for the new patient indicates a risk of the new patient experiencing kidney failure or a 40% or greater decline of the eGFR for the new patient (see Cha, page 31, lines 9-15; the system can determine CKD progression based on a 40% decline).
With regard to claim 9, Cha in view of Jain teach wherein the risk of the new patient experiencing kidney failure comprises an indication that the new patient is at risk of (i) requiring chronic dialysis, (ii) requiring a kidney transplant, or (iii) experiencing a glomerular filtration rate of less than 10 ml/min/1.73m2 (see Cha, page 8, lines 19-23; page 49, lines 2-9; the system can evaluate and predict risk of a patient of various events including kidney failure).
With regard to claim 10, Cha in view of Jain teach determining that the risk score output indicating CKD progression for the new patient indicates a predicted risk of the new patient experiencing CKD within a particular time period that satisfies one or more predicted risk threshold values; and (i) generating a notification that the new patient may need an interventive kidney treatment; (ii) generating a recommendation of an interventive kidney treatment for the new patient based on the risk score output indicating CKD progression for the new patient; (iii) generating a recommendation of a frequency of monitoring of CKD progression for the new patient based on the risk score output indicating CKD progression for the new patient; or (iv) administering an interventive kidney treatment to the new patient (see Cha, page 4, lines 8-24; see page 6, lines 26-28; the system can make a risk score for the patient over a time period and be able to determine a treatment recommendation and transmit a notification of the treatment recommendation).
With regard to claim 11, Cha in view of Jain teach wherein the one or more predicted risk threshold values are based upon the particular time period associated with the risk score output indicating CKD progression for the new patient (see Cha, page 25, line 30 through page 26, line 2; the threshold values can be based or associated with a time period).
With regard to claim 12, Cha in view of Jain teach wherein the recommendation of the interventive kidney treatment or the recommendation of the frequency of monitoring of CKD progression is further based upon at least some of the second set of medical laboratory data associated with the new patient (see Cha, page 25, lines 3-12; the patient information or second data records are utilized to determine the risk score for the patient and what workflow actions should be performed including providing any notifications of recommendations).
With regard to claim 13, Cha in view of Jain teach wherein the interventive kidney treatment comprises one or more of: renin-angiotensin-aldosterone system (RAAS) inhibition, blood pressure control, sodium-glucose cotransporter-2 (SGLT2) inhibitor medication, mineralocorticoid receptor antagonists (MRAs) therapy, or preparation for nephrology consultation, home dialysis, dialysis access, or kidney transplant (see Cha, page 26, lines 18-21; Figure 2; various treatment options are available).
With regard to claim 16, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above.
With regard to claim 17, this claim is substantially similar to claim 3 and is rejected for similar reasons as discussed above.
With regard to claim 18, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above.
With regard to claim 19, this claim is substantially similar to claim 3 and is rejected for similar reasons as discussed above.
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Cha et al [WO 2020/006571 A1] (from IDS) in view of Jain et al [US 2018/0055885 A1] in further view of Hsich et al, Identifying Important Risk Factors for Survival in Systolic Heart Failure Patients Using Random Survival Forests (from IDS).
With regard to claim 14, Cha in view of Jain teach all the claim limitations of claim 1.
Cha in view of Jain do not appear to explicitly teach wherein the first set of medical laboratory data comprises one or more imputed values in place of missing values.
Hsich teaches wherein the first set of medical laboratory data comprises one or more imputed values in place of missing values (see page 2, last paragraph through the top paragraph on page 3; the system could use informed imputation to fill in missing values).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the testing and data collection process as taught by Cha in view of Jain by incorporating imputation as taught by Hsich in order to ensure that data sets are complete and provide reasonable data values so that missing data from datasets doesn’t skew the analysis via having too few datasets with values to consider for initial training and potentially giving more undue weight to the non-missing features thus helping the prediction process of the machine learning model to be as informed and accurate as possible so that the most accurate predictions can be given to a patient.
With regard to claim 15, Cha in view of Jain in further view of Hsich teach wherein the first set of medical laboratory data indicates, with a degree of value imputation of 30% or less, eGFR, urine ACR, urea, potassium, hemoglobin, platelet count, albumin, calcium, glucose, bilirubin, sodium, bicarbonate, and GGT (see Hsich, see page 2, last paragraph through the top paragraph on page 3; the system could use informed imputation to fill in missing values, e.g. 10%; see Jain, paragraphs [0420]-[0427] and [0134] for entire list of examples and individual readings that can be measured and used, or have their respective values be imputed).
Declaration Under 37 C.F.R. § 1.132
The Declaration states at Items 4-9 that the Cha reference doesn’t teach certain predictive features being used as predictive inputs and that the Naylor reference discusses receiving medical data readings but in a manner unrelated to predicting renal function decline nor provide any basis for incorporating them into renal function decline. The Declaration continues by stating that, as such, a person of ordinary skill in the art would not have been motivated to incorporate those variables in Naylor into Cha’s model, which none of the measurements were recognized as predictors of renal function decline.
The Examiner notes that applicant’s Declaration alleges at what someone would do and would not know with no evidence to support their claims that the respective well-known laboratory measurement data would never be considered by one of ordinary skill in the art. Per MPEP 716.01(c)(II) that arguments presented by the applicant cannot take the place of evidence in the record. Additionally, per MPEP 716.01(c)(III), “[i] n assessing the probative value of an expert opinion, the examiner must consider the nature of the matter sought to be established, the strength of any opposing evidence, the interest of the expert in the outcome of the case, and the presence or absence of factual support for the expert’s opinion”. Since the expert is the actual inventor, the interest of the expert in the outcome of the case is substantial. Although the inventor is an expert per their Declaration, the Declaration did not recite nor provide any factual support for their statements/opinions. As illustrated above, upon further search and consideration, a new reference was found to provide evidence of the various variables that are being examined in association with the treatment of chronic kidney disease (Jain et al [US 2018/0055885 A1]) with illustrates at Table 8 in paragraphs [0420]-[0427] and [0134] various parameters/variables that are considered and collected/measured from laboratory assessments/tests. As such, it illustrates that the various variables were considered, in some capacity, when evaluating chronic kidney disease. Therefore, applicant’s Declaration is not persuasive and the respective rejections still stand.
Response to Arguments
Applicant's arguments (see the third paragraph on page 8 through the third from last paragraph on page 9) have been fully considered but they are not persuasive. The applicant argues that claims that require specific machine-implemented processing that cannot practically be performed in the human mind do not recite a mental process (see last two paragraphs on page 8) and that the claim limitations cannot reasonably be interpreted as a human “making a prediction” or “evaluating data to form a decision” since they recite a trained machine learning model generated by configuring model parameters through a multi-patient training dataset that includes specific laboratory features and CKD clinical outcomes and the subsequent processing of new patient input data through via those configured parameters to generate a risk score. Therefore, applicant concludes that, when properly construed, the claims cannot be properly characterized as reciting a judicial exception. The Examiner respectfully disagrees. As indicated in the 35 USC 101 rejections, at least the last limitation recites using the model to generate a risk score of a patient, which relates to an abstract idea of evaluating/analyzing patient information which amounts to both certain methods of organizing human activity (doctor/patient relationship/interactions) and mental process steps (analyzing medical data to form a judgement/decision). Although applicant indicates a specifically defined dataset, the claims merely list the data with no details regarding how that particular data is utilized (or utilized differently from prior art) and thus is non-functional descriptive material since it is merely describing the field of use of the judicial exception and the intended meaning of the data that is being utilized. Additionally, applicant indicates that the claims recite a specific machine-implemented process; however, no details were provided to illustrate the specific machine learning process, which, as discussed in the 35 USC 101 rejections above indicated, the claims merely indicate “a machine learning model” that went from untrained to trained with no details on that specific process. The recitation of “machine learning model” in of itself is at a high-level of generality and does not amount to being a specific machine-implemented process as applicant alleges. Therefore, applicant’s arguments are not persuasive and the 35 USC 101 rejections still stand.
Applicant's arguments (see the last two paragraphs on page 9 through the second paragraph on page 11) have been fully considered but they are not persuasive. The applicant argues that (a) the claims reflect an improvement to the technical field of clinical risk prediction for chronic kidney disease which is achieved by configuring machine learning model parameters using a specifically recited combination of training features that generate CKD progression risk score outputs. The applicant points to the 132 Declaration as support for the technical advance in how machine learning models are trained and applied for CKD risk prediction (see last two paragraphs on 9 through second to last paragraph on page 10); (b) each independent claim is integrated into a practical application because they involve a particular machine, such as a “machine learning model…generated by applying a training dataset to an untrained machine learning model” (see last paragraph on page 10 through second paragraph on page 11) where the specific set of features that cannot be interpreted as corresponding to a generic computer implementation. The Examiner respectfully disagrees.
With regard to applicant’s arguments regarding an improvement to the functioning of a computer or to any other technology or technical field, the Examiner notes that, per MPEP 2106.05(a), that “[a]n important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107.” (emphasis added). Additionally, it is important to note that “the judicial exception alone cannot provide the improvement” and that “the claim reflects the asserted improvement”. The Cha reference provided examples of exemplary lab tests and associated lab test variables but did not recite that those listed variables were limiting. Additionally, the claims are recited in a broad manner indicating a variety of input data is provided; however, the input data is recited as merely existing and being used/input into the system. The Examiner notes that, similar to a multiplexor in circuit design, multiple inputs can be provided but not all necessarily receive the same weight in consideration, or even any weight at all. In other words, the system may disregard particular input (i.e. little to no weight) and merely indicating that data exists doesn’t rise to the level of covering a particular solution to the problem or a particular way to achieve the desired outcome but rather merely claiming the idea of a solution using a wide variety of potential variables. Therefore, applicant’s arguments are not persuasive.
Applicant's arguments (see the last two paragraphs on page 11 through the second paragraph on page 11) have been fully considered but they are not persuasive. The applicant argues that the claims include a specific limitation other than what is well-understood, routine, and conventional in the field including configuration of machine learning parameters and generating risk scores by processing patient data with those parameters including particular data variables that are not well-understood, routine, and conventional. The Examiner respectfully disagrees. As noted in the 35 USC 101 rejections, the claims illustrate widely-known medical variables which are known and can be readily collected in a conventional and well-known manner. With regards to its application with a machine learning model, as noted above, merely listing a series of variables that are available to be analyzed recites the idea of a solution and does not provide any specific solution to the problem and generally recites field of use limitations describing the particular data that is used with their machine learning model. The applicant provided footnotes with regard to pages 24 and 25; where the respective previous remarks indicated that applicant’s specification recited 22-variable random forest model with the claims reciting the specific combination of data that is used to facilitate this improvement. As noted in the previous Office Action, the specification recites various features in greater detail while the claims recite the concept/idea of a solution by using a list of data without any specifics of how that data is utilized or impacts the decision/score generation process. With regard to the comments of claim 5 about not being separately addressed, the Examiner reviewed the Office Action and notes that claim 5 was separately addressed.
Applicant's arguments (see the first paragraph on page 12 through the second to last paragraph on page 13) have been fully considered but they are not persuasive. The applicant argues the Naylor reference is not analogous art since it isn’t pertinent to the field of inventors endeavor or reasonably pertinent to the problem. The Examiner respectfully disagrees. In response to applicant's argument that Naylor is nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, as noted by applicant, the Cha reference relates to the field of endeavor with discussion of non-limiting variables and tests that are considered but rather other data can be considered too ((see Cha, page 2, lines 23-31; page 4, lines 1-13; page 9, lines 15-22; page 18, line 27 through page 19, line 7). Thus Cha illustrates that "any number of features relating to lab tests information" can be used/employed where Cha provides examples of the features with Naylor illustrating other features that relate to the human body that can be considered. As noted in the 35 USC 103 rejections, Naylor would be considered analogous art since Naylor relates to the acquisition of particular medical data. As noted in the Office Action, Cha did not limit itself to only a finite set of data to only ever be considered but allowed for consideration of other data, where, someone of ordinary skill in the art when viewing Cha’s teachings, that wanted other medical data to consider, even if for statistical analysis to see if any correlation exists, would look at other medical-related references to determine what variables and respective tests would and/or should be gathered/performed. As noted in the 35 USC 103 rejections, Naylor modifies Cha's teaching to illustrate the usage of additional test data that can be used/considered by Cha's system which seeks to use all relevant test data (see page 52, lines 13-18). Therefore, Naylor is in the field of inventor's endeavor in that Naylor provides various medical data that can be accessed and used for analysis, thus making Naylor reasonably pertinent to the problem of medical data analysis via acquiring medical data variables via various widely used and known medical tests.
Applicant's arguments (see the last paragraph on page 13 through the second to last paragraph on page 13) have been fully considered but they are not persuasive. The applicant argues that the reasoning for combining the references provided by the Examiner fails to identify any teaching, suggestion or rationale for selecting the specific laboratory features as recited in the independent claim since Cha provides no guidance that would have led a person of ordinary skill in the art to select the particular combination of laboratory features as recited. The Examiner respectfully disagrees. As noted in the 35 USC 103 rejections, the recited claim features are recited at a broad-level of generality that appear to mostly indicate that a computerized tool (machine learning model) can receive and analyze a lot of different variables limitations which relates to statistical analysis/classification. As discussed above, one of ordinary skill in the art would look to other medical references for identification of other variables and tests; with respect to the 35 USC 103 rejections, the particular process is taught by Cha with Naylor being used to illustrate that other known variables are known and can be used/analyzed (i.e. gathered via medical tests).
Applicant's arguments (see the third from last paragraph on page 14 through second paragraph on page 15) have been fully considered but they are not persuasive. The applicant discusses the Declaration and why the absence of particular variables in Cha’s reference means that Cha does not regard them as relevant predictors and thus the instant claims provide a difference from the cited prior art; including, that Naylor provides laboratory measurements of various data items unrelated to renal function decline. Thus, Naylor provides no teaching or suggestion to use those measurements in a CKD progression. The Examiner respectfully disagrees. As discussed above with regard to the Declaration discussion, the prior art references do not themselves have to recite the desire or teaching of having to consider other known variables in order for references to be considered by one of ordinary skill in the art. As illustrated by the Examiner, under broadest reasonable interpretation, the claims are recited at a high-level of generality where a medical record can be provided to the machine learning model; the examiner notes that medical records (e.g. a table with dates of exam/measurements and respective columns for variables with cells being measurements) could be sparse (i.e. no values or some default value). The machine learning model (an automated computer program) is recited as receiving this data to determine importance of particular variables in order to make a score/decision based on the input data; there is no discussion of what weight or how much weight is applied to the variables just as long as that data exists where the Examiner indicated in the combination that the medical record of patients can have a wide variety of medical tests and corresponding variables. If particular weights of variables or how they are determined, whether statically/dynamically per patient (e.g. certain variable being in some range of value causes the importance of some other subset of variables to be more important in the analysis) can help differentiate from the teachings of the prior art. However, due to the broad recitation, merely indicating that common data that can be acquired from well-known tests can be considered does not make the acquisition of such data non-obvious. Therefore, applicant’s arguments are not persuasive.
Applicant's arguments (see the fourth from last paragraph on page 15) have been fully considered but they are not persuasive. The applicant argues that the prior art do not teach the claim limitations since Cha merely indicates that renal outcomes may be evaluated over different timeframes and does not teach that the prediction horizon as an explicit input to the machine learning model. The Examiner respectfully disagrees. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. As illustrated in the 35 USC 103 rejections, Cha teaches being able to perform the analysis/prediction including being able to make predictions for 1, 2, and 5 years (see Cha, page 51, lines 17-18). The Examiner notes that the model received these parameters in order to be able to make predictions for those time periods. Additionally, as support, Cha at page 21 and lines 20-31 illustrates how the model is configured to form predictions for a time period. Therefore, since Cha teaches the claim limitation as discussed above, the applicant’s argument is not persuasive and the respective 35 USC 103 rejection still stands.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Fabian et al [US 2016/0180049 A1] teaches at paragraph [0042] that the medical test results for patients can include platelet count, hematocrit, and hemoglobin.
Berg et al [US 2014/0228296 A1] teaches at paragraphs [0017], [0006], and [p] generating a risk or determining disease severity based on a control sample associated with average values from the population so that the system can diagnosis or determine likelihood of developing kidney disease.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARC S SOMERS whose telephone number is (571)270-3567. The examiner can normally be reached M-F 11-8 EST.
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/MARC S SOMERS/Primary Examiner, Art Unit 2159 4/18/2026
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