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 .
Response to Arguments
Applicant’s arguments filed on Applicant’s arguments, filed on January 13, 2026 with respect to the rejections of claims 1-16 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the previous 103 rejections have been withdrawn. However, upon further search and consideration, a new ground(s) of rejections have been made as can be further seen below.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3 and 5-16 are rejected under 35 U.S.C. 101 because the claims are directed to a mental processes without significantly more.
All of claims 1-3 and 5-16 are directed to a monitoring system for managing a cardiac device, which could be performed by the human mind and/or by a human with a physical aid as with pen and paper.
Claim 1 recites a mental process including the steps such as:
Receive potassium data associated with the potassium measurements from the sensor electronics module.
Process the potassium data to determine one or more potassium metrics comprising a potassium clearance rate based on the potassium data.
Determine an indication of kidney function from the patient based on the one or more potassium metrics.
This judicial exception is not integrated into a practical application because the claims merely implement the mental process using generic processing technology and add insignificant extra-solution activity. Specifically, the step of:
“Receiv[ing] potassium data associated with the potassium measurements from the sensor electronics module…” Is considered a mental step as noted above, however, if this step was not a mental process, it would be considered as insignificant pre-solution activity of mere data gathering and processing, since it merely collects the data necessary to carry out the mental process.
Furthermore, merely carrying out mental steps using generic computing technology such as a "memory" and " one or more processors" is well established to not amount to an integration into a practical application under the § 101 analysis. See, e.g., MPEP ss 2106.04(a)(2)(III)(C) and 2106.04(d)(I) and 2106.05(f).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements recited in the claims are:
Continuous analyte sensor comprising a substrate, a working electrode and a reference electrode disposed on the substrate, wherein the continuous analyte sensor is a continuous potassium sensor, and wherein the continuous analyte sensor further comprises a continuous glucose sensor for collecting glucose measurements, wherein the kidney disease prediction is further based on the glucose measurements.
Sensor electronics module configured to collect and process potassium measurements and glucose data associated with glucose measurements.
Memory comprising executable instructions configured to receive potassium data to determine one or more potassium metrics comprising the potassium clearance rate based on the potassium data, and determining an indication of kidney function based on the potassium metric(s).
Processor(s) configured to receive non-analyte sensor data from a patient to generate the kidney disease prediction based on the non-analyte sensor data, wherein the non-analyte sensors include at least one of an insulin pump, an accelerometer, a temperature sensor, an electrocardiogram (ECG) sensor, a heart rate monitor, a blood pressure sensor, an impedance, or a respiratory sensor. Furthermore, the processor is configured to generate a kidney disease prediction using a model trained on historical population data from patients with varying stages of kidney disease.
The Examiner takes official notice that these are basic, generic components which are well-understood, routine and conventional in the medical diagnostic arts, and the claims here merely use them for their well-understood, routine and conventional functions. As such, those additional elements cannot be considered “significantly more” than the judicial exception in Step 2B of the § 101 analysis.
Regarding claim 2, the additional steps of: “……wherein the analyte measurements generated by the continuous analyte sensor correspond to an electromotive force at least in part based on a potential difference generated between the working electrode and the reference electrode.” Is considered insignificant pre-solution activity of mere data gathering and processing, since it merely collects the data necessary to carry out the mental process.
Regarding claim 3, the additional step of: “wherein: the continuous analyte sensor comprises a continuous potassium sensor.” Is considered insignificant pre-solution activity of mere data gathering and processing, since it merely collects data necessary to and carry out the mental process.
Regarding claim 5, the additional steps of: “ wherein the kidney disease prediction is indicative of at least one of:
a risk of future kidney disease in the patient; a current presence of kidney disease in the patient; a severity of kidney disease in the patient; or a level of improvement or deterioration of the kidney disease in the patient” Is reciting additional mental steps, therefore following the same analysis as previous discussed above.
Regarding claim 6, the additional steps of: “wherein the severity of kidney disease corresponds to a stage of chronic kidney disease.” Is reciting additional mental steps, therefore following the same analysis as previous discussed above.
Regarding claim 7, the additional steps of: “generating one or more recommendations for treatment or prevention of kidney disease based, at least in part, on the indication of kidney function.” Is reciting additional mental steps, therefore following the same analysis as previous discussed above.
Regarding claim 8, the additional step of: “wherein the one or more recommendations comprise at least one of: a lifestyle modification recommendation; a medication recommendation; an intervention recommendation; or a recommendation to seek additional diagnostic testing.” Is reciting additional mental steps, therefore following the same analysis as previous discussed above.
Regarding claim 9, the additional step of: “wherein the one or more recommendations comprise a recommendation to administer a kidney function challenge test.” Is reciting additional mental steps, therefore following the same analysis as previous discussed above.
Regarding claim 11, the additional steps of: “receiving glucose data associated with the glucose measurements from the sensor electronics module” is considered insignificant pre-solution activity of mere data gathering and processing, since it merely collects the data necessary to carry out the mental process.
Regarding claim 12, the additional steps of: “receiv[ing] non-analyte sensor data generated from the patient using the one or more non-analyte sensors, wherein the kidney disease prediction is generated based on the non-analyte sensor data.” Is considered insignificant pre-solution activity of mere data gathering and processing, since it merely collects the data necessary to carry out the mental process.
Regarding claim 13, the additional steps of: “wherein the one or more non-analyte sensors comprise at least one of: an insulin pump, an accelerometer, a temperature sensor, an electrocardiogram (ECG) sensor, a heart rate monitor, a blood pressure sensor, an impedance, or a respiratory sensor.” Is considered insignificant pre-solution activity of mere data gathering and processing, since it merely collects the data necessary to carry out the mental process.
Regarding claim 15, the additional steps of: “obtain[ing] at least one of demographic information, food consumption information, activity level information, medication information, health and sickness information, disease information, or kidney disease stage information related to the patient; and wherein the kidney disease prediction is generated based on at least one of the food consumption information, the activity level information, the medication information, the health and sickness information, disease information, or the kidney disease stage information related to the patient.” Is considered insignificant pre-solution activity, since it merely collects and evaluates the data necessary to carry out the mental process.
Regarding claim 10, the additional step of: “wherein the one or more recommendations comprise an alert or alarm indicating at least one of: an abnormal analyte level; an abnormal analyte rate of change; an abnormal analyte clearance rate; or an abnormal analyte variance.” Is considered insignificant post-solution activity since it merely outputs the result of the mental process using a generic output modality.
Regarding claim 14, the additional steps of: “wherein the kidney disease prediction is generated using a model trained based on population data including records of historical patients with varying stages of kidney disease…” amount to merely carrying out mental steps using generic computing technology such as a model trained on historical data, which is well established to not amount to an integration into a practical application under the § 101 analysis. See, e.g., MPEP ss 2106.04(a)(2)(III)(C) and 2106.04(d)(I) and 2106.05(f).
Regarding claim 16, the additional step of: “wherein the one or more processors are further configured to provide a kidney disease prediction based on the indication of kidney function.” Is considered insignificant post-solution activity, since it merely outputs the result of the mental process using a generic output modality.
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.
Claim(s) 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over US 2010/0179408 A1 to Kamath et al. (hereinafter “Kamath”) in view of US 2004/0267100 A1 to Faupel et al. (hereinafter “Faupel”).
Regarding claim 1, Kamath teaches:
A monitoring system (abstract, fig. 1, and para 0116), comprising:
a continuous analyte sensor (abstract and para 0089) configured to generate analyte measurements (data or signals) associated with analyte levels (glucose levels) in the interstitial fluid of a patient (para 0002, para 0011, para 0113: “ The term "analyte" as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a substance or chemical constituent in a biological fluid (for example, blood, interstitial fluid, cerebral spinal fluid, lymph fluid or urine) that can be analyzed” , para 0022, para 0126, para 0153-0154);
and a sensor electronics module (the sensor data transformation module or processor module) coupled to the continuous analyte sensor and configured to receive and process the analyte measurements (See fig. 17A, para 0017, para 0422, and para 0474, lines 1-7), a
memory comprising executable instructions/sensor information (para 0385 and para 0424 );
and one or more processors in data communication with the memory (para 0385 and para 0424) and configured to execute the executable instructions (para 0385 and para 0424),
but does not explicitly disclose wherein the continuous analyte sensor is configured to generate potassium measurements associated with potassium levels,
and wherein the sensor electronics module is coupled to the sensor and configured to receive and process potassium measurements, and wherein the one or more processors is configured to:
receive potassium data associated with the potassium measurements from the sensor electronics module;
process the potassium data to determine one or more potassium metrics comprising a potassium clearance rate based on the potassium data;
and determine an indication of kidney function for the patient based on the one or more potassium metrics.
However, Faupel teaches a system and method for monitoring an analyte in a fluid in order to monitor and treat a health condition (see abstract). The system and method (figs. 1 and 3) teach generating potassium measurements associated with potassium levels (para 0069),
and wherein the sensor/sensing device is configured to receive and process potassium measurements (para 0069, para 0084, and claim 6), and wherein the system is configured to:
receive potassium data associated with the potassium measurements from the sensor system (see fig. 3, para 0069, and para 0084);
process the potassium data (analyte potassium reading) to determine one or more potassium metrics comprising a potassium clearance rate (also known as renal excretion) based on the potassium data/potassium analyte measurement;
and determine an indication of kidney function for the patient based on the one or more potassium metrics (see para 0069: “If evaluating determines a level of potassium indicating one or more of these metabolic disorders, an agent or treatment effective for treating the disorder can be administered. Such agents and treatments include oral or intravenous potassium salt solutions, kidney dialysis, and the like.” – if the potassium level indicates inadequate renal exertion, the system determines an indication of the kidney function, and an agent or treatment is administered to the patient in order to improve their kidney function).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Kamath with the potassium detection and diagnostic system of Faupel to arrive at the claimed invention, since such modification would result in a reasonable expectation for success, due to the prior art showing successful implementation of the potassium clearance rate/renal excretion as an accurate indication of a patient kidney function in order to provide adequate treatment for the patient.
Regarding claim 2, Kamath as modified teaches the monitoring system of claim 1 (fig. 1), wherein the continuous analyte sensor comprises:
a substrate/electrode membrane domain (para 0165, para 0201, 0272, 0278, 0308, 0242, 0245, 0248-0249), a working electrode disposed on the substrate (see annotated fig. 5C below, para 0066, 0216, and para 0242-0243), a reference electrode disposed on the substrate (see fig. 4C, annotated fig. 5B-5C below, para 0216, and para 0242-0243), wherein the analyte measurements generated by the continuous analyte sensor (para 0081, 0089, and para 0116 ) correspond to an electromotive force/voltage at least in part based on a potential difference generated between the working electrode and the reference electrode (para 0119, 0183,and para 0381-para 0383). The potentiostat controls the electromotive force (voltage) based on the potential difference generated between the working and reference electrode while also measuring the current output between the working electrode and counter electrode, which ultimately produces the electrochemical reaction (analyte measurements), and a raw signal indicative of the concentration of glucose in the user’s body.
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Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Kamath in view of Faupel, and further in view of US 2018/0263539 A1 to Javey et al. (hereinafter “Javey”).
Regarding claim 3, Kamath as modified teaches the monitoring system of claim 1 (fig. 1), but does not disclose wherein:
the continuous analyte comprises a continuous potassium sensor.
However, Javey teaches a wearable sensing platform that includes sensors and circuits used to monitor a user’s physical state (abstract, para 0005, and para 0093), wherein the continuous analyte comprises a continuous potassium sensor (para 0005, 0044, 0051, 0054, 0084, para 0095, and claim 42), and the analyte measurements include potassium measurements/potassium ions (para 0046-0047). The potassium sensors are one of many sensors that are used to continuously monitor the patient’s state.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified teachings of Kamath with the continuous potassium analyte sensor of Javey to arrive at the claimed invention. Such combination would improve the system by properly detecting premature signs of a kidney issue occurring (as a result of dehydration or Hyperkalemia), ultimately preventing kidney damage/disease, death, and/or other serious long-term health issues from occurring.
Claims 5-6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kamath in view of Faupel, and further in view of US 2014/0316219 A1 to Paz et al. (hereinafter “Paz”) and US 2022/0093261 A1 to Noshay et al. (hereinafter “Noshay”).
Regarding claim 5, Kamath as modified teaches:
The monitoring system of claim 16 (fig. 1), but does not disclose wherein the kidney disease prediction is indicative of at least one of:
a risk of future kidney disease in the patient;
a current presence of kidney disease in the patient;
a severity of kidney disease in the patient;
or a level of improvement or deterioration of the kidney disease in the patient.
However, Paz teaches wherein the kidney disease prediction is indicative of a level of deterioration/ improvement of the kidney disease in the patient (monitoring of at least one dynamic trend, such as the kidney state, indicative of the body malfunction in the patient) (see abstract and para 0056).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified teachings of Kamath with the prediction system of Paz to arrive at the claimed invention. Such combination would improve the system by properly detecting premature signs of a kidney issue occurring, ultimately preventing additional kidney damage/disease, death, and/or additional serious long-term health issues from occurring.
Regarding claim 6, Kamath as modified teaches the monitoring system of claim 5, but does not disclose wherein the severity of kidney disease corresponds to a stage of chronic kidney disease.
However, Noshay teaches a chronic kidney disease (CKD) machine learning prediction system (see abstract, line 1). The system (fig. 1) estimates/predicts a patient’s CKD, and wherein the severity of the kidney disease corresponds to a stage of chronic kidney disease (see para 0017).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified teachings of Kamath with that of Noshay to arrive at the claimed invention, since such combination would improve the system by properly detecting more severe signs of additional kidney damage, ultimately ensuring urgent dialysis treatment can be provided in order to prevent death or additional serious long-term health issues from occurring in a patient.
Regarding claim 15, Kamath as modified teaches the monitoring system of claim 16 containing one or more processors (see para 0017) and containing a prediction algorithm (see para 0520), and wherein the one or more processor is further configured to:
obtain at least one of demographic information, food consumption information, activity level information, medication information, health and sickness information, disease information, or kidney disease stage information related to the patient (para 0431, lines 1-8), but does not explicitly disclose wherein the kidney disease prediction is generated based on at least one of the food consumption information, the activity level information, the medication information, the health and sickness information, disease information, or the kidney disease stage information related to the patient.
However, Noshay teaches wherein the kidney disease prediction is generated based on at least one of the food consumption information, the activity level information, the medication information, the health and sickness information, disease information, or the kidney disease stage information related to the patient (see abstract and para 0017, lines 1-17).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified teachings of Kamath with the teachings of Noshay to arrive at the claimed invention. Such combination would improve the system, since using disease information and/or kidney disease stage information related to the patient will allow the model to be trained more effectively for properly determining the next disease stage and properly treating the patient, ultimately preventing additional kidney damage/disease, death, and/or additional serious long-term health issues from occurring.
Claims 7-8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kamath in view of Faupel, and further in view of US 2003/0125983 A1 to Flack et al. (hereinafter “Flack”).
Regarding claim 7, Kamath as modified teaches:
The monitoring system of claim 1, but does not explicitly disclose wherein the system further comprises generating one or more recommendations for treatment or prevention of kidney disease based, at least in part, on the indication of kidney function.
However, Faupel teaches immediately administering an agent or dialysis therapy based on the potassium levels present in the renal excretion of a patient (see para 0069), but does not explicitly disclose providing recommendations for treatment or prevention of kidney disease based, at least in part, on the indication of kidney function.
However, Flack teaches a computer-implemented method for managing and evaluating a patient’s healthcare and kidney function (see abstract, lines 1-2). The system (figs. 1-2) provides one or more recommendations for treatment based on the kidney disease prediction (see abstract, claim 1, and claim 19).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified teachings of Kamath with the system of Faupel to arrive at the claimed invention. Such combination would improve the system by providing treatment recommendations that will ultimately help prevent additional health issues from occurring while ensuring a patient receives proper CKD treatment to prevent further illness or death in the patient as well.
Regarding claim 8, Kamath as modified teaches the monitoring system of claim 7, but does not explicitly disclose wherein the one or more recommendations comprise at least one of:
a lifestyle modification recommendation; a medication recommendation; an intervention recommendation; or a recommendation to seek additional diagnostic testing.
However, Flack teaches wherein the one or more recommendations comprises a recommendation to seek additional diagnostic testing, an intervention recommendation/ medical treatment recommendation, and a medication recommendation (figs. 21 and 23, para 0101, para 0105, and claim 1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified teachings of Kamath with the prediction and recommendations of Flack to arrive at the claimed invention. Such combination would improve the system by providing proper treatment recommendations that will ultimately help prevent additional health issues from occurring while ensuring a patient receives proper CKD treatment to prevent further illness or death in the patient as well.
Regarding claim 10, Kamath as modified teaches the monitoring system of claim 7, wherein the one or more recommendations comprise an alert or alarm indicating:
an abnormal analyte level/analyte levels that exceed a predetermined threshold (see para 0093).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Kamath in view Faupel and Flack, and further in view of WO 2020146745 A1 to Chiofolo et al. (hereinafter “Chiofolo”).
Regarding claim 9, Kamath as modified teaches the monitoring system of claim 7, but does not disclose wherein the one or more recommendations comprise a recommendation to administer a kidney function challenge test.
However, Chiofolo teaches a system and method for assessing kidney health (abstract, line 1). The system (figs. 1-2) teaches wherein one or more recommendations (following the evaluation of a kidney health score and confidence level) comprises administering a kidney function challenge test (such as a recommendation to conduct one or more diagnostic test for evaluating the kidney function ) (see para 0051, 00156, para 00186, and claim 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified teachings of Kamath with the kidney function challenge test of Chiofolo to arrive at the claimed invention. Such combination would improve the system by providing an additional treatment tests to further validate the current state of the kidneys in each patient, ultimately ensuring proper treatment is provided to the patient to prevent additional damage to the kidneys.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Kamath in view Faupel and Noshay, and further in view of US 2011/0245634 A1 to Ray et al. (hereinafter “Ray”).
Regarding claim 11, Kamath as modified teaches the monitoring system of claim 16 containing a continuous analyte glucose sensor, wherein the analyte measurements further include glucose measurements (see para 0081, 0174, 0178, and 0382), and wherein
the processor is further configured to receive glucose data associated with the glucose measurements from the sensor electronics module/input module (para 0017 and para 0422), and wherein the identification of kidney failure is further based on glucose data (para 0003-0005), but does not explicitly disclose wherein a kidney disease prediction is based on glucose data.
However, Ray teaches a method for analyzing an analyte distribution from continuous and quasi-continuous measurements (abstract). The system (fig. 21) discloses wherein a kidney disease prediction is based on glucose data (see fig. 21 and para 0126, first two sentences).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified teachings of Kamath with the teachings of Ray to arrive at the claimed invention. Such combination would improve the system by ensuring renal/kidney issues are properly predicted to prevent further kidney complications, since elevated glucose readings are highly correlated with kidney damage due to damaging the blood vessels in the kidneys that are needed for properly filtering out waste.
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Kamath in view of Faupel, Noshay, and Javey.
Regarding claim 12, Kamath as modified teaches the monitoring system of claim 16, further comprising: one or more non-analyte sensors/temperature sensors and ECG/heart rate sensors (see para 0431) containing a processor (para 0017), and wherein the processor (para 0062) is further configured to:
receive non-analyte sensor data (heart rate/ECG, accelerometer, or temperature sensor data) generated for the patient using the one or more non-analyte sensors (see para 0431), but does not explicitly disclose wherein the kidney disease prediction is generated based on the non-analyte sensor data.
However, Javey teaches wherein the kidney disease prediction (can be kidney disease as well as other diseases) is generated based on the non-analyte sensor data/temperature sensor data (see para 0077 and 0093).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified teachings of Kamath with the teachings of Javey to arrive at the claimed invention, since such combination would improve the system by providing more a more definitive prediction of kidney disease in a patient, ultimately allowing for better and more accurate kidney treatment for each patient.
Regarding claim 13, Kamath as modified teaches the monitoring system of claim 12, wherein the one or more non-analyte sensors comprise at least one of:
an insulin pump, an accelerometer, a temperature sensor, an electrocardiogram (ECG) sensor, a heart rate monitor, a blood pressure sensor, an impedance, or a respiratory sensor (para 0431).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Kamath in view of Faupel and Noshay, and further in view of US 2016/0357923 A1 to Dong et al. (hereinafter “Dong”).
Regarding claim 14, Kamath as modified teaches the monitoring system of claim 16, but does not disclose wherein the kidney disease prediction is generated using a model trained based on population data including records of historical patients with varying stages of kidney disease.
However, Noshay teaches wherein the kidney disease prediction is generated using a model trained based on population data including records of patients with varying stages of kidney disease/known CKD progression data (para 0009, 0011, para 0013), but does not explicitly disclose wherein the population data includes records of past/historical patients.
However, Dong teaches a dialysis predictive model used to predict the likelihood that a Chronic kidney disease (CKD) will cause end-stage kidney disease that requires dialysis (see title and abstract, lines 1-3). The predictive model (fig. 1) uses historical/past health related data from a plurality of sources for one or more patients, and applies multiple models to identify whether a patient with CKD is more likely to need dialysis based one model from a group of applied models (see claims 8 and 15).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified teachings of Kamath with the teachings of Noshay and Dong to arrive at the claimed invention. Such combination would improve the system, since the historical patient data with varying levels of Kidney disease will allow the model to be trained more effectively, ultimately providing more accurate diagnosis and prediction of the current stage of Kidney disease in each patient.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Kamath in view of Pan and Noshay.
Regarding claim 16, Kamath as modified teaches:
The monitoring system of claim 1, but does not disclose wherein the one or more processors are further configured to provide a kidney disease prediction based on the indication of kidney function.
However, Noshay teaches wherein the one or more processors are further configured to provide a kidney disease prediction based on the indication of kidney function (see abstract, para 0051: “The machine learning algorithms disclosed herein include dynamic, multifactorial predictive algorithms that are programmed to consider clinical, pharmacological, and extra-clinical factors that adversely impact kidney function….. In some instances, the predictions may be used for selecting a treatment plan, a dialysis treatment, and/or RRT.”, and para 0057: “The CKD management server 102 also includes an analytics processor 106 configured to apply patient characteristic data for a patient under analysis to the one or more predictive machine learning algorithms to assess or predict the patient's CKD progression probably, progression rate, and probably of needing urgent-start dialysis.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified teachings of Kamath with the teachings of Noshay to arrive at the claimed invention. Such modification would improve the system by providing more a more definitive prediction of kidney disease in a patient, ultimately allowing for better and more accurate kidney treatment for each patient.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Coe et al. (US 10, 290, 369 B2) teaches a method for a clinical laboratory-based disease program containing automated patient-specific device configured to recommend a course of action based on test result data.
Blanchard et al. (US 2019/0046112 A1) teaches a system and method for estimating the progression of chronic kidney of a patient and for providing clinical interventions/treatment.
Rebec et al. (US 20130303865 A1) teaches an end of stage renal disease monitoring system comprising an implantable sensor configured to sense a group of analytes (which includes potassium).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KARMEL J WEBSTER whose telephone number is (703)756-5960. The examiner can normally be reached Monday-Friday 7:30am-5:00pm.
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/K.J.W./Examiner, Art Unit 3792
/JOHN R DOWNEY/Primary Examiner, Art Unit 3792
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