DETAILED ACTION
Applicant's response, filed 13 August 2025, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Claim Status
Claims 1-20 have been cancelled.
Claims 21-40 are currently pending and under exam herein.
Terminal Disclaimer
The terminal disclaimer filed on 13 August 2025 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US Patent 10,978,176 has been reviewed and is NOT accepted. The Terminal Disclaimer form filed by Applicant is incomplete as it fails to include any information pertaining to the Application Number; filed date; owner; or prior patent number. Further, the Terminal Disclaimer is not signed or dated nor does it contain any further information. Applicant is requested to please re-submit the Terminal Disclaimer. No fee is further required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 21-40 remain rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Any newly recited portions herein are necessitated by claim amendment.
The instant rejection reflects the framework as outlined in the MPEP at 2106.04:
Framework with which to Evaluate Subject Matter Eligibility:
(1) Are the claims directed to a process, machine, manufacture or composition of matter;
(2A) Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
(2B) If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step (1) Evaluation
With respect to step (1): yes, the claims 21-40 are directed to a system for determining risk of renal function decline for a plurality of patients.
(2A)(1) Evaluation
With respect to step (2A)(1), the claims recite abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the (2A)(1) evaluation, the claims are found herein to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information).
The claims further include natural phenomena.
Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
Claim 21 steps to abstract ideas are as follows:
machine-learning system is configured to determine a weight for each of the plurality of features based on an importance of the respective feature in determining a probability that a patient will experience an outcome relating to a decline in renal function within a prediction time period; calculate a feature value for each feature based on the respective weight; determine…a current risk score based on the feature values calculated for the respective patient record…wherein operations for “determinations” and “calculation”, under their plain meaning and when viewed with respect to the broadest reasonable interpretation (BRI) are directed to mental operations. Save for the recitation of the computer implementation that includes machine learning herein, said steps could be performed mentally using pen and paper, as there is nothing in the claim that precludes said interpretation. Simply including “machine learning” without any steps beyond those words, does not remedy the interpretation, because as recited the machine learning is a generic operation on a generic computer. For example, there are no steps in the claim to actually training any machine learning model with particular parameters such that a machine is altered. Rather, the system merely uses machine learning to perform said abstract steps. As such, the steps are directed to a mental process.
Claim 38:
determine that the current risk score satisfies a workflow rule…determine a treatment recommendation…wherein given the plain meaning of “determine” one could mentally assess parameters and make an educated guess. Under its broadest reasonable interpretation consistent with the specification, the plain and ordinary meaning of this limitation requires an assessment using mental threshold determinations [0149].
The claims further recite a law of nature because they describes the naturally occurring relationship of patient “feature” information (laboratory results) with renal decline. As there are no bright lines between the types of judicial exceptions, and many of the concepts identified by the courts as exceptions can fall under several exceptions, MPEP 2106.04, subsection I instructs examiners to “identify... the claimed concept (the specific claim limitation(s) that the examiner believes may recite an exception) [that] aligns with at least one judicial exception.” While limitation of prediction (comparisons) can be categorized under several exceptions (a mathematical concept-type abstract idea, a mental process-type abstract idea, and a law of nature), it is adequate for an examiner to identify the limitation as falling under at least one judicial exception and to base further analysis on that identification. The remainder of this discussion is premised on the recited exception as an abstract idea. See MPEP 2106.04, subsection II.B.
As discussed above, the abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined herein to each cover performance either in the mind because they cover concepts performed in the mind that include mental predictions, save for implementation using a machine learning computing system. See MPEP 2106.04(a)(2), subsection II.
(2A)(2) Evaluation
Because the claims do recite judicial exceptions, direction under (2A)(2) provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application (MPEP 2106.04(d). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim is said to fail to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
Claim 1 recites the following additional elements:
plurality of data sources in communication with a network, the plurality of data sources storing patient information relating to each of a plurality of patients, the patient information for a respective patient comprising: a plurality of demographics associated with the patient comprising: a demographic relating to an age of the patient; and a demographic relating to a gender of the patient wherein the additional elements represents mere data gathering because all uses of the judicial exception require collection of patient demographic information. See MPEP 2106.05(g). As such, said steps are insignificant extra-solution activity.
a plurality of lab tests associated with the patient, each lab test of the plurality of lab tests associated with lab test information comprising a date, a variable and a value relating to the variable wherein said laboratory results are those from data gathering techniques. See MPEP 2106.05(g). As such, said steps are insignificant extra-solution activity.
ML system in communication with the network; computer in electrical communication with the plurality of data sources…; transmit at least one treatment recommendation… include parts of a computing network wherein said network operates the recited judicial exception only and is not a specific system. Further step to transmitting are akin to extra-solution activity whereby the data are provided without practical application herein.
Further with respect to the additional elements in the instant claims, those steps directed to data gathering perform functions of collecting the data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or on how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
Further steps herein directed to additional non-abstract elements of the above computing elements do not describe any specific computational steps by which the “computer parts” perform or carry out the abstract idea, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer/computer network which performs the functions that constitute the abstract idea. Machine learning systems are not defined beyond the generic machine learning system as no actual training of the system takes place, for example. Hence, these are mere instructions to apply the abstract idea using a computer or using a machine learning model, and therefore the claim does not integrate that abstract idea into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer. (see MPEP 2106.05(f)).
None of the recited dependent claims recite additional elements which would integrate a judicial exception into a practical application.
(2B) Evaluation
As such, the claims are lastly evaluated using the (2B) analysis, wherein it is determined that because the claims recite abstract ideas, and do not integrate that abstract ideas into a practical application, the claims also lack a specific inventive concept. Applicant is reminded that the judicial exception alone cannot provide the inventive concept or the practical application and that the identification of whether the additional elements amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements of data gathering described above do not rise to the level of significantly more than the judicial exception. As set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to additional elements in claim 21, as included in step 2A, prong two above, said claims were found to recite additional elements which are insignificant extra-solution activity that amounts to mere data gathering incidental to the operation of steps of the claim in (d)-(g). See MPEP 2106.05(g). Dependent claims 22-40 further include the various types of data elements and are also found to include the same.
Under 2B the claims are then further evaluated and said assessment takes into account whether or not said extra-solution activities are well-known, routine and conventional. The data gathering activities in limitation above are recited at a high level of generality and have been recognized by the courts as being routine laboratory techniques, for example, with respect to patient data. See, as example, Genetic Techs. v. Merial 34 LLC, 818 F.3d 1369, 1377 (Fed. Cir. 2016) (analyzing DNA to provide sequence information or to detect allelic variants is conventional in the art); MPEP 2106.05(d), subsection II. The specification otherwise only describes carrying out said methods by conventional techniques. See pages 16-28 describing routine laboratory techniques to perform said operations.
The claims to systems and computers and trained machine learning systems represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer. (see MPEP 2106.05(f)).
Under 2B the claims recite no details about a particulars of the system or model. The machine learning model is used to generally apply the abstract idea (i.e., perform the abstract calculations for assessing laboratory data) without placing any limitation on how the network structure operates. The claim omits any details as to how the machine learning system, for example, solves a technical problem and instead recites only the idea of a solution or outcome wherein the claim is limited by “transmit a notification of an updated patient record” which include steps that artificial intelligence models operationally are positioned to achieve in any environment, including that of the analysis using the data that is patient laboratory information as instantly claimed. See MPEP 2106.05(f). Therefore, the limitation represents no more than mere instructions to implement the abstract idea which is equivalent to adding the words “apply it” to the recited judicial exception. In addition, the claim confines the use of the judicial exception to the technological environment of a machine learning by generally linking the use of the judicial exception to the recited model. Therefore, this general model recitation does not integrate the judicial exception into a practical application. See MPEP 2106.05(h). Therefore, it can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to a particular field of use or a technological environment. Further the recited computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than an abstract idea (see MPEP 2106.05(b)I-III).
Consequently, for the reasons discussed above, the additional elements individually or in combination with the judicial exception do not provide an inventive concept; so, the claim as a whole does not amount to significantly more than a generic instruction to “apply” the judicial exception. (Step 2B: NO).
Response to Applicant’s Arguments
1. Applicant states that the claims have been amended to include, “determine a weight for each of a plurality of features based on an importance…” and further that the computer is “configured to transmit at least one treatment recommendation…”. Applicant includes that, “the independent claims, particularly in light of the above amendments, integrate any alleged abstract idea into a practical application and are therefore eligible. For example, the claims are directed to improving the functioning of machine learning systems for treating renal function decline. The claims recite, for example, a trained machine learning system configured to (1) determine a weight for each of a plurality of features based on an importance of the respective feature in determining a probability that a patient will experience an outcome relating to a decline in renal function within a prediction time period, (2) calculate a feature value for each feature based on the respective weight, and (3) determine, for each of the received patient records, a current risk score based on the feature values calculated for the respective patient record, the current risk score relating to the probability that the respective patient will experience the outcome within the prediction time period. Additionally, the steps performed by the machine learning system are specifically tied to treating renal function decline, as the output of the machine learning system is used to determine and transmit a treatment recommendation for renal function decline”.
It is respectfully submitted that this is not persuasive. The steps outlined above with respect to determine a weight…calculate a feature value…and determine a current risk score… are steps that recite judicial exceptions in the claims, as detailed above. Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.").
2. Applicant states, “the claims also do not merely recite a generic model that is applied to the alleged abstract ideas. Rather, the claims recite a specially configured and unconventional machine learning system for treating renal function decline. Thus, the claims are not so abstract and sweeping such that they would monopolize the alleged abstract ideas”.
It is respectfully submitted that this is not persuasive. As stated above, the claims fail to recite any particular method for “training” a machine learning system such that it affects the operation of the system. Rather, the machine learning of the instant claims provides the tool to perform said abstract idea herein. The system is “configured” to make determinations and calculate, which are abstract ideas here as there are no steps by which to perform said functions in the claim. Further, Applicant has not provided any evidence that such is an improvement over conventional machine learning systems. As such, the claims remain ineligible under 35 USC 101.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
1. Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 10,978,176. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims being examined are, for example, generic to the sub-genus claimed in said patent wherein the entire scope of the reference claims fall within the scope of claims 21-40 herein. The following chart exemplifies the anticipation under non-statutory double-patenting:
Instant claims 21-40
US Patent 10,978,176 (parent)
Equivalents
21. A system for determining a risk of renal function decline for a plurality of patients comprising:
a plurality of data sources in communication with a network, the plurality of data sources storing patient information relating to each of a plurality of patients, the patient information for a respective patient comprising:
a plurality of demographics associated with the patient comprising:
a demographic relating to an age of the patient; and a demographic relating to a gender of the patient; and a
plurality of lab tests associated with the patient, each lab test of the plurality of lab tests associated with lab test information comprising a date, a variable and a value relating to the variable;
a trained machine-learning system in communication with the network, wherein the trained machine-learning system has previously been trained with training data such that the system has identified important features from a plurality of potential features and has calculated and stored weights associated with such important features based on an importance of the respective feature in determining a probability that a patient will experience an outcome relating to a decline in renal function within a prediction time period, the important features comprising: a plurality of demographic features; and a plurality of lab test features; wherein the trained machine-learning system is configured to calculate, for each of a plurality of received patient records, a feature value for each of the important features based on patient information associated with the respective patient record and the stored weight associated with the respective important feature, and wherein the trained machine-learning system is configured to determine, for each of the received patient records, a current risk score based on the feature values calculated for the respective patient record, the current risk score relating to the probability that the respective patient will experience the outcome within the prediction time period; and a computer in electrical communication with the plurality of data sources and the trained machine-learning system via the network, the computer comprising a memory storing a plurality of patient records configured to be analyzed by the trained machine-learning system, each of the patient records storing patient information corresponding to a unique patient from the plurality of patients, the computer configured to continuously monitor each of the plurality of data sources and, upon determining that new patient information is available:
automatically ingest the new patient information; preprocess the new patient information in accordance with a centralized data schema to create preprocessed data records; correlate each of the preprocessed data records to the patient records stored in the memory to update the patient records with the correlated preprocessed data records; transmit the updated patient records to the machine-learning system; receive the current risk scores from the trained machine-learning system; store the current risk scores in the memory, each current risk score associated with the updated patient record to which it corresponds; and determine whether the current risk score associated with each of the updated patient records is greater than a predetermined threshold and, if so, transmit a notification comprising at least a portion of the respective updated patient record to one or more providers associated with the respective patient.
1. A method of determining and automatically updating a risk of renal function decline for a plurality of patients via a machine-learning system, the method comprising:
A. storing, in a memory of a computer, a plurality of patient records configured to be analyzed by a trained machine-learning system in communication with the computer via a network, each of the patient records storing patient information corresponding to a unique patient from a plurality of patients, the patient information comprising:
a plurality of demographics associated with the patient comprising:
a demographic relating to an age of the patient; and
a demographic relating to a gender of the patient;
a plurality of lab tests associated with the patient, each lab test of the plurality of lab tests associated with lab test information comprising a date, a variable and a value relating to the variable, the plurality of lab tests comprising:
a first set of lab tests, each associated with a biomarker variable relating to one of:
tumor necrosis factor receptor-1 (“TNFR1”), tumor necrosis factor receptor-2 (“TNFR2”), and kidney injury molecule-1 (“KIM1”);
a second set of lab tests comprising:
a first lab test associated with a first date and a first lab test variable; and
a second lab test associated with a second date that is different than the first date and a second lab test variable that is different than the first lab test variable,
wherein each lab test of the second set of lab tests is associated with a lab test variable relating to at least one of: estimated glomerular filtration rate (“eGFR”), urine albumin-creatinine-ratio (“UACR”), serum creatinine, blood urea nitrogen (“BUN”), serum sodium, serum potassium, serum chloride, serum bicarbonate, serum calcium, serum albumin, urine creatinine, urine albumin, urine microalbumin, urine protein, complete blood count (“CBC”) panel, liver function panel, lipid profile panel, a coagulation panel, magnesium, phosphorus, brain natriuretic peptide (“BNP”), hemoglobin A1 c (“HbA1c”), uric acid and endostatin;
B. transmitting, by the computer, to the trained machine-learning system, the patient records, wherein the trained machine-learning system has previously been trained with training data such that the system has identified important features from a plurality of potential features and has calculated and stored weights associated with such important features based on an importance of the respective feature in determining a probability that a patient will experience an outcome relating to a decline in renal function within a prediction time period, the important features comprising:
a plurality of demographic features, each relating to at least one demographic of
the plurality of demographics; and
a plurality of lab test features, each relating to at least one lab test of the plurality of lab tests, the plurality of lab test features comprising a feature relating to the first lab test variable, the second lab test variable, and a time period that includes both the first date and the second date;
wherein the trained machine-learning system is configured to calculate, for each of the transmitted patient records, a feature value for each of the important features based on the patient information associated with the respective patient record and the stored weight associated with the respective important feature, and
wherein the trained machine-learning system is configured to determine, for each of the transmitted patient records, a current risk score based on the feature values calculated for the respective patient record, the current risk score relating to the probability that the respective patient will experience the outcome within the prediction time period;
C. receiving, by the computer, from the trained machine-learning system, the current risk scores;
D. storing, by the computer, the current risk scores in the memory, each current risk score associated with the patient record to which it corresponds;
E. determining, by the computer, whether the current risk score associated with each of the patient records is greater than a predetermined threshold and, if so, transmitting a notification comprising at least a portion of the respective patient record to one or more providers associated with the respective patient; and
F. continuously determining, by the computer, for each of a plurality of data sources in communication with the computer via the network, the plurality of data sources comprising an electronic health records (“EHR”) system, whether new patient information is available and, if so:
automatically ingesting, by the computer, the new patient information;
preprocessing, by the computer, the new patient information in accordance with a centralized data schema to create preprocessed data records;
correlating, by the computer, each of the preprocessed data records to one of the patient records stored in the memory to update the patient records with the correlated preprocessed data records; and
repeating steps B-E for the updated patient records to thereby determine current risk scores and transmit notifications to providers when such current risk scores are greater than the predetermined threshold.
Claims 21 and claim 1 are directed to the system and method, respectively and wherein the method of claim 1 operates in a system environment and includes machine learning and fully is encompasses in the systems as instantly claimed.
Claim 23 herein: A system according to claim 21, wherein the plurality of lab tests comprises a lab test associated with a variable relating to at least one of: estimated glomerular filtration rate ("eGFR"), urine albumin-creatinine-ratio ("UACR"), serum creatinine, blood urea nitrogen ("BUN"), serum sodium, serum potassium, serum chloride, serum bicarbonate, serum calcium, serum albumin, urine creatinine, urine albumin, urine microalbumin, urine protein, complete blood count ("CBC"), liver function, lipid profile, a coagulation panel, magnesium, phosphorus, brain natriuretic peptide ("BNP"), hemoglobin Alc ("HbAlc"), uric acid, endostatin, tumor necrosis factor receptor-1 ("TNFR1"), tumor necrosis factor receptor-2 ("TNFR2"), and kidney injury molecule-1 ("KIMi").
Claim 24 herein: A system according to claim 21, wherein the plurality of lab tests comprises: a first set of lab tests, each associated with a biomarker variable relating to one of: tumor necrosis factor receptor-1 ("TNFR1"), tumor necrosis factor receptor-2 ("TNFR2"), and kidney injury molecule-1 ("KIM1"); and a second set of lab tests, each associated with a lab test variable relating to at least one of: estimated glomerular filtration rate ("eGFR"), urine albumin-creatinine-ratio ("UACR"), serum creatinine, blood urea nitrogen ("BUN"), serum sodium, serum potassium, serum chloride, serum bicarbonate, serum calcium, serum albumin, urine creatinine, urine albumin, urine microalbumin, urine protein, complete blood count ("CBC") panel, liver function panel, lipid profile panel, a coagulation panel, magnesium, phosphorus, brain natriuretic peptide ("BNP"), hemoglobin Alc ("HbAlc"), uric acid and endostatin.
22. A system according to claim 21, wherein the plurality of demographics further comprises a demographic relating to a race of the patient.
2. A method according to claim 1, wherein the plurality of demographics further comprises a demographic relating to a race of the patient.
3. A method according to claim 1, wherein the first set of lab tests comprises:
a lab test associated with TNFR1;
a lab test associated with TNFR2; and
a lab test associated with KIM1.
See instant claim 24 above
4. A method according to claim 1, wherein the lab test variable associated with each of the second set of lab tests relates to at least one of: eGFR, serum creatinine, BUN, serum bicarbonate, serum phosphorus, serum calcium, urine creatinine, urine albumin, urine microalbumin, urine protein, and UACR.
See claim 23 above
26. A system according to claim 21, wherein the lab test information further comprises one or more of: a lab test identifier, a lab test date, a unit relating to the lab test value, a reference range of values, a sample type, facility identification information, provider information, radiological imaging data, and clinical notes.
5. A method according to claim 1, wherein the lab test information further comprises one or more of: a lab test identifier, a unit relating to the lab test value, a reference range of values, a sample type, facility identification information, provider information, radiological imaging data, and clinical notes.
27. A system according to claim 21, wherein: the patient information further comprises one or more diagnoses associated with the patient, each diagnosis of the one or more diagnoses associated with diagnosis information comprising a diagnosis identifier; and the plurality of features further comprises a plurality of diagnosis features, each diagnosis feature of the plurality of diagnosis features relating to at least one diagnosis of the one or more diagnoses.
6. A method according to claim 1, wherein:
the patient information further comprises one or more diagnoses associated with the patient, each diagnosis of the one or more diagnoses associated with diagnosis information comprising a diagnosis identifier; and
the plurality of features further comprises a plurality of diagnosis features, each diagnosis feature of the plurality of diagnosis features relating to at least one diagnosis of the one or more diagnoses.
28. system according to claim 27, wherein the one or more diagnoses comprises a first diagnosis associated with a kidney issue or a comorbidity.
7. A method according to claim 6, wherein the one or more diagnoses comprises a first diagnosis associated with a kidney issue or a comorbidity.
29. A system according to claim 28, wherein: the first diagnosis is associated with the kidney issue; and the kidney issue relates to one of the group consisting of: polycystic kidney disease, renal agenesis, Alport Syndrome, rapidly progressive glomerulonephritis, focal segmental glomerulosclerosis, IgA nephropathy, membranous nephropathy, membranoproliferative glomerulopathy, mesangial proliferative glomerulopathy, minimal change disease, nephritis syndrome, nephrotic syndrome, nephrolithiasis, hypertensive nephropathy, analgesic nephropathy, diabetic nephropathy, lithium nephropathy, renal artery stenosis, Lupus nephritis, kidney myeloma, kidney amyloidosis, anti-glomerular basement disease, fatigue or weakness, edema, and proteinuria.
8. A method according to claim 7, wherein:
the first diagnosis is associated with the kidney issue; and
the kidney issue relates to one of the group consisting of: polycystic kidney disease, renal agenesis, Alport Syndrome, rapidly progressive glomerulonephritis, focal segmental glomerulosclerosis, IgA nephropathy, membranous nephropathy, membranoproliferative glomerulopathy, mesangial proliferative glomerulopathy, minimal change disease, nephritic syndrome, nephrotic syndrome, nephrolithiasis, hypertensive nephropathy, analgesic nephropathy, diabetic nephropathy, lithium nephropathy, renal artery stenosis, Lupus nephritis, kidney myeloma, kidney amyloidosis, anti-glomerular basement disease, fatigue or weakness, edema, and proteinuria.
30. A system according to claim 28, wherein: the first diagnosis is associated with the comorbidity; and the comorbidity relates to one of the group consisting of: alcohol abuse, anemia deficiency, rheumatoid arthritis, blood loss anemia, cardiac arrhythmia, congestive heart failure ("CHF"), chronic pulmonary disease ("CPD"), coagulopathy, acquired immunodeficiency syndrome ("AIDS") or human immunodeficiency virus ("HIV"), depression, diabetes, drug abuse, hypertension, hypothyroidism, liver disease, lymphoma, a fluid or electrolyte disorder, metastatic cancer, a neurological disorder, obesity, paralysis, peripheral vascular disease, psychosis, and pulmonary circulation disorder.
9. A method according to claim 7, wherein
the first diagnosis is associated with the comorbidity; and
the comorbidity relates to one of the group consisting of: alcohol abuse, anemia deficiency, rheumatoid arthritis, blood loss anemia, cardiac arrhythmia, congestive heart failure (“CHF”), chronic obstructive pulmonary disease (“COPD”), coagulopathy, acquired immunodeficiency syndrome (“AIDS”) or human immunodeficiency virus (“HIV”), depression, diabetes, drug abuse, hypertension, hypothyroidism, liver disease, lymphoma, a fluid or electrolyte disorder, metastatic cancer, a neurological disorder, obesity, paralysis, peripheral vascular disease, psychosis, and pulmonary circulation disorder.
31. A system according to claim 30, wherein the plurality of diagnosis features further comprises a feature relating to a Charlson Comorbidity Index ("CCI") score calculated for the first diagnosis.
10. A method according to claim 9, wherein the plurality of diagnosis features further comprises a feature relating to a Charlson Comorbidity Index (“CCI”) score calculated for the first diagnosis.
32. A system according to claim 27, wherein the diagnosis information further comprises one or more of: a diagnosis date, provider information, equipment information, clinical notes and vital signs information.
11. A method according to claim 6, wherein the diagnosis information further comprises one or more of: a diagnosis date, provider information, equipment information, clinical notes and vital signs information.
33. A system according to claim 21, wherein: the patient information further comprises one or more medications associated with the patient, each medication of the one or more medications associated with medication information comprising a medication identifier; and the plurality of features further comprises a plurality of medication features, each medication feature of the plurality of medication features relating to at least one medication of the one or more medications.
12. A method according to claim 1, wherein:
the patient information further comprises one or more medications associated with the patient, each medication of the one or more medications associated with medication information comprising a medication identifier; and
the plurality of features further comprises a plurality of medication features, each medication feature of the plurality of medication features relating to at least one medication of the one or more medications.
34. A system according to claim 33, wherein the medication information further comprises at least one of the group consisting of: a medication date, a medication type, a concentration, a quantity, an amount, date information, refill information, provider information, and clinical notes.
13. A method according to claim 12, wherein the medication information further comprises at least one of the group consisting of: a medication date, a medication type, a concentration, a quantity, an amount, date information, refill information, provider information, and clinical notes.
35. A system according to claim 34, wherein the one or more medications comprises one or more of the group consisting of: an antibiotic medication; a non-steroidal anti-inflammatory drug ("NSAID") medication; a beta-adrenergic receptor blocker medication; a dihydropyridine medication; an angiotensin II receptor blocker ("ARB") medication; an angiotensin-converting enzyme ("ACE") inhibitor medication; a sodium-glucose Cotransporter-2 (SGLT2) inhibitor medication; a Thiazide-class diuretic medication; a Loop-diuretic medication; and a HMG-CoA reductase inhibitor medication.
14. A method according to claim 13, wherein the one or more medications comprises one or more of the group consisting of: an antibiotic medication; a non-steroidal anti-inflammatory drug (“NSAID”) medication; a beta-adrenergic receptor blocker medication; a dihydropyridine medication; an angiotensin II receptor blocker (“ARB”) medication; an angiotensin-converting enzyme (“ACE”) inhibitor medication; a sodium-glucose Cotransporter-2 (SGLT2) inhibitor medication; a Thiazide-class diuretic medication; a Loop-diuretic medication; and a HMG-CoA reductase inhibitor medication.
36. A system according to claim 21, wherein the patient information comprises genetic information indicating that one or more risk variant alleles in an Apolipoprotein L1 gene ("APOL1") of the patient are expressed.
15. A method according to claim 1, wherein the patient information comprises genetic information indicating that one or more risk variant alleles in an Apolipoprotein L1 gene (“APOL1”) of the patient are expressed.
37. A system according to claim 36, wherein the plurality of features further comprises one or more features relating to the genetic information.
16. A method according to claim 15, wherein the plurality of features further comprises one or more features relating to the genetic information.
38. A system according to claim 21, wherein the computer is further configured to: determine that the current risk score satisfies a workflow rule associated with a patient workflow; and determine a treatment recommendation for the patient, based on said determining that the current risk score satisfies the workflow rule.
17. A method according to claim 1, further comprising:
determining that the current risk score associated with one of the patient records satisfies a workflow rule associated with a patient workflow; and
executing the patient workflow.
39. A system according to claim 38, wherein said notification further comprises the treatment recommendation to one or more recipients.
18. A method according to claim 17, wherein said executing the patient workflow comprises:
determining a treatment recommendation for the patient, based on the current risk score,
wherein the notification further comprises the treatment recommendation.
40. A system according to claim 21, wherein the plurality of data sources comprises at least one of the group consisting of: an electronic health records ("EHR") system, a health facility system, an insurance system, a payment system, a user device, a medical device, a biometric device, and an engagement system.
19. A method according to claim 1, wherein the plurality of data sources further comprises at least one of the group consisting of: a health facility system, an insurance system, a payment system, a user device, a medical device, a biometric device, and an engagement system.
Response to Applicant’s Arguments
1. Applicant includes a terminal disclaimer to over comet he rejection herein. However, the Terminal Disclaimer filed is not accepted herein (see above) and as a result, the rejection under Double Patenting is maintained.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
1. Claims 21-40 are rejected under 35 U.S.C. 103 as being unpatentable over 2012/0077690 to Singbartl et al. in view of 2017/0115310 to Colhoun et al. as evidenced by Tibshirani (Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society Series B-Methodological. 1996; 58(1): 267-88). The instant rejection is necessitated by claim amendment.
It is noted that references to the prior art citations are italicized herein.
The instant claims are made obvious by the art of Singbartl et al. in view of Colhoun et al. and as evidenced by Tibshirani, wherein the claims are drawn to a system of determining a risk of renal function decline for a plurality of patients.
As to claim 21, Singbartl discloses determining risk of renal function decline for a plurality of patients comprising (Abstract, diagnosis, differential diagnosis, risk stratification - subjects suffering or at risk of suffering from injury to renal function, reduced renal function and/or acute renal failure through measurement of one or more kidney injury markers, para [0007]-[0013])
a plurality of data sources in communication with a network, the plurality of data sources storing p