DETAILED ACTION
This action is made in response to the amendments/remarks filed on December 4, 2025. This action is made final.
Claims 1-20 are pending. Claims 1, 4, 5, 7, 9 and 15 have been amended. Claims 1, 9, and 15 are independent claims.
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 December 4, 2025 have been fully considered but are not persuasive.
Applicant argues their specification (page 2 of Remarks) identifies several technical problems with existing approaches to cardiovascular disease (CVD) and reflects an improvement to the operation of a machine learning model (page 1 of Remarks). However, the examiner respectfully disagrees.
MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem (e.g., current models relying on correlation rather than causation, current models not accurate, large data sets as identified on page 3 of Remarks) are not technological problems caused by the machine learning models. The problem of making accurate predictions of an individual patient’s risk of developing an atherosclerotic cardiovascular disease is not a problem cause by the machine learning models, but is a problem that existed and/or exists regardless of whether a machine learning models is involved in the process. At best, Applicant’s identified problem is a healthcare problem. Because no technological problem is present, the claims do not provide a practical application.
Applicant further argues the claims are analogous to the claims in Dejardins. However, the examiner respectfully disagrees. The Examiner respectfully submits that there is no improvement to the claim machine learning. Initially, the Examiner notes that Ex parte Desjardins does not represent a substantive change in subject matter eligibility analysis; there has been no indication by the Office that this decision impacts the how claims involving machine learning are analyzed at the Examiner level. The decision is specific to the facts before the Appeals Review Panel and follows the subject matter eligibility analysis set forth in MPEP 2106. As found by the Panel, the claimed “training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" represents “technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” This analysis represents implementation of the practical application-“improvement” analysis of MPEP 2106.04(d)(I) to the facts before the Panel.
Applicant’s claims do not provide such an improvement. Initially, there is no training within the claims so this argument falls on its face from the outset. Even assuming there was, there is no indication in the cited portion of the Specification that the claimed invention provides an improvement as to how model is trained. Improving the accuracy of a machine learning model by supplying it with specific data is not an improvement to how the model is trained within the meaning of Desjardins. This is how all machine learning models are optimized (i.e., select training data, train the model, compare the output to validation data, receive feedback, adjust the parameters of the training data according to the comparison/feedback, and repeat until an accuracy threshold is met). Put another way, the particular way the machine learning model of applicant’s invention uses the data to train itself is not improved, which is the holding of Desjardins. Applicant is merely improving the accuracy of the model by optimizing the data selected/used by the model. Improving the accuracy of a model is not an improvement by any measure in MPEP 2106.
Examiner’s position is also supported by the decision in Recentive Analytics, Inc. v. Fox Corp. Recentive held that non-specifically claimed training of an [AI/ML] algorithm is insufficient to provide a practical application or significantly more because it does not result in “improving the mathematical algorithm or making machine learning better.” Recentive at 12. The decision further instructed that “[i]terative training using selected training material…are incident to the very nature of machine learning” and thus does not provide for an improvement. Recentive at 12.
Applicant’s argument with respect to the Urdea reference has been fully considered but is not persuasive.
Applicant argues Urdea fails to teach any particular supervised machine learning technique. However, the examiner respectfully disagrees. Notably, the claims do not recite any particular supervised machine learning technique, but merely recites a “predictor” that has been trained with training data. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). As such, Urdea, having taught various functions/algorithms trained on historical populations (e.g., see [0100], [0226]), therefore reads upon the claimed limitation.
Applicant further argues Urdea fails to teach “ground truth”, “the number of training patient records (is) orders of magnitude great than the number of causal risk factors” and “wherein the risk factor training values and outcomes were obtained over a training data collection period of at least a length of the predefined time interval, and wherein none of the training patients has shown an atherosclerotic cardiovascular disease outcome up to a beginning of the training data collection period”. However, the examiner respectfully disagrees. As a first matter, 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.
Urdea is directed to, among other things, methods of predicting subjects risk of arteriovascular disease, wherein the risk factors and parameter data are used as inputs, including those identified as confirmed or true values (i.e., ground truth) to train a formula or model to develop reference values in which to compare the patient to for determining whether or not the patient will develop an arteriovascular disease. Urdea further teaches the training data is collected from subjects who are both asymptomatic and lack risk factors for the disease and continue to be absent of the disease for a predetermined period of time, wherein the training data is of a sufficiently large study population, such as greater than 6000 (e.g., orders of magnitude than the risk factors) (e.g., see [0100], [0123]-[0125], [0189], [0201], [0202], [0290]). As such, Urdea teaches the claimed limitations.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-8 recite a method of predicting a risk of developing a health disease, which is within the statutory category of a process. Claims 9-14 recite a system of predicting a risk of developing a health disease, which is within the statutory class of a machine. Claims 15-20 recite a program product of predicting a risk of developing a health disease, which is within the statutory class of a manufacture.
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. ___ (2014). Claims 1-20, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
MPEP 2106 Step 2A – Prong 1:
The limitations of:
Claims 1, 9, and 15 (claim 1 being representative)
receiving a current set of risk factors associated with a risk of atherosclerotic plaque build-up in the patient's arteries, wherein the current set comprises a set of causal risk factors covering multiple biological levels of the patient to capture potential interactions or correlations detected between molecules in different biological levels, with at least demographic risk factors, biomarker risk factors, comorbidity risk factors, family history risk factors, and lifestyle risk factors, said causal risk factors satisfying proven causal relationships to thereby be established as potential causes for developing atherosclerotic plaque build-up; providing the current set as test input to a predictor, wherein the predictor has been trained with a training data set including a number of training patient records, the number of training patient records being orders of magnitude greater than the number of causal risk factors, the training data set including risk factor training values corresponding to the same causal risk factors as included in the test input of said patient, and serving as training inputs, the training data set further including atherosclerotic cardiovascular disease outcomes of the training patients serving as ground truth, wherein the risk factor training values and the outcomes were obtained over a training data collection period of at least a length of the predefined time interval, and wherein none of the training patients has shown an atherosclerotic cardiovascular disease outcome up to a beginning of the training data collection period; and in response to the test input, obtaining from the predictor a predicted risk value for developing an atherosclerotic cardiovascular disease for said patient within the predefined time interval.
as presently drafted, under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components. That is, other than reciting (claim 9) a computer system; (claim 15) a computer program product, a non-transitory computer-readable storage medium, a computing device, the claimed invention amounts to managing personal behavior. For example, but for the noted computer elements, the claim encompasses a person following rules or instructions to receive and process data in the manner described in the abstract idea. The examiner further notes that “methods of organizing human activity” includes a person’s interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). If the claim limitation, under its broadest reasonable interpretation, covers managing persona behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
MPEP 2106 Step 2A – Prong 2:
This judicial exception is not integrated into a practical application because there are no meaningful limitations that transform the exception into a patent eligible application. The additional elements merely amount to instructions to apply the exception using generic computer components (“a computer system”, “a computer program product”, "a non-transitory computer readable medium”, “a computing device”—all recited at a high level of generality). Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claim further recites the additional elements of (1) a predictor that has been trained with a training data set and (2) obtaining results from the predictor. When given the broadest reasonable interpretation in light of the nonexistent description of predictor training in the disclosure, training of a, presumed to be, AI model with the noted data amounts to a mathematical concept that creates data associations. As such, this training of the AI model/predictor is interpreted to be subsumed within the identified abstract idea and the use of the trained model provides nothing more than mere instructions to implement the abstract idea, supra. July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of item (c) at Pgs. 7-9. Regarding (2), the use of the AI model/predictor provides nothing more than mere instructions to implement an abstract idea on a generic computer (“apply it”). See MPEP 2106.05(f). MPEP 2106.05(f); July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of items (d) and (e) at Pgs. 8-9.
The claims only manipulate abstract data elements into another form. They do not set forth improvements to another technological field or the functioning of the computer itself and instead use computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. None of the additional elements recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
At the levels of abstraction described above, the claims do not readily lend themselves to a finding that they are directed to a nonabstract idea. Therefore, the analysis proceeds to step 2B. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016) ("The Enfish claims, understood in light of their specific limitations, were unambiguously directed to an improvement in computer capabilities. Here, in contrast, the claims and their specific limitations do not readily lend themselves to a step-one finding that they are directed to a nonabstract idea. We therefore defer our consideration of the specific claim limitations’ narrowing effect for step two.") (citations omitted).
MPEP 2106 Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as presented in Step 2A Prong 2. Moreover, the additional elements recited are known and conventional generic computing elements (“a computer system”, “a computer program product”, "a non-transitory computer readable medium”, “a computing device”—see Specification Fig. 5, [0070]-[0090] describing the various components as general purpose, common, standard, known to one of ordinary skill, and at a high level of generality, and in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements). Therefore, these additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept that amounts to significantly more. See MPEP 2106.05(f).
The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, translating, and displaying data—see Specification above as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements (1) a predictor that has been trained with a training data set and (2) obtaining results from the predictor were considered to be part of the abstract idea and “apply it,” respectively. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. Regarding (1), the training of the predictor is considered part of the abstract idea and thus cannot provide a practical application. Regarding (2), the use of the predictor represented saying “apply it.” Item (2) has been revaluated under the “significantly more” analysis and does not provide “significantly more” to the abstract idea. MPEP 2106.05(A) indicates also indicates that merely adding the words “apply it” or equivalent use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Dependent Claims
The limitations of dependent but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. Claims 2, 3 (10, 16, 17) merely recites the type of data for the risk factors, claims 4, 5 (12, 13, 18, 19) merely recite identifying a risk based on a value and providing a intervention suggestion, claim 6 (14) merely recites repeating the steps over a time interval and claim 7 (20) merely recites the type of diseases identified, which covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions).
Claims 8 (11) further refine the abstract idea described in the independent claim and merely recites the model utilizing various statistical models. These additional elements are considered to be subsumed within the identified abstract idea, as detailed in the analysis 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.
Claim(s) 1, 6-9, 11, 14, 15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Urdea et al. (USPPN: 2008/0057590; hereinafter Urdea) in further view of Shankar et al. (USPPN: 2017/0277841; hereinafter Shankar).
As to claim 1, Urdea teaches A computer-implemented method for predicting an absolute risk of a patient to develop an atherosclerotic cardiovascular disease within a predefined time interval, the predefined time interval specifying a minimum time period which it takes for development of said disease (e.g., see Abstract, [0011], [0110] teaching a method for predicting a patient’s absolute risk for developing arteriovascular events, including atherosclerosis disease, for a specific time period) the method comprising:
receiving a current set of risk factors associated with a risk of atherosclerotic plaque build-up in the patient's arteries, wherein the current set comprises a set of causal risk factors covering multiple biological levels of the patient to capture potential interactions or correlations detected between molecules in different biological levels, with at least demographic risk factors, biomarker risk factors, comorbidity risk factors, family history risk factors, and lifestyle risk factors, said causal risk factors satisfying proven causal relationships to thereby be established as potential causes for developing atherosclerotic plaque build-up (e.g., see Fig. 1, [0022], [0024], [0084], [0089], [0095], [0096], [0106], [0121], [0122], [0126], [0213] wherein various risk factors are received, including demographics, lifestyle, and biomarkers, etc. (which is consistent with Applicant specification of “biological levels” as per [0006]), and are used to determine a patient’s risk for atherosclerotic disease and/or plaque build up);
providing the current set as test input to a predictor, wherein the predictor has been trained with a training data set including a number of training patient records, the number of training patient records being orders of magnitude greater than the number of causal risk factors, the training data set including risk factor training values corresponding to the same causal risk factors as included in the test input of said patient, and serving as training inputs, the training data set further including atherosclerotic cardiovascular disease outcomes of the training patients serving as ground truth, wherein the risk factor training values and the outcomes were obtained over a training data collection period of at least a length of the predefined time interval, and wherein none of the training patients has shown an atherosclerotic cardiovascular disease outcome up to a beginning of the training data collection period (e.g., see [0100], [0123], [0124], [0201], [0202], [0290] wherein the risk factors and parameter data are used as inputs to train a formula or model to develop reference values in which to compare the patient to for determining whether or not the patient will develop an arteriovascular disease, wherein the training data is collected from subjects who are both asymptomatic and lack risk factors for the disease and continue to be absent of the disease for a predetermined period of time. The training data is provided from a sufficiently large study population); and
in response to the test input, obtaining from the predictor a predicted risk value for developing an atherosclerotic cardiovascular disease for said patient within the predefined time interval (e.g., see [0128], [0129], [0150], [0151], [0204], [0254] wherein a patient is predicted to have a risk of developing an arteriovascular disease within a predetermined time, including atherosclerotic cardiovascular disease, based on a result compared to a “normal”/”reference”/”index” value).
While Urdea teaches a plurality of risk factors used to determine a patient’s risk for atherosclerotic disease and/or plaque build up, wherein the risk factors would reasonably read upon the claimed “causal risk factors”. However, should the features upon which the examiner rely not provide sufficient support, additionally cited Shankar teaches causal risk factors satisfying proven causal relationships to thereby be established as potential causes for developing atherosclerotic plaque build-up (e.g., see [0038]-[0042] wherein various risk factors are based on medical knowledge information and related to medical rules, identifying risk contributors and diagnosing a patient based on the risk factors known to lead to cardiovascular disease, such as family history, lifestyle, biological information, etc.).
Accordingly, it would have been obvious to modify Urdea in view of Shankar before the effective date of the present application with a reasonable expectation of success. One would have been motivated to make the modification in order to agument risk assessment and improve diagnosis (e.g., see [0045] of Shankar).
As to claim 6, the rejection of claim 1 is incorporated. Urdea further teaches repeating the previous steps once per predefined monitoring time interval for said patient (e.g., see [0124], [0130] wherein the patient can be monitored for various time intervals).
As to claim 7, the rejection of claim 1 is incorporated. Urdea further teaches wherein a atherosclerotic cardiovascular disease outcome is any of the following: Coronary/Ischaemic heart disease, Heart attack, Angina, Stroke, Cardiac Arrest, Congestive Heart Failure, Left ventricular failure, Myocardial Infarction, Aortic valve stenosis, Cerebral artery occlusions, Nontraumatic haemorrhages (e.g., see [0097], [0135], [0206] wherein the diagnosed disease can include coronary heart disease, angina, congestive heart failure, stroke, heart attack, etc.).
As to claim 8, the rejection of claim 1 is incorporated. Urdea further teaches wherein the predictor is implemented as a LogisticRegression Predictor or as an Extreme Gradient Boosting Predictor (e.g., see Table 10 wherein various classifiers can be used, including logistic regression).
As to claim 9, 11, and 14 the claims are directed to the system implementing the method of claims 1, 8, and 6 and are similarly rejected.
As to claims 15 and 20, the claims are directed to a program product implementing the method of claims 1 and 7 and are similarly rejected.
Claim(s) 2-3, 10, 16, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Urdea and Shankar, as applied above, and in further view of Perls (USPPN: 2007/0118398; hereinafter Perls).
As to claim 2, the rejection of claim 1 is incorporated. Urdea further teaches the current set of risk factors comprising a selection of 25 risk factors out of a plurality of more than 200 potentially relevant risk factors (e.g., see [0233] wherein at least 2-12 biomarkers are selected from a sufficiently large panel. While Urdea fails to explicitly recite 25 out of potentially more than 200, Urdea nonetheless teaches at least 2 from a sufficiently large pool and therefore the claimed range overlaps or lie inside the range disclose by the prior art and is, therefore, obvious, see MPEP 2144.05), wherein:
demographic risk factors comprise at least the patient's age and gender (e.g., see [0259] wherein clinical parameters include age and gender);
the biomarker risk factors comprise at least the patient's waist circumference, systolic blood pressure, total Cholesterol, LDL Cholesterol, and total Cholesterol HDL ratio (e.g., see [0096] [0121], [0123] wherein risk factors include waist circumference, systolic blood pressure, total cholesterol, LDL cholesterol, and LDL to HDL ratio);
the comorbidity risk factors comprise at least the patient's hypertension and other Heart Arrhythmias different from Atrial Fibrillations (e.g., see [0121], [0205] wherein risk factors include patient’s hypertension and heart arrhythmias different from atrial fibrillations);
genetic risk factors comprise at least the patient's familial CVD history (e.g., see [0123] wherein risk factors include family history of CVD); and
the lifestyle risk factors comprise at least the patient's smoking status, number of cigarettes smoked per day (e.g., see [0004], 0121], [0208] wherein risk factors include smoking status and number of cigarettes a day, and physical activity).
While Urdea identifies several risk factors, Urdea fails to explicitly teach the risk factors comprise: Sleep problems: Not at all, Sleep problems: Several days; indicator if alcohol is usually consumed with meals, indicator if alcohol is sometimes consumed with meals, social visits: 2-4 times a week, social visits: about once a week, social visits: about once/month, social visits: almost daily, social visits: once every few months, walking pace: brisk pace, and walking pace: steady average pace, indicator if a self-rated overall health rating is excellent, indicator if a self- rated overall health rating is poor.
However, in the same field of endeavor of determining risk assessments of a patient, Perls teaches risk factors comprise: Sleep problems: Not at all, Sleep problems: Several days; indicator if alcohol is usually consumed with meals, indicator if alcohol is sometimes consumed with meals, social visits: 2-4 times a week, social visits: about once a week, social visits: about once/month, social visits: almost daily, social visits: once every few months, walking pace: brisk pace, and walking pace: steady average pace, indicator if a self-rated overall health rating is excellent, indicator if a self- rated overall health rating is poor (e.g., see [0040], [0113]-[0116], [0129]-[0137], [0218]-[0227], [0285]-[0294], [0321]-[0328] wherein risk factors include sleep problems, amount of alcohol consumption and in between food, amount of physical activity and social interactions and overall health feedback). Accordingly, it would have been obvious to modify Urdea in view of Perls before the effective date of the application with a reasonable expectation of success. One would have been motivated to make the modification as a simple substitution of known risk factors for another to yield the predictable results of better assessing patient health based on known (See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); MPEP 2143 and [0088] of Perls)
As to claim 3, the rejection of claim 2 is incorporated. Urdea further teaches wherein: the demographic risk factors further comprise the patient's ethnicity (e.g., see Table 3 wherein a clinical parameter includes patient’s ethnicity);
the biomarkers risk factors further comprise any of the patient's further weight-related measurements, blood-sample related measurements, heart rate, and glucose (e.g., see [0096], Table 3 wherein a risk includes weight-related measurements, blood sample measurements, heart rate and glucose);
the comorbidity risk factors further comprise any of the patient's atrial fibrillation, diabetes type 2, diabetes type 1, chronic kidney disease, migraines, rheumatoid arthritis, systemic lupus erythematosus, schizophrenia, bipolar, depression, psychosis, diagnosis or treatment of erectile dysfunction, and atypical antipsychotic medication, inflammation markers, steroids, or medication for any of said comorbidities (e.g., see [0004], [0096], [0205], table 2 listing several risk factors including atrial fibrillation, diabetes type I or II, inflammation, medication use, kidney disease, migraines, psychosocial issues, etc.);
the genetic risk factors further comprise any of the patient's family history of stroke, diabetes, high cholesterol, and high blood pressure (e.g., see [0004] wherein family history includes history of arteriovascular disease);
the lifestyle risk factors further comprise any of the patient's stress, overall health rating, physical activity, and sleep status (e.g., see [0121] wherein risk factor’s include patient physical activity); and
wherein the risk factors further comprise environmental risk factors with any of the patient's exposure to tobacco smoke, work, housing, and other socio-economic factors (e.g., see [0004] wherein risk factors include smoking).
As to claim 10, the claim is directed to the system implementing the method of claim 2 and is similarly rejected.
As to claims 16 and 17, the claims are directed to a program product implementing the method of claims 2 and 3 and are similarly rejected.
Claim(s) 4-5, 12-13, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Urdea and Shankar, as applied above, and in further view of Goldner et al. (USPPN: 2021/0391081; hereinafter Goldner).
As to claim 4, the rejection of claim 1 is incorporated. Urdea further teaches further comprising: mapping the obtained predicted risk to a corresponding predefined risk level (e.g., see [0150], [0315] wherein the risks are compared to a predetermined threshold/cutoff points to determine a risk).
While Urdea teaches identifying subjects at risk for a health risk event and their need for therapeutic interventions or treatment, Urdea fails to explicitly teach notifying the patient and/or a medically trained person accordingly.
However, in the same filed of endeavor of predictive guidance system for personalized health care, Goldner teaches in case the corresponding predefined risk level indicates a need for therapeutic intervention, notifying the patient and/or a medically trained person accordingly (e.g., see [0139], [0142] wherein in response to a likelihood of a health condition to occur, a notification is sent to the user device to prevent or adjust the outcome, wherein the notification is sent to the patient and their healthcare provider).
Accordingly, it would have been obvious to modify Urdea in view of Goldner before the effective date of the application with a reasonable expectation of success. One would have been motivated to make the modification in order to encourage the user to perform a mitigating action to avoid the health event (e.g., see [0034] of Goldner).
As to claim 5, the rejection of claim 1 is incorporated. Urdea further teaches mapping the obtained predicted risk to a corresponding predefined risk level (e.g., see [0150], [0315] wherein the risks are compared to a predetermined threshold/cutoff points to determine a risk).
in case the corresponding predefined risk level indicates a need for therapeutic intervention: for each risk factor of the current set: checking if the respective risk factor exceeds a corresponding maximum value, or falls below a corresponding minimum value (e.g., see [0104], [0150], [0315] wherein each risk factor can be associated with threshold/limit for defining disease values).
While Urdea teaches identifying subjects at risk for a health risk event and their need for therapeutic interventions or treatment, Urdea fails to explicitly teach if the respective risk factor exceeds the corresponding maximum value, or falls below the corresponding minimum value, retrieving from an intervention database one or more suggestions for interventions which, when applied to the patient, are appropriate to reduce the predicted risk for said patient; providing the retrieved one or more suggestions to the patient and/or to a medically trained person.
However, in the same filed of endeavor of predictive guidance system for personalized health care, Goldner teaches if the respective risk factor exceeds the corresponding maximum value, or falls below the corresponding minimum value, retrieving from an intervention database one or more suggestions for interventions which, when applied to the patient, are appropriate to reduce the predicted risk for said patient; providing the retrieved one or more suggestions to the patient and/or to a medically trained person (e.g., see [0031]-[0035], [0092]-[0093], [0139], [0142] wherein in response to a likelihood of a health condition to occur, one or more recommendations for mitigating the event is received from a database and a notification is sent to the user device to prevent or adjust the outcome, wherein the notification is sent to the patient and their healthcare provider).
Accordingly, it would have been obvious to modify Urdea in view of Goldner before the effective date of the application with a reasonable expectation of success. One would have been motivated to make the modification in order to encourage the user to perform a mitigating action to avoid the health event (e.g., see [0034] of Goldner).
As to claim 12 and 13, the claims are directed to the system implementing the method of claims 4 and 5 and are similarly rejected.
As to claims 18 and 19, the claims are directed to a program product implementing the method of claims 4 and 5 and are similarly rejected.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/STELLA HIGGS/Primary Examiner, Art Unit 3681