DETAILED ACTION
This office action is based on the claim set filed on 03/27/2026.
Claims 22, 24, 31, 37-39, and 41 have been amended.
Claims 23, 30, and 32-36 have been canceled.
Claims 22, 24-29, 31, and 37-41 are currently pending and have been examined.
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 .
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 Langi, 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 l.32I(c) or l.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
§ § 706.02(1)(1) - 706.02(1)(3) 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 l.32l(b).
The USPTO Internet website contains terminal disclaimer forms which may be used.
Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-inf o-1.js p.
Claims 39-41 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 12 and 17 of App. No. 18/739,226. The claims at issue are not identical and they are not patentably distinct from each other because both are directed toward addressing a similar scope. The table/chart below exhibits the similarity* between the independent claim(s) while claim 39 of the current application ‘099 discloses equivalent elements for as in claim 12 and 17 of the reference application ‘226.
*Similarities highlighted in BOLD
App. No. 18/873,099 (current application) Claim 39
App. No. 18/739,226 (reference patent) Claim 12
A method of operating a clinical decision support system for pulmonary hypertension, the method comprising
A method comprising
receiving, from a database, a first set of input variable data of a set of input variables
receiving, from a database, a first set of input variable data of a set of input variables
determining, via one or more pulmonary arterial hypertension risk algorithms, a first set of risk score values associated with a patient surviving within a given time period using the first set of input variable data, for one or more time instances,
determining, via one or more pulmonary arterial hypertension risk algorithms, a first set of risk score values associated with a patient surviving within a given time period using electronic medical records from the first set input variable data for one or more time instances
outputting, via a visualization output of a graphical user interface associated with a user's device, the first set of risk score values associated with a patient surviving within the given time period in a plotted line;
outputting, via a visualization output of a graphical user interface associated with a user device, the first set of risk score values associated with the patient surviving within the given time period
presenting, via the graphical user interface, a set of input variables for a second set of input variable data, wherein the second set of input variable data includes a portion or all of the set of input variables
presenting, via the graphical user interface, a set of input variables for a second set of input variable data, wherein the second set of input variable data includes a portion or all of the first set of input variables;
receiving, from the user's device, a user modified second set of input variable data provided by the user through the graphical user interface;
receiving, from the user device, the second set of input variable data provided by a user through the graphical user interface to simulate a clinical scenario
determining, via the one or more pulmonary arterial hypertension risk algorithms, a second set of risk score values associated with the patient surviving within the given time period using the user modified second set of input variable data;
determining, via the one or more pulmonary arterial hypertension risk algorithms, a second set of risk score values associated with the patient surviving within the given time period using the second set of input variable data
outputting, via the visualization output of the graphical user interface, the second set of risk score values associated with a patient surviving within the given time period, wherein the second set of risk score values is concurrently presented with the first set of risk score values in the visualization output
outputting, via the visualization output of the graphical user interface, the second set of risk score values associated with a patient surviving within the given time period, wherein the second set of risk score values is concurrently presented with the first set of risk score values in the visualization output to provide a comparative risk assessment between the first set of input variable data and the simulated clinical scenario
wherein the one or more pulmonary arterial hypertension risk algorithms comprise an ensemble of one or more tree augmented Naive Bayes (TAN) networks associated with at least one of: a clinical data model, an imaging data model, an ECHO data model, or a genomic biomarker model
Claim 17
wherein the first set of risk score values and the second set of risk score values are calculated using a Tree-Augmented Naive Bayes (TAN) network trained on a registry of pulmonary arterial hypertension patients
As per the above chart, it shows the similarities in the reference application ‘226 and the current application ‘099 independent claim(s) which exhibits a broader and more generic than application ‘099.
Thus, this is a provisional nonstatutory double patenting rejection because the patentably indistinct claims are obvious variations of one another.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “unit” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a means for input and output” in Claim(s) 22 has been interpreted under 112(f) as a means plus function limitation because of the combination of a non-structural term “means” and functional language without reciting sufficient structure to achieve the function.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 22, 24-29, 31, and 37-41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 22, 24-29, 31, and 37-38 are drawn to a system and Claim 39-41 are drawn to a method, and each of which is within the four statutory categories (i.e., a machine and a process). Claims 22, 24-29, 31, and 37-41 are further directed to an abstract idea on the grounds set out in detail below.
Under Step 2A, Prong 1, the steps of the claim for the invention represents an abstract idea of a series of steps that recite a process for predicting health condition risk and evaluating survival rate. Collecting a patient data to estimate risk score for a period of time and measure survival rate for the time period are steps that could have been performed by a human mind but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Metal Process that can be performed using human mind with the aid of pencil and paper.
Independent Claim 22 recites the steps of:
“a processor; a memory having instructions stored thereon; and
a means for input and output, wherein at least one set of input variable data are provided by the input means,
wherein execution of the instructions by the processor causes the processor to execute one or more pulmonary arterial hypertension risk algorithms configured to generate a risk score value associated with a patient surviving within a given time period,
wherein the one or more pulmonary arterial hypertension risk algorithms comprises an ensemble of one or more tree augmented Naive Bayes (TAN) networks associated with at least one of: a clinical data model, an imaging data model, an ECHO data model, or a genomic biomarker model, and
wherein the clinical decision support system is configured to display a set of risk score values associated with a patient surviving within a given time period computed by the one or more pulmonary arterial hypertension risk algorithms associated with a first set of input variable data in a plotted line on a graphical user interface, and further configured to receive a user- modified second set of input variable data via the means for input, determine a second set of risk score values using the second set of input variable data, and concurrently present the second set of risk score values as future risk score values alongside the set of risk score values from the first set as historical risk score values in a visualization output”.
Independent Claim 39 recites similar steps as in Claim 22:
“receiving, from a database, a first set of input variable data of a set of input variables;
determining, via one or more pulmonary arterial hypertension risk algorithms, a first set of risk score values associated with a patient surviving within a given time period using the first set of input variable data, for one or more time instances, wherein the one or more pulmonary arterial hypertension risk algorithms comprise an ensemble of one or more tree augmented Naive Bayes (TAN) networks associated with at least one of: a clinical data model, an imaging data model, an ECHO data model, or a genomic biomarker model;
outputting, via a visualization output of a graphical user interface associated with a user's device, the first set of risk score values associated with a patient surviving within the given time period in a plotted line;
presenting, via the graphical user interface, a set of input variables for a second set of input variable data, wherein the second set of input variable data includes a portion or all of the set of input variables;
receiving, from the user's device, a user modified second set of input variable data provided by the user through the graphical user interface;
determining, via the one or more pulmonary arterial hypertension risk algorithms, a second set of risk score values associated with the patient surviving within the given time period using the user modified second set of input variable data; and
outputting, via the visualization output of the graphical user interface, the second set of risk score values associated with a patient surviving within the given time period,
wherein the second set of risk score values is concurrently presented with the first set of risk score values in the visualization output
These limitations, as drafted, given the broadest reasonable interpretation cover performance of the limitations by a human mind with aid of pen and paper reciting an abstract idea for Mental Process along with Organizing Human Activity and Mathematical concepts, but for the recitation of generic computer components. For example, using tree augmented Naive Bayes (TAN) networks is a mathematical and probabilistic concept for pulmonary arterial hypertension risk. The limitations encompass a user the ability to collect a patient data to evaluate pulmonary arterial hypertension risk, compute risk score, determine survival rate and display outputs, which are steps that that could have been performed by a human to implement the abstract idea and are steps reciting mental process that could have been performed using a human mind with aid of pen and paper and mathematical concepts, but other than the mere nominal recitation of "processor, memory, user device", to implement the abstract idea for performing the steps of observing, evaluating, judgment and opinion which can be performed using a human mind with the aid of pencil and paper, MPEP § 2106.04(a)(2)(III) and Electric Power Group v. Alstom., S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). Accordingly, the claim limitations (in BOLD) recite an abstract idea. Any limitations not identified above as part of the Mental Process are deemed "additional elements," and will be discussed in further detail below.
Under Step 2A, Prong 2, this judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, linking the abstract idea to a particular technological environment. In particular, the claims recite the additional elements such as “processor, memory, clinical decision support system, input means, database, graphical user interface (GUI), user device, tree augmented Naive Bayes (TAN) networks” that iteratively takes input data and analyzes said data to determine an output to performing generic computer functions, e.g., display[ing] risk score and survival rate, such that it amounts no more than adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f), generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 "merely include[ing] instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application. Accordingly, looking at the claim as a whole, individually and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Under step 2B, the claims do not include additional elements that are sufficient to amount to "significantly more" than the judicial exception because as mentioned above, the additional elements amount to no more than generic computing components, recited at a high level of generality, do not present improvements to another technology or technical field, nor do they affect an improvement to the functioning of the computer itself, that amount to no more than mere instruction to perform the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f). 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 and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, See Alice, 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention."). The claims are not patent eligible.
Dependent Claims 24-29, 31, 37-38, and 40-41 include all of the limitations of claim(s) 22 and 39, and therefore likewise incorporate the above-described abstract idea. While the depending claims add additional limitations, such as
As for claims 24-25, 28, 37-38, 40, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible.
As for claims 26-27, 29, 31, 41, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process. The claims recite additional elements “processor, decision system, processor, memory, database, Bayesian networks, GUI” that implement the identified abstract idea. These hardware components are recited at a high level of generality to perform the steps, e.g., “display[ing], store[ing]”, that amounts to no more than the words "apply it" with a computer because it appears to intend to do so, which would still amount to mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Additionally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements amount to more than mere instruction to apply the exception using generic computer component and have been re-evaluated under the “significantly more” analysis. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claims 29, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process along with mathematical calculations and relationships that constitute Mathematical Concepts but for the recitation of generic computer components. For example, calculating weights is/are Mathematical Concepts, but for the recitation of generic computer components. The claims recite additional elements “processor” that implement the identified abstract idea. These hardware components are recited at a high level of generality to perform the steps that amounts to no more than the words "apply it" with a computer because it appears to intend to do so, which would still amount to mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Additionally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements amount to more than mere instruction to apply the exception using generic computer component and have been re-evaluated under the “significantly more” analysis. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
Claim Rejections - 35 USC § 103
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.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 22, 24-29, 31, and 38-41 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US 2025/0253050 A1- “Kim”) in view of Ciolko et al. (“Intelligent Clinical Decision Support Systems Based on SNOMED CT”- “Ciolko”) in view of Galie et al. (Guidelines for the diagnosis and treatment of pulmonary hypertension – “Galie”)
Regarding Claim 22 (Currently Amended), Kim teaches a clinical decision support system comprising:
a processor; a memory having instructions stored thereon; and a means for input and output, wherein at least one set of input variable data are provided by the input means, Kim discloses receiving variables of a patient from different sources, e.g., cardiopulmonary and cutaneous parameters, demographics, etc. via communication interface using input devices such as keyboard, pointing device (Kim: [Fig. 1, 3], [0023-0027], [0040-0041])
wherein execution of the instructions by the processor causes the processor to execute one or more pulmonary arterial hypertension risk algorithms configured to generate a risk score value associated with a patient surviving within a given time period Kim discloses using the different variables for calculating the probability of the patient risk to be cross validated via algorithm to predict critical event(s) projecting a trajectory over a time period (Kim: [Fig. 7-9], [0041-0043], [0048], [0054-0055]);
wherein the clinical decision support system is configured to display a set of risk score values associated with a patient surviving within a given time period (e.g., in a plotted line, the measured metrics of the patient) computed by the one or more pulmonary arterial hypertension risk algorithms associated with a first set of input variable data in a plotted line on a graphical user interface, and further configured to receive a user- modified second set of input variable data via the means for input, determine a second set of risk score values using the second set of input variable data, and concurrently present the second set of risk score values as future risk score values alongside the set of risk score values from the first set as historical risk score values in a visualization output Kim discloses plotting subject health trajectories on a visualization and analysis (VA) tool to display the probabilities associated with the predicted risk level trajectory over the projected time period and surviving probability based on the estimated risk where the user may change the trajectory to analyze the risk score as a future risk score for selected variables as such predicting and comparing the score next to the current score in 6, 12, 18, and 24 month in the future and to the current risk score (Kim: [Fig. 7-9], [0034-0035], [0038], [0051-0052], [0055]).
Kim discloses the trajectories are estimated using a Bayesian multivariate linear mixed effects model (MLMM) [0041]. However, Kim does not expressly disclose a tree-augmented Naïve Bayes (TAN) associated with model(s) and associating with the different model.
Ciolko teaches
wherein the one or more pulmonary arterial hypertension risk algorithms comprises an ensemble of one or more tree augmented Naive Bayes (TAN) networks associated with at least one of: a clinical data model, an imaging data model, an ECHO data model, or a genomic biomarker model Ciolko discloses a decision support system that learn the relationship between variables and diseases such as disease of lung, using plurality of Bayesian networks such as that is/are tree augmented Naives Bayes (TAN) networks asscoaited with different model in a SNOMED CT such as patient HER including symptoms, images, etc. (Ciolko: [p. 6782-6783. Section C]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Kim decision support model to incorporate decision support using plurality of Bayesian networks/ tree augmented Naives Bayes (TAN) networks, as taught by Ciolko, helps dealing with uncertainties providing robust capabilities (Ciolko: [p. 6782-6783. Section C]).
Galie discloses decision support system for evaluating a risk of a disease based on biomedical information such as presence of the disease based on historical clinical data, lab test, etc., biomedical information such as presence of the disease based on imaging data or studies, using echocardiography, biomarker and developing diagnostics classifiers decision tree (Galie: [7.1.1, 7.1.2, 7.1.3, 7.1.4, 7.1.5, 7.2.3, 7.2.6]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have the combination of Kim and Ciolko decision support system using plurality of Bayesian networks/ augmented Naives Bayes (TAN) networks to incorporate decision support using clinical data, imaging, biomarker, as taught by Galie, helps evaluating the risk of a future disease and improve diagnosis accuracy (Galie: [7.1.10]).
Regarding Claim 24 (Currently Amended), the combination of Kim, Ciolko, and Galie teaches the clinical decision support system of claim 22, wherein the first and/or second risk score value associated with a patient surviving within a given time period is categorized into low risk, intermediate risk, high risk Kim discloses scores associated with patient and risk threshold such as high risk (Kim: [Fig. 7-9]), however does not disclose the feature(s) as underlined.
Galie teaches risk assessment score of pulmonary arterial hypertension (PAH) over a period of time, e.g., 3-6 month (Galie: [Table 9, 12, 14], [p. 2507]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Kim decision support risk assessment to incorporate categorization of the patient risk for decision support, as taught by Galie, which plays a key role in the choice of the initial treatment, the evaluation of the response to therapy, and the possible escalation of therapy if needed (Galie: [p. 2507]).
Regarding Claim 25 (Previously Presented), the combination of Kim, Ciolko, and Galie teaches the clinical decision support system of claim 24, wherein low risk, intermediate risk, and high risk are defined by clinical guidelines Galie discloses risk scores categorization based on clinical guidelines, e.g., WHO, Registry, ESC/ERS pulmonary arterial hypertension guideline, etc. (Galie: [p. 2495-2497]).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 24, and incorporated herein.
Regarding Claim 26 (Previously Presented), the combination of Kim, Ciolko, and Galie teaches the clinical decision support system of claim 24, wherein execution of the instructions by the processor causes the processor to query a lookup table of clinical treatment guidelines for the risk category of the first risk score value associated with a patient surviving within a given time period (i.e., the measured metrics of the patient) Galie teaches the risk assessment and categorization over a time period and provide information guide for treatment decision based on the risk assessment results (Galie: [Fig. 1], [Table 11, 16, 17, 19], [7.2.6])
The motivations to combine the above-mentioned references are discussed in the rejection of claim 24, and incorporated herein.
Regarding Claim 27 (Previously Presented), the combination of Kim, Ciolko, and Galie teaches the clinical decision support system of claim 22, wherein the memory further comprises a database for storing input variable data for one or more input instances Kim discloses inputting variables based on historical and current observations data (Kim: [Fig. 7-9], [0033], [0041-0042]).
Regarding Claim 28 (Previously Presented), the combination of Kim, Ciolko, and Galie teaches the clinical decision support system of claim 27, wherein the one or more input instances are one or more time-dependent input instances Kin discloses inputting variables based on historical and current observations data (Kim: [Fig. 7-9], [0033], [0041-0042]).
Regarding Claim 29 (Previously Presented), the combination of Kim, Ciolko, and Galie teaches the clinical decision support system of claim 22, wherein execution of the instructions by the processor causes the processor to calculate the relative weights of each input variable of the set of input variable data Kim discloses calculating a risk of future events where different variables may influence the risk level for example, calculating value higher than an input variable such as pFVC may indicate a high risk of ventilatory defect but low or not cardiomyopathy (Kim: [Fig. 9], [0054-0056]).
Regarding Claim 31 (Currently Amended), the combination of Kim, Ciolko, and Galie teaches the clinical decision support system of claim 22, wherein the ensemble of one or more Bayesian networks is a trained neural network Ciolko discloses the decision support system that learn the relationship between variables and diseases using plurality of Bayesian networks with supervised learning (Ciolko: [p. 6782-6783. Section C]).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 22, and incorporated herein
Regarding Claims 38 (Currently Amended), the combination of Kim, Ciolko, and Galie teaches the clinical decision support system of claim 22, wherein the genomic biomarkers include at least one of ST-2, GDF-15, NT-ProBNP, endostatin, HDGF, Gal3, IL6, or a combination thereof Galie discloses the biomarkers associated with the medical event, e.g., NT-ProBNP, etc. (Galie: [7.2.3]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have the combination of Kim and Ciolko decision support system using plurality of Bayesian networks/ augmented Naives Bayes (TAN) networks to incorporate decision support using clinical data, as taught by Galie, helps evaluating the risk of a future disease and improve diagnosis accuracy (Galie: [7.1.10]).
Regarding Claims 39 (Currently Amended), Kim teaches a method of operating a clinical decision support system for pulmonary hypertension, the method comprising:
receiving, from a database, a first set of input variable data of a set of input variables Kim discloses receiving variables of a subject(s) from different sources, e.g., cardiopulmonary and cutaneous parameters, demographics, etc. (Kim: [Fig. 3, 5], [0033], [0040-0041], [0044])
determining, via one or more pulmonary arterial hypertension risk algorithms, a first set of risk score values associated with a patient surviving within a given time period using the first set of input variable data, for one or more time instances wherein the one or more pulmonary arterial hypertension risk algorithms comprise an ensemble of one or more tree augmented Naive Bayes (TAN) networks associated with at least one of: a clinical data model, an imaging data model, an ECHO data model, or a genomic biomarker model; Kim discloses using the different variables for calculating the probability of the subject risk based on current and historical observation to be cross validated via algorithm to predict critical event(s) projecting a trajectory into a future such as in the next 6 month, 12 month, etc. (Kim: [Fig. 7-9], [0033], [0041-0043], [0048], [0054-0055]);
outputting, via a visualization output of a graphical user interface associated with a user's device, the first set of risk score values associated with a patient surviving within the given time period in a plotted line Kim discloses the output the probabilities associated with the predicted risk level trajectory over the projected time period and surviving probability based on the estimated risk (Kim: [Fig. 7-9], [0034-0035], [0055])
presenting, via the graphical user interface, a set of input variables for a second set of input variable data, wherein the second set of input variable data includes a portion or all of the set of input variables Kim discloses the plotted trajectory filtering tool to compare and display the patient trajectory data to a similar patient(s) associated with the risk probability/surviving rate within the assigned time period (Kim: Fig. 7-9], [0037-0039], [0051-0052], [0055])
receiving, from the user's device, a user modified second set of input variable data provided by the user through the graphical user interface (Kim: Fig. 7-9], [0051-0052], [0054-0055])
determining, via the one or more pulmonary arterial hypertension risk algorithms, a second set of risk score values associated with the patient surviving within the given time period using the user modified second set of input variable data (Kim: [Fig. 7-9], [0033], [0041-0043], [0048], [0054-0055])
outputting, via the visualization output of the graphical user interface, the second set of risk score values associated with a patient surviving within the given time period, wherein the second set of risk score values is concurrently presented with the first set of risk score values in the visualization output Kim discloses plotting subject health trajectories on a visualization and analysis (VA) tool to display the probabilities associated with the predicted risk level trajectory over the projected time period and surviving probability based on the estimated risk where the user may change the trajectory to analyze the risk score as a future risk score for selected variables as such predicting and comparing the score next to the current score in 6, 12, 18, and 24 month in the future and to the current risk score (Kim: [Fig. 7-9], [0033-0035], [0038], [0041-0043], [0048], [0051-0052], [0054-0055]).
Kim discloses risk scores and future scores trajectories are estimated using a Bayesian multivariate linear mixed effects model (MLMM) [0041]. However, Kim does not expressly disclose the features such as survival in time period and a tree-augmented Naïve Bayes (TAN) associated with model(s) as underlined.
Galie teaches risk assessment score of pulmonary arterial hypertension (PAH) and patient survival over a period of time, e.g., 3-6 month (Galie: [Table 9, 12, 14], [p. 2507]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Kim decision support risk assessment to incorporate categorization of the patient risk for decision support, as taught by Galie, which plays a key role in the choice of the initial treatment, the evaluation of the response to therapy, and the possible escalation of therapy if needed (Galie: [p. 2507]).
Ciolko teaches
wherein the one or more pulmonary arterial hypertension risk algorithms comprise an ensemble of one or more tree augmented Naive Bayes (TAN) networks associated with at least one of: a clinical data model, an imaging data model, an ECHO data model, or a genomic biomarker model Ciolko discloses a decision support system that learn the relationship between variables and diseases such as disease of lung, using plurality of Bayesian networks such as that is/are tree augmented Naives Bayes (TAN) networks asscoaited with different model in a SNOMED CT such as patient HER including symptoms, images, etc. (Ciolko: [p. 6782-6783. Section C]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Kim decision support model to incorporate decision support using plurality of Bayesian networks/ tree augmented Naives Bayes (TAN) networks, as taught by Ciolko, helps dealing with uncertainties providing robust capabilities (Ciolko: [p. 6782-6783. Section C]).
Regarding Claims 40 (Previously Presented), the combination of Kim, Ciolko, and Galie teaches the method of operating a clinical decision support system for pulmonary hypertension of claim 39, wherein the visualization output is configured to (i) present a current risk score value of the first set of set of risk score values, including for a first time instance, (ii) present historical risk score values of the first set of risk score values, including at least for a second time instance and a third time instance, and (iii) present future risk score values of the second set of risk score values Kim discloses inputting variables based on historical and occurrences of sentinel events and current observations data determining risk score and predict future score (Kim: [Fig. 7-9], [0033], [0039], [0041-0042]).
Regarding Claims 41 (Currently Amended), the combination of Kim, Ciolko, and Galie teaches the method of operating a clinical decision support system for pulmonary hypertension of claim 39, further comprising:
determining relative weights of each input variable of the set of input variables in determining the first set of risk score values associated with the patient surviving within the given time period Kim discloses factors that impact the risk score of a disease and health trajectory (Kim: [Fig. 7, 8], [0048], [0051]). Galie discloses risk factors for the disease risk development such as factors that my have definite associations with the disease and other may be unlikely associated with the disease (Galie: [2501]).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 39, and incorporated herein.
outputting, via the graphical user interface, one of more indicators of determined relative weights of the candidate variable inputs Kim discloses outputting the impact of the different risk factors on the health trajectory and outcomes (Kim: [Fig. 7, 8], [0048], [0052]).
Claims 37 is rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US 2025/0253050 A1- “Kim”) in view of Ciolko et al. (“Intelligent Clinical Decision Support Systems Based on SNOMED CT”- “Ciolko”) in view of Galie et al. (Guidelines for the diagnosis and treatment of pulmonary hypertension – “Galie”) in view of Gomes et al. (“Signal Transduction during Metabolic and Inflammatory Reprogramming in Pulmonary Vascular Remodeling”- “Gomes”)
Regarding Claims 37 (Currently Amended), the combination of Kim, Ciolko, and Galie teaches the clinical decision support system of claim 22, wherein the genomic biomarkers may be related to at least one of: Pentose Phosphate, IL-22, Phospholipase C signaling, Endocannabinoid related pathways, Thioredoxin pathway, or a combination thereof Gomes biomarkers for the diagnosis and prognosis of Pulmonary arterial hypertension (PAH) such as Pentose Phosphate, phospholipase, thioredoxin, etc. (Gomes: [Fig. 1], [2.2]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have the combination of Kim and Ciolko decision support system using plurality of Bayesian networks/ augmented Naives Bayes (TAN) networks to incorporate biomarkers such as pentose phosphate, as taught by Gomes, helps understand the metabolic pathway of the disease (Gomes: [2.2]).
Examiner note: the listing of genomic biomarkers among the wide range of disease biomarkers, it does not add significant patentable weight to the claim because the markers for use of analyzing a disease can be a wide spectrum of biomarkers.
Response to Amendment
Applicant's arguments filed 03/27/2026 have been fully considered by the Examiner and addressed as the following:
In the remarks, Applicant argues in substance that:
Applicant's arguments with respect to Double Patenting (DP) rejection on page 6.
In response to the Applicant argument that “Applicant does not agree but has filed a terminal disclaimer herewith to overcome the rejections”, Examiner affirms that up to the date of this OA, no terminal disclaimer was filed. In addition, Examiner, finds that the additional limitation as amended is/are further narrow the current application claims however the steps recites the similar concept as recited in the reference application, see claim 17 reference app. Therefore, Examiner remains the DP rejection.
Applicant's arguments with respect to the 35 U.S.C. § 112(b) rejection on page 6.
In response to the Applicant amendment for claims, 22 and 23, Examiner withdraws the 112(b) rejection.
Applicant's arguments with respect to the 35 U.S.C. § 101 rejection on page 6-7.
On page 6 of the remarks, the Applicant argues “The amended independent claims 22 and 39 are not directed to the abstract idea of a mental process. First, the claims now include the execution of an ensemble of tree augmented Naive Bayes (TAN) networks ... Second, the claims recite an interactive graphical user interface configured to receive "user-modified" input variables to determine”, Examiner respectfully disagree. The claims are given their broadest reasonable interpretation for the purpose of determining whether they encompass a judicial exception. The claim limitations, given their broadest reasonable interpretation, recite steps, i.e., receiving information of a user to evaluate and predict pulmonary arterial hypertension risk, computing risk score, and provide visualization outputs, which have been analyzed under Step 2A, Prong One reciting a process for obtaining/collecting, determining, comparing (analyzing) and predicting or provide an opinion, which are steps of observing, evaluating, judgment, and opinion that are citing a process for which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III), but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Mental Process.
Furthermore, the step of user modified input which a process that can be implemented also using a human mind and using pen and paper to perform the assessment as such the analysis is recited at high level of generality such that they while the user interface which is recited as a additional element to perform displaying results as the court found that “collecting information, analyzing it, and displaying certain results of the collection and analysis”, could practically be performed in the human mind, see MPEP 2106.04(a)(2)(III)(A), and Electric Power Group v. Alstom.
Moreover, the amended step recited in independent claims that the risk algorithm comprising a tree-augmented Naïve Bayes (TAN) which performs a mathematical process and in the present claims is to measure and assess the risk, where the court found “organizing information and manipulating information through mathematical correlations” is a mathematical concepts, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., while the tree-augmented Naïve Bayes (TAN) networks has/have been analyzed under Step 2A, Prong Two as an additional element cited as a tool for implementing claim steps that amounts to no more than mere instructions to implement “apply” the exception using a generic computer component and no more than adding the words "apply it" (or an equivalent) with the judicial exception.
On page 7 of the remarks, the Applicant argues “Furthermore, even if the claims are directed to an abstract idea, which Applicant does not concede, the claims integrate he abstract idea into a practical application. The claims provide a an interactive computational tool driven by a complex TAN architecture which improves the graphical user interface and clinical utility of a system”, Examiner respectfully disagree. As described above, the claim, under BRI, describes obtaining information and analyzing the information to generate risk score and output a visualization of the score, which are steps identified as abstract while the claims nor the specification do not provide any technological or technical improvement or attributes an improvement in computer technology or functionality to the claimed invention or that otherwise indicates that the claimed invention uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The fact that the judicial exception, identified in the rejection above, relies upon gathering data and analyzing user information to generate a risk score and provide a visualized output does not impart an improvement to any existing computer, or any other technology or technical field. At best, this gathering of data to be used by a risk algorithm comprising the tree-augmented Naïve Bayes (TAN) may improve the abstract idea of analyzing risk and provide outputs. However, improving upon an abstract idea does not make the abstract idea any less abstract. The claims as a whole are therefore directed to an abstract idea.
Therefore, the Examiner has addressed the Applicant argument(s) and found this argument is not found to be persuasive. Hence, Examiner remains the 101 rejections of claims which have been updated to address Applicant's amendments.
Applicant's arguments with respect to the 35 U.S.C. § 102/103 rejection on page 7-8.
On page 7 of the remarks, the Applicant argues “Independent claims 22 and 39 have been amended to include the features... These features are not taught or suggested by the prior art of record ... Kim completely fails to teach receiving a user-modified second set of input variables to generate and concurrently plot a hypothetical future trajectory alongside a historical one”, Examiner respectfully disagree. Although Applicant argument is directed to a new feature that was not examined in the prior analysis, Examiner has elaborated on the reference “Kim” disclosing the new feature where in Kim, the
Therefore, Examiner finds that the Applicant argument against the references is moot.
Prior Art Cited but not Applied
The following document(s) were found relevant to the disclosure but not applied:
Westerlund et al. “Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence - PMC”
Loghmanpour et al. Cardiac Health Risk Stratification System (CHRiSS): A Bayesian-Based Decision Support System for Left Ventricular Assist Device (LVAD) Therapy - PMC
Saxena et al, Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine - PMC
Sherif et al. Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks - PMC
Ferreira-Santos et al. “Improving Diagnosis in Obstructive Sleep Apnea with Clinical Data: A Bayesian Network Approach | IEEE Conference Publication | IEEE Xplore
Ouyang et al. “TCM syndromes diagnostic model of hypertension: Study based on Tree Augmented Naive Bayes | IEEE Conference Publication | IEEE Xplore”
The references are relevant since it discloses estimating survival in pulmonary arterial hypertension patients.
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 extension fee 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 date of this final action.
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/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687