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 .
DETAILED ACTION
Response to Amendment
In the Election dated 18 February 2026, Group I (Claims 1-16 and 18) was elected.
The present office action represents the first action on the merits.
Claims 1-16, 18, and 26-28 are pending with Claims 26-28 having been withdrawn from consideration. Claims 1-16 and 18 are examined.
Priority
This application claims priority to U.S. Provisional Patent Application No. 63/625,656 dated 26 January 2024.
Election
Applicant's election of Group I (Claims 1-16 and 18) without traverse in the reply filed on 18 February 2026 is acknowledged.
Information Disclosure Statement
The Information Disclosure Statement(s) (lDS) submitted on 16 June 2025 is/are in compliance with the provisions of 37 CFR 1.97 and has/have been fully considered by the Examiner.
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 1-16 and 18 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 and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a method and system for determining an escalation recommendation for a patient having an implanted mechanical circulatory support device, which are within a statutory category.
Step 2A1
The limitations of (Claim 1 being representative) receiving, in association with a mechanical circulatory support device, an indication to determine an escalation recommendation for a patient; determining values for a set of features, wherein the set of features includes one or more first features associated with the mechanical circulatory support device and one or more second features associated with the patient; providing the values for the set of features as input to a trained model to generate a model output; and displaying on the user interface, an escalation recommendation for the patient based, at least in part, on the model output, as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to determine an escalation recommendation for a patient (see Spec. Para. 0018 describing determining an escalation recommendation for a patient as a human activity) in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “receiving…determining…providing…and displaying” as indicated supra.
Other than reciting generic computer components (discussed infra), i.e., a computer-implemented method, the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of (Claim 1) a computer having a user interface or (Claim 16) a controller that implements the identified abstract idea. The computer and controller are not described by the applicant and are recited at a high-level of generality (i.e., a generic computer performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The Examiner notes that the only description of the controller is that it is general purpose hardware having a processor (see Spec. Para. 0033) and thus the controller is presumed to be a generic computer. Even assuming that the controller is part of the MSC device, the MSC device is described as a device that is known in the art (see Spec. Para. 0018). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of (Claim 1) a computer having a user interface or (Claim 16) a controller to perform the noted steps amounts 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 (“significantly more”).
Claims 2-15 and 18 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim(s) 2, 3, 12, 18 merely describe(s) how/where data is displayed, which further defines the abstract idea.
Claim(s) 4, 5, 6, 7, 8, 9, 10 merely describe(s) the features, which further defines the abstract idea.
Claim(s) 11 merely describe(s) merely describes the model, which further defines the abstract idea.
The Examiner notes that the training of the model (which is not yet disclosed as being a machine learning model) is recited in the claim. The type of training utilized by the claimed invention is not described by the Applicant. As such the Examiner is required to analyze the training step given the broadest reasonable interpretation. The step(s) performed to train step(s) of the model is/are considered to be part of the abstract idea because it/they fall(s) under data manipulations that humans perform (i.e., fitting a model to data) and thus are interpreted to be part of the abstraction--the rules or instructions that fall under Certain Methods of Organizing Human Activity. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 12 (Fed. Cir. April 18, 2025) (finding that “[i]terative training using selected training material…are incident to the very nature of machine learning.”).
Claim(s) 13 merely describe(s) the type of procedure, which further defines the abstract idea.
Claim(s) 14 merely describe(s) tracking values and updating the recommendation accordingly, which further defines the abstract idea.
Claim(s) 15 merely describe(s) the model, which further defines the abstract idea.
The Examiner notes that the claim recites that the model is “a machine learning model.” This in interpreted to be part of the abstract idea. A review of the Specification indicates that the particular type of model employed is described as “any suitable classification model may be used, examples, of which include…a decision trees model […], or a logistic regression model. See Spec. Para. 0022. As such, the “machine learning model” encompasses simplistic mathematical models that are part of the rules or instructions that a person or persons would follow; a person having skill in the art would be able to perform the noted types of data manipulation. While these particular limitations may be considered mathematical relationships and/or mental process consistent with the analysis in Example 42, Claim2, the claim as a whole is directed towards a method of organizing human activity.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1-15 each recite a “computer-implemented method” without any recitation in the body of each of the claims describing which step is implemented by a computer or how the computer may be involved. Each of the limitations purely pertain to data manipulation without describing whether a computer may be involved in any particular step or how it may be involved. See, e.g., Ex Parte Langemyr, Appeal No. 2008-1495 at Pg. 20, 2008 Pat App. LEXIS 13 (B.P.A.I. May 28, 2008) (finding that nominal recitation of computer-implementation in the preamble is insufficient to tie the particular steps of the method to the computer). Accordingly, it is unclear where and to what extent the computer-implementation described in the preamble may take place within the body of the claim. The Examiner suggests reciting “wherein each of the following steps are performed by the computer” or similar language.
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 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 of this title, 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) 1-7, 9, 14-16, and 18 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Fodness-Bonfhus et al. (hereinafter “FB;” U.S. Pre-Grant Patent Publication No. 2025/0090831) in view of El Katerji et al. (U.S. Pre-Grant Patent Publication No. 2020/0376183).
REGARDING CLAIM 1
FB teaches the claimed computer-implemented [Para. 0070, 0073 teaches that a controller performs the disclosed functions.] method of determining an escalation
recommendation for a patient having an implanted mechanical circulatory support device, [Fig. 1, Para. 0050 teaches that the blood pump is implanted in the patient.] the method comprising:
receiving, via a user interface associated with a mechanical circulatory support device, an indication to determine an escalation recommendation for a patient; [Para. 0083 assessing effectiveness of a pump, i.e., determining whether escalation is needed/recommended, is determined in response to user input. As Para. 0079 indicates, a user interface is the only way for a user to input data.]
determining values for a set of features, [Para. 0083, 0083 teaches that parameters and characteristics (values) are determined.] wherein the set of features includes
one or more first features associated with the mechanical circulatory support device and [Para. 0083, 0095 teaches that blood pump parameters are monitored, such as motor speed.]
one or more second features associated with the patient; [Para. 0085 teaches that patient characteristics are monitored such as arterial and ventricle pressure and flow rate (second features).]
providing the values for the set of features as input to a […] model to generate a model output; and [Para. 0098, 0102 teaches that the various patient and blood pump data are determined and analyzed via statistical analysis (a trained model, which is undefined in the claim) to determine whether a transition / escalation is required/recommended.]
displaying on the user interface, an escalation recommendation for the patient based, at least in part, on the model output. [Para. 0102, 0104 teaches that the recommended transition / escalation is displayed on a user interface.]
FB may not explicitly teach
a trained model
El Katerji at abstract, Para. 0007, 0011, 0093, 0053, 0055, 0095, 0096 teaches that it was known in the art of computerized healthcare, at the time of filing, to analyze patient data using a trained machine learning model
a trained model [El Katerji at Abstract, Para. 0053, 0055, 0098 teaches receiving intra-aortic pressure information for a patient that has an implanted mechanical circulatory support device installed. El Katerji at Para. 0007, 0011, 0093, 0095 teaches that the data is analyzed by a trained machine learning model to make a prediction. Para. 0096 teaches that the training data is from a cohort.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, at the time of filing, to modify the escalation determination system of FB to analyze patient data using a trained machine learning model as taught by El Katerji, with the motivation of improving clinicians ability to forecast the condition of a patient, which improves patient outcomes (see El Katerji at Para. 0005).
REGARDING CLAIM 2
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claim 1. FB/El Katerji further teaches
wherein the user interface associated with the mechanical circulatory support device is displayed by a controller of the mechanical circulatory support device. [FB at Fig. 4, Para. 0070, 0104 teaches that the user interface associated with the controller (which is associated with the MCS) displays the data.
REGARDING CLAIM 3
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claim 1. FB/El Katerji further teaches
wherein the user interface associated with the mechanical circulatory support device is displayed by a computing device communicatively coupled to the mechanical circulatory support device. [FB at Fig. 4, Para. 0070, 0104 teaches that the user interface associated with the controller (which is associated with the MCS) displays the data. FB at Fig. 4, Para. 0067 teaches that the controller communicates with the MCS.]
REGARDING CLAIM 4
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claim 1. FB/El Katerji further teaches
wherein the one or more first features associated with the mechanical circulatory support device include a feature associated with operation of the mechanical circulatory support device. [FB at Para. 0085 teaches that the parameters of the MCS include flow rate (a feature associated with operation of the MCS).]
REGARDING CLAIM 5
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claims 1 and 4. FB/El Katerji further teaches
wherein the feature associated with operation of the mechanical circulatory support device includes one or more of motor current, pressure information, pump speed or blood flow. [FB at Para. 0085 teaches that the parameters of the MCS include flow rate (blood flow).]
REGARDING CLAIM 6
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claim 1. FB/El Katerji further teaches
wherein the one or more second features associated with the patient include one or more patient physiological features. [FB at Para. 0092, 0110 teaches that patient data is mean arterial pressure (MAP; see Spec. Para. 0007, 0019, Claim 7).]
REGARDING CLAIM 7
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claims 1 and 6. FB/El Katerji further teaches
wherein the one or more patient physiological features include one or more of left ventricular end diastolic pressure, heart rate, pulsatility, contractility, mean arterial pressure, ejection fraction, or cardiac output. [FB at Para. 0092, 0110 teaches that patient data is mean arterial pressure (MAP; see Spec. Para. 0007).]
REGARDING CLAIM 9
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claim 1. FB/El Katerji further teaches
wherein at least one value of the values in the set of features is a derived value determined over a particular time window. [FB at Para. 0092, 0110 teaches that patient data is mean arterial pressure (a value, typically in mmHg) that is calculated (derived). MAP, by definition, is determined over one cardiac cycle, which is a particular time window.]
REGARDING CLAIM 14
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claim 1. FB/El Katerji further teaches
tracking values for the set of features over time during performance of a medical procedure; and [FB at Para. 0083, 0085, 0101, 0102 teaches that key patient characteristics are monitored over time. The Examiner notes that the implantation and use of a blood pump is itself a medical procedure; however, FB at Para. 0048 explicitly teaches that the blood pump of the disclosed invention is used during a PCI procedure, etc.]
updating the escalation recommendation for the patient displayed on the user interface during the medical procedure based on the tracked values. [FB at Para. 0102, 0104 teaches that the result of the escalation analysis is continuously provided to the user interface, i.e., it is continuously updated.]
REGARDING CLAIM 15
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claim 1. FB/El Katerji further teaches
wherein the trained model comprises a machine learning model. [El Katerji at Abstract. Para. 0077, 0078, etc. teaches that the trained model is a machine learning model.]
REGARDING CLAIM(S) 16
Claim(s) 16 is/are analogous to Claim(s) 1, thus Claim(s) 16 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
FB/El Katerji further teaches
a heart pump including at least one sensor configured to sense operation data of the heart pump; and [FB at Fig. 6, Para. 0050, 0068 teaches a blood pump (heart pump) that has a speed sensor.]
[…];
wherein the values for the one or more first features are determined based, at least in part, on the operation data of the heart pump; [Para. 0098, 0102 teaches that the blood pump motor speed data are determined and analyzed via statistical analysis (a model, which is undefined in the claim) to determine whether a transition / escalation is required/recommended.]
REGARDING CLAIM(S) 18
Claim(s) 18 is/are analogous to Claim(s) 1 and 3, thus Claim(s) 18 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1 and 3.
FB/El Katerji further teaches
by transmitting an indication [FB at Fig. 4, Para. 0071 teaches that the controller utilizes control signals to display information of the user interface, thus the recommendation is transmitted to the user interface.]
Claim(s) 8 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Fodness-Bonfhus et al. (hereinafter “FB;” U.S. Pre-Grant Patent Publication No. 2025/0090831) in view of El Katerji et al. (U.S. Pre-Grant Patent Publication No. 2020/0376183) in view of Pappada et al. (U.S. Pre-Grant Patent Publication No. 2018/0168516).
REGARDING CLAIM 8
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claim 1. FB/El Katerji may not explicitly teach
wherein the one or more second features associated with the patient include one or more features derived from an electronic health record associated with the patient.
Pappada at Abstract, Para. 0034, 0039, 0041 teaches that it was known in the art of computerized healthcare, at the time of filing, to analyze real-time mean arterial pressure data for a patient from the patient’s EMR
wherein the one or more second features associated with the patient include one or more features derived from an electronic health record associated with the patient. [Pappada at Abstract, Para. 0034, 0039, 0041 teaches making a prediction (the escalation determination of FB) using real-time data received from a patient’s EMR (i.e., derived from the EMR) such as respiration rate, temperature, and urine output].
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, at the time of filing, to modify the escalation determination system of FB having the analysis of patient data using a trained machine learning model of El Katerji to analyze real-time mean arterial pressure data for a patient from the patient’s EMR as taught by Pappada, with the motivation of providing a complete picture of the patient’s medical situation.
Claim(s) 10 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Fodness-Bonfhus et al. (hereinafter “FB;” U.S. Pre-Grant Patent Publication No. 2025/0090831) in view of El Katerji et al. (U.S. Pre-Grant Patent Publication No. 2020/0376183) in view of Plfug et al. (U.S. Pre-Grant Patent Publication No. 2022/0240798).
REGARDING CLAIM 10
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claim 1. FB/El Katerji may not explicitly teach
wherein at least one value of the values in the set of features is a measure of variability determined over a particular time window.
Pflug at Para. 0043, 0048 teaches that it was known in the art of computerized healthcare, at the time of filing, to collect and utilize patient HRV data by determining the standard deviant of heart beats
wherein at least one value of the values in the set of features is a measure of variability determined over a particular time window. [Pflug at Para. 0043, 0048 teaches determining the standard deviation of heart beats to arrive at heart rate variability (HRV) which is then used in patient analysis (the escalation determination of FB).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, at the time of filing, to modify the escalation determination system of FB having the analysis of patient data using a trained machine learning model of El Katerji to collect and utilize patient HRV data by determining the standard deviant of heart beats as taught by Pflug, with the motivation of providing a complete picture of the patient’s medical situation while also providing more accurate patient information.
Claim(s) 11 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Fodness-Bonfhus et al. (hereinafter “FB;” U.S. Pre-Grant Patent Publication No. 2025/0090831) in view of El Katerji et al. (U.S. Pre-Grant Patent Publication No. 2020/0376183) in view of Thiele et al. (Extracorporeal life support in patients with acute myocardial infarction complicated by cardiogenic shock - Design and rationale of the ECLS-SHOCK trial).
REGARDING CLAIM 11
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claim 1. FB/El Katerji further teaches
wherein the trained model is a model trained on historical patient cohort data associated with patients [El Katerji at Para. 0007, 0011, 0093, 0095 teaches that the patient intra-aortic pressure information is analyzed by a trained machine learning model to make a prediction. Para. 0096 teaches that the training data is from a cohort (a historical cohort).]
FB/El Katerji may not explicitly teach
patients that have undergone escalation from a first type of mechanical circulatory support device to a second type of mechanical circulatory support device, the second type of mechanical circulatory support device having a higher maximum output than the first type of mechanical circulatory support device.
Thiele teaches
patients that have undergone escalation from a first type of mechanical circulatory support device to a second type of mechanical circulatory support device, the second type of mechanical circulatory support device having a higher maximum output than the first type of mechanical circulatory support device. [Theile at Fig. 1, Pg. 5, right column to Pg. 6, left column teaches that patient data which includes whether the particular patient has undergone device escalation is collected. Escalation is the switching from a first mechanical circulatory support device to a second having higher flow rate (see, e.g., FB at Para. 0084).]
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to combine the patient information concerning patients that have undergone escalation of Thiele with teaching of FB/El Katerji since the combination of the references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the patient information concerning patients that have undergone escalation of Thiele for cohort patient intra-aortic pressure training information of FB. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claim(s) 12 and 13 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Fodness-Bonfhus et al. (hereinafter “FB;” U.S. Pre-Grant Patent Publication No. 2025/0090831) in view of El Katerji et al. (U.S. Pre-Grant Patent Publication No. 2020/0376183) in view of Itu et al. (U.S. Pre-Grant Patent Publication No. 2021/0251577).
REGARDING CLAIM 12
FB/El Katerji teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claim 1. FB/El Katerji may not explicitly teach
wherein displaying on the user interface, an escalation recommendation for the patient based, at least in part, on the model output comprises displaying the escalation recommendation during performance of a medical procedure on the patient.
Itu at Para. 0026, 0027, 0059, 0067 teaches that it was known in the art of computerized healthcare, at the time of filing, to output a recommendation to a user based on model output during a percutaneous coronary intervention (PCI)
wherein displaying on the user interface, an escalation recommendation for the patient based, at least in part, on the model output comprises displaying the escalation recommendation during performance of a medical procedure on the patient. [Itu at Para. 0026, 0027, 0059, 0067 teaches a model (the model of FB) that determines the risk of a PMI during a PCI procedure and outputs a suggested course of action (the recommendation of FB) to a medical professional during the procedure (i.e., peri-procedure).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, at the time of filing, to modify the escalation determination system of FB having the analysis of patient data using a trained machine learning model of El Katerji to output a recommendation to a user based on model output during a percutaneous coronary intervention (PCI) as taught by Pflug, with the motivation of improving patient outcomes.
REGARDING CLAIM 13
FB/El Katerji/Itu teaches the claimed computer-implemented method of determining an escalation recommendation for a patient having an implanted mechanical circulatory support device of Claims 1 and 12. FB/El Katerji/Itu further teaches
wherein the medical procedure comprises a percutaneous coronary intervention procedure. [Itu at Para. 0026 teaches that the procedure is PCI.]
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Sampson et al. (WIPO Publication No. WO2022/086913) which discloses assessing patients having heart failure for heart failure with preserved ejection fraction or reduced ejection fraction.
Freeman et al. (WIPO Publication No. WO2021/202292) which discloses a portable pulmonary treatment device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON S TIEDEMAN whose telephone number is (571)272-4594. The examiner can normally be reached 7:00am-4:00pm, off alternate Fridays.
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/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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