DETAILED ACTION
Claims 1-20 are hereby under examination.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/17/2024 is being considered by the examiner.
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.
Claim 10 is 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 10, the claim recites the limitation "the third probability value" in line 1. There is insufficient antecedent basis for this limitation in the claim. In light of the specification, it is currently unclear what “the third probability value” is. For the purposes of examination, “the third probability value” is being interpreted as any probability value.
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.
Analysis of independent claims 1 and 16:
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 1 is directed to a system, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Claim 16 is directed to a method, which describes one of the four statutory categories of patentable subject matter, i.e., a process. Therefore, further consideration is necessary.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claims 1 and 16 recite an abstract idea. In particular, the claims recite the following:
Determine, from a first data segment included in the physiological data, a first data portion satisfying a signal-quality-index (“SQI”) condition;
Determine a first probability value for the first data portion with a model developed via machine learning using training data, wherein the first probability value indicates a probability that the first data portion is associated with an exacerbation event; and
Determine whether an exacerbation condition is satisfied based on the first probability value.
These elements recited in claims 1 and 16 are drawn to an abstract idea since (1) they involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations; and/or (2) they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Determining if first data portion satisfies a signal-quality-index (“SQI”) condition is drawn to a mathematical concept. For example, determining if the first data portion satisfies a SQI condition can be drawn to determining if the variability of a first set of data points is within a set range. Further, determining if first data portion satisfies a signal-quality-index (“SQI”) condition is drawn to a mental process that can be practically performed in the human mind, using pen and paper. For example, a person can view a set of data on a piece of paper and determine if a SQI is met by determining if the data does not include extreme outliers. There is nothing to suggest an undue level of complexity in the determining of a SQI condition step.
Determining if a first probability value for the first data portion with a model indicates a probability that the first portion is associated with an exacerbation event is drawn to a mathematical concept. For example, the values of the first data portion can be input into a mathematical model to output a probability. Further, determining if a first probability value for the first data portion with a model indicates a probability associated with an exacerbation event is drawn to a mental process that can be practically performed in the human mind, using pen and paper. For example, a person utilize a model (no specifics are recited regarding what the model is), for example, known values that are associated with exacerbation events, and compare the first data portion to the model to determine a probability. There is nothing to suggest an undue level of complexity in the determining if a first probability value for the first data portion with a model indicates a probability associated with an exacerbation event step.
Determining whether an exacerbation condition is satisfied based on the first probability value is drawn to a mental process that can be practically performed in the human mind, using pen and paper. For example, a person can mentally determine if the probability is above or below a threshold and/or if the probability is indicated as a yes, to determine the exacerbation condition is satisfied. There is nothing to suggest an undue level of complexity in the determine whether the exacerbation condition is satisfied step.
Prong Two: Claims 1 and 16 do not recite additional elements that integrate the exception into a practical application. Therefore, the claims are “directed to” the abstract idea. The additional elements merely:
Recite the words “apply it” or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “one or more electronic processors” (claim 1 and claim 16)), and
Add insignificant extra-solution activity (the pre-solution activity of: using generic data-gathering components (e.g. “receive physiological data associated with a patient” (claim 1 and claim 16) - with no structure recited, “developed via machine learning using training data” (claim 1)); the post-solution activity of: (e.g. “generate and transmit an exacerbation alert associated with the patient” (claim 1 and claim 16) - with no structure recited); using generic data-outputting components (e.g. N/A)).
As a whole, the additional elements merely serve to gather information to be used by the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example, “receiving physiological data associated with a patient” is well-understood, routine, and conventional, as disclosed by Inan et al. (US 20230293082 A1) - para. [0171-0172].
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example, “a model developed via machine learning using training data” is well-understood, routine, and conventional, as disclosed by Inan et al. (US 20230293082 A1) - para. [0276].
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example, “generate and transmit an exacerbation alert” is well-understood, routine, and conventional, as disclosed by Inan et al. (US 20230293082 A1) - para. [0185].
Further, “one or more electronic processors” does not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1 and 16 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above. E.g., all elements are directed to pre-solution and/or post-solution activity, with no structure recited, which merely facilitate the abstract idea.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Analysis of the dependent claims:
Claims 2-15 and 17-20 depend from the independent claims. The dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely
Further describe the abstract idea (“determine a set of data segments included in the physiological data; and determine a set of data portions from the set of data segments, wherein each data portion is included in a corresponding data segment and is a representative signal of the corresponding data segment, wherein the first data segment is included in the set of data segments and the first data portion is included in the set of data portions” (claim 5), “the set of data segments includes a series of non-overlapping time windows” (claim 6), “the SQI condition includes a predetermined threshold of 0.5” (claim 7), “determine, from a second data segment included in the physiological data, a second data portion satisfying the SQI condition; and determine a second probability value for the second data portion with the model developed via machine learning using the training data” (claim 8), “determine whether the exacerbation condition is satisfied based on the first probability value and the second probability value” (claim 9), “the third probability value is a mean pulmonary capillary wedge pressure of the first probability value and the second probability value” (claim 10), “determine a third probability value based on the first probability value and the second probability value, determine whether the exacerbation condition is satisfied based on the third probability value” (claim 11), “determine whether the exacerbation condition is satisfied by comparing the third probability value to a pressure threshold” (claim 12), “the third probability value satisfies the exacerbation condition when the third probability value exceeds the pressure threshold” (claim 13), “the pressure threshold is 18 mmHg” (claim 14), “the exacerbation condition indicates an elevated cardiac pressure that is indicative of an onset of heart failure exacerbation” (claim 15), “determining a set of data segments included in the physiological data; and determining a set of data portions from the set of data segments, wherein each data portion is included in a corresponding data segment and is a representative signal of the corresponding data segment” (claim 17), “determining a plurality of data portions, wherein each data portion satisfies the SQI condition, and wherein the first data portion is included in the plurality of data portions; determining a plurality of probability values using the model relating to the physiological data to cardiac pressure, wherein the first probability value is included in the plurality of probability values and wherein each probability value indicates a probability that each data portion is associated with a corresponding exacerbation event; determining a combined probability value based on the plurality of probability values; determining whether the exacerbation condition is satisfied based on the combined probability value; and in response to determining that the exacerbation condition is satisfied, generating and transmitting the exacerbation alert associated with the patient” (claim 18), “determining the combined probability value includes determining a mean pulmonary capillary wedge pressure based on the plurality of probability values” (claim 19), “determining the plurality of probability values includes determining a plurality of probability values, wherein each probability value is associated with a different data portion of the plurality of data portions” (claim 20)),
Further describe the pre-solution activity (or the structure used for such activity) (“the physiological data includes electrocardiogram ("ECG") data collected by an ECG device associated with the patient, wherein the ECG data is single-lead ECG data” (claim 2)),
Further describe the computer implementation (“the one or more electronic processors are configured to: receive the physiological data continuously from a remote monitoring device associated with the patient” (claim 3), “the one or more electronic processors are configured to: receive the physiological data intermittently from a remote monitoring device associated with the patient” (claim 4), “one or more electronic processors” (claim 5), “one or more electronic processors” (claim 8), “one or more electronic processors” (claim 9), “one or more electronic processors” (claim 11), “one or more electronic processors” (claim 12)), and
Further describe the post-solution activity (N/A) (recited at a high level of generality).
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example, “an ECG device” is well-understood, routine, and conventional, as disclosed by Inan et al. (US 20230293082 A1) - para. [0172], para. [0210].
Further, “one or more electronic processors” does not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Taken alone or in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. The additional elements do not add anything significantly more than the abstract idea. The collective functions of the additional elements merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subjection matter. Claims 1-20 are rejected.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 15, and 16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Inan et al. (US 20230293082 A1), hereinafter referred to as Inan.
The claims are generally directed towards a system for detecting abnormal cardiac pressures in patients with heart failure, the system comprising: one or more electronic processors configured to: receive physiological data associated with a patient; determine, from a first data segment included in the physiological data, a first data portion satisfying a signal-quality-index ("SQI") condition; determine a first probability value for the first data portion with a model developed via machine learning using training data, wherein the first probability value indicates a probability that the first data portion is associated with an exacerbation event; determine whether an exacerbation condition is satisfied based on the first probability value; and in response to determining that the exacerbation condition is satisfied, generate and transmit an exacerbation alert associated with the patient.
Regarding claim 1, Inan discloses a system for detecting abnormal cardiac pressures in patients with heart failure (Abstract, para. [0015]), the system comprising:
one or more electronic processors (Fig. 1B, element 170, para. [0178], “microprocessor …”) configured to:
receive physiological data associated with a patient (para. [0181-0182], “receiving data from a first sensor … an ECG signal of a user … receiving data from a second sensor … a SCG signal …”);
determine, from a first data segment included in the physiological data, a first data portion satisfying a signal-quality-index ("SQI") condition (Fig. 2A, para. [0220], “SQI, was applied separately to each channel of SCG to extract high quality SCG beats …”);
determine a first probability value for the first data portion with a model developed via machine learning using training data, wherein the first probability value indicates a probability that the first data portion is associated with an exacerbation event (para. [0202-0203], “trained a population regression model to predict the mean pressure values … tracking the changes in the pressures …”, para. [0206], “machine learning based regression model to measure PCWP …”, para. [0224], “support vector machines for both classification and regression tasks …”);
determine whether an exacerbation condition is satisfied based on the first probability value (para. [0184], “assessing, based on the filling characteristics of the heart, heart health … classification of a clinical status … assessment of heart health can include data indicative of changes in filling characteristics …”, para. [0202-0203], “trained a population regression model to predict the mean pressure values … tracking the changes in the pressures …”); and
in response to determining that the exacerbation condition is satisfied, generate and transmit an exacerbation alert associated with the patient (para. [0185], “outputting the heart health assessment to a user … an alert to a user when the heart health assessment changes …”).
Regarding claim 2, Inan discloses the system of claim 1, wherein the physiological data includes electrocardiogram ("ECG") data collected by an ECG device associated with the patient, wherein the ECG data is single-lead ECG data (para. [0181], “receiving data from a first sensor … an ECG signal of a user”, para. [0197], “single-lead ECG”).
Regarding claim 3, Inan discloses the system of claim 1, wherein the one or more electronic processors are configured to: receive the physiological data continuously from a remote monitoring device associated with the patient (para. [0241], “wearable ECG and SCG signals were recorded continuously …”).
Regarding claim 15, Inan discloses the system of claim 1, where the exacerbation condition indicates an elevated cardiac pressure that is indicative of an onset of heart failure exacerbation (para. [0184-0185], “assessing, based on the filling characteristics of the heart, heart health … classification of a clinical status of heart failure… assessment of heart health can include data indicative of changes in filling characteristics … treat patients with heart failure”, para. [0202-0203], “trained a population regression model to predict the mean pressure values … tracking the changes in the pressures …”, para. [0273], “elevated PCWP”).
Regarding claim 16, Inan discloses a method for detecting abnormal cardiac pressures in patients with heart failure (Abstract, para. [0015]), the method comprising:
receiving physiological data associated with a patient (para. [0181-0182], “receiving data from a first sensor … an ECG signal of a user … receiving data from a second sensor … a SCG signal …”);
determining, with one or more electronic processors, a first data portion satisfying a signal-quality-index ("SQI") condition (Fig. 1B, element 170, Fig. 2A, para. [0178], “microprocessor …”, para. [0220], “SQI, was applied separately to each channel of SCG to extract high quality SCG beats …”);
determining, with the one or more electronic processors, a first probability value for the first data portion with a model relating the physiological data to cardiac pressure, wherein the first probability value indicates a probability that the first data portion is associated with an exacerbation event (para. [0202-0203], “trained a population regression model to predict the mean pressure values … tracking the changes in the pressures …”, para. [0206], “machine learning based regression model to measure PCWP …”, para. [0224], “support vector machines for both classification and regression tasks …”);
determining, with the one or more electronic processors, whether an exacerbation condition is satisfied based on the first probability value (para. [0184], “assessing, based on the filling characteristics of the heart, heart health … classification of a clinical status … assessment of heart health can include data indicative of changes in filling characteristics …”, para. [0202-0203], “trained a population regression model to predict the mean pressure values … tracking the changes in the pressures …”); and
in response to determining that the exacerbation condition is satisfied, generating and transmitting, with the one or more electronic processors, an exacerbation alert associated with the patient (para. [0185], “outputting the heart health assessment to a user … an alert to a user when the heart health assessment changes …”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4-6, 8-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Inan et al. (US 20230293082 A1), hereinafter referred to as Inan as applied to claims 1 and 16 above, and further in view of Dani et al. (US 20200353271 A1), hereinafter referred to as Dani.
Regarding claim 4, Inan discloses the system of claim 1.
However, Inan does not explicitly disclose wherein the one or more electronic processors are configured to: receive the physiological data intermittently from a remote monitoring device associated with the patient.
Dani teaches an analogous system for detecting abnormal cardiac events in patients (Abstract, Fig. 5, para. [0006]). Dani teaches receiving physiological data associated with a patient (para. [0060], para. [0090]). Dani further teaches one or more electronic processors are configured to: receive the physiological data intermittently from a remote monitoring device associated with the patient (Fig. 1, para. [0027-0032], para. [0035]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Inan to additionally receive the physiological data intermittently from a remote monitoring device associated with the patient, as taught by Dani. This is because Dani teaches patient data can be periodically obtained and received, which one of ordinary skill in the art would recognize as requiring less power (para. [0035]).
Regarding claim 5, Inan discloses the system of claim 1.
However, modified Inan does not explicitly disclose wherein the one or more electronic processors are configured to: determine a set of data segments included in the physiological data; and determine a set of data portions from the set of data segments, wherein each data portion is included in a corresponding data segment and is a representative signal of the corresponding data segment, wherein the first data segment is included in the set of data segments and the first data portion is included in the set of data portions.
Dani teaches an analogous system for detecting abnormal cardiac events in patients (Abstract, Fig. 5, para. [0006]). Dani teaches receiving physiological data associated with a patient (para. [0060], para. [0090]). Dani further teaches, determining a set of data segments included in the physiological data; and determine a set of data portions from the set of data segments, wherein each data portion is included in a corresponding data segment and is a representative signal of the corresponding data segment, wherein the first data segment is included in the set of data segments and the first data portion is included in the set of data portions (para. [0039], para. [0084-0085]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Inan to additionally include determining a set of data segments included in the physiological data; and determine a set of data portions from the set of data segments, wherein each data portion is included in a corresponding data segment and is a representative signal of the corresponding data segment, wherein the first data segment is included in the set of data segments and the first data portion is included in the set of data portions, as taught by Dani. This is because Dani teaches obtaining a plurality of data segments and data portions allows for a plurality of probability values to adjust probability thresholds (para. [0039]).
Regarding claim 6, modified Inan discloses the system of claim 5.
However, modified Inan does not explicitly disclose wherein the set of data segments includes a series of non-overlapping time windows.
Dani further teaches the set of data segments includes a series of non-overlapping time windows (para. [0084]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system taught by modified Inan to additionally include a series of non-overlapping time windows, as taught by Dani. This is because Dani teaches non-overlapping time windows allows for samples to be contained within one temporal window (para. [0084]), which allows for errors to be reduced in calculations.
Regarding claim 8, Inan discloses the system of claim 1.
However, Inan does not explicitly disclose wherein the one or more electronic processors are configured to: determine, from a second data segment included in the physiological data, a second data portion satisfying the SQI condition; and determine a second probability value for the second data portion with the model developed via machine learning using the training data.
Dani teaches an analogous system for detecting abnormal cardiac events in patients (Abstract, Fig. 5, para. [0006]). Dani teaches receiving physiological data associated with a patient (para. [0060], para. [0090]). Dani further teaches determining a plurality of data segments included in the physiological data, determining a plurality of portions that satisfy an SQI condition, and determining a plurality of probability values with the model (Fig. 5, para. [0073-0074], para. [0083]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system taught by Inan to additionally include determining, from a second data segment included in the physiological data, a second data portion satisfying the SQI condition; and determining a second probability value for the second data portion with the model developed via machine learning using the training data, as taught by Dani. This is because Dani teaches obtaining a plurality of data segments and data portions allows for a plurality of probability values to adjust probability thresholds (para. [0039]).
Regarding claim 9, modified Inan discloses the system of claim 8.
However, modified Inan does not explicitly disclose wherein the one or more electronic processors are configured to: determine whether the exacerbation condition is satisfied based on the first probability value and the second probability value.
Dani further teaches the one or more electronic processors are configured to: determine whether the exacerbation condition is satisfied based on the first probability value and the second probability value (Fig. 5, para. [0083], para. [0091-0092]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system taught by modified Inan to additionally determine whether the exacerbation condition is satisfied based on the first probability value and the second probability value, as taught by Dani. This is because Dani teaches utilizing a plurality of probability values allows for a more tailored diagnostic approach, as compared to a binary prediction (para. [0037-0038]).
Regarding claim 10, modified Inan discloses the system of claim 8, wherein the third probability value is a mean pulmonary capillary wedge pressure of the first probability value and the second probability value (para. [0243], “estimate changes in … pulmonary capillary mean pressure”).
Regarding claim 11, modified Inan discloses the system of claim 8.
However, modified Inan does not explicitly disclose wherein the one or more electronic processors are configured to: determine a third probability value based on the first probability value and the second probability value, wherein the one or more electronic processors are configured to determine whether the exacerbation condition is satisfied based on the third probability value.
Dani further teaches the one or more electronic processors are configured to: determine a third probability value based on the first probability value and the second probability value, wherein the one or more electronic processors are configured to determine whether the exacerbation condition is satisfied based on the third probability value (Fig. 5, para. [0083], para. [0091-0092]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system taught by modified Inan to additionally determine a third probability value based on the first probability value and the second probability value, wherein the one or more electronic processors are configured to determine whether the exacerbation condition is satisfied based on the third probability value, as taught by Dani. This is because Dani teaches utilizing a plurality of probability values allows for a more tailored diagnostic approach, as compared to a binary prediction (para. [0037-0038]).
Regarding claim 12, modified Inan discloses the system of claim 11, wherein the one or more electronic processors are configured to: determine whether the exacerbation condition is satisfied by comparing the third probability value to a pressure threshold (para. [0184], para. [0208]).
Regarding claim 13, modified Inan discloses the system of claim 12, wherein the third probability value satisfies the exacerbation condition when the third probability value exceeds the pressure threshold (para. [0184], para. [0208]).
Regarding claim 14, modified Inan discloses the system of claim 12.
Inan suggests, but does not explicitly disclose wherein the pressure threshold is 18 mmHg. Inan suggests this by disclosing a mean PCWP threshold of 20 mmHg or more was utilized, however, the thresholds may be overridden based on additional data (para. [0208]). As such, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the pressure threshold taught by modified Inan to explicitly be 18 mmHg through routine experimentation, as suggested by Inan (see MPEP 2144.05, I and II).
Regarding claim 17, Inan discloses the method of claim 16.
However, Inan does not explicitly disclose the method further comprises: determining a set of data segments included in the physiological data; and determining a set of data portions from the set of data segments, wherein each data portion is included in a corresponding data segment and is a representative signal of the corresponding data segment.
Dani teaches an analogous method for detecting abnormal cardiac events in patients (Abstract, Fig. 5, para. [0007]). Dani teaches receiving physiological data associated with a patient (para. [0060], para. [0090]). Dani further teaches determining a set of data segments included in the physiological data; and determining a set of data portions from the set of data segments, wherein each data portion is included in a corresponding data segment and is a representative signal of the corresponding data segment (para. [0039], para. [0084-0085]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Inan to additionally include determining a set of data segments included in the physiological data; and determining a set of data portions from the set of data segments, wherein each data portion is included in a corresponding data segment and is a representative signal of the corresponding data segment, as taught by Dani. This is because Dani teaches obtaining a plurality of data segments and data portions allows for a plurality of probability values to adjust probability thresholds (para. [0039]).
Regarding claim 18, modified Inan discloses the method of claim 16.
However, modified Inan does not explicitly disclose the method further comprises: determining a plurality of data portions, wherein each data portion satisfies the SQI condition, and wherein the first data portion is included in the plurality of data portions; determining a plurality of probability values using the model relating to the physiological data to cardiac pressure, wherein the first probability value is included in the plurality of probability values and wherein each probability value indicates a probability that each data portion is associated with a corresponding exacerbation event; determining a combined probability value based on the plurality of probability values; determining whether the exacerbation condition is satisfied based on the combined probability value; and in response to determining that the exacerbation condition is satisfied, generating and transmitting the exacerbation alert associated with the patient.
Dani further teaches determining a plurality of data portions, wherein each data portion satisfies the SQI condition, and wherein the first data portion is included in the plurality of data portions (Fig. 5, para. [0073-0074], para. [0083]); determining a plurality of probability values using the model relating to the physiological data to cardiac pressure, wherein the first probability value is included in the plurality of probability values and wherein each probability value indicates a probability that each data portion is associated with a corresponding exacerbation event (Fig. 5, para. [0073-0074], para. [0083], para. [0091]); determining a combined probability value based on the plurality of probability values (Fig. 5, para. [0091-0092]); determining whether the exacerbation condition is satisfied based on the combined probability value (Fig. 5, para. [0091-0093]); and in response to determining that the exacerbation condition is satisfied, generating and transmitting the exacerbation alert associated with the patient (Fig. 5, para. [0091-0093]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method taught by modified Inan to additionally include determining a plurality of data portions, wherein each data portion satisfies the SQI condition, and wherein the first data portion is included in the plurality of data portions; determining a plurality of probability values using the model relating to the physiological data to cardiac pressure, wherein the first probability value is included in the plurality of probability values and wherein each probability value indicates a probability that each data portion is associated with a corresponding exacerbation event; determining a combined probability value based on the plurality of probability values; determining whether the exacerbation condition is satisfied based on the combined probability value; and in response to determining that the exacerbation condition is satisfied, generating and transmitting the exacerbation alert associated with the patient, as taught by Dani. This is because Dani teaches utilizing a plurality of probability values allows for a more tailored diagnostic approach, as compared to a binary prediction (para. [0037-0038]).
Regarding claim 19, modified Inan discloses the method of claim 18, wherein determining the combined probability value includes determining a mean pulmonary capillary wedge pressure based on the plurality of probability values (para. [0243], “estimate changes in … pulmonary capillary mean pressure”).
Regarding claim 20, modified Inan discloses the method of claim 18.
However, modified Inan does not explicitly disclose wherein determining the plurality of probability values includes determining a plurality of probability values, wherein each probability value is associated with a different data portion of the plurality of data portions.
Dani further teaches determining the plurality of probability values includes determining a plurality of probability values, wherein each probability value is associated with a different data portion of the plurality of data portions (Fig. 5, para. [0083-0085], para. [0091]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method taught by modified Inan to additionally include wherein determining the plurality of probability values includes determining a plurality of probability values, wherein each probability value is associated with a different data portion of the plurality of data portions, as taught by Dani. This is because Dani teaches determining the probability value for a plurality of different data portions allows for more probability values to be utilized for a more tailored diagnostic approach, as compared to a binary prediction (para. [0037-0038]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Inan et al. (US 20230293082 A1), hereinafter referred to as Inan.
Regarding claim 7, Inan discloses the system of claim 1.
Inan suggests, but does not explicitly disclose, wherein the SQI condition includes a predetermined threshold of 0.5. Inan suggests this because Inan discloses a percentage of the total values can be utilized for further processing (para. [0220]). As such, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the threshold taught by modified Inan to explicitly be 0.5 through routine experimentation and processing requirements, as suggested by Inan (see MPEP 2144.05, I and II).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Schlesinger et al. (“A Deep Learning Model for Inferring Elevated Pulmonary Capillary Wedge Pressures From the 12-Lead Electrocardiogram”) - discloses a machine learning model for identifying elevated mPCWP using information from the ECG along (Conclusion).
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/K.W.K./Examiner, Art Unit 3791
/RENE T TOWA/Primary Examiner, Art Unit 3791
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