DETAILED ACTION
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 .
Election/Restrictions
Applicant’s election of Species e (claims 1-14, 17, 20-21, 23-26, 28-29, and 33-34) in the reply filed on 05/15/2026 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)).
Claim 27 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 05/15/2026.
Claims 15-16, 18-19, 22, 30-32, and 35-69 are cancelled.
Claims 1-14, 17, 20-21, 23-26, 28-29, and 33-34 are pending and under examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/06/2026 and 10/23/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 11 is objected to because of the following informalities: the abbreviation for pulmonary artery pressure “PAP” is the same abbreviation given for pulmonary artery pulsatility “PAP” of claim 10. Examiner suggests amending pulmonary artery pulsatility to its art-given abbreviation of “PAPi” Appropriate correction is required.
Claim 11 is objected to because of the following informalities: Examiner requests clarification on whether the PAP of claim 10 is the same as PAPI of claim 11. That is because pulmonary artery pulsatility is the same as pulmonary artery pressure index.
Claim 12 is objected to because of the following informalities: Examiner requests clarification on whether the PAdia and PAsys of claim 12 is the same as systolic and diastolic PAP of claim 11. That is because it is known in the art that PAPI is calculated using systolic and diastolic PAP.
Claim 34 is objected to because of the following informalities: the phrase “from the first area”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-14, 17, 20-21, 23-26, 28-29, and 33-34 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.
When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may “express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure." Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340 (Fed. Cir. 2008) (internal citation omitted).It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015).
Applicant’s first, second, and third machine learning (ML) algorithm for generating a first, second, and third probabilities (claims 1-7, 9, 14, 20, 29, and 34) lacks written description support. The mere statement and recitation of the usage of models such as ML algorithms provides insufficient disclosure of the type of algorithm and/or calculations that are used to determine the probabilities. The instant specification fails to describe the type of data that is used to train the model and the inputs and outputs of the models to achieve the probability. Specifically, although the type of data that is inputted is disclosed ([0042]-[0044]), the specification fails to disclose what within the data/how the data is analyzed (data [0044]) to make the determination of the probability and/or the algorithm that is used that uses the data to determine the probability. Furthermore, the specification fails to detail the features within a trained data set to determine the probabilities. The instant specification fails to detail the classification designations of the trained data for the different probabilities, how the trained data is associated with the collected data for making the determination of the probability, and what within the trained data is observed (peaks, troughs, time delays, peak-to-peak intervals, area under the curve, or many other data extraction techniques) to make the determination. Nevertheless, the instant specification fails to detail the way in which each of the machine learning models is used in their distinctive manner. Ultimately, the specification is written generically such that it only defines the invention in functional language specifying a desired result but does not sufficiently identify how the function is performed or the result is achieved (see MPEP §2161.01).
Claims 1-14, 17, 20-21, 23-26, 28-29, and 33-34 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The analysis of whether the specification complies with the written description requirement calls for the examiner to compare the scope of the claim with the scope of the description to determine whether applicant has demonstrated that the inventor was in possession of the claimed invention. Such a review is conducted from the standpoint of one of ordinary skill in the art at the time the application was filed (see, e.g., Wang Labs., Inc. v. Toshiba Corp., 993 F.2d 858, 865, 26 USPQ2d 1767, 1774 (Fed. Cir. 1993)) and should include a determination of the field of the invention and the level of skill and knowledge in the art. For some arts, there is an inverse correlation between the level of skill and knowledge in the art and the specificity of disclosure necessary to satisfy the written description requirement. Information which is well known in the art need not be described in detail in the specification. See, e.g., Hybritech, Inc. v. Monoclonal Antibodies, Inc., 802 F.2d 1367, 1379-80, 231 USPQ 81, 90 (Fed. Cir. 1986). However, sufficient information must be provided to show that the inventor had possession of the invention as claimed. See MPEP 2163 (II)(2).
A "representative number of species" means that the species which are adequately described are representative of the entire genus. See MPEP 2163(III)(a)(ii).
The Federal Circuit has explained that a specification cannot always support expansive claim language and satisfy the requirements of 35 U.S.C. 112 "merely by clearly describing one embodiment of the thing claimed." LizardTech v. Earth Resource Mapping, Inc., 424 F.3d 1336, 1346, 76 USPQ2d 1731, 1733 (Fed. Cir. 2005). The issue is whether a person skilled in the art would understand inventor to have invented, and been in possession of, the invention as broadly claimed. In LizardTech, claims to a generic method of making a seamless discrete wavelet transformation (DWT) were held invalid under 35 U.S.C. 112, first paragraph, because the specification taught only one particular method for making a seamless DWT and there was no evidence that the specification contemplated a more generic method. Id.; see also Tronzo v. Biomet, 156 F.3d at 1159, 47 USPQ2d at 1833 (Fed. Cir. 1998)(holding that the disclosure of a species in a parent application did not provide adequate written description support for claims to a genus in a child application where the specification taught against other species). See MPEP 2163(III)(a)(ii).
Claims 1-7, 9, 14, 20, 29, and 34 fail to sufficiently describe the determining the probability of any condition existing in a second (and third and fourth) area based on data from a first area in enough detail for one skilled in the art to have possession of the broadly claimed genus. Although the first, second, third, and fourth areas is used in the instant specification, the instant specification only defines the following locations for determining a probability of a condition: [0010] “the at least one processor may be configured to receive third data, after receiving the second data, the third data relating to a fourth area of the subject's body that is different from the first area, second area, and third area. For example, if the first, second, and third areas are the left heart, right heart, and vena cava, the fourth area may be, e.g., the pulmonary artery”. There are no other areas described in the instant specification for any of the first, second, third, and fourth areas. Further, the instant specification fails to detail the determining of any “condition”. The instant specification only defines the condition of right heart failure ([0039],[0048], and [0069]). Similar to Lizardtech, there is no evidence that the specification contemplated a more generic method of determining the probability of (any) condition of a user at a second/third/fourth area (of anywhere in the body) via by collecting data from a first area (of anywhere in the body). The only embodiment/species the instant specification details are the condition being RHF, the first area is the left hear, the second area is the right heart, the third area is vena cava, and the fourth area is the pulmonary artery. The instant specification fails to disclose any other embodiment/species, and therefore does not have a representative number of species to claim the genus, as instantly claimed. Therefore, claims 1-14, 17, 20-21, 23-26, 28-29, and 33-34 do not provide sufficient detail for a person skilled in the art to have been in possession of the invention as broadly claimed.
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 1-14, 17, 20-21, 23-26, 28-29, and 33-34 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1 and 34, it is unclear of the condition of line 8 is the same or different than the “conditions” of line 1. It is unclear if the “condition” encompasses one of the “conditions” or if the “condition” is a different condition that the “conditions”.
Regarding claims 2-4, it is unclear how there are 2 processors because under BRI, claim 1 requires only 1 processor based on the phrase “at least one processor”. Claim 2 is indefinite because it is unclear how many processors are used.
Regarding claim 2, it is unclear if the “data” of lines 3-4, 6, and 9 the same or different than the data of claim 1 line 6.
Claim 2 recites the limitation "the trained machine learning algorithm" in line 7. There is insufficient antecedent basis for this limitation in the claim. Claim 1 recites “at least one trained machine learning algorithm”.
Regarding claim 6, it is unclear how there are 2 ML algorithms because under BRI, claim 1 requires only 1 ML algorithm based on the phrase “at least one trained ML algorithm”. Claim 6 is indefinite because it is unclear how many ML algorithms are used.
Regarding claim 6, it is unclear if the “single medical device product line” the same or different than the “plurality of medical device product lines” of claim 5. It is unclear if the single medical device is a standalone different than the plurality or encompassed with the plurality of medical devices.
Regarding claim 9, it is unclear how the second data is received from the user, when claim 7, or any of the previous claims make no mention of the second data received from the user. Claim 7 recites that the second data “relating to a third area of the subject’s body”, which a user is different from a subject’s body, and is not necessarily received/collected off of a user or subject.
Regarding claim 11, it is unclear if the “CVP” of line 4 the same or different than the CVP of claim 8 line 2.
Regarding claim 12, it is unclear if the “PAPI” of line 1 the same or different than the PAPI of claim 11 line 7.
Claim 17 recites the limitation "the patient’s body" in line 2. There is insufficient antecedent basis for this limitation in the claim. Claim 1 recites “a subject’s body”.
Regarding claim 20, it is unclear if the “data” of lines 2 the same or different than the data of claim 1 line 6.
Regarding claim 21, it is unclear if the “value” relating to ECG of line 2 the same or different than the value related to blood oxygen of claim 21 line 2.
Regarding claim 26, the phrase “also includes” is ambiguous as to whether the limitation is required or if the limitation is optional. Examiner will interpret the limitation as optional.
Regarding claim 28, it is unclear if the “trend over time” of line 11 the same or different than the trend over time of claim 28 line 5.
Claim 28 recites the limitation "the probability" in line 6 and 9. There is insufficient antecedent basis for this limitation in the claim. Claim 1 recites “a first probability”.
Claim 28 recites the limitation "the first predetermined threshold" in line 10. There is insufficient antecedent basis for this limitation in the claim. Claim 28 recites “a first threshold” in line 3.
Claim 28 recites the limitation "the second predetermined threshold" in line 10. There is insufficient antecedent basis for this limitation in the claim. Claim 28 recites “a second threshold” in line 3.
Claim 29 recites the limitation "the second predetermined threshold" in line 3. There is insufficient antecedent basis for this limitation in the claim. Claim 28 recites “a second threshold” in line 3.
Claim 29 recites the limitation "the first predetermined threshold" in line 3. There is insufficient antecedent basis for this limitation in the claim. Claim 28 recites “a first threshold” in line 3.
Claim 29 recites the limitation "the probability" in line 5. There is insufficient antecedent basis for this limitation in the claim. Claim 1 recites “a first probability”.
Claim 33 recites the limitation "the probability" in line 5. There is insufficient antecedent basis for this limitation in the claim. Claim 1 recites “a first probability”.
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 1-14, 17, 20-21, 23-26, 28-29, and 33-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP 2106(III) outlines steps for determining whether a claim is directed to statutory subject
matter. The stepwise analysis for the instant claim is provided here.
Step 1 – Statutory categories
Claim 1 is directed to a system (i.e. machine) and thus meets the step 1 requirements.
Claim 34 is directed to a method and thus meets the step 1 requirements.
Step 2A – Prong 1 – Judicial exception (j.e.)
Regarding claims 1 and 34, the following step is an abstract idea:
“determine… a first probability of a condition existing in a second area of the subject's body based on the received data”, which is a mental process when given its broadest reasonable interpretation. As discussed in MPEP 2106.04(a)(2)(II), the mental process grouping includes observations, evaluations, judgements, and opinions. In this case, a human could determine a probability through the evaluation of the condition based on data.
Step 2A – Prong 2 – additional elements to integrate j.e. into a practical application
Regarding claims 1 and 34, the abstract idea is not integrated into a practical application.
The following claim elements do not add any meaningful limitation to the abstract idea:
- “a hemodynamic support device”, and “a processor” are recited at a high level of generality amounting to generic computer components for implementing abstract idea [MPEP 2106.05(b)];
It is noted that the machine learning algorithm is by definition automating the human thinking process with a computer.
- “sensor” of claim 17 and 20-21 is data gathering structures for the insignificant extra-solution activity of data gathering [MPEP 2106.05(b)];
- “first probability”, “first and second areas”, “condition” and “data” are data (gathering, selecting, and displaying) that is necessary to implement the abstract idea on a computer amounting to insignificant extra-solution activity [MPEP 2106.05(g)];
- “machine learning algorithm” is merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Step 2B – significantly more/inventive concept
The following claim elements do not add any meaningful limitation to the abstract idea:
- “a hemodynamic support device”, and “a processor” are recited at a high level of generality amounting to generic computer components for implementing abstract idea [MPEP 2106.05(b)];
It is noted that the machine learning algorithm is by definition automating the human thinking process with a computer.
- “sensor” of claim 17 and 20-21 is data gathering structures for the insignificant extra-solution activity of data gathering [MPEP 2106.05(b)];
- “first probability”, “first and second areas”, “condition” and “data” are data (gathering, selecting, and displaying) that is necessary to implement the abstract idea on a computer amounting to insignificant extra-solution activity [MPEP 2106.05(g)];
- “machine learning algorithm” is merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
The additional elements of claims 1 and 34, when considered separately and in combination, do not add significantly more (ie. an inventive concept) to the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the hemodynamic support device, and processor, along with their associated functions, are recited at a high level of generality and simply amount to implementing the abstract idea on a computer. Specifically, the hemodynamic support device, is a generic device that contains a generic computing device, similar to Venkatraman et al. (US 20210259560) (Fig. 5(505)), that is well-understood, routine, and conventional.
Dependent claims 2-14, 17, 20-21, 23-26, 28-29, and 33 do not integrate the abstract idea into a practical application and do not add significantly more to the abstract idea of claim 1 and 10. The dependent claim limitations are directed to the type of data (claims 2-14, 23-26, 28-29, and 33), and generic data gathering structure (claims 17, 20-21) and to processing user input (claims 5-9, 12, and 15-19), which are insignificant extra-solution activity and do not amount to more than what is well-understood, routine, and conventional.
In summary, claims 1-14, 17, 20-21, 23-26, 28-29, and 33-34 are directed to an abstract idea without significantly more and, therefore, are patent ineligible.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1-2, 4-11, 13-14, 17, 20-21, 23-26, 28-29, and 33-34 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Venkatraman et al. (US 20210259560)(IDS)(Hereinafter Venkatraman).
Regarding claims 1 and 34, Venkatraman teaches A system and method for detecting and/or inferring conditions (Abstract “The present disclosure provides methods, devices, and systems for determining a state or condition of a subject. A method for determining a state”), comprising:
a hemodynamic support device configured to be positioned in a first area of a subject's body (Fig. 3 shows different positions. [0054] “The position of the monitoring device 300 may be varied with respect to anatomical features of the subject 340 depending on the state or condition to be characterized. For example, the position of the monitoring device 300 may be external to the skin near the subject's heart. As another example, the position of the monitoring device may be near the subject's lung.”);
at least one processor operably coupled to the hemodynamic support device (Fig. 5(505) [0078] “The monitoring device may comprise a microprocessor or microprocessing unit (MPU) 505.”), the at least one processor configured to:
receive data from the hemodynamic support device ([0182] “After the button 120 is depressed, patient data may be collected, such as ECG data, intrathoracic impedance data, accelerometer data, and audio data. The collected data may be pre-processed on the monitoring device 100.”); and
determine, with at least one trained machine learning (ML) algorithm, a first probability of a condition existing in a second area of the subject's body based on the received data, the second area being different from the first area ([0102] “The algorithm may be trained by a training set that is specific to a given application, such as, for example classifying a state or condition (e.g., a disease). The algorithm may be different for heart disease and lung disease, for example. The algorithm may be trained for application in a first use case (e.g., arrhythmia) using a training set that is different than training the algorithm for application in a second use case (e.g., pneumonia). The algorithm may be trained using a training set of subjects with known states or conditions (e.g., disorders).” [0127] “The model may be subsequently used to evaluate audio data alone, ECG data alone, intrathoracic impedance data, or a combination of two or more data types to determine the presence or absence of a state or condition of an organ, such as a murmur of a heart…The network may output a probability of state or condition of a heart for each segment. These probabilities may then be averaged across all or a fraction of the segments. The average may then be thresholded to make a determination of whether a state or condition of an organ, such as a heart murmur is present.” [0054] “The position of the monitoring device 300 may be varied with respect to anatomical features of the subject 340 depending on the state or condition to be characterized. For example, the position of the monitoring device 300 may be external to the skin near the subject's heart.” [0191] “The analysis software 1308 comprises the following algorithms of the present disclosure: … (2) a murmur detection algorithm that uses a neural network model to process heart audio data to detect the presence of murmurs” Examiner notes that the external device is the first area (external to the heart on the skin), and the organ (heart) is the second area, which is being classified as a heart murmur.).
Regarding claim 2, Venkatraman teaches wherein the at least one processor comprises:
a first processor (Fig. 5(505) [0078] “The monitoring device may comprise a microprocessor or microprocessing unit (MPU) 505.”) configured to:
receive data from the hemodynamic support device ([0182] “After the button 120 is depressed, patient data may be collected, such as ECG data, intrathoracic impedance data, accelerometer data, and audio data. The collected data may be pre-processed on the monitoring device 100.”); and
transmit the data to a second processor over a network ([0168] “FIG. 7 shows a computer system (also referred to herein as a “computing device”) 701 that is programmed or otherwise configured to receive ECG data, intrathoracic impedance data, accelerometer (e.g., motion and orientation) data, and audio data from a monitoring device (e.g., the monitoring device 100 shown in FIGS. 1A and 1B).” See Fig. 7 that moves data through the “network” ([0169]).); and
a second processor ([0169] “The computer system 701 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 705,”) configured to:
receive data from the first processor ([0168] “FIG. 7 shows a computer system (also referred to herein as a “computing device”) 701 that is programmed or otherwise configured to receive ECG data, intrathoracic impedance data, accelerometer (e.g., motion and orientation) data, and audio data from a monitoring device (e.g., the monitoring device 100 shown in FIGS. 1A and 1B).” See Fig. 7 that moves data through the “network” ([0169]).); and
determine, with the trained machine learning algorithm, the first probability of the condition existing in the second area of the subject's body based on the received data ([0170] “The instructions can be directed to the CPU 705, which can subsequently program or otherwise configure the CPU 705 to implement methods of the present disclosure.” [0102] “The algorithm may be trained by a training set that is specific to a given application, such as, for example classifying a state or condition (e.g., a disease). The algorithm may be different for heart disease and lung disease, for example. The algorithm may be trained for application in a first use case (e.g., arrhythmia) using a training set that is different than training the algorithm for application in a second use case (e.g., pneumonia). The algorithm may be trained using a training set of subjects with known states or conditions (e.g., disorders).” [0127] “The model may be subsequently used to evaluate audio data alone, ECG data alone, intrathoracic impedance data, or a combination of two or more data types to determine the presence or absence of a state or condition of an organ, such as a murmur of a heart…The network may output a probability of state or condition of a heart for each segment. These probabilities may then be averaged across all or a fraction of the segments. The average may then be thresholded to make a determination of whether a state or condition of an organ, such as a heart murmur is present.” [0054] “The position of the monitoring device 300 may be varied with respect to anatomical features of the subject 340 depending on the state or condition to be characterized. For example, the position of the monitoring device 300 may be external to the skin near the subject's heart.” [0191] “The analysis software 1308 comprises the following algorithms of the present disclosure: … (2) a murmur detection algorithm that uses a neural network model to process heart audio data to detect the presence of murmurs”).
Regarding claim 4, Venkatraman teaches further comprising a remote device (),
wherein the second processor is further configured to transmit the first probability to the remote device ([0169] “The network 530, in some examples with the aid of the computer system 701, can implement a peer-to-peer network, which may enable devices coupled to the computer system 701 to behave as a client or a server. The computer system 701 can include or be in communication with an electronic display 735 that comprises a user interface (UI) 740 for providing, for example, an output indicative a state or condition of a user.”); and
wherein the remote device is configured to display the first probability, or a text or image representative of the first probability ([0169] “The computer system 701 can include or be in communication with an electronic display 735 that comprises a user interface (UI) 740 for providing, for example, an output indicative a state or condition of a user.”).
Regarding claim 5, Venkatraman teaches wherein the at least one trained ML algorithm comprises a first ML algorithm trained on historical data gathered from a plurality of medical device product lines ([0102] “The algorithm may be trained by a training set that is specific to a given application, such as, for example classifying a state or condition (e.g., a disease)…The algorithm may be trained using a training set of subjects with known states or conditions (e.g., disorders). In some examples, the training set (e.g., type of data and size of the training set) [historical data] may be selected such that, in validation, the algorithm yields an output having a predetermined [historical] accuracy, sensitivity and/or specificity (e.g., an accuracy of at least 90% when tested on a validation or test sample independent of the training set).” [0103] “The trained algorithm may be a neural network. The neural network may comprise an unsupervised learning model or a supervised learning model. The audio and/or ECG data [plurality of medical device product lines] may be input into the neural network.”).
Regarding claim 6, Venkatraman teaches wherein the at least one trained ML algorithm further comprises a second ML algorithm trained on data gathered from a single medical device product line ([0118] “Independent training samples may be associated with presence of the state or condition (e.g., training samples comprising datasets of ECG data and/or audio data [single medical device product line] and associated output values obtained or derived from a plurality of subjects known to have the state or condition)…A plurality of different trained algorithms may be trained, such that each of the plurality of trained algorithms is trained using a different set of independent training samples (e.g., sets of independent training samples corresponding to presence or absence of different states or conditions).”).
Regarding claim 7, Venkatraman teaches wherein the at least one processor is configured to receive second data, after receiving the data from the hemodynamic support device, the second data relating to a third area of the subject's body that is different from the first area and the second area ([0046] “Data from vibration, force, or pressure sensors may be used, individually or in combination with data received from the other sensors of the monitoring device 100, to detect or identify a state or condition of the subject, such as a pressure [second data]” [0044] “The monitoring device 100 may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more sensors of the same or of different types. The sensors may be various types of sensors, such as ECG sensors, audio sensors, temperature sensors, pressure sensors, vibration sensors, force sensors, respiratory monitors or sensors (e.g., a device, device part, or sensor capable of measuring a respiration rate), heart rate monitors or sensors, intrathoracic impedance monitors or sensors (e.g., a device, device part, or sensor capable of measuring an intrathoracic impedance), accelerometers, and/or other types of sensors. The sensors may be part of the monitoring device 100.” [0054] “For example, the position of the monitoring device 300 may be external to the skin near the subject's heart. As another example, the position of the monitoring device may be near the subject's lung. As still another example, the position of the monitoring device 300 may be near the subject's bowel. In yet another example, the position of the monitoring device 300 may be near the subject's fistula (e.g., a diabetic fistula).” Examiner notes the different locations where the data can be collected.); and
wherein the at least one trained ML algorithm is further configured to determine a second probability of the condition based on the data from the hemodynamic support device and the second data ([0046] “Data from vibration, force, or pressure sensors may be used, individually or in combination with data received from the other sensors of the monitoring device 100, to detect or identify a state or condition of the subject, such as a pressure [second data]” [0099] “The algorithm can, for example, be used to process a suitable combination of data measured using the monitoring device (e.g., ECG data, audio data, intrathoracic impedance data, accelerometer data, and/or data from any combination of the one or more sensors and/or any of the sensor modalities) to determine the physiological or biological state or condition of an organ or organ system the subject, such as heart, lung, bowel, or any other organ or organ system.” [0118] “A plurality of different trained algorithms may be trained, such that each of the plurality of trained algorithms is trained using a different set of independent training samples (e.g., sets of independent training samples corresponding to presence or absence of different states or conditions [this allows for a plurality of probabilities]).” [0115] “a classification of subjects may assign an output value of “positive” or 1 if the subject has a probability of having the state or condition” Examiner notes that the second probability is outputted from one of the plurality of algorithms. ).
Regarding claim 8, Venkatraman teaches wherein the second data includes a value relating to a central venous pressure (CVP) ([0046] “Data from vibration, force, or pressure sensors may be used, individually or in combination with data received from the other sensors of the monitoring device 100, to detect or identify a state or condition of the subject, such as a pressure (e.g., an increased pressure or filling pressure) inside the heart, such as pulmonary artery pressure, pulmonary arterial wedge pressure, central venous pressure”).
Regarding claim 9, Venkatraman teaches wherein the at least one processor is configured to receive third data, after receiving the second data from the user, the third data relating to a fourth area of the subject's body that is different from the first area, second area, and third area ([0108] “Implantable sensors [fourth area] comprise implantable devices capable of providing real-time hemodynamic data such as Heart Failure (HF) systems further comprising CardioMEMS, right ventricular (RV) sensors, pulmonary artery (PA) sensors …The neural network may combine all of the three metrics to arrive at a combined score which is proportional to or related to the ejection fraction of the subject. In another example, the combined score can predict pulmonary artery pressure [third data] as measured by an implantable sensor like the CardioMEMS HF system.” [0044] “The monitoring device 100 may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more sensors of the same or of different types. The sensors may be various types of sensors, such as ECG sensors, audio sensors, temperature sensors, pressure sensors, vibration sensors, force sensors, respiratory monitors or sensors (e.g., a device, device part, or sensor capable of measuring a respiration rate), heart rate monitors or sensors, intrathoracic impedance monitors or sensors (e.g., a device, device part, or sensor capable of measuring an intrathoracic impedance), accelerometers, and/or other types of sensors. The sensors may be part of the monitoring device 100.” [0054] “For example, the position of the monitoring device 300 may be external to the skin near the subject's heart. As another example, the position of the monitoring device may be near the subject's lung. As still another example, the position of the monitoring device 300 may be near the subject's bowel. In yet another example, the position of the monitoring device 300 may be near the subject's fistula (e.g., a diabetic fistula).” Examiner notes the different locations where the data can be collected.); and
wherein the at least one trained ML algorithm is further configured to determine a third probability of the condition based on the data from hemodynamic support device, second data, and third data ([0108] “Implantable sensors [fourth area] comprise implantable devices capable of providing real-time hemodynamic data such as Heart Failure (HF) systems further comprising CardioMEMS, right ventricular (RV) sensors, pulmonary artery (PA) sensors …The neural network may combine all of the three metrics to arrive at a combined score which is proportional to or related to the ejection fraction of the subject. In another example, the combined score can predict pulmonary artery pressure [third data] as measured by an implantable sensor like the CardioMEMS HF system.” [0099] “The algorithm can, for example, be used to process a suitable combination of data measured using the monitoring device (e.g., ECG data, audio data, intrathoracic impedance data, accelerometer data, and/or data from any combination of the one or more sensors and/or any of the sensor modalities) to determine the physiological or biological state or condition of an organ or organ system the subject, such as heart, lung, bowel, or any other organ or organ system.” [0118] “A plurality of different trained algorithms may be trained, such that each of the plurality of trained algorithms is trained using a different set of independent training samples (e.g., sets of independent training samples corresponding to presence or absence of different states or conditions [this allows for a plurality of probabilities]).” [0115] “a classification of subjects may assign an output value of “positive” or 1 if the subject has a probability of having the state or condition” Examiner notes that the second probability is outputted from one of the plurality of algorithms.).
Regarding claim 10, Venkatraman teaches wherein the third data includes a value relating to pulmonary artery pulsatility (PAP) ([0108] “Implantable sensors comprise implantable devices capable of providing real-time hemodynamic data such as Heart Failure (HF) systems further comprising CardioMEMS, right ventricular (RV) sensors, pulmonary artery (PA) sensors…The neural network may combine all of the three metrics to arrive at a combined score which is proportional to or related to the ejection fraction of the subject. In another example, the combined score can predict pulmonary artery pressure as measured by an implantable sensor like the CardioMEMS HF system.” PAP is related to PAPi since PAP is used to determine PAPi.).
Regarding claim 11, Venkatraman teaches wherein the at least one processor is further configured to derive at least one parameter, and the at least one trained ML algorithm is configured to determine the second probability and/or the third probability further based on the at least one parameter, where the at least one parameter is central venous pressure (CVP), a right atrial pressure (RAP), a min, max, and/or mean of RAP, right ventricle end-diastolic pressure (RVEDP), pulmonary artery pressure (PAP), a mean, systolic, and/or diastolic PAP, a pulmonary artery pressure index (PAPI), and/or an echo based parameter of right heart function (0046] “Data from vibration, force, or pressure sensors may be used, individually or in combination with data received from the other sensors of the monitoring device 100, to detect or identify a state or condition of the subject, such as a pressure (e.g., an increased pressure or filling pressure) inside the heart, such as pulmonary artery pressure, pulmonary arterial wedge pressure, central venous pressure” [0099] “The algorithm can, for example, be used to process a suitable combination of data measured using the monitoring device (e.g., ECG data, audio data, intrathoracic impedance data, accelerometer data, and/or data from any combination of the one or more sensors and/or any of the sensor modalities) to determine the physiological or biological state or condition of an organ or organ system the subject, such as heart, lung, bowel, or any other organ or organ system.” [0118] “A plurality of different trained algorithms may be trained, such that each of the plurality of trained algorithms is trained using a different set of independent training samples (e.g., sets of independent training samples corresponding to presence or absence of different states or conditions [this allows for a plurality of probabilities]).” [0115] “a classification of subjects may assign an output value of “positive” or 1 if the subject has a probability of having the state or condition” Examiner notes that the second probability is outputted from one of the plurality of algorithms using CVP of the pressure sensor and the audio data.).
Regarding claim 13, Venkatraman teaches wherein the echo-based parameter of right heart function is a right ventricle (RV) diameter, an RV volume, RV stroke volume index (RVSVI) value, RV stroke work index (RVSWI) value, and/or a tricuspid annular plane systolic excursion (TAPSE) value ([0108] “Implantable sensors comprise implantable devices capable of providing real-time hemodynamic data such as Heart Failure (HF) systems further comprising CardioMEMS, right ventricular (RV) sensors, pulmonary artery (PA) sensors, and left atrial pressure (LAP) sensors, diagnostic features in implantable cardiac resynchronization therapy (CRT) devices and implantable cardioverter defibrillator (ICD) devices.” RV volume is calculation from RV sensors.).
Regarding claim 14, Venkatraman teaches further comprising a remote device, the remote device configured to send the second data and third data to the at least one processor, and to receive the first probability, second probability, and third probability from the at least one processor ([0164] “Subject data such as ECG data, intrathoracic impedance data, and audio data may be analyzed on a remote server via a cloud computing network. The remote server [computing device which is interpreted as the processor that received the second and third data] may perform calculations (such as analyzing data) with greater computational cost that a mobile device of a user.” [0165] “The computing device, such as mobile device or a remote computing device may include a user interface. The ECG data and audio data or other data from any sensor or sensor modality provided herein may be transmitted to the computing device for display on the user interface [which includes the first probability, second probability, and third probability].”).
Regarding claim 17, Venkatraman teaches further comprising an additional device operably coupled to the at least one processor, the additional device including a sensor, the sensor being positioned in or on a third area of the patient's body ([0046] “Data from vibration, force, or pressure sensors may be used, individually or in combination with data received from the other sensors of the monitoring device 100, to detect or identify a state or condition of the subject, such as a pressure (e.g., an increased pressure or filling pressure) inside the heart, such as pulmonary artery pressure, pulmonary arterial wedge pressure, central venous pressure” [0044] “The monitoring device 100 may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more sensors of the same or of different types. The sensors may be various types of sensors, such as ECG sensors, audio sensors, temperature sensors, pressure sensors, vibration sensors, force sensors, respiratory monitors or sensors (e.g., a device, device part, or sensor capable of measuring a respiration rate), heart rate monitors or sensors, intrathoracic impedance monitors or sensors (e.g., a device, device part, or sensor capable of measuring an intrathoracic impedance), accelerometers, and/or other types of sensors. The sensors may be part of the monitoring device 100.” [0054] “For example, the position of the monitoring device 300 may be external to the skin near the subject's heart. As another example, the position of the monitoring device may be near the subject's lung. As still another example, the position of the monitoring device 300 may be near the subject's bowel. In yet another example, the position of the monitoring device 300 may be near the subject's fistula (e.g., a diabetic fistula).” Examiner notes the different locations where the data can be collected.).
Regarding claim 20, Venkatraman teaches wherein the at least one trained ML algorithm is configured to determine the first probability further based on data received from the sensor of the additional device ([0044] “The monitoring device 100 may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more sensors of the same or of different types. The sensors may be various types of sensors, such as ECG sensors, audio sensors, temperature sensors, pressure sensors, vibration sensors, force sensors, respiratory monitors or sensors (e.g., a device, device part, or sensor capable of measuring a respiration rate), heart rate monitors or sensors, intrathoracic impedance monitors or sensors (e.g., a device, device part, or sensor capable of measuring an intrathoracic impedance), accelerometers, and/or other types of sensors.” [0102] “The algorithm may be trained by a training set that is specific to a given application, such as, for example classifying a state or condition (e.g., a disease). The algorithm may be different for heart disease and lung disease, for example. The algorithm may be trained for application in a first use case (e.g., arrhythmia) using a training set that is different than training the algorithm for application in a second use case (e.g., pneumonia). The algorithm may be trained using a training set of subjects with known states or conditions (e.g., disorders).” [0127] “The model may be subsequently used to evaluate audio data alone, ECG data alone, intrathoracic impedance data, or a combination of two or more data types to determine the presence or absence of a state or condition of an organ, such as a murmur of a heart…The network may output a probability of state or condition of a heart for each segment. These probabilities may then be averaged across all or a fraction of the segments. The average may then be thresholded to make a determination of whether a state or condition of an organ, such as a heart murmur is present.” [0054] “The position of the monitoring device 300 may be varied with respect to anatomical features of the subject 340 depending on the state or condition to be characterized. For example, the position of the monitoring device 300 may be external to the skin near the subject's heart.” [0191] “The analysis software 1308 comprises the following algorithms of the present disclosure: … (2) a murmur detection algorithm that uses a neural network model to process heart audio data to detect the presence of murmurs” Examiner notes that the external device is the first area (external to the heart on the skin), and the organ (heart) is the second area, which is being classified as a heart murmur.).
Regarding claim 21, Venkatraman teaches wherein the data received from the sensor of the additional device includes a heart rate, a value related to blood oxygen, a value relating to an electrocardiogram (ECG), a skin temperature, or an acceleration ([0044] “The monitoring device 100 may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more sensors of the same or of different types. The sensors may be various types of sensors, such as ECG sensors, audio sensors, temperature sensors, pressure sensors, vibration sensors, force sensors, respiratory monitors or sensors (e.g., a device, device part, or sensor capable of measuring a respiration rate), heart rate monitors or sensors, intrathoracic impedance monitors or sensors (e.g., a device, device part, or sensor capable of measuring an intrathoracic impedance), accelerometers, and/or other types of sensors.”).
Regarding claim 23, Venkatraman teaches wherein the data from the hemodynamic support device includes first information related to left heart contractile function, and second information related to suction or pump flow in the left heart ([0046] “Data from vibration, force, or pressure sensors may be used, individually or in combination with data received from the other sensors of the monitoring device 100, to detect or identify a state or condition of the subject, such as a pressure (e.g., an increased pressure or filling pressure) inside the heart, such as pulmonary artery pressure, pulmonary arterial wedge pressure, central venous pressure, jugular venous pressure, left ventricle end-diastolic pressure (LVEDP), or other kinds of pressure. The states or conditions that can be detected using a vibration or force sensor, individually or in combination with other sensors of the monitoring device 100 may further comprise conditions such as a contraction. A contraction may be present, for example, in the heart of the subject. A contraction may comprise a [left] ventricular contraction [first information] (e.g., a premature ventricular contraction), an atrial contraction (e.g., a premature atrial contraction), or other types of contraction.” [0108] “Ejection fraction (EF) is a measurement, expressed as a percentage, of how much blood the left ventricle pumps out with each contraction [second information].”).
Regarding claim 24, Venkatraman teaches wherein the first information includes left ventricle (LV) contractility, aortic (AO) pulse pressure and/or pulsatility, or a combination thereof ([0046] “Data from vibration, force, or pressure sensors may be used, individually or in combination with data received from the other sensors of the monitoring device 100, to detect or identify a state or condition of the subject, such as a pressure (e.g., an increased pressure or filling pressure) inside the heart, such as pulmonary artery pressure, pulmonary arterial wedge pressure, central venous pressure, jugular venous pressure, left ventricle end-diastolic pressure (LVEDP), or other kinds of pressure. The states or conditions that can be detected using a vibration or force sensor, individually or in combination with other sensors of the monitoring device 100 may further comprise conditions such as a contraction. A contraction may be present, for example, in the heart of the subject. A contraction may comprise a [;eft] ventricular contraction (e.g., a premature ventricular contraction), an atrial contraction (e.g., a premature atrial contraction), or other types of contraction.”).
Regarding claim 25, Venkatraman teaches wherein the data from the hemodynamic support device includes aortic (AO) pressure, left ventricular (LV) pressure, pump motor speed, pump motor current, LV-AO pressure gradient, pump flow, cardiac output, native cardiac output, LV pulse rate, AO pulse rate, or a combination thereof ([0054] “The monitoring device 300 may be used to obtain indications to change a heart failure medication prescription, dosage, or frequency, such as a diuretic or ace inhibitor, based upon the cardiac output, systolic time intervals, or lung fluid status.” [0046] “Data from vibration, force, or pressure sensors may be used, individually or in combination with data received from the other sensors of the monitoring device 100, to detect or identify a state or condition of the subject, such as a pressure (e.g., an increased pressure or filling pressure) inside the heart, such as pulmonary artery pressure, pulmonary arterial wedge pressure, central venous pressure, jugular venous pressure, left ventricle end-diastolic pressure (LVEDP), or other kinds of pressure.” [0108] “Ejection fraction (EF) is a measurement, expressed as a percentage, of how much blood the left ventricle pumps out with each contraction.” EF is related to LV contractibility because EF is calculated using LV contractibility.).
Regarding claim 26, Venkatraman teaches wherein the data from the hemodynamic support device also includes LV volume via conductance, heart rate, heart rhythm, arterial pressure, blood oxygenation, or a combination thereof ([0046] “Data from vibration, force, or pressure sensors may be used, individually or in combination with data received from the other sensors of the monitoring device 100, to detect or identify a state or condition of the subject, such as a pressure (e.g., an increased pressure or filling pressure) inside the heart, such as pulmonary artery pressure, pulmonary arterial wedge pressure, central venous pressure, jugular venous pressure, left ventricle end-diastolic pressure (LVEDP), or other kinds of pressure.” [0059] “The sensors may comprise ECG sensors, audio sensors, temperature sensors, pressure sensors, vibration sensors, force sensors, respiratory monitors or sensors (e.g., a device, device part, or sensor capable of measuring a respiration rate), heart rate monitors or sensors, intrathoracic impedance monitors or sensors (e.g., a device, device part, or sensor capable of measuring an intrathoracic impedance), and/or other types of sensors.” Examiner notes the phrase “also includes” is interpreted as optional as it is unclear if the limitation is required.).
Regarding claim 28, Venkatraman teaches wherein the at least one processor is further configured to:
determine if the first probability is above a first threshold and/or below a second threshold ([0144] “Some of the output values may be assigned based on one or more cutoff values. For example, a binary classification of subjects may assign an output value of “positive” or 1 if the subject has at least a 50% probability of having the state or condition. For example, a binary classification of subjects may assign an output value of “negative” or 0 if the subject has less than a 50% probability of having the state or condition.”);
determine a trend over time in probabilities of the condition in the subject, and optionally if a rate of change described by the trend is above a threshold rate and/or if the probability will be above the first threshold or below the second threshold within a predetermined period of time should the trend continue ();
identify one or more primary factors that result in the probability being above the first predetermined threshold and/or below the second predetermined threshold ();
determine a trend over time in identified primary factors; or a combination thereof (Examiner notes only 1 limitation is required.).
Regarding claim 29, Venkatraman teaches wherein the at least one processor is further configured to alert a user when the first probability is determined to be above the first predetermined threshold or below the second predetermined threshold, when the rate of change described by the trend is above the threshold rate, and/or when it is determined, should the trend continue, the probability will be above the first threshold or below the second threshold within the predetermined period of time ([0114] “Some of the output values may be assigned based on one or more cutoff values. For example, a binary classification of subjects may assign an output value of “positive” or 1 if the subject has at least a 50% probability of having the state or condition. For example, a binary classification of subjects may assign an output value of “negative” or 0 if the subject has less than a 50% probability of having the state or condition.” [0137] “An output indicative of the physiological or biological state or condition of the heart of the subject may then be provided on the computing device. The output may be an alert indicative of an adverse state or condition of the heart.” Examiner notes only 1 limitation is required.).
Regarding claim 33, Venkatraman teaches wherein the at least one processor is further
configured to track the probability of the condition over time to determine if a provided treatment is effective at lowering risk of the condition ([0064] “a longitudinal dataset comprising subject data may be collected. A longitudinal data set may be used to track a state or condition of a subject (such as the state or condition of a heart of a subject) over an extended period of time. A monitoring device may track an output comprising a state or condition of a subject over time.” [0112] “Such descriptive labels may provide an identification or indication of a state or condition of the subject, and may comprise, for example, positive, negative, high-risk, intermediate-risk, low-risk, or indeterminate. Such descriptive labels may provide an identification of a treatment for the state or condition of the subject, and may comprise, for example, a therapeutic intervention, a duration of the therapeutic intervention, and/or a dosage of the therapeutic intervention suitable to treat the state or condition of the subject.”).
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.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman et al. (US 20210259560)(Hereinafter Venkatraman).
Regarding claim 3, Venkatraman teaches wherein the second processor is further configured to transmit the first probability to the first processor ([0169] “The network 530 in some examples is a telecommunication and/or data network. The network 530 can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network 530, in some examples with the aid of the computer system 701, can implement a peer-to-peer network, which may enable devices coupled to the computer system 701 to behave as a client or a server. The computer system 701 can include or be in communication with an electronic display 735 that comprises a user interface (UI) 740 for providing, for example, an output indicative a state or condition of a user.” [0127] “The model may be subsequently used to evaluate audio data alone, ECG data alone, intrathoracic impedance data, or a combination of two or more data types to determine the presence or absence of a state or condition of an organ, such as a murmur of a heart. For example, the model may be used to detect a murmur of a heart based on the above criteria. The model may be further used to determine the type of the murmur detected. Heart murmurs may comprise systolic murmurs, diastolic murmurs, continuous murmurs, holosystolic or pansystolic murmurs, and plateau or flat murmurs. In some examples, the audio data may split into segments, as described herein with respect to training. The audio data over a period may be analyzed independently by the network. The network may output a probability of state or condition of a heart for each segment.” [0170] “The instructions can be directed to the CPU 705, which can subsequently program or otherwise configure the CPU 705 to implement methods of the present disclosure.”).
However, Venkatraman does not teach the transmitting to the first processor. Although Venkatraman teaches the capability to communicate across platforms and compute the algorithm (see citations above), the second processor can transmit the first probability to the first processor to enable communication amongst the devices. It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to transmit the first probability to the first processor, for the purpose of communicating with other devices, since it has been held to be within the general skill of a worker in the art to enable communication on the basis of its suitability for the intended use as a matter of obvious design choice. In re Leshin, 125 USPQ 416.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman et al. (US 20210259560)(Hereinafter Venkatraman) in view of Lim et al. (“Pulmonary artery pulsatility index: physiological basis and clinical application”, European Journal of Heart Failure, First published: 28 November 2019 https://doi.org/10.1002/ejhf.1679)(Hereinafter Lim).
Regarding claim 12, Venkatraman teaches the invention of claim 1. Venkatraman does not teach PAPI is calculated by subtracting a diastolic pulmonary artery value (PAdia) from a systolic pulmonary artery value (PAsys), and the difference is then divided by RAP or CVP. Lim, in the same field of endeavor, teaches determining hemodynamic parameters relating to heart failure (Abstract), and further teaches wherein PAPI is calculated by subtracting a diastolic pulmonary artery value (PAdia) from a systolic pulmonary artery value (PAsys), and the difference is then divided by RAP or CVP (Pg. 33 left col. lines 14-15 “The PAPi is calculated as the PAPP divided by the right atrial or central venous pressure: PAPi = (PASP − PADP)∕RAP”) to indicate right heart function (Pg. 33 left col. lines 18-20). It would have been obvious to one skilled in the art, prior to the effective filing date of the invention, to modify the systen of Venkatraman, with the PAPI is calculated by subtracting a diastolic pulmonary artery value (PAdia) from a systolic pulmonary artery value (PAsys), and the difference is then divided by RAP or CVP of Lim, because such a modification would allow to indicate right heart function.
Conclusion
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/MOUSSA HADDAD/Examiner, Art Unit 3796
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