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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are: computing device in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
Claims 9 and 11 are objected to because of the following informalities:
in claim 9, line 3: “a” should be inserted before “memory”;
in claim 11, line 3: “at least one of: the lung perfusion” should be “the at least one of the lung perfusion”.
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 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.
Claim 1 recites “executing by the computing device a machine learning engine, the machine learning engine using a predictive model stored at the computing device for inferring outputs based on inputs, the inputs comprising the determined n * m voltage values, the outputs comprising a matrix of values representative of at least one of: lung perfusion, pulse transit time values and pulmonary artery pressure values” in lines 14-19, which is clearly a computer-implemented recitation. Under the current guidelines of 35 USC 112, the specification fails to support a claim that defines the invention in functional language specifying a desired result when the specification does not sufficiently identify how the invention achieves the claimed function. For there to be sufficient disclosure for a computer-implemented claim limitation, it is not enough that one skilled in the art could write a program to achieve the claimed function. Rather, the specification must disclose the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that performs the claimed function in sufficient detail such that one of ordinary skill can reasonably conclude that the inventor invented the claimed subject matter. See Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, Fed. Reg. Vol. 76, No. 27, February 9, 2011, p. 7162-7175 (“the Supplementary Examination Guidelines”). With respect to claim 1, this claim is rejected under §112, first paragraph, based on lack of written description because the specification fails to provide the algorithm (e.g., the necessary steps and/or flowcharts) that performs the claimed function of executing by the computing device a machine learning engine, the machine learning engine using a predictive model stored at the computing device for inferring outputs based on inputs, the inputs comprising the determined n * m voltage values, the outputs comprising a matrix of values representative of at least one of: lung perfusion, pulse transit time values and pulmonary artery pressure values. In particular, the specification does not provide any specifics, flow chart, or explanation that details how to execute the machine learning engine, how to use the predictive model, and how to infer the claimed outputs from the claimed inputs from the predictive model.
Claims 2-8 are rejected by virtue of their dependence from claim 1.
Claim 9 recites “execute a machine learning engine, the machine learning engine using a predictive model stored at the computing device for inferring outputs based on inputs, the inputs comprising the plurality of voltage values, the outputs comprising a matrix of values representative of at least one of lung perfusion values, pulse transit time (PTT) values and the pulmonary artery pressure (PAP) values” in lines 11-16, which is clearly a computer-implemented recitation. See the rejection of claim 1 and the Supplementary Examination Guidelines regarding sufficient disclosure for a computer-implemented claim limitation. With respect to claim 9, this claim is rejected under §112, first paragraph, based on lack of written description because the specification fails to provide the algorithm (e.g., the necessary steps and/or flowcharts) that performs the claimed function of executing a machine learning engine, the machine learning engine using a predictive model stored at the computing device for inferring outputs based on inputs, the inputs comprising the plurality of voltage values, the outputs comprising a matrix of values representative of at least one of lung perfusion values, pulse transit time (PTT) values and the pulmonary artery pressure (PAP) values. In particular, the specification does not provide any specifics, flow chart, or explanation that details how to execute the machine learning engine, how to use the predictive model, and how to infer the claimed outputs from the claimed inputs from the predictive model.
Claims 10-13 are rejected by virtue of their dependence from claim 9.
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 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.
Claim 1 recites “the chest” in line 7 in which there is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites “m pairs of electrodes located on the belt” in lines 8-9, but it is not clear if “a pair of electrodes, the pair of electrodes being located on a belt” from claim 1, lines 5-6 are a part of these m pairs of electrodes”. As such, the number of pairs of electrodes on the belt is not clear.
Claims 2-8 are rejected by virtue of their dependence from claim 1.
Claim 2 recites “m pairs of adjacent electrodes located on the belt” in lines 2-3, but it is not clear if this recitation is the same as, related to, or different from “m pairs of electrodes located on the belt” of claim 1, lines 8-9. The relationship between these two recitations should be made clear.
Claim 6 recites “wherein the voltage values comprise average voltage values, Root Mean Square (RMS) voltage values or maximum values of alternating voltage waveforms measured between the pairs of adjacent electrodes” in lines 1-4, but it is not clear if the action of “measuring” the average voltage values, Root Mean Square (RMS) voltage values or maximum values of alternating voltage waveforms is supposed to be the same as “determining corresponding voltage values” of claim 1, line 8. Measuring and determining connote two different actions and the claim does not link these two actions together. This ambiguity renders claim 6 indefinite.
Claim 6 recites “the pairs of adjacent electrodes” in lines 3-4, but it is not clear if this recitation is the same as, related to, or different from “m pairs of electrodes located on the belt” of claim 1, lines 8-9. The relationship between these two recitations should be made clear. If they are different, there is insufficient antecedent basis for “the pairs of adjacent electrodes” in claim 6.
Claim 9 recite “different pairs of electrodes of the belt” in lines 8-9, but it is not clear if this recitation is the same as, related to, or different from “pairs of electrodes of a belt” of claim 9, line 6. The relationship between these two recitations should be made clear.
Claim 9 recites “the chest” in line 9 and “the pulmonary artery pressure (PAP) values” in line 16 in which there are insufficient antecedent bases for these limitations in the claim.
Claim 9 recites “a predictive model stored at the computing device” in line 12, but it is not clear if this recitation is the same as, related to, or different from “a predictive model” of claim 9, line 3 that is stored in the memory. If they are the same, “a predictive model stored at the computing device” in line 12 should be “the predictive model stored in the memory”. If they are different, their relationship between these two recitations should be made clear, they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements), and any subsequent recitation of “the predictive model” should make it clear which recitation is being referred to.
Claims 10-14 are rejected by virtue of their dependence from claim 9.
Claim 10 recites “the matrix of at least one of values representative of the lung perfusion, the PTT values and the PAP values” in lines 2-4, but it is not clear if this recitation is the same as, related to, or different from “a matrix of values representative of at least one of lung perfusion values, pulse transit time (PTT) values and the pulmonary artery pressure (PAP) values” of claim 9, lines 14-16. If they are the same, consistent terminology should be used. If they are different, their relationship between these two recitations should be made clear. If they are different, there is insufficient antecedent basis for “the matrix of at least one of values representative of the lung perfusion, the PTT values and the PAP values” in claim 10.
Claims 11 and 14 are rejected by virtue of their dependence from claim 10.
Claim 11 recites “a visual representation” in line 1, but it is not clear if this recitation is the same as, related to, or different from “a visual representation” of claim 10, line 2. If they are the same, “a visual representation” in line 1 should be “the visual representation”. If they are different, their relationship between these two recitations should be made clear, they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements), and any subsequent recitation of “the visual representation” should make it clear which recitation is being referred to.
Claim 11 recites “further displaying a trend corresponding to the matrix of at least one of: the lung perfusion, the PTT values and the PAP values on a display of the computing device” in lines 4-7 which is a method step in an apparatus claim. A single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, because it creates confusion as to when direct infringement occurs. (MPEP 2173.05(p) citing In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 97 USPQ2d 1737 (Fed. Cir. 2011)).
Claim 11 recites “the matrix of at least one of: the lung perfusion, the PTT values and the PAP values” in lines 5-6, but it is not clear if this recitation is the same as, related to, or different from “a matrix of values representative of at least one of lung perfusion values, pulse transit time (PTT) values and the pulmonary artery pressure (PAP) values” of claim 9, lines 14-16 and/or “the matrix of at least one of values representative of the lung perfusion, the PTT values and the PAP values” of claim 10, lines 2-4. If they are the same, consistent terminology should be used. If they are different, their relationship among these recitations should be made clear. If they are different, there is insufficient antecedent basis for “the matrix of at least one of: the lung perfusion, the PTT values and the PAP values” in claim 11.
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 9-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 9-14 are directed to a method of outputting a matrix of values representative of at least one of lung perfusion values, pulse transit time (PTT) values and the pulmonary artery pressure (PAP) values using a computational algorithm, which is an abstract idea. Claims 9-14 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019).
The analysis of claim 9 is as follows:
Step 1: Claim 9 is drawn to a 9 machine.
Step 2A – Prong One: Claim 9 recites an abstract idea. In particular, claim 9 recites the following limitations:
[A1] receive a plurality of voltage values;
[B1] execute a machine learning engine, the machine learning engine using a predictive model for inferring outputs based on inputs,
[C1] the inputs comprising the plurality of voltage values,
[D1] the outputs comprising a matrix of values representative of at least one of lung perfusion values, pulse transit time (PTT) values and the pulmonary artery pressure (PAP) values.
These elements [A1]-[D1] of claim 9 are drawn to an abstract idea since 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.
Step 2A – Prong Two: Claim 9 recites the following limitations that are beyond the judicial exception:
[A2] a communication interface;
[B2] a memory;
[C2] a processing unit comprising one or more processors; and
[D2] a plurality of voltage values between pairs of electrodes of a belt comprising a plurality of electrodes, the voltage values being determined by sequentially injecting an alternating electrical current between different pairs of electrodes of the belt, the belt being positioned around the chest of a person.
These elements [A2]-[C2] of claim 9 do not integrate the exception into a practical application of the exception. In particular, the elements [A2]-[C2] are merely instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). Further, the element [D2] merely describes the nature of the voltage value since the electrodes and the belt are not part of the claimed computing device.
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the elements [A2]-[C2] do not qualify as significantly more because these limitations are 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)).
Also, the element [D2] does not qualify as significantly more because this limitation merely describes the nature of the voltage value since the electrodes and the belt are not part of the claimed computing device.
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.
Claims 10-14 depend from claim 9, and recite the same abstract idea as claim 9. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm), with the following exceptions:
Claim 10: the processing unit; and
Claim 11: displaying a trend corresponding to the matrix of at least one of: the lung perfusion, the PTT values and the PAP values on a display of the computing device.
Each of these claims limitations does not integrate the exception into a practical application. In particular, the element of claim 10 is merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). Further, the element of claim 11 is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere outputting the results of the algorithm - see MPEP 2106.04(d) and MPEP 2106.05(g).
Also, each of these limitations does not recite additional elements that amount to significantly more than the judicial exception itself. The element of claim 10 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 is, one of display) 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); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)). The element of claim 11 is merely insignificant extrasolution activity to the judicial exception, e.g., simply displaying the results of the algorithm that uses conventional, routine, and well known elements.
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 of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, 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.
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.
Claims 1-5, 9-10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2022/0160310 (Shetty), in view of U.S. Patent No. 7,096,061 (Arad), and further in view of U.S. Patent Application Publication No. 2014/0148678 (Drori).
Shetty teaches an implantable medical device (IMD) including one or more electrodes configured to measure an electrogram (EGM). The IMD may determine tissue perfusion based on impedance sensed via the electrodes, and/or oxygen saturation using an optical sensor. The processing circuitry may determine a pulse transit time (PTT) associated with the patient based on the EGM, the impedance, the measured oxygen saturation, or any combination thereof (. PTT is correlated with blood pressure (paragraph 0035 of Shetty). Shetty also teaches that the impedance may be, for example, subcutaneous impedance, an intrathoracic impedance, or an intracardiac impedance (paragraphs 0045, 0064, and 0107 of Shetty). Further, Shetty teaches that, alternatively, one or more patient parameters can be measured based on signals collected by one or more external devices such as patch devices, wearable devices (e.g., smart watches), wearable sensors, or any combination thereof (paragraph 0027 of Shetty).
Arad teaches that impedance measuring device that uses 8 electrodes placed around the chest for measuring impedance of the thoracic cavity (col 10, lines 15-50 of Arad). Additionally Arad teaches how to determine impedance readings with such electrodes (FIG. 2 and col. 8, lines 20-50 of Arad). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the 8 impedance electrodes about the chest to measuring the impedance of the thoracic cavity and the process for making the impedance measurements of Arad as a substitute for the impedance electrodes of Shetty since (1) it is a simple substitution of one known element for another to obtain predictable results, (2) Shetty teaches that impedance can be measured by an external device and Arad teaches one such device and a process of implementation. Drori teaches that such electrodes are mounted on a band 210 (paragraph 0059 of Drori). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to mount the electrodes of the combination on the band of Drori for application to the chest of the patient since it provides a more stable affixation onto the patient.
With respect to claim 1, the combination teaches or suggests a method using machine learning for pulmonary artery pressure measurement based on electrical impedance tomography (EIT), the method comprising:
repeating the following steps n times:
injecting an alternating electrical current between a pair of electrodes (the current injection of Arad using the electrodes of Arad; FIG. 2 and col. 8, lines 20-50 of Arad), the pair of electrodes being located on a belt (the electrodes of Arad on the band of Drori), the belt being positioned around the chest of a person (col. 7, lines 15-40 of Arad); and
determining corresponding voltage values between m pairs of electrodes located on the belt (the voltage measurements of Arad using the electrodes of Arad; FIG. 2 and col. 8, lines 20-50 of Arad);
transmitting the determined n * m voltage values to a computing device (transmitting the measurement data to the processing circuitry 14 on the externa device 12 of Shetty; paragraph 0025 of Shetty);
receiving by the computing device the determined n * m voltage values (receiving the measurement data to the processing circuitry 14 on the external device 12 of Shetty; paragraph 0025 of Shetty); and
executing by the computing device a machine learning engine, the machine learning engine using a predictive model stored at the computing device for inferring outputs based on inputs (the neural network of Shetty; paragraphs 0053, 0106, 0115 of Shetty), the inputs comprising the determined n * m voltage values (the measurement data of Shetty; paragraph 0025 of Shetty), the outputs comprising a matrix of values representative of at least one of: lung perfusion, pulse transit time values and pulmonary artery pressure values (the tissue perfusion values (paragraphs 0031 and 0035 of Shetty) and the PTT values of Shetty (paragraph 00035 of Shetty) over time (paragraphs 0028, 0035 and 0056 of Shetty); the tissue perfusion values are considered lung perfusion values since lung function is detected (paragraph 0030 of Shetty) and the thoracic cavity contains the lungs).
With respect to claim 2, the combination teaches or suggests that the determination of the corresponding voltage values is performed between m pairs of adjacent electrodes located on the belt (the voltage measurements of Arad using the electrodes of Arad; FIG. 2 and col. 8, lines 20-50 of Arad).
With respect to claim 3, the combination teaches or suggests that the m pairs of electrodes used for determining the corresponding voltage values do not include the electrodes of the pair of electrodes used for injecting the alternating electrical current (the current injection and voltage measurement of Arad using the electrodes of Arad includes this aspect; FIG. 2 and col. 8, lines 20-50 of Arad).
With respect to claim 4, the combination teaches or suggests that the inputs of the machine learning engine further comprise at least one of the following: a frequency of the alternating electrical current, an electrical current value of the alternating electrical current and a voltage matrix containing electrocardiogram (ECG) values (the ECGs of Shetty; paragraphs 0030, 0035 and 0107 of Shetty).
With respect to claim 5, the combination teaches or suggests that the alternating electrical current has a sinusoidal waveform (the sinusoidal current source of Arad; col. 10, lines 15-50 of Arad; it would have been obvious to add such a current source to provide a sinusoidal current since it is a simple substitution or one known element for another to obtain predictable results and/or a type of current source is required and Arad teaches one such source).
With respect to claim 9, the combination teaches or suggests a computing device comprising:
a communication interface (the communication circuitry 82 of Shetty; paragraph 0066 of Shetty);
memory (the memory 84 of Shetty) storing a predictive model (the neural network of Shetty; paragraphs 0053, 0106, 0115 of Shetty); and
a processing unit comprising one or more processors (the processors of the processing circuity 14 of Shetty; paragraph 0040 of Shetty) configured to:
receive via the communication interface a plurality of voltage values between pairs of electrodes of a belt comprising a plurality of electrodes (receiving the measurement data to the processing circuitry 14 on the external device 12 of Shetty; paragraph 0025 of Shetty), the voltage values being determined by sequentially injecting an alternating electrical current between different pairs of electrodes (the current injection of Arad using the electrodes of Arad; FIG. 2 and col. 8, lines 20-50 of Arad) of the belt (the electrodes of Arad on the band of Drori), the belt being positioned around the chest of a person (col 7, lines 15-40 of Arad);
execute a machine learning engine, the machine learning engine using a predictive model stored at the computing device for inferring outputs based on inputs (the neural network of Shetty; paragraphs 0053, 0106, 0115 of Shetty), the inputs comprising the plurality of voltage values (the measurement data of Shetty; paragraph 0025 of Shetty), the outputs comprising a matrix of values representative of at least one of lung perfusion values, pulse transit time (PTT) values and the pulmonary artery pressure (PAP) values (the tissue perfusion values (paragraphs 0031 and 0035 of Shetty) and the PTT values of Shetty (paragraph 00035 of Shetty) over time (paragraphs 0028, 0035 and 0056 of Shetty); the tissue perfusion values are considered lung perfusion values since lung function is detected (paragraph 0030 of Shetty) and the thoracic cavity contains the lungs).
With respect to claim 10, Shetty teaches tissue perfusion values (paragraphs 0031 and 0035 of Shetty) over time (paragraphs 0028 and 0056 of Shetty). The tissue perfusion values are considered lung perfusion values since lung function is detected (paragraph 0030 of Shetty) and the thoracic cavity contains the lungs. Shetty also teaches the use of a display (paragraph 0096 of Shetty). Arad teaches that a display is used for presenting results (col. 10- lines 15-50 of Arad). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the display of Shetty to display the results of tissue perfusion over time since it provides the medical professional and/or the patient with an update on the status of tissue perfusion over time. With respect to claim 10, the combination teaches or suggest that the processing unit further generates a visual representation of a lung perfusion array based on the matrix of at least one of values representative of the lung perfusion, the PTT values and the PAP values (the display of the results of tissue perfusion over time based on the tissue perfusion values over time).
With respect to claim 12, the combination teaches or suggests that the inputs of the machine learning engine further comprise at least one of the following: a frequency of the alternating electrical current, an electrical current value of the alternating electrical current and a voltage matrix containing electrocardiogram (ECG) values (the ECGs of Shetty; paragraphs 0030, 0035 and 0107 of Shetty).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2022/0160310 (Shetty), in view of U.S. Patent No. 7,096,061 (Arad), and further in view of U.S. Patent Application Publication No. 2014/0148678 (Drori), and further in view of U.S. Patent Application Publication No. 2010/0036379 (Prakash).
The combination teaches the voltage measurements of Arad using the electrodes of Arad (FIG. 2 and col. 8, lines 20-50 of Arad). Also, Arad teaches a sinusoidal current source of Arad (col. 10, lines 15-50 of Arad). It would have been obvious to add such a current source to provide a sinusoidal current since it is a simple substitution or one known element for another to obtain predictable results and/or a type of current source is required and Arad teaches one such source. Prakash teaches that RMS values of the voltage are used with sinusoidal current injection to determine the magnitude of issue impedance (paragraph 0052 of Prakash). It would have been obvious to convert the voltage measurements into RMS voltage values since it is a simple substitution or one known element for another to obtain predictable results and/or a type of voltage measurement is required and Prakash teaches one such measurement.
With respect to claim 6, the combination teaches or suggests that the voltage values comprise average voltage values, Root Mean Square (RMS) voltage values (the RMS voltage values of Prakash) or maximum values of alternating voltage waveforms measured between the pairs of adjacent electrodes.
Claims 7-8 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2022/0160310 (Shetty), in view of U.S. Patent No. 7,096,061 (Arad), and further in view of U.S. Patent Application Publication No. 2014/0148678 (Drori), and further in view of U.S. Patent Application Publication No. 2009/0099474 (Pineda).
The combination teaches the neural network of Shetty (paragraphs 0053, 0106, 0115 of Shetty). Pineda teaches a structure for the neural network (paragraph 0078-0081 and FIG. 6 of Pineda). It would have been obvious to use the structure of the neural network of Pineda as the neural network of Shetty since a structure is required and Pineda teaches one such structure and/or it is a simple substitution or one known element for another to obtain predictable results.
With respect to claim 7, the combination teaches or suggests that the machine learning engine is a neural network inference engine implementing a neural network (the structure of the neural network of Pineda as the neural network of Shetty), the neural network using the predictive model for inferring the outputs based on the inputs (the inputs comprising the plurality of voltage values of Shetty (paragraph 0025 of Shetty); the outputs being the tissue perfusion values (paragraphs 0031 and 0035 of Shetty) and the PTT values of Shetty (paragraph 00035 of Shetty) over time (paragraphs 0028, 0035 and 0056 of Shetty); the tissue perfusion values are considered lung perfusion values since lung function is detected (paragraph 0030 of Shetty) and the thoracic cavity contains the lungs), the predictive model comprising weights of the neural network (the weights of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda).
With respect to claim 8, the combination teaches or suggests that the neural network comprises an input layer (the input layer of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda), followed by fully connected hidden layers (the fully connected hidden layers of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda), followed by an output layer (the output layer of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda); the input layer comprising neurons receiving the inputs (the neurons of the input layer of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda); the output layer comprising neurons outputting the outputs (the neurons of the output layer of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda); the weights of the neural network (the weights of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda) being applied to the fully connected hidden layers.
With respect to claim 13, the combination teaches or suggests that the machine learning engine is a neural network inference engine implementing a neural network (the structure of the neural network of Pineda as the neural network of Shetty), the neural network using the predictive model for inferring the outputs based on the inputs (the inputs comprising the plurality of measurement data of Shetty (paragraph 0025 of Shetty); the outputs being the tissue perfusion values (paragraphs 0031 and 0035 of Shetty) and the PTT values of Shetty (paragraph 00035 of Shetty) over time (paragraphs 0028, 0035 and 0056 of Shetty); the tissue perfusion values are considered lung perfusion values since lung function is detected (paragraph 0030 of Shetty) and the thoracic cavity contains the lungs), the predictive model comprising weights of the neural network (the weights of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda).
With respect to claim 14, the combination teaches or suggests that the neural network comprises an input layer (the input layer of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda), followed by fully connected hidden layers (the fully connected hidden layers of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda), followed by an output layer (the output layer of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda); the input layer comprising neurons receiving the inputs (the neurons of the input layer of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda); the output layer comprising neurons outputting the outputs (the neurons of the output layer of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda); the weights of the neural network (the weights of Pineda; paragraph 0078-0081 and FIG. 6 of Pineda) being applied to the fully connected hidden layers.
Claims 1-5 and 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2022/0160310 (Shetty), in view of U.S. Patent No. 7,096,061 (Arad), and further in view of U.S. Patent Application Publication No. 2014/0148678 (Drori), and U.S. Patent Application Publication No. 2015/0216443 (Baier-Lowenstein).
Shetty teaches an implantable medical device (IMD) including one or more electrodes configured to measure an electrogram (EGM). The IMD may determine tissue perfusion based on impedance sensed via the electrodes, and/or oxygen saturation using an optical sensor. The processing circuitry may determine a pulse transit time (PTT) associated with the patient based on the EGM, the impedance, the measured oxygen saturation, or any combination thereof. PTT is correlated with blood pressure (paragraph 0035 of Shetty). Shetty also teaches that the impedance may be, for example, subcutaneous impedance, an intrathoracic impedance, or an intracardiac impedance (paragraphs 0045, 0064, and 0107 of Shetty). Further, Shetty teaches that, alternatively, one or more patient parameters can be measured based on signals collected by one or more external devices such as patch devices, wearable devices (e.g., smart watches), wearable sensors, or any combination thereof (paragraph 0027 of Shetty).
Arad teaches an impedance measuring device that uses 8 electrodes placed around the chest for measuring impedance of the thoracic cavity (col. 10, lines 15-50 of Arad). Additionally Arad teaches how to determine impedance readings with such electrodes (FIG. 2 and col. 8, lines 20-50 of Arad). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the 8 impedance electrodes about the chest to measuring the impedance of the thoracic cavity and the process for making the impedance measurements of Arad as a substitute for the impedance electrodes of Shetty since (1) it is a simple substitution of one known element for another to obtain predictable results, (2) Shetty teaches that impedance can be measured by an external device and Arad teaches one such device and a process of implementation. Drori teaches that such electrodes are mounted on a band 210 (paragraph 0059 of Drori). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to mount the electrodes of the combination on the band of Drori for application to the chest of the patient since it provides a more stable affixation onto the patient.
Shetty teaches the determination of the tissue perfusion values (paragraphs 0031 and 0035 of Shetty) over time (paragraphs 0028, 0035 and 0056 of Shetty) using impedance measurements. Baier-Lowenstein teaches that regional distribution of a parameter for lung perfusion is determined from impedance electrodes placed on the thoracic cavity and the display of the section plane of the lung perfusion as a function of time (abstract and paragraphs 0002, 0009-0010, 0022, 0029, and 0030 of Baier-Lowenstein). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine the regional distribution of lung perfusion from the impedance measurements of the combination using the neural network of Shetty and display the section planes so as to monitor the blood distribution within the lungs.
With respect to claim 1, the combination teaches or suggests a method using machine learning for pulmonary artery pressure measurement based on electrical impedance tomography (EIT), the method comprising:
repeating the following steps n times:
injecting an alternating electrical current between a pair of electrodes (the current injection of Arad using the electrodes of Arad; FIG. 2 and col. 8, lines 20-50 of Arad), the pair of electrodes being located on a belt (the electrodes of Arad on the band of Drori), the belt being positioned around the chest of a person (col 7, lines 15-40 of Arad); and
determining corresponding voltage values between m pairs of electrodes located on the belt (the voltage measurements of Arad using the electrodes of Arad; FIG. 2 and col. 8, lines 20-50 of Arad);
transmitting the determined n * m voltage values to a computing device (transmitting the measurement data to the processing circuitry 14 on the external device 12 of Shetty; paragraph 0025 of Shetty);
receiving by the computing device the determined n * m voltage values (receiving the measurement data to the processing circuitry 14 on the external device 12 of Shetty; paragraph 0025 of Shetty); and
executing by the computing device a machine learning engine, the machine learning engine using a predictive model stored at the computing device for inferring outputs based on inputs (the neural network of Shetty; paragraphs 0053, 0106, 0115 of Shetty), the inputs comprising the determined n * m voltage values (the measurement data of Shetty; paragraph 0025 of Shetty), the outputs comprising a matrix of values representative of at least one of: lung perfusion, pulse transit time values and pulmonary artery pressure values (the distribution of lung perfusion values of Baier-Lowenstein over time).
With respect to claim 2, the combination teaches or suggests that the determination of the corresponding voltage values is performed between m pairs of adjacent electrodes located on the belt (the voltage measurements of Arad using the electrodes of Arad; FIG. 2 and col. 8, lines 20-50 of Arad).
With respect to claim 3, the combination teaches or suggests that the m pairs of electrodes used for determining the corresponding voltage values do not include the electrodes of the pair of electrodes used for injecting the alternating electrical current (the current injection and voltage measurement of Arad using the electrodes of Arad includes this aspect; FIG. 2 and col. 8, lines 20-50 of Arad).
With respect to claim 4, the combination teaches or suggests that the inputs of the machine learning engine further comprise at least one of the following: a frequency of the alternating electrical current, an electrical current value of the alternating electrical current and a voltage matrix containing electrocardiogram (ECG) values (the ECGs of Shetty; paragraphs 0030, 0035 and 0107 of Shetty).
With respect to claim 5, the combination teaches or suggests that the alternating electrical current has a sinusoidal waveform (the sinusoidal current source of Arad; col. 10, lines 15-50 of Arad; it would have been obvious to add such a current source to provide a sinusoidal current since it is a simple substitution or one known element for another to obtain predictable results and/or a type of current source is required and Arad teaches one such source).
With respect to claim 9, the combination teaches or suggests a computing device comprising:
a communication interface (the communication circuitry 82 of Shetty; paragraph 0066 of Shetty);
memory (the memory 84 of Shetty) storing a predictive model (the neural network of Shetty; paragraphs 0053, 0106, 0115 of Shetty); and
a processing unit comprising one or more processors (the processors of the processing circuity 14 of Shetty; paragraph 0040 of Shetty) configured to:
receive via the communication interface a plurality of voltage values between pairs of electrodes of a belt comprising a plurality of electrodes (receiving the measurement data to the processing circuitry 14 on the external device 12 of Shetty; paragraph 0025 of Shetty), the voltage values being determined by sequentially injecting an alternating electrical current between different pairs of electrodes (the current injection of Arad using the electrodes of Arad; FIG. 2 and col. 8, lines 20-50 of Arad) of the belt (the electrodes of Arad on the band of Drori), the belt being positioned around the chest of a person (col 7, lines 15-40 of Arad);
execute a machine learning engine, the machine learning engine using a predictive model stored at the computing device for inferring outputs based on inputs (the neural network of Shetty; paragraphs 0053, 0106, 0115 of Shetty), the inputs comprising the plurality of voltage values (the measurement data of Shetty; paragraph 0025 of Shetty), the outputs comprising a matrix of values representative of at least one of lung perfusion values, pulse transit time (PTT) values and the pulmonary artery pressure (PAP) values (the distribution of lung perfusion values of Baier-Lowenstein over time).
With respect to claim 10, the combination teaches or suggest that the processing unit further generates a visual representation of a lung perfusion array based on the matrix of at least one of values representative of the lung perfusion, the PTT values and the PAP values (the display of the distribution of lung perfusion values of Baier-Lowenstein over time).
With respect to claim 11, the combination teaches or suggest that generating a visual representation comprises applying at least one algorithm to the matrix of values representative of at least one of: the lung perfusion, the PTT values and the PAP values to generate an array of lung perfusion values (the conversion of the lung perfusion values over time to signals for generating the display), and further displaying a trend corresponding to the matrix of at least one of: the lung perfusion, the PTT values and the PAP values on a display of the computing device (the display of the distribution of lung perfusion values of Baier-Lowenstein over time).
With respect to claim 12, the combination teaches or suggests that the inputs of the machine learning engine further comprise at least one of the following: a frequency of the alternating electrical current, an electrical current value of the alternating electrical current and a voltage matrix containing electrocardiogram (ECG) values (the ECGs of Shetty; paragraphs 0030, 0035 and 0107 of Shetty).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW KREMER whose telephone number is (571)270-3394. The examiner can normally be reached Monday - Friday 8 am to 6 pm; every other Friday off.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JACQUELINE CHENG can be reached at (571) 272-5596. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
Prosecution Insights