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
While not necessarily unclear, it is conceivable that the following terms could be interpreted in ways other than the manner in which they are interpreted herein. Accordingly, Examiner seeks correction or confirmation of the following claim interpretations.
Under the broadest reasonable interpretation “a layer” is being interpreted as a processing step.
Claim Interpretation- 35 USC § 112(f)
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 limitations are:
“a communication interface” (claim 1), which lacks corresponding structure in the specification (see section Claim Rejections- 35 USC § 112 below).
“a processor” (claim 1), which as defined in Applicant’s specification [0016] comprises a central processing unit (CPU), a graphics processing unit (GPU), a microcontroller unit (MCU), a multi-processor, a distributed processing system, an application specific integrated circuit (ASIC) and/or a suitable processing unit.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they 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 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 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 them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-20 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.
In re claim 1,
There is insufficient antecedent basis for the limitation “the lead electrocardiograms”. For examination purposes “the lead electrocardiograms” will be interpreted as “the plurality of lead electrocardiograms”.
There is insufficient antecedent basis for the limitation “the groups”. For examination purposes the limitation “the groups” will be interpreted as “the plurality of groups”.
There is insufficient antecedent basis for the limitation “the electrocardiogram features”. For examination purposes “the electrocardiogram features” will be interpreted as “the plurality of electrocardiogram features”.
Claim limitation “a communication interface” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph (see * below for additional information).
The limitation “attribute data” raises a clarity concern when viewed in combination with applicant’s specification [0042] and FIG. 2. As discussed in Applicant’s specification [0042] attribute data is defined as “information of the electrocardiograms other than the signal, comprising P-R-T axes (i.e., the electrical axis of the heart) and/or QRS duration”. Examiner asserts that the attribute data information, that is the electrical axis of the heart and QRS duration, would have to be derived from an electrocardiogram signal. Therefore, it is unclear why FIG. 2 does not depict an arrow between box “E” and box “AD”.
In re claim 2, see above (In re claim 1 (i) and (ii)).
In re claim 3,
The limitation “segmenting a plurality of heartbeat electrocardiograms corresponding to a heartbeat from the lead electrocardiograms based on a waveform of one of the lead electrocardiograms; and extracting the electrocardiogram features from the heartbeat electrocardiograms respectively based on the groups” promotes a clarity concern. Examiner asserts that “heartbeat electrocardiogram” is not an art recognized term. Moreover, it is unclear how an “heartbeat electrocardiogram” is different from “the lead electrocardiogram”, given that an electrocardiogram by definition records the electrical activity of the heart. Because Examiner cannot ascertain how the limitation “segmenting a plurality of heartbeat electrocardiograms corresponding to a heartbeat from the lead electrocardiograms based on a waveform of one of the lead electrocardiograms;” further limits the claim, claim 3 will be interpreted as requiring the operation of extracting the electrocardiogram features to further comprise: extracting a heartbeat or heartrate from the lead electrocardiogram respectively based on the groups.
see above (In re claim 1 (i), (ii), and (iii)).
In re claim 4, see above (In re claim 1 (i) and (iii)).
In re claim 5, see above (In re claim 1 (i) and (iii)).
In re claim 6, see above (In re claim 1 (iii)).
In re claim 7, see above (In re claim 1 (iii)).
In re claim 8, see above (In re claim 1 (iii)).
In re claim 9, see above (In re claim 1 (i) and (iii)).
In re claim 11, see above (In re claim 1).
In re claim 12, see above (In re claim 2).
In re claim 13, see above (In re claim 3).
In re claim 14, see above (In re claim 4).
In re claim 15, see above (In re claim 5).
In re claim 16, see above (In re claim 6).
In re claim 17, see above (In re claim 7).
In re claim 18, see above (In re claim 8).
In re claim 19, see above (In re claim 9).
*Regarding point (iv) Applicant may:
Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
*Regarding point (iv), If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Independent claims 1 and 11 are directed to a left ventricular mass estimation apparatus and a left ventricular mass estimation method. Thus, the claims are directed to statutory categories of invention. (Step 1: YES)
Step 2A, Prong 1
Independent claims 1 and 11 recite the following limitations:
“classify the lead electrocardiograms into a plurality of groups based on the body surface positions” (mental process and/or mathematical calculation).
“extracting a plurality of electrocardiogram features from the lead electrocardiograms respectively based on the groups” (mental process and/or mathematical calculation).
“estimating a left ventricular mass corresponding to the electrocardiogram to be tested based on the electrocardiogram features, attribute date corresponding to the electrocardiogram to be tested, and demographic data corresponding to the electrocardiogram to be tested” (mental process)
The dependent claims recite the following limitations:
“classifying the lead electrocardiograms into a limb group and a precordial group, wherein the limb group comprises the lead electrocardiograms measured from human limbs, and the precordial group comprises the lead electrocardiograms measured from human chest.” (mental processes and/or mathematical calculation) (claim 2, claim 12)
“segmenting a plurality of heartbeat electrocardiograms corresponding to a heartbeat from the lead electrocardiograms based on a waveform of one of the lead electrocardiograms; and extracting the electrocardiogram features from the heartbeat electrocardiograms respectively based on the groups.” (mental process and/or mathematical calculations)(claim 3, claim 13)
“extracting the electrocardiogram features from the lead electrocardiograms based on a plurality of timing attributes of a plurality of signals in the lead electrocardiograms.” (further limiting the abstract idea of “extracting the electrocardiogram features”) (claim 4) (claim 14)
“wherein the operation of extracting the electrocardiogram features further comprises: selecting one of a plurality of feature extraction layers based on gender data of the demographic data; and extracting the electrocardiogram features from the lead electrocardiograms based on the selected one of the feature extraction layers.” (further limiting the abstract idea of “extracting the electrocardiogram features”) (claim 5, claim 15)
“wherein the operation of estimating the left ventricular mass further comprises: projecting the electrocardiogram features onto a first vector based on a first projection layer; and estimating the left ventricular mass corresponding to the electrocardiogram to be tested based on the first vector, the attribute data, and the demographic data” (further limiting the abstract idea of “estimating left ventricular mass”) (claim 6, claim 16)
“wherein the operation of estimating the left ventricular mass further comprises: projecting the attribute data and the demographic data onto a second vector based on a second projection layer; and estimating the left ventricular mass corresponding to the electrocardiogram to be tested based on the second vector and the electrocardiogram features.” (further limiting the abstract idea of “estimating the left ventricular mass”) (claim 7, claim 17)
“wherein the operation of estimating the left ventricular mass further comprises: selecting one of a plurality of prediction layers based on gender data of the demographic data; and generating a prediction result by using the selected one of the prediction layers based on the electrocardiogram features, the attribute data, and the demographic data, wherein the prediction result comprises the left ventricular mass.” (further limiting the abstract idea of “estimating the left ventricular mass”)(claim 8, claim 18)
“extracts the electrocardiogram features from the lead electrocardiograms based on a feature extraction layer” (further limiting the abstract idea of “extracting electrocardiogram features”) (claim 9, claim 19)
“estimates the left ventricular mass corresponding to the electrocardiogram to be tested based on a prediction layer;” (further limiting the abstract idea of “estimating the left ventricular mass”)(claim 9, claim 19)
“wherein the attribute data comprises a QRS wave duration and an electrical axis corresponding to the electrocardiogram to be tested” (further limiting abstract idea of “estimating a left ventricular mass…based on…attribute data”) (claim 10, claim 20)
Regarding the limitations directed to a mental process, the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper to be an abstract idea. Examples of mental processes include observation, judgement, evaluation and opinion. The limitations listed above nothing more than an operator estimating a left ventricular mass of a patient by considering ECG features and demographic information of the patient. Examiner notes that the courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. See MPEP 2106.04(a)(2)(III).
Regarding the limitations directed to a mathematical calculation, under the broadest reasonable interpretation a mathematical calculation is a mathematical operation or an act of calculating using mathematical methods to determine a number of variable. See MPEP 2106.04(a)(2)(I). Examiner notes, as discussed in MPEP 2106.04(a)(2)(I), the claim does not need to recite the word “calculating” in order to be considered a mathematical calculation.
For the reasons above, Examiner asserts that the claims recite a judicial exception, specifically an abstract idea (Step 2A, Prong 1: Yes).
Step 2A, Prong 2
Independent claims 1 and 11 recite the following additional elements:
“a communication interface” (generic computer component)
“configured to receive an electrocardiogram to be tested” (insignificant extra solution activity)
“wherein the electrocardiogram to be tested comprises a plurality of lead electrocardiograms corresponding to a plurality of body surface positions” (further limiting additional element directed to receiving an “electrocardiogram to be tested”)
“a processor, electrically connected to the communication interface” (generic computer component).
“an electronic apparatus” (generic computer component)
The dependent claims recite the following additional elements:
“the processor” (generic computer component) (claim 9)
The above additional elements are examples of generic computer components or insignificant extra solution activity.
Specifically, the additional elements “a communication interface”, “a processor, electrically connected to the communication interface”, “an electronic apparatus”, and “the processor” fail to provide significantly more because they amount to merely applying the abstract idea using generic computer components.
Additionally, the remaining additional elements fail to provide significantly more because they amount to insignificant extra solution activity, specifically data gathering.
Step 2B
The claims do not include any additional elements that amount to significantly more than the judicial exception.
As discussed above, in Step 2A, Prong 2 the remaining additional elements amount to no more than insignificant extra solution activity and generic computer components that are used to apply the abstract idea.
Regarding the limitation “wherein the electrocardiogram to be tested comprises a plurality of lead electrocardiograms corresponding to a plurality of body surface positions”, see Guzzetta et al. (US 2007/02194554) which discloses a method for determining a probability of cardiac disease by processing electrocardiogram data. As shown in FIG. 3, the electrocardiogram data comprises a plurality of lead electrocardiograms that correspond to a plurality of body surface positions. Thus, the limitations “wherein the electrocardiogram to best tested comprises a plurality of lead electrocardiograms corresponding to a plurality of body surface positions” are well-understood, routine and conventional (as evidenced by Guzzetta)
Moreover, reconsidering the claim limitations individually and as an ordered combination, the claims fail to meet the requirements for eligibility under 35 U.S.C. 101. (Step 2B: NO)
Claim Rejections - 35 USC § 101
Section 33(a) of the America Invents Act reads as follows:
Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism.
Claim 2 is rejected under 35 U.S.C. 101 and section 33(a) of the America Invents Act as being directed to or encompassing a human organism. See also Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (indicating that human organisms are excluded from the scope of patentable subject matter under 35 U.S.C. 101).
In re claim 2, the phrase “the lead electrocardiograms measured from human limbs” implies that the “human limbs” are a part of the claimed invention. As stated in MPEP 2105(III), claims directed to or encompassing a human organism are excluded from patentability. For the purposes of examination, the limitation “the lead electrocardiograms measured from human limbs”", will be interpreted to mean “the lead electrocardiograms configured to be measured from human limbs”. Examiner notes that the same analysis applies to the phrase “the lead electrocardiograms measured from human chest”.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 6, 7, 9-11, 13, 16-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Khurshid et al. (Khurshid Shaan et al., “Deep Learning to Predict Cardiac Magnetic Resonance-Derived Left Ventricular Mass and Hypertrophy From 12-Lead ECGs.” Circulation: Cardiovascular Imaging, vol. 14, no. 6, 2021, pg. 485-495), in view of Kwon et al. (Kwon Joon-Myoung et al., “Comparing the performing of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography.”, EP Eur., 2020, vol. 22, pg. 412-419).
In re clam 1, Khurshid discloses a left ventricular mass estimation apparatus (title: “Predict…Left Ventricular Mass”; Figure 1: left panel) , comprising:
a communication interface (pg. 486, Data Processing: interface that receives “downloaded” ECG data), configured to receive an electrocardiogram to be tested (pg. 486, Data Processing: “Resting 12 lead-ECG data”),
wherein the electrocardiogram to be tested comprises a plurality of lead electrocardiograms (pg. 486, Data Processing: “Resting 12 lead ECG”) corresponding to a plurality of body surface positions (apparent as a 12 lead ECG includes 12-leads located at different “body surface positions”);
a processor (pg. 487, LVM-AI: component responsible for executing “LVM-AI”, that is, “a one-dimensional convolutional neural network”), electrically connected to the communication interface (Figure 1: left panel), configured to execute the following operations:
classifying the lead electrocardiograms into a plurality of groups based on the body surface positions (pg. 487: “LVM-AI”: where each lead of the “12-lead ECG waveform” is a single “group”); and
estimating a left ventricular mass (pg. 487, LVM-AI: “infer LV mass”) corresponding to the electrocardiogram to be tested based on
electrocardiogram features (pg. 487, LVM-AI: “infer LV mass using 12-lead ECG”), and
demographic data corresponding to the electrocardiogram to be tested (pg. 487, LVM-AI: “infer LV mass using…participant age, sex, and BMI”)
Khurshid does not disclose the processor being configured to execute the following operations:
extracting a plurality of electrocardiogram features from the lead electrocardiograms respectively based on the groups
estimating a left ventricular mass corresponding to the electrocardiogram to be tested based on
the electrocardiogram features,
-attribute data corresponding to the electrocardiogram to be tested, and
demographic data corresponding to the electrocardiogram to be tested.
Kwon discloses an apparatus (FIG. 2) that, like Khurshid, detects an increase in left ventricular mass using electrocardiogram data and demographic information (abstract; pg. 413, Introduction, ¶ 1). As shown in FIG. 2, Kwon further discloses detecting an increase in left ventricular mass using extracted electrocardiogram features (“ECG features”) in addition to demographic information and electrocardiogram data. Examples of extracted electrocardiogram features include heart rate, presence of atrial fibrillation, QT interval, and QTc (pg. 414, ¶ 1), as well as attribute data like QRS duration, R-wave axis and T-wave axis (pg. 414, ¶ 1). As discussed in Kwon, using a combination of electrocardiogram data, demographic information and extracted ECG features was shown to improve prediction accuracy (Kwon, pg. 416, Results: ¶2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of Khurshid to extract a plurality of electrocardiogram features from the lead electrocardiograms and estimate a left ventricular mass based on the electrocardiogram features and attribute data, as taught by Kwon. One would have been motivated to make this modification to improve prediction accuracy (Kwon, pg. 416, Results: ¶2). Accordingly, such a modification would yield a processor that estimates a left ventricular mass based on “the electrocardiogram features, attribute data corresponding to the electrocardiogram to be tested and demographic data corresponding to the electrocardiogram to be tested”.
Regarding extracting a plurality of electrocardiogram features from the lead electrocardiograms “based on the groups”, Examiner asserts it is apparent that feature extraction would be based on the lead given that each lead records electrical activity of the heart from a unique spatial perspective. However, in so far as this is not explicitly stated claim 1 is alternatively rejected under 35 U.S.C. 103 as follows:
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the feature extraction disclosed by the proposed combination to be based on the groups given that established electrocardiogram-based rules used to diagnose left ventricular hypertrophy involve isolating waveforms from specific electrocardiogram leads, as discussed in Khurshid (see Khurshid supplementary material, pg. 6, Table 1).
In re claim 3, the proposed combination yields (all mapping directed to Kwon), wherein the operation of extracting the electrocardiogram features further comprises:
segmenting a plurality of heartbeat electrocardiograms corresponding to a heartbeat from the lead electrocardiograms based on a waveform of one of the lead electrocardiograms (pg. 414, ¶ 1, lines 1-5: “features of ECG such as heart rate”; additionally,” see section Claim Rejection 35 USC 112 (In re claim 3)); and
extracting the electrocardiogram features from the *heartbeat electrocardiograms (“extracting” indicated by arrow in FIG.2 between “12 lead by 5000 number” and “ECG features:” box) respectively based on the groups (regarding “the groups” see above In re claim 1).
*Regarding “heartbeat electrocardiograms” see above section Claim Rejections 35 USC 112 (In re claim 3)
In re claim 6, the proposed combination yields (all mapping directed to Kwon unless indicated otherwise),
wherein the operation of estimating the left ventricular mass further comprises:
projecting the electrocardiogram features onto a first vector based on a first projection layer (occurs during “Preprocessing and Normalization” step shown in Figure 2) and
estimating the left ventricular mass corresponding to the electrocardiogram to be tested based on the first vector, the attribute data, and the demographic data (see modification above In re claim 1; apparent as “the first vector” includes both “attribute data” and “demographic data”) .
In re claim 7, the proposed combination yields (all mapping directed to Kwon unless indicated otherwise),
wherein the operation of estimating the left ventricular mass further comprises:
projecting the attribute data and the demographic data onto a second vector based on a second projection layer (occurs during “Preprocessing and Normalization” step shown in Figure 2); and
estimating the left ventricular mass corresponding to the electrocardiogram to be tested based on the second vector and the electrocardiogram features (see proposed modification above (In re claim 1); apparent as “the second vector” also includes “the electrocardiogram features).
In re claim 8, the proposed combination yields (all mapping directed to Khurshid unless indicated otherwise), wherein the operation of estimating the left ventricular mass further comprises
generating a prediction result based on the electrocardiogram features, the attribute data, and the demographic data, wherein the prediction result comprises the left ventricular mass.
The proposed combination does not yield wherein the operation of estimating the left ventricular mass comprises:
selecting one of a plurality of prediction layers based on gender data of the demographic data; and
generating a prediction result by using the selected one of the prediction layers based on the electrocardiogram features, the attribute data, and the demographic data, wherein the prediction result comprises the left ventricular mass.
Khurshid further discloses using gender data to improve left ventricular mass prediction (see Khurshid Supplementary Material, pg. 3, “Methods Figure D” and associated caption: “recalibration of LVM-AI predicted mass values by fitting a linear model…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the operation of estimating the left ventricular mass to comprise selecting one of a plurality of prediction layers based on gender data of the demographic data, as further taught by Khurshid. One would have been motivated to make this modification because doing so has been shown to improve model accuracy (Khurshid, Supplementary Material, pg. 3, “Methods Figure D” caption). Accordingly, such a modification would yield “generating a prediction result by using the selected one of the prediction layers”.
In re claim 9, the proposed combination yields (all mapping directed to Khurshid unless indicated otherwise), wherein:
the processor extracts the electrocardiogram features from the lead electrocardiograms based on a feature extraction layer (Kwon, Figure 2: “ECG features”; pg. 414, ¶1, where “a feature extraction layer” is the processing step that dictates extraction of “ECG features”);
the processor estimates the left ventricular mass corresponding to the electrocardiogram to be tested based on a prediction layer (pg. 487, LVM-AI: “LVM-AI is a one-dimensional convolutional neural network designed to infer LV mass using 12-lead ECG”); and
the feature extraction layer and the prediction layer are generated through the following operations:
generating a prediction result by using an initial feature extraction layer and an initial prediction layer based on a plurality of training electrocardiograms (pg. 488, Figure 2: “LVM-AI training set”; pg. 487, LVM-AI: “we trained LVM-AI” );
adjusting a plurality of parameters of the initial feature extraction layer and the initial prediction layer based on the prediction result and a plurality of known left ventricular masses corresponding to the training electrocardiograms (pg. 487, “LVM-AI”: “we trained LVM-AI; apparent as such adjustment is a part of a traditional training process); and
taking the adjusted initial feature extraction layer and the adjusted initial prediction layer as the feature extraction layer and the prediction layer (pg. 488, Figure 2 caption: “LVM-AI was evaluated” using “UK Biobank test set”).
In re claim 10, the proposed combination yields (all mapping directed to Kwon) wherein the attribute data comprises
a QRS wave duration (Figure 2; pg. 414, ¶1: “QRS duration”) and
an electrical axis corresponding to the electrocardiogram to be tested (Figure 2; pg. 414, ¶1: “R-wave axis, and T-wave axis”).
In re claim 11, see above (In re claim 1).
The proposed combination also yields, a method (Khurshid, pg. 486, “METHODS”).
In re claim 13, see above (In re claim 3).
In re claim 16, see above (In re claim 6).
In re claim 17, see above (In re claim 7).
In re claim 19, see above (In re claim 9).
In re claim 20, see above (In re claim 10).
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Khurshid et al. (Khurshid Shaan et al., “Deep Learning to Predict Cardiac Magnetic Resonance-Derived Left Ventricular Mass and Hypertrophy From 12-Lead ECGs.” Circulation: Cardiovascular Imaging, vol. 14, no. 6, 2021, pg. 485-495), in view of Kwon et al. (Kwon Joon-Myoung et al., “Comparing the performing of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography.”, EP Eur., 2020, vol. 22, pg. 412-419), in view of Anastasia et al. (US 2021/0315506), in view of Wang et al. (US 2021/0065876).
In re claim 2, the proposed combination does not yield, wherein the operation of classifying the lead electrocardiograms into the groups further comprises:
classifying the lead electrocardiograms into a limb group and a precordial group,
wherein the limb group comprises the lead electrocardiograms measured from human limbs, and the precordial group comprises the lead electrocardiograms measured from human chest.
Wang discloses an electrocardiogram analysis technique (FIG. 3, abstract) that involves separating electrocardiogram data (310) into groups (330) that each share certain types of features or have common properties [0035].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the operation of classifying the lead electrocardiograms into the groups to further comprise classifying the lead electrocardiograms into a limb group and a precordial group, given that grouping electrocardiogram data by certain types of features or common properties is a known technique employed in the art (as evidenced by Wang). Moreover, Examiner asserts that one of ordinary skill in the art would have the ability to select a grouping criteria that would best meet their needs. Accordingly, such a modification would yield “wherein the wherein the limb group comprises the lead electrocardiograms measured from human limbs, and the precordial group comprises the lead electrocardiograms measured from human chest”, given that limb leads, and precordial leads are known to be measured from a patient’s limbs and chest respectively.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Khurshid et al. (Khurshid Shaan et al., “Deep Learning to Predict Cardiac Magnetic Resonance-Derived Left Ventricular Mass and Hypertrophy From 12-Lead ECGs.” Circulation: Cardiovascular Imaging, vol. 14, no. 6, 2021, pg. 485-495), in view of Kwon et al. (Kwon Joon-Myoung et al., “Comparing the performing of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography.”, EP Eur., 2020, vol. 22, pg. 412-419), in view of Anastasia et al. (US 2021/0315506).
In re claim 4, the proposed combination yields (all mapping directed to Kwon), wherein the operation of extracting the electrocardiogram features further comprises: extracting the electrocardiogram features from the lead electrocardiograms (Figure 2: “ECG features”; pg. 414, ¶1)
The proposed combination does not yield extracting the electrocardiogram features from the lead electrocardiograms based on a plurality of timing attributes of a plurality of signals in the lead electrocardiograms.
Anastasia discloses a method for electrocardiogram signal feature extraction (abstract) that involves segmenting an electrocardiogram signal into temporal windows based on an R-peak identifier [0102; 0105].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the electrocardiogram feature extraction of the proposed combination to be based on a plurality of timing attributes of a plurality of signals in the lead electrocardiogram, as taught by Anastasia. One would have been motivated to make this modification because both the feature extraction technique of the proposed combination and feature extraction technique disclosed by Anastasia are functionally equivalent (i.e., they both result in extracted ECG features). Moreover, one of ordinary skill in the art would have the ability to select an extraction technique that would best meet their needs.
In re claim 14, see above (In re claim 4).
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Khurshid et al. (Khurshid Shaan et al., “Deep Learning to Predict Cardiac Magnetic Resonance-Derived Left Ventricular Mass and Hypertrophy From 12-Lead ECGs.” Circulation: Cardiovascular Imaging, vol. 14, no. 6, 2021, pg. 485-495), in view of Kwon et al. (Kwon Joon-Myoung et al., “Comparing the performing of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography.”, EP Eur., 2020, vol. 22, pg. 412-419), in view of Moss et al. (Moss, Arthur J. “Gender differences in ECG parameters and their clinical implications.” Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc vol. 15,1 (2010))
In re claim 5, the proposed combination yields (all mapping directed to Kwon), wherein the operation of extracting the electrocardiogram features further comprises:
selecting one of a plurality of feature extraction layers and extracting the electrocardiogram features from the lead electrocardiograms based on the selected one of the feature extraction layers (Figure 2: “ECG features”; pg. 414, ¶1)
The proposed combination does not yield,
selecting on of a plurality of feature extraction layers based on gender data of the demographic data
Moss discloses an overview of meaningful differences in measured ECG parameters between females and males (title). As disclosed by Moss, women often experience longer a longer QT interval (pg. 1, ¶2), a longer QTc interval (pg. 1, ¶2), and a shorter QRS duration compared to men (pg. 1, ¶3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the operation of extracting the electrocardiogram features to further comprise selecting a plurality of feature extraction layers based on gender data of the demographic data, given the recognized differences in measured ECG parameters between men and women as disclosed by Moss. One would have been motivated to make this modification because doing so would help avoid inaccurate predictions.
In re claim 15, see above (In re claim 5).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rabkin et al. (Rabkin, Simon, “Estimating Left Ventricular Mass from the Electrocardiogram across the Spectrum of LV Mass from Normal to Increased LV Mass in an Older Age Group”, Cardiology Research and Practice, vol. 2024, Article ID 6634222, 9 pages) discloses a method for estimating left ventricular mass using electrocardiogram data (title) comprising measuring electrocardiogram data (pg. 2, 2.2 Measurements) corresponding to a plurality of body positions (pg. 2, 2.2 Measurements: “12 lead ECG”), extracting a plurality of features based on the plurality of body positions (pg. 2, 2.2 Measurements: “amplitude of R waves of leads aVL, I, III, V4, V5 and V6”, “amplitude of the S waves of leads V1, V2, V3 and V4”, “deepest S-wave in any precordial lead”), and estimating a left ventricular mass based on the plurality of features extracted (pg. 2, Results; FIGURE 1).
Friedman et al. (US 2024/0378437) discloses a method for analyzing ECG features using machine learning (abstract).
Golan et al. (US 2023/0397888) discloses a system (FIG. 5B) for cardiac signal processing (abstract) that includes a communication interface (module responsible for “Signal Ingestion”) and several machine learning models (“Ensemble of Neural Networks”).
Yu et al. (US 11,571,161) discloses a method and system for electrocardiogram analysis (abstract; FIG. 6) that performs feature extraction using a convolutional neural network (abstract).
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLIVIA WALKER whose telephone number is (571)272-7052. The examiner can normally be reached M-F: 7-4pm CT.
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, David Hamaoui can be reached at (571)-270-5625. 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.
/OLIVIA WALKER/Examiner, Art Unit 3796
/DAVID HAMAOUI/SPE, Art Unit 3796
Prosecution Insights