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 .
Response to Amendment
The following is in response to the amendment filed on April 1, 2026.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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, 2, 4, 6-12, 14 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al, (“A fully-automated paper ECG digitization algorithm using deep learning) in view of Attia et al., US 2020/0397313.
With respect to claim 1:
Wu teaches:
An apparatus for time series data format conversion and analysis using machine-learning, wherein the apparatus comprises:
At least a processor; and
A memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
Receive a static image comprising at least a time series of measured values, wherein the at least a time series of measured values represents an electrocardiogram (ECG) of a subject; (Page 2, Paragraph 2, discloses receiving printed 12 lead ECGs, ECGs comprise time series data)
Convert the at least a time series of measured values from the static image to a target domain protocol, wherein the conversion comprises: (Fig. 1 Step 11, discloses converting the extracted data to a cropped lead image)
Parsing the at least a time series of measured values from the static image to ECG data comprising data representing the ECG of the subject, wherein the data points represent lead signal and time and parsing the at least a time series comprises: (Pages 3-4, disclose the processing steps to digitize the ECG wherein the data points representing lead signal and time are extracted)
Parsing the at least a time series to an interrogable format of the data points, wherein the interrogable format is a format in which the data points are quantified and accessible by the at least a processor; and (Page 6, Step IV, discloses parsing the analyzed ECG data into a spreadsheet with values that are quantified and accessible)
Wu doesn’t appear to explicitly disclose:
predict, using an ejection-fraction prediction model, an estimated ejection fraction characteristic as a function of the ECG data, wherein the predicting the estimated ejection fraction characteristic comprises:
inputting the ECG data representing the ECG of the subject into the ejection-fraction prediction model;
predicting, using the ejection-fraction prediction model, an estimated ejection-fraction characteristic of the subject as a function of the ECG data; and
outputting, using the ejection-fraction prediction model, the estimated ejection-fraction characteristic.
Attia teaches:
predict, using an ejection-fraction prediction model, an estimated ejection fraction characteristic as a function of the ECG data (e.g. The system 110 further includes one or more ejection-fraction prediction models 118. These models 118 are generally configured to process one or more predictive inputs that characterize a patient's ECG data and, based on the predictive inputs, generate an estimated ejection-fraction characteristic for the patient 102, Attia: [0052]), wherein the predicting the estimated ejection fraction characteristic comprises:
inputting the ECG data representing the ECG of the subject into the ejection-fraction prediction model (e.g. The system provides a predictive input that was derived from the ECG data to an ejection fraction predictive mode, Attia: [0009]);
predicting, using the ejection-fraction prediction model, an estimated ejection-fraction characteristic of the subject as a function of the ECG data (e.g. The system 110 further includes one or more ejection-fraction prediction models 118. These models 118 are generally configured to process one or more predictive inputs that characterize a patient's ECG data and, based on the predictive inputs, generate an estimated ejection-fraction characteristic for the patient 102, Attia: [0052]); and
outputting, using the ejection-fraction prediction model, the estimated ejection-fraction characteristic (e.g. The predictive input can be processed using the ejection-fraction predictive model to generate an estimated ejection-fraction characteristic. The system then provides, for output, the estimated ejection-fraction characteristic, Attia: [0009], [0021], [0058]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Wu and Attia because this would make the calculation of ejection fraction more efficient, faster and economical (See Attia, Paragraph [005]).
Regarding claim 2, Attia further teaches, wherein:
the static image comprises an ECG format comprising multiple leads (e.g. The ECG data can include multiple channels, each channel including a subset of the ECG data that describes a respective one of multiple leads of the ECG of the mammal over the period of time. The predictive input can characterize the multiple leads of the ECG for each of the multiple channels of the ECG data, Attia: [0012]); and
parsing the at least a time series of measured values from the static image to the ECG data comprises parsing the at least a time series of measured values from the static image to the ECG data, wherein the ECG data comprises ECG data for multiple leads (e.g. the predictive input generator 116 may normalize and vectorize ECG data from one or more channels (corresponding to one or more leads)[as parsing]. Each lead provides a different view of the patient's cardiac electrical activity as a result of the different angles formed by the different pairs of electrodes for the different leads. The signal from each lead can be recorded simultaneously for a period of time (e.g., 5, 10, or 15 seconds)[as data points] to capture information about the timing and location of electrical activity along different radial directions, Attia: [0047], [0055]).
Regarding claim 4, Wu further discloses, wherein parsing the at least a time series of measured values from the static image to the ECG data comprises:
Extracting at least a feature from the static image, wherein the at least a feature is constrained by a technical lexicon associated with the time series types of data; and verifying that the data points align with the target domain protocol as a function of the at least a feature of the static image. (Page 3, discloses extracted ECG specific features and aligning the data to assist in analyzing it)
Regarding claim 6, Attia further teaches, wherein the processor is further configured to:
input the ECG data into a feature extractor (e.g. the system 110 includes a feature extractor 126 that analyzes ECG data and determines values of any applicable morphological features to include the predictive input that will be processed by an ejection-fraction prediction model 118, Attia: [0056]);
analyze, using the feature extractor, the ECG data to determine morphological features of the ECG (e.g. the system 110 includes a feature extractor 126 that analyzes ECG data and determines values of any applicable morphological features to include the predictive input that will be processed by an ejection-fraction prediction model 118, Attia: [0056]); and
predict, using the ejection-fraction prediction model, the ejection-fraction prediction as a function of the morphological features of the ECG (e.g. the system 110 includes a feature extractor 126 that analyzes ECG data and determines values of any applicable morphological features to include the predictive input that will be processed by an ejection-fraction prediction model 118, Attia: [0056]).
Regarding claim 7, Attia further teaches, wherein the ejection-fraction prediction model comprises a neural network trained, using gradient descent machine-learning techniques, with a set of multiple training data pairs (e.g. the ejection-fraction prediction model(s) 118 may be regression models, machine-learning models, or both. In some implementations, the model(s) 118 are feedforward, recurrent, or convolutional neural networks, or a capsule network. Neural network models may have fully connected layers and may employ an auto-encoder network, Attia: [0053], [0064]), wherein the set of multiple training data pairs comprises:
exemplary time-series data (e.g. the predictive inputs represent a time-series of values for the ECG waveform, Attia: [0058]); and
a target ejection-fraction characteristic (e.g. the system obtains a set of multiple training data pairs. Each pair includes an ECG predictive input that characterizes a particular patient's ECG and a target ejection-fraction characteristic for the patient, Attia: [0064]).
Regarding claim 8, Attia further teaches, wherein the at least a processor is further configured to select the ejection-fraction prediction model as a function of one or more characteristics of the subject (e.g. selecting and using an appropriate ejection-fraction prediction model that corresponds to the characteristics of a patient, Attia: [0061]), wherein selecting the ejection-fraction prediction model comprises:
identifying a set of characteristics for the subject (e.g. the system identifies a set of characteristics for the patient for whom an estimated ejection fraction characteristic is to be determined, Attia: [0061]);
selecting the ejection-fraction prediction model from one or more ejection-fraction prediction models as a function of the set of characteristics for the subject (e.g. the system selects one of the ejection-fraction prediction models that corresponds to the identified set of characteristics for the patients, Attia: [0061]); and
generating the estimated ejection-fraction characteristic using the selected ejection- fraction prediction model and the ECG data of the subject (e.g. the system generates an ejection-fraction prediction using the selected ejection-fraction prediction model that corresponds to the patient's characteristics, Attia: [0061]).
Regarding claim 9, Attia further teaches, wherein the processor is further configured to classify the estimated ejection-fraction characteristic into a risk category comprising one or more thresholds (e.g. the ejection-fraction model may be trained to classify a patient's ejection fraction into one of two, three, or more possible ejection-fraction categories defined by specified threshold ejection-fraction values, Attia: [0058]).
Regarding claim 10, Attia further teaches, wherein the at least a processor is further configured to determine whether the estimated ejection-fraction characteristic meets one or more screening criteria comprising a threshold ejection-fraction (e.g. ECG-based estimates of a patient's ejection fraction can be useful screening procedure, further evaluation of a patient may be warranted based on the results of an ECG-based screening procedure. The estimated ejection-fraction characteristic may be an absolute value that indicates the predicted ejection fraction of a patient and the screening criteria may include a threshold ejection fraction, Attia: [0062]), wherein determining whether the estimated ejection-fraction characteristic meets one or more screening criteria comprises:
comparing the one or more screening criteria to the estimated ejection-fraction characteristic (e.g. the system determines whether the estimated ejection-fraction characteristic, and optionally additional factors, meet one or more screening criteria that are to guide a decision whether to further evaluation of the patient's condition is warranted. For example, the estimated ejection-fraction characteristic may be an absolute value that indicates the predicted ejection fraction of a patient and the screening criteria may include a threshold ejection fraction (e.g., 35-percent or 50-percent). If the estimated ejection fraction of the patient is below the threshold, a follow-on procedure for further evaluation can be performed on the patient, Attia: [0062]).
Claims 11, 12, 14 and 16-20 are rejected according to the claims above.
Claims 3, 5, 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al, (“A fully-automated paper ECG digitization algorithm using deep learning) in view of Attia et al., US 2020/0397313 and Bordaweker et al (US PG Pub 2019/0294953).
With respect to claim 3:
The combination of Wu and Attia does not explicitly disclose:
wherein parsing the at least a time series of measured values from the static image to the ECG data comprises: scaling the data points along a time axis and
aligning the data points along a lead signal axis
Bordaweker teaches:
wherein parsing the at least a time series of measured values from the static image to the ECG data comprises:
scaling the data points along a time axis (e.g. time is graphed on the horizontal axis, Bordaweker: [0025] and Fig. 2); and
aligning the data points along a lead signal axis (e.g. the data value in the Time Series 210a-c at the corresponding time is graphed on the vertical axis, Bordaweker: [0025] and Fig. 2).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Wu and Attia because this would include a complete and non-limiting way to compare time series data. (See Bordaweker, Paragraph [003]).
Regarding claim 5, Bordaweker further discloses, wherein parsing the at least a time series of measured values from the static image to ECG data comprises:
inputting the static image into a machine-learning model trained (e.g. the generated similarity measures are used to train one or more machine learning models to predict a future data series corresponding to an input data series, Bordaweker: [0045]) using synthetic image data generated from digital ECG data; and
outputting, by the machine-learning model (e.g. the Neural Network 300 includes a plurality of Nodes 302-324 (often referred to as neurons) and is trained to generate an output Time Series 355 when provided with an input Time Series 350, Bordaweker: [0034]), the ECG data.
Claims 13 and 15 are rejected according to claims 3 and 5 above.
Response to Arguments
Claim Rejections - 35 USC § 101
Applicant’s arguments and instant amendment have overcome the 35 USC 101 rejection, the rejection has been removed.
Claim Rejections - 35 USC § 103
Applicant’s arguments are moot in view of the new ground(s) of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIELA D REYES whose telephone number is (571)270-1006. The examiner can normally be reached Monday-Friday, 7:30 am -5:00 pm.
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/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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