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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 16/816,581, filed on March 12, 2020.
This application constitutes as a continuation-in-part of prior Application No. 16/816,581, filed March 12, 2020, because it repeats a substantial portion and adds disclosure not presented in the prior application, and names the inventor or at least one joint inventor named in the prior application. A substantial portion of the claims in this application are directed to subject matter not presented in the prior application. This includes but is not limited to limitations directed to the plurality of attention heads
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 7-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because it lacks a tangible, physical form required for a machine or manufacture. The plurality of attention heads, classifier and combination layer recited in claim 7 do not present a tangible, physical form. Paragraph [0079] of the published application defines the plurality of attention heads as, “a block configured to attend to each of different features of the target biosignal and extract a feature vector related to the feature” , paragraph [0084] states, “The classifier may be configured as a three-layer fully connected neural network” and paragraph [0081] states, “a trainable combination layer” but these are not sufficient of a tangible form for a machine or manufacture.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to limitations reciting a mental process or mathematical concepts without significantly more. The claim(s) recite(s) combining extracted feature vectors by weighting and averaging feature vectors on the basis of weights of the respective attention heads to derive a unified feature vector; determining a result of a prediction of a type of arrhythmic state to which the target biosignal corresponds to a classification probability, on the basis of the unified feature vector, and determining a level of confidence in the prediction; and combining the classification probability and the prediction confidence level to derive a final probability of the type of arrhythmic state to which the target biosignal corresponds. This judicial exception is not integrated into a practical application because the claims recite generic components or insignificant extra-solution activity. The plurality of attention heads, the classifier and the combination layer are recited at a high level of generality without substantially more. Extracting feature vectors related to different types of arrhythmic state from a target biosignal of a subject with respect to the respective attention heads is regarded as merely a collection of information or gathering of information for analysis which the courts have indicates as not sufficient to show an improvement to technology. Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) and TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are recited at a high level of generality, does not effect a transformation or reduction of a particular article to a different state or thing, and is not an unconventional step that confines the claim to a particular useful application of the judicial exception.
Dependent claims 2-5 and 8-11 only further limit the judicial exception and are rejected to for the same reasons as claims 1 and 7.
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.
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2012/0310102) in view of Johnson et al. (US 2010/0211125) and Givental et al. (US 2021/0264025).
Regarding claims 1 and 7, Li et al. discloses a system and a method performed in a system for estimating arrhythmia using artificial neural networks, the system comprising a plurality of attention heads ([0083]), the method comprising the steps of: by the plurality of attention heads, extracting feature vectors related to different types of arrhythmic state from a target biosignal of a subject with respect to the respective attention heads (“A plurality of morphological features are extracted from the arrhythmic waveform. The arrhythmic feature vector (b) is associated with the plurality of morphological features” [0084]), and combining the extracted feature vectors by weighting and averaging the feature vectors on the basis of weights of the respective attention heads to drive a unified feature vector (“fuzzy score” [0056], [0080], [0087-0088]).
Li et al. does not expressly disclose the system comprising a classifier, and the step of: by the classifier, determining a result of a prediction of a type of arrhythmic state to which the target biosignal corresponds as a classification probability, on the basis of the unified feature vector, and determining a level of confidence in the prediction. Johnson et al. teaches it is known in the art to determine a result of a prediction of a type of arrhythmic state to which a target biosignal corresponds ([0051]) to as a classification probability ([0053]), and determining a level of confidence in the prediction (“validation metric values” [0053-0055]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Li et al. to determine a result of a prediction of a type of arrhythmic state to which the target biosignal corresponds to a classification probability, on the basis of the disclosed determined unified feature vector, and to further determine a level of confidence in the prediction as taught by Johnson et al. in order to present a single index that can be used to rapidly identify possible misclassified arrhythmic episodes and prompt the clinician to further investigate such episodes ([0045]).
Li et al. also does not expressly disclosed the system uses artificial neural networks, the system comprising a combination layer, and the step of: by the combination layer, combining the classification probability and the prediction confidence to derive a final probability of the type of arrhythmic state to which the target biosignal corresponds. Givental et al. teaches it is known in the art to rely on machine learning based anomaly detection mechanisms to reduce manual efforts ([0020]), and to combine a classification probability and prediction confidence value to generate an overall probability/confidence score ([0036], [0061]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Li et al. to combine the probability and prediction confidence value to generate an overall probability/confidence score as taught by Givental et al. in order to provide an overall ranking score from values across a variety of artificial networks ([0066]) the results of such a modification being reasonably predictable in the art.
Regarding claims 2 and 8, Li et al. discloses the target biosignal is an electrocardiogram signal (“A plurality of morphological features are extracted from the arrhythmic waveform” [0084]).
Regarding claims 3 and 9, Li et al. discloses the weights of the plurality of attention heads are independently determined ([0080]).
Regarding claims 4 and 10, Li et al. discloses wherein the classification probability is determined as a vector of N dimensions where N is the number of arrhythmic state types ([0088]), and Li et al. in view of Johnson et al. discloses the prediction confidence level is determined as a single scalar value (“validation metric values” [0053-0055]).
Regarding claims 5 and 11, Li et al. in view of Givental et al. discloses wherein the combination layer is configured to learn logic for optimally combining the classification probability and the prediction confidence level through end-to-end learning (“convolutional neural networks (CNNs)” [0026], known in the art to be end-to-end learning models).
Regarding claim 6, Li et al. in view of Givental et al. discloses a non-transitory computer-readable recording medium ([0049]) having stored thereon a computer program for executing the method of claim 1 ([0006]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERICA S LEE whose telephone number is (571)270-1480. The examiner can normally be reached M-F 8-7pm, flex.
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/ERICA S LEE/Primary Examiner, Art Unit 3796
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