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 .
Drawings
The drawings are objected to because figures 4, 5, 6, 7, 9, and 11-21 include stray marks or blurry features that are illegible. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al (Chen, Genlang, et al. "A cascaded classifier for multi-lead ECG based on feature fusion." Computer methods and programs in biomedicine 178 (2019): 135-143, copy provided with this Office action).
Regarding claim 1, Chen discloses a system for performing a machine learning task which is a regression task on a neural network input derived from electrocardiogram (ECG) data received from one or more ECG leads to generate an output using a neural network, the system comprising: one or more computers configured to perform one or more operations to implement a neural network configured to perform the machine learning task (pages 135-136 which detail automatic classification of ECG signals via neural network [as in pages 138-139] which inherently requires the use of a computer considered to be configured to perform operations to implement the neural network), the neural network comprising: a feature extraction sub-neural network that is configured to process the neural network input to generate one or more feature extraction network outputs (pages 136-137 where ten signal features, including signal quality indices and morphological characters etc., are chosen and run on each of the 12 leads separately; features could also be extracted from time); and a task sub-neural network that is configured to process one or more inputs derived from the one or more feature extraction network outputs and generate a neural network output as a function of the one or more inputs derived from the one or more feature extraction network outputs (pages 138-139 which detail cascaded classification involving processing a neural network input to generate network outputs; after the process of feature fusion, the feature vector is fed into the cascaded classification system. Random forest and multilayer perceptron (MLP) are chosen to be the first and second layer of the classification system).
Regarding claim 11, Chen discloses a method of performing a machine learning task which is a regression task on a neural network input derived from electrocardiogram (ECG) data received from one or more ECG leads to generate a neural network output, the method comprising: processing, using one or more computers and a feature extraction sub-neural network (pages 135-136 which detail automatic classification of ECG signals via neural network [as in pages 138-139] which inherently requires the use of a computer considered to be configured to perform operations to implement the neural network), the neural network input to generate one or more feature extraction network outputs, each of the one or more feature extraction network outputs describing temporal features from a different ECG lead of the one or more ECG leads represented in the neural network input (pages 136-137 where ten signal features, including signal quality indices and morphological characters etc., are chosen and run on each of the 12 leads separately; features could also be extracted from time); and processing, using the one or more computers and a task sub-neural network, one or more inputs derived from the one or more feature extraction network outputs (pages 138-139 which detail cascaded classification involving processing a neural network input to generate network outputs; after the process of feature fusion, the feature vector is fed into the cascaded classification system. Random forest and multilayer perceptron (MLP) are chosen to be the first and second layer of the classification system); generating, using the one or more computers and the task sub-neural network, a neural network output as a function of the one or more inputs derived from the one or more feature extraction network outputs (pages 138-139 which detail cascaded classification involving processing a neural network input to generate network outputs; after the process of feature fusion, the feature vector is fed into the cascaded classification system. Random forest and multilayer perceptron (MLP) are chosen to be the first and second layer of the classification system).
Allowable Subject Matter
Claims 2-10 and 12-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lindsey G Wehrheim whose telephone number is (571)270-5181. The examiner can normally be reached Monday - Friday 9 a.m. - 5 p.m. EST.
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, Niketa Patel can be reached at (571) 272-4156. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Lindsey G Wehrheim
Primary Examiner
Art Unit 3799
/LINDSEY G WEHRHEIM/Primary Examiner, Art Unit 3799
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