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 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 1-11 are rejected under 35 U.S.C. 101 because of the following analysis:
Step 1: Do the claims recite one of the statutory categories of matter (i.e. method, apparatus, etc.)? YES, claims 1-6 recite a method and claims 7-11 recite an apparatus.
Step 2A Prong 1: Is there an abstract idea involved? YES, the claim language recites generating a model (making determinations/mathematics) training a machine learning model (observation/analysis), performing feature extraction and identification of features (observation/analysis) and predicting an intracardiac electrical behavior of a patient (making determinations). These limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in mind or by a person using a pen and paper. For example, a trained physician can observe and learn the conditions (model) of a heart attack and then be able to predict other heart attacks based on analyzing patient data.
Step 2a Prong 2: Do the claims recite additional elements that integrate the exception into a practical application? NO, the claims recite a computing system that is recited at a high level of generality and is recited as performing generic computer functions. i.e., data processing and display. The elements amount to mere 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 2106.05(f)). Accordingly, each of the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea.
The dependent claims recite the same abstract idea as the independent claims and contain recitations that further limit the abstract idea. Therefore, they are also rejected.
Step 2B: Do the additional elements amount to “Significantly More” than the judicial exception? The emphasized elements cited above do not amount to significantly more than the judicial exception 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’I, 110 USPQ2d 1976 (2014)).
In view of the above, the additional elements individually 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 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. Simply appending well-understood, routine, conventional activities previously known to 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 to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, and 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 2016/0210435 A1).
Regarding claims 1 and 7, Neumann discloses a process for generating a machine learning model capable of predicting intracardiac electrical behavior of a patient using non-invasively acquired data, the process comprising: generating simulated electrocardiograms (ECGs) (eg. Fig. 1-2, 4-5, Para. 39-41) and simulated intracardiac electrical behavior data for each of a plurality of cardiac geometries (eg. Fig. 1-2, 4-5, 12 Para. 26-31, 37, 59-62); and training a machine learning model to predict intracardiac electrical behavior based on ECG characteristics (eg. Para. 40-42, 59), wherein training the machine learning model comprises performing feature extraction on the simulated ECGs and simulated intracardiac electrical behavior data to identify features of the simulated ECGs and features of corresponding intracardiac electrical behavior (eg. Para. 40, 51-67), and generating model weights that represent correlations between the features of the simulated ECGs and features of the corresponding intracardiac electrical behavior (eg. Fig. 8, Para. 54-65, regression; said process performed by a computing system comprising one or more computing devices (eg. Para. 25, 78).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the embodiments of Neumann to have personalized weights and values using multiple models as shown in Fig. 5 and 8 to provide the predictable result of having a more personalized technique on patients with a large variety in phenotype and improving the diagnosis of diseases (eg. Neumann, Para. 55-57).
Regarding claims 2 and 8, Neumann discloses the invention of claim 1, but does not disclose extracting features of the simulated ECGs comprises performing wavelet decomposition of the ECGs (eg. Fig. 5 and 7, Para. 51, calculate named ECG figures such as QT, QRS, etc.).
Regarding claims 3 and 9, Neumann discloses performing validation of the trained machine learning model using actual ECG and corresponding actual intracardiac electrical behavior of patients (eg. Neumann, Para. 65, 69-72).
Regarding claim 4, Neumann discloses comprising using the trained machine learning model to predict intracardiac electrical behavior of a patient based on an ECG of the patient (eg. Fig. 2, 12-13, Para. 41, 66-68, 73-76).
Claim(s) 5-6 and 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 2016/0210435 A1) in view of Walters (US 10459954 A1).
Regarding claims 5 and 10, Neumann discloses the invention of claim 1, but does not disclose machine learning model comprises a sequence-to-sequence machine learning mode.
Walters teaches a machine learning algorithm that uses Seq2Seq models and neural networks on big data including biological data (Eg. Col. 9, Ln. 40-Col. 10, Ln. 52).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Neumann with a Seq2Seq neural network model since it is a common type of machine learning model in the art of processing big data such as biological data.
Regarding claims 6 and 11, Neuman discloses the sequence-to-sequence machine learning model comprises a sequence-to-sequence neural network (eg. Walters, Col. 9, Ln. 40-Col. 10, Ln. 52).
Conclusion
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/MICHAEL J LAU/Examiner, Art Unit 3796
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