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 .
Status of Claims
Claims 1 and 6 are currently amended.
Claims 2-4 and 7-9 are cancelled.
Response to Arguments
Applicant's arguments, page 5-8, filed 10/1/2025, have been fully considered but they are not
persuasive.
35 U.S.C. 101
Regarding claim 1, applicant argue that the claimed invention is not a mental process because
the steps require computational resources and algorithmic processing that are not practically performable in the human mind. The examiner respectfully argues that although the claim recites additional elements such as a catheter electrode, non-transitory computer-readable memory, and a processor with a five layer neural network, these elements are recited as general computer components to perform the abstract idea. Furthermore, each additional elements are well-known, routine and conventional in the art. Specifically for the five layer neural network, the applicant’s specification recite that “NN model 55 may have about ten (10) layers (or any other suitable number of layers, e.g., at least five layers) and may be based on the architecture of AlexNet NN supplied by Alex Krizhevsky (Toronto, Canada), or on the TensorFlow open-source machine learning platform supplied by Google AI, a subsidiary of Google (Mountain View, California, US), or any other suitable type of NN architecture.” This architecture is utilized in many different technological platforms and is not inventive.
Applicant is reminded that abstract ideas cannot provide a practical application or significantly more (e.g., an improvement). Both Step 2A Prong 2 and Step 2B require an additional element, not an abstract idea, to provide a practical application or significantly more (e.g., an improvement). See Genetic Technologies Limited v. Merial LLC (Fed Cir 2016). Here, the additional elements of claim 1 are merely generically recited computer elements used as tools for executing the abstract ideas or insignificant extra-solution activity.
Regarding claim 1, applicant argues that the amended claims are directed to a technological
improvement in the processing of ECG signals as supported by McRO v. Bandai Namco, 837 F 3d. 1299 (Fed. Cir. 2016). The examiner respectfully disagrees and argues that claim 1 is not directed to a specific process for automatically animating characters using particular information and techniques as discussed in McRO. Thus, the applicability of McRO to these claims seems questionable. Further, unlike the claims at issue in McRO, Claim 1 is merely applying an abstract idea to a computer and do not either improve the performance of the computer itself or computer animation in any way.
Regarding claim 1, applicant argues that the claims are patent eligible because they incorporate
mathematical modeling and machine learning techniques to enhance the operations associated with ECG processing as supported by Thales Visionix Inc. v. United States, 850 F.3d 1343 (Fed. Cir. 2017). The examiner respectfully disagrees and argues that the claims at issue in Thales were focused on specific systems and methods that use inertial sensors in a non-conventional manner to reduce errors in measuring the relative position and orientation of a moving object on a moving reference frame. According to the court, the Thales claims specify a particular configuration of inertial sensors and a particular method of using the raw data from the sensors in order to more accurately calculate the position and orientation of an object on a moving platform. The mathematical equations are a consequence of the arrangement of the sensors and the unconventional choice of reference frame in order to calculate position and orientation. Far from claiming the equations themselves, the claims seek to protect only the application of physics to the unconventional configuration of ECG sensors as disclosed. However, none of Claims 1 is directed to an unconventional configuration of sensors or a specific process for reducing errors in measuring the relative position and orientation of a moving object on a moving reference frame as discussed in Thales. Thus, the applicability of Thales to these claims is questionable, at best. Further, unlike the claims at issue in Thales, Claims 1 merely apply an abstract idea to a computer and do not either improve the performance of the computer itself or computer animation in any way.
Regarding claim 1, applicant argues that the claims are directed towards an improvement to
computer functionality, specifically ECG processing, as supported by Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016). The examiner respectfully disagrees and argues the improvement is towards the abstract idea of improving the accuracy of ECG processing by suppressing interference utilizing generic computer components.
Regarding claim 1, applicant argues that the claims integrate the judicial exception into a
practical application. The examiner respectfully disagrees and argues that in light of Applicant’s specification, the claimed term neural network is reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available technology, with their already available basic functions, to use as tools in executing the claimed process. See MPEP 2106.05(f).
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 and 5-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 and 6 recite a method and device with instructions for performing operations comprising:
receiving, from one or more sources external to the heart, one or more external signals that concurrently sense the interference during acquisition of the first ECG signal;
training a Neural Network (NN), by a processor configured with an autoencoder neural network (NN) architecture having at least five layers, a Neural Network (NN) using (i) one or more training ECG signals that are not distorted by the interference, and (ii) one or more training interference signals each having one or more respective spectral lines and one or more respective harmonics;
producing a second ECG signal, in which the interference is suppressed relative to the first ECG signal, by applying trained Neural Network (NN) to the first ECG signal and to the one or more external signals, and wherein training the NN comprises training an autoencoder artificial NN having at least five lavers.
To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is
evaluated according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.05. The instant claims are evaluated according to such analysis.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 is directed to a method, claim 6 is directed to a system to perform the steps of the method, and thus meet the requirements for step 1.
Step 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
Claims 1 and 6 recite a method and device with instructions to perform the method
comprising:
receiving, from one or more sources external to the heart, one or more external signals that concurrently sense the interference during acquisition of the first ECG signal;
training a Neural Network (NN), by a processor configured with an autoencoder neural network (NN) architecture having at least five layers, a Neural Network (NN) using (i) one or more training ECG signals that are not distorted by the interference, and (ii) one or more training interference signals each having one or more respective spectral lines and one or more respective harmonics;
producing a second ECG signal, in which the interference is suppressed relative to the first ECG signal, by applying trained Neural Network (NN) to the first ECG signal and to the one or more external signals, and wherein training the NN comprises training an autoencoder artificial NN having at least five lavers.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the
limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Therefore, claims 1 and 6 recite an abstract idea of a mental process.
Claims 1 and 6 recite the abstract idea of a mental process. The limitations as drafted in the claims, under its broadest reasonable interpretation, covers performance of the claimed steps in the mind, but for the recitation of a generic processor. Other than reciting a generic data gathering device (which is interpreted as a processer in a data gathering device) and memory, nothing in the elements of the claims precludes the step from practically being performed in the mind or manually by a clinician. For example:
“Receiving, from one or more sources external to the heart, one or more external signals that concurrently sense the interference during acquisition of the first ECG signal.” A physician may receive ECG signal from a multitude of outside sources such as electrodes, heartbeat sensors, and wristwatches.
“Producing a second ECG signal, in which the interference is suppressed relative to the first ECG signal, by applying a trained Neural Network (NN) to the first ECG signal and to the one or more external signals.” A physician may manually provide filtering for a second ECG signal by using filtering equations and display the results for a patient.
“Training a Neural Network (NN), by a processor configured with an autoencoder neural network (NN) architecture having at least five layers, a Neural Network (NN) using (i) one or more training ECG signals that are not distorted by the interference, and (ii) one or more training interference signals each having one or more respective spectral lines and one or more respective harmonics;” A physician gather ECG signals with one or more spectral lines and perform math equation to acquire respective harmonics. The signals may by not distorted by interference by applying filtering equations to the frequency signals.
“Producing a second ECG signal, in which the interference is suppressed relative to the first ECG signal, by applying trained Neural Network (NN) to the first ECG signal and to the one or more external signals, and wherein training the NN comprises training an autoencoder artificial NN having at least five lavers.” A physician may obtain a second ECG signal by filtering/suppressing interference from a first ECG signal using a filtering equation through five layers of steps.
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial
exception into a practical application?
Claims 1 and 6 recite the additional elements of a “one or more external source” and a
“processor” which are being interpreted as a processor of a generic data gathering device.
acquiring a first electrocardiogram (ECG) signal from one or more electrodes located on a catheter positioned inside the heart of the patient; The catheter and electrodes are recited as pre-solution activity to gather ECG data.
execute the NN using a trained model stored in non-transitory computer-readable memory; The generic computer components, NN and memory, are recited computer implementation to execute processing steps already indicated as an abstract idea.
However, these elements are recited at a high level of generality performing the function of generic data processing such that they amount to no more than mere instructions to simply implement the abstract idea using generic computer components. See MPEP 2106.05(b) and (f).
Accordingly, the additional elements do not integrate the abstract idea into a practical
application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
The additional elements when considered individually and in combination are not enough to
qualify as significantly more than the abstract idea.
acquiring a first electrocardiogram (ECG) signal from one or more electrodes located on a catheter positioned inside the heart of the patient; The catheter and electrodes are recited as pre-solution activity to gather ECG data.
execute the NN using a trained model stored in non-transitory computer-readable memory; The generic computer components, NN and memory, are recited computer implementation to execute processing steps already indicated as an abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, “one or more external source” and a “processor” which are being interpreted as a processor of a generic data gathering device, as recited to perform the steps of:
receiving, from one or more sources external to the heart, one or more external signals that concurrently sense the interference during acquisition of the first ECG signal;
training a Neural Network (NN), by a processor configured with an autoencoder neural network (NN) architecture having at least five layers, a Neural Network (NN) using (i) one or more training ECG signals that are not distorted by the interference, and (ii) one or more training interference signals each having one or more respective spectral lines and one or more respective harmonics;
producing a second ECG signal, in which the interference is suppressed relative to the first ECG signal, by applying trained Neural Network (NN) to the first ECG signal and to the one or more external signals, and wherein training the NN comprises training an autoencoder artificial NN having at least five lavers.
amount to no more than mere instructions to apply the exception using generic computer
components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. These additional elements are well‐understood, routine (For example FONTANARAVA et al (U.S. Patent Application Publication Number: US 2019/0298204 A1, hereinafter Fontanarava) teaches a data gathering device with a processor and conventional limitations that amount to mere instructions or elements to implement the abstract idea. In addition, the end result of the system/method, the essence of the whole, is a patent-ineligible concept. Therefore, the claims are not patent eligible.
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 THIEN J TRAN whose telephone number is (571)272-0486. The examiner can normally be reached M-F. 8:30 am - 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached on 571-270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.J.T./Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792