DETAILED ACTION
1. This communication is in response to the request for continued examination and corresponding amendments filed on April 24, 2026 for Application No. 18/878,007 in which Claims 1, 5-11, 13-16, and 61-88 are presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
3. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/24/2026 has been entered.
Response to Arguments
4. The amendments filed on April 24, 2026 have been considered. Claims 1, 6-7, and 16 have been amended. Claims 62-88 have been newly added. Thus, Claims 1, 5-11, 13-16, and 61-88 are pending and presented for examination.
5. Applicant’s arguments and corresponding amendments filed April 24, 2026, with respect to the 35 U.S.C. 103 rejection of Claims 1, 5-11, 13-16, and 61 have been fully considered and are persuasive. Thus, the 35 U.S.C. 103 rejection for these claims have been withdrawn. Note: While Claims 1, 5-11, 13-16, 61-67, 72-73, and 81-88 have no prior art rejection, they are still rejected under 35 U.S.C. 101 – abstract idea. Further, Examiner maintains use of the Anastasia (US PG-PUB 20210315506) reference of record in the new 35 U.S.C. 103 rejection of the newly added claims (Claims 68-71 and 74-80) – however, as the newly added claims are drafted differently from Independent Claim 1, Applicant’s arguments regarding the Anastasia reference are now moot.
6. Applicant's arguments filed April 24, 2026 with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Applicant’s Arguments on Pgs. 15-16 of Arguments/Remarks state:
“As explained in MPEP § 2106.04(a)(2), a claim does not recite a mental process where the claimed steps cannot practically be performed in the human mind, such as where they require computer-based operations on digital data that humans cannot practically carry out. The Examiner's analysis improperly fragments the claim and ignores its computational scale and precision.
Claim 1 recites "training the ML model." The training of an ML model cannot be performed in the human mind. Example 39 of the Subject Matter Eligibility Examples: Abstract Ideas, January 7, 2019, addresses training an ML model.
[…]
This claim recites applying a transformation to facial images and modifying the facial images using, for example, rotating the images. Clearly, a person could have a picture of someone and manually rotate that facial image. A person could also create a first training data set by collecting the facial images, the modified facial images, and non- facial images (e.g., a picture of a hand). A person could also create a second training data set that is the first training data set and non-facial images that have been incorrectly detected as facial images.
A person, however, cannot practically perform the steps needed to train a neural network (i.e., an ML model). To train the first neural network, facial images need to be input sequentially to the neural network (given its current weights) to determine if the output categorization of facial or non-facial is correct. The weights of the neural network, which would typically number in the hundreds or more, must be then adjusted (e.g., using a gradient descent analysis) to help ensure that facial images are correctly identified. This process is repeated for each facial image or sets of facial images.
The analysis of Example 39 states "the claim does not recite a mental process because the steps cannot practically be performed in the human mind."
Applicant's claim 1 similarly recites training an ML model which cannot practically be performed in the human mind. Thus, applicant's claims are not directed to a mental process.
Nevertheless, applicant has amended claim 1 to recite that the ML model is a neural network.
Accordingly, claim 1 is not directed to a mental process. Also, the newly added claims are not directed to a mental process for similar reasons.”
Examiner respectfully disagrees. First, the limitation “training the ML model” of the Independent claim is not considered to be a mental process at Step 2A Prong 1. Instead, the limitation “training the ML model using the training data wherein the trained ML model […]” is considered at Step 2A Prong 2 and Step 2B as adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f). The training of the ML model is recited at a very high level of generality – training data is merely “generated” and accordingly “used” to train the model without significantly more. There is no degree to which the model is trained and/or optimized in order to ensure accuracy of the model – the model is simply just “trained” with previously determined data in order to complete the specific operation of “output[ting] base data as subject data for a subject region” without significantly more. This cannot provide an inventive concept.
Further, regarding Applicant’s evaluation of Example 39 of the Subject Matter Eligibility examples, Examiner respectfully disagrees. Applicant states, with regards to Example 39, “This claim recites applying a transformation to facial images and modifying the facial images using, for example, rotating the images. Clearly, a person could have a picture of someone and manually rotate that facial image” – this is an incorrect interpretation of Example 39. Example 39 specifically recites “digital facial images” and furthermore recites very specific operations for applying transformations to these digital facial images alongside creating training datasets comprising these digital facial images. A human clearly cannot modify a digital facial image and/or create a training set of digital facial images by mental process – hence, these limitations are eligible at Step 2A Prong 1. Further, Applicant’s subsequent assertion regarding the training of the network and the number of weights used regarding Example 39 are misleading – the independent claim of example 39 does not disclose any information about weights, gradients, etc.
When considering instant claim 1 in view of Example 39, Examiner asserts that the instant claims still recite an abstract idea as a human is clearly capable of observing/analyzing a plurality of mappings (which are simply accessed/received without significantly more according to the claim language) and accordingly deriving a plurality of derived regions from the base region and the base cardiogram of that mapping and accordingly generating training data that includes the derived region labeled with the base data. For example, a user may observe/analyze the plurality of mappings and an according base cardiogram (by visually observing) and use judgement/evaluation to determine derived regions (which may be manually annotated) based on the mappings and according analysis. Furthermore, the user may manually generate training data (with the aid of pen and paper) including the derived region that is labeled (manually annotated with the aid of pen and paper) with the base data (such as a start and end time, source location of arrhythmia, etc.) of that mapping. Accordingly, claim 1 is directed towards a mental process.
Applicant’s Arguments on Pgs. 17-18 of Arguments/Remarks state:
“Step 2A, Prong Two: The Claims Are Integrated Into a Practical Application
Even assuming arguendo that an abstract idea is recited, the claims are integrated into a practical application under MPEP § 2106.05.
A. Technical Improvement (MPEP & 2106.05(a))
The Office Action characterizes the claims as merely "training a machine learning model using training data." This overgeneralization ignores the claimed technical solution.
The claims recite a specific training data generation technique in which multiple derived cardiogram regions are generated from a base region by systematic temporal offsetting, and are labeled using base data. This improves ML robustness to physiological timing variation.
Under MPEP § 2106.05(a), an improvement to a technical field-here, machine learning applied to cardiogram signal processing-constitutes a practical application.
Step 2B: The Claims Are Directed to a Practical Application
Applicant's claims provide a solution to a problem that occurs because a person manually selects a portion of a cardiogram. A person cannot reliably select the optimal region of a cardiogram that should be used to inform treatment of a patient. The selection of a suboptimal region may lead to a suboptimal outcome. For example, if a person selects a suboptimal region, the wrong source location (e.g., base data) of an arrhythmia may be identified. If the patient is treated (e.g., an ablation procedure) using the wrong source location, the outcome of the treatment would be suboptimal.
The claimed ML model inputs such a suboptimal region and outputs base data (e.g., source location) which is considered to be the optimal base data. A patient can then be treated based on the optimal base data. So, the treatment is more likely to have an optimal outcome even though a suboptimal region was selected.
Using a computer system to overcome a problem caused by a person's inability to reliably select an optimal base region of a cardiogram is a practical application.
Applicant has also amended the claims to specifically recite that the treatment is an ablation procedure and that the base data is a source location of an arrhythmia. The claims are directed to the technical field of cardiac ablation procedures.
Accordingly, claim 1 is directed to a practical application and thus to statutory subject matter. The newly added claims are similarly directed to a practical application for similar reasons.”
Examiner respectfully disagrees. Applicant states above that the claims “recite a specific training data generation technique in which multiple derived cardiogram regions are generated from a base region by systematic temporal offsetting” – however, this is not evident by the currently drafted claim language. Instead, as stated in the preceding response to arguments, the machine learning model is generically trained using a training data set that is generated by “deriving regions” of accessed mappings.
Furthermore, Applicant states that the claims provide a solution to a problem that occurs because a person manually selects a portion of a cardiogram and that a person may not reliably select the optimal region of a cardiogram that should be used to inform treatment of a patient. However, even with the application of the machine learning model, the supposed improvement is not reflected into the currently drafted claim language – the machine learning model is also not iteratively trained to improve performance/accuracy or evaluated against any degree/requisite/threshold to determine if the output is accurate as well. Instead, again, the machine learning model is merely “trained” and then “applied” to provide an output without significantly more. Such improvements are not reflected in the claim language, as the claim language is still recited at a high-level of generality and does not properly integrate the judicial exception into practical application.
Furthermore, regarding the amendment that now specifies that the treatment is an ablation procedure, Examiner asserts that this limitation is still recited at a high-level of generality – regarding Independent claim 1, at Step 2A Prong 2 and Step 2B, this amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Further, the ablation procedure may be coordinated by an ablation device (See Applicant’s specification Par. [0074] for support), hence simply adding the generic limitation “performing an ablation on the patient based on the identified source location” amounts to mere instructions to apply the exception using generic computer components and cannot provide an inventive concept. Applicant should consider further amending the claims to better detail the training of the machine learning model such that it correspondingly highlights how the use of an ML model improves the process of identifying arrhythmias and performing ablation.
Thus, the 35 U.S.C. 101 rejection is maintained.
7. Applicant’s arguments filed April 24, 2026 with respect to the objection of the specification have been fully considered and are persuasive. Thus, the objection of the specification has been withdrawn.
Claim Rejections - 35 USC § 101
8. 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.
9. Claims 1, 5-11, 13-16, and 61-88 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a method type claim. Therefore, Claims 1, 5-11, 13-16, and 61 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: 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. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
[…] identifying data associated with a region of a cardiogram […] (mental process – identifying data associated with a region of a cardiogram may be performed manually by a user observing/analyzing the cardiogram and accordingly using judgement/evaluation to identify data associated with a particular region of said cardiogram)
for each of the plurality of mappings, deriving a plurality of derived regions from the base region and the base cardiogram of that mapping (mental process – deriving a plurality of regions from the base region and base cardiogram may be performed manually by a user observing/analyzing the plurality of mappings that each map a base region of a base cardiogram to base data and accordingly using judgement/evaluation to derive a plurality of derived regions based on the user’s review of each of the mappings)
for each of the plurality of the derived regions, generating training data that includes the derived region labeled with the base data of that mapping (mental process – generating training data may be performed manually by a user observing/analyzing each of the derived regions and accordingly using judgement/evaluation to generate training data which includes the labeled/manually annotated (with the aid of pen and paper) derived region with the base data)
wherein the derived start times and the derived end times for derived regions that are derived from a base region are derived by adding a positive or negative increment to the base start time of that base region and/or adding a positive or negative increment to the base end time of that base region, wherein at least one derived region has either a negative increment added to its start time and/or a positive increment added to its end time (mental process – adding a positive or negative increment to the base start/end time may be performed manually by a user observing/analyzing the base start time and/or base end time and accordingly using judgment/evaluation to either add a positive or negative increment to either the base start time and/or base end time)
[…] the patient base region being selected by a person (mental process – selecting a patient base region may be performed manually by a user observing/analyzing the cardiogram and accordingly using judgement/evaluation to select a base region of the cardiogram)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
one or more computing systems for training a machine learning model (ML) […] (recited at a high-level of generality (i.e., as a generic computing system for generically training a machine learning model without significantly more) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
accessing a plurality of mappings that each map a base region of a base cardiogram to base data, a base region being a portion of a base cardiogram (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
training the ML model using the training data (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
wherein the trained ML model, when applied to a subject region of a subject cardiogram, outputs base data as subject data for the subject region (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
[…] the ML model being a neural network (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the ML model is a neural network does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
wherein a base region of a base cardiogram has a base start time and a base end time within that base cardiogram and wherein the derived regions that are derived from a base region have derived start times and derived end times that are derived from the base start time and the base end time of that base region (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the base region of the cardiogram has a base start/end time and the derived regions have a derived start/end time does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
wherein a patient base region of a patient cardiogram collected from a patient is input to the ML model […] (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
[…] which outputs a patient derived region […] (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
[…] the patient is treated based on the patient derived cardiogram (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the patient is treated simply based on the patient derived cardiogram does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
[…] the base data being a source location of an arrhythmia and the treatment being an ablation procedure (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the base data is a source location of an arrhythmia and the treatment is an ablation procedure does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
one or more computing systems for training a machine learning model (ML) […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
accessing a plurality of mappings that each map a base region of a base cardiogram to base data, a base region being a portion of a base cardiogram (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
training the ML model using the training data (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data. This cannot provide an inventive concept)
wherein the trained ML model, when applied to a subject region of a subject cardiogram, outputs base data as subject data for the subject region (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
[…] the ML model being a neural network (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the ML model is a neural network does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
wherein a base region of a base cardiogram has a base start time and a base end time within that base cardiogram and wherein the derived regions that are derived from a base region have derived start times and derived end times that are derived from the base start time and the base end time of that base region (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the base region of the cardiogram has a base start/end time and the derived regions have a derived start/end time does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
wherein a patient base region of a patient cardiogram collected from a patient is input to the ML model […] (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
[…] which outputs a patient derived region […] (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
[…] the patient is treated based on the patient derived cardiogram (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the patient is treated simply based on the patient derived cardiogram does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
[…] the base data being a source location of an arrhythmia and the treatment being an ablation procedure (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the base data is a source location of an arrhythmia and the treatment is an ablation procedure does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 5-11, 13-16, and 61. The additional limitations of the dependent claims are addressed below.
Regarding Claim 5:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 5 depends on.
Step 2A Prong 2 & Step 2B:
wherein the ML model comprises a convolutional neural network that inputs an image of a derived region and outputs the base data (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the ML model is a convolutional neural network that inputs an image and outputs base data does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 6:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 6 depends on.
generates a latent vector representing an image of the derived region […] (mathematical process – generating a latent vector representing an image of the derived region may be performed by mathematical process/calculation using an equation/algorithm for generating a latent vector)
Step 2A Prong 2 & Step 2B:
wherein the ML model comprises a portion of an autoencoder […] and another ML model that inputs the generated latent vector and outputs the base data (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 7:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 7 depends on.
Step 2A Prong 2 & Step 2B:
wherein the ML model is trained using features derived from the derived region (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner' s note: high level recitation of training a machine learning model with previously determined data. This cannot provide an inventive concept)
the features being one or more of a time-voltage series specifying voltages and time increments of the derived region, images and time- voltage series of portions of the derived region, length in seconds of various intervals of the derived region, QRS integral of the derived region, maximum, minimum, mean, and variance of voltages of the derived region, a maximal vector of QRS loop and angle of a vector derived from vectorcardiogram of the derived region, and location of a peak or zero crossing relative to a maximum peak in an interval of the derived region (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying the features without significantly more does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 8:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 8 depends on.
[…] generate an encoding […] (mathematical process – generating an encoding may be performed by mathematical process using an algorithm/equation for generating an encoding)
Step 2A Prong 2 & Step 2B:
wherein the ML model comprises an encoder of a transformer adapted to an image to […] and a neural network inputs the encoding and other features of a feature vector based on the training data (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 9:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 9 depends on.
Step 2A Prong 2 & Step 2B:
wherein the ML model comprises a neural network that inputs a feature vector representing the derived region (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the ML model comprises a neural network that inputs a feature vector does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 10:
Step 2A Prong 1:
See the rejection of Claim 9 above, which Claim 10 depends on.
Step 2A Prong 2 & Step 2B:
wherein the feature vector representing the derived region includes a time-voltage series of the derived region (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the feature vector represents the derived region including a time-voltage series does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 11:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 11 depends on.
Step 2A Prong 2 & Step 2B:
wherein at least some of the mappings have a base time range of the base cardiogram that is selected by a person and have base data that is specified based on treatment of an arrhythmia (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the mappings have a base time range selected by a person and have base data that is specified base on treatment of an arrhythmia does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 13:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 13 depends on.
determining overall subject data based on analysis of the lead subject data for the leads (mental process – determining overall subject data may be performed manually by a user observing/analyzing the lead subject data for the leads and accordingly using judgement/evaluation to determine overall subject data)
Step 2A Prong 2 & Step 2B:
wherein a base cardiogram includes multiple leads and wherein a base region of each lead is mapped to base data and wherein a machine learning model is trained for each lead (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the base cardiogram includes multiple leads and wherein a base region of each lead is mapped to base data and wherein a machine learning model is trained for each lead does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
receiving lead subject regions of multiple leads of a subject cardiogram (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
for each of the multiple leads, applying a trained machine learning model for that lead to the lead subject region of that lead, which outputs lead subject data for that lead(Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner' s note: high level recitation of training a machine learning model with previously determined data. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 14:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 14 depends on.
Step 2A Prong 2 & Step 2B:
wherein the base data is a source location of an arrhythmia (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the base data is a source location of an arrhythmia does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 15:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 15 depends on.
Step 2A Prong 2 & Step 2B:
wherein the base data is a region of a cardiogram (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the base data is a region of a cardiogram does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 16:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 16 depends on.
Step 2A Prong 2 & Step 2B:
wherein the base data is a start time and an end time of a region of a cardiogram (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the base data is a start time and an end time of a region of a cardiogram does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 61:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 61 depends on.
wherein the treating of the patient includes identifying a source location of an arrhythmia based on the patient derived cardiogram (mental process – identifying a source location of an arrhythmia and performing an ablation may be performed manually by a user observing/analyzing the patient derived cardiogram and using judgement/evaluation to identify a source location of an arrhythmia)
Step 2A Prong 2 & Step 2B:
performing an ablation on the patient based on the identified source location (Applicant’s specification Par. [0074] specifies that the performing of an ablation may be coordinated by an ablation device – hence, mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 62:
Step 1: Claim 62 is a method type claim. Therefore, Claims 62-67 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: 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. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
[…] identifying a source location of the arrhythmia […] (mental process – other than reciting “under control of one or more computing systems”, identifying a source location of an arrhythmia may be performed manually by a user observing/analyzing a subject cardiogram and accordingly using judgement/evaluation to identify a source location of an arrhythmia based on said analysis)
for each of the plurality of mappings, deriving a plurality of derived regions from the base region and the base cardiogram of that mapping (mental process – deriving a plurality of regions from the base region and base cardiogram may be performed manually by a user observing/analyzing the plurality of mappings that each map a base region of a base cardiogram to base data and accordingly using judgement/evaluation to derive a plurality of derived regions based on the user’s review of each of the mappings)
for each of the plurality of the derived regions, generating training data that includes the derived region labeled with the base data of that mapping (mental process – generating training data may be performed manually by a user observing/analyzing each of the derived regions and accordingly using judgement/evaluation to generate training data which includes the labeled/manually annotated (with the aid of pen and paper) derived region with the base data)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a method performed for treating an arrhythmia of a patient, the method comprising: under control of one or more computing systems […] (recited at a high-level of generality (i.e., as a generic method performed by a generic computing system) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
displaying a subject cardiogram of the patient that is collected during an arrhythmia episode of the patient (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
receiving a selection by a user of a subject region of the subject cardiogram (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
applying a machine learning model to the subject region to identify a subject source location, the machine learning model being trained by […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more)
accessing a plurality of mappings that each map a base region of a base cardiogram to base data, a base region being a portion of a base cardiogram (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
training the machine learning model using the training data […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more)
wherein the trained machine learning model, when applied to the subject region of the subject cardiogram, outputs base data as the subject source location for the subject region […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of applying a generically trained machine learning model with previously determined data without significantly more)
outputting an indication of the subject source location (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
performing an ablation procedure on the patient to treat the arrhythmia (recited at a high-level of generality (i.e., as generically performing an ablation procedure which may be performed by a generic ablation device – See Applicant’s specification Par. [0074]) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a method performed for treating an arrhythmia of a patient, the method comprising: under control of one or more computing systems […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
displaying a subject cardiogram of the patient that is collected during an arrhythmia episode of the patient (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
receiving a selection by a user of a subject region of the subject cardiogram (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
applying a machine learning model to the subject region to identify a subject source location, the machine learning model being trained by […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
accessing a plurality of mappings that each map a base region of a base cardiogram to base data, a base region being a portion of a base cardiogram (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
training the machine learning model using the training data […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
wherein the trained machine learning model, when applied to the subject region of the subject cardiogram, outputs base data as the subject source location for the subject region […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of applying a generically trained machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
outputting an indication of the subject source location (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
performing an ablation procedure on the patient to treat the arrhythmia (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
For the reasons above, Claim 62 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 63-67. The additional limitations of the dependent claims are addressed below.
Regarding Claim 63:
Step 2A Prong 1:
See the rejection of Claim 62 above, which Claim 63 depends on.
Step 2A Prong 2 & Step 2B:
wherein the outputting of the indication of the subject source location includes displaying the indication of the subject source location (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim. Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 62.
Regarding Claim 64:
Step 2A Prong 1:
See the rejection of Claim 62 above, which Claim 64 depends on.
Step 2A Prong 2 & Step 2B:
wherein the outputting of the indication of the subject source location includes providing the indication of the subject source location to an ablation device […] (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
[…] ablation device that coordinates the performing of the ablation procedure (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 62.
Regarding Claim 65:
Step 2A Prong 1:
See the rejection of Claim 62 above, which Claim 65 depends on.
Step 2A Prong 2 & Step 2B:
wherein a base region of a base cardiogram has a base start time and a base end time within that base cardiogram and wherein each derived region is derived from a base region having a derived start time and a derived end time that are derived from the base start time and the base end time of that base region. (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the base region has a base start and end time and each derived region has a derived start and end time does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 62.
Regarding Claim 66:
Step 2A Prong 1:
See the rejection of Claim 62 above, which Claim 66 depends on.
Step 2A Prong 2 & Step 2B:
wherein the derived regions are labeled with base source locations associated with the base cardiograms (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the derived regions are labeled does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 62.
Regarding Claim 67:
Step 2A Prong 1:
See the rejection of Claim 62 above, which Claim 67 depends on.
Step 2A Prong 2 & Step 2B:
wherein a cardiogram has multiple leads (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that a cardiogram has multiple leads does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
wherein each lead has a lead subject region corresponding to the selected subject region (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that each lead has a lead subject region corresponding to the selected subject region does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
wherein the applying of the machine learning model applies a lead machine learning model for each lead to the lead subject region of that lead to identify a lead subject source location for that lead (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner's note: high level recitation of applying a trained machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
wherein the subject source location is derived from the lead subject source locations (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the subject source location is derived from the lead subject source locations does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 62.
Regarding Claim 68:
Step 1: Claim 68 is a method type claim. Therefore, Claims 68-78 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: 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. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
identifying a base region of a cardiogram to inform a treatment decision for an arrhythmia of a patient (mental process – other than reciting “one or more computing systems”, identifying a base region of a cardiogram to inform a treatment decision for an arrhythmia of a patient may be performed manually by a user observing/analyzing the cardiogram and accordingly using judgement/evaluation to identify a base region of the cardiogram, based on said analysis, to inform a treatment decision regarding an arrhythmia of a patient)
identifying a subject base region of the subject cardiogram based on mappings of derived regions of base cardiograms to base regions of base cardiograms, each derived region being derived from the base region of a base cardiogram to which the derived region is mapped (mental process – identifying a subject base region of a subject cardiogram based on mappings of derived regions of base cardiograms to base regions of base cardiograms may be performed manually by a user observing/analyzing the subject cardiogram and according mappings and using judgement/evaluation to identify a subject base region based on said analysis)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a method performed by one or more computing systems […] (recited at a high-level of generality (i.e., as a generic method performed by generic systems) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
accessing a subject region of a subject cardiogram collected from the patient (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
applying a machine learning (ML) model to the subject base region to identify base data, the ML model trained using training data that includes base regions labeled with base data (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of applying a trained machine learning model with previously determined data without significantly more)
outputting an indication of the base data to inform an ablation procedure to treat the arrhythmia of the patient (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a method performed by one or more computing systems […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
accessing a subject region of a subject cardiogram collected from the patient (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
applying a machine learning (ML) model to the subject base region to identify base data, the ML model trained using training data that includes base regions labeled with base data (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of applying a trained machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
outputting an indication of the base data to inform an ablation procedure to treat the arrhythmia of the patient (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
For the reasons above, Claim 68 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 69-78. The additional limitations of the dependent claims are addressed below.
Regarding Claim 69:
Step 2A Prong 1:
See the rejection of Claim 68 above, which Claim 69 depends on.
Step 2A Prong 2 & Step 2B:
wherein at least some of the base regions were used in identifying a source location of an arrhythmia that resulted in successful treatment of the arrhythmia (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that at least some of the base regions were used in identifying a source location of an arrhythmia that was successfully treated does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 68.
Regarding Claim 70:
Step 2A Prong 1:
See the rejection of Claim 68 above, which Claim 70 depends on.
Step 2A Prong 2 & Step 2B:
wherein the base data is a source location of an arrhythmia (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the base data is a source location of an arrhythmia does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 68.
Regarding Claim 71:
Step 2A Prong 1:
See the rejection of Claim 68 above, which Claim 71 depends on.
Step 2A Prong 2 & Step 2B:
wherein the identifying of the subject base region is based on similarity between the subject region and the derived regions (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that identifying the subject base region is based on a generic similarity between the subject and derived regions does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 68.
Regarding Claim 72:
Step 2A Prong 1:
See the rejection of Claim 68 above, which Claim 72 depends on.
Step 2A Prong 2 & Step 2B:
inputting the subject region to a second ML model (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
[…] outputs the subject base region (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 68.
Regarding Claim 73:
Step 2A Prong 1:
See the rejection of Claim 72 above, which Claim 73 depends on.
Step 2A Prong 2 & Step 2B:
wherein the second ML model is trained based on the mappings (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner' s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 68.
Regarding Claim 74:
Step 2A Prong 1:
See the rejection of Claim 68 above, which Claim 74 depends on.
Step 2A Prong 2 & Step 2B:
wherein a base region of a base cardiogram has a base start time and a base end time within that base cardiogram and wherein the derived regions that are derived from a base cardiogram have derived start times and derived end times that are derived from the base start time and the base end time of that base region. (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the base region has a base start and end time and the derived region has a derived start and end time does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 68.
Regarding Claim 75:
Step 2A Prong 1: See the rejection of Claim 74, which Claim 75 depends on.
wherein the derived start times and the derived end times for derived regions that are derived from a base region are derived by adding positive or negative increments to the base start time of that base region and/or adding positive or negative increments to the base end time of that base region (mental process – adding a positive or negative increment to the base start/end time may be performed manually by a user observing/analyzing the base start time and/or base end time and accordingly using judgment/evaluation to either add a positive or negative increment to either the base start time and/or base end time)
Step 2A Prong 2 & Step 2B:
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 68.
Regarding Claim 76:
Step 2A Prong 1:
See the rejection of Claim 68 above, which Claim 76 depends on.
Step 2A Prong 2 & Step 2B:
wherein a region is specified as a time range within a cardiogram, as a voltage-time series, or as an image of a portion of a cardiogram (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that a region is a time range, voltage-time series, or an image does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 68.
Regarding Claim 77:
Step 2A Prong 1:
See the rejection of Claim 68 above, which Claim 77 depends on.
Step 2A Prong 2 & Step 2B:
displaying the subject cardiogram (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
receiving a selection of the subject region of the displayed subject cardiogram (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
displaying an indication of the subject region on the displayed subject cardiogram (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 68.
Regarding Claim 78:
Step 2A Prong 1:
See the rejection of Claim 77 above, which Claim 78 depends on.
Step 2A Prong 2 & Step 2B:
displaying an indication of the subject base region on the displayed subject cardiogram (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 68.
Regarding Claim 79:
Step 1: Claim 79 is a method type claim. Therefore, Claim 79 is directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: 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. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
[…] identifying a base region of a cardiogram to inform a treatment decision for a patient […] (mental process – other than reciting “under control of one or more computing systems”, identifying a base region of a cardiogram may be performed manually by a user observing/analyzing a cardiogram and accordingly identifying a base region of the cardiogram based on said analysis, in order to inform a treatment decision for a patient)
identifying a subject base region of the subject cardiogram based on mappings of derived regions to base regions of base cardiograms, each derived region being derived from the base region of a base cardiogram to which the derived region is mapped (mental process – identifying a subject base region of a subject cardiogram based on mappings of derived regions to base regions of base cardiograms may be performed manually by a user observing/analyzing the given mappings of derived regions to base regions of base cardiograms and accordingly using judgement/evaluation to identify a subject based region based on said analysis)
treating the patient based on the indication of the subject base region (mental process – treating the patient based on the indication of the subject base region may be performed manually by a user (such as a doctor or expert in the field) observing/analyzing the indication of the subject base region and accordingly using judgement/evaluation to recommend a treatment plan (with the aid of pen and paper) based on said analysis)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a method for treating a patient, the method comprising: under control of one or more computing systems […] (recited at a high-level of generality (i.e., as a generic method utilizing generic systems) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
accessing a subject region of a subject cardiogram collected from a patient (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
outputting an indication of the subject base region of the subject cardiogram to inform a treatment for the patient (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a method for treating a patient, the method comprising: under control of one or more computing systems […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
accessing a subject region of a subject cardiogram collected from a patient (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
outputting an indication of the subject base region of the subject cardiogram to inform a treatment for the patient (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
For the reasons above, Claim 79 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 80:
Step 1: Claim 80 is a method type claim. Therefore, Claim 80 is directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: 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. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
receiving a selection by a user of a subject region of the subject cardiogram […] (mental process – receiving a selection by a user of a subject region of a subject cardiogram may be performed manually by a user observing/analyzing a subject cardiogram and accordingly using judgement/evaluation to select a subject region of the subject cardiogram based on said analysis)
[…] identify a subject source location […] (mental process – identifying a subject source location may be performed manually by a user observing/analyzing the subject cardiogram and accordingly using judgement/evaluation to identify a subject source location on the cardiogram, based on said analysis)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a method performed by one or more computing systems for identifying a source location of an arrhythmia of a patient […] (recited at a high-level of generality (i.e., as a generic method performed by generic systems) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
displaying a subject cardiogram of the patient that is collected during an arrhythmia episode of the patient (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
applying a machine learning model to the subject region to identify a subject source location, the machine learning model trained based on mappings of derived regions of derived cardiograms to base regions of base cardiograms […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more)
each derived region being derived from the base region of a base cardiogram to which the derived region is mapped, each base region associated with a source location (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that each derived region is derived from the base region and each base region is associated with a source location does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
outputting an indication of the subject source location (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a method performed by one or more computing systems for identifying a source location of an arrhythmia of a patient […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
displaying a subject cardiogram of the patient that is collected during an arrhythmia episode of the patient (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
applying a machine learning model to the subject region to identify a subject source location, the machine learning model trained based on mappings of derived regions of derived cardiograms to base regions of base cardiograms […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
each derived region being derived from the base region of a base cardiogram to which the derived region is mapped, each base region associated with a source location (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that each derived region is derived from the base region and each base region is associated with a source location does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
outputting an indication of the subject source location (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
For the reasons above, Claim 80 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 81:
Step 1: Claim 81 is a method type claim. Therefore, Claims 81-88 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: 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. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
receiving a selection by a user of a subject region of the subject cardiogram (mental process – receiving a selection by a user of a subject region of a subject cardiogram may be performed manually by a user observing/analyzing a subject cardiogram and accordingly using judgement/evaluation to select a subject region of the subject cardiogram based on said analysis)
[…] identify a subject base region of the subject cardiogram […] (mental process – identifying a subject base region of a subject cardiogram may be performed manually by a user observing/analyzing the subject cardiogram and accordingly using judgement/evaluation to identify a subject base region based on said analysis)
[…] identify a subject source location […] (mental process – identifying a subject source location may be performed manually by a user observing/analyzing the subject cardiogram and accordingly using judgement/evaluation to identify a subject source location on the cardiogram, based on said analysis)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a method for treating an arrhythmia of a patient, the method comprising: identifying a source location of an arrhythmia of a patient by, under control of one or more computing systems […] (recited at a high-level of generality (i.e., as a generic method utilizing generic computing systems) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
displaying a subject cardiogram of the patient that is collected during an arrhythmia episode of the patient (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
applying a region machine learning model to the subject region to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of applying a trained machine learning model with previously determined data without significantly more)
applying a source location machine learning model to the subject base region to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of applying a trained machine learning model with previously determined data without significantly more)
outputting an indication of the subject source location (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
performing an ablation procedure to treat the arrhythmia of the patient, the ablation procedure being informed by the subject source location (recited at a high-level of generality (i.e., as generically performing an ablation procedure which may be performed by a generic ablation device – See Applicant’s specification Par. [0074]) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a method for treating an arrhythmia of a patient, the method comprising: identifying a source location of an arrhythmia of a patient by, under control of one or more computing systems […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
displaying a subject cardiogram of the patient that is collected during an arrhythmia episode of the patient (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
applying a region machine learning model to the subject region to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of applying a trained machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
applying a source location machine learning model to the subject base region to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of applying a trained machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
outputting an indication of the subject source location (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
performing an ablation procedure to treat the arrhythmia of the patient, the ablation procedure being informed by the subject source location (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
For the reasons above, Claim 81 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 82-88. The additional limitations of the dependent claims are addressed below.
Regarding Claim 82:
Step 2A Prong 1:
See the rejection of Claim 81 above, which Claim 82 depends on.
Step 2A Prong 2 & Step 2B:
wherein the outputting of the indication of the subject source location includes displaying the indication of the subject source location (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim. Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 81.
Regarding Claim 83:
Step 2A Prong 1:
See the rejection of Claim 81 above, which Claim 83 depends on.
Step 2A Prong 2 & Step 2B:
wherein the outputting of the indication of the subject source location includes providing the indication of the subject source location to an ablation device […] (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
[…] ablation device that coordinates the performing of the ablation procedure (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 81.
Regarding Claim 84:
Step 2A Prong 1:
See the rejection of Claim 81 above, which Claim 84 depends on.
Step 2A Prong 2 & Step 2B:
wherein a cardiogram has multiple leads (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that a cardiogram has multiple leads does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
wherein each lead has a subject region corresponding to the selected subject region (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that each lead has a subject region corresponding to the selected subject region does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
wherein the applying of the region machine learning model applies a lead region machine learning model for each lead to the subject region of that lead to identify a lead subject base region for that lead and applies a lead source location machine learning model for each lead to the lead subject base region for that lead to identify a lead subject source location for that lead (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner' s note: high level recitation of applying a trained machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
wherein the subject source location is derived from the lead subject source locations (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the subject source location is derived from the lead subject source location does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 81.
Regarding Claim 85:
Step 2A Prong 1:
See the rejection of Claim 81 above, which Claim 85 depends on.
Step 2A Prong 2 & Step 2B:
wherein the region machine learning model is trained using derived regions of base cardiograms labeled with base regions of the base cardiograms (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner' s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 81.
Regarding Claim 86:
Step 2A Prong 1:
See the rejection of Claim 81 above, which Claim 86 depends on.
Step 2A Prong 2 & Step 2B:
wherein the source location machine learning model is trained using derived regions of base cardiograms labeled with base source locations associated with the base cardiograms (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner' s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 81.
Regarding Claim 87:
Step 2A Prong 1:
See the rejection of Claim 81 above, which Claim 87 depends on.
Step 2A Prong 2 & Step 2B:
wherein the region machine learning model and the source location machine learning model are supervised or unsupervised machine learning models (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the region and source location machine learning models are either supervised or unsupervised models does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 81.
Regarding Claim 88:
Step 2A Prong 1:
See the rejection of Claim 87 above, which Claim 88 depends on.
Step 2A Prong 2 & Step 2B:
wherein a supervised machine learning model is a classifier or a regressor (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that a supervised machine learning model is a classifier or a regressor does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 81.
Claim Rejections - 35 USC § 103
10. 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.
11. Claims 68-71, 74-76, and 79 are rejected under 35 U.S.C. 103 as being unpatentable over Anastasia et al. (hereinafter Anastasia) (US PG-PUB 20210315506), in view of Robinson et al. (hereinafter Robinson) (US PG-PUB 20210137384).
Regarding Claim 68, Anastasia teaches a method performed by one or more computing systems for identifying a base region of a cardiogram to inform a treatment decision for an arrhythmia of a patient (Anastasia, Abstract, “A computer-implemented method of facilitating electrocardiogram (“ECG”) analysis involves receiving one or more sensed ECG traces for a patient, each of the sensed ECG traces representing sensed patient heart activity over a sensed time period […]” & Par. [0077-0078], “In some embodiments, the display 16 and/or any or all of the system 10 may be included in the surgical theatre to facilitate enhanced monitoring of diagnostic or therapeutic procedures for a patient in the surgical theatre for the purpose of monitoring diagnostic and therapeutic procedures, including an enhanced monitoring of diagnostic drug dose-response or of the therapeutic outcome following interventional procedures, including catheter ablation, for example. In some embodiments, the display 16 and/or any or all of the system 10 may be included in a continuous cardiac monitoring system to facilitate detection of primary electronic disorder arrhythmias in ambulatory or sedentary patients, for example.”, therefore, a method performed by one or more computing systems for identifying a base region of a cardiogram to inform a treatment decision for an arrhythmia of a patient is disclosed), the method comprising:
accessing a subject region of a subject cardiogram collected from the patient (Anastasia, Claim 1, “receiving one or more sensed ECG traces for a patient, each of the sensed ECG traces representing sensed patient heart activity over a sensed time period;”, thus, a subject region of a subject cardiogram (one or more sensed ECG traces) are collected from a patient);
identifying a subject base region of the subject cardiogram based on mappings of derived regions of base cardiograms to base regions of base cardiograms, each derived region being derived from the base region of a base cardiogram to which the derived region is mapped (Anastasia, Claim 1, “for each of the one or more sensed ECG traces: identifying a plurality of corresponding sensed ECG trace segments, each of the sensed ECG trace segments representing sensed patient heart activity for the patient over a segment of the sensed time period; and determining a representative ECG trace based on at least one of the identified corresponding sensed ECG trace segments;”, therefore, a subject base region of the subject cardiogram (representative ECG trace) may be identified based on mappings of derived regions of base cardiograms to base regions, each derived region being derived from the base region (ECG trace segments which are derived from the sensed ECG traces of the base ECG));
applying a machine learning (ML) model to the subject base region to identify base data, the ML model trained using training data that includes base regions labeled with base data (Anastasia, Claim 14, “causing at least one neural network classifier to be trained using the representative training ECG traces and the diagnoses, the at least one neural network classifier configured to output one or more diagnostically relevant scores related to at least one diagnosis:,”, therefore, a machine learning model/neural network classifier is trained using the training ECG traces and diagnoses (base regions labeled with base data), such that when applied to a subject region of a cardiogram, the classifier outputs base data (diagnosis data) for the subject region); and
outputting an indication of the base data (Anastasia, Par. [0072], “In various embodiments, the ECG analyzer 12 may cause at least one neural network classifier or function to be applied to the one or more determined representative ECG traces to determine one or more diagnostically relevant scores related to the diagnosis of the patient. For example, in some embodiments, the ECG analyzer 12 may have stored therein data defining a BrS diagnosis neural network classifier, which may be configured to include in its input, 12 representative ECG traces, and to output a diagnosis score, such as a BrS diagnosis score.”, therefore, the neural network classifier may output an indication of the base data in the form of a diagnosis to inform treatment) to inform an ablation procedure to treat the arrhythmia of the patient (While Anastasia broadly discloses diagnosing the patient, which may include diagnosing an arrhythmia per Anastasia Par. [0078] and interventional procedures including catheter ablation per Anastasia Par. [0077], Anastasia does not explicitly disclose outputting an indication of the base data to inform an ablation procedure to treat the arrhythmia of the patient – See introduction of Robinson reference which teaches outputting an indication of the base data to inform an ablation procedure to treat the arrhythmia of the patient).
As disclosed above, although Anastasia broadly discloses outputting base data including diagnosis data which may be used in corresponding treatment plans, Anastasia does not explicitly disclose outputting an indication of the base data to inform an ablation procedure to treat the arrhythmia of the patient.
However, Robinson teaches outputting an indication of the base data to inform an ablation procedure to treat the arrhythmia of the patient (Robinson, Par. [0006], “In some aspects, the method may further include generating a decision support module. The decision support module may include an output of the one or more cardiac arrhythmia targets and one or more of a description of a scar pattern, a description of a scar burden size and location, a suggested volume to achieve ablation, a confidence score of the combined mappings, a listing of at-risk structures, a general recommendation, an expected success rate with non-invasive therapy, or an expected success rate with alternative treatment modalities.”, therefore, an indication of the base data to inform an ablation procedure to treat the arrhythmia of the patient is output)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method performed by one or more computing system for identifying a base region of a cardiogram to inform a treatment decision for an arrhythmia of a patient, as disclosed by Anastasia to include outputting an indication of the base data to inform an ablation procedure to treat the arrhythmia of the patient, as disclosed by Robinson. One of ordinary skill in the art would have been motivated to make this modification to improve identification of cardiac arrhythmias and correspondingly enhance decision support for the selection of therapy and interventional procedures, such as ablation (Robinson, Par. [0117], “Further provided herein is a decision support module to provide an informed ablation plan to the physician and provide metrics for support about success and risks of various treatment option and opportunities to improve patient outcomes. After a target has been identified, the identified target(s) may be presented to a physician in the form of a decision support module.”).
Regarding Claim 69, Anastasia in view of Robinson teaches the method of claim 68 wherein at least some of the base regions were used in identifying a source location of an arrhythmia that resulted in successful treatment of the arrhythmia (Robinson, Par. [0117], “Further provided herein is a decision support module to provide an informed ablation plan to the physician and provide metrics for support about success and risks of various treatment option and opportunities to improve patient outcomes. After a target has been identified, the identified target(s) may be presented to a physician in the form of a decision support module. In addition, the decision support module may include one or more of a description of the scar pattern, a description of the scar burden size and location, a suggested volume to achieve ablation (e.g. full-thickness ablation, partial-thickness ablation, etc.), a confidence score of the combined mappings, a listing of at-risk structures, general recommendations, expected success with SBRT, expected success with alternative treatment modalities (e.g., catheter RF, antiarrhythmic drug (e.g. amiodarone), etc.), or combinations thereof.”, thus, at least some of the base regions (target) may be used in identifying a source location of an arrhythmia that resulted in successful treatment – see Par. [0123]).
The reasons of obviousness have been noted in the rejection of Claim 68 above and applicable herein.
Regarding Claim 70, Anastasia in view of Robinson teaches the method of claim 68 wherein the base data is a source location of an arrhythmia (Anastasia, Par. [0063], “In some embodiments, ECG signals from various medical devices (including implantable and wearable devices as well as stationary and portable clinical and home monitors) may suffice for the system to facilitate diagnosis and/or recognition of arrhythmia signals associated with BrS or other arrhythmogenic cardiomyopathy.” & Par. [0138], “In some embodiments, the patient data may include, for example, age, sex, and/or patient and/or family histories of arrhythmogenic cardiomyopathy-associated conditions and comorbidities. These may include, for example, numerical or Boolean representations of personal and/or family history of arrhythmogenic cardiomyopathy diagnoses including of BrS, family history of sudden cardiac death (SCD), personal history of syncope, and/or personal history of cardiac arrest. In some embodiments, for example, the patient data may include any or all of the following: […]”, thus, the base data may include locations of an arrhythmia – this is similarly taught by Robinson as outlined in the rejection of claim 68 above).
Regarding Claim 71, Anastasia in view of Robinson teaches the method of claim 68 wherein the identifying of the subject base region is based on similarity between the subject region and the derived regions (Anastasia, Par. [0066], “The ECG analyzer 12 may then, for each of the one or more sensed ECG traces, identify a plurality of corresponding sensed ECG trace segments, each of the sensed ECG trace segments representing sensed patient heart activity for the patient over a segment of the sensed time period, and determine a representative ECG trace based on at least one of the identified corresponding sensed ECG trace segments. In some embodiments, the sensed ECG trace segments may be chosen such that each ECG trace segment represents sensed ECG data for a single heartbeat. In various embodiments, each of the ECG trace segments may be generally similar, having repeated features.”, thus, the identifying of a subject base region (representative ECG trace) may be based on similarity, or common features, between the subject region and derived region (ECG trace segments)).
Regarding Claim 74, Anastasia in view of Robinson teaches the method of claim 68 wherein a base region of a base cardiogram has a base start time and a base end time within that base cardiogram and wherein the derived regions that are derived from a base cardiogram have derived start times and derived end times that are derived from the base start time and the base end time of that base region (Anastasia, Claim 22, “The method of claim 14 wherein identifying the plurality of corresponding training ECG trace segments comprises identifying respective common features in the training ECG trace segments and identifying respective start and end times for each of the plurality of training ECG trace segments relative to the identified common features.”, thus, each ECG trace segment, including both the base ECG traces and segmented/derived ECG traces, has an associated start time and end time),
Regarding Claim 75, Anastasia in view of Robinson teaches the method of claim 74 wherein the derived start times and the derived end times for derived regions that are derived from a base region are derived by adding positive or negative increments to the base start time of that base region and/or adding positive or negative increments to the base end time of that base region (Anastasia, Par. [0106], “In various embodiments, block 304 may direct the analyzer processor 100 to identify the start and end times by subtracting and adding, respectively, half of the temporal length of the window from each R-peak identifier.”, therefore, the segmented/derived start and end times may be identified by subtracting and adding to the base times).
Regarding Claim 76, Anastasia in view of Robinson teaches the method of claim 68 wherein a region is specified as a time range within a cardiogram, as a voltage-time series, or as an image of a portion of a cardiogram (Anastasia, Claim 1, “receiving one or more sensed ECG traces for a patient, each of the sensed ECG traces representing sensed patient heart activity over a sensed time period;”, thus, a region may be specified as a time range/time period within an ECG – this is similarly supported by Par. [0093]).
Regarding Claim 79, Anastasia teaches a method for treating a patient, the method comprising: under control of one or more computing systems, identifying a base region of a cardiogram to inform a treatment decision for a patient (Anastasia, Abstract, “A computer-implemented method of facilitating electrocardiogram (“ECG”) analysis involves receiving one or more sensed ECG traces for a patient, each of the sensed ECG traces representing sensed patient heart activity over a sensed time period […]” & Par. [0077-0078], “In some embodiments, the display 16 and/or any or all of the system 10 may be included in the surgical theatre to facilitate enhanced monitoring of diagnostic or therapeutic procedures for a patient in the surgical theatre for the purpose of monitoring diagnostic and therapeutic procedures, including an enhanced monitoring of diagnostic drug dose-response or of the therapeutic outcome following interventional procedures, including catheter ablation, for example. In some embodiments, the display 16 and/or any or all of the system 10 may be included in a continuous cardiac monitoring system to facilitate detection of primary electronic disorder arrhythmias in ambulatory or sedentary patients, for example.”, therefore, a method performed by one or more computing systems for identifying a base region of a cardiogram to inform a treatment decision for an arrhythmia of a patient is disclosed), by:
accessing a subject region of a subject cardiogram collected from a patient (Anastasia, Claim 1, “receiving one or more sensed ECG traces for a patient, each of the sensed ECG traces representing sensed patient heart activity over a sensed time period;”, thus, a subject region of a subject cardiogram (one or more sensed ECG traces) are collected from a patient);
identifying a subject base region of the subject cardiogram based on mappings of derived regions to base regions of base cardiograms, each derived region being derived from the base region of a base cardiogram to which the derived region is mapped (Anastasia, Claim 1, “for each of the one or more sensed ECG traces: identifying a plurality of corresponding sensed ECG trace segments, each of the sensed ECG trace segments representing sensed patient heart activity for the patient over a segment of the sensed time period; and determining a representative ECG trace based on at least one of the identified corresponding sensed ECG trace segments;”, therefore, a subject base region of the subject cardiogram (representative ECG trace) may be identified based on mappings of derived regions of base cardiograms to base regions, each derived region being derived from the base region (ECG trace segments which are derived from the sensed ECG traces of the base ECG)); and
outputting an indication of the subject base region of the subject cardiogram to inform a treatment for the patient; and treating the patient based on the indication of the subject base region (While Anastasia broadly discloses diagnosing the patient, which may include diagnosing an arrhythmia per Anastasia Par. [0078] and interventional procedures including catheter ablation per Anastasia Par. [0077], Anastasia does not explicitly disclose outputting an indication of the subject base region of the subject cardiogram to inform a treatment for the patient; and treating the patient based on the indication of the subject base region – See introduction of Robinson reference which teaches outputting an indication of the subject base region of the subject cardiogram to inform a treatment for the patient; and treating the patient based on the indication of the subject base region).
As disclosed above, although Anastasia broadly discloses outputting base data including diagnosis data which may be used in corresponding treatment plans, Anastasia does not explicitly disclose outputting an indication of the subject base region of the subject cardiogram to inform a treatment for the patient; and treating the patient based on the indication of the subject base region
However, Robinson teaches outputting an indication of the subject base region of the subject cardiogram to inform a treatment for the patient (Robinson, Par. [0006], “In some aspects, the method may further include generating a decision support module. The decision support module may include an output of the one or more cardiac arrhythmia targets and one or more of a description of a scar pattern, a description of a scar burden size and location, a suggested volume to achieve ablation, a confidence score of the combined mappings, a listing of at-risk structures, a general recommendation, an expected success rate with non-invasive therapy, or an expected success rate with alternative treatment modalities.”, therefore, an indication of the base region (target) to inform an ablation procedure to treat the arrhythmia of the patient is output); and treating the patient based on the indication of the subject base region (Robinson, Par. [0062], “The disclosed technology addresses the need in the art for automatic identification of one or more cardiac arrhythmias in a patient and an objective treatment plan that is user independent. Disclosed are systems, methods, and computer-readable storage media for implementing a multi-modal technique for the identification of one or more arrhythmic components of a ventricular tachycardia and generating a treatment plan”, thus, patients may be treated based on the indication of the subject base region specified by a generated treatment plan)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method for treating a patient, the method comprising: under control of one or more computing systems, identifying a base region of a cardiogram to inform a treatment decision for a patient, as disclosed by Anastasia to include outputting an indication of the subject base region of the subject cardiogram to inform a treatment for the patient; and treating the patient based on the indication of the subject base region, as disclosed by Robinson. One of ordinary skill in the art would have been motivated to make this modification to improve identification of cardiac arrhythmias and correspondingly enhance decision support for the selection of therapy and interventional procedures, such as ablation (Robinson, Par. [0117], “Further provided herein is a decision support module to provide an informed ablation plan to the physician and provide metrics for support about success and risks of various treatment option and opportunities to improve patient outcomes. After a target has been identified, the identified target(s) may be presented to a physician in the form of a decision support module.”).
12. Claims 77-78 and 80 are rejected under 35 U.S.C. 103 as being unpatentable over Anastasia et al. (hereinafter Anastasia) (US PG-PUB 20210315506), in view of Robinson et al. (hereinafter Robinson) (US PG-PUB 20210137384), further in view of Thiagalingam (hereinafter Thiaga) (US PG-PUB 20090099468).
Regarding Claim 77, Anastasia in view of Robinson teaches the method of claim 68.
Anastasia in view of Robinson does not explicitly disclose displaying the subject cardiogram, receiving a selection of the subject region of the displayed subject cardiogram, and displaying an indication of the subject region on the displayed subject cardiogram.
However, Thiaga teaches displaying the subject cardiogram, receiving a selection of the subject region of the displayed subject cardiogram, and displaying an indication of the subject region on the displayed subject cardiogram (Thiaga, Par. [0089-0090], “To display data and collect input from the user, a computer program in accordance with an embodiment of the invention uses ‘windows’ or graphical displays listed in Table 2. Electrogram window This display shows the reference electrogram as well as the raw and differentiated mapping catheter electrograms. The user can navigate to any portion of the recorded electrograms by using the slider controls. The electrograms can be displayed at a variety of magnification levels (‘sweep speeds’). Automated map window This display shows the cardiac chamber as a 3-dimensional model. The colours at each point on the map represent the analysis results of electrograms recorded from that point. Zoomed electrogram window This displays the mapping electrograms that were recorded from a user selected region of interest. The data is represented in two-dimensions and each beat is shown as a scaled electrogram. This view allows the user to remove electrogram data points that have incorrect or artefactual data from the substrate map by clicking on them.”, therefore the subject cardiogram may be displayed (electrogram window) and then a selection of the subject region of the displayed subject cardiogram and the indication of the subject region may be displayed (zoomed electrogram window))
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of claim 68 for identifying a base region of a cardiogram to inform a treatment decision for an arrhythmia of a patient, as disclosed by Anastasia in view of Robinson to include displaying the subject cardiogram, receiving a selection of the subject region of the displayed subject cardiogram, and displaying an indication of the subject region on the displayed subject cardiogram, as disclosed by Thiaga. One of ordinary skill in the art would have been motivated to make this modification to enable selecting and displaying the subject region within a subject cardiogram, such that abnormal areas (arrhythmic areas) may be visually identified and pinpointed, hence improving system efficiency for identifying arrhythmias and corresponding treatment decisions (Thiaga, Par. [0083], “The embodiments of the invention may be implemented in software that acts as a digital assistant to identify and catalogue every beat that occurs during the study. These beats can then be selected on a variety of criteria such as spatial location, morphology (eg. ‘sinus beats’ or ‘ventricular ectopics’), electrogram voltage or slope, and time. Benefits of this technology include the ability to collect high quality data quickly. Further, the results can be compiled and displayed in the form of spatial maps to show the locations of abnormal areas within the heart.”).
Regarding Claim 78, Anastasia in view of Robinson in view of Thiaga teaches the method of claim 77 further comprising displaying an indication of the subject base region on the displayed subject cardiogram (Thiaga, Par. [0162], “The zoomed electrogram window is a dynamic displaying allowing the user to interact with and modify the electrogram data set. When the user clicks on the zoomed electrogram window, the program records the ‘X’ and ‘Y’ locations of the user click. These ‘X’ and ‘Y’ values are converted into the appropriate data values (eg. in the preceding example the ‘X’ axis is used to represent the time of atrial activation). By this means, the beat that the user selected on the zoomed electrogram window can be identified within the data set. Depending on the software toggles that user has selected a variety of actions can be performed on this beat (eg. identifying this beat as showing as particular feature, removing data from this beat from the substrate map, or ‘jumping’ to this time point within the electrogram window so that this beat can be examined in greater detail)”, therefore, an indication of the subject base region may be displayed in the zoomed electrogram window).
The reasons of obviousness have been noted in the rejection of Claim 77 above and applicable herein.
Regarding Claim 80, Anastasia teaches a method performed by one or more computing systems for identifying a source location of an arrhythmia of a patient (Anastasia, Abstract, “A computer-implemented method of facilitating electrocardiogram (“ECG”) analysis involves receiving one or more sensed ECG traces for a patient, each of the sensed ECG traces representing sensed patient heart activity over a sensed time period […]” & Par. [0077-0078], “In some embodiments, the display 16 and/or any or all of the system 10 may be included in the surgical theatre to facilitate enhanced monitoring of diagnostic or therapeutic procedures for a patient in the surgical theatre for the purpose of monitoring diagnostic and therapeutic procedures, including an enhanced monitoring of diagnostic drug dose-response or of the therapeutic outcome following interventional procedures, including catheter ablation, for example. In some embodiments, the display 16 and/or any or all of the system 10 may be included in a continuous cardiac monitoring system to facilitate detection of primary electronic disorder arrhythmias in ambulatory or sedentary patients, for example.”, therefore, a method performed by one or more computing systems for identifying a source location of an arrhythmia is disclosed), the method comprising:
displaying a subject cardiogram of the patient that is collected during an arrhythmia episode of the patient (See introduction of Thiaga reference below for teaching of displaying a subject cardiogram of the patient that is collected during an arrhythmia episode);
receiving a selection by a user of a subject region of the subject cardiogram (See introduction of Thiaga reference below for teaching of receiving a selection by a user of a subject region of the subject cardiogram);
applying a machine learning model to the subject region to identify a subject source location (While Anastasia discloses applying and training a machine learning model as disclosed below, Anastasia does not explicitly disclose applying a machine learning model to the subject region to identify a subject source location. See introduction of Robinson reference below for teaching of applying a machine learning model to the subject region to identify a subject source location), the machine learning model trained based on mappings of derived regions of derived cardiograms to base regions of base cardiograms (Anastasia, Claim 14, “causing at least one neural network classifier to be trained using the representative training ECG traces and the diagnoses, the at least one neural network classifier configured to output one or more diagnostically relevant scores related to at least one diagnosis:,”, therefore, a machine learning model/neural network classifier is trained using the training ECG traces and diagnoses (representative ECG traces which are derived from the traces and trace segments), such that when applied to a subject region of a cardiogram, the classifier outputs base data (diagnosis data) for the subject region), each derived region being derived from the base region of a base cardiogram to which the derived region is mapped, each base region associated with a source location (Anastasia, Claim 1, “for each of the one or more sensed ECG traces: identifying a plurality of corresponding sensed ECG trace segments, each of the sensed ECG trace segments representing sensed patient heart activity for the patient over a segment of the sensed time period; and determining a representative ECG trace based on at least one of the identified corresponding sensed ECG trace segments;”, therefore, a subject base region of the subject cardiogram (representative ECG trace) may be identified based on mappings of derived regions of base cardiograms to base regions, each derived region being derived from the base region (ECG trace segments which are derived from the sensed ECG traces of the base ECG)); and
outputting an indication of the subject source location (See introduction of Robinson reference below for teaching of an outputting an indication of the subject source location).
Anastasia does not explicitly disclose:
applying a machine learning model to the subject region to identify a subject source location
outputting an indication of the subject source location.
However, Robinson teaches:
applying a machine learning model to the subject region to identify a subject source location (Robinson, Par. [0081], “Any modeling approach can be used to learn the image features that predict the location of an abnormality (e.g., VT) on each input mapping. For example, a deep convolutional neural network can be used. Because the model only includes two classes (VT / no VT), a deep network may not be needed.”, therefore, a neural network may be applied to the subject region (of a cardiogram/electrical input mapping) to identify a subject source location (location of an abnormality))
outputting an indication of the subject source location (Robinson, Par. [0006], “In some aspects, the method may further include generating a decision support module. The decision support module may include an output of the one or more cardiac arrhythmia targets and one or more of a description of a scar pattern, a description of a scar burden size and location, a suggested volume to achieve ablation, a confidence score of the combined mappings, a listing of at-risk structures, a general recommendation, an expected success rate with non-invasive therapy, or an expected success rate with alternative treatment modalities.”, therefore, an indication of the subject source location (arrhythmia targets) may be outputted)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method performed by one or more computing systems for identifying a source location of an arrhythmia of a patient, as disclosed by Anastasia to include applying a machine learning model to the subject region to identify a subject source location and outputting an indication of the subject source location, as disclosed by Robinson. One of ordinary skill in the art would have been motivated to make this modification to improve identification of the location of cardiac arrhythmias and correspondingly enhance decision support for the selection of therapy and interventional procedures, such as ablation (Robinson, Par. [0117], “Further provided herein is a decision support module to provide an informed ablation plan to the physician and provide metrics for support about success and risks of various treatment option and opportunities to improve patient outcomes. After a target has been identified, the identified target(s) may be presented to a physician in the form of a decision support module.”).
Anastasia does not explicitly disclose:
displaying a subject cardiogram of the patient that is collected during an arrhythmia episode of the patient; receiving a selection by a user of a subject region of the subject cardiogram;
However, Robinson teaches:
displaying a subject cardiogram of the patient that is collected during an arrhythmia episode of the patient; receiving a selection by a user of a subject region of the subject cardiogram (Thiaga, Par. [0089-0090], “To display data and collect input from the user, a computer program in accordance with an embodiment of the invention uses ‘windows’ or graphical displays listed in Table 2. Electrogram window This display shows the reference electrogram as well as the raw and differentiated mapping catheter electrograms. The user can navigate to any portion of the recorded electrograms by using the slider controls. The electrograms can be displayed at a variety of magnification levels (‘sweep speeds’). Automated map window This display shows the cardiac chamber as a 3-dimensional model. The colours at each point on the map represent the analysis results of electrograms recorded from that point. Zoomed electrogram window This displays the mapping electrograms that were recorded from a user selected region of interest. The data is represented in two-dimensions and each beat is shown as a scaled electrogram. This view allows the user to remove electrogram data points that have incorrect or artefactual data from the substrate map by clicking on them.”, therefore the subject cardiogram may be displayed (electrogram window) and then a selection of the subject region of the displayed subject cardiogram and the indication of the subject region may be displayed (zoomed electrogram window))
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method performed by one or more computing systems for identifying a source location of an arrhythmia of a patient, as disclosed by Anastasia in view of Robinson to include displaying a subject cardiogram of the patient that is collected during an arrhythmia episode of the patient and receiving a selection by a user of a subject region of the subject cardiogram, as disclosed by Thiaga. One of ordinary skill in the art would have been motivated to make this modification to enable selecting and displaying the subject region within a subject cardiogram, such that abnormal areas (arrhythmic areas) may be visually identified and pinpointed, hence improving system efficiency for identifying arrhythmias and corresponding treatment decisions (Thiaga, Par. [0083], “The embodiments of the invention may be implemented in software that acts as a digital assistant to identify and catalogue every beat that occurs during the study. These beats can then be selected on a variety of criteria such as spatial location, morphology (eg. ‘sinus beats’ or ‘ventricular ectopics’), electrogram voltage or slope, and time. Benefits of this technology include the ability to collect high quality data quickly. Further, the results can be compiled and displayed in the form of spatial maps to show the locations of abnormal areas within the heart.”).
Allowable Subject Matter
14. No prior art rejection is made for Claims 1, 5-11, 13-16, 61-67, 72-73, and 81-88. However, these claims are still rejected under 35 U.S.C. 101 – abstract idea.
15. Examiner has disclosed Anastasia et al. (US PG-PUB 20210315506), Robinson et al. (US PG-PUB 20210137384), and Xue et al. (US PG-PUB 20230326601), which are the closest prior art as compared to the instant application. In particular, Anastasia discloses methods and systems for facilitating electrocardiogram (ECG) analysis involving receiving one or more sensed ECG traces, identifying a plurality of sensed ECG trace segments, determining a representative ECG trace based on at least one of the identified ECG trace segments, and causing at least one neural network classifier to be applied to the one or more representative ECG traces to determine diagnostically relevant scores related to a patient diagnosis. Further, Robinson teaches methods and systems for determining one or more cardiac arrhythmia targets for ablation, utilizing a neural network for learning image features that predict the location of an abnormality within an electrical input mapping. Additionally, Xue teaches utilizing deep neural networks (DNN) for ECG interpretation, where a first DNN classifies ECG rhythm characteristics, a second DNN classifiers ECG morphology characteristics, and a third DNN identifies global ECG parameters, forming an ensemble DNN for ECG interpretation.
However, Anastasia, Robinson, and Xue seemingly do not explicitly disclose the limitations of Independent Claim 1 including “for each of the plurality of mappings, deriving a plurality of derived regions from the base region and the base cardiogram of that mapping; and for each of the plurality of the derived regions, generating training data that includes the derived region labeled with the base data of that mapping; and training the ML model using the training data wherein the trained ML model, when applied to a subject region of a subject cardiogram, outputs base data as subject data for the subject region, the ML model being a neural network […] wherein a patient base region of a patient cardiogram collected from a patient is input to the ML model which outputs a patient derived region and the patient is treated based on the patient derived cardiogram, the patient base region being selected by a person, the base data being a source location of an arrhythmia and the treatment being an ablation procedure.” & the similar limitations of Independent Claim 62 including “for each of the plurality of mappings: deriving a plurality of derived regions from the base region and the base cardiogram of that mapping; and for each of the plurality of the derived regions, generating training data that includes the derived region labeled with the base data of that mapping; and training the machine learning model using the training data wherein the trained machine learning model, when applied to the subject region of the subject cardiogram, outputs base data as the subject source location for the subject region; outputting an indication of the subject source location; and performing an ablation procedure on the patient to treat the arrhythmia” in combination with the remaining limitations of the Independent claims.
Further, Anastasia, Robinson, and Xue seemingly do not explicitly disclose the limitations of dependent claim 72 (and instant claim 73 which depends on claim 72) including “The method of claim 68 wherein the identifying of the subject base region includes inputting the subject region to a second ML model that outputs the subject base region.” & the similar limitations of Independent Claim 81 including “receiving a selection by a user of a subject region of the subject cardiogram; applying a region machine learning model to the subject region to identify a subject base region of the subject cardiogram; applying a source location machine learning model to the subject base region to identify a subject source location; and outputting an indication of the subject source location; and performing an ablation procedure to treat the arrhythmia of the patient, the ablation procedure being informed by the subject source location.” in combination with the remaining limitations of the Independent claims. This similarly applies to instant claims 82-88 which are dependent upon claim 81.
Conclusion
16. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devika S Maharaj whose telephone number is (571)272-0829. The examiner can normally be reached Monday - Thursday 8:30am - 5:30pm.
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/DEVIKA S MAHARAJ/Examiner, Art Unit 2123
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