DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
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 allowance or after an Office action under Ex Parte Quayle, 25 USPQ 74, 453 O.G. 213 (Comm'r Pat. 1935). 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, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on 11/17/2025 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/17/2025 was filed after the mailing date of the Allowability Notice on 08/26/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Regarding the arguments against the rejection of claims under 35 USC 101, the Examiner respectfully disagrees. Applicant argues that the claims do not recite methods of organizing human activity. Examiner asserts that the use of the machine learning model with the training data recites additional elements that have been analyzed to not be significant as noted in the Step 2A Prong 2 analysis as represented below, not under Prong 1. The use of the multimodal data with the fusion model recites generic computing components as claimed and do not recite a technical improvement. There is no indication in the current claims of the iterative loop for the training. The improvement to the dosage determinations recites an improvement to the abstract idea, not a technology improvement, See MPEP 2106.05(a)II, particularly “Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”
Applicant further argues the similarities of the claimed invention to Example 47. Examiner asserts that this example recites an actual technology improvement related to networks. The claimed invention on the other hand recites a trained model that is used as a generic tool for the abstract idea of determining treatment dosages, where the training of the model is recited at a generic level. Applicant further argues the similarities of the claimed invention to Example 48. Examiner asserts that this example recites a technical improvement related to speech signal processing, which is a specific technical improvement. The claimed invention on the other hand recites a trained model that is used as a generic tool for the abstract idea of determining treatment dosages, where the training of the model is recited at a generic level.
Applicant further argues that the additional elements amount to significantly more than the judicial exception, as the additional elements are non-conventional and specific for an improvement to a technical field. Examiner asserts that there is no indication of a particular arrangement of the elements to demonstrate a technology improvement. As noted in the rejection, the use of the multiple modalities and the fusion module recites mere data gathering elements, and therefore is insignificant extra solution activity related to the gathering or forwarding of information which is well understood, routine, and conventional. The continuously monitored data and the training of the model using the data recites generic implementation of training a model.
As noted in the rejection represented below, there is no rejection under 35 USC 103.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-24 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
It is appropriate for the Examiner to determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance to the Subject Matter Eligibility Test as recited in the following Steps: 1, 2A, and 2B, see MPEP 2106(III.).
Patent Subject Matter Eligibility Test: Step 1:
First, the Examiner is to establish whether the claim falls within any statutory category including a process, a machine, manufacture, or composition of matter, see MPEP 2106.03(II.) and MPEP 2106.03(I).
Claims 1-12 are related to a system, and claims 13-24 are also related to a method (i.e., a process). Accordingly, these claims are all within at least one of the four statutory categories.
Patent Subject Matter Eligibility Test: Step 2A- Prong One:
Step 2A of the Subject Matter Eligibility Test demonstrates whether a clam is directed to a judicial exception, see MPEP 2106.04(I.). Step 2A is a two-prong inquiry, where Prong One establishes the judicial exception. Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes, see MPEP 2106.04(II.)(A.)(1.) and 2106.04(a)(2).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A system for determining dosage parameters to ensure durability in treatment processes, the system comprising:
at least a processor;
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive a plurality of historical data, wherein the historical data comprises:
a plurality of historical outcomes;
a plurality of correlated historical physiological parameters; and
a plurality of historical dosage parameters;
train a machine learning model using training data comprising the plurality of historical data;
receive current physiological data, wherein the current physiological data comprises data of at least two different modalities, and wherein the data of the at least two different modalities is joined using a fusion module;
continuously monitor the current physiological data using one or more sensors, wherein the continuously monitored physiological data is used as subsequent training data for the machine learning model;
determine dosage parameters using the current physiological data and the trained machine learning model, wherein determining dosage parameters comprises:
inputting the current physiological data into the trained machine learning model; and
outputting the dosage parameters from the machine learning model; and
initiate a treatment process using the dosage parameters.
The Examiner submits that the foregoing underlined limitations constitute “certain methods of organizing human activity”, more specifically managing interactions between people as the following abstract limitations are related to the outputting of dosage parameters to ensure durability in treatment processes and initiate a treatment process using the dosage parameters:
continuously monitor the current physiological data, which is an abstract limitation of observation of the physiological data over time which is used for the further steps,
determine dosage parameters using the current physiological data, which is an abstract limitation related to an analysis of data and judgment based on the analysis,
outputting the dosage parameters, which is an abstract limitation related to the management of the dosage parameters that is related to a treatment,
initiate a treatment process using the dosage parameters, which is an abstract limitation related to the management of the treatment related to the determined dosage parameters.
The claim limitations as a whole recite steps for the outputting of dosage parameters to ensure durability in treatment processes and initiate a treatment process using the dosage parameters, where this analysis and management of the treatment and therefore recite managing interactions between people.
The abstract idea recited in claim 13 is similar to that of claim 1.
Any limitations not identified above as part of the abstract idea are deemed “additional elements” (i.e., processor) and will be discussed in further detail below.
Accordingly, the claim as a whole recites at least one abstract idea.
Furthermore, dependent claims further define the at least one abstract idea, and thus fails to make the abstract idea any less abstract as noted below:
Claims 2, 3, 14 and 15 further describe the dosage parameters that are determined as being related to PFA dosage parameters, thus further describing the abstract idea. Claims 9, 10, 21 and 22 recite further abstract limitations related to changing the dosage parameters related to a PF ablation procedure and can be changed from a specific treatment to another treatment, thus further describing the abstract idea.
Patent Subject Matter Eligibility Test: Step 2A- Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrates the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exceptions into a “practical application,” see MPEP 2106.04(II.)(A.)(2.) and 2106.04(d)(I.).
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A system for determining dosage parameters to ensure durability in treatment processes, the system comprising:
at least a processor;
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
receive a plurality of historical data, wherein the historical data comprises:
a plurality of historical outcomes;
a plurality of correlated historical physiological parameters; and
a plurality of historical dosage parameters;
train a machine learning model using training data comprising the plurality of historical data (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
receive current physiological data, wherein the current physiological data comprises data of at least two different modalities, and wherein the data of the at least two different modalities is joined using a fusion module (merely data gathering steps as noted below, see MPEP 2106.05(g) and Symantec);
continuously monitor the current physiological data using one or more sensors (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)), wherein the continuously monitored physiological data is used as subsequent training data for the machine learning model (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
determine dosage parameters using the current physiological data and the trained machine learning model, wherein determining dosage parameters comprises:
inputting the current physiological data into the trained machine learning model; and (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f))
outputting the dosage parameters from the machine learning model; and (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f))
initiate a treatment process using the dosage parameters.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitation of the system comprising of a processor and memory, receiving a plurality of historical data as described that is used to train a machine learning model, use of the machine learning model to determine and output dosage parameters where the continuously monitored physiological data is used as subsequent training data for the machine learning model, use of the sensor, and inputting current physiological data into the machine learning model, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [0013] of Applicant’s Specification recites the use of a computing system and processors to carry out the system. [0014, 0020, 0021] recites the gathering and use of historical data for training a machine learning model. [0020] recites the use of the model to carry out aspects of the abstract idea. [0024] recites inputting current data into the machine learning model. [0042] recites the use of the continuously monitored data as training data for the model where the model is generically trained. [0017] recites the use of a sensor as a generic tool for the monitoring. These limitations recite the use of generic computing technologies such as training a machine learning model to carry out the abstract idea without a technological improvement related to machine learning or other computing systems, and is thus mere computer implementation.
Regarding the additional limitation of receive current physiological data, wherein the current physiological data comprises data of at least two different modalities, and wherein the data of the at least two different modalities is joined using a fusion module, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). [0024, 0042] of Applicant’s Specification recites the gathering of current physiological data. [0128] recites the fusion model that is used for fusing the modalities of the data together. This module is only used for the data gathering aspect of the abstract idea, and thus recites insignificant pre solution activity.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to determine dosage parameters, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception, see MPEP 2106.04(d), 2106.05(a), 2106.05(b).
For these reasons, the independent claims do not recite additional elements that integrate the judicial exception into a practical application.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set below:
Claims 2, 3, 14, and 15 recite further detail on the historical outcomes that are used for the training of the model without providing a technology improvement, thus merely recites mere computer implementation. Claims 4, 5, 16 and 17 recite further detail of the physiological data that is received, thus further describing the mere pre solution activity. Claims 6, 7, 8, 18, 19 and 20 recite further detail of the machine learning model that is used and the output of the feature vector, however there is no technology improvement related to the computer models and thus recite mere computer implementation of the abstract idea. Claims 11, 12, 23 and 24 recite further detail of the multimodal neural network receiving EGM and CT data, however there is no specific configuration of the model to show a technology improvement and thus recite mere computer implementation of the abstract idea.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Patent Subject Matter Eligibility Test: Step 2B:
Regarding Step 2B of the Subject Matter Eligibility Test, the independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application, see additionally MPEP 2106.05(II.). Further, it may need to be established, when determining whether a claim recites significantly more than a judicial exception, that the additional elements recite well understood, routine, and conventional activities, see MPEP 2106.05(d).
Regarding the additional limitation of the system comprising of a processor and memory, receiving a plurality of historical data as described that is used to train a machine learning model, use of the machine learning model to determine and output dosage parameters where the continuously monitored physiological data is used as subsequent training data for the machine learning model, use of the sensor, and inputting current physiological data into the machine learning model, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [0013] of Applicant’s Specification recites the use of a computing system and processors to carry out the system. [0014, 0020, 0021] recites the gathering and use of historical data for training a machine learning model. [0020] recites the use of the model to carry out aspects of the abstract idea. [0024] recites inputting current data into the machine learning model. [0042] recites the use of the continuously monitored data as training data for the model where the model is generically trained. [0017] recites the use of a sensor as a generic tool for the monitoring. These limitations recite the use of generic computing technologies such as training a machine learning model to carry out the abstract idea without a technological improvement related to machine learning or other computing systems, and is thus mere computer implementation and does not recite significantly more than the judicial exception.
Regarding the additional limitation of receive current physiological data, wherein the current physiological data comprises data of at least two different modalities, and wherein the data of the at least two different modalities is joined using a fusion module, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)”). [0024, 0042] of Applicant’s Specification recites the gathering of current physiological data. [0128] recites the fusion model that is used for fusing the modalities of the data together. This module is only used for the data gathering aspect of the abstract idea, and thus recites insignificant pre solution activity and does not recite significantly more than the judicial exception. The receiving of the current physiological data recites the use of a computer or forwarding information from one computing device to another in order to carry out the abstract idea, and thus recites well understood, routine, and conventional activities.
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exceptions for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
For the reasons stated, the claims fail the Subject Matter Eligibility Test and therefore claims 1-24 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Conclusion
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/CONSTANTINE SIOZOPOULOS/
Examiner
Art Unit 3686