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 .
Response to Arguments
Response to Applicant’s Arguments Regarding 35 U.S.C. § 101
Applicant’s arguments presented in the remarks filed June 10, 2026, at pages 5-7, concerning the rejection of claims 1, 3, 5-7, and 9-10 under 35 U.S.C. § 101 have been fully considered. The arguments are not persuasive for the reasons set forth below.
Applicant argues that amended claims 1, 3, 5-7, and 9-10 overcome the 101 rejection because the claims now recite a practical application and the SMED “must be considered.”
Examiner respectfully disagrees that SMED it changes the result. Under MPEP 2106, eligibility turns on the claims under BRI. Claim 1 still recites “apply a K-means algorithm” and output “a predicted outcome representing a likelihood,” without administering treatment, adjusting therapy, or controlling a device or otherwise improves the computer, model-training process, or neuromodulation technology itself.
Applicant argues that the claims solve a practical neuromodulation problem because prior SCS selection relied on “physician experience,” “psychological screening,” and “relatively simple statistical correlations.”
Examiner respectfully disagrees. Those facts show a useful clinical problem, but Prong Two requires the claim to integrate the exception into a practical application. Claim 1 recites accepting patient data and outputting “a predicted outcome representing a likelihood.” Claim 6 likewise outputs “a predicted outcome ... representing a likelihood.” The claims produce decision-support information, but do not recite device-control action or specific improvement to a technology.
Applicant argues that the clustered ML approach is eligible because the SMED reports better performance: AUC “approximately 0.732” versus “approximately 0.653.”
Examiner respectfully disagrees that those performance metrics establish a technological improvement or practical application under § 101. The SMED evidence is considered and supports improved predictive accuracy, but the improvement is to the recited prediction itself. Claim 1 still applies a K-means algorithm and a cluster-specific predictive model to provide a predicted outcome representing a likelihood. Under MPEP 2106, improving the accuracy of the mathematical prediction used to estimate neuromodulation-response likelihood does not, without more, integrate the exception into a practical application or improve the server, user interface, neuromodulation device, or model-training technology.
Applicant argues that a “clear and direct nexus” exists because the invention solved unreliable prediction of long-term SCS success.
Examiner respectfully disagrees. The asserted nexus is to the usefulness of the prediction, not to a claimed practical application. Claim 1 stops at outputting “a predicted outcome representing a likelihood that the neuromodulation treatment will produce a positive response.” The claim does not require using that likelihood to administer, adjust, or control neuromodulation treatment.
Applicant argues under Prong One that the claims do not merely automate a mental process because they use more than thirty features, K-means clustering, nested cross-validation, feature selection, and cluster-specific models.
Examiner respectfully disagrees. Even assuming if the full asserted workflow is not practically performed mentally, claim 1 expressly recites mathematical concepts: “apply a K-means algorithm” and predictive models including “logistic regression, random forest, XGBoost, elasticnet, support vector machine, Naive Bayes.” Applicant pinpoint mathematical part does not overcome mental part, claims still recite analyzing patient-related information and producing predictive classifications or outputs. Applicant also relies on unclaimed detail. Claim 1 recites first features including patient age, pain duration, baseline NRS score, and baseline PCS score; claim 5 only requires second features selected from at least one of listed categories; and the claims do not recite nested cross-validation or feature-selection steps. Under BRI, the claims still recite mathematical analysis of patient data to output a predictive likelihood.
Applicant argues under Prong Two that the invention has clinical and economic significance because it can reduce “failed implants” and improve “allocation of neuromodulation therapies.”
Examiner respectfully disagrees that such outcomes amount to a technological improvement or practical application under the 101 analysis. Those are intended downstream benefits of using the predicted information. Claim 6 recites collecting features, applying “a K-means algorithm,” using a predictive model, and outputting “a predicted outcome ... representing a likelihood.” It does not recite reducing an implant failure, selecting a therapy, administering treatment, or controlling a neuromodulation device or practical application.
Response to Applicant’s Arguments Regarding 35 U.S.C. § 103
Applicant’s arguments presented in the remarks filed June 10, 2026, at pages 8-10, concerning the rejection of claims 1, 3, 5-7, and 9-10 under 35 U.S.C. § 103 over Neumann in view of Hoydonckx and Schnetz have been fully considered.
Applicant argues that claims 1 and 6 require identifying a patient cluster, selecting a predictive model specific to that cluster, and applying that model to different known-outcome patient data, and that the prior combination of Neumann, Hoydonckx, and Schnetz did not disclose or render obvious that arrangement.
Examiner respectfully agrees that the previous combination did not clearly establish selection and application of a predictive model specific to the identified cluster.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art of record fails to teach or suggest, alone or in combination, every limitation of independent claims 1 and 6 arranged as claimed. In particular, the art does not disclose using first known-outcome patient data for K-means clustering, selecting a predictive model specific to the identified cluster, and using different patient data in that cluster-specific prediction stage to predict an SCS response.
Upon completion of an updated prior-art search, the closest related art includes:
Neumann, US 2021/0057048 A1, discloses healthcare machine learning, prognostic probabilities, listed model types, and using unsupervised clusters in supervised learning, but not selection and application of a separately trained model specific to the identified cluster using the claimed different datasets.
Hoydonckx discloses spinal cord stimulation and baseline patient measures including NRS and PCS, but not the claimed two-stage cluster-specific machine-learning arrangement.
Villongco, US 2019/0333643 A1, discloses K-means clustering, generating a classifier for each cluster, and applying the identified cluster’s classifier to a new patient, but not SCS-response prediction using the claimed first and different second datasets from known-outcome patients.
Schnetz, US 2019/0046122 A1, discloses K-means clustering of a test-patient vector with reference-patient vectors having known outcomes and determining prognosis from the identified cluster, but not selecting a separate predictive model specific to that cluster and applying it to a different patient dataset.
Although the references disclose individual aspects of the claims, the record provides no teaching or reason to restructure their systems into the claimed ordered workflow. Specifically, the art does not suggest using one known-outcome patient dataset to identify a cluster, using that cluster to select a corresponding predictive model, and using different patient features in the selected model to predict the likelihood of a positive SCS response. Accordingly, the prior art, alone or in combination, does not disclose or render obvious independent claims 1 and 6. Claims 3, 5, 7, and 9–10 are allowable by dependency.
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.
Subject Matter Eligibility Analysis:
Claims 1, 3, 5-7, and 9-10 are rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception (an abstract idea) without reciting elements that integrate the exception into a practical application or provide an inventive concept amounting to significantly more than the exception itself.
Step 1: Statutory Categories Analysis
The claims fall within the statutory categories of invention.
Process (Claims 6-7, 9-10): The language reciting "collecting a first plurality of patient features... using a machine learning engine to apply a K-means algorithm... to output a predicted outcome" defines a series of acts or steps, fulfilling the definition of a process in MPEP § 2106.03.
Machine (Claims 1, 3, 5): The language reciting "A system for predicting an outcome... comprising: a server... and a machine learning engine" describes a concrete thing consisting of parts, fulfilling the definition of a machine in MPEP § 2106.03.
Step 2A, Prong One: Judicial Exception Analysis
Step 2A, Prong One determines whether the claims are directed to a judicial exception, such as an abstract idea, under MPEP 2106.04.
The whole invention is related to an approach that uses machine learning predictive modeling to predict patient response to spinal cord stimulation treatment based on historical patient characteristics. Refer to Spec., para. [0004], [0005], and Figure 3 for further details.
More specify, claims 1, 3, 5-7, and 9-10 are directed to an abstract idea, specifically mathematical concepts and mental processes. The invention focuses on an approach that uses machine learning predictive modeling to forecast patient response to spinal cord stimulation treatment based on historical patient characteristics (Spec., para. [0004], [0005], Figure 3). Claims 1 and 6 recite receiving clinical patient data sets, mathematically grouping the patient into a cluster using a K-means algorithm, and applying a mathematical predictive model corresponding to that cluster to output a likelihood of treatment success using a second set of data.
Independent Claim 1 Recites the following non-bold parts abstract idea:
A system for predicting an outcome of a neuromodulation treatment, comprising:
a server providing a user interface configured to accept a first set of data representing a first plurality of features from a new patient for whom a prediction of spinal cord stimulation is desired, wherein the first plurality of features include at least a patient age, a pain duration, a baseline NRS score, and a baseline PCS score, and to accept a second set of data representing a second plurality of features that is different than the first set of features;
and a machine learning engine in communication with the server, wherein the machine learning engine is configured to apply a K-means algorithm to perform a cluster stage to evaluate the plurality of patient features to identify a cluster from a plurality of clusters that corresponds to the plurality of features of the new patient based on a first set of data from a set of patients with known outcomes and then to perform a prediction stage using a predictive model selected from the group consisting of logistic regression, random forest, XGBoost, elasticnet, support vector machine, Naïve Bayes, and combinations thereof that provides a predicted outcome representing a likelihood that the neuromodulation treatment will produce a positive response for the new patient by selecting the predictive model that is specific to the cluster identified in the cluster stage and then applying the predictive model that is specific to the cluster identified by the cluster stage to a second set of data from the set of patients with known outcomes that is different than the first set of data.
Claim Abstract Classification Rationale
Under their Broadest Reasonable Interpretation (MPEP § 2111), the independent claims recite gathering patient data, executing mathematical formulas to group the data, and performing mathematical calculations to output a probability.
Mathematical Concepts (MPEP § 2106.04(a)(2)(I)):
The claims recite mathematical concepts because they rely on mathematical relationships, formulas, and calculations. Independent claims 1 and 6 recite applying "a K-means algorithm to perform a cluster stage", a mathematical formula that minimizes within-cluster sum of squares to group data points, and applying "a predictive model selected from the group consisting of logistic regression, random forest, XGBoost, elasticnet, support vector machine, Naïve Bayes" each of which is a mathematical calculation that computes a probability output. These mathematical formulas receive numerical patient data as inputs and produce a numerical likelihood as output.
Mental Process (MPEP § 2106.04(a)(2)(III)):
At a higher level of abstraction, the claims also recite a mental process. Independent claims 1 and 6 recite "evaluate the plurality of patient features to identify a cluster" and "provides a predicted outcome representing a likelihood." The specification frames the invention as a tool to "provide an objective datapoint to augment the clinician's decision about when to pursue alternate therapies" (Spec., para. [0002]). The evaluation of patient characteristics to identify a patient subgroup and the judgment of likely treatment outcomes are cognitive steps, observation, evaluation, and opinion that a clinician performs when assessing whether a patient is a good candidate for spinal cord stimulation.
Manual Replication Scenario (Human Equivalence)
A clinician gathers the patient's age, pain duration, NRS, and PCS scores. The clinician reviews historical patient profiles and, based on experience, mentally groups the patient with similar past patients. The clinician then recalls the success rates observed for that patient group and forms a judgment about the likelihood of treatment success. The claims merely automate these fundamental observation-grouping-prediction steps by substituting the clinician's mental heuristics with specific mathematical formulas (like K-means and logistic regression).
Dependent Claims Analysis
The dependent claims are also directed to the abstract ideas of mathematical concepts and mental processes.
Claims 3, 7, and 9: Claim 3 recites a "machine learning algorithm trained with data representing the plurality of features." Claim 7 recites "K-means clustering of data." Claim 9 recites algorithms "selected from the group consisting of logistic regression, random forest, XGBoost, elasticnet, support vector machine, Naïve Bayes." These claims identify specific mathematical formulas and statistical training iterations, falling strictly under Mathematical Concepts.
Claims 5 and 10: Claim 5 recites the second plurality of features are "selected from at least one of demographics, pain descriptors, pain questionnaire data, psychiatric comorbidities, spinal imaging, activity, medications, non-psychiatric comorbidities, and past spinal cord stimulation results." Claim 10 recites identical language. These claims identify specific categories of medical data, falling under Mental Process / Data Collection.
Because the claims are directed to an abstract idea, the analysis proceeds to Step 2A, Prong Two to determine if it is integrated into a practical application.
Step 2A, Prong Two: Integration into a Practical Application
The claims do not integrate the abstract idea into a practical application. The additional elements simply provide a generic technological environment to perform the data analysis and fail to impose meaningful limits on the abstract idea.
Evaluation of Independent Claims 1 and 6 Additional Elements Generic Hardware (server and user interface): The recitation of a server and user interface is a mere instruction to implement the abstract idea on a generic computer (MPEP § 2106.05(f)). The server and interface are invoked solely to "accept a first set of data" and "accept a second set of data." Obtaining data inputs for mathematical calculations is insignificant extra-solution activity (MPEP § 2106.05(g)). The claims do not recite any structural modifications that improve the functioning of the server or user interface itself (MPEP § 2106.05(a)).
Generic Software and Data Architecture (machine learning engine):
The claims require the machine learning engine to accept a "first set of data" for the cluster stage and a "second set of data" for the prediction stage. This two-stage data architecture reflects a generic mathematical data-organization methodology, using different feature subsets for clustering versus classification to prevent overfitting, rather than a technological improvement to the computer's functioning (MPEP § 2106.05(a)). The separation of data inputs is an inherent part of the mathematical modeling, not a structural enhancement of the machine learning engine or the server. Reciting the engine in the context of "predicting an outcome of a neuromodulation treatment" merely links the mathematical abstract idea to a particular technological environment (MPEP § 2106.05(h)).
Combination as a Whole:
The specific ordered combination of a server accepting data, grouping it via K-means clustering, performing a cluster-specific prediction using a second data set, and outputting the result does not transform the abstract idea. The sequence is the logical execution of the mathematical methodology itself. The elements operate in their generic capacities—the server receives data, and the processor calculates the formulas, without demonstrating any technical synergy that improves the underlying computing system.
Dependent Claims Analysis The dependent claims do not add new technical additional elements; they merely narrow the abstract idea.
Claims 3, 7, and 9: Reciting specific algorithms ("logistic regression, random forest," etc.) or training requirements fails to improve computer functionality (MPEP § 2106.05(a)) because it simply identifies the specific mathematical formula to be calculated by the generic processor.
Claims 5 and 10: Reciting specific medical data variables ("demographics, pain descriptors," etc.) is a mere field-of-use limitation (MPEP § 2106.05(h)) that restricts the data gathering to the medical field without providing a practical, technical application.
When viewed as a whole, the combination of these elements in the dependent and independent claims does not integrate the abstract idea into a practical application because the claims merely direct the application of statistical algorithms to specific medical data using a generically invoked server.
Step 2B: Inventive Concept Analysis
The claims lack an inventive concept because the additional elements, alone and in combination, represent well-understood, routine, and conventional activities in the field that do not amount to significantly more than the abstract idea itself.
Evaluation of Independent Claims 1 and 6 Additional Elements Generic Hardware (server and user interface):
The invocation of a server and user interface provides no inventive concept. The specification admits these components are well-understood, routine, and conventional off-the-shelf components. The specification explicitly states that "GUI files will be located and loaded from a server 16, such as Amazon web services (AWS)" and "As is known in the art, GUIs can require user authentication and login..." (Spec., para. [0022]). This is a mere instruction to apply the exception using generic commercial cloud components (MPEP § 2106.05(f)).
Generic Software (machine learning engine):
Applying existing machine learning algorithms to evaluate data is well-understood, routine, and conventional in the field of data science. The specification admits "The K-means algorithm is one of the simplest and most frequently used clustering algorithms" (Spec., para. [0024]) and identifies the predictive models as generic tools like "logistic regression, random forest, XGBoost" (Spec., para. [0005]). Utilizing "frequently used" algorithms on a generic engine does not constitute a technological improvement (MPEP § 2106.05(a)). The two-stage data feature separation is likewise a conventional data science technique to optimize mathematical model performance. Refer also to Mars, US10296848, Col. 6, ll. 1 – 50, Charles, US20190108912A, par. 0017, 0063, 0053, Mamta, US20190325354A1, par. 0031, 0052-0053
Combination as a Whole:
The ordered combination of using a generic web server to receive patient data and passing it to a conventional machine learning engine executing generic K-means and logistic regression formulas is a well-understood, routine, and conventional arrangement. The whole is no greater than the sum of its generic parts, merely automating an abstract idea.
Dependent Claims Analysis The dependent claims do not introduce an inventive concept.
Claims 3, 7, and 9: These claims add specific mathematical algorithms ("XGBoost, elasticnet," etc.). This is MPEP § 2106.05(f) - Mere Instructions to apply the exception using specific formulas. The specification confirms this is a generic selection from known options: "The machine learning algorithm may comprise logistic regression, random forest, XGBoost... or combinations thereof." (Spec., para. [0005]).
Claims 5 and 10: These claims add types of medical data ("psychiatric comorbidities, spinal imaging," etc.), which is insignificant pre-solution activity (g) and a mere field-of-use limitation (h). The specification confirms these are generic clinical data inputs for assessing neuromodulation (Spec., para. [0021]).
As a whole, the combination of the dependent and independent claims merely automates the abstract idea of medical outcome prediction using generic, off-the-shelf cloud computing and generic statistical methodologies.
Therefore, Claims 1, 3, 5-7, and 9-10 are rejected under 35 U.S.C. § 101.
Relevant Prior Art
US20190333643A1- Recites specific-model-selection rule, par. 0158 and par. 0161.
US20210118559A1 – Recites receiving subject data, identifying similar-subject cohort, processing that cohort, and presenting the output to a user, par. 0100, 0209, 0023, 0094, 0204, 0207, 0223, 0304, 0306, fig.4.
US20160213314A1- Recites SCS-specific outcome/benefit and include Naïve Bayes, and K-nearest neighbor, par. 0012-0013, 0073, 0122-01223, 0131, fig. 2 -3.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA DAMIAN RUIZ whose telephone number is (571)272-0409. The examiner can normally be reached 0800-1800.
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/JOSHUA DAMIAN RUIZ/Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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