CTNF 18/974,449 CTNF 92810 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Specification 06-31 AIA The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-9, 11-20 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Woods (US 20230293899 A1; 9/21/2003) . Regarding claim 1 , Woods teaches a method for machine-learning based automated neurostimulation programming ([0004]; [0102][005]-[0059]), the method comprising: generating a database of previously tested stimulation settings and their clinical effect (Fig. 2A, 214; Fig. 11A-11F); identifying a region of interest surrounding at least one lead (Fig. 2A, 200; Fig. 11A-11F); determining a stimulation setting corresponding to one or more specific clinical effects associated with the region of interest, the one or more specific clinical effects including at least one beneficial clinical effect or at least one detrimental clinical effect (Fig. 2A, 212; Fig. 11A-11F; [0057]-[0059]); employing a trained machine-learning model to identify a stimulation parameter value for a stimulation setting to set in a patient of interest based on the one or more specific clinical effects, the trained machine-learning model configured to classify the stimulation parameter value for the stimulation setting based at least in part on raw imaging data (Fig. 2B; Fig. 11A-11F; [0060]; [0102]); and generating an output providing the stimulation parameter value for the stimulation setting for the patient of interest (Fig. 2B; Fig. 11A-11F; [0060]; [0102]). Claim 16 is rejected under substantially the same basis as claim 1 above. Claim 20 is rejected under substantially the same basis as claim 1 above. Regarding claim 2 , Woods teaches determining a similarity metric between the patient of interest and a plurality of patients according to the region of interest (Fig. 12A-12F; [0060]; [0097]; [0098]; [0099] “predicted treatment response”; [0100]). Regarding claim 3 , Woods teaches using a single similarity metric or multiple similarity metrics (Fig. 12A-12F; [0060]; [0097]; [0098]; [0099] “predicted treatment response”); and clustering stimulation settings from similar patients identified among the plurality of patients according to the region of interest (Fig. 11A-11F; Fig. 12A-12F; [0060]; [0097]; [0098]; [0099] “predicted treatment response”; [0100]). Claim 17 is rejected under substantially the same basis as claims 2-3 above. Regarding claim 4 , Woods teaches suggesting or avoiding the stimulation settings based on cluster features identified among the plurality of patients according to the region of interest (Fig. 11A-11F; Fig. 12A-12F; [0097]; [0098]; [0099] “predicted treatment response”; [0100]). Regarding claim 5 , Woods teaches identify one or more commonalities between the patient of interest and the plurality of patients in the region of interest around the lead (Fig. 11A-11F; Fig. 12A-12F; [0060]; [0097]; [0098]; [0099] “predicted treatment response”; [0100]). Regarding claim 6 , Woods teaches employing the trained machine-learning model to identify the one or more commonalities based at least in part on additional data types beyond raw imaging data (Fig. 2A, 212, 214; [0069] “cognitive test”; [0077] “Introducing additional neuroimaging modalities and clinical data into the model may further enhance performance and predictive value”; claim 12). Regarding claim 7 , Woods teaches wherein the additional data types beyond raw imaging data include at least one of disease state, comorbidities, patient states, or clinician evaluation (Fig. 2A, 212, 214; [0069] “cognitive test”; [0077] “Introducing additional neuroimaging modalities and clinical data into the model may further enhance performance and predictive value”; claim 12). Claim 18 is rejected under substantially the same basis as claims 5-7 above. Regarding claim 8 , Woods teaches identifying the stimulation parameter value for the stimulation setting to set in the patient of interest based on data-drive phenotypes (Fig. 7A; non-responders vs responders; Fig. 2A, 214; Fig. 12A-12F; [0060]; [0100]-[0102]). Regarding claim 9 , Woods teaches identifying the stimulation parameter value for the stimulation setting to set in the patient of interest based on a hierarchical organization ([0061] “feature selection is to filter the features…significant group-level difference”; [0102]). Claim 19 is rejected under substantially the same basis as claims 8-9 above. Regarding -20 , Woods teaches employing the trained machine-learning model to confirm that the model-identified stimulation parameter value for the stimulation setting to set in the patient of interest based on the one or more specific clinical effects matches known target settings for a patient (Fig. 11A-11F; Fig. 12A-12F; [0060] “The inventors of the present invention recognized that many parameter values of the electric field from step 210 do not considerably vary between the first group and second group of subjects, whereas other parameter values of the electric field do considerably vary between the first group and second group of subjects. The inventors of the present invention recognized that if those parameter values of the electric field that distinguish the first group of subjects from the second group of subjects were determined, they could be used to efficiently predict whether a new subject will experience cognitive function improvement based on parameter values of the electric field in the new subject.”; [0102] “It was shown that the optimal stimulation parameters reduce between-subject variability, elevated current intensity within targeted brain regions, and closely resembled the current distributions observed in treatment responders.”). Regarding claim 12 , Woods teaches wherein the confirmation is performed through use of at least one of computational modeling ([0099] “Computational models…predicted treatment response with over 86% accuracy”), imaging, clinical evaluation, symptomology scoring (Fig. 2A-2B; Fig. 11A-11F; Fig. 12A-12F; [0060]; [0102]), or invasive testing. Regarding claim 13 , Woods teaches employing the trained machine-learning model to determine target similarity metrics and thresholds for grouping patients by anatomical phenotypes (Fig. 2A-2B; Fig. 7A-7D; Fig. 10A-10D; Fig. 11A-11F; Fig. 12A-12F; [0072]; [0099]). Regarding claim 14 , Woods teaches wherein grouping patients by the anatomical phenotypes includes using different similarity metric correlations, varying the thresholds to evaluate accuracy of predictions, identifying the thresholds that maximize accuracy, compute different similarity metrics on various image features, or selecting the various image features that produce distinct patient groupings (Fig. 2A-2B; Fig. 7A-7D; Fig. 10A-10D; Fig. 11A-11F; Fig. 12A-12F; [0072]; [0099]). Regarding claim 15 , Woods teaches validating, by the trained machine-learning models, a particular similarity metric and threshold by testing prediction accuracy on a new patient ([0077] “we used two-level nested cross-validation to increase the number independent test samples and used 10 permutations to assess the retest reliability of these models. Average accuracy and confidence intervals across these permutations were used to estimate model performance on novel datasets.”; [0099] “Computational models of current intensity and direction predicted treatment response with over 86% accuracy”) . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim (s) 10 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The prior art of record does not disclose or fairly suggest either singly or in combination the claimed invention of claim 10 when taken as a whole, comprising, in addition to the other recited claim elements, wherein the hierarchical organization includes a first level providing a best contact, a second level providing a best contact fraction, and a third level providing a best amplitude. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan T Kuo whose telephone number is (408)918-7534. The examiner can normally be reached M-F 10 a.m. - 6 p.m. PT. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Niketa Patel can be reached at 571-272-4156. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JONATHAN T KUO/Primary Examiner, Art Unit 3792 Application/Control Number: 18/974,449 Page 2 Art Unit: 3792 Application/Control Number: 18/974,449 Page 3 Art Unit: 3792 Application/Control Number: 18/974,449 Page 4 Art Unit: 3792 Application/Control Number: 18/974,449 Page 5 Art Unit: 3792 Application/Control Number: 18/974,449 Page 6 Art Unit: 3792 Application/Control Number: 18/974,449 Page 7 Art Unit: 3792
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