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 .
Formal Matters
Applicant's response, filed 12 December 2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1, 5-12, 15-18, 21-23 are currently pending and have been examined.
Claims 1, 5, 12, and 18 have been amended.
Claims 2-4, 13-14, and 19-20 have been canceled.
Claims 21-23 have been added.
Claims 1, 5-12, 15-18, 21-23 have been rejected.
Priority
The instant application claims the benefit of priority under 35 U.S.C 119(e) or under 35 U.S.C. § 120, 121, or 365(c). Accordingly, the effective filing date for the instant application is 17 December 2021 claiming benefit to Provisional Applications 63/291275, 63/330753, and 63/415949.
Subject Matter Eligibility
Under the Subject Matter Eligibility Test for Products and Processes, patent eligible subject matter must be analyzed following a two-step test – first, the examiner must see if the claimed invention is directed towards a process, machine, manufacture, or composition of matter – the claimed invention is directed towards a method or system. Therefore, the claims are directed towards a statutory category. Second, the examiner must analyze if the claims are directed towards a judicial exception. While the claimed invention does contain claim language directed towards methods of organizing human activity under Step 2A Prong One (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people), the claims qualify as eligible subject matter.
When determining if a particular treatment and prophylaxis as a practical application under Step 2A Prong Two, Examiner considered the factors presented in MPEP § 2106.04(d)(2).
Factor A. The treatment plan determined from the abstract idea is "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s). Here, the treatment delivered is specifically anti-VEGF therapy to treat neovascular age-related macular degeneration (nAMD).
Factor B. The treatment limitation has a significant relationship to the judicial exception – it integrates the law of nature into a practical application. The judicial exception is utilized to determine the treatment frequency for the delivery of the therapy.
Factor C. The treatment or prophylaxis limitation imposes meaningful limits on the judicial exception. There is a positively recited administering step of the anti-VEGF therapy according to a generated treatment plan.
Therefore, the claims only recite the prophylactic step as a tool which only serves to as insignificant post solution activity (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
Claim Rejections - 35 USC § 103
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.
Claims 1, 5-12, 15-18, 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Peng et al. (US Patent App No 2019/0180441)[hereinafter Peng] in view of Eifert et al. (US Patent Application No. 2019/0180441)[hereinafter Eifert] in further view of Pfau et al., Probabilistic Forecasting of Anti-VEGF Treatment Frequency in Neovascular Age-Related Macular Degeneration, 10 Translational Vision Science & Technology (June 2021)[hereinafter Pfau].
As per claim 1, Peng teaches on the following limitations of the claim:
a method comprising: receiving baseline data for a subject diagnosed with neovascular age-related macular degeneration (nAMD) is taught in the Detailed Description in ¶ 0064 and ¶ 0093 (teaching on receiving current nAMD patient image data (treated as synonymous to baseline data));
forming a plurality of predictor inputs for an outcome predictor using the baseline data and regimen data for a plurality of treatment regimens is taught in the Detailed Description in ¶ 0095 (teaching on processing input data including fundus image and other patient data to generate a set of treatment outcome scores);
wherein the outcome predictor comprises at least one machine learning model is taught in the Detailed Description in ¶ 0095 (teaching on the treatment scoring processing including machine learning );
generating, via the outcome predictor, a plurality of treatment scores for the plurality of treatment regimens using the plurality of predictor inputs is taught in the Detailed Description in ¶ 0095 (teaching on processing input data including fundus image and other patient data to generate a set of treatment outcome scores);
selecting one of the plurality of treatment regimens as a selected treatment regimen for the subject is taught in the Detailed Description in ¶ 0099 (teaching on selecting the treatment with the highest treatment outcome score).
Peng fails to teach the following limitation of claim 1. Pfau, however, does teach the following:
wherein the plurality of predictor inputs comprises a different predictor input for each of the plurality of treatment regimens is taught in the § Introduction on p. 2 col 1, § Machine-Learning on p. 3 col 2, § Probabilistic Forecasting on p. 3 col 2 - p. 5 col 1, § Discussion on p. 6 col 2 - p. 7 col 2, and p. 8 col 1 (teaching on analyzing treatment dosage efficacy for single v multi-dosage regimens with ten different hyperparameters for three models, each used to predict different treatment frequencies);
determining, based on the plurality of treatment scores, whether a single treatment regimen of the plurality of treatment regimens or multiple treatment regimens of the plurality of treatment regimens meets a score criterion is taught in the § Introduction on p. 2 col 1, § Discussion on p. 6 col 2 - p. 7 col 2, and p. 8 col 1 (teaching on analyzing treatment dosage efficacy for single or multi-dosage regimens of anti-VEGF agents treating AMD via a machine learning model utilizing a NGBoost probabilistic forecast wherein a single treatment meets a predictive certainty threshold (treated as synonymous to a score criterion));
responsive to the single treatment regimen meeting the score criterion, selecting the single treatment regimen as the selected treatment regimen for the subject; or responsive to the multiple treatment regimens meeting the score criterion, identifying a treatment regimen of the multiple treatment regimens that meets a set of treatment burden criteria for the subject as the selected treatment regimen; and is taught in the § Introduction on p. 2 col 1, § Discussion on p. 6 col 2 - p. 7 col 2, and p. 8 col 1 (teaching on analyzing treatment dosage efficacy for single or multi-dosage regimens of anti-VEGF agents treating AMD via a machine learning model utilizing a NGBoost probabilistic forecast wherein a single treatment meets a predictive certainty threshold - Examiner notes that the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (see MPEP 2111.04(II) on contingent limitations). Because the multiple treatment meeting a score criterion condition contingent step is not satisfied, the performance recited by the step need not be carried out in order for the claimed method to be performed);
delivering an anti-vascular endothelial growth factor (anti-VEGF) therapy to the subject based on the selected treatment regimen is taught in the § Introduction on p. 2 col 1, § Discussion on p. 6 col 2 - p. 7 col 2, and p. 8 col 1 (teaching on analyzing treatment dosage efficacy for single or multi-dosage regimens of anti-VEGF agents treating AMD via a machine learning model utilizing a NGBoost probabilistic forecast and delivering said treatment to the patient on the determined schedule).
One of ordinary skill in the art before the effective filing date would combine the nAMD machine learning model for evaluating treatments of Peng with the treatment dose efficacy determination of Pfau with the motivation of “allow[ing] physicians to use the prediction for clinical decision making about upcoming anti-VEGF therapy approaches” (Pfau in the § Introduction on p. 2 col 1).
Independent method claim 12 is rejected under the same rational. Independent claim 18 is rejected under the same rational; Examiner notes that the broadest reasonable interpretation of a system (or apparatus or product) claim having structure that performs a function, which only needs to occur if a condition precedent is met, requires structure for performing the function should the condition occur (see MPEP § 2111.04(II) on contingent limitations). Because the condition of having a score for multiple injections is not met, the remaining limitations of claim 18 are not required by the claim and the processing structure is adequately taught by the prior art applied to the independent claim.
As per claim 5, the combination of Peng and Pfau discloses all of the limitations of claim 1. Peng fails to teach the following; Pfau, however, does disclose:
the method of claim 1, wherein the set of treatment burden criteria comprises at least one of a fewest number of injections, a lowest frequency of injections, a lowest dosage, a lowest drug strength, a reduced amount of monitoring, or reduced side effects is taught in the § Introduction on p. 2 col 1, § Discussion on p. 6 col 2 - p. 7 col 2, and p. 8 col 1 (teaching on analyzing treatment dosage efficacy for single or multi-dosage regimens of anti-VEGF agents treating AMD via a machine learning model utilizing a NGBoost probabilistic forecast wherein a single treatment meets a predictive certainty threshold - Examiner notes that the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (see MPEP 2111.04(II) on contingent limitations). Because the multiple treatment meeting a score criterion triggering the treatment burden analysis condition contingent step is not satisfied, the performance recited by the step need not be carried out in order for the claimed method to be performed).
One of ordinary skill in the art before the effective filing date would combine the nAMD machine learning model for evaluating treatments of Peng with the treatment dose efficacy determination of Pfau with the motivation of “allow[ing] physicians to use the prediction for clinical decision making about upcoming anti-VEGF therapy approaches” (Pfau in the § Introduction on p. 2 col 1).
As per claim 21, the combination of Peng and Pfau discloses all of the limitations of claim 5. Peng fails to teach the following; Pfau, however, does disclose:
the system of claim 18, wherein identifying the treatment regimen of the multiple treatment regimens meeting the score criterion as the selected treatment regimen comprises: determining the treatment regimen with the lowest treatment burden based on the set of treatment burden criteria, wherein the treatment regimen with the lower treatment burden comprises the treatment regimen with the lowest frequency of injections is taught in the § Introduction on p. 2 col 1, § Discussion on p. 6 col 2 - p. 7 col 2, and p. 8 col 1 (teaching on analyzing treatment dosage efficacy for single or multi-dosage regimens of anti-VEGF agents treating AMD via a machine learning model utilizing a NGBoost probabilistic forecast wherein a single treatment meets a predictive threshold - Examiner notes that the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (see MPEP 2111.04(II) on contingent limitations). Because the multiple treatment meeting a score criterion triggering the treatment burden analysis condition contingent step is not satisfied, the performance recited by the step need not be carried out in order for the claimed method to be performed).
One of ordinary skill in the art before the effective filing date would combine the nAMD machine learning model for evaluating treatments of Peng with the treatment dose efficacy determination of Pfau with the motivation of “allow[ing] physicians to use the prediction for clinical decision making about upcoming anti-VEGF therapy approaches” (Pfau in the § Introduction on p. 2 col 1).
Dependent claims 22 and 23 are rejected under the same rational.
As per claim 6, the combination of Peng and Pfau discloses all of the limitations of claim 1. Peng fails to teach the following; Pfau, however, does disclose:
the method of claim 1, wherein the generating comprises: processing, via the outcome predictor, each of the plurality of predictor inputs independently to generate the plurality of treatment scores is taught in the § Introduction on p. 2 col 1, § Machine-Learning on p. 3 col 2, § Probabilistic Forecasting on p. 3 col 2 - p. 5 col 1, § Discussion on p. 6 col 2 - p. 7 col 2, and p. 8 col 1 (teaching on analyzing treatment dosage efficacy for single v multi-dosage regimens with ten different hyperparameters for three models, each used to predict different treatment frequencies).
One of ordinary skill in the art before the effective filing date would combine the nAMD machine learning model for evaluating treatments of Peng with the treatment dose efficacy determination of Pfau with the motivation of “allow[ing] physicians to use the prediction for clinical decision making about upcoming anti-VEGF therapy approaches” (Pfau in the § Introduction on p. 2 col 1).
Dependent claims 22 and 23 are rejected under the same rational.
As per claim 7, the combination of Peng and Pfau discloses all of the limitations of claim 1. Peng also discloses the following:
the method of claim 1, wherein: the outcome predictor comprises a plurality of predictor models; each of the plurality of predictor models comprises at least one machine learning model; and each of the plurality of predictor models is configured to generate a treatment score of the plurality of treatment scores for a corresponding treatment regimen of the plurality of treatment regimens using a predictor input of the plurality of predictor inputs that corresponds to the treatment regimen is taught in the Detailed Description in ¶ 0033 and ¶ 0095-97 (teaching on utilizing an ensemble method (treated as synonymous to a plurality of learned models) for determining the machine learning output of each treatment score for a plurality of treatments).
As per claim 8, the combination of Peng and Pfau discloses all of the limitations of claim 1. Peng also discloses the following:
the method of claim 1, wherein the baseline data comprises at least one of: optical coherence tomography (OCT) image data is taught in the Detailed Description in ¶ 0038 (teaching on the input feature data including an OCT image);
clinical data that includes at least one of a visual acuity measurement, a central subfield thickness, a low-luminance deficit, age, or sex; or retinal feature data extracted from segmented image data that has been generated from OCT image data corresponding to a baseline point in time is taught in the Detailed Description in ¶ 0040 (teaching on the input feature data including central corneal thickness, age, and/or gender).
Dependent claim 15 is rejected under the same rational.
As per claim 9, the combination of Peng and Pfau discloses all of the limitations of claim 1. Peng fails to teach the following; Pfau, however, does disclose:
the method of claim 1, wherein the regimen data for a corresponding one of the plurality of treatment regimens identifies a treatment and at least one of an administration frequency, a dosage schedule, or a monitoring schedule for the treatment is taught in the § Discussion on p. 6 col 2 - p. 7 col 2 (teaching on analyzing treatment efficacy of anti-VEGF agents treating AMD via a machine learning model utilizing a NGBoost probabilistic forecast).
One of ordinary skill in the art before the effective filing date would combine the nAMD machine learning model for evaluating treatments of Peng with the treatment dose efficacy determination of Pfau with the motivation of “allow[ing] physicians to use the prediction for clinical decision making about upcoming anti-VEGF therapy approaches” (Pfau in the § Introduction on p. 2 col 1).
As per claim 10, the combination of Peng and Pfau discloses all of the limitations of claim 1. Peng fails to teach the following; Pfau, however, does disclose:
the method of claim 1, wherein each of the treatment scores is a predicted visual acuity measurement is taught in the § Introduction on p. 2 col 1 (teaching on the treatment efficacy being a measure of visual acuity outcomes).
It would have been obvious to one of ordinary still in the art to include in the nAMD machine learning model for evaluating treatments of Peng with visual acuity prediction as taught by Pfau since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably generating a score for each treatment that represents the visual capability of the patient utilizing a known measure of sight.
Dependent claim 16 is rejected under the same rational.
As per claim 11, the combination of Peng and Pfau discloses all of the limitations of claim 1. Peng fails to teach the following; Pfau, however, does disclose:
the method of claim 1, wherein the at least one machine learning model comprises at least one of a linear regression model, a random forest (RF) model, a Gradient Boosting Machine (GBM) model, an Extreme Gradient Boosting (XGBoost), or a Support Vector Machine (SVM) model is taught in the § Discussion on p. 6 col 2 - p. 7 col 2 (teaching on analyzing treatment dosage efficacy of anti-VEGF agents treating AMD via a machine learning model utilizing a NGBoost probabilistic forecast).
One of ordinary skill in the art before the effective filing date would combine the nAMD machine learning model for evaluating treatments of Peng and Eifert with the boosting algorithm of Pfau with the motivation of “adequately reflect[ing] the needed antiVEGF injection frequency” (Pfau in the § Introduction on p. 2 col 2).
Dependent claim 17 is rejected under the same rational.
Response to Arguments
Applicant's arguments filed 12 December 2025 with respect to 35 USC § 101 have been fully considered and are persuasive in view of the amendment. The subject matter eligibility rejection has been withdrawn.
Applicant's arguments filed 12 December 2025 with respect to 35 USC § 102 and § 103 have been fully considered but they are not persuasive in view of the amendments. Applicant merely asserts that Pfau fails to cure the deficiencies of Peng and previously relied upon Eifert. Examiner disagrees. Pfau clearly teaches on utilizing a plurality of machine learning models to determine the best treatment for a patient – forecasting a patient’s needs in a year for a singular treatment to a plurality of treatments, comparing each treatment frequency to find the optimized score. Examiner notes Pfau solves the same problem as the instant claims of differentiating between responders (low/single dosage patients) v non-responders (multi-dosage patients). While the claims are currently drafted with contingent limitations, Pfau would also read on the selection from multiple treatments, the optimum treatment plan as the plan with the highest score would necessarily include a “treatment burden” of successfully treating the disease – that is Pfau assigns the treatment frequency based on the side-effect of responding to treatment. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Pfau, as per the rejection above.
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 JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M Vazquez can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857