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 .
Claim Status
Claims 1-20 are pending.
Priority
This application claims benefit of application no. 63/337,992, filed 05/03/2022. The instant application has the effective filing date of 03 May 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/03/2023, 10/23/2023, 8/13/2024, and 11/26/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings, submitted on 04/27/2023, are accepted by the examiner.
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 1-20 are rejected under U.S.C 101 because the claimed invention is directed to abstract ideas without significantly more, as detailed in the analysis below.
Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106:
Claims 1-9 are directed to a statutory category (method).
Claim 10-18 are directed to a statutory category (system).
Claim 19-20 are directed to a statutory category (product).
Therefore, in accordance with MPEP § 2106.03 all claims have patent eligible subject matter.
[Eligibility Step 1: YES]
Eligibility Step 2A: This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106.
Eligibility Step 2A -- Prong One: Limitations are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon). Possible judicial exceptions are explored below.
Recitations of Judicial Exceptions:
Claim 1, 10, and 19: identifying a dosage of an active ingredient in a treatment that is being provided to or that is being considered for provision to a subject; (mental process)
predicting a level of local amyloid beta based on the dosage of the active ingredient; (mental process, mathematical concept)
predicting a severity of amyloid-related imaging abnormalities (ARIA) manifested as hyperintensities on T2-weighted fluid attenuated inversion recovery (FLAIR) images (ARIA-E) based on the predicted removal of local amyloid beta, wherein predicting severity of ARIA-E includes predicting an extent of vascular wall disturbance; (mental process, mathematical concept)
Claims 2, 11, and 20: wherein the active ingredient includes an anti-amyloid monoclonal antibody. (mental process)
Claims 3 and 12: using a pharmacokinetic model to predict a time course of a concentration of an active ingredient in the treatment in plasma, wherein the prediction of the local amyloid beta is based on at least one predicted concentration of the active ingredient in the predicted time course. (mathematical concept)
Claims 4 and 13: wherein predicting the level of local amyloid beta includes: estimating a baseline level of local amyloid beta; calculating a rate of removal of the local amyloid beta based on the dosage of an active ingredient and the baseline level of local amyloid beta; and predicting the level of local amyloid beta based on the calculated rate of removal of the local amyloid beta. (mental process, mathematical concept)
Claims 5 and 14: wherein predicting the level of local amyloid beta includes solving a pharmacodynamic differential equation that assumes a rate of change of amyloid beta is proportional to a product between concentrations of the active ingredient in the subject and local amyloid beta levels (mathematical concept)
Claims 6 and 15: wherein predicting the level of vascular wall disturbance includes solving a differential equation that includes the level of local amyloid beta. (mathematical concept)
Claims 7 and 16: wherein predicting the severity of ARIA-E includes solving an algebraic equation that assumes a non-linear relationship between the level of vascular wall disturbance and the Barkhof Grand Total Score (BGTS) (mathematical concept)
Claims 8 and 17: identifying a potential schedule for monitoring for ARIA events based on the predicted severity of ARIA, wherein the result characterizes the potential schedule (mental process)
Claims 9 and 18: identifying a potential recommendation of the dosage of the active ingredient for the subject based on the predicted severity of ARIA, wherein the result characterizes the potential recommendation dosage. (mental process)
Step 2A – Prong One Analysis:
Analysis techniques such as identifying data, making estimations, drawing conclusions based on data, and some calculations, requiring nothing more than the human mind and pen/paper, read on observations, evaluations, judgments, and opinions, and fall under the mental process grouping of abstract ideas. Limitations that merely provide additional information regarding the data being analyzed in this manner are similarly categorized (claim 2, 11, 20).
Analysis techniques such as predicting variables using differential equations, pharmacokinetic models, calculations, products, and ratios recite mathematical formulas, calculations, and relationships that fall under the mathematical concept grouping of abstract ideas.
Therefore, the claims are found to recite judicial exceptions.
[Eligibility Step 2A – Prong One: YES]
Eligibility Step 2A – Prong Two: A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. If the claim contains no additional claim elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)). Additional elements are recited, categorized, and analyzed below.
Data Outputting Elements:
Claim 1, 10, and 19: outputting a result corresponding to the predicted ARIA-E severity
Computer Components Elements:
Claim 1: Computer-implemented method
Claim 10: system comprising one or more data processors; and a non-transitory computer-readable medium containing instructions which, when executed on the one or more data processors
Claim 19: computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform a set of actions comprising:
Step 2A – Prong Two Analysis:
Necessary data gathering and outputting steps equate to insignificant extra-solution activities that do not integrate the judicial exceptions into practical application per MPEP 2106.05 (g).
Generic computer components and implementations provide mere instructions to implement the abstract ideas onto a technological environment per Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
As such, the additional elements, when viewed separately and in the context of a whole claimed invention, do not integrate the judicial exceptions into practical application.
[Eligibility Step 2A – Prong Two: NO]
Eligibility Step 2B: Claim elements are probed for inventive concept equating to significantly more than the judicial exception (MPEP 2106.04(II)).
Step 2B Analysis:
The generic outputting limitation is found well-understood, routine, and conventional per Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) of MPEP 2016.05 (g).
The computer components are found to be well-understood, routine, and conventional per Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 for storing and retrieving information in memory and Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (MPEP 2106.05 (a)).
As such, the additional elements are further found to lack inventive concept.
[Eligibility Step 2B: NO]
Therefore, claims 1-20 are directed to judicial exceptions without significantly more and are rejected under 35 U.S.C 101.
Claim Rejections - 35 USC § 103
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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 10-14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sperling et al. (IDS filed 05/03/2023; NPL; cite no. 32; 2011) in view of Lin et al. (Pharmaco & Systems Pharmacology; Vol. 11: 3; 2022).
Sperling et al. describes ARIA including inclusion/exclusion criteria and potential areas of research which might help increase our understanding of these events.
Claims 1, 10, and 19 are directed to methods, systems, and computer-readable mediums with instructions for: identifying a dosage of an active ingredient in a treatment that is being provided to or that is being considered for provision to a subject; predicting a level of local amyloid beta based on the dosage of the active ingredient; predicting a severity of amyloid-related imaging abnormalities (ARIA) manifested as hyperintensities on T2-weighted fluid attenuated inversion recovery (FLAIR) images (ARIA-E) based on the predicted removal of local amyloid beta, wherein predicting severity of ARIA-E includes predicting an extent of vascular wall disturbance; and outputting a result corresponding to the predicted ARIA-E severity.
Sperling et al. teaches the highest dose group of 5mg/kg developed transient signal abnormalities (page 2, column 1), such as hyperintensities (page 3, column 1), on T2 weighted/fluid attenuation inversion recovery (FLAIR) sequences, approximately 4–6 weeks after a single dose of bapineuzumab (page 2, column 1).
Sperling et al. further teaches ARIA-E should be interpreted both for severity as well as relevance to clinical symptoms (page 17, column 1); a detailed reporting form covering the spectrum of findings should be provided to the local radiologist that specifically asks about presence or absence of signal abnormalities consistent with ARIA-E, such as the presence of increased signal on FLAIR sequences consistent with parenchymal edema or effusions in the leptomeninges or sulcal space (page 17, column 1); and It may also be useful to develop a more quantitative scoring or rating scale for ARIA, that might include detailed information about anatomic location of ARIA-E and/or ARIA-H, and a severity index (page 17, column 1).
Sperling et al. does not explicitly teach predicting ARIA severity based on the removal of local amyloid beta, nor predicting an extent of vascular wall disturbance (claims 1, 10, and 19).
Sperling et al. teaches the FLAIR and T2*-GRE abnormalities observed in ARIA might be related to altered vascular permeability (page 9, column 1); direct removal of amyloid from the vessel wall may be associated with compromise in the vascular integrity (page 9, column 1); and focal amyloid-related vascular inflammation may play a role in some cases of ARIA (page 9, column 1).
Therefore, Sperling et al. teaches determining the severity of ARIA-E based on identifying T2 and FLAIR hyperintensities; and that hyperintensities may be related to vascular disturbances, caused by the removal of local amyloid beta.
As such, Sperling et al. provides motivation so that it would be obvious to one of ordinary skill in the art predict ARIA-E severity based on the removal of local amyloid beta, as well as predicting the extent of vascular wall disturbance, in the form of focal amyloid-related vascular inflammation.
Claims 2, 11, and 20 are directed to the active ingredient including an anti-amyloid monoclonal antibody.
Sperling et al. teaches an unexpected type of MRI signal alteration was first observed in the single dose-ascending Phase I trial of a monoclonal antibody against amyloid-β (page 2, column 1).
Sperling et al. also does not teach predicting a level of local amyloid beta based on the dosage of the active ingredient (claims 1, 10, and 19).
Lin et al. describes a quantitative systems pharmacology model for Alzheimer’s disease to predict the effect of aducanumab on brain amyloid.
Lin et al. teaches calibrating a model to describe total Amyloid Beta (Aβ) data with i.v. administration of aducanumab across a range of doses from 0.3 mg/kg to 60 mg/kg from the SAD study in patients with AD (page 4, column 2).
Claims 3 and 12 are directed to using a pharmacokinetic model to predict a time course of a concentration of an active ingredient in the treatment in plasma, wherein the prediction of the local amyloid beta is based on at least one predicted concentration of the active ingredient in the predicted time course.
Lin et al. teaches the model was also calibrated to adequately describe aducanumab plasma PK (Figure 2a), CSF to plasma drug concentration ratio at steady state (Figure S2), and total Aβ data (Figure 2b) with i.v. administration of aducanumab across a range of doses from 0.3 mg/kg to 60 mg/kg (page 4, column 2).
Claims 4 and 13 are directed to predicting the level of local amyloid beta by estimating a baseline level of local amyloid beta; calculating a rate of removal of the local amyloid beta based on the dosage of an active ingredient and the baseline level of local amyloid beta; and predicting the level of local amyloid beta based on the calculated rate of removal of the local amyloid beta.
Lin et al. teaches calibrating the model outputs to match the baseline concentrations of different Aβ species (page 4, column 2); using the final model to simulate plaque reduction with long-term treatment of 1–10 mg/kg aducanumab q4w up to 10 years (page 5, column 2), resulting in plaque reduction from baseline (page 6, column 1); and leveraging the model to predict Aβ oligomer changes with 10-year treatment of aducanumab (page 6, column 2).
Claims 5 and 14 are directed to predicting the level of local amyloid beta with a method that includes solving a pharmacodynamic differential equation that assumes a rate of change of amyloid beta is proportional to a product between concentrations of the active ingredient in the subject and local amyloid beta levels.
Lin et al. teaches the following differential equation within Supplementary Materials 3, that shows the rate of change of local amyloid beta (Abetaplasma) being proportional to variables including the product of the concentration of the active ingredient (mABplasma) and local amyloid beta (Abetaplasma), as shown below (page 12, eq. 8):
PNG
media_image1.png
145
901
media_image1.png
Greyscale
Therefore Sperling et al. teaches exploring the relationships between identified dosages of an anti-amyloid beta monoclonal antibody, amyloid-related imaging abnormalities (ARIA) manifested as hyperintensities on T2-weighted fluid attenuated inversion recovery (FLAIR) images, and vascular wall disturbances. Lin et al. teaches building quantitative models that predict the level of local amyloid beta using two identical parameters. As such, it would be obvious to one of ordinary skill in the art to combine the prior art elements with each element merely performing the same function as they do separately with an expectation of predictable results.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sperling et al. (IDS filed 05/03/2023; NPL; cite no. 32; 2011) in view of Lin et al. (Pharmaco & Systems Pharmacology; Vol. 11: 3; 2022), as applied to claims 1-5, 10-14, and 19-20 above, and in further view of Baron et al. (J Cereb Blood Flow & Metab; Vol. 34; 2014) and Vernhet et al. (Alzheimer's Dement; Vol. 16; 2020).
Sperling et al. in view of Lin et al. teach modelling dosages of an anti-amyloid beta monoclonal antibody, ARIA, and vascular wall disturbances, as described above.
Claims 6 and 15 are directed to wherein predicting the level of vascular wall disturbance includes solving a differential equation that includes the level of local amyloid beta.
Sperling et al. in view of Lin et al. do not teach solving a differential equation that includes the level of local amyloid beta.
Baron et al. describes the diagnostic utility of amyloid PET in cerebral amyloid angiopathy-related symptomatic intracerebral hemorrhage.
Baron et al. teaches by detecting β-amyloid (Aβ) in the wall of cortical arterioles, amyloid positron emission tomography (PET) imaging might help diagnose cerebral amyloid angiopathy (CAA) in patients with lobar intracerebral hemorrhage (l-ICH) (page 1, column 1); and no previous study has directly assessed the diagnostic value of 11C-Pittsburgh compound B (PiB) PET in probable CAA-related l-ICH against healthy controls (HCs) (page 1, column 1).
Baron et al. further teaches obtaining C-PiB-PET and magnetic resonance imaging (MRI) including T2* in 11 nondemented patients fulfilling the Boston criteria for probable CAA-related symptomatic l-ICH (sl-ICH), (page 1, column 1); performing quantitative analysis by optimal spatial normalization (page 1, column 1); and obtaining cerebral spinal fluid (CSF)-corrected PiB distribution volume ratios (DVRs) (page 1, column 1).
Baron et al. teaches estimating cerebral spinal fluid-corrected ROI distribution volume ratio (DVR) using the reference tissue Logan graphical method (page 3, column 1); and concluding despite the poor specificity of PiB for CAA, our finding that almost all CAA patients were PiB+, particularly with the quantitative analysis, indicate that PiB imaging has high sensitivity for CAA.
Though Baron et al. teaches using Logan graphical modelling, Baron et al. does not explicitly teach using differential equations to model the variables.
Vernhet et al. describes modelling early accumulation of amyloid with differential equations.
Vernhet et al. teaches the rate of overall amyloid burden is closely associated with baseline level of amyloid; developing a formal mathematical model capturing this phenomenon (page 1, column 1); and finding that an autonomous ordinary differential equation allows for a good explanatory model of the PiB PET-DVR (distribution volume ratio) data (page 1, column 1).
Therefore Baron et al. teaches predicting vascular wall disturbance, in the form of cerebral amyloid angiopathy and lobar intracerebral hemorrhages by quantitative analysis of the PiB distribution volume ratio that includes, local amyloid beta levels, measured by PiB PET imaging. Vernhet et al. provides sufficient to model PiB PET DVRs using differential equations. As such, it would be obvious to one of ordinary skill in the art to use differential equations to predict vascular wall disturbances, in a model that incorporates local amyloid beta levels.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sperling et al. (IDS filed 05/03/2023; NPL; cite no. 32; 2011) in view of Lin et al. (Pharmaco & Systems Pharmacology; Vol. 11: 3; 2022), as applied to claims 1-5, 10-14, and 19-20 previously, and in further view of Ketter et al. (Journal of Alzheimer's Disease; Vol. 57; 2017) and Jack et al. (The Lancet Neurology; Vol. 9; 2010).
Sperling et al. in view of Lin et al. teach modelling dosages of an anti-amyloid beta monoclonal antibody, ARIA, and vascular wall disturbances, as described previously.
Claims 7 and 16 are directed to wherein predicting the severity of ARIA-E includes solving an algebraic equation that assumes a non-linear relationship between the level of vascular wall disturbance and the Barkhof Grand Total Score (BGTS).
Sperling et al. further teaches increased signal intensity on FLAIR images, associated with ARIA-E (page 17, column 1), may represent leakage or effusion of proteinaceous fluid from meningeal vessels; and movement of amyloid into cerebral vessel walls might also relate to increased incident microhemorrhages (mH), if the vessel wall integrity is sufficiently impaired to permit small amounts of red blood cell passage (page 9, column 1).
Therefore Sperling et al. teaches ARIA-E, which severity can be measured with a Barkhof Grand Total score, may be related to vascular wall disturbances.
Sperling et al. in view of Lin et al. do not teach predicting the severity of ARIA-E, while assuming a non-linear relationship between vascular wall disturbance and the Barkhof Grand Total Score.
Ketter et al. reviews Amyloid-related imaging abnormalities (ARIA) in Two Phase III clinical trials of bapineuzumab in mild-to-moderate Alzheimer’s Disease patients.
Ketter et al. teaches objectives of generating a MRI Final Read include: 1) characterizing ARIA-E radiologic severity; and 3) characterizing the relationship of incident hemosiderin deposits (HDs), with ARIA-E (page 4, column 2).
Ketter et al. teaches rating the radiologic severity of ARIA with a total maximum score based on summing across the 12 regional scores, ranging from 0–60 (page 4, column 2); and recording the numbers of definite and possible small hemosiderin deposits (HDs), microhemorrhages (mHs), and large HDs detected as foci of decreased signal on T2*-weighted gradient recalled echo (GRE) MRI sequence for each brain region.
Ketter et al. does not explicitly teach solving an algebraic equation that assumes a non-linear relationship between the Total Score and vascular wall disturbance variables.
Jack et al. describes hypothetical modelling dynamic biomarkers of the Alzheimer's pathological cascade.
Jack et al. teaches focusing on the five most widely studied biomarkers of AD pathology, including: decreased CSF Aβ42, increased CSF tau, decreased fluorodeoxyglucose uptake on PET (FDG-PET), PET amyloid imaging, and structural MRI measures of cerebral atrophy (page 2, column 2); and finding rates of change in each biomarker change over time to follow a non-linear time course, which we hypothesise to be sigmoid shaped (page 5, column 1).
Jack et al. further teaches non-linearity has been clearly shown in MRI studies, in which atrophy rates accelerate as patients approach clinical dementia (page 5, column 1); a sigmoid shape as a function of time implies that the maximum effect of each biomarker varies over the course of disease progression (page 5, column 1); and comprehensive biomarker-based staging of disease in an individual at a given point in time should be possible from measures of the magnitude and slope of several different biomarkers (page 5, column 1).
Therefore Sperling et al. provides motivation for one of ordinary skill in the art to further explore and quantify the relationship between ARIA-E severity and vascular wall disturbances. Ketter et al. teaches predicting the severity of ARIA-E via quantitative analysis of vascular wall disturbances, in the form of microhemorrhages, hemosiderin deposits, and the Barkhof grand total score; and deriving the data for the variables from MRI analysis. Jack et al. teaches MRI biomarkers of Alzheimer’s disease clearly follow a non-linear rate of change over time when modelling the staging or severity of the disease. As such, Jack et al. provides sufficient motivation for one of ordinary skill in the art to predict ARIA-E severity using an equation that assumes a non-linear relationship between the MRI-derived biomarkers of vascular wall disturbance and Barkhof Grand Total Score (BGTS).
Claims 8-9 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sperling et al. (IDS filed 05/03/2023; NPL; cite no. 32; 2011) in view of Lin et al. (Pharmaco & Systems Pharmacology; Vol. 11: 3; 2022), as applied to claims 1-5, 10-14, and 19-20 previously, and in further view of Cummings et al. (J Prev Alz Dis; Vol. 9: 2; 2022).
Sperling et al. in view of Lin et al. teach modelling dosages of an anti-amyloid beta monoclonal antibody, ARIA, and vascular wall disturbances, as described previously.
Claims 8 and 17 are directed to identifying a potential schedule for monitoring ARIA events based on the predicted severity of ARIA, wherein the result characterizes the potential schedule.
Sperling et al. in view of Lin et al. do not teach identifying a potential schedule for monitoring ARIA events, based on severity.
Cummings et al. describes appropriate use and recommendations of aducanumab.
Cummings et al. teaches if ARIA is detected, specific management strategies are indicated; if ARIA is of mild radiographic severity, dosing can continue with monthly MRI to detect any worsening; If the asymptomatic ARIA is moderate or severe or if the mild ARIA progresses to become moderate or severe, we recommend that dosing be interrupted, and MRI repeated monthly (page 6, column 1); and in view of the emerging information that ARIA is most likely to occur before the 10 mg/kg dose is reached, the AUR update proposes that MRIs be obtained routinely before the 5th, 7th, 9th, and 12th doses (page 1, column 1).
Claims 9 and 18 are directed to identifying a potential recommendation of the dosage of the active ingredient for the subject based on the predicted severity of ARIA, wherein the result characterizes the potential recommendation dosage.
Cummings et al. teaches for the most severe symptomatic cases of ARIA, we recommend beginning high-dose glucocorticoid therapy; a regimen to be considered is methylprednisolone 1 gm intravenously per day for 5 days followed by oral prednisone, 60 mg per day, slowly tapered over weeks or months (page 2, table 1).
Cummings et al. further teaches if ARIA is of mild radiographic severity, aducanumab dosing can continue; if the asymptomatic ARIA is moderate or severe or if the mild ARIA progresses to become moderate or severe, we recommend that dosing be interrupted; and treatment at the same dose the patient was receiving when the dosing was postponed may be re-initiated once ARIA-E resolves or ARIA-H stabilizes (page 6, column 1).
Cummings et al. further teaches deploying resources of this type could be considered by those developing monoclonal antibodies (page 7, column 1).
Therefore Cummings et al. teaches monitoring and dosing recommendations based on ARIA severity; and provides sufficient motivation for one of ordinary skill in the art to combine the methods with those of other monoclonal antibody protocols, such as that of Sperling et al. in view of Lin et al., with predictable results and each element merely performing the same function as they do separately.
Conclusion
No claims are currently allowed.
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Milana Thompson whose telephone number is (571)272-8740. The examiner can normally be reached Monday - Friday, 9:00-6:00 ET.
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, Karlheinz Skowronek can be reached at (571) 272-1113. 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.
/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
Prosecution Insights