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 05/18/26 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/18/26 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 13 is objected to under 37 CFR 1.75 as being a substantial duplicate of claim 10. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 lines 5-7 recite “wherein the plurality of wells in the one or more plates to receive biological cells” (emphasis added by Examiner). It appears this is a typo and the word “to” should be removed.
Claims 2-13 are rejected due to their dependency on claim 1.
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 1-2, 10, 13-15 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Recursion Pharmaceuticals WO 2020/190585 (hereinafter “Recursion”, cited in the IDS) in view of Dodkins et al. A Rapid, High Throughput, Viral Infectivity Assay using Automated Brightfield Microscopy with Machine Learning (hereinafter “Dodkins”, cited in the IDS).
Regarding claim 1, Recursion discloses a method for drug discovery assays using one or more artificial intelligence (Al) models (0017-0020, 0122), the
method comprising: receiving an assay layout defining one or more positive
phenotype controls, at least one negative control, a plurality of drug compounds, a plurality of drug concentrations and their replicates in a plurality of wells of one or more plates, wherein the plurality of wells in the one or more plates to receive biological cells, drug compounds at specified concentrations, drug solvents, and/or carriers (see fig. 2A-2C, par 0038 "a screening evaluation module 214, e.g., for evaluating the effects of a perturbation and/or candidate therapeutic compound on a
cell context; • a feature measurement database 220, e.g., for storing assay data sets 222 that include one or more of plate control data 224 (e.g., plate control features measurements 226), assay control data 228 (e.g., assay
control features measurements 230), and test data 232 (e.g., test features measurements 234); • a vector database 240, e.g., for storing assay vectors set 242 that include one or more of plate control vectors 244 (e.g., perturbation vectors 246), assay control vectors 248 (e.g., perturbation vectors 250), and test vectors 252 (e.g., perturbation vectors 254);" specific concentrations: see
par 00144; positive and negative controls are in every assay: see par 0161, 0163);
receiving one or more images of each of the plurality of wells in the one or more plates, wherein each image includes a plurality of tiles or one or more sub-image
regions (see Fig. 3 (306), par 0043 "In some embodiments, referring to Figure 3, these feature measurements are acquired by capturing images 306 of the multi-well plates using, for example, epifluorescence microscopy using an
epifluorescence microscope 304. The images 306 are then used as a basis for obtaining the measurements of the different features from each of the wells in the multi-well plates");
training one or more
trained (see par 0033 "Referring to Figure 1, the present disclosure provides a method 100 for evaluating an effect of one or more perturbations and/or therapeutic candidate compounds on cells. The method includes obtaining (102)
feature data from a first set of control states and a first set of test states, e.g., which may or may not also include a therapeutic candidate compound. Each control state in the set of control states and each test state in the set of
test states includes a common cellular context. The method then includes training (104) a variability model based on feature data from first set of control states. As described below, in some embodiments, the variability model is based
on only a subset of all features measured from the control states. Likewise, in some embodiments, the feature values used to train the variability model are normalized, standardized, and/or centered based on feature measurements
from a separate set of control states (e.g., that is specific to the multi-well plate from which the control state was located). ", 0034 "The method then includes
evaluating (108) one or more screening conditions (e.g., the effect of a perturbation and/or candidate therapeutic compound on a cellular context) within the mathematical space defined by the trained variability model. Multiple
iterations of subsequent screening steps 110 and 112 can be performed. ") ;
Recursion does not explicitly disclose the training of the second all-control AI model and the evaluation of the two models.
As the two models have not been clearly defined in claim 1, i.e. there appears to
be no difference in input (positive and negative controls) nor in the output
(probabilities of an input image being one of the positive or negative), it appears
to be merely the use of several AI models in order to distinguish which one
performs better. This is however well-known in the art and does not need the
use of inventive activity by the skilled person (see also for example Dodkins, fig. 1,
pages 4-6, 8).
Recursion and Dodkins are analogous art because they are from the same field of endeavor of machine learning for viral assays.
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine Recursion and Dodkins to use several AI models in order to distinguish which one performs better. The motivation would be to determine the best AI model.
Regarding claim 2, Dodkins discloses generating one or more visual representations of the set of probabilities to evaluate the phenotypes induced by the plurality of drug compounds and drug concentrations for their similarity to the control phenotypes (see figures 3 and 4).
Regarding claim 10, Dodkins discloses all sample images participate in training of the AI models and prediction with the AI models using N fold cross validation (see page 13 fourth paragraph to page 14 third paragraph).
Claim 13 is similarly analyzed to claim 10.
Claim 14 is similarly analyzed to claim 1.
Claim 15 is similarly analyzed to claim 2.
Claim 21 is similarly analyzed to claim 2.
Allowable Subject Matter
Claims 3-9, 11-12, 16-20, and 22-24 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 (note the 112 rejections must also be overcome).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN B STREGE whose telephone number is (571)272-7457. The examiner can normally be reached M-F 9-5 (PST).
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, Chan Park can be reached at (571)272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN B STREGE/Primary Examiner, Art Unit 2669
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