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 .
This office action is in response to communication filed on 8/13/2026. Claims 1-20 are pending on this application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-4, 8-11, and 15-18 have been considered but are moot in view of the new grounds of rejection.
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.
Claim(s) 1-4, 8-11, and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gulsun et al (US20240029868) in view of He et al (US12266160) and Kim et al (“Self-supervision advances morphological profiling by unlocking powerful image representations”, bioRxiv preprint doi: https://doi.org/10.1101/2023.04.28.538691, 4/29/2023, pages 1-25, retrieved from the Internet on 5/7/2026).
Regarding claim 1, Gulsun teaches a computer-implemented method comprising:
generating a masked training microscopy representation by applying a mask to remove a portion of a training microscopy representation (para. [0043], Training images 402 comprise image patches 404 and masked patches 420); and
training a generative machine learning model (para. [0041], Encoder network 304 is trained during a prior offline or training stage) to generate microscopy representation embeddings (para. [0041], the plurality of input medical images is encoded into embeddings using a machine learning based encoder network) by:
generating, utilizing the generative machine learning model, a predicted microscopy representation from the masked training microscopy representation (para. [0043], Decoder 414 learns to decode embeddings 410 and learned embeddings 412 based on positional encoding 416 to generate reconstructions of training images 402 as reconstructed images 418).
Gulsun fails to teach generating a measure of loss between the predicted microscopy representation and the training microscopy representation; and
modifying parameters of the generative machine learning model utilizing the measure of loss.
However He teaches generating a measure of loss between a predicted representation and a training representation (col. 10 lines 29-32, The loss function may compute a mean squared error (MSE) between the reconstructed image 120 and the original/input image 102 in the pixel space); and
modifying parameters of a generative machine learning model utilizing the measure of loss (col. 9 lines 29-48, The machine-learning model may be updated based on a loss function…In particular embodiments, the model may be updated and/or evaluated only based on results corresponding to masked patches 108).
Therefore taking the combined teachings of Gulsun and He as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of He into the method of Gulsun. The motivation to combine He and Gulsun would be to reduce computations (col. 4 lines 64-67 of He).
Gulsun also fails to teach generating, utilizing the trained generative machine learning model, a plurality of microscopy representation embeddings for a plurality of microscopy representations; and
generating similarity measures between the plurality of microscopy representation embeddings by comparing the plurality of microscopy representation embeddings.
However Kim teaches generating, utilizing a trained generative machine learning model (page 3 last paragraph, a masked autoencoder (MAE) the encoder network acts as a mapping function), a plurality of microscopy representation embeddings for a plurality of microscopy representations (page 3 last paragraph, A representation learning model maps an input image x to a d-dimensional embedding space; page 4 first paragraph, At inference time, each microscopy image 𝐼, corresponding to a single field of view (FOV), was split into 224x224 image crops {𝑥", 𝑖 = 1, …, Ncrops}. All image crops were passed through the backbone or encoder to generate crop embeddings); and
generating similarity measures between the plurality of microscopy representation embeddings by comparing the plurality of microscopy representation embeddings (page 17 second paragraph, To compute perturbation mAP, k nearest neighbors of each batch-aggregated profile were queried in the representation space, and the number of neighbors with matching perturbation labels (true
positives, TP) and those with non-matching labels (false positives, FP) were counted; page 17 fourth paragraph, To calculate these metrics, pairwise distances were computed between all samples in the representation space, and for each sample, the nearest neighbor from a different batch (NSB) or with a different perturbation label (NSBP) was identified; page 18 second paragraph, Each profile is connected to its closest k neighbors with respect to the cosine distance).
Therefore taking the combined teachings of Gulsun and He with Kim as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Kim into the method of Gulsun and He. The motivation to combine Kim, He and Gulsun would be to provide significantly faster processing speed (page 3 first paragraph of Kim).
Regarding claim 2, the modified method of Gulsun teaches a computer-implemented method wherein generating the masked training microscopy representation comprises generating a masked training phenomic image by applying the mask to remove a portion of a training phenomic image (page 3 second paragraph of Kim, using MAE to extract feature representations from 5-channel Cell Painting images; page 15 last paragraph, Given a partially masked input image, MAE reconstructs the missing regions using an asymmetric encoder-decoder architecture, with a significantly smaller decoder. The masking ratio was set to 50%) portraying a cell subjected to a perturbation (page 3 first paragraph of Kim, Using the JUMPCP data, we assessed all methods on evaluation sets with both compound and genetic perturbations; page 12 first paragraph, Taken together, these findings demonstrate the applicability of self-supervised methods for analyzing multichannel microscopy images as well as their transferability across domain-relevant distribution shifts such as perturbation types).
Regarding claim 3, the modified method of Gulsun teaches a computer-implemented method wherein generating the predicted microscopy representation comprises generating, utilizing a masked autoencoder generative model, a predicted phenomic image from the masked training phenomic image (col. 4 lines 46-52 of He; abstract of Kim).
Regarding claim 4, the modified method of Gulsun teaches a computer-implemented method wherein:
generating the measure of loss comprises comparing the predicted phenomic image and the training phenomic image utilizing a loss function (col. 10 lines 29-32 of He; abstract of Kim); and
modifying the parameters comprises modifying the masked autoencoder generative model utilizing the measure of loss (col. 9 lines 29-48 of He).
Regarding claim 8, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 9, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
Regarding claim 10, the claim recites similar subject matter as claim 3 and is rejected for the same reasons as stated above.
Regarding claim 11, the claim recites similar subject matter as claim 4 and is rejected for the same reasons as stated above.
Regarding claim 15, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 16, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
Regarding claim 17, the claim recites similar subject matter as claim 3 and is rejected for the same reasons as stated above.
Regarding claim 18, the claim recites similar subject matter as claim 4 and is rejected for the same reasons as stated above.
Allowable Subject Matter
Claims 5-7, 12-14, and 19-20 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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 LEON VIET Q NGUYEN whose telephone number is (571)270-1185. The examiner can normally be reached Mon-Fri 11AM-7PM.
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/LEON VIET Q NGUYEN/Primary Examiner, Art Unit 2663
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