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 .
DETAILED ACTION
This communication is a non-Final office action in merits. Claims 1-18, are presently pending and have been elected and considered below.
Request for Continued Examination
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 7/8/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/24/2024, 1/9/2025, and 7/8/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over “A Deep Learning-Based Model of Normal Histology”, Sing et al. BioRxiv, December 5, 2019, an IDS submission, (hereinafter Sing) in view of US 2019/0228037 A1, Nicol et al. (hereinafter Nicol).
As to claim 1, Sing discloses a method for training and using a neural network for medical image analysis using mammalian transfer learning, the method comprising the steps of:
a) pre-processing a plurality of data sets (page 11, par 3, test set parches being created and saved before the learning process), each comprising labeled image data associated with a species, in a standardized format (page 3, pars 3-5; page 5, last par, mammalian tissues being recognized by standard convolutional neural networks trained on small patches of input image for cross-species predictions and transfer learning; page 9, par 2 from bottom; page 10, par 3, labeled data);
b) segmenting the stored data to define image chunks, wherein the size of each of the image chunks is selected to obfuscate a species of the source of the image while still being large enough to classify features (page 2, par 3; page 4, par 3; page 6, par 2 from bottom; page 9, par 4; page 10, par 3, data segmentation with appropriate patch/chunk size);
c) using the image chunks to train a neural network (see citations in b);
d) saving the model parameters resulting from the training (page 11, par 2, save training dataset and training model (e.g. parameters/weights);
e) until the saved model parameters yield sufficient accuracy, performing zero or more subsequent epochs of training on additional image chunks (page 3, pars 3-5, upon reaching sufficient accuracy level of training, no subsequent trainings required);
applications comprises applications that differ in at least two of:
i. species, ii. set of class labels used (page 4, pars 2-3; page 5, last par; page 6, par 1, cross-species classification without retraining),
iii. data modality, and iv. scan type.
Sing does not expressly disclose f) when the saved model parameters yield sufficient accuracy, saving a checkpoint of the neural network;
g) using the checkpointed neural network to process input data from any of a first plurality of applications and provide output data to any of a second plurality of applications.
Nicol, in the same or similar field of endeavor, further teaches when the saved model parameters yield sufficient accuracy, saving a checkpoint of the neural network (Figs 1-2; pars 0026, 0030, 0033-0034, 0038-0039, checkpoint established and stored);
and using the checkpointed neural network to process input data from any of a first plurality of applications and provide output data to any of a second plurality of applications (pars 0026, 0030, 0033-0034, 0078, stored weights at checkpoint being applied to future use).
Therefore, consider Sing and Nicol’s teachings as a whole, it would have been obvious to one of skill in the art before the filing date of invention to incorporate Nicol’s teachings in Sing’s method to utilized well trained model or system for more applications without involving additional training burden.
As to claim 2, Sing as modified discloses the method of claim 1, further comprising, between the saving and performing steps, the step of: d1) using an optimizer to fine tune performance of the neural network based on the saved model parameters (Nicol: pars 0030, 0033, 0050-0051, weights adjustment).
As to claim 3, Sing as modified discloses the method of claim 2, further comprising, after the using step, the step of: d2) determining and saving one or more parameters improved by the optimizer; wherein the performing step uses the saved, optimized parameters ((Nicol: pars 0064, 0091).
As to claim 4, Sing as modified discloses the method of claim 1, wherein labels associated with each image chunk each comprise one of: a set of per-pixel characteristics that describe pixels of the chunk (Sing: Abstract; page 3, last par; page 6, last par); or a set of per-voxel characteristics that describe voxels of the chunk.
As to claims 5-9, input/out data being associated with different species including first, second, and/or third species. Sing as modified teaches cross-species predictions and transfer learning which inherently include a plurality of species as test dataset for training (Sing: page 3, par 5; page 5, last par – page 6, par).
As to claim 10, it is a system claim encompassed claim 1. Rejection of claim 1 is therefore incorporated herein.
As to claims 11-18, they are rejected with the same reason as set forth in claims 2-9, respectively.
Examiner’s Note
Examiner has cited particular column, line number, paragraphs and/or figure(s) in the reference(s) as applied to the claims for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the reference(s) in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUN SHEN whose telephone number is (571)270-7927. The examiner can normally be reached on Mon-Fri 8:30-5:50 PT.
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/QUN SHEN/
Primary Examiner, Art Unit 2662
Prosecution Insights