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
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 13 are rejected under 35 U.S.C. 102(a)(1) based upon a public use or sale or other public availability of the invention by Cao et al. (“Establishment of morphological atlas of Caenorhabditis elegans embryo with cellular resolution using deep-learning-based 4D segmentation”, bioRxiv preprint doi: https://doi.org/10.1101/797688; this version posted October 8, 2019).
With respect to claim 1, Cao et al. teach a method for generating a morphological atlas of an embryo comprising the steps of (pages 2-3, I. Introduction): receiving a plurality of 3D images of the embryo representative of the morphological process of embryonic cells from a first predetermined cell population to a second predetermined cell population (page 2, we generate a time-lapse framework of cellular shape and migration for C. elegans embryos from 4- to 350-cell stage ; pages 7-8; Dataset);
processing the plurality of 3D images to derive nucleus lineage information associated with each nucleus of the embryonic cells during the morphological process (Fig. 4, cell nucleus lineage);
performing a membrane segmentation procedure to segment the 3D images into membrane segments (Fig. 4, membrane image, automatic segmentation); and
combining the nucleus lineage information and the membrane segments to generate the morphological atlas of the embryo (Fig. 4, cell shape lineage).
Claim 13 is rejected as same reason as claim 1 above.
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 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 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claim 2, 3, 14-20 are rejected under 35 USC 103 as being unpatentable over Cao et al. (“Establishment of morphological atlas of Caenorhabditis elegans embryo with cellular resolution using deep-learning-based 4D segmentation”, bioRxiv preprint doi: https://doi.org/10.1101/797688; this version posted October 8, 2019) in view of Hatamizadeh et al. (“UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation”, arXiv:2204.00631v2 [eess.IV] 5 Apr 2022).
With respect to claim 2, Cao et al. teach all the limitations of claim 1 as applied above from which claim 2 respectively depend.
Cao et al. do not teach expressly that the membrane segmentation procedure includes a machine learning processor comprising an UNet Transformer (TUNETr) arranged to be pre-trained by annotated images representing a range of embryonic developmental stages and imaging conditions.
Hatamizadeh et al. teach medical image segmentation procedure includes a machine learning processor comprising an UNet Transformer arranged to be pre-trained by annotated medical images (Introduction, we propose a unified framework consisting of hybrid and transformer-based architectures, referred to as UNetFormer and UNetFormer+, which utilize a 3D Swin Transformer as the encoder and CNN-based and Swin Transformer-based decoders respectively).
At the time of effective filing, it would have been obvious to a person of ordinary skill in the art to try well known method to segment membrane in the method of Cao et al..
The suggestion/motivation for doing so would have been that has a better performance by directly utilizing 3D tokens.
Therefore, it would have been obvious to combine Hatamizadeh et al. with Cao et al. to obtain the invention as specified in claim 2.
With respect to claim 3, Hatamizadeh et al. teach UNetFormer adapted to utilize the capability of a large deep neural network for convoluting voxels of 3D volumes with corresponding neighboring voxels. (Introduction, we propose a unified framework consisting of hybrid and transformer-based architectures, referred to as UNetFormer and UNetFormer+, which utilize a 3D Swin Transformer as the encoder and CNN-based and Swin Transformer-based decoders respectively).
With respect to claim 14, Hatamizadeh et al. teach the Encoding Module comprises a Swin Transformer Convolution Encoder Module (Introduction, we propose a unified framework consisting of hybrid and transformer-based architectures, referred to as UNetFormer and UNetFormer+, which utilize a 3D Swin Transformer as the encoder and CNN-based and Swin Transformer-based decoders respectively).
With respect to claim 15, Hatamizadeh et al. teach the Swin Transformer Convolution Encoder Module comprises a Patch Partition layer, a Linear Embedding layer, and a plurality of Swin Transformer blocks (STBs) and Linear Patch Merging (LPM) layers. (page 2-3, Fig. 1, 3D tokens are evenly partitioned into non-overlapping regions, Patch Embed, transformer, Patch Merge).
With respect to claim 16, Hatamizadeh et al. teach each of the Swin Transformer blocks is immediately followed by a Linear Patch Merging (LPM) layer (page 2-3, 2.1 Model Architecture, Fig. 1)
With respect to claim 17, Hatamizadeh et al. teach the Encoder Module comprises four Swin Transformer blocks and four Linear Patch Merging layers (page 2-3, 2.1 Model Architecture, Fig. 1).
With respect to claim 18, Hatamizadeh et al. teach the Decoder Module comprises a plurality of Convolution Residual Regression Modules, wherein each of the Convolution Residual Regression Modules is adapted to take an output from a corresponding layer in the Encoder Module and added to an output from the previous layer of Decoder Module. (page 2-3, 2.1 Model Architecture, Fig. 1, CNN-based and transformer-based).
With respect to claim 19, Hatamizadeh et al. teach each of the Swin Transformer blocks comprises one or more consecutive Transformer Blocks (page 2-3, 2.1 Model Architecture, Fig. 1).
With respect to claim 20, Hatamizadeh et al. teach each of the Transformer Blocks comprises a shifted window based multi-headed self-attention (MSA) module and a Multilayer Perceptron (MLP) layer, wherein each MSA module and MLP layer has a Layer Normalisation (LN) layer is applied therebefore, and a residual connection is applied after each module. (page 2-4, 2.1 Model Architecture).
Allowable Subject Matter
1. Claims 4-12 are objected to as being dependent upon a rejected base claim, but would be allowable of rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Randolph Chu whose telephone number is 571-270-1145. The examiner can normally be reached on Monday to Thursday from 7:30 am - 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached on (571) 272-7778.
The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RANDOLPH I CHU/
Primary Examiner, Art Unit 2663
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