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 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.
Claim(s) 23-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over McKim (US 2007/0218457) in view of Koh et al. (WO 2019/035766).
Regarding to claim 23:
McKim discloses a computer-implemented method comprising:
obtaining a two-dimensional (2D) hepatocyte model on test wells of a multi-well plate, wherein the 2D hepatocyte model in each test well of a plurality of test wells on the multi-well plate is treated with at least one training molecule selected from a plurality of training molecules (paragraphs [0142]-[0143]: The cells are seeded in multiwell plates. Various concentration of the compounds being tested are added to the media and the cells);
obtaining biochemical readouts of the 2D hepatocyte model from a biochemical assay
applied to the multi-well plate, each biochemical readout representing a biochemical response of the 2D hepatocyte model to the training molecule applied to the corresponding test well (paragraph [0235]: The test compound is evaluated in primary hepatocyte via one or two biochemical markers for cell health).
Mckim however does not disclose obtaining images of cellular features of the two-dimensional (2D) hepatocyte model on test wells of a multi-well plate, and applying an image-based model to the images of the cellular features of the 2D hepatocyte model to obtain a cellular output for each image representing a cellular response of the 2D hepatocyte model to the training molecule applied to the corresponding test well, generating a plurality of training examples, each training example derived from one test well and comprising data identifying the training molecule applied to the test well, the cellular output for the test well from the image-based model, and the biochemical readout for the test well from the biochemical assay, and training a prediction model as a neural network with the plurality of training examples, wherein the prediction model is configured to input data identifying a test compound and to output a predicted measure of a toxic effect of the test compound, the cellular output for the test well from the image-based model.
Koh discloses a method for measuring drug response comprising obtaining images of cellular features of a two-dimensional (2D) model on test wells of a multi-well plate (FIG. 19, elements 1902, 1904, 1906. Paragraph [0094]: Acquisition 1904 of bright-field images 1906 was obtained. FIG. 3 and paragraph [0016]: FIG. 3 shows a brightfield image of spheroids grown in an ultra-low attachment 384-well plate), and applying an image-based model to the images of the cellular features of the 2D model to obtain a cellular output for each image representing a cellular response of the 2D model to a training molecule applied to the corresponding test well (FIG. 19: The model of the data preprocessing such as “Spheroid Peeling” processes imaging data (1904-1906) to output image descriptors 1916), further comprising generating a plurality of training examples, each training example derived from one test well and comprising data identifying the training molecule applied to the test well, the cellular output for the test well from the image-based model (FIG. 19: The image descriptors 1916 are for training supervised learning models 1920), and the biochemical readout for the test well from the biochemical assay (FIG. 19, element 1918: The output from viability measurement), and training a prediction model as a neural network with the plurality of training examples (FIG. 19, element 1920: Artificial Neural Network), wherein the prediction model is configured to input data identifying a test compound and to output a predicted measure of a drug response of the test compound (FIG. 19:The training of the neural network 1920 forming the drug response model 1922).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify McKim’s method to include obtaining images of cellular features of the two-dimensional (2D) model on the test wells of the multi-well plate to provide the cellular response for further training a neural network to form a learning model for predicting the drug/compound as taught by Koh (FIG. 19).
McKim further teaches the following claims:
Regarding to claim 24: wherein the 2D hepatocyte model comprises a culture of primary human hepatocytes (paragraph [0137]: The cell is a human liver, a human hepatic).
Regarding to claims 25-26: wherein the images of cellular features are obtained by staining one or more cellular structures selected from: DNA, endoplasmic reticulum (ER), plasma membrane, RNA, Golgi apparatus, mitochondria, and lysosomes, wherein each cellular structure is stained with a distinct fluorescent dye (paragraph [0162]: Mitochondrial function can be used as an indicator of cytotoxicity and cell proliferation. Paragraph [0265]: Cell proliferation in each well was measured with propidium iodide. This specific nucleic acid binding dye fluoresces when interacts within the nucleic acids).
Response to Arguments
Applicant's arguments filed 4/15/2026 have been fully considered but they are not persuasive.
In response to Applicant’s Remarks, the Examiner cites that, in Koh’s FIG. 19, the technique of “Spheroid Peeling” refers to a process of exposing the spheroid layer by layer for analyzing 3D tumor spheroid as a whole. As a result, the spheroid layers are sequentially exposed (by peeling) and studied via 2D imaging of each layer. “Spheroid Peeling” in Koh is thus considered as a 2D imaging to be used for studying a 3D spheroid.
Furthermore, Koh in FIG. 19 also teaches that the measure viability 1918 to provide biochemical readouts and the imaging to output images (1904, 1910) are from the same multi-well plate 1902, and the outputs are used for building the drug response model 1922 via the supervised learning training 1920.
Conclusion
THIS ACTION IS MADE FINAL. 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 LAM S NGUYEN whose telephone number is (571)272-2151.
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/LAM S NGUYEN/ Primary Examiner, Art Unit 2853
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