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 .
All amendments filed on 2/5/26 have been entered and the action follows:
Response to Arguments
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 123-140 and 142 are rejected under 35 U.S.C. 103 as being unpatentable over Inoue et al (US Pub. 2022/0215543) in view of CONAN: Complementary pattern augmentation for rare disease detection, by Cui et al. and Martin et al (US Pub. 2024/0221360).
With respect to claim 123, Inoue discloses A method comprising:
obtaining or having obtained one or more cells of a common state; capturing a plurality of images corresponding to the one or more cells, (see figure 3, steps S100 and S102); and
analyzing the plurality of images using a predictive model to predict a presence or absence of a known disease state for the one or more cells, the predictive model trained to distinguish between morphological profiles of healthy cells and cells in a known disease state, (see figure 3, step S104, the prediction model “a predictive model”, and paragraph 0008, wherein … which information indicating at least a neurodegenerative disease is associated with an image obtained by imaging cells of the neurodegenerative disease differentiated from pluripotent stem cells, and predict onset of the neurodegenerative disease of the subject or effects of drugs on the neurodegenerative disease, based on output results of the model to which the images were input),
wherein the predictive model is trained using training data generated from at least one cohort of [synthetically] pooled cells of the known disease state, (see paragraph 0008, wherein … model trained on data “training data” in which information indicating at least a neurodegenerative disease is associated with …imaging cells of the neurodegenerative disease “training data generated from at least one cohort of neurodegenerative disease of the subject or effects of drugs on the neurodegenerative disease…),
However, Inoue fails to explicitly disclose the predictive model is trained using training data generated from at least one cohort of synthetically pooled cells of the known disease state, (emphasis added) as claimed.
Cui teaches generated from at least one cohort of synthetically pooled cells of the known disease state, (see page 615, right hand column, wherein …GANs have shown superior performance in image generation, and also demonstrated initial success in generating synthetic patient data to address the data limitation..), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of disease detection using image analysis. The teaching of Cui to generate synthetic patient data for the training purpose can be incorporated into Inoue’s system as suggested (see Inoue figure 6 step S200 generate training data), for suggestion, and modifying the system yields a better model that is trained on more training data, for motivation.
Martin teaches wherein the at least one cohort of synthetically pooled cells is formed by in silico combining embeddings or fixed feature vectors of randomly selected single cells, (see paragraph 0053, wherein …each synthetic singleplex image of the set of synthetic singleplex images, the image-processing system “in silico combining embeddings or fixed feature vectors of randomly selected single cells” can apply a machine-learning model to the synthetic singleplex image to predict a phenotype of each detected cell depicted in the synthetic singleplex image, in which the phenotype relates to a corresponding type of biomarker. In some instances, the machine-learning model is trained to process a first synthetic singleplex image that depict cells stained for a first type of biomarker, and a different machine-learning model is trained to process a second synthetic singleplex image stained for a second type of biomarker), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of disease detection using image analysis. The teaching of Martin to generate synthetic data using the image processing can be incorporated into Inoue and Cui’s system as suggested (see Inoue figure 6 step S200 generate training data), for suggestion, and modifying the system yields a better model that is trained on more training data, for motivation.
With respect to claim 124, combination of Inoue, Cui and Martin further discloses wherein: the at least one cohort of synthetically pooled cells are combined from a plurality of sources, which causes source-specific variations to be smoothened and state-specific features to be highlighted when training the predictive model, the at least one cohort of synthetically pooled cells is built by randomly selecting the number of single cells or randomly selecting a number of tiles, the embeddings or fixed feature vectors of the randomly selected single cells are in silico combined without physically pooling together the randomly selected single cells, and the combining comprises averaging the embeddings or fixed feature vectors of the randomly selected single cells, or the plurality of cell lines are obtained from different subjects of the known disease state or healthy state, (see Inoue figure 6 and paragraph 0008, and 0072, the model trained on data in which information indicating at least a disease differentiated from pluripotent stem cells, and paragraph 0051-0053 for a plurality of regions R such as 128x128 pixels are created), as claimed.
With respect to claim 125, combination of Inoue, Cui and Martin further discloses wherein the predictive model trained to distinguish between the morphological profiles of healthy cells and cells in the known disease state achieves an AUC of at least 0.95 or an accuracy of at least 0.88, (see Inoue paragraph 0008, wherein …program capable of accurately “accuracy of at least 0.88” predicting future diseases of a subject based on images of cells differentiated from pluripotent stem cells derived from the subject), as claimed.
With respect to claim 126, combination of Inoue, Cui and Martin further discloses wherein the predictive model is trained by: capturing a plurality of images corresponding to the randomly selected single cells of the known disease state or healthy state; and using the plurality of images corresponding to the randomly selected single cells of the known disease state or healthy state to train the predictive model to distinguish between the morphological profiles of cells of the known disease state and cells of the healthy state, wherein using the plurality of images corresponding to the randomly selected single cells of the known disease state or healthy state to train the predictive model further comprises averaging embeddings of the plurality of images corresponding to the randomly selected single cells of the known disease state or healthy state, (see Inoue paragraph 0066-0068 for training and paragraph 0112), as claimed.
With respect to claim 127, combination of Inoue, Cui and Martin further discloses wherein: the one or more cells of a common state comprise cells of a single cell line from a single subject, the predictive model is trained to predict the presence or absence of the known disease state with a prediction probability, or the healthy cells or the cells in the known disease state serve as a reference ground truth for training the predictive model, (see Inoue paragraph 0067 for generating the training data; and paragraph 0074, the error is computed between the output of the model and the reference “ground truth”), as claimed.
With respect to claim 128, combination of Inoue, Cui and Martin further discloses wherein, to distinguish between the morphological profiles of healthy cells and cells in the known disease state for the one or more cells of a common state, the predictive model is trained to compare an averaged embedding of the one or more cells of a common state to an averaged embedding of the plurality of images corresponding to the randomly selected single cells of the known disease state or healthy state, (see Inoue paragraph 0112), as claimed.
With respect to claim 129, combination of Inoue, Cui and Martin further discloses prior to capturing the plurality of images corresponding to the one or more cells of a common state, providing a perturbation to the one or more cells of a common state, the perturbation causing the one or more cells from a known disease state to an unknown disease state; subsequent to analyzing the plurality of images of the one or more cells of a common state, comparing the predicted state of the one or more cells to the known disease state of the one or more cells known before providing the perturbation; and based on the comparison, identifying the perturbation as having one of a therapeutic effect, a detrimental effect, or no effect, (see Inoue paragraph 0091, for administering an agent prior to taking images for analysis also see paragraph 0133), as claimed.
With respect to claim 130, combination of Inoue, Cui and Martin further discloses prior wherein: the predictive model is one of a neural network, random forest, or regression model, (see Inoue paragraph 0055, wherein …. prediction model MDL …a convolutional neural network (CNN)), as claimed.
With respect to claim 131, combination of Inoue, Cui and Martin further discloses wherein: each of the morphological profiles comprises values of imaging features or comprise a transformed representation of images that define a known disease state or a healthy state of a cell, (see Inoue paragraph 0082, wherein …in the present embodiment, since the prediction model MDL including the plurality of models …it is possible to expect …changes in the cell structure and relative positional relationships between the cells…), as claimed.
With respect to claim 132, combination of Inoue, Cui and Martin further discloses wherein each cell in the one or more cells of a common state is one of a stem cell, a partially differentiated cell, or a terminally differentiated cell, (see Inoue paragraph 0038, wherein …stem cells…), as claimed.
With respect to claim 133, combination of Inoue, Cui and Martin further discloses wherein each cell in the one or more cells of a common state is a somatic cell selected from a fibroblast or a peripheral blood mononuclear cell (PBMC), (see Inoue paragraph 0108, wherein … peripheral blood mononuclear cell…), as claimed.
With respect to claim 134, combination of Inoue, Cui and Martin further discloses wherein the one or more cells of a common state are obtained from a subject through a tissue biopsy or blood draw, (see Inoue paragraph 0108, wherein … peripheral blood mononuclear cell “blood draw”…), as claimed.
With respect to claim 135, combination of Inoue, Cui and Martin further discloses wherein the morphological profile is extracted from a layer of a penultimate deep learning neural network, (see Inoue paragraph 0058), as claimed.
With respect to claim 136, combination of Inoue, Cui and Martin further discloses prior to capturing the plurality of images corresponding to the one or more cells of a common state, staining or having stained the one or more cells of a common state using one or more fluorescent dyes, (see Inoue paragraph 0112), as claimed.
With respect to claim 137, combination of Inoue, Cui and Martin further discloses wherein: at least 5 or 30 cell features derive from fluorescently labeled biomarkers identifying plasma membrane, at least 5 or 25 cell features derive from fluorescently labeled biomarkers identifying cell nucleus, at least 5 or 10 cell features derive from fluorescently labeled biomarkers identifying endoplasmic reticulum, at least 5 or 35 cell features derive from fluorescently labeled biomarkers identifying mitochondria, at least 5 or 10 cell features derive from fluorescently labeled biomarkers identifying RNA, or at least 20 or 60 correlated cell features derive from various fluorescence channels, (see Inoue paragraph 0112), as claimed.
With respect to claim 138, combination of Inoue, Cui and Martin further discloses each of the plurality of images corresponding to the one or more cells of a common state corresponds to a fluorescent channel, and the steps of obtaining or having obtained the one or more cells of a common state and capturing the plurality of images corresponding to the one or more cells of a common state are performed in a high-throughput format using an automated array, (see Inoue paragraph 0129 the image of column A through column H is the green fluorescent region, and a high through put format is obvious in the art), as claimed.
With respect to claim 139, combination of Inoue, Cui and Martin further discloses a common state is one of a common disease state, a common source, a common processing state, or a common growth state, the disease state of the cell predicted by the predictive model is a classification of at least two categories, (see Inoue figure 3, S108 and S110 two categories, and see paragraph 0040 for disease cells), as claimed.
With respect to claim 140, combination of Inoue, Cui and Martin further discloses wherein the at least two categories comprise a presence or absence of a neurodegenerative disease, and the neurodegenerative disease is any one of Parkinson’s Disease (PD), Alzheimer’s Disease, Amyotrophic Lateral Sclerosis (ALS), Infantile Neuroaxonal Dystrophy (INAD), Multiple Sclerosis (MS), Amyotrophic Lateral Sclerosis (ALS), Batten Disease, Charcot-Marie-Tooth Disease (CMT), Autism, post-traumatic stress disorder (PTSD), schizophrenia, frontotemporal dementia (FTD), multiple system atrophy (MSA), and a synucleinopathy, (see Inoue figure 3 and paragraph 0040 for the diseases), as claimed.
Claim 142 is rejected for the same reasons as set forth in the rejections of claim 123, because claim 142 is claiming subject matter of similar scope as claimed in claim 123.
Allowable Subject Matter
Claim 141 is 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
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.
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663
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