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 .
Status of the Claims
Claims 1-236 are canceled.
Claims 237-256 are pending.
Claims 237-256 are rejected.
Priority
The application is a continuation of application PCT/US20/67648 which has an effective filing date of 1/2/2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 2 December 2022 and 15 July 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings filed on 6/06/2022 were considered.
Response to Arguments
Applicant’s arguments with respect to claims 237-253 and 255-256 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. Examiner mistakenly omitted claim 254 from the previous office action. Claim 254 has been addressed for the first time in this office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 237-256 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations);
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 237-256 are directed to a methods, systems, and tools for image analysis of cells
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if
so, then it is determined in Prong Two whether the recited judicial exception is integrated into a
practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception,
examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a
law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 237 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating a training set comprising a first subset of images, the first subset of images including images of cells modified with one of a plurality of agents, and a second subset of images, the second subset of images including images of cells that have not been modified with an agent; (mathematical concept, under the broadest reasonable interpretation this can include matrix transformations)
training a machine learned model using the training set, the machine-learned model configured to predict an effect of an agent on a state of a cell (mathematical concept, under the broadest reasonable interpretation training a machine learning model can be regression)
applying the machine learned model to each image in the set of images to identify a set of candidate compounds predicted to modify a corresponding state of a corresponding cell from the first state of the cell to a second state; (mathematical concept, under the broadest reasonable interpretation applying a machine learning model can be regression)
Dependent claim 238 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the state of the cell is a predicted age of the cell, and wherein the machine-learned model is configured to predict an effect of an agent on the predicted age of a cell (mathematical concept, this just limits what is predicted)
Dependent claim 239 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the set of candidate compounds predicted to modify the corresponding state of the corresponding cell from the first state to the second state are predicted to reduce a predicted age of a cell by restoring one or more aspects of an immune response of the cell (mathematical concept, this just limits what is predicted)
Dependent claim 240 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the state of the cell is a function of the cell, and wherein the machine learned model is configured to predict an effect of an agent on the function of a cell (mathematical concept, this just limits what is predicted)
Dependent claim 241 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the function of the cell is the immune cell function of the cell, and wherein the agent modifies the immune cell function from a first function to a second function (mathematical concept, this just limits what is predicted)
Dependent claim 242 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the identified set of compounds is predicted to modify the corresponding state of the corresponding cell based on at least one of: a functional signature of the cell, a morphological signature of the cell, or a marker of the cell (mathematical concept, this just limits what is predicted)
Dependent claim 243 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein generating the training set further comprises: preprocessing one or more images in the first subset of images and one or more images in the second subset of images; and wherein preprocessing an image includes at least one of: rotating the image, inverting the image left, inverting the image right, inverting the image up, or inverting the image down. (mathematical concept, under the broadest reasonable interpretation these are function which transform a matrix)
Dependent claim 244 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein applying the machine learned model to each image in the set of images further comprises: preprocessing each image in the set of images; and wherein preprocessing includes at least one of: normalization, image enhancement, image correction, contrast enhancement, brightness enhancement, filtering, transformation, adjusting image resolution, adjusting bit resolution, adjusting image size, adjusting field-of-view, background subtraction, image subtraction, or compression (mathematical concept, under the broadest reasonable interpretation these are function which transform a matrix)
Independent claim 246 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating a training set comprising a first subset of images, the first subset of images including images of cells modified with one of a plurality of agents, and a second subset of images, the second subset of images including images of cells that have not been modified with an agent (mathematical concept)
training a machine learned model using the training set, the machine- learned model configured to predict an effect of an agent on a state of a cell (mathematical concept)
applying the machine learned model to each image in the set of images to identify a set of candidate compounds predicted to modify a corresponding state of a corresponding cell from the first state of the cell to a second state; (mathematical concept)
Dependent claim 247 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the state of the cell is a predicted age of the cell, and wherein the machine learned model is configured to predict an effect of an agent on the predicted age of a cell (mathematical concept, this just limits what is predicted)
Dependent claim 248 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the set of candidate compounds predicted to modify the corresponding state of the corresponding cell from the first state to the second state are predicted to reduce a predicted age of a cell by restoring one or more aspects of an immune response of the cell (mathematical concept, this just limits what is predicted)
Dependent claim 249 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the state of the cell is a function of the cell, and wherein the machine learned model is configured to predict an effect of an agent on the function of a cell (mathematical concept, this just limits what is predicted)
Dependent claim 250 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the function of the cell is the immune cell function of the cell, and wherein the agent modifies the immune cell function from a first function to a second function (mathematical concept, this just limits what is predicted)
Dependent claim 251 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the identified set of compounds is predicted to modify the corresponding state of the corresponding cell based on at least one of: a functional signature of the cell, a morphological signature of the cell, or a marker of the cell (mathematical concept, this just limits what is predicted)
Dependent claim 252 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein generating the training set further comprises: preprocessing one or more images in the first subset of images and one or more images in the second subset of images; and wherein preprocessing an image includes at least one of: rotating the image, inverting the image left, inverting the image right, inverting the image up, or inverting the image down. (mathematical concept, under the broadest reasonable interpretation these are function which transform a matrix)
Dependent claim 253 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein applying the machine learned model to each image in the set of images further comprises: preprocessing each image in the set of images; and wherein preprocessing includes at least one of: normalization, image enhancement, image correction, contrast enhancement, brightness enhancement, filtering, transformation, adjusting image resolution, adjusting bit resolution, adjusting image size, adjusting field-of-view, background subtraction, image subtraction, or compression. (mathematical concept, under the broadest reasonable interpretation these are function which transform a matrix)
Dependent claim 254 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
applying the machine learned model to the training set to generate a prediction of an effect of an agent on a state of a cell; (mathematical concept, under the broadest reasonable interpretation a set of weights can be two terms in a regression function)
and updating the initial set of weights based on the predictions and a label associated with each image in the training set, the label indicating a known effect of the agent on a corresponding cell (mathematical concept, under the broadest reasonable interpretation a set of weights can be two terms in a regression function)
Independent claim 255 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating a training set comprising a first subset of images, the first subset of images including images of cells modified with one of a plurality of agents, and a second subset of images, the second subset of images including images of cells that have not been modified with an agent (mathematical concept, under the broadest reasonable interpretation this can include matrix transformations)
training a machine learned model using the training set, the machine-learned model configured to predict an effect of an agent on a state of a cell (mathematical concept, under the broadest reasonable interpretation applying a machine learning model can be regression)
applying the machine learned model to each image in the set of images to identify a set of candidate compounds predicted to modify a corresponding state of a corresponding cell from the first state of the cell to a second state (mathematical concept, under the broadest reasonable interpretation applying a machine learning model can be regression)
Dependent claim 256 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein applying the machine learned model to each image in the set of images further comprises: processing each image in the set of images; and wherein processing includes at least one of: normalization, image enhancement, image correction, contrast enhancement, brightness enhancement, filtering, transformation, adjusting image resolution, adjusting bit resolution, adjusting image size, adjusting field-of-view, background subtraction, image subtraction, or compression. (mathematical concept, under the broadest reasonable interpretation this can include matrix transformations)
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 237-256 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further
examination is performed that analyzes if the claim recites additional elements that when examined as a
whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that
integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception
in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements
are analyzed to determine if the abstract idea is integrated into a practical application (MPEP
2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract
idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
The additional element in independent claim 237 includes:
A computer-implemented method, the computer implemented method comprising:
accessing a set of images, each image in the set of images including a cell, each cell associated with a first state
and providing the set of candidate compounds to an entity associated with the set of images.
The additional element in independent claim 246 includes:
A system comprising: a processor; and a non-transitory computer-readable storage medium storing executing instructions that, when executed, cause the processor to perform steps comprising:
accessing a set of images, each image in the set of images including a cell, each cell associated with a first state
and providing the set of candidate compounds to an entity associated with the set of images
The additional element in dependent claim 254 includes:
wherein training the machine learned model comprises: accessing an initial set of weights; initializing the machine learned model with the initial set of weights;
The additional element in independent claim 255 includes:
A non-transitory computer-readable storage medium storing executable computer instructions that, when executed by a processor, causes the processor to perform steps comprising
accessing a set of images, each image in the set of images including a cell, each cell associated with a first state
and providing the set of candidate compounds to an entity associated with the set of images.
The additional elements of accessing a set of images, each image in the set of images including a cell, each cell associated with a first state (Claim 237), accessing a set of images, each image in the set of images including a cell, each cell associated with a first state (Claim 246), wherein training the machine learned model comprises: accessing an initial set of weights; initializing the machine learned model with the initial set of weights (Claim 254), accessing a set of images, each image in the set of images including a cell, each cell associated with a first state (Claim 255) are insignificant extra-solution activity that are part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)).
The additional elements of a computer-implemented method, the computer implemented method comprising (Claim 237), and providing the set of candidate compounds to an entity associated with the set of images (Claim 237), a system comprising: a processor; and a non-transitory computer-readable storage medium storing executing instructions that, when executed, cause the processor to perform steps comprising (Claim 246), and providing the set of candidate compounds to an entity associated with the set of images (Claim 246), a non-transitory computer-readable storage medium storing executable computer instructions that, when executed by a processor, causes the processor to perform steps comprising (Claim 255), and providing the set of candidate compounds to an entity associated with the set of images (Claim 255) fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
The additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 237-256 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 237-256 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
The additional elements recited in claims 237-256 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of accessing a set of images, each image in the set of images including a cell, each cell associated with a first state (Claim 237), accessing a set of images, each image in the set of images including a cell, each cell associated with a first state (Claim 246), wherein training the machine learned model comprises: accessing an initial set of weights; initializing the machine learned model with the initial set of weights (Claim 254), accessing a set of images, each image in the set of images including a cell, each cell associated with a first state (Claim 255) are conventional and part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)). Evidence for conventionality is shown by MPEP 2106.05 (d)which states receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
The additional elements of a computer-implemented method, the computer implemented method comprising (Claim 237), and providing the set of candidate compounds to an entity associated with the set of images (Claim 237), a system comprising: a processor; and a non-transitory computer-readable storage medium storing executing instructions that, when executed, cause the processor to perform steps comprising (Claim 246), and providing the set of candidate compounds to an entity associated with the set of images (Claim 246), a non-transitory computer-readable storage medium storing executable computer instructions that, when executed by a processor, causes the processor to perform steps comprising (Claim 255), and providing the set of candidate compounds to an entity associated with the set of images (Claim 255) are conventional fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Evidence for conventionality is shown by MPEP 2106.05 (d) (See directly above).
When taken alone, all additional elements in claims 237-256 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 237-256 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
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.
The factual inquiries 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 237, 245, 246, 254 and 255 are rejected under 35 U.S.C. 103 as being unpatentable over Kensert et al. (Kensert, A.; Harrison, P. J.; Spjuth, O. Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes. SLAS DISCOVERY: Advancing the Science of Drug Discovery 2019, 24 (4), 466–475.) in view of Simm et al. (Simm et al. Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery. Cell chemical biology 2018, 25 (5), 611-618.e3.). The instant application claims are italicized.
With respect to the limitations of Claims 237, 246, 255, Kensert et al. teaches the training and the use of convolutional neural network (CNN) architecture to predict cell mechanisms of action in response to chemical perturbations (abstract). The CNN was trained on images from the BBBC21v1 which contains images of cells before and after treatment with accompanying mechanism of action labels (pg. 467. Col. 2, paragraph 2, a computer-implemented method, the computer-implemented method comprising (Claim 237), a system comprising: a processor; and a non-transitory computer-readable storage medium storing executing instructions that, when executed, cause the processor to perform steps comprising (claim 246), a non-transitory computer-readable storage medium storing executable computer instructions that, when executed by a processor, causes the processor to perform steps comprising (claim 255), generating a training set comprising a first subset of images, the first subset of images including images of cells modified with one of a plurality of agents, and a second subset of images, the second subset of images including images of cells that have not been modified with an agent; training a machine learned model using the training set (Claim 237, Claim 246, Claim 255), training a machine learned model using the training set, the machine-learned model configured to predict an effect of an agent on a state of a cell (Claim 237, Claim 246, Claim 255), When training a CNN is trained images of before and after a drug is used on a cell. This was done over multiple drug candidates. When training the CNN the leave one out approach is used where each individual image is accessed iteratively to update the weights and the CNN is tested on the image that was left out. (pg. 470, col. 1, paragraph 3, accessing a set of images, each image in the set of images including a cell, each cell associated with a first state (Claim 237, Claim 246, Claim 255)
With respect to the limitations of Claims 245, 254, Kensert et al. teaches the getting an initial set of weights from CNNs trained on image net and then updating those weights based on changes in cell morphology in response to drugs (abstract, wherein training the machine learned model comprises: accessing an initial set of weights; initializing the machine learned model with the initial set of weights; applying the machine learned model to the training set to generate a prediction of an effect of an agent on a state of a cell; and updating the initial set of weights based on the predictions and a label associated with each image in the training set, the label indicating a known effect of the agent on a corresponding cell (Claim 245, Claim 254)
Kensert et al. does not explicitly teach
applying the machine learned model to each image in the set of images to identify a set of candidate compounds predicted to modify a corresponding state of a corresponding cell from the first state of the cell to a second state; and providing the set of candidate compounds to an entity associated with the set of images (Claim 237, Claim 246, Claim 255),
With respect to the limitations of Claims 237, 246, 245, Simm et al. teaches features extracted from images of cells are used by machine-learning methods to model all available activity data from previously performed assays. Assays with good predictivity on the test data are then selected for testing a relatively small number of predicted-active compounds, chosen from a large set of compounds profiled in the imaging assay. This applies the machine learning algorithms to images in order to identify the best candidate drugs that will produce the desired effect on the cell based (pg. 613, Figure 2 Caption, applying the machine learned model to each image in the set of images to identify a set of candidate compounds predicted to modify a corresponding state of a corresponding cell from the first state of the cell to a second state; and providing the set of candidate compounds to an entity associated with the set of images (Claim 237, Claim 246, Claim 255),
A person having ordinary skill in the art would be motivated to combine the method of using machine learning to classify morphological changes in response to drugs taught by Kensert et al. with the machine learning on images to identify and select candidate drugs of Simm et al. in order to build a machine learning model that can identify a drug in order to induce a morphological change. Both works directly deal in the same field of endeavor of machine learning on cell image analysis in response to drugs. There is a reasonable expectation of success because each author performed analysis on images in order to detect morphological changes and each method works independently. The foundations of the methods are not changing so there is a reasonable expectation of success they will work combined as they work separated. A person having ordinary skill in the art would also be motivated to use the transfer learning on cell images taught by Kensert et al. to the machine learning model in order to improve model performance. There is a reasonable expectation of success because transfer learning was previously used to increase performance of cell imaging models.
Claims 238 and 247 are rejected under 35 U.S.C. 103 as being unpatentable over Kensert et al. in view of Simm et al. as applied to claims 237, 245, 246, 254 and 255 under 35 U.S.C. 103 above, and further in view of Oja et al. (Oja, S., Komulainen, P., Penttilä, A. et al. Automated image analysis detects aging in clinical-grade mesenchymal stromal cell cultures. Stem Cell Res Ther 9, 6 (2018)).
Kensert et al. in view of Simm et al. teach a system, non-transitory computer-readable storage medium, and method for analyzing images and providing a set of candidate compounds associated with the images as applied to claims 237, 245, 246, 254 and 255 above.
Kensert et al. in view of Simm et al. does not explicitly teach the limitation of claims 238 and 247 wherein the state of the cell is a predicted age of the cell, and wherein the machine-learned model is configured to predict an effect of an agent on the predicted age of a cell.
Regarding the limitation of dependent claims 238 and 247, wherein the state of the cell is a predicted age of the cell, and wherein the machine-learned model is configured to predict an effect of an agent on the predicted age of a cell, Oja et al. teaches Imaging analysis of cell morphology is a useful tool for evaluating aging in cell cultures throughout the lifespan of MSCs (abstract)
A person having ordinary skill in the art would be motivated to combine the method of Kensert et al. in view of Simm et al. with the use of cellular morphology aging indicator taught by Oja et al. in order to develop a model to predict an effect of a drug on the age of a cell. One would have had a reasonable expectation of success because of the relationship between cellular morphology and aging. Image analysis would be able to detect the changes in morphology which directly relate to gaining age.
Claims 239-242 and 248-251 are rejected under 35 U.S.C. 103 as being unpatentable over Kensert et al. in view of Simm et al. as applied to claims 237, 245, 246, 254 and 255 under 35 USC 103 above, in view of Oja et al. as applied to claims 238 and 247 under 35 U.S.C. 103 above, and further in view of Kyriazis et al. (Kyriazis, A.; Shahriar Noroozizadeh; Amir Refaee; Choi, W.; Chu, L.-T.; Bashir, A.; Cheng, W. K.; Zhao, R.; Dhananjay Namjoshi; Salcudean, S. E.; Wellington, C. L.; Nir, G. An End-To-End System for Automatic Characterization of Iba1 Immunopositive Microglia in Whole Slide Imaging. 2019, 17 (3), 373–389).
Kensert et al. in view of Simm et al. in view of Jackson et al. in view of Oja et al. a
a system, non-transitory computer-readable storage medium, and method for analyzing images and providing a set of candidate compounds associated with the images as applied to claims 237, 245, 246, 254 and 255 above as well as the use of cellular morphology as an indicator for age as applied to claims 238 and 247 above.
Kensert et al. in view of Simm et al. in view of Oja et al. does not explicitly teach the limitations of wherein the set of candidate compounds predicted to modify the corresponding state of the corresponding cell from the first state to the second state are predicted to reduce a predicted age of a cell by restoring one or more aspects of an immune response of the cell (claim 239, claim 248); wherein the state of the cell is a function of the cell, and wherein the machine learned model is configured to predict an effect of an agent on the function of a cell (claim 240, claim 249); wherein the function of the cell is the immune cell function of the cell, and wherein the agent modifies the immune cell function from a first function to a second function (claim 241, claim 250); wherein the identified set of compounds is predicted to modify the corresponding state of the corresponding cell based on at least one of: a functional signature of the cell, a morphological signature of the cell, or a marker of the cell (claim 242, claim 251).
In regards to the limitations of dependent claims 240-242 and 248-251, wherein the set of candidate compounds predicted to modify the corresponding state of the corresponding cell from the first state to the second state are predicted to reduce a predicted age of a cell by restoring one or more aspects of an immune response of the cell (claim 239, claim 248); wherein the state of the cell is a function of the cell, and wherein the machine learned model is configured to predict an effect of an agent on the function of a cell (claim 240, claim 249); wherein the function of the cell is the immune cell function of the cell, and wherein the agent modifies the immune cell function from a first function to a second function (claim 241, claim 250); wherein the identified set of compounds is predicted to modify the corresponding state of the corresponding cell based on at least one of: a functional signature of the cell, a morphological signature of the cell, or a marker of the cell (claim 242, claim 251) Kyriazis et al. teaches training and deploying a machine learning model to classify microglia based on images of microglia processes. Microglia processes play an important role in morphology, immune response and function of microglia (abstract).
A person having ordinary skill in the art would be motivated to combine the machine learning model of Kensert et al. in view of Simm et al. in view of Oja et al. with the knowledge of immune cell response, morphology, and function from Kyriazis et al. in order to make a machine learning model to predict function, immune response or morphology of individual cells. One would have had a reasonable expectation of success because machine learning models have successfully detected morphology changes in cells and function, immune response or morphology changes of individual cells can be detected via images.
Claims 243-244, 252-253, and 256 are rejected under 35 U.S.C. 103 as being unpatentable over Kensert et al. in view of Simm et al. as applied to claims 237,245, 246, 254 and 255 under 35 U.S.C. 103 above, and further in view of Montserrat et al. (Montserrat, D. M.; Lin, Q.; Allebach, J.; Delp, EdwardJ. Training Object Detection and Recognition CNN Models Using Data Augmentation. Electronic Imaging 2017, 2017 (10), 27–36).
Kensert et al. in view of Simm et al. teach a system and method for analyzing images and providing a set of candidate compounds associated with the images as applied to claims 237,245, 246, 254 and 255 above. Kensert et al. in view of Simm et al. does not explicitly teach wherein generating the training set further comprises: preprocessing one or more images in the first subset of images and one or more images in the second subset of images; and wherein preprocessing an image includes at least one of: rotating the image, inverting the image left, inverting the image right, inverting the image up, or inverting the image down (claim 243, claim 252); wherein applying the machine learned model to each image in the set of images further comprises: preprocessing each image in the set of images; and wherein preprocessing includes at least one of: normalization, image enhancement, image correction, contrast enhancement, brightness enhancement, filtering, transformation, adjusting image resolution, adjusting bit resolution, adjusting image size, adjusting field-of-view, background subtraction, image subtraction, or compression (claim 244, claim 253, claim 256).
Regarding the limitations of dependent claims 243-244, 252-253, and 256, wherein generating the training set further comprises: preprocessing one or more images in the first subset of images and one or more images in the second subset of images; and wherein preprocessing an image includes at least one of: rotating the image, inverting the image left, inverting the image right, inverting the image up, or inverting the image down (claim 243, claim 252); wherein applying the machine learned model to each image in the set of images further comprises: preprocessing each image in the set of images; and wherein preprocessing includes at least one of: normalization, image enhancement, image correction, contrast enhancement, brightness enhancement, filtering, transformation, adjusting image resolution, adjusting bit resolution, adjusting image size, adjusting field-of-view, background subtraction, image subtraction, or compression (claim 244, claim 253, claim 256) Montserrat et al. does teach a method of augmenting training data through the use of data augmentation methods where linear and nonlinear transforms are done on the training data to create “new” training images (abstract).
A person of ordinary skill in the art would be motivated to combine the machine learning model taught by Kensert et al. in view of Simm et al. with the training data augmentation taught by Montserrat et al. in order to increase the available training data for model training in order to increase performance. There is a reasonable expectation of success because linear and nonlinear transformations have previously been performed on images.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Connor Beveridge whose telephone number is 571-272-2099. The examiner can normally be reached Monday - Thursday 9 am - 5 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at 571-272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/C.H.B./
Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
Prosecution Insights