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 .
Information Disclosure Statement(s)
The Information disclosure statement (IDS) filed on August 06th, 2024 has been acknowledged and considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
1) “receiving, by one or more computing devices, a plurality of images……,” as recited in claim 1, in lines 2, 5, 7, 9 and 13 and mentioned in claims 2, 5 and 7;
The specification provide sufficient structure, material and/or act for the recited computing devices to perform the recited functions as disclosed in the instant specification’s [0074] wherein the computing device has a form of a computing computer which includes a storage device to store instructions to perform the functions , and installation device, a network interface, and I/O controller, display devices, a keyboard and a pointing device such as a mouse and a central processing unit.
Limitations that use words like “means” (or “step”) or similar terms with functional language but do not invoke 35 U.S.C. 112(f):
“one or more computing devices comprising one or more processors configured to….,” as recited in claim 11;
Such claim limitation(s) is/are:
(i) “computing devices comprising one or more processors” have a structure associated, recited in the claim, with it a processor to perform the recited function hence, a special programmed processor.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-20 are rejected under 35 U.S.C. 101
(best understood based on the 112f interpretation above) Regarding Independent Claim 1 and its dependent claims 2-10,
Step 1 Analysis: Claim 1 is directed to a method/process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part:
“classifying, by the one or more computing devices, each image of the plurality of images as treated or untreated;
calculating, by the one or more computing devices, a classification accuracy for each subset of the plurality of images;
determining, by the one or more computing devices, a concentration of the candidate drug corresponding to a subset of the plurality of images having a greatest change in classification accuracy relative to a subset corresponding to a next-highest or next-lowest concentration; and
identifying, by the one or more computing devices, the determined concentration as corresponding to an inhibitory concentration”
The limitations as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes/Mathematical Concept” grouping of abstract ideas. The limitations of:
“classifying each image of the plurality of images as treated or untreated” is a step, under BRI, a human mind can perform through process of observation and evaluation, such as, the human mind can observe images and evaluate and classify the images with treated or untreated results.
“calculating a classification accuracy….plurality of images” is a calculation, a mathematical operation performance within abstract idea category.
“determining a concentration of the candidate drug….next-highest or next-lowest concentration” is a step, under BRI, a human mind can perform through process of observation and evaluation using pen and paper such as the human mind can observation images and determine a concentration of a candidate drug having certain condition or criteria met such as recited in the claim of “having a greatest change….next-lowest concentration.”
“identifying the determined concentration as corresponding to an inhibitory concentration” is a step of which a human mind can perform, under BRI, such as through a process of observation and evaluation such as the human mind can observed some given/already resulted data/information here being the determined concentration, as corresponding to an inhibitory concentration.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. particular, the claim recites the following additional element(s) –
“receiving, by one or more computing devices, a plurality of images of cells treated with a candidate drug, the plurality of images comprising subsets of one or more images, each subset corresponding to a different concentration of the candidate drug;
…., by the one or more computing devices,….”
The additional element of instances of “one or more computing devices” - recited at a high level of generality (i.e. a computer to have computer with computer components such as a processor, memory, etc., as interpreted above in the 112f section, components executing the instructions of the invention, etc.) such that it amounts to no more than mere instructions to apply the exception.
The additional elements include steps of insignificant extra-solution activity of data gathering, data receiving [acquiring data/information, transmitting data/info., outputting data/information, displaying data/info., converting data/info., generating data/info., etc.] .
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.. Please see MPEP §2106.04.(d).III.C.
Step 2B Analysis: there are no additional elements, such as for these additional elements as indicated above, that amount to significantly more than the judicial exception. Please see MPEP §2106.05. The claim is directed to an abstract idea.
For all of the foregoing reasons, claim 1 does not comply with the requirements of 35 USC 101.
Accordingly, the dependent claims 2-10 do not provide elements that overcome the deficiencies of the independent claim 1. Moreover, claim 2 recites, in part, “receiving, by the one or more computing devices, a second plurality of images of untreated cells” comprises additional elements of insignificant extra-solution activity of data gathering of receiving data/information using a generic recited at high level of generality additional element of the one or more computing devices. claim 3 recites, in part, “dividing the plurality of images…..into a first set of training data and a second set of test data” is a step a human mind can perform, under BRI, mental through a process of observation and evaluation using pen and paper such as the human mind can observe some images and divide them into certain sets such as laid out in this claimed limitation. Claim 4 recites, in part, “augmenting the plurality of images….with the candidate drug by creating additional images via…manipulations of the received images” is a step of insignificant extra-solution of data/gathering of augmenting data/information using some manipulation of images well-known in the art recited at high level of generality of image augmentation. Claim 5 recites, in part, “filtering each image of the plurality of images” is a step of insignificant extra-solution of well-known activity of data filtering of filtering images recited at high level of generality, “to identify edges within the image” is a step of which a human mind can also perform, using process of observation and evaluation such as, the human mind can observe some already resulted/given filter images and identify edges. Claim 6 recites, in part, “applying a Sobel filter to each image” is a well-known process in the art of a mathematical operation abstract idea of Sobel filter. Claim 7 recites, in part, “providing each image to one or more vision transformers executed” is a step of insignificant extra-solution activity additional element of data gathering data transmitting of providing data/information images to vision transformers which is another additional element of generic high level of vision transformers recited at high level of generality. Claim 8 recites, in part, “comparing the classification of each image to a predetermined treatment classification for the image” is a step, under BRI, a human mind can perform through a process of observation and evaluation such as the human mind can observe some classification results and compare them according to some given criteria or condition. Claim 9 recites, in part, “determining a Hill curve corresponding to the calculated classification accuracies” is a well-known operation in the field involving mathematical operation and concept of abstract ideas recited at high level of generality; “and identifying a concentration of the candidate drug corresponding to a portion of the Hill curve having a greatest slope” is a step that the human mind can also perform, under BRI, through a process of observation and evaluation such as human mind can observe the result of the Hill curve operation and make an identification of a concentration of the candidate drug according to a certain condition such as laid out in the limitation. Claim 10 recites, in part, “determining a slope between each pair of adjacent concentrations and corresponding classification accuracies and identifying a concentration of the candidate drug corresponding to a maximum determined slope” is a series of steps that the human mind can perform using pen and paper through a process of observation and evaluation and also steps of mathematical operation to determine a slope by observing given data/information such as recited in the claim.
Accordingly, the dependent claims 2-10 are not patent eligible under 101.
Regarding the independent claim 11 and its dependent claims 12-20:
The independent claim 11 recites analogous limitations to the independent claim 1 hence, are rejected under 101 for the same reason under the same approach, moreover, claim 11 recited further additional elements of “a system,” “one or more computing devices comprising one or more processors configured to” which are well-known generic computer and computer component additional elements well-known in the art recited at high level of generality to perform generic functions.
Dependent claims 12-20 recite analogous limitations to the dependent claims 2-10 hence, are rejected under 101 for the same reason under the same approach.
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.
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.
Claims 1-4, 7-9, 11-14 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Stefan Kallenberger et. al. (“US 2022/0406434 A1” hereinafter as “Kallenberger”) in view of Kookrae Cho et. al. (“Numerical Learning of Deep Features from Drug-Exposed Cell Images to Calculate IC50 Without Staining, 2022, Scientific Reports 12:6610m Nature Portfolio” hereinafter as “Cho”).
(best understood based on the 112f interpretation above) Regarding claim 1, Kallenberger discloses a method for determining drug inhibitory concentrations (title and abstract), comprising: receiving, by one or more computing devices, a plurality of images of cells treated with a candidate drug ([0054-0055] discloses biological probes being treated with substance being candidate drug [according to 0174-0176] and being acquired images of them as part of the invention processing by a computer according to [0007] which is analogous to the recited computing device; [0007] discloses the system including the use of a computer including processor to execute the invention), the plurality of images comprising subsets of one or more images, each subset corresponding to a different concentration of the candidate drug ([0054] discloses the probes are being administered a sequence of substance concentrations; therefore, the acquired images includes images of the different concentrations of the substance [candidate drug] therefore, analogous to the recited subsets of one or more images); classifying, by the one or more computing devices, each image of the plurality of images as treated or untreated ([0044] discloses classifying the biological state of the cells as living or dead cells [can be understood to be analogous to treated/living or untreated/dead as claimed] through the images [0041]); calculating, by the one or more computing devices, a classification accuracy for each subset of the plurality of images ([0065] discloses series of optical measurements are used for refitting the model parameters with improved accuracy, therefore, the optical measure for refitting the model parameters is a process analogous to calculating classification accurate for each set of the images since, as further disclosed in [0155] as the biological model is based on accurate estimation of the values of the model parameters hence has an increase accuracy).
However, Kallenberger does not explicitly disclose determining, by the one or more computing devices, a concentration of the candidate drug corresponding to a subset of the plurality of images having a greatest change in classification accuracy relative to a subset corresponding to a next-highest or next-lowest concentration; and identifying, by the one or more computing devices, the determined concentration as corresponding to an inhibitory concentration.
In the same field of deep machine learning for determining effect of drugs (abstract and title, Cho) Cho discloses determining, by the one or more computing devices, a concentration of the candidate drug corresponding to a subset of the plurality of images having a greatest change in classification accuracy (“Prediction of IC50 using label-free cell images” section of page 7, 2nd par., discloses the accuracy of IC50 reflected the precise cell viability estimated by the CNN models; moreover, the cell viability is determined based on a correlation coefficient [according to page 5, 2nd to the last paragraph] such as illustrated in Fig. 3 and table 1, wherein the coefficient or the difference is largest for the allosteric models [according to page 4, 2nd to the last par.] which is analogous to a concentration with the largest change in classification accuracy) relative to a subset corresponding to a next-highest or next-lowest concentration (as discussed previously, the accuracy of IC50 reflected based on the precise cell viability [the correlation coefficients as discussed previously being the classification accuracy] estimated by the CNN models; wherein the IC50 value is determined based on the equation 1 of page 3, 1st par., concerning the Max and minimum concentrations of the candidate drug, therefore, the concentration of the greatest change in classification accuracy being relative [or related] to the equation 1 having the min and max values of the candidate drug concentration); and identifying, by the one or more computing devices, the determined concentration as corresponding to an inhibitory concentration (the IC50 determined as discussed previously being the inhibitory concentration).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Kallenberger to perform calculating, by the one or more computing devices, a classification accuracy for each subset of the plurality of images; determining, by the one or more computing devices, a concentration of the candidate drug corresponding to a subset of the plurality of images having a greatest change in classification accuracy relative to a subset corresponding to a next-highest or next-lowest concentration; and identifying, by the one or more computing devices, the determined concentration as corresponding to an inhibitory concentration as taught by Cho to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to predict IC50 value more accurately (abstract, Cho).
(best understood based on the 112f interpretation above) Regarding claim 2, Kallenberger in view of Cho discloses the method of claim 1, further comprising receiving, by the one or more computing devices, a second plurality of images of untreated cells (since the experience using different candidate drugs therefore, it can be understood as a second set of images of untreated cells is also being used according to Cho’s page 3, 2nd to the last par.). The motivation for combination of arts is the same as for claim 1 above.
Regarding claim 3, Kallenberger in view of Cho discloses the method of claim 2, further comprising dividing the plurality of images of treated cells and the second plurality of images of untreated cells into a first set of training data and a second set of test data (since the experience using different candidate drugs therefore, it can be understood as a second set of images of untreated cells is also being used according to Cho’s page 3, 2nd to the last par. which is also used for training according to Cho’s page 2, “Image pre-processing” section, and also into testing according to page 2, “Model construction for the prediction of cell viability” section). The motivation for combination of arts is the same as for claim 1 above.
Regarding claim 4, Kallenberger in view of Cho discloses the method of claim 1, further comprising augmenting the plurality of images of cells treated with the candidate drug by creating additional images via one or more image manipulations of the received images (Cho’s page 2, “Image pre-processing” section, discloses data augmentation to creating images of cells treated with the candidate drug by creating additional images through image manipulations of the input images). The motivation for combination of arts is the same as for claim 1 above.
(best understood based on the 112f interpretation above) Regarding claim 7, Kallenberger in view of Cho discloses the method of claim 1, wherein classifying each image of the plurality of images as treated or untreated comprises providing each image to one or more vision transformers executed by the one or more computing devices (Cho’s page 2, “Image Pre-processing” section, discloses the pre-processing process of the images for the CNN models includes random cropping of the images [vision transformers as claimed]). The motivation for combination of arts is the same as for claim 1 above.
Regarding claim 8, Kallenberger in view of Cho discloses the method of claim 1, wherein calculating the classification accuracy for each concentration of the candidate drug further comprises comparing the classification of each image to a predetermined treatment classification for the image (as discussed above in claim 1, as taught by Cho, the classification accuracy is determined based on the relationship between the predicted and the measured, therefore, there is a predetermined treatment classification [being the known ground truth] such as disclosed in Cho’s page 10, 1st par.). The motivation for combination of arts is the same as for claim 1 above.
Regarding claim 9, Kallenberger in view of Cho, wherein Cho discloses the method of claim 1, wherein determining the concentration of the candidate drug corresponding to the subset of the plurality of images having the greatest change in classification accuracy relative to a subset corresponding to a next-highest or next-lowest concentration comprises determining a Hill curve corresponding to the calculated classification accuracies (page 3, 1st par., discloses the cell viability [classification accuracy such as already discussed above in claim1] for determining of the IC50 is based on determining a regression curve of the Hill equation [Hill curve] corresponding to the correlation coefficients [classification accuracies such as already discussed above in claim 1] according to page 4, 2nd to the last par. of Cho); and identifying a concentration of the candidate drug corresponding to a portion of the Hill curve having a greatest slope (and the concentration of the candidate drug is based on the portion of the curve’s slopes of the linear regression curves according to Cho’s page 4, 2nd to the last par.; these slopes are being used to identify each concentration even including the greatest slope therefore, covers the scope of the claim). The motivation for combination of arts is the same as for claim 1 above.
Regarding claim 11, Kallenberger discloses a system for determining drug inhibitory concentrations, comprising: (title and abstract): one or more computing devices comprising one or more processors configured to ([0007] discloses the system including the use of a computer including processor to execute the invention): receive a plurality of images of cells treated with a candidate drug ([0054-0055] discloses biological probes being treated with substance being candidate drug [according to 0174-0176] and being acquired images of them as part of the invention processing by a computer according to [0007] which is analogous to the recited computing device), the plurality of images comprising subsets of one or more images, each subset corresponding to a different concentration of the candidate drug ([0054] discloses the probes are being administered a sequence of substance concentrations; therefore, the acquired images includes images of the different concentrations of the substance [candidate drug] therefore, analogous to the recited subsets of one or more images); classify each image of the plurality of images as treated or untreated ([0044] discloses classifying the biological state of the cells as living or dead cells [can be understood to be analogous to treated/living or untreated/dead as claimed] through the images [0041]); calculate a classification accuracy for each subset of the plurality of images ([0065] discloses series of optical measurements are used for refitting the model parameters with improved accuracy, therefore, the optical measure for refitting the model parameters is a process analogous to calculating classification accurate for each set of the images since, as further disclosed in [0155] as the biological model is based on accurate estimation of the values of the model parameters hence has an increase accuracy).
However, Kallenberger does not explicitly disclose determine a concentration of the candidate drug corresponding to a subset of the plurality of images having a greatest change in classification accuracy relative to a subset corresponding to a next-highest or next-lowest concentration; and identify the determined concentration as corresponding to an inhibitory concentration.
In the same field of deep machine learning for determining effect of drugs (abstract and title, Cho) Cho discloses determine a concentration of the candidate drug corresponding to a subset of the plurality of images having a greatest change in classification accuracy (“Prediction of IC50 using label-free cell images” section of page 7, 2nd par., discloses the accuracy of IC50 reflected the precise cell viability estimated by the CNN models; moreover, the cell viability is determined based on a correlation coefficient [according to page 5, 2nd to the last paragraph] such as illustrated in Fig. 3 and table 1, wherein the coefficient or the difference is largest for the allosteric models [according to page 4, 2nd to the last par.] which is analogous to a concentration with the largest change in classification accuracy) relative to a subset corresponding to a next-highest or next-lowest concentration (as discussed previously, the accuracy of IC50 reflected based on the precise cell viability [the correlation coefficients as discussed previously being the classification accuracy] estimated by the CNN models; wherein the IC50 value is determined based on the equation 1 of page 3, 1st par., concerning the Max and minimum concentrations of the candidate drug, therefore, the concentration of the greatest change in classification accuracy being relative [or related] to the equation 1 having the min and max values of the candidate drug concentration); and identify the determined concentration as corresponding to an inhibitory concentration (the IC50 determined as discussed previously being the inhibitory concentration).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Kallenberger to perform calculate a classification accuracy for each subset of the plurality of images; determine a concentration of the candidate drug corresponding to a subset of the plurality of images having a greatest change in classification accuracy relative to a subset corresponding to a next-highest or next-lowest concentration; and identify the determined concentration as corresponding to an inhibitory concentration as taught by Cho to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to predict IC50 value more accurately (abstract, Cho).
Regarding claim 12, Kallenberger in view of Cho discloses the system of claim 11, wherein the one or more processors are further configured to receive a second plurality of images of untreated cells (since the experience using different candidate drugs therefore, it can be understood as a second set of images of untreated cells is also being used according to Cho’s page 3, 2nd to the last par.). The motivation for combination of arts is the same as for claim 11 above.
Regarding claim 13, Kallenberger in view of Cho discloses the system of claim 12, wherein the one or more processors are further configured to divide the plurality of images of treated cells and the second plurality of images of untreated cells into a first set of training data and a second set of test data (since the experience using different candidate drugs therefore, it can be understood as a second set of images of untreated cells is also being used according to Cho’s page 3, 2nd to the last par. which is also used for training according to Cho’s page 2, “Image pre-processing” section, and also into testing according to page 2, “Model construction for the prediction of cell viability” section). The motivation for combination of arts is the same as for claim 11 above.
Regarding claim 14, Kallenberger in view of Cho discloses the system of claim 11, wherein the one or more processors are further configured to augment the plurality of images of cells treated with the candidate drug by creating additional images via one or more image manipulations of the received images (Cho’s page 2, “Image pre-processing” section, discloses data augmentation to creating images of cells treated with the candidate drug by creating additional images through image manipulations of the input images). The motivation for combination of arts is the same as for claim 11 above.
Regarding claim 17, Kallenberger in view of Cho discloses the system of claim 11, wherein the one or more processors are further configured to apply one or more vision transformers to each image (Cho’s page 2, “Image Pre-processing” section, discloses the pre-processing process of the images for the CNN models includes random cropping of the images [vision transformers as claimed]). The motivation for combination of arts is the same as for claim 11 above.
Regarding claim 18, Kallenberger in view of Cho discloses the system of claim 11, wherein the one or more processors are further configured to compare the classification of each image to a predetermined treatment classification for the image (as discussed above in claim 11, as taught by Cho, the classification accuracy is determined based on the relationship between the predicted and the measured, therefore, there is a predetermined treatment classification [being the known ground truth] such as disclosed in Cho’s page 10, 1st par.). The motivation for combination of arts is the same as for claim 11 above.
Regarding claim 19, Kallenberger in view of Cho, wherein Cho discloses the system of claim 11, wherein the one or more processors are further configured to determine a Hill curve corresponding to the calculated classification accuracies; (page 3, 1st par., discloses the cell viability [classification accuracy such as already discussed above in claim 11] for determining of the IC50 is based on determining a regression curve of the Hill equation [Hill curve] corresponding to the correlation coefficients [classification accuracies such as already discussed above in claim 11] according to page 4, 2nd to the last par. of Cho); and identify a concentration of the candidate drug corresponding to a portion of the Hill curve having a greatest slope (and the concentration of the candidate drug is based on the portion of the curve’s slopes of the linear regression curves according to Cho’s page 4, 2nd to the last par.; these slopes are being used to identify each concentration even including the greatest slope therefore, covers the scope of the claim). The motivation for combination of arts is the same as for claim 11 above.
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Stefan Kallenberger et. al. (“US 2022/0406434 A1” hereinafter as “Kallenberger”) in view of Kookrae Cho et. al. (“Numerical Learning of Deep Features from Drug-Exposed Cell Images to Calculate IC50 Without Staining, 2022, Scientific Reports 12:6610m Nature Portfolio” hereinafter as “Cho”) and E. Bengtsson et. al. (“Robust Cell Image Segmentation Methods, Dec. 2003, Pattern Recognition and Image Analysis, Vol. 14, No. 2, pp. 157-167” hereinafter as “Bengtsson”).
(best understood based on the 112f interpretation above) Regarding claim 5, Kallenberger in view of Cho discloses the method of claim 1, further comprising (as discussed above in claim 1) filtering, by the one or more computing devices, each image of the plurality of images (Kallenberger’s [0157] discloses the images are determined the cells based on image segmentation [analogous to filtering the images]).
However, Kallenberger in view of Cho does not explicitly disclose to identify edges within each image.
In the same field of cell image segmentation (title and abstract, Bengtsson) Bengtsson discloses to identify edges within each image (the image segmentation of the cells includes edge-based segmentation according to section 2.5, 1st 2 paragraphs).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Kallenberger in view of Cho apparatus to perform filtering, by the one or more computing devices, each image of the plurality of images to identify edges within each image as taught by Bengtsson to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to segment cells more accurately in cell images (abstract, Bengtsson).
Regarding claim 6, Kallenberger in view of Cho and Bengtsson discloses the method of claim 5, wherein filtering each image of the plurality of images further comprises applying a Sobel filter to each image (Bengtsson, section 3.2.2, discloses the segmentation includes applying Sobel filter to each image). The motivation for combination of arts is the same as for claim 5 above.
Regarding claim 15, Kallenberger in view of Cho discloses the system of claim 11, wherein the one or more processors are further configured to filter each image of the plurality of images (Kallenberger’s [0157] discloses the images are determined the cells based on image segmentation [analogous to filtering the images]).
However, Kallenberger in view of Cho does not explicitly disclose to identify edges within each image.
In the same field of cell image segmentation (title and abstract, Bengtsson) Bengtsson discloses to identify edges within each image (the image segmentation of the cells includes edge-based segmentation according to section 2.5, 1st 2 paragraphs).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Kallenberger in view of Cho apparatus to perform filtering, by the one or more computing devices, each image of the plurality of images to identify edges within each image as taught by Bengtsson to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to segment cells more accurately in cell images (abstract, Bengtsson).
Regarding claim 16, Kallenberger in view of Cho and Bengtsson discloses the system of claim 15, wherein filtering each image of the plurality of images further comprises applying a Sobel filter to each image (Bengtsson, section 3.2.2, discloses the segmentation includes applying Sobel filter to each image). The motivation for combination of arts is the same as for claim 15 above.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Stefan Kallenberger et. al. (“US 2022/0406434 A1” hereinafter as “Kallenberger”) in view of Kookrae Cho et. al. (“Numerical Learning of Deep Features from Drug-Exposed Cell Images to Calculate IC50 Without Staining, 2022, Scientific Reports 12:6610m Nature Portfolio” hereinafter as “Cho”) and C.E. Stephan (“Methods for Calculating an LC50, 1977, Aquatic Toxicology and Hazard Evaluation, ASTM STEP 634, American Society for Testing and Materials, pp. 65-84” hereinafter as “Stephan”).
Regarding claim 10, Kallenberger in view of Cho discloses the method of claim 1, wherein determining the concentration of the candidate drug corresponding to the subset of the second plurality of images having the greatest change in classification accuracy relative to a subset corresponding to a next-highest or next-lowest concentration (as discussed above in claim 1).
However, Kallenberger in view of Cho does not explicitly disclose comprises determining a slope between each pair of adjacent concentrations and corresponding classification accuracies and identifying a concentration of the candidate drug corresponding to a maximum determined slope.
In the same field of concentration of potency determination (title and abstract, Steph) Stephen discloses comprises determining a slope between each pair of adjacent concentrations and corresponding classification accuracies (page 67, 2nd to the last par., discloses toxicant concentration is determined based on the ratio between adjacent concentrations [a slope between pair of adjacent concentrations] and also is based on the confidence limits [classification accuracies] which is further explained in page 66, 2nd par. to 3rd par., wherein the measure of dispersion is basing on the standard error and the confidence limits indicating accuracies of the classification) and identifying a concentration of the candidate drug corresponding to a maximum determined slope (the concentration of LC50 is determined based on the slope of the maximum determined slope according to page 77, 2nd par.).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Kallenberger in view of Cho to perform calculating, by the one or more computing devices, a classification accuracy for each subset of the plurality of images; determining, by the one or more computing devices, a concentration of the candidate drug corresponding to a subset of the plurality of images having a greatest change in classification accuracy relative to a subset corresponding to a next-highest or next-lowest concentration; and identifying, by the one or more computing devices, the determined concentration as corresponding to an inhibitory concentration wherein determining the concentration of the candidate drug corresponding to the subset of the second plurality of images having the greatest change in classification accuracy relative to a subset corresponding to a next-highest or next-lowest concentration comprises determining a slope between each pair of adjacent concentrations and corresponding classification accuracies and identifying a concentration of the candidate drug corresponding to a maximum determined slope as taught by Stephen to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to predict potency concentration accurately and efficiently (abstract, Stephen).
Regarding claim 20, Kallenberger in view of Cho discloses the system of claim 11, wherein the one or more processors are further configured to (as discussed above in claim 11).
However, Kallenberger in view of Cho does not explicitly disclose to determine a slope between each pair of adjacent concentrations and corresponding classification accuracies and identify a concentration of the candidate drug corresponding to a maximum determined slope.
In the same field of concentration of potency determination (title and abstract, Steph) Stephen discloses to determine a slope between each pair of adjacent concentrations and corresponding classification accuracies and (page 67, 2nd to the last par., discloses toxicant concentration is determined based on the ratio between adjacent concentrations [a slope between pair of adjacent concentrations] and also is based on the confidence limits [classification accuracies] which is further explained in page 66, 2nd par. to 3rd par., wherein the measure of dispersion is basing on the standard error and the confidence limits indicating accuracies of the classification) identify a concentration of the candidate drug corresponding to a maximum determined slope (the concentration of LC50 is determined based on the slope of the maximum determined slope according to page 77, 2nd par.).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Kallenberger in view of Cho to perform calculate a classification accuracy for each subset of the plurality of images; determine a concentration of the candidate drug corresponding to a subset of the plurality of images having a greatest change in classification accuracy relative to a subset corresponding to a next-highest or next-lowest concentration; and identify the determined concentration as corresponding to an inhibitory concentration wherein determining the concentration of the candidate drug corresponding to the subset of the second plurality of images having the greatest change in classification accuracy relative to a subset corresponding to a next-highest or next-lowest concentration comprises determining a slope between each pair of adjacent concentrations and corresponding classification accuracies and identifying a concentration of the candidate drug corresponding to a maximum determined slope as taught by Stephen to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to predict potency concentration accurately and efficiently (abstract, Stephen).
Pertinent Prior Art(s)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Pelin Armutlu et. al. (“Classification of Drug Molecules Considering Their IC50 Values using Mix-Integer Linear Programming based Hyper-Boxes Method, Oct 2008, BMC Bioinformatics 2008, 9:411”) discloses determining of IC50 concentration (page 4, 1st col., 4th to the last par.), further being based on classification results and average accuracy (page 4, 1st col, 5th to the last par.) using determination of differences in accuracies (page 2, last 3 paragraphs).
Weidi Zhang et. al. (“SIC50: Determination of IC50 by an Optimized Sobel Operator and a Vision Transformer, August 15th, 2022, bioRxiv preprint”) discloses observing accuracy of binary classification to predict IC50 based on average value of two adjacent concentrations and their classification accuracies using vision transformer (page 3, last par.).
Conclusion
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/PHUONG HAU CAI/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673