DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/14/2025 has been entered.
Claims 1, 3-6, 9-11, 13, 15-16, 19-20 are amended. Claims 1-20 are pending.
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 .
Response to Arguments
The Applicant’s arguments regarding the rejection of above claims have been fully considered.
In reference to Applicant’s arguments about:
35 USC 101 rejections.
Examiner’s response:
Applicant’s arguments are fully considered but are not persuasive. Applicant asserts the claims are patent eligible under 35 USC 101, however, examiner respectfully disagrees. Examiner re-considered the claimed limitations as amended and still concludes the limitations to be directed to judicial exception without significantly more.
In regards to claim amendments, Examiner first would like to point out to the MPEP at section 2106.04 (a)(2) I C. which recites: “There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation”. Therefore, by merely replacing the word “computing” by “generating”/”determining” does not change the interpretation taken in view of the Specification. The instant application specification recites at [0018]: “For this reason, for each of the heat maps, the training application applies a wavelet transform to the heat map to determine a set of low frequency spatial patterns”, and at [0098]: “The aggregation engine 280 can generate the feature vector 138 in any technically feasible fashion. For instance, in some embodiments, the aggregation engine 280 concatenates the spatial feature sets 270(0)-270(6) to generate the feature vector 138. The feature vector 138 is also referred to herein as a "set of features" ”. A wavelet transform is a mathematical calculation, and the concatenation occurring to generate a feature vector, also referred as the set of features, are further mathematical calculations. Therefore, in view of this interpretation, Examiner concludes that these limitations do recite mathematical concepts, not only involve them.
In regards to Applicant’s arguments about the trained machine learning model based (emphasis added) on the calculated set of features, Examiner further reconsidered this limitation and understands that the mathematical concepts recited in the first two limitations are used to train a machine learning model for classification of microwell plates, therefore, the judicial exception is applied to the technological field of computer science and biochemical analysis/ microwell plates. Therefore, this limitation does no more than generally linking a judicial exception to a particular technological environment. Applicant further points out to the instant application specification at [0063-0064] and [0113] for allegedly providing an improvement, however, after further reconsideration, no improvement is being described in these paragraphs. Paragraph [0063] simply recites different types of machine learning models, paragraph [0064] simply recite different types of training applications, and paragraph [0113] simply recites receiving input via a GUI.
Applicant further compares Example 39 of the 2019 guidance with the current claim, however, Examiner respectfully disagrees. Example 39 was considered eligible because it did not recite any abstract ideas (emphasis added), in contrast, the current claims do recite abstract ideas, as explained above. Applicant further asserts that the claim recite a particular manner of training the machine learning model, however, Examiner respectfully disagrees. Claim limitations merely recite “generating a trained machine learning model” based on the mathematical concepts recited beforehand, and this do not integrate the abstract idea into a practical application.
In reference to Applicant’s arguments about:
35 USC 103 rejections.
Examiner’s response:
Applicant’s arguments have been fully considered but are not persuasive. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant mainly directs the arguments towards the Amthor reference, however, Examiner respectfully would like to point out that the claims are rejected based on the combination (emphasis added) of Amthor, Sammack and Qu.
After further re-consideration of the claim limitations and the prior art of record, in particular, Amthor, Examiner still considers that it teaches the claim limitations as amended. As explained above in the rejection, Examiner understands that Amthor teaches the limitation “generating first spatial information based on a first heat map that indicates results of a plurality of experiments generated using a first microwell plate”, as Amthor teaches an image sensor for recording digital images of a sample, recording digital images using the image sensor during regular operation of the microscope system, and providing at least one recorded digital image as at least one input data set to an image analysis system. Furthermore, Amthor recites that the time at which the recorded images were produced, or at which the corresponding experiment was performed, is plotted on the x-axis and experiment measurement values are plotted as an example on the y-axis; therefore, Amthor does disclose the application of its teachings to experiments. In addition, Amthor explains that the sample describes an object to be examined such as a multiwell plate. In summary, Examiner understands that Amthor’s teachings would have make obvious to a person having ordinary skill in the art to compute first spatial information based on a first heat map that indicates results of a plurality of experiments generated using a first microwell plate, as it shows in its teachings. Regarding the last limitation, as amended, Examiner still considers Amthor to disclose this limitation as it presents an image of the microscope system while specially marking the component that is to be maintained and outputs an indication relating to the type of maintenance procedure (for example cleaning the optical system, replacing the illumination). As previously explained, Amthor’s teachings are directed to experiments, as the time at which the recorded images were produced, or at which the corresponding experiment was performed, is plotted on the x-axis and experiment measurement values are plotted as an example on the y-axis. Applicant further asserts that Amthor fails to teach the use of a trained machine learning model, however, paragraph [0021] of Amthor recites “The use of technologies of artificial intelligence, image recognition and machine learning now permits evaluation of the results of the microscope system, specifically of one or more recorded images during regular operation of the microscope system”. Further, at [0026] and [0063], Amthor teaches the training of this machine learning model (0026: “The method may here additionally comprise training the learning system using a set of digital images by way of machine learning for generating a learning model for identifying the at least one feature—such as anomalies or other peculiarities—in one of the recorded digital images of samples during regular operation of the microscope system”, 0063: “The term “training of the learning system” means that for example a machine learning system adjusts, by way of a plurality of exemplary images, parameters in, for example, a neural network by partially repeated evaluation of the exemplary images so as to associate after the training phase even unknown images with one or more categories with which the learning system has been trained”).
Further, as explained below in this Office Action, Sammack teaches generating first spatial information, as it can be seen at [0089]: “Once a specific window with a specific width is chosen, that window is applied across the entire signal. The result is a single resolution analysis of the image. A multi-resolution analysis, where the image is analyzed over windows of varying widths, would provide a more detailed and accurate analysis of the signal. This is precisely what the wavelet transform achieves”. In view of the instant application specification, the spatial information is a result of a wavelet transform. Sammack further teaches determining a first set of features based on the first spatial information as it can be seen at [0012]: “the image features of the invention may be derived by a texture analysis method. Exemplary texture analysis methods include, without limitation, wavelet decomposition, multiresolution analysis, time frequency analysis, dilate and erode, co-occurrence matrices, Fourier spectral analysis, Gabor methods, wavelet packet decomposition, statistical analysis of wavelet coefficients, Markov Random fields, autoregression models, spatial domain filters, Fourier and Wavelet domain filters, and texture segmentation. Further at [0097]: “Applying Equations 2.10 and 2.12 across all B subbands in the wavelet decomposition of an image yields the wavelet energy signature of that image. Thus, the vector, x=[E.sub.1, E.sub.2, . . . , E.sub.B, MD.sub.1, MD.sub.2, . . . , MD.sub.B] (2.13) is the resulting wavelet energy textural feature representation”. The instant application specification states that the features are referred as a feature vector. Finally, Qu explicitly teaches computing first spatial information based on a first heat map, as it can be seen at [0053]: “a first attention module of the first branch 500 may overlay a first spatial attention heat map onto the first set of data received by the first branch 500, and a second attention module of the second branch 502 may overlay a second spatial attention heat map onto the second set of data received by the second branch 502. The attention module may include a space map network (e.g., corresponding to the spatial attention heat map) which is adjusted based on a final predicted label (error type/no error) of an input image” and at [0058]: “The CBAM provides a spatial heat map and color-channel attention feature. Thus, the additional channel attention module may focus attention on the channel that is associated with the target element that is of interest to the particular defect type”. Therefore, Examiner understands that the combination of Amthor, Sammack and Qu teaches the claim limitations.
Examiner respectfully would like to remind applicant about the claim limitations which are deemed to contain allowable subject matter (claims 5 and 15, as they are only rejected under 35 USC 101). Examiner would like to suggest applicant to include these limitations into independent claims in order to withdraw the prior art rejections.
For these reasons above, rejections are still maintained.
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 stand rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 analysis:
In the instant case, the claims are directed to a method, a non-transitory computer-readable media and a system. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A analysis:
Based on the claims being determined to be within of the four categories (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically the abstract idea of Mathematical Concepts (including mathematical relationships, formulas, and/or calculations).
Step 2A: Prong 1 analysis:
The claim(s) recite(s):
Claim 1:
“generating first spatial information based on a first heat map that indicates results of a plurality of experiments generated using a first microwell plate” - this limitation amounts to generating spatial information, which under BRI, amounts to applying a wavelet transform, being a mathematical technique/concept. MPEP 2106.04 (a)(2) recites “a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation”;
“determining a first set of features based on the first spatial information” - this limitation amounts to a determining a set of features, which under BRI, amounts to calculating a feature vector by performing concatenation, being a mathematical calculation/concept. MPEP 2106.04 (a)(2) recites “a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation”.
Step 2A: Prong 2 analysis:
This judicial exception is not integrated into a practical application because it only recites these additional elements:
“executing one or more machine learning operations based on the first set of features to generate a trained machine learning model, wherein, in operation. the trained machine learning model classifies sets of features associated with different microwell plates with respect to a plurality of labels indicating a plurality of executional artifacts caused by a different plurality of experiments that correspond to the plurality of experiments and that are executed in the different microwell plates” - generating a trained machine learning model based on the calculated feature vector (as recited in the previous steps) for further classification of microwell plates is recited at a high level of generality, therefore, this does no more than generally linking a judicial exception to a particular technological environment (the calculation of the spatial information using wavelet transforms and the calculation of feature set being a feature vector being applied to the technical field of computer science and biochemical analysis/ microwell plates).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B analysis:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements explained above merely indicate a field of use or technological environment in which to apply a judicial exception. The claims are not patent eligible.
Independent claims 11 and 20 are analogous claims, therefore the same rejection and rationale applies to it.
In addition, Claim 11 recites the additional elements analyzed under Step 2A: prong 2 and Step 2B:
Claim 11: “One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors…”- this computer readable media amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computers merely as a tool to perform an existing process (see MPEP 2106.05(f) (2)). The processor is considered a generic computer component. Further, under Step 2B, including instructions corresponds to storing information in the memory, being well-understood, routine and conventional (see MPEP 2106.05 (d) II (iv)).
Claim 20: “A system comprising: one or more memories storing instructions; and one or more processors coupled to the one or more memories that, when executing the instructions, perform the steps of…”- these memories storing instructions amount to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computers merely as a tool to perform an existing process (see MPEP 2106.05(f) (2)). The processors are considered generic computer components. Further, under Step 2B, storing instructions corresponds to storing information in the memory, being well-understood, routine and conventional (see MPEP 2106.05 (d) II (iv)).
Dependent claim(s) 2-10, 12-19 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The claims are reciting further embellishment of the judicial exception.
Claim 2: this claim recites executing a clustering algorithm, being mathematical calculations (mathematical concept). Claim 12 is analogous.
Claim 3: this claim recites classification of features related to microwell plates, which merely indicates a field of use or technological environment in which to apply a judicial exception. Claim 13 is analogous.
Claim 4: this claim recites further mathematical transforms/calculations (mathematical concept). Claim 14 is analogous.
Claim 5: this claim recites extracting data which amounts to mere data gathering, considered a pre-solution activity (data gathering) which is an insignificant extra solution activity (see 2106.05(g) (3)). It further recites details about the computing/mathematical concepts by aggregating, which amounts to mathematical calculations. Claim 15 is analogous.
Claim 6: this claim recites more mathematical computations; then displaying information in a GUI and displaying data is one of the examples that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see 2106.05(h) (vi)- displaying the results of collection and analysis of data. Furthermore, a GUI is part of a general purpose computer (see MPEP 2106.05(b)); finally recites determining a label which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. Claim 16 is analogous.
Claim 7: this claim recites cell counts in the microwell plate, which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception per 2106.05(h)). Claim 17 is analogous.
Claim 8: this claim recites machine learning process recited at a high level of generality, which under BRI, invokes computers or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f)). Claim 18 is analogous.
Claim 9: this claim recites machine learning process recited at a high level of generality, which under BRI, invokes computers or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f)).
Claim 10: this claim recites further mathematical computations, and then generating a reference guide which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. Claim 19 is analogous
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 8-9, 11-14, 18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Amthor et al (US 2020/0202508- submitted in IDS dated 10/18/2021, hereinafter Amthor) in view of Sammak et al (US 2006/0039593- submitted in IDS dated 10/18/2021, hereinafter Sammak) and further in view of Qu et al (US 2022/0343140- hereinafter Qu).
Referring to Claim 1, Amthor teaches a computer-implemented method for training a machine learning model to detect executional artifacts in experiments involving microwell plates, the method comprising:
generating first spatial information based on a first heat map that indicates results of a plurality of experiments generated using a first microwell plate (see Amthor at [0074]: “The method includes providing 102 a microscope system that includes an image sensor for recording digital images of a sample, recording 104 digital images using the image sensor during regular operation of the microscope system, and providing 106 at least one recorded digital image as at least one input data set to an image analysis system”. Also at [0075: “determining 108 at least one feature by way of the image analysis system in the at least one recorded digital image”. Further at [0077]: “The time at which the recorded images were produced, or at which the corresponding experiment was performed, is plotted on the x-axis (time/date combination). Experiment measurement values are plotted as an example on the y-axis”. Previously at [0059], Amthor explains that the sample describes an object to be examined such as a multiwell plate, therefore, this determination of features from the image analysis during experiments corresponds to the claimed ‘spatial information based on a first heat map’);
determining a first set of features based on the first spatial information (see [0075]: “determining 108 at least one feature by way of the image analysis system in the at least one recorded digital image, wherein the feature correlates to a malfunction of the microscope system during regular operation, producing 110 a status signal for a state of the microscope system by way of the image analysis system based on the at least one digital image and the at least one feature contained therein”. Therefore, this determination of the features for correlation and status signal productions correspond to the claimed ‘computing a first set of features”); and
executing one or more machine learning operations based on the first set of features to generate a trained machine learning model, wherein, in operation, the trained machine learning model classifies sets of features associated with different microwell plates with respect to a plurality of labels indicating a plurality of executional artifacts caused by a different plurality of experiments that correspond to the plurality of experiments and that are executed in the different microwell plates (see Amthor at [0075]: “This can be done by way of text or by way of a graphic, by presenting an image of the microscope system while specially marking the component that is to be maintained. It is additionally possible to output an indication relating to the type of maintenance procedure (for example cleaning the optical system, replacing the illumination etc.”. Further at [0076]: “This includes determining 202 a time interval for a necessary or recommended maintenance procedure at the microscope system, capturing 204 further parameter values of components of the microscope system, training 206 the learning system in a different form (supervised/unsupervised learning), capturing 208 parameter values of operating elements—possibly also in time-dependent fashion—using 210 a combination of parameter values for generating a status signal for an ascertained maintenance procedure for the microscope system, and using 212 the further parameter values as input data for the learning system”. Finally at [0077]: “The time at which the recorded images were produced, or at which the corresponding experiment was performed, is plotted on the x-axis (time/date combination). Experiment measurement values are plotted as an example on the y-axis”. Therefore, Amthor’s determination of proper maintenance according to the feature of the image during experiments by a learning system corresponds to the claimed “trained classifier”).
Even though Amthor broadly teaches generating first spatial information based on a first heat map that indicates results of a plurality of experiments generated using a first microwell plate, Sammak, in an analogous system, teaches generating first spatial information (see Sammak at [0089]: “Once a specific window with a specific width is chosen, that window is applied across the entire signal. The result is a single resolution analysis of the image. A multi-resolution analysis, where the image is analyzed over windows of varying widths, would provide a more detailed and accurate analysis of the signal. This is precisely what the wavelet transform achieves”);
Sammak further teaches determining a first set of features based on the first spatial information (see Sammak at [0012]: “the image features of the invention may be derived by a texture analysis method. Exemplary texture analysis methods include, without limitation, wavelet decomposition, multiresolution analysis, time frequency analysis, dilate and erode, co-occurrence matrices, Fourier spectral analysis, Gabor methods, wavelet packet decomposition, statistical analysis of wavelet coefficients, Markov Random fields, autoregression models, spatial domain filters, Fourier and Wavelet domain filters, and texture segmentation. Further at [0097]: “Applying Equations 2.10 and 2.12 across all B subbands in the wavelet decomposition of an image yields the wavelet energy signature of that image. Thus, the vector, x=[E.sub.1, E.sub.2, . . . , E.sub.B, MD.sub.1, MD.sub.2, . . . , MD.sub.B] (2.13) is the resulting wavelet energy textural feature representation”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Amthor with the above teachings of Sammak by computing information indicating results of experiments using a first microwell plate, as taught by Amthor, wherein the information is spatial information, as taught by Sammak. The modification would have been obvious because one of ordinary skill in the art would be motivated to image the whole area of the microplate more efficiently (see Sammak at [0053]: “The smaller format of a microplate increases the overall efficiency of the system by minimizing the quantities of the reagents, storage and handling during preparation and the overall movement required for the scanning operation. In addition, the whole area of the microplate can be imaged more efficiently”).
In addition, Qu explicitly teaches, in an analogous system, computing first spatial information based on a first heat map (see Qu at [0053]: “a first attention module of the first branch 500 may overlay a first spatial attention heat map onto the first set of data received by the first branch 500, and a second attention module of the second branch 502 may overlay a second spatial attention heat map onto the second set of data received by the second branch 502. The attention module may include a space map network (e.g., corresponding to the spatial attention heat map) which is adjusted based on a final predicted label (error type/no error) of an input image” and at [0058]: “The CBAM provides a spatial heat map and color-channel attention feature. Thus, the additional channel attention module may focus attention on the channel that is associated with the target element that is of interest to the particular defect type”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amthor and Sammak with the above teachings of Qu by computing spatial information associated with a first microwell plate, as taught by Amthor and Sammak, wherein the computing is based on a heat map, as taught by Qu. The modification would have been obvious because one of ordinary skill in the art would be motivated to represent a spatial relationship between the input image and the final predicted label (see Qu at [0055]: “The attention module may include a space map network (e.g., corresponding to the spatial attention heat map) which is adjusted based on a final predicted label (error type/no error) of an input image. The space map network may represent a spatial relationship between the input image and the final predicted label”).
Referring to Claim 2, the combination of Amthor, Sammak and Qu teaches the computer-implemented method of claim 1, further comprising, prior to executing the one or more machine learning operations, executing a clustering algorithm on a plurality of sets of features to generate the plurality of labels (see Amthor at [0036]: “Such time series may also be used by way of the learning system—in particular by unsupervised learning during regular operation of the microscope system—for an assessment of the sensor parameters, a determination of deviations and a determination or recommendation of maintenance procedures or maintenance time points”. Therefore, the unsupervised learning that Amthor teaches for the system to learn the correlation between sensor parameters and determination of recommendation (grouping them together, or clustering) corresponds to the claims ‘clustering algorithm’).
Referring to Claim 3, the combination of Amthor, Sammak and Qu teaches the computer-implemented method of claim 1, wherein the trained machine learning model classifies a given set of features for a particular microwell plate by estimating a label confidence for a label included in the plurality of labels, wherein the label confidence indicates a likelihood that the label applies to the particular microwell plate (see Sammak at [0053]: “For the purpose of the following discussion, the terms `well` and `microwell` refer to a location in an array of any construction to which cells adhere and within which the cells are imaged”. Further at [0125-0126]: “We interpret h(x) as the confidence in the binary decision. Larger positive/negative values of h(x) show higher confidence in the corresponding +1/-1 decision. The most beneficial properties of SVM is that the SVM attempts to minimize not only empirical error but also the more appropriate generalization error by maximizing margin”. Further at [0171]: “Each analysis calculated a binary classification f(x.sub.i) and confidence value h(x.sub.i) for each pixel x.sub.i in the stem cell colony image being reclassified”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Amthor with the above teachings of Sammak by computing information associated with a first microwell plate, as taught by Amthor, and having a confidence level, as taught by Sammak. The modification would have been obvious because one of ordinary skill in the art would be motivated to image the whole area of the microplate more efficiently (see Sammak at [0053]: “The smaller format of a microplate increases the overall efficiency of the system by minimizing the quantities of the reagents, storage and handling during preparation and the overall movement required for the scanning operation. In addition, the whole area of the microplate can be imaged more efficiently”).
Referring to Claim 4, the combination of Amthor, Sammak and Qu teaches the computer-implemented method of claim 1, wherein generating the first spatial information comprises applying a wavelet transform to the first heat map (see Sammak at [0089]: “Once a specific window with a specific width is chosen, that window is applied across the entire signal. The result is a single resolution analysis of the image. A multi-resolution analysis, where the image is analyzed over windows of varying widths, would provide a more detailed and accurate analysis of the signal. This is precisely what the wavelet transform achieves”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Amthor with the above teachings of Sammak by computing information associated with a first microwell plate, as taught by Amthor, and applying a wavelet transform, as taught by Sammak. The modification would have been obvious because one of ordinary skill in the art would be motivated to image the whole area of the microplate more efficiently (see Sammak at [0053]: “The smaller format of a microplate increases the overall efficiency of the system by minimizing the quantities of the reagents, storage and handling during preparation and the overall movement required for the scanning operation. In addition, the whole area of the microplate can be imaged more efficiently”).
Referring to Claim 8, the combination of Amthor, Sammak and Qu teaches the computer-implemented method of claim 1, wherein a first machine learning operation included in the one or more machine learning operations comprises a supervised machine learning operation, an unsupervised machine learning operation, a semi-supervised machine learning operation, or a reinforcement learning operation (see Amthor at [0044]: “the learning system may be a neural network—or for example a support vector machine. The neural network may be trained using supervised learning or unsupervised learning”).
Referring to Claim 9, the combination of Amthor, Sammak and Qu teaches the computer-implemented method of claim 1, wherein the trained machine learning model comprises a trained random forest, a trained neural network, a trained decision tree, or a trained support vector machine (see Amthor at [0044]: “the learning system may be a neural network—or for example a support vector machine. The neural network may be trained using supervised learning or unsupervised learning”).
Referring to independent Claim 11 and Claim 20, they are rejected on the same basis as independent claim 1 since they are analogous claims. The only slight difference is the use of the term “value array” instead of “heat map”, however, the instant application specification defines these terms as analogous (see instant application specification at [0042]: “Each heat map is a 2D array of measurement values or a visual representation of a 2D array of measurement values”).
Referring to dependent Claim 12, it is rejected on the same basis as dependent claim 2 since they are analogous claims.
Referring to dependent Claim 13, it is rejected on the same basis as dependent claim 3 since they are analogous claims.
Referring to dependent Claim 14, it is rejected on the same basis as dependent claim 4 since they are analogous claims.
Referring to dependent Claim 18, it is rejected on the same basis as dependent claim 8 since they are analogous claims.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Amthor et al (US 2020/0202508- hereinafter Amthor) in view of Sammak et al (US 2006/0039593- hereinafter Sammak) and further in view of Qu et al (US 2022/0343140- hereinafter Qu) and further in view of Joy et al (US 2015/0354012- hereinafter Joy).
Referring to Claim 6, the combination of Amthor, Sammak and Qu teaches the computer-implemented method of claim 1, however, fails to teach further comprising, prior to executing the one or more machine learning operations:
generating a mean heat map based on a plurality of heat maps that includes the first heat map; displaying the mean heat map via a graphical user interface ("GUI"); and determining a first label included in the plurality of labels based on input that is received via the GUI and is associated with the mean heat map.
Joy teaches, in an analogous system, further comprising, prior to executing the one or more machine learning operations:
generating a mean heat map based on a plurality of heat maps that includes the first heat map; displaying the mean heat map via a graphical user interface ("GUI"); and determining a first label included in the plurality of labels based on input that is received via the GUI and is associated with the mean heat map (see Joy at [0027-0028], figs 8-10; ‘FIG. 9 depicts, in accordance with various embodiments of the invention, consensus k-means heat maps for k=2 to 10 generated with AKT pathway genes in the discovery dataset (GBM195). Red indicates total consensus (consensus index of 1) while white indicates no consensus (consensus index of 0).’ And ‘FIG. 10 depicts, in accordance with various embodiments of the invention, average expression of AKT pathway genes in subgroups’).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amthor, Sammak and Qu with the above teachings of Joy by computing spatial information associated with a first microwell plate wherein the computing is based on a heat map, as taught by Amthor, Sammak and Qu, and computing a mean heat map, as taught by Joy. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve clinical trial design, decreasing their cost and maximizing the number of therapeutics that can be evaluated (see Joy at [0241]: “It suggests incorporating AKT classification can improve clinical trial design, decreasing their cost and maximizing the number of therapeutics that can be evaluated. In addition, AKT based classification can enhance drug discovery since new pathways and drug targets will be easier to find in molecularly homogeneous samples”).
Referring to dependent Claim 16, it is rejected on the same basis as dependent claim 6 since they are analogous claims.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Amthor et al (US 2020/0202508- hereinafter Amthor) in view of Sammak et al (US 2006/0039593- hereinafter Sammak), in view of Qu et al (US 2022/0343140- hereinafter Qu) and further in view of Housey et al (US 2010/0016298- hereinafter Housey).
Referring to Claim 7, the combination of Amthor, Sammak and Qu teaches the computer-implemented method of claim 1, however, fails to teach wherein the first heat map specifies a plurality of cell counts, and each cell count included in the plurality of cell counts is associated with a different well that is included in the first microwell plate.
Housey teaches, in an analogous system, wherein the first heat map specifies a plurality of cell counts, and each cell count included in the plurality of cell counts is associated with a different well that is included in the first microwell plate (see Housey at [0670]: All of the figures displaying cell count and viability assays utilized this system for data acquisition and analysis. Simultaneously with the cell count performed, the system is also capable of determining overall cell viability by distinguishing counted, imaged cells that have excluded trypan blue (counted as "viable" cells) from cells which have taken up the trypan blue dye (counted as "non-viable" cells). Injection of trypan blue into the cell sample occurs immediately prior to the sample being sequentially injected into the microcell for simultaneous cell counting and imaging).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amthor, Sammak and Qu with the above teachings of Housey by computing spatial information associated with a first microwell plate wherein the computing is based on a heat map, as taught by Amthor, Sammak and Qu, wherein the first heat map specifies a plurality of cell counts, as taught by Housey. The modification would have been obvious because one of ordinary skill in the art would be motivated to incorporate different colors having different meanings or ‘intensities’, given the advantage of a human grasping the meaning of the information faster than of a digital nature (see Housey at [0670]: “All of the figures displaying cell count and viability assays utilized this system for data acquisition and analysis. Simultaneously with the cell count performed, the system is also capable of determining overall cell viability by distinguishing counted, imaged cells that have excluded trypan blue (counted as "viable" cells) from cells which have taken up the trypan blue dye (counted as "non-viable" cells)”).
Referring to dependent Claim 17, it is rejected on the same basis as dependent claim 7 since they are analogous claims.
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Amthor et al (US 2020/0202508- hereinafter Amthor) in view of Sammak et al (US 2006/0039593- hereinafter Sammak), in view of Qu et al (US 2022/0343140- hereinafter Qu) and further in view of Constandt (US 2016/0210337- hereinafter Constandt).
Referring to Claim 10, the combination of Amthor, Sammak and Qu teaches the computer-implemented method of claim 1, however, fails to teach further comprising: generating a plurality of mean heat maps based a plurality of heat maps that is associated with the plurality of labels; and generating a reference guide that is associated with the trained machine learning model based on the plurality of mean heat maps and the plurality of labels.
Constandt teaches, in an analogous system, further comprising: generating a plurality of mean heat maps based a plurality of heat maps that is associated with the plurality of labels; and generating a reference guide that is associated with the trained machine learning model based on the plurality of mean heat maps and the plurality of labels (Constandt, 0079, figs 1 and 2: “This is for example shown in FIG. 2, in which two search result facets 26, one for “drugs” and one for “target diseases””. In the example shown in FIG. 2, this could mean that the heat map visualisation type 42 of the “drugs” search result facet 26 is for example changed to a histogram visualisation type 42 by the user by accessing a number of choices offered by means of the visualisation type modifier 54. Here Constandt discloses a plurality of heat maps for different drugs).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amthor, Sammak and Qu with the above teachings of Constandt by computing spatial information associated with a first microwell plate wherein the computing is based on a heat map, as taught by Amthor, Sammak and Qu, and having a plurality of heat maps associated with a plurality of labels, as taught by Constandt. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve the visualization of the results by a user (see Constandt at [0079]: “Additionally the visualisation module 50 presents visualisation modifiers 54 to the users configured. By means of these visualisation modifiers 54, also shown in FIG. 2, the users are able to request a visualisation type modification 56. This means that these visualisation modifiers 54 allow the users to change the visualisation type 42 of the presented visualisation”).
Referring to dependent Claim 19, it is rejected on the same basis as dependent claim 10 since they are analogous claims.
Allowable Subject Matter
For claims 5 and 15, no art rejection is made for these claims, they are only rejected under 35 USC 101 as explained above in this office action.
Conclusion
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/LUIS A SITIRICHE/Primary Examiner, Art Unit 2126
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