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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/8/24 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “processing a microscope image with an ordinal classification model in order to calculate a classification with respect to classes that form an order” and “determining a confidence of the classification based on a consistency of the classification estimates of the binary classifiers”. The claims recite language related to the mental process and mathematics calculations groupings of abstract ideas. Specifically, the broadest reasonable interpretation of “processing a microscope image with an ordinal classification model in order to calculate a classification with respect to classes that form an order” and “determining a confidence of the classification based on a consistency of the classification estimates of the binary classifiers” are that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III A user is able to view an image and classify.
The broadest reasonable interpretation of “determining a confidence of the classification based on a consistency of the classification estimates of the binary classifiers” encompasses mathematical concepts (e.g., determining a confidence) that can be performed mentally.
Limitations “processing a microscope image with an ordinal classification model in order to calculate a classification with respect to classes that form an order” and “determining a confidence of the classification based on a consistency of the classification estimates of the binary classifiers” are recited as being performed by a computer. The computer is recited at a high level of generality. In the limitations, the computer is used as a tool to perform the generic computer function of processing an image and calculate a confidence performing the abstract idea, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
The claims recite mere instructions to implement an abstract idea on a computer by reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. There are no additional elements that would integrate the recited judicial exception into a practical application, therefore claim is directed to the judicial exception.
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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.
Claims 1-8, 12-17 are rejected under 35 U.S.C. 103 as being unpatentable over Arcadu et al., United States Patent Publication 20230047100 (hereinafter “Arcadu”), in view of Murphy et al., United States Patent Publication 20180247107 (hereinafter “Murphy”).
Claim 1:
Arcadu discloses:
A computer-implemented method for determining a confidence of a calculated classification, comprising:
processing a medical image with an ordinal classification model in order to calculate a classification with respect to classes that form an order (see paragraph [0007]). Arcadu teaches processing a medical image with an ordinal classification model to calculate a classification based on classes that form an order;
wherein the ordinal classification model comprises a plurality of binary classifiers which, instead of calculating calculation estimates with respect to the classes, calculate classification estimates with respect to cumulative auxiliary classes, wherein the cumulative auxiliary classes differ in how many consecutive classes of the order are combined (see paragraphs [0025], [0028] and [0034]). Arcadu teaches the ordinal classification model comprises binary classifiers that ca;
wherein the classification is calculated from the classification estimates of the binary classifiers (see paragraph [see paragraph [0034]-[0037] and [0040]). Arcadu teaches the classification is calculated/estimated using binary classifiers ; and
determining a confidence of the classification based on a consistency of the classification estimates of the binary classifiers (see paragraphs [0065]-[0066], [0168], [0177]). Arcadu teaches determining a confidence score of the classification based on the binary classifier and the consistency.
Arcadu fails to expressly disclose processing a microscope image.
Murphy discloses:
processing a microscope image (see paragraph [0051]). Murphy teaches processing and classifying a microscopic image.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify the method disclosed by Arcadu to include processing a microscopic image for the purpose of efficiently screen out irrelevant endoscopic images and detect endoscopic images in which abnormal cell or tissue growth is present. as recited by Murphy.
Claim 2:
Arcadu discloses:
wherein the classification of the ordinal classification model classifies into one of a plurality of classes, which relate to one of the following:
a number, confluence or size of depicted objects in the microscope image; or a quality statement regarding a sample, an image acquisition or an employed microscope component (see paragraph [0008]). Arcadu teaches the classifications relate to a quality state regarding training data; or
brightness values of pixels of an output image calculated by the ordinal classification model, in particular a virtually stained, noise-reduced or resolution-enhanced output image.
Claim 3:
Arcadu discloses:
wherein the confidence is determined to be lower, the more pronounced inconsistencies between the classification estimates of the binary classifiers are (see paragraph [0066]). Arcadu teaches the confidence is low when there are inconsistencies with classification estimates.
Claim 4:
Arcadu discloses:
wherein the binary classifiers form a series corresponding to the order of the classes; wherein the consistency is determined based on a curve of the classification estimates over the series of the binary classifiers, wherein each classification estimate indicates a probability of an applicability of the corresponding auxiliary class (see paragraphs [0076], [0079], [0080]). Arcadu teaches having the binary classifiers having an order of classes and the consistency being based on a curve of the classifications estimates. The estimates indicate a probability according to the different additional classes.
Claim 5:
Arcadu discloses:
wherein the confidence is determined to be lower, the more the curve deviates from a monotonic curve (see figure 5B). Arcadu teaches when the curve deviates from the curve, the confidence is getting lower.
Claim 6:
Arcadu discloses:
wherein an edge is determined in the curve of the classification estimates between classification estimates that indicate an applicability of the corresponding auxiliary classes and classification estimates that indicate a non-applicability of the corresponding auxiliary classes (see figure 5B). Arcadu teaches based on the curve, the estimates indicate low confidence in the applicability of the classification and a high confidence in the applicability;
wherein the confidence is determined to be lower, the greater a width of the edge or the flatter a slope of the edge is (see figure 5B and paragraph [0101]). Arcadu teaches the confidence when the slope flattens;
wherein the confidence is estimated to be lower if more than one edge is determined in the curve of classification estimates (see figure 5B and paragraph [0101]). Arcadu teaches determining classification characteristics of the confidence based on the curve.
Claim 7:
Arcadu discloses:
wherein the confidence is determined to be lower if an edge in the curve of the classification estimates extends up to a start or an end of the series of the binary classifiers (see paragraph [0181] and figure 5B). Arcadu teaches characteristics about the estimate curve based on a series of binary classifiers.
Claim 8:
Arcadu discloses:
wherein the confidence is determined to be lower, the more the curve of the classification estimates deviates from a point symmetry (see figure 5B). Arcadu teaches if the curve deviates then the confidence is low.
Claim 12:
Arcadu discloses:
wherein each classification estimate indicates a probability of an applicability of the corresponding auxiliary class, wherein the probability is indicated with a value between 0 and 1 (see paragraphs [0082] and [0083]). Arcadu teaches each estimate indicates a probability of applicability between 0 and 1.
wherein the confidence is determined to be higher, the closer all estimated probabilities are to 0 or 1 (see paragraphs [0082] and [0083]). Arcadu teaches when the confidence is higher, the probability is closer to 0 or 1.
Claim 13:
Arcadu discloses:
wherein the confidence is determined to be lower, the higher an entropy of the classification estimates is (see paragraph [0168]). Arcadu teaches the confidence is low the higher the decline of predictability.
Claim 14:
Arcadu discloses:
wherein the classification estimates or quantities derived therefrom are input into a machine-learned confidence estimation model that was trained using training data to calculate a confidence of the classification from classification estimates or quantities derived therefrom (see paragraph [0015] and [0101]). Arcadu teaches estimating classifications based on a machine learning confidence estimation model.
Claim 15:
Arcadu discloses:
wherein the ordinal classification model is trained with training data comprising images and associated auxiliary class annotations, wherein, in a training of the ordinal classification model, deviations of the classification estimates of the binary classifiers from the auxiliary class annotations are captured in at least one loss function to be minimized (see paragraph [0034] and [0040]). Arcadu teaches the ordinal classification model being trained with images and associated auxiliary class.
Arcadu fails to expressly disclose processing a microscope image.
Murphy discloses:
processing a microscope image (see paragraph [0051]). Murphy teaches processing and classifying a microscopic image.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify the method disclosed by Arcadu to include processing a microscopic image for the purpose of efficiently screen out irrelevant endoscopic images and detect endoscopic images in which abnormal cell or tissue growth is present. as recited by Murphy.
Claims 16, 17:
Although Claim 16 is a system claims and Claim 17 is a computer program claim, they are interpreted and rejected for the same reasons as the method of Claim 1.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Arcadu and Murphy, in view of Nakae et al., United States Patent Publication 20230047100 (hereinafter “Nakae”).
Claim 9:
Arcadu and Murphy fails to expressly disclose the curve fitting a sigmoid function.
Nakae discloses:
wherein the curve of the classification estimates is evaluated by fitting a sigmoid function to the curve of classification estimates (see paragraph [0227]). Nakae teaches the curve of the estimates is evaluating by fitting a sigmoid function, and
wherein the confidence is determined based on deviations of the classification estimates from the fitted sigmoid function (see paragraph [0227]). Nakae teaches the confidence is determined based on the fitting of the sigmoid function.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Arcadu and Murphy to include having the curve fit a sigmoid function, for the purpose of efficiently classifying and determining various treatment which can be minimally administered and/or the therapeutic effects can be classified, as recited by Nakae.
Claim 10:
Arcadu and Murphy fails to expressly disclose the curve fitting a sigmoid function.
Nakae discloses:
wherein a deviation of a classification estimate from the sigmoid function is given a greater weight, the further away said classification estimate is from an inflection point in the sigmoid function (see paragraph [0227]). Nakae teaches the confidence is lower based on the distance away from the fitting of the sigmoid function.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Arcadu and Murphy to include having the curve fit a sigmoid function, for the purpose of efficiently classifying and determining various treatment which can be minimally administered and/or the therapeutic effects can be classified, as recited by Nakae.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Arcadu and Murphy, in view of Sychra et al., “Fourier Classification Images in Cardiac Nuclear Medicine” (hereinafter “Sychra”).
Claim 11:
Arcadu and Murphy fails to expressly disclose using a Fourier analysis to classify images.
Sychra discloses:
wherein the curve of the classification estimates is evaluated based on a Fourier analysis (see page 3, Section “C. Discriminant Analysis…”). Sychra teaches determining the classification of images using a Fourier analysis.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Arcadu and Murphy to include using a Fourier analysis to classify images for the purpose of efficiently extract information of interest from multiple radiological images as they may contain an overwhelming amount of information (and noise) that is only remotely or not at all related to the diagnosis being seeked, as recited by Sychra.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIONNA M BURKE whose telephone number is (571)270-7259. The examiner can normally be reached M-F 8a-4p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Hong can be reached at (571)272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TIONNA M BURKE/Examiner, Art Unit 2178 5/2/26
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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