DETAILED ACTION
The Applicant’s filing, received 18 September 2023, has been fully considered. The following rejections and/or objections constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claims 7 and 18 are objected to.
Priority
This application claims benefit of 63/376,558, filed 21 September 2022.
Information Disclosure Statement
The information disclosure statement (IDS) received 29 January 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings received 18 September 2023 have been accepted.
Claim Objections
Claims 7 and 18 are objected to because of the following informalities: The word “great” in line five should be replaced with the word “greater.”
Appropriate correction is required.
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 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Claims 1-11 recite a system for tumor analysis comprising a memory and one or more processors coupled to the memory (i.e., a machine and/or a manufacture); claims 12-19 recite a method for tumor analysis (i.e., a process); and claim 20 recites a non-transitory computer-readable medium having instructions stored thereon (i.e., a machine and/or a manufacture).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites a system for tumor analysis comprising a memory and one or more processors coupled to the memory, the memory including instructions which, when executed by the one or more processors, cause the one or more processors to execute the mental processes and/or mathematical concepts recited by independent claim 12, as noted below.
Independent claim 12 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
determining, via a machine learning component, a characteristic of the tumor based on the one or more input features, the characteristic being indicative of patient survivability (i.e., mental processes and mathematical concepts, e.g., a machine learning model can also be calculated manually (i.e., in the human mind with the aid of pen and paper) depending on the type of model, e.g., the simplest type of machine learning model could be a linear regression, which predicts a continuous numerical output by fitting a straight line (y = mx + b) to data, by using an input variable (x) and an output variable (y), making its mathematical relationship easy to calculate and interpret).
Independent claim 20 recites a non-transitory computer-readable medium having instructions stored thereon, that when executed by one or more processors, cause the one or more processors to execute the mental processes and/or mathematical concepts recited by independent claim 12, as noted above.
Dependent claims 2-10 and 13-19 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
compute the value indicating the circularity of the tumor based on an area of the tumor divided by an area of a circle having a same perimeter length as the tumor (i.e., mental processes and mathematical concepts).
Dependent claim 3 further recites:
the one or more input features further include a value indicating a discrepancy between a shape of the tumor and a shape of a lumen associated with the tumor (i.e., mental processes, i.e., information).
Dependent claim 4 further recites:
the one or more input features further include a value indicating an eccentricity of the tumor (i.e., mental processes, i.e., information).
Dependent claim 5 further recites:
the tumor is one of a plurality of neighboring tumors (i.e., mental processes, i.e., information), and
the one or more input features further include a value indicating a coherence between orientations of the plurality of neighboring tumors (i.e., mental processes, i.e., information).
Dependent claim 6 further recites:
calculate the value indicating the coherence based on an angle between principal axes of at least two of the plurality of neighboring tumors (i.e., mental processes and mathematical concepts).
Dependent claim 7 further recites:
the tumor is one of a plurality of tumors (i.e., mental processes, i.e., information), and
the one or more input features include a quantity of the plurality of tumors that have a number of whitespace features greater than a threshold (i.e., mental processes and mathematical concepts).
Dependent claim 8 further recites:
the one or more input features includes a number of instances of cytoplasmic vacuolization, signet ring cells, and shearing detected for the tumor (i.e., mental processes, i.e., information).
Dependent claim 9 further recites:
the one or more input features include a measure of an evolution of an architecture of the tumor (i.e., mental processes, i.e., information).
Dependent claim 10 further recites:
the machine learning component is trained using a labeled dataset associated with geometric shapes of tumors (i.e., mathematical concepts).
Dependent claim 13 further recites:
computing the value indicating the circularity of the tumor based on an area of the tumor divided by an area of a circle having a same perimeter length as the tumor (i.e., mental processes and mathematical concepts).
Dependent claim 14 further recites:
the one or more input features further include a value indicating a discrepancy between a shape of the tumor and a shape of a lumen associated with the tumor (i.e., mental processes, i.e., information).
Dependent claim 15 further recites:
the one or more input features further include a value indicating an eccentricity of the tumor (i.e., mental processes, i.e., information).
Dependent claim 16 further recites:
the tumor is one of a plurality of neighboring tumors (i.e., mental processes, i.e., information), and
the one or more input features further include a value indicating a coherence between orientations of the plurality of neighboring tumors (i.e., mental processes, i.e., information).
Dependent claim 17 further recites:
calculating the value indicating the coherence based on an angle between principal axes of at least two of the plurality of neighboring tumors (i.e., mental processes and mathematical concepts).
Dependent claim 18 further recites:
the tumor is one of a plurality of tumors (i.e., mental processes, i.e., information), and
the one or more input features include a quantity of the plurality of tumors that have a number of whitespace features greater than a threshold (i.e., mental processes and mathematical concepts).
Dependent claim 19 further recites:
the one or more input features includes a number of instances of cytoplasmic vacuolization, signet ring cells, and shearing detected for the tumor (i.e., mental processes, i.e., information).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., determine, via a machine learning component, a characteristic of the tumor based on the one or more input features, the characteristic being indicative of patient survivability), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., determine, via a machine learning component, a characteristic of the tumor based on the one or more input features, the characteristic being indicative of patient survivability) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-20 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
In the instant application, the independent claims provide additional elements to receive ‘receive one or more input features associated with a geometric shape of a tumor’, however once the data is received, the subsequent step only uses machine learning to calculate a characteristic of the tumor, followed by outputting an indication of the characteristic. However, the claims do not recite any limitations to which the ‘indication of the characteristic’ (i.e., the result of the data analysis) is practically applied.
Dependent claims 3-5, 7-10, and 13-19 do not further recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
a memory;
one or more processors coupled to the memory;
receive one or more input features associated with a geometric shape of a tumor, the one or more input features including at least a value indicating a circularity of the tumor (i.e., receive data); and
output an indication of the characteristic (i.e., output data).
The additional elements in independent claim 12 include:
receiving one or more input features associated with a geometric shape of a tumor, the one or more input features including at least a value indicating a circularity of the tumor (i.e., receiving data); and
outputting an indication of the characteristic (i.e., outputting data).
The additional elements in independent claim 20 include:
a non-transitory computer-readable medium having instructions stored thereon;
one or more processors;
receive one or more input features associated with a geometric shape of a tumor, the one or more input features including at least a value indicating a circularity of the tumor (i.e., receive data); and
output an indication of the characteristic (i.e., output data).
The additional elements in dependent claims 2, 6 and 11 include:
one or more processors (claims 2 and 6); and
to output the indication of the characteristic, the one or more processors are configured to output an indication of a biological aggressiveness of the tumor (claim 11).
The additional elements of a memory (claim 1); one or more processors coupled to the memory (claim 1); a non-transitory computer-readable medium having instructions stored thereon (claim 20); one or more processors (claims 2, 6 and 20); and the one or more processors are configured to output an indication of a biological aggressiveness of the tumor (claim 11); invoke a computer and/or computer-related components merely as tools for use in the claimed process, such that they amount to no more than mere instructions to apply the exceptions using a generic computer (MPEP 2106.05(f)), and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional elements of receive/receiving one or more input features associated with a geometric shape of a tumor, the one or more input features including at least a value indicating a circularity of the tumor (i.e., receive/receiving data) (claims 1, 12 and 20); is merely a pre-solution activity of gathering data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of output/outputting an indication of the characteristic (i.e., output/outputting data) (claims 1, 12 and 20); is merely a post-solution activity of outputting data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and as such, when all limitations in claims 1-20 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-20 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 3-5, 7-10, and 13-19 do not further recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1, 12 and 20 and dependent claims 2, 6 and 11 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a memory (claim 1); one or more processors coupled to the memory (claim 1); a non-transitory computer-readable medium having instructions stored thereon (claim 20); one or more processors (claims 2, 6 and 20); the one or more processors are configured to output an indication of a biological aggressiveness of the tumor (claim 11); receive/receiving data (claims 1, 12 and 20); and output/outputting data (claims 1, 12 and 20); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
Therefore, when taken alone (i.e., individually), all additional elements in claims 1-20 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-20 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 10, 11, 12, 13, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hafez et al. (“Predicting likelihood and site of metastasis from patient records.” US 2021/0319906).
Independent claims 1, 12, and 20 encompass steps for tumor analysis comprising receiving one or more input features associated with a geometric shape of a tumor, the one or more input features including at least a value indicating a circularity of the tumor; determining, via a machine learning component, a characteristic of the tumor based on the one or more input features, the characteristic being indicative of patient survivability; and outputting an indication of the characteristic.
Dependent claims 2, 10, 11, and 13 further define the computation of a value indicating the circularity of the tumor (claims 2 and 13); further define the machine learning component as being trained using a labeled dataset associated with geometric shapes of tumors (claim 10); and further define the output indication as one of a biological aggressiveness of the tumor (claim 13).
Hafez et al. teaches systems and methods for predicting metastasis of a cancer in a subject by obtaining a plurality of data elements for the patient’s cancer and applying one or more models to the plurality of data elements to determine one or more indications of whether the cancer will metastasize (Abstract).
Regarding independent claims 1, 12, and 20, Hafez et al. shows generating and modeling predictions of patient objectives that are generated from patient information represented by feature modules implemented by a combination of both hardware and software and further shows that the information acquired from patients’ electronic medical records (EMR), unstructured text, genetic sequencing, imaging, and various other information can be converted into features that are used for training a plurality of machine learning models (paras. [0043] & [0044]); features comprise imaging features extracted from digital images, wherein the imaging features can be divided into two categories: biologically meaningful features and geometrically meaningful features, wherein the geometrically meaningful features include average tumor cell circularity (para. [0098]); the model is a trained survival function, also known as a complementary cumulative distribution function, wherein the survival function relates the time that passes, before some event occurs (e.g., metastasis), to one or more covariates (e.g., features comprising imaging data) associated with that quantity of time (para. [0437]); and objective modules that may comprise a plurality of modules such as Observed Survival, Progression Free Survival, wherein each module may be associated with one or more targets, e.g., “Death”, and time periods “6, 12, 24, and 60 months” (para. [0070]).
Regarding dependent claims 2 and 13, Hafez et al. shows computing a value for average cell circularity (calculated as cell area divided by the square of the perimeter, averaged over all cells, ranges between 0 and 1) (para. [0098]).
Regarding dependent claim 10, Hafez et al. shows a type of classifier that use labeled data (para. [0434]) and further shows that the information acquired from patients’ electronic medical records (EMR) (e.g., imaging) can be converted into features that are used for training a plurality of machine learning models (para. [0044]).
Regarding dependent claim 11, Hafez et al. shows an imaging module for features associated with patient information, wherein the features may include characteristics such as tumor budding, size, aggressiveness, metastasis (para. [0057]) and an output module comprising one or more indications of whether the cancer will metastasize in the subject (para. [0311]).
Thus, Hafez et al. anticipates instant claims 1, 2, 10, 11, 12, 13, and 20.
Claim Rejections - 35 USC § 103
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.
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, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Da Col et al. (“Image analysis of circulating tumor cells and leukocytes predicts survival and metastatic pattern in breast cancer patients.” Frontiers in Oncology, 2022, vol. 12, article 725318, pp. 1-11).
Independent claims 1, 12, and 20 encompass steps for tumor analysis comprising receiving one or more input features associated with a geometric shape of a tumor, the one or more input features including at least a value indicating a circularity of the tumor; determining, via a machine learning component, a characteristic of the tumor based on the one or more input features, the characteristic being indicative of patient survivability; and outputting an indication of the characteristic.
Dependent claims 2-4 and 13-15 further define the computation of a value indicating tumor circularity (claims 2 and 13) and input values representing geometric features (claims 3, 4, 14 and 15).
Dependent claim 10 further defines the trained machine learning model.
Dependent claim 11 further defines the output of the indication of the characteristic.
Da Col et al. teaches a method for predicting survival and metastatic pattern in breast cancer patients using image analysis of circulating tumor cells and leukocytes.
Regarding independent claims 1, 12 and 20, Da Col et al. shows image-based features of epithelial circulating tumor cells (eCTC) and leukocyte (CD45pos) cells were used as inputs and overall survival (OS) (≤30 vs. >30 months) or bone metastasis (BM) (absence vs. presence) as output (page 6, col. 1, para. 2); from each cell image the following parameters were provided: circularity (using 2 algorithms, named circularity and circularityOV, the second being more effective on cells with irregular membranes), diameter, and perimeter (page 4, col. 1, para. 1); and using various machine learning approaches for predicting overall survival and bone metastasis (page 5, col. 2, and page 6, col. 1).
Regarding dependent claims 2 and 13, Da Col et al. further shows that the most represent morphological aspect was circularity, which is the most prevalent feature, in various channels and statistical variables, and it is defined as 4π x Area / Perimeter2, and circularity is thus inversely proportional to the square perimeter, meaning that membranes with higher complexity (frequency and extent of indentations) have lower levels of circularity (page 8, col. 2, paras. 1-2).
Regarding dependent claims 3, 4, 14 and 15, Da Col et al. further shows that higher circularity values (simpler membranes) are linked to poor survival, and that in patients with lower overall survival, both nucleus and membrane of eCTC have higher circularity, and in a purely speculative way, in the attempt to attribute a meaning to this information, the ideal representation of a cell with a highly circular membrane and nucleus is a small basal-like or stem-like cell with low differentiation, which might be more responsible of cancer progression, and thus, the increased average circularity of CTC population might indicate an increased proportion of such highly aggressive cells (page 8, col. 2, para. 3) (e.g., Da Col et al. considers the circularity of both the nucleus and the cell membrane, which directly impact the inner space of the cell; and Da Col et al. further considers the increased average circularity of cells which suggests giving consideration to how much the physical shape deviates from a circle of sphere, i.e., eccentricity).
Regarding dependent claim 10, Da Col et al. further shows that the models were trained (page 6, col. 1) although Da Col et al. does not specifically show using a labeled dataset, however the use of machine learning models for supervised learning is ideal for precise tasks like image classification, object detection and medical diagnostics, e.g., training a model on thousands of labeled X-ray images to detect tumors.
Regarding dependent claim 11, Da Col et al. further shows that the increased average circularity of a circulating tumor cell (CTC) population might indicate an increased proportion of such highly aggressive cells (page 8, col. 2, para. 3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Da Col et al., e.g., by incorporating an alternative calculation to derive a circularity value, and using a labeled dataset for machine learning training, as discussed above. One of ordinary skill in the art would have been motivated to modify the methods of Da Col et al., because Da Col et al. shows methods for predicting overall survival using the predictive feature of circularity, because higher circularity values are linked to poor survival. This modification would have had a reasonable expectation of success given that Da Col et al. discloses the combination of quantitative image analysis of morphological features of cells (e.g., circularity) and machine learning models to predict survival and metastatic pattern.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, and further in view of Wang et al. (“Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predicts survival outcome.” Scientific Reports, 2018, vol. 8:10393, pp. 1-9).
Dependent claims 7 and 16 further define the input features of the machine learning model.
Wang et al. teaches an automated tumor region recognition system for lung cancer pathology images that uses a deep convolutional neural network (CNN).
Regarding dependent claims 7 and 16, Wang et al. shows a machine learning model that distinguishes tumor patches from non-malignant and white (empty region) image patches that were extracted from lung pathology images (page 3, para. 1; and Figure 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, by incorporating methods for distinguishing tumor patches from non-malignant and white (empty region) image patches that were extracted from lung pathology images, as shown by Wang et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, with the methods of Wang et al., because Wang et al. shows methods for discovering tumor shape and boundary features that predict survival outcome. This modification would have had a reasonable expectation of success given that both Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, and Wang et al. disclose methods for classifying morphological features of cells to predict survival outcome.
Claims 5, 6, 9, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, and further in view of Lee et al. (“Co-occurring gland angularity in localized subgraphs: Predicting biochemical recurrence in intermediate-risk prostate cancer patients.” PLoS ONE, 2014, vol. 9(5): e97954, pp. 1-14).
Dependent claims 5, 6, 9, 16 and 17 further define the input features of the machine learning model.
Lee et al. teaches the application of advanced computational image analysis to reproducibly describe disease appearance on digitized histopathology images.
Regarding dependent claims 5, 6, 16 and 17, Lee et al. shows using an approach, called co-occurring gland angularity (CGA), where in benign or less aggressive prostate cancer, gland orientations within local neighborhoods are similar to each other, but are more chaotically arranged in aggressive disease (Abstract); and further shows that with increasing degree of malignancy and disease aggressiveness, the coherence of gland orientations within localized regions is completely disrupted, in other words, the entropy (which captures disorder) in gland orientations tends to increase as a function of malignancy (page 2, col. 2, para. 2); and further shows that for each gland, ascribing an angle that reflects the dominant orientation of the gland based off the major axis as shown in Figure 1(a) (page 2, col. 2, para. 4).
Regarding dependent claim 9, Lee et al. further shows that the CGAs provide a compact, interpretable and quantitative representation of gland architecture and prostate cancer morphology which can be employed to distinguish (a) cancer from benign regions, and (b) biochemical recurrence (BCR) from non-biochemical recurrence (NR) cases (page 3, col. 2, para. 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, by incorporating methods for calculating the co-occurring gland angularity (CGA) for gland characteristics of interest, as shown by Lee et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, with the methods of Lee et al., because Lee et al. shows that malignant prostate glands lose their capability to orient themselves and display no preferred directionality, and further shows that the CGA features represent the only feature set which show statistically significant differentiation in the survival outcomes of its predicted patient cohorts. This modification would have had a reasonable expectation of success given that both Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, and Lee et al. disclose methods for classifying morphological features of tumor cells to predict survival outcome.
Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, and further in view of Makki (“Diversity of breast carcinoma: Histological subtypes and clinical relevance.” Clinical Medicine Insights: Pathology, 2015, vol. 8, pp. 23-31) and Xu et al. (“In vivo classification of breast masses using features derived from axial-strain and axial-shear images.” Ultrasonic Imaging, 2012, vol. 34(4), pp. 222-236).
Dependent claims 8 and 19 further define the input features of the machine learning model.
Makki reviews the diversity of breast carcinoma and explores how breast cancer manifests through a wide spectrum of morphological features, unique histopathological variants, and distinct immunohistochemical profiles.
Xu et al. teaches classification of breast masses using features extracted from ultrasound-based, axial-strain and axial-shear images of breast masses for breast-mass differentiation.
Regarding dependent claims 8 and 19, Makki shows that signet ring carcinoma ILC (invasive lobular carcinoma) usually shows cytoarchitectural features of classic ILC combined with significant number of signet ring cells with eccentric, semilunar nucleic and transparent cytoplasmic vacuoles, and that it is important to differentiate this variant from mucinous carcinoma with signet ring features because of their different prognoses (page 29, col. 2, para. 3).
Regarding dependent claims 8 and 19, Makki does not show shearing detected for the tumor.
Regarding dependent claims 8 and 19, Xu et al. shows classification results for breast-mass features including stiffness contrast, size ratio, and a normalized axial-shear strain (Abstract) and shows on composite shear strain images, areas depicted in red represent positive values of the axial-shear, while those in blue depict negative or opposite shearing strains (page 4, para. 4; and page 12, Figure 1).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, by incorporating pathomorphological features of breast cancer, as shown by Makki and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, with the methods of Makki, because Makki shows that breast carcinoma is a heterogeneous group of tumors that exhibit a wide scope of morphological features, different immunohistochemical profiles, and unique histopathological subtypes that have specific clinical course and outcome. This modification would have had a reasonable expectation of success given that both Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, and Makki disclose using morphological features of tumors for classification.
It would have been further prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, by incorporating methods for using a normalized axial-shear strain to derive breast-mass features for classification of in vivo breast masses, as shown by Xu et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, with the methods of Xu et al., because Xu et al. shows that there is a need for sensitive, noninvasive methods to differentiate breast masses. This modification would have had a reasonable expectation of success given that both Da Col et al. as applied to claims 1, 2, 3, 4, 10, 11, 12, 13, 14, 15, and 20 above, and Xu et al. disclose methods for classifying morphological features of tumor cells.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application.
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/STEVEN W. BAILEY/Examiner, Art Unit 1687
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