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 .
Claim Status
Claims 1-15 are pending.
Claims 1-15 were considered.
Claims 1-15 are rejected.
Priority
The instant application filed on 21 August 2023 claims priority to a PCT filed 1 March 2022 and foreign priority to EP 21159970.9 filed 1 March 2021. As such, claims 1-15 have an effective filing date of 1 March 2021.
Information Disclosure Statement
The IDS filed 21 August 2023 was considered by the examiner.
Drawings
The drawings filed 21 August 2023 are accepted.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“Parameter providing module” in claims 1 - 5, 7, 9, 12, and 14
“Artificial intelligence providing module” in claims 1 and 4 – 15
“Temporal breast cancer risk determination module” in claims 1, 4 – 9, 12, and 14
“Input unit” in claim 10
“Training data sets providing unit” in claims 11, 13, and 15
“Training module” in claims 11, 13, and 15
In review of the claims and specification, the functions of these units are disclosed in the specification as:
The parameter providing module is configured to determine the probability parameter and/or the density parameter based on the provided breast image data set and to provide the determined probability parameter and/or the determined the density parameter, respectively. In an embodiment, the parameter providing module is configured to determine the probability parameter by using a further artificial intelligence... the parameter providing module can also be configured to use another software product for determining the probability parameter like known computer-aided detection (CAD) software products which provide a probability parameter being indicative of a probability of having breast cancer. [0029] The parameter providing module can also just be a receiving module configured to receive the respective parameter(s) from another module, which determines the respective parameter(s), and provide the received respective parameter(s). Moreover, the parameter providing module can also be a storage medium in which a determined parameter is stored and from which it is retrieved for providing the stored parameter. [0033]
The artificial intelligence providing module is configured to determine the artificial intelligence by training as described further below with respect to a training apparatus, i.e. the artificial intelligence providing module can be such a training apparatus. However, the artificial intelligence providing module can also just be a receiving module configured to receive the artificial intelligence from another module, which, for instance, determines the artificial intelligence by training, and provides the received artificial intelligence. Moreover, the artificial intelligence providing module can also be a storage medium in which the already trained artificial intelligence is stored and from which it is retrieved for providing the stored artificial intelligence. [0034]
The temporal breast cancer risk determination module computes short term risk based on parameter information from the parameter providing module and the artificial intelligence providing module. [Figures 1 and 2]
The input unit includes a keyboard, a computer mouse, a touchpad, et cetera, in order to allow a user to interact with the apparatus and to provide, for instance, further parameters to be considered by the temporal breast cancer risk determination module. [0081]
The training data sets providing unit configured to provide training data sets as part of the training apparats. [Figure 3]
The training module is part of the training apparatus and configured to train the artificial intelligence by using the training data sets. [Figure 3]
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 14 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to a computer readable storage medium. The specification states: a computer program may be stored/distributed on a suitable medium, such as an optical storage medium…but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. [0117] MPEP § 2106.3, subsection I, states that non-limiting examples of claims that are not directed to any of the statutory categories include transitory forms of signal transmission (often referred to as "signals per se"), such as a propagating electrical or electromagnetic signal or carrier wave. The MPEP further clarifies that a transitory, propagating signal does not fall within any statutory category. (See Mentor Graphics Corp. v. EVE-USA, Inc., 851 F.3d 1275, 1294, 112 USPQ2d 1120, 1133 (Fed. Cir. 2017); Nuijten, 500 F.3d at 1356-1357, 84 USPQ2d at 1501-03.) [MPEP § 2106.03, subsection I] As such, claims 14 and 15 are not directed to one of the four statutory subject matter categories.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to abstract ideas without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step
1: YES) are then analyzed to determine if the claims recite any concepts that equate to an
abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application,
the claims recite the following limitations that equate to abstract ideas:
Claim 1 and dependent claims 2-10, claim 12, and claim 14 recite:
A parameter providing module
An artificial intelligence providing module
A temporal breast cancer risk determination module
Claim 2 and dependent claim 3 recite:
A dense density parameter
A density distribution parameter
Claims 11, 13, and 15 recite:
A training data sets providing unit
An artificial intelligence providing module
A training module
The limitations for the listed claims are evaluations or judgements that can be made through mental observations or mathematical calculations which fall under the “mental processes” and “mathematical concepts” groupings of abstract ideas. Under the broadest reasonable interpretation, the abstract ideas recited in the claims are determined to cover performance either in the mind (calculations by hand or pen and paper) or by mathematical operation (calculations/algorithms). See MPEP § 2106.04(a)(2), subsection III. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation (see, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674: noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. V. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016): holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person's mind" (see Versata Dev. Group V. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016): holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
While claims 14 and 15 recite performing aspects of the methods with a “computer processor”, “computer”, and/or “computer readable storage medium”, there are no additional limitations that indicate that the computer processor, computer, or computer readable storage medium would require anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation on generic computer components, then it falls into the “mental processes” grouping of abstract ideas. As such, claims 1-15 recite abstract ideas (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further
analyzed to determine if the claims as a whole integrate the recited judicial exception into a
practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a
practical application because the claims do not recite an additional element that reflects an
improvement to technology or applies or uses the recited judicial exception in some other
meaningful way. Rather, the instant claims recite additional elements that amount to mere
instructions to implement the abstract idea or insignificant extra-solution activity. Specifically,
the claims recite the following additional elements:
Claim 5 recites:
At least one further parameter extracted from a dense breast tissue thickness map
Claims 14 and 15 recite:
A computer processor
A computer
The limitations for defining terms describe mental processes with additional elements. This judicial exception is not integrated into a practical application because these additional
elements do not add any meaningful limitations. The claims do not include additional elements
that are sufficient to amount to significantly more than the judicial exception because they only
describe more specificity to the types of variables. As such, these limitations equate to mere
instructions to implement the abstract ideas.
There are no limitations that indicate that the computer processor or computer would require anything other than a generic computing system. As such, these limitations equate to mere instructions to implement the abstract ideas on a generic computer that the courts have stated do not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The above recited additional elements do not provide a practical application of the recited
judicial exception. As such, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2:NO).
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are known and commonly used techniques in the art and are mere instructions to apply the recited exception in a generic computing environment.
As discussed above, there are no additional limitations to indicate that the claimed
method requires more than routine use of technology. Additionally, there are no additional limitations to indicate that the computer programs or processor require anything other than generic computer components in order to carry out the recited abstract ideas in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. In addition, mere display of collected and analyzed information that could be performed by the human mind do not render an abstract idea eligible. See Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-15 are not patent eligible.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 9 – 15 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Akselrod-Ballin et al. (US 2020/0395123 A1, 16 June 2019) (Herein referred to as Akselrod-Ballin.)
With respect to claims 1, 12, and 14, Akselrod-Ballin teaches a model able to correctly identify cancer in 34 of the 71 (48%) women where initial radiologist diagnosis was negative, but cancer was detected within a year. [0042] Akselrod-Ballin further teaches using BI-RADS assessments. [0019, 0047, 0087] BI-RADS is a method known in the art to provide a parameter indicative of the probability of breast cancer based on breast tissue density. Fowler et al discloses that mammographic density is an important breast cancer risk factor and the Breast Imaging Reporting and Data System (BI-RADS) lexicon was developed to standardize clinical reporting in mammography. (Medical Physics, 21 October 2013, pages 113502-1 - 113502-9) (Page 113502-1, left column, paragraph 1) Fowler et al further discloses that BI-RADS includes a breast composition classification system that is a four-category ordinal scale comprised of both a qualitative description and an estimate of the percentage of fibroglandular (glandular) tissue content of the breast. (Page 113502-1, left column, paragraph 1 – right column, lines 1-3) Fowler additionally discloses that these descriptors have been used as a discrete measure of breast density for breast cancer risk assessments. (Page 113502-2, left column, lines 2-4) Akselrod-Ballin teaches a neural network component trained for outputting an intermediate value indicative of likelihood of malignancy for a target tissue of a target patient, in response to an input of one or more anatomical images depicting the target tissue of the target patient, and the selected sub-set of non-imaging clinical parameters of the target patient. [0111] Akselrod-Ballin additionally teaches a model for selecting patients for treatment according to a computed indication of likelihood of malignancy in a target tissue of a target patient outputted by the model. [0117]
With respect to claim 9, Akselrod-Ballin teaches all the limitations of claim 1 as described above. Akselrod-Ballin further teaches utilizing clinical parameters including past final BIRADS assessment DM left, past final BIRADS assessment DM right, past final BIRADS assessment US left, and past final BIRADS assessment US right. [0019]
With respect to claim 10 Akselrod-Ballin teaches all the limitations of claim 1 as described above. Akselrod-Ballin further teaches that different models may be provided, for example, per type of anatomical imaging modality (e.g., CT, x-ray, MRI, nuclear medicine scan, PET, ultrasound), and/or per target tissue (e.g., breast, prostate, colon, esophagus, liver, pancreas, brain, lung). [0077] Akselrod-Ballin additionally teaches a computing device with a user interface(s) that includes a mechanism designed for a user to enter data (e.g., select patient anatomical images and/or non-imaging data) and/or view the computed indication of malignancy. [0075] Akselrod-Ballin further states that exemplary user interfaces include one or more of a touchscreen, a display, a keyboard, a mouse, and voice activated software using speakers and microphone. [0075]
With respect to claims 11, 13, and 15, Akselrod-Ballin teaches a method of training a model used for selecting patients for treatment, comprises: training at least one neural network component of the model for outputting an intermediate value indicative of likelihood of malignancy for a target tissue of a target patient in response to an input of at least one anatomical image depicting a target tissue of the target patient, and at least some non-imaging clinical parameters of the target patient, according to a training dataset. [0004] Akselrod-Ballin further teaches defining the non-imaging clinical parameters based on the training dataset by computing a statistical correlation between each non-imaging clinical parameter and ground truth indication of malignancy, and selecting the non-imaging clinical parameters according to a requirement of the statistical correlation. [0020] Akselrod-Ballin additionally teaches a method for computing an indication of likelihood of malignancy in a target tissue of a patient by a trained model based on a combination of images and non-imaging clinical data [0059] and the non-imaging data of sample patients may be included in training dataset for training the model [0066].
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 - 4 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Akselrod-Bellin in view of Huo et al. (US 6,282,305 B1, 5 June 1998) (Herein referred to as Huo.)
Akselrod-Ballin teaches the limitations of claim 1 as applied above under 35 U.S.C. 102.
With respect to claim 2, dependent claim 3, and claim 4, Akselrod-Ballin does not specifically teach a dense density parameter or a density distribution parameter.
Huo teaches a computerized method to automatically extract features that characterize mammographic parenchymal patterns (density distribution parameter) and relate to breast cancer risk. [Column 9, last paragraph]
With respect to claim 3, Akselrod-Ballin does not teach that the dense density parameter is indicative of an amount of glandular and connective breast tissue of the breast as the dense breast tissue having a density being larger than the density of the other breast tissue of the breast.
Huo teaches regions of brightness in mammography associated with fibroglandular tissue are referred to as mammographic density (dense density) and the brightness of these regions can be quantified to yield information regarding the denseness of the region. [Column 13, lines 54-59]
With respect to claim 8, Akselrod-Ballin does not teach breast cancer risks for different predetermined time intervals.
Huo teaches employing multivariate statistical models, such as linear regression analysis and artificial neural networks, to merge multiple computer-extracted features including age into models to predict risk, including both lifetime risk and the 10-year risk. [Column 19, lines 37-42]
It would have been prima facie obvious to one of ordinary skill in the art at the effective
filing date of the invention to have combined the breast cancer risk prediction methods of Akselrod-Ballin and Huo. Regarding claims 2-4, Huo discloses that several groups have conducted experiments to examine the masking hypothesis and researchers found that the masking of cancer did occur in breasts with dense parenchyma; however, their results showed that the effect of the masking on estimation of breast cancer risk was small. [Column 7, lines 54-61] Huo further explains previous studies concluded that women with dense breasts have two disadvantages: 1) they were at increased risk of developing breast cancer, and 2) cancers occurring in dense breast parenchyma were more difficult to detect. [Column 7, lines 61-65] Huo describes additional research that determined the magnitude of the risk of breast cancer associated with mammographic density and found that the estimated relative risk of developing breast cancer depended on the methods that were used to classify mammographic patterns, concluding that women with dense breasts have an increased risk of breast cancer relative to those with fatty breasts. [Column 8, lines 6-15] Huo states that computerized techniques have been investigated to quantitatively evaluate mammographic parenchyma and density to identify women that are at risk of developing breast cancer. [Column 8, lines 19-23 and 51-55] Huo additionally discloses that the development of a computerized method to automatically extract features that characterize mammographic parenchymal patterns and relate to breast cancer risk would potentially benefit women seeking information regarding their individual breast cancer risk. [Column 8, lines 61-65] Regarding claim 8, Huo discloses age is the most important factor for the prediction of breast cancer risk and, as a result, both the lifetime and 10-year risks are included in many models. [Column 21, lines 23-30] Huo further discloses findings that the inclusion of age has little effect in predicting lifetime risk; however, the inclusion of age substantially improved the correlation coefficients in predicting 10-year risk, which implies that age is more important than the mammographic features in predicting 10-year risk, whereas, the mammographic features are more important than age in predicting lifetime risk. [Column 24, lines 7-14] Therefore, one of ordinary skill in the art would have been motivated to combine the methods of Akselrod-Ballin and Huo to incorporate additional features to improve the ability to predict breast cancer risk. One of ordinary skill in the art would have been motivated to include additional parameters related to known breast cancer risk factors in order to provide a more accurate prediction method. The invention is therefore prima facie obvious.
Claims 1 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Akselrod-Ballin in view of Holland et al. (Breast Cancer Research and Treatment, 4 February 2017, pages 541-548) (Herein referred to as Holland.)
Akselrod-Ballin teaches the limitations of claim 1 as applied above under 35 U.S.C. 102.
Akselrod-Ballin does not teach providing a further parameter from a dense breast tissue thickness map.
Holland teaches volumetric breast density maps were computed, which provide the dense tissue thickness for each pixel location. (Page 541, Abstract: Methods) Holland further teaches that three measurements were derived from these maps: percent dense volume (PDV), percent area where dense tissue thickness exceeds 1 cm (PDA), and dense tissue masking model (DTMM). Holland additionally teaches that these three automated measurements (PDV, PDA, and DTMM) were investigated to estimate masking risk. (Page 543, left column, lines 8-16) The instant application references that a parameter extracted from a breast density thickness map could include: a percentage dense area (PDA) or a dense tissue masking model (DTMM) parameter. [0040]
It would have been prima facie obvious to one of ordinary skill in the art at the effective
filing date of the invention to have combined Akselrod-Ballin’s breast cancer risk prediction method with Holland’s quantification of masking risk based on breast density maps. Holland discloses that fibroglandular tissue may mask cancers, and therefore sensitivity of mammography decreases with an increase in breast density.(Page 541, right column, last paragraph) Holland additionally discloses the distribution of dense tissue may play a role in masking and this is reflected in the new BI-RADS definition that considers the densest area of fibroglandular tissue within the breast. (Page 542, left column, paragraph 3) Holland further discloses investigating the ability of several measurements of masking risk to distinguish false negative screening mammograms from true negative screening mammograms to find a method that can identify women who are at high risk to be diagnosed with an interval cancer after a negative screening
exam. (Page 544, right column, Discussion paragraph 1) Holland teaches that the proposed masking risk measurements may have a better performance than visual BI-RADS assessment in distinguishing false negative screening mammograms from true negative screening mammograms and these measurements may be considered as predictive masking measure when
implementing supplemental screening for women at a high risk for interval cancers. (Page 546, right column, last paragraph) Therefore, one of ordinary skill in the art would have been motivated to incorporate known methods for quantifying density-based masking risk into a tissue density-based method for predicting interval breast cancer. The invention is therefore prima facie obvious.
Claims 1, 6, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Akselrod-Ballin in view of Allman.(US 2020/0102617 A1, 23 January 2018)
Akselrod-Ballin teaches the limitations of claim 1 as applied above under 35 U.S.C. 102.
With respect to claims 6 and 7, Akselrod-Ballin does not specifically teach temporal breast cancer risks for different types of cancer or different probability parameters for the different cancer types.
Allman teaches methods that can be used to assess risk of a human female subject developing breast cancer and the term “breast cancer” encompasses any type of breast cancer that can develop in a female subject, including the breast cancer may be characterized as Luminal A (ER+ and/or PR+, HER2−, low Ki67), Luminal B (ER+ and/or PR+, HER2+(or HER2− with high Ki67), Triple negative/basal-like (ER−, PR−, HER2−) or HER2 type (ER−, PR−, HER2+). [0052] Allman discloses that breast cancer may be estrogen receptor positive or estrogen receptor negative [0134, lines 1-5], the methods that are not limited to assessing the risk of developing a particular type or subtype of breast cancer, and these methods can be used to assess the risk of developing estrogen receptor positive or estrogen receptor negative breast cancers. [0134, lines 5-11] Allman additionally teaches methods used to assess the risk of developing metastatic breast cancer. [0134, lines 17-18] Allman further teaches performing breast cancer risk assessments based on a variety of parameters including if the cancer is estrogen receptor positive or estrogen receptor negative and using risk assessment outcomes to determine future use of prophylactic anti-breast cancer therapy. [Claims 1, 13, and 14]
It would have been prima facie obvious to one of ordinary skill in the art at the effective
filing date of the invention to have combined the breast cancer risk prediction methods of Akselrod-Ballin and Allman. Allman teaches combining simplified clinical risk assessment and genetic risk assessment to improve risk analysis for developing breast cancer. [Abstract] Allman states that breast cancer is a heterogeneous disease with distinct clinical outcomes and it is discussed in the art that breast cancer may be estrogen receptor positive or negative. [0134, lines 1-4] Therefore, one of ordinary skill in the art would have been motivated to apply a breast cancer risk prediction model to multiple types of breast cancer. It would be obvious to try applying the methods of Allman looking at different types of breast cancer to the breast cancer prediction method of Akselrod. The invention is therefore prima facie obvious.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ha and Chang teach using a convolutional neural network for determining a risk of developing breast cancer based on images and tissue density. (US 2020/0321130 A1, 12 May 2020) Sun et al teach a SVM using multiscale texture and density features for near-term breast cancer risk analysis. (Medical Physics, 18 May 2015, pages 2853-2862) MEng et al teach a deep learning mammography-based model for improved breast cancer risk prediction. (Radiology, 7 May 2019, pages 60-66) Hinton et al teach a neural net to aid in classifying risk of interval cancer compared to using BI-RADS density. (Proc. of SPIE 10718, 6 July 2018, pages 1-8) Lang et al teach using AI to reduce the rate of interval cancer based on mammography screening. (European Radiology, 23 January 2021, pages 5940–5947) Akselrod-Ballin et al teach predicting breast cancer by applying deep learning to linked health records and mammograms. (Radiology, 18 June 2019, pages 1-12) Tagliafico et al teach an overview of radiomics in breast cancer diagnosis and prognostication, including the use of artificial intelligence applied to image analysis. (The Breast, 6 November 2019, pages 74-80)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARON LEVINE GRAFF whose telephone number is (571)317-0219. The examiner can normally be reached Mon - Fri 7:30 AM - 4 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.L.G./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
Prosecution Insights