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 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.
Claims 1-3, 6-12, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Stavros et al, (US-PGPUB 2016/0343132) in view of Zhang et al, (“Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision”, scientific report, 5:11085, June 5, 2015)
In regards to claim 1, Stavros discloses a method, comprising:
utilizing one or more processors in connection with, (Par. 0029, “processor”),
receiving opto-acoustic ultrasound (OA/US) feature scores obtained from OA/US images collected from a non-invasive examination of a volume of interest in a patient, (see at least: Par. 0035, obtaining a plurality of images of a volume of tissue and spatially aligns the images. Such images may include images produced by various imagining technologies including but not limited to MRI, CT scan, X-ray, Ultrasound, Optoacoustic, among other modalities, [i.e., collecting OA/US images from a non-invasive examination of a volume of interest in a patient]. Further, Par. 0073-0080, discloses the assessing six specific features of optoacoustic images or other parametric maps on an ordinal scale, [i.e., implicitly receiving opto-acoustic ultrasound (OA/US) feature scores obtained from OA/US images]);
Stavros does not expressly disclose comparing the OA/US feature scores to a feature score-to-molecular subtype (FSMS) table that provides correlations between different values of the OA/US feature scores and different molecular subtypes of breast cancer including one or more of Luminal A (LumA), Luminal B (LumB), Triple-negative (TRN), or HER2 amplified (HER2+); and determining, from the FSMS table, an indicated molecular subtype of the breast cancer based on comparing the OA/US feature scores to the FSMS table.
Zhang discloses comparing the OA/US feature scores to a feature score-to-molecular subtype (FSMS) table that provides correlations between different values of the OA/US feature scores and different molecular subtypes of breast cancer including one or more of Luminal A (LumA), Luminal B (LumB), Triple-negative (TRN), or HER2 amplified (HER2+), (see at least: Page 4, where table 1 implicitly shows correlation between different values of the ultrasound feature scores for (age, size, shape, ….BI-RADS scores) , and different molecular subtypes of breast cancer including one or more of (LA, LB, HER2, and TN, for features of FV, cutoff, frequencies, and final selection scores), [i.e., the feature score-to-molecular subtype (FSMS) table, “Table 1”, provides correlations between different values of the OA/US feature scores, “ultrasound features scores for age, size, shape, ….BI-RADS”, and different molecular subtypes of breast cancer including one or more of Luminal A (LumA), Luminal B (LumB), Triple-negative (TRN), or HER2 amplified (HER2+), “LA, LB, HER2, and TN”]). Further, the models are constructed using the ensemble decision approach, by randomly selected 80% of the data from each sample category (LA, LB, HER2 and TN) to construct the training set, corresponding to 256, 249, 84 and 87 patients, for a total of 676 patients, [i.e., the training set of decision approach is constructed by implicitly using data from table 1]; and from Pages 2, 4-6, and Figs. 3a, 4a, 5a, identifying (LA, LB, HER2 and TN) breast cancer based on ultrasound features, using the decision approach that integrated multiple decision trees based on an ensemble decision theory to select the special features of each subtype, [i.e., determining, from the FSMS table, “implicit by constructing the models that integrated multiple decision trees, by using the features of the table 1”, an indicated molecular subtype of the breast cancer, “identifying (LA, LB, HER2 and TN) breast cancer”, based on comparing the OA/US feature scores to the FSMS table, “implicitly by using the correlation between ultrasound features scores and different molecular subtypes of breast cancer scores”]).
Stavros and Zhang are combinable because they are both concerned with pathology prediction. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Stavros, to use the table 1 that correlates ultrasound features scores and different molecular subtypes of breast cancer scores, as though by Zhang, in order to construct the decision approach that integrated multiple decision trees, in order to identifying (LA, LB, HER2 and TN) breast cancer, (Zhang, Pages 2, 4-6, and Figs. 3a, 4a, 5a).
In regards to claim 2, the combine teaching Stavros and Zhang as whole discloses the limitations of claim 1.
Stavros further discloses wherein the OA/US feature scores that are received include at least one of: a) multiple ultrasound (US) feature scores and no opto-acoustic (OA) feature scores; b) multiple OA feature scores and no US feature scores; or c) at least one US feature score and at least one OA feature score, (see at least: Par. 0073, assessing six specific features of optoacoustic images on an ordinal scale from 0-5 or 0-6, namely: 1) internal vascularity and de-oxygenation, 2) peritumoral boundary zone vascularity and deoxygenation, 3) internal deoxygenated blush, 4) internal total blood, 5) external peritumoral radiating vessels, and 6) interfering artifact, [i.e., multiple US feature scores only, and no OA feature scores]).
In regards to claim 3, the combine teaching Stavros and Zhang as whole discloses the limitations of claim 1.
Zhang further discloses wherein the correlations between the different values of the OA/US feature scores and the different molecular subtypes of the breast cancer in the FSMS table are first correlations, (see at least: Table 1), and the FSMS table also defines second correlations between the different values of the OA/US feature scores and different histologic grades of the breast cancer, (see at least: Page 9, under “Histological examination”, and the Table on Page 10, which shown correlations between the different values of the OA/US feature scores and different histologic grades of the breast cancer).
In regards to claim 6, the combine teaching Stavros and Zhang as whole discloses the limitations of claim 1.
Stavros further discloses wherein the OA/US feature scores that are received include (a) at least one of an ultrasound (US) or an opto-acoustic (OA) boundary zone and (b) at least one of a US or an OA peripheral zone feature score, (see at least: Par. 0072-0073, the interior region and the peripheral region of a lesion of optoacoustic images may be used to classify the lesion; and Par. 0079, discloses that the one or more features of optoacoustic images are graded on an ordinal scale from 0-6).
In regards to claim 7, the combine teaching Stavros and Zhang as whole discloses the limitations of claim 1.
Stavros further discloses wherein the OA/US feature scores that are received include (a) at least one of an ultrasound (US) or an opto-acoustic (OA) boundary zone feature score and (b) at least one OA/US internal or peripheral feature score from: a US internal zone shape feature score, a US internal zone echotexture feature score, a US internal zone sound transmission feature score, a US peripheral zone feature score, an OA internal deoxygenated blood feature score, an OA internal total hemoglobin feature score, or an OA peripheral zone feature score, (Stavros, Par. 0072-0073, 0079, optoacoustic peripheral region of a lesion feature score, [i.e., OA or US peripheral zone feature score]).
In regards to claim 8, the combine teaching Stavros and Zhang as whole discloses the limitations of claim 1.
Stavros further discloses displaying the indicated molecular subtype of the breast cancer as a collection of probabilities of malignancy (POM) associated with a collection of the molecular subtypes, (Stavros, see at least: step 207 in Fig. 2, and Par. 0070, and Fig. 6).
In regards to claim 9, the combine teaching Stavros and Zhang as whole discloses the limitations of claim 1.
Stavros further discloses wherein receiving the OA/US feature scores, comparing the OA/US feature scores to the FSMS table, and determining the indicated molecular subtype are performed in connection with a combination of a US data set, an OA data set, US images, OA images, US feature scores, and OA feature scores, (Stavros, see at least: Paragraphs 0033, and 0070, 0153, opto-acoustics (OA), a dual energy laser technology co-registered with diagnostic ultrasound).
In regards to claim 10, Stavros discloses a system, (see at least: Par. 0034, “system 100”), comprising:
a memory storing program instructions, and one or more processors that, while executing the program instructions, (Par. 0034, the system 100 is implemented on a standalone system or general-purpose computer, which the computer implicitly comprises a processor and a memory), are configured to:
receive the OA/US feature scores obtained from OA/US images collected from a non-invasive examination of a volume of interest in a patient, (see at least: Par. 0035, obtaining a plurality of images of a volume of tissue and spatially aligns the images. Such images may include images produced by various imagining technologies including but not limited to MRI, CT scan, X-ray, Ultrasound, Optoacoustic, among other modalities, [i.e., collecting OA/US images from a non-invasive examination of a volume of interest in a patient]. Further, Par. 0073-0080, discloses the assessing six specific features of optoacoustic images or other parametric maps on an ordinal scale, [i.e., implicitly receiving opto-acoustic ultrasound (OA/US) feature scores obtained from OA/US images]).
Stavros does not expressly disclose a memory storing a feature score-to-molecular subtype (FSMS) table that provides correlations between different values of opto-acoustic ultrasound (OA/US) feature scores and different molecular subtypes of breast cancer including one or more of Luminal A (LumA), Luminal B (LumB), Triple-negative (TRN), or HER2 amplified (HER2+); compare the OA/US feature scores to the FSMS table; and determine, from the FSMS table, an indicated molecular subtype of the breast cancer based on comparing the OA/US feature scores to the FSMS table.
However, Zhang discloses a memory storing a feature score-to-molecular subtype (FSMS) table, (see at least: Page 9, “ultrasound examination”, the static images and cine clips from B-mode and Doppler ultrasound were saved in the database for double-blind analysis, where the examined ultrasound criteria were listed, illustrated and defined in Table 2, “implicitly using of storage/or memory for storing a feature score-to-molecular subtype (FSMS) table”. Furthermore, Zhang discloses that the feature score-to-molecular subtype (FSMS) table provides correlations between different values of opto-acoustic ultrasound (OA/US) feature scores and different molecular subtypes of breast cancer including one or more of Luminal A (LumA), Luminal B (LumB), Triple-negative (TRN), or HER2 amplified (HER2+); compare the OA/US feature scores to the FSMS table; and determine, from the FSMS table, an indicated molecular subtype of the breast cancer based on comparing the OA/US feature scores to the FSMS table, (see at least: Page 4, where table 1 implicitly shows correlation between different values of the ultrasound feature scores for (age, size, shape, ….BI-RADS scores) , and different molecular subtypes of breast cancer including one or more of (LA, LB, HER2, and TN, for features of FV, cutoff, frequencies, and final selection scores), [i.e., the feature score-to-molecular subtype (FSMS) table, “Table 1”, provides correlations between different values of the OA/US feature scores, “ultrasound features scores for age, size, shape, ….BI-RADS”, and different molecular subtypes of breast cancer including one or more of Luminal A (LumA), Luminal B (LumB), Triple-negative (TRN), or HER2 amplified (HER2+), “LA, LB, HER2, and TN”]). Further, the models are constructed using the ensemble decision approach, by randomly selected 80% of the data from each sample category (LA, LB, HER2 and TN) to construct the training set, corresponding to 256, 249, 84 and 87 patients, for a total of 676 patients, [i.e., the training set of decision approach is constructed by implicitly using data from table 1 by comparing the OA/US feature scores to the FSMS table]; and from Pages 2, 4-6, and Figs. 3a, 4a, 5a, identifying (LA, LB, HER2 and TN) breast cancer based on ultrasound features, using the decision approach that integrated multiple decision trees based on an ensemble decision theory to select the special features of each subtype, [i.e., determining, from the FSMS table, “implicit by constructing the models that integrated multiple decision trees, by using the features of the table 1”, an indicated molecular subtype of the breast cancer, “identifying (LA, LB, HER2 and TN) breast cancer”, based on comparing the OA/US feature scores to the FSMS table, “implicitly by using the correlation between ultrasound features scores and different molecular subtypes of breast cancer scores”]).
Stavros and Zhang are combinable because they are both concerned with pathology prediction. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Stavros, to use the table 1 that correlates ultrasound features scores and different molecular subtypes of breast cancer scores, as though by Zhang, in order to construct the decision approach that integrated multiple decision trees, in order to identifying (LA, LB, HER2 and TN) breast cancer, (Zhang, Pages 2, 4-6, and Figs. 3a, 4a, 5a).
Regarding claim 11, claim 11 recites substantially similar limitations as set forth in claim 2. As such, claim 11 is rejected for at least similar rational.
Regarding claim 12, claim 12 recites substantially similar limitations as set forth in claim 3. As such, claim 12 is rejected for at least similar rational.
Regarding claim 15, claim 15 recites substantially similar limitations as set forth in claim 6. As such, claim 15 is rejected for at least similar rational.
Regarding claim 16, claim 16 recites substantially similar limitations as set forth in claim 7. As such, claim 16 is rejected for at least similar rational.
Regarding claim 17, claim 17 recites substantially similar limitations as set forth in claim 8. As such, claim 17 is rejected for at least similar rational.
Regarding claim 18, claim 18 recites substantially similar limitations as set forth in claim 9. As such, claim 18 is rejected for at least similar rational.
Allowable Subject Matter
Claims 4-5, 13-14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
With respect to claim 4, the prior art of record, alone or in reasonable combination, does not teach or suggest, the following limitation(s), (in consideration of the claim as a whole):
“wherein the FSMS table includes the correlations between the different values of the OA/US feature scores and different pairs of the different molecular subtypes of the breast cancer”.
The closest non-patent-literature to Zhang, (“Identifying ultrasound and clinical
features of breast cancer molecular subtypes by ensemble decision”, scientific report, 5:11085, June 5, 2015) discloses comparing the OA/US feature scores to a feature score-to-molecular subtype (FSMS) table that provides correlations between different values of the OA/US feature scores and different molecular subtypes of breast cancer including one or more of Luminal A (LumA), Luminal B (LumB), Triple-negative (TRN), or HER2 amplified (HER2+), (see at least: Page 4, where table 1 implicitly shows correlation between different values of the ultrasound feature scores for (age, size, shape, ….BI-RADS scores) , and different molecular subtypes of breast cancer including one or more of (LA, LB, HER2, and TN, for features of FV, cutoff, frequencies, and final selection scores), [i.e., the feature score-to-molecular subtype (FSMS) table, “Table 1”, provides correlations between different values of the OA/US feature scores, “ultrasound features scores for age, size, shape, ….BI-RADS”, and different molecular subtypes of breast cancer including one or more of Luminal A (LumA), Luminal B (LumB), Triple-negative (TRN), or HER2 amplified (HER2+), “LA, LB, HER2, and TN”]). Further, the models are constructed using the ensemble decision approach, by randomly selected 80% of the data from each sample category (LA, LB, HER2 and TN) to construct the training set, corresponding to 256, 249, 84 and 87 patients, for a total of 676 patients, [i.e., the training set of decision approach is constructed by implicitly using data from table 1]; and from Pages 2, 4-6, and Figs. 3a, 4a, 5a, identifying (LA, LB, HER2 and TN) breast cancer based on ultrasound features, using the decision approach that integrated multiple decision trees based on an ensemble decision theory to select the special features of each subtype, [i.e., determining, from the FSMS table, “implicit by constructing the models that integrated multiple decision trees, by using the features of the table 1”, an indicated molecular subtype of the breast cancer, “identifying (LA, LB, HER2 and TN) breast cancer”, based on comparing the OA/US feature scores to the FSMS table, “implicitly by using the correlation between ultrasound features scores and different molecular subtypes of breast cancer scores”]).
However, while disclosing a table that provides correlation between ultrasound features scores for age, size, shape, ….BI-RADS”, and the different molecular subtypes of breast cancer including LA, LB, HER2, and TN, (see table 1); Zhang fails to teach or suggest, either alone or in combination with the other cited references, that the FSMS table includes the correlations between the different values of the OA/US feature scores and different pairs of the different molecular subtypes of the breast cancer”.
A further prior art of record, Stavros (US-PGPUB 2016/0343132), discloses a
method, comprising: using one or more processors, (Par. 0029, “processor”), in connection with receiving opto-acoustic ultrasound (OA/US) feature scores obtained from OA/US images collected from a non-invasive examination of a volume of interest in a patient, (see at least: Par. 0035, obtaining a plurality of images of a volume of tissue and spatially aligns the images. Such images may include images produced by various imagining technologies including but not limited to MRI, CT scan, X-ray, Ultrasound, Optoacoustic, among other modalities, [i.e., collecting OA/US images from a non-invasive examination of a volume of interest in a patient]. Further, Par. 0073-0080, discloses the assessing six specific features of optoacoustic images or other parametric maps on an ordinal scale, [i.e., implicitly receiving opto-acoustic ultrasound (OA/US) feature scores obtained from OA/US images]); but fails to teach or suggest, either alone or in combination with the other cited references, the above limitations (as combined with the other claimed limitations).
Regarding claim 5, claim 5 is also in condition for allowance in view of its dependency from claim 4.
With respect to claim 13, the prior art of record, alone or in reasonable combination, does not teach or suggest, the following limitation(s), (in consideration of the claim as a whole):
“wherein the FSMS table includes the correlations between the different values of the OA/US feature scores and different pairs of the different molecular subtypes of the breast cancer, and the one or more processors are configured to determine at least one of the different pairs of the different molecular subtypes of the breast cancer from comparing the OA/US feature scores with the FSMS table”
The cited prior art above to Zhang, (“Identifying ultrasound and clinical features
of breast cancer molecular subtypes by ensemble decision”, scientific report, 5:11085, June 5, 2015), and Stavros (US-PGPUB 2016/0343132), with respect to claim 4, apply also to claim 13; but none, either alone or in combination, teach or suggest the above underlined claimed limitations of claim 13.
Regarding claim 14, claim 14 is also in condition for allowance in view of its dependency from claim 13.
The following is a statement of reasons for the indication of allowable subject matter:
-- Claim 19 is allowable over the prior art of record.
-- Claim 20 is allowable in view of its dependency from claim 19.
With respect to claim 19, the prior art of record, alone or in reasonable combination, does not teach or suggest, the underlined following limitation(s), (in consideration of the claim as a whole):
“the FSMS model including a table associating pairs of molecular subtypes of breast cancer and the OA/US features scores, the table including a correlation index indicative of an extent to which the corresponding OA/US feature scores differentiate between the corresponding pair of the molecular subtypes, ….”
The cited prior art above to Zhang, (“Identifying ultrasound and clinical features
of breast cancer molecular subtypes by ensemble decision”, scientific report, 5:11085, June 5, 2015), and Stavros (US-PGPUB 2016/0343132), with respect to claim 4, apply also to claim 19; but none, either alone or in combination, teach or suggest the above underlined claimed limitations of claim 19.
Regarding claim 20, claim 20 is also in condition for allowance in view of its dependency from claim 19.
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/AMARA ABDI/Primary Examiner, Art Unit 2668 06/18/2026
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