DETAILED ACTION
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
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.
Claim(s) 42-43, 45-69 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (US2020/0211695) in view of Klassen et al. (US2022/0068449).
To claim 42, Zheng teach a computer-implemented method for processing at least one image of a location of a body of a subject (Figs. 1-2), comprising:
(a) obtaining, by a computer, said at least one image of said location of a body of said subject (301 of Fig. 3A, receive chest radiograph);
(b) using a trained algorithm (paragraph 0056) to classify said at least one image or a derivative thereof to a category among a plurality of categories comprising a first category and a second category, wherein said classifying comprises applying a image processing algorithm to said at least one image or derivative thereof (paragraphs 0059, 0064); and
(c) based at least in part on said classifying of said at least one image or derivative thereof in (b), (i) designating said at least one image or derivative thereof as having a first priority for radiological assessment if said at least one image is classified to said first category, or (ii) designating said at least one image or derivative thereof as having a second priority for radiological assessment, if said at least one image is classified to a second category among said plurality of categories, wherein said second priority has a lower priority or urgency than said first priority (paragraphs 0022; 0061, classifying a medical image, such as used for generating a grade based on severity and/or urgency); and
(d) generating an electronic assessment of said subject based at least in part on said designating, wherein, responsive to said designating at least one image or derivative thereof as having said second priority (paragraph 0077, diagnosis and reporting).
But, Zheng do not expressly disclose said electronic assessment comprises a negative report indicative of said subject not having a health condition.
Klassen teach electronic assessment comprises a negative report indicative of said subject not having a health condition (Figs. 1-2; paragraphs 0065, 0067).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Klassen into the method of Zhang, in order to implement electronic assessment report.
To claim 68, Zheng and Klassen teach a non-transitory computer readable medium comprising machine executable code that, upon execution by one or more computer processors, implements a method for processing at least one image of a location of a body of a subject (as explained in response to claim 42 above).
To claim 69, Zheng and Klassen teach a computer-implemented method for processing at least one image of a location of a body of a subject (as explained in response to claim 42 above, wherein Klassen teach implementing multiple trained machine learning models in paragraphs 0053, 0055, 0064).
To claim 43, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein said negative report comprises a negative BI-RADS assessment and/or a density assessment (Klassen, paragraphs 0030, 0039).
To claim 45, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein said first category is labeled as having a high priority (Zheng, paragraphs 0061, 0081, classification based on severity and/or urgency).
To claim 46, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein said second category is labeled as having a low priority (Zheng, paragraphs 0061, 0081, classification based on severity and/or urgency).
To claim 47, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein said second category is labeled “non-suspicious” or “clear” for said health condition (Klassen, paragraph 0067, class true negative).
To claim 48, Zheng and Klassen teach claim 47.
Zheng and Klassen teach further comprising performing false-negative tracking of said negative report having a “non-suspicious” label that is indicative of said subject not having said health condition (Klassen, paragraph 0069, class false negative).
To claim 49, Zheng and Klassen teach claim 48.
Zheng and Klassen teach wherein said false-negative tracking continues through subsequent radiological assessments of said subject for said health condition (Klassen, paragraphs 0022, 0053, obvious timeline tracking task).
To claim 50, Zheng and Klassen teach claim 48.
Despite lack of disclosure on wherein said false-negative tracking ends when (i) a test result is obtained that is indicative of whether said subject has said health condition, or (ii) a vigilance time window expires subsequent to said radiological assessment, it would be obvious to one of ordinary skill in the art to recognize having a track ending condition in the basis of Klassen’s teaching on timeline tracking, and having a expiration time window is also well-known in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate into the method of Zheng and Klassen, in order to implement automation, hence Official Notice is taken).
To claim 51, Zheng and Klassen teach claim 50.
Zheng and Klassen teach wherein said test result is indicative of a benign outcome or absence of disease, thereby determining that said electronic assessment of said subject is a true negative case (Klassen, paragraph 0067).
To claim 52, Zheng and Klassen teach claim 50.
Zheng and Klassen teach wherein said test result is indicative of a malignant outcome or presence of disease, thereby determining that said electronic assessment of said subject is a false negative case (Klassen, paragraph 0069).
To claim 53, Zheng and Klassen teach claim 50.
Zheng and Klassen teach wherein said vigilance time window expires subsequent to said radiological assessment and the presence of disease has not been detected, and said electronic assessment of said subject is assumed to be a true negative case (as explained in response to claim 50 above, Official Notice is taken).
To claim 54, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein applying said image processing algorithm comprises providing a high-priority classification at an operating point on the receiver operating characteristic curve with high specificity or positive predictive value, and providing a low-priority classification at an operating point on the receiver operating characteristic curve with high sensitivity or negative predictive value (Klassen, obvious in paragraphs 0062-0063, 0065-0070; high specificity or positive predictive value and high sensitivity or negative predictive value are also well-known in the art, hence Official Notice is also taken).
To claim 55, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein said health condition comprises a cancer (Klassen, paragraph 0031).
To claim 56, Zheng and Klassen teach claim 55.
Zheng and Klassen teach wherein said cancer is breast cancer (Klassen, paragraph 0030).
To claim 57, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein said image is a radiological image (Klassen, paragraph 0030).
To claim 58, Zheng and Klassen teach claim 57.
Zheng and Klassen teach wherein said radiological image is generated using an imaging modality selected from the group consisting of mammography, X-ray, fluoroscopy, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and a combination thereof (Klassen, paragraph 0030).
To claim 59, Zheng and Klassen teach claim 58.
Zheng and Klassen teach wherein said imaging modality is mammography (Klassen, paragraph 0030).
To claim 60, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein said trained algorithm comprises a trained machine learning classifier (Zheng, paragraph 0057).
To claim 61, Zheng and Klassen teach claim 60.
Zheng and Klassen teach wherein said trained machine learning classifier is selected from the group consisting of a neural network, a Random Forest model, or a support vector machine (Zheng, paragraph 0057).
To claim 62, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein generating said electronic assessment in (d) is at least partially computer-automated or completely computer-automated without human intervention (Zheng, paragraph 0027; Klassen, paragraph 0021).
To claim 63, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein generating said electronic assessment in (d) is performed in real-time or near real-time relative to obtaining said at least one image in (a) (Zheng, paragraph 0077; Klassen, paragraphs 0048, 0050).
To claim 64, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein said plurality of categories comprises a third category, wherein (c) further comprises designating said at least one image or derivative thereof as requiring a manual diagnostic examination if said at least one image is classified to said third category (Zheng, paragraphs 0016-0017, manual review; Klassen, paragraphs 0020, 0024, 0037-0038, user input; paragraph 0057, critical finding requires immediate attention).
To claim 65, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein said plurality of categories comprises an additional category, wherein (c) further comprises designating said at least one image or derivative thereof as immediate priority for radiological assessment if said at least one image is classified to said additional category (Zheng, paragraph 0009, obvious in grading severity and urgency; Klassen, paragraph 0057, critical finding requires immediate attention).
To claim 66, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein an image of said at least one image or derivative thereof classified as having a first priority for radiological assessment is presented to a first group of one or more radiologists, and an image of said at least one image or derivative thereof classified as having a second priority for radiological assessment is presented to a second group of one or more radiologists, wherein said first group is distinct from said second group (Klassen, paragraphs 0047-0048, 0053, 0064, 0072-0073).
To claim 67, Zheng and Klassen teach claim 42.
Zheng and Klassen teach wherein an image of said at least one image or derivative thereof classified as having a first priority for radiological assessment is presented to one or more radiologists at a first time and an image of said at least one image or derivative thereof classified as having a second priority for radiological assessment is presented to said one or more radiologists at a second time, wherein said first time is distinct from said second time (Klassen, paragraph 0022, timeline tracking).
Claim(s) 44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (US2020/0211695) in view of Klassen et al. (US2022/0068449) and Liu et al. (CN112990374).
To claim 44, Zheng and Klassen teach claim 42.
Though obvious, but Zheng and Klassen do not expressly disclose wherein said first category is labeled “uncategorized.”
Liu teach a category labeled “uncategorized” (paragraphs 0060, 0126), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the method of Zheng and Klassen, in order to implement category labeling.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 February 22, 2026