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
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 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 of this title, 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, and 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 (Perek et al., “Classification of contrast-enhanced spectral mammography (CESM) images”, Int J Comput Assist Radiol Surg. 2019 Feb;14(2):249-257) in view of D2 (Zhao et al., “Background Parenchymal Enhancement on Contrast-Enhanced Spectral Mammography: Influence of Age, Breast Density, Menstruation Status, and Menstrual Cycle Timing”, Sci Rep 10, 8608 (2020)).
With regards to claims 1, 13 , and 14, D1 teaches an information processing apparatus for processing contrast enhanced spectral mammography (CESM) images comprising:
at least one processor,
wherein the processor is configured to:
generate a difference image representing a difference between a low-energy image captured by irradiating a breast, in which a contrast agent is injected, with radiation having first energy and a high-energy image captured by irradiating the breast with radiation having second energy higher than the first energy (Abstract; Methods); and further teaches processing the CESM image using a machine learned model and incorporating additional non-image information through multimodal classification, wherein BIRADS descriptors are converted into numerical inputs and incorporated into the neural network as additional inputs (BIRADS encoding; BIRADS fusion).
However, D1 does not teach determine an enhancement level of background mammary gland parenchyma of the breast based on the difference image and external information that is information other than the difference image.
D2 teaches determining an enhancement level of background parenchymal enhancement (BPE) on CESM images by acquiring low-energy and high energy breast images following contrast injection and generating CESM recombined images from the low and high energy images (Imaging technique), and determining BPE level and BPE intensity from the CESM images, wherein enhancement intensity is quantitatively measured from the CESM images using regions of interest with maximum and minimum and difference pixel values being recorded (Image analysis). It would have been well known, obvious, and predictably suitable to one with ordinary skill in the art to modify D1 with the claimed configuration as taught by D2 since D2 teaches that external factors influence BPE levels and may be used to predict BPE category and intensity.
With regards to claim 2, D1 discloses wherein the processor is configured to: extract a first feature value by inputting the difference image to a first machine learned model; and determine the enhancement level based on the first feature value and the external information (The networks; BIRADS encoding; Fig. 3).
With regards to claim 11, D2 teaches wherein the external information includes at least any one of imaging-related information that is information related to imaging or person-under-examination-related information that is information related to a person under an examination (Statistical analysis; Results) .
With regards to claim 12, D1 teaches wherein the imaging-related information includes at least one of an elapsed time after contrast agent injection (Imaging technique), an injection amount of the contrast agent, a tube voltage, a breast thickness, a mammary gland volume, or a breast type, and the person-under-examination-related information includes at least one of age (Statistical analysis), a menstrual cycle, a body weight, presence or absence of menopause, a heart rate, or a blood pressure.
Allowable Subject Matter
Claims 3-10 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.
The following is a statement of reasons for the indication of allowable subject matter:
None of the prior art teach a processor configured to:
combine the external information with the first feature and determines the enhancement level by inputting the first feature with which the external information has been combined into a second machine-learned model; and/or
extract a first feature by inputting the difference image into a first machine-learned model, extract a second feature by inputting the external information into a third machine-learned model, and determine the enhancement level on the basis of the first feature and the second feature.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARCUS H TANINGCO whose telephone number is (571)272-1848. The examiner can normally be reached Monday-Friday 9am-6pm EST.
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/MARCUS H TANINGCO/ Primary Examiner, Art Unit 2884
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