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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/21/2022 in is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Preliminary Response to Amendment
The preliminary amendment filed on 09/21/2022 have been acknowledged.
Claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14 have been amended
Claims 1-14 are pending.
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 are: “reconstruction module is configured to output a breast micro-calcification magnetic resonance image” in claim 1, “tissue identification module is configured for providing the fibroglandular tissue segmentation” in claim 5, and “phase image calculation module is implemented as any one of the following: - using a dipole kernel calculation module” in claim 6
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.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Claim 13 defines a computer program embodying functional descriptive material. However, the claim does not define a computer-readable medium or memory and is thus non-statutory for that reason (i.e., “When functional descriptive material is recorded on some computer-readable medium it becomes structurally and functionally interrelated to the medium and will be statutory in most cases since use of technology permits the function of the descriptive material to be realized” – Guidelines Annex IV). That is, the scope of the presently claimed computer program can range from paper on which the program is written, to a program simply contemplated and memorized by a person. The examiner suggests amending the claim to embody the program on “non-transitory computer-readable medium” or equivalent in order to make the claim statutory. Any amendment to the claim should be commensurate with its corresponding disclosure.
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 ,2, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (Wang hereinafter US 8781197 B2) in view of Andersson et al (Andersson hereinafter SEPARATION OF WATER AND FAT SIGNAL IN WHOLE‐BODY GRADIENT ECHO SCANS USING CONVOLUTIONAL NEURAL NETWORKS) in further view of Ardekani et al (Ardekani hereinafter IDENTIFICATION OF BREAST CALCIFICATION USING MAGNETIC RESONANCE IMAGING).
As per claim 1, Wang teaches a medical system comprising: a memory configured to store machine executable instructions (Figure 1) and a computational system configured for controlling the medical system that can receive the gradient echo MRI data (Column 5 line 10 “the computer executable code includes instructions for collecting magnetic resonance signals“ and Column 12 line 9 “The invention also includes the followings: (63) 1. Developing data acquisition sequence. A fast multiple gradient echo imaging sequence is developed for effective high-resolution mapping…”) as well as receive the DIXON MRI data (Column 5 line 10 “the computer executable code includes instructions for collecting magnetic resonance signals“ and column 47 line 13 “We propose extending IDEAL…The signal at a given voxel can be modeled as…(Equation 7-7)…w and f are the water and fat magnetization at the echo center…Maps of the three parameters water w, fat f and field map … can be estimated by fitting the signal model to the measured multiple echo data at each voxel…” The Dixon technique is a way to separate fat and water signals in MRI. IDEAL is a modern high-performance variant of the 3-point DIXON technique. Thus, Wang shows a system capable of receiving DIXON imaging data.) Wangs system also has the framework to generate calcification MR images using the received data (column 9 line 56 “information has been used to generate high contrast-to-noise ratio high resolution images at high field strength; positive phase is used to identify the presence of calcification. The use of phase information in field inversion as proposed in this research, will lead to accurate susceptibility quantification.” The system also shows the ability to hold the “ instructions for collecting magnetic resonance signals emitted by an object and for generating an image of the object from the magnetic resonance signals” column 5 line 10. )
Wang is not relied upon for the objective of breast microcalcification localization, the reconstruction module , the output of breast micro calcification localization MRI, the phase image nature of the GRE data, nor the reconstruction module being a neural network. Although Wang speaks on using this system to track calcification and its genetic roots in breast tissue (as well as other types mineralization in other organs) through MRI (Column 49 line 34-67) Wang is used mainly for the medical system’s general architecture and capability.
Andersson teaches a image reconstruction module (Abstract “water-fat signal separation of whole-body gradient echo scans using convolutional neural networks” Andersson’s CNN is a computational reconstruction module that processes MRI data to reconstruct and output MRI images. This satisfies the “image reconstruction module” ), inputting gradient echo MRI data and DIXON MRI data (“A 3D spoiled gradient echo sequence was used. A total of 5 bipolar echoes were collected. The following parameters were used: voxel size = 2.07 × 2.07 × 8 mm3- and “As input to the networks 1 2D axial slice with 2 channels for each echo, 1 for the real and 1 for the imaginary component, was used” To clarify Andersson processes gradient echo MRI data to perform Dixon type water fat separation. This means the input dataset inherently includes Dixon MRI signal components (water, fat, in-phase, opposed-phased) derived from the GRE acquisition) , wherein the gradient echo MRI data is a phase image (Andersson’s is doing chemical -shift based separation on gradient echo scans. Chemical shift (Dixon) separation relies on phase differences across gradient echo signals. The GRE data implicitly includes phase information Andersson further states in the Neural Network section that “The networks were trained using different configurations of the available echoes as input. Networks were either trained with echoes of both polarities or only echoes of 1 polarity (i.e., only odd or only even numbered echoes). For the 3 different sets of echoes used, all possible configurations using the first available echo and different numbers of consecutive echoes were used.” sTating “all possible configurations” are used includes phase image), wherein the DIXON MRI data and the gradient echo MRI data are spatially matched (Andersson is doing a “whole‐body gradient echo scans” Dixon signal components are acquired from the same GRE acquisition and are implicitly spatially matched.), wherein the module is implemented as a trained neural network (Anderson is using a U-net as seen in figure 1)
Ardekani teaches using SWI which is described in Optimizing imagining parameters section as “a 3D fast gradient echo sequence” to identify and localize breast calcifications. Ardekani explains that “Corrected phase and magnitude images acquired using SWI allowed identification and correlation of all calcifications” and that “As the approach is a 3D technique, it could potentially allow for more accurate localization and biopsy” For the claim limitation that the output image is “a breast micro-calcification magnetic resonance image” Ardekani teaches MRI output images tailored to breast calcification depictions because it discloses “Corrected phase and magnitude images acquired using SWI” that are used for calcification identification. For the limitation that the output image is “descriptive of a location of breast micro-calcifications” “Ardekani teaches both identification and localization of calcifications. Ardekani states that the corrected SWI images “identification and correlation of all calcifications” and that “3D technique, it could 1potentially allow for more accurate localization”. Ardekani supplies the clinical imagining objective. It should also be noted that the SWI images are generally phase images to help differentiate materials like iron or calcium.
It should be noted for clarification that the fact that Andersson and Wang do not expressly mention breast calcification does not circumvent the purpose of an obviousness analysis. This is because they are not being used for the clinical target; they are being used for the technical mechanism and system environment that a person of ordinary skill in the art would have applied to that target being breast calcification image. Andersson supplies the core reconstruction technology i.e. a neural network operating on GRE echo/DIXON data, with GRE phase and DIXON data used as an input and MRI image that is the output offered by the neural network. Wang supplies the surrounding MRI system framework and the understanding that MR signals are collected, processed and used to generate diagnostic images and detect features such as a calcified deposit. This keeps the combination in the same MRI detection and susceptibility imagining realm. Ardekani provides the missing specific problem endpoint: in breast imaging, susceptibility weighted imaging ( high resolution 3D MRI that uses magnitude and phase image information that is sensitive to calcification) permits identification of calcifications in the breast and a more accurate localization. In essence Andersson shows how to implement a neural MRI reconstruction on the relevant data through his neural network architecture, Wang teaches the MRI system context in which signals are collected and images are generated which include calcification related diagnostics and Ardekani teaches the reasoning a person of ordinary skill in the art would direct this pipeline towards breast microcalcification localization MRI output.
Accordingly, a person of ordinary skill in the art at the time this invention was effectively filed would have found it obvious to implement Andersson’s CNN MRI processing within the MRI system framework of Wang and to direct the resulting MRI output toward the breast calcification identification and localization objective taught by Ardekani. This is because the references address complementary parts of the same phase sensitive MRI flow. Andersson teaches using CNN networks for water-fat signal separation of whole-body gradient echoes cans i.e. a trained model that processes gradient echo, Dixon MRI data to generate MRI domain outputs. Wang enables the deployable MRI system context and Ardekani teaches why a person of ordinary skill in the art would target that processing to breast calcifications namely that corrected phase and magnitude images acquired by GRE derived SWI allows for identification and correlation to calcifications. Essentially, they work together as follows: Andersson supplies the image reconstruction/model mechanism, Wang supplies the computer implemented MRI system architecture that receives MR data and generates images and Ardekani supplies the breast specific diagnostic target for the output image. Ardekani teaches that phase sensitive MRI processing such as SWI corrected phase and magnitude imagining enhances the visibility of breast micro calcifications and enables their identification and localization because calcifications produce susceptibility induced phase effects in MRI data. Both Andersson and Ardekani rely on extracting diagnostically relevant information from phase sensitive GRE MRI signals and the modification simply directs a known reconstructions technique toward a known diagnostic objective using the same underlying data. A person of ordinary skill in the art would have recognized that the same phase sensitive information used in Andersson’s U-net is the same class of information used in Fatemi to visualize calcifications, namely, signal variations arising from magnetic susceptibility and chemical shift effects in gradient echo MRI data. The advantage of this combination is a system that uses phase sensitive GRE/Dixon MRI data in a trained computational pipeline to produce an MRI image tailored for breast micro calcification with improved suppression of confounding tissue components and improved utility for diagnosis. This allows for breast microcalcification diagnosis imagining to be MRI based rather than CT based which eliminates the patients need for X-ray exposure of a traditional Mammogram.
As per claim 2
Wang, Jafari and Ardekani cover claim 1’s limitations in claim 1’s 103 rejection. Please see claim 1’s 103 rejection.
Ardekani calculates a filtered breast micro-calcification image (132) by applying a high-pass spatial filter (130) to the breast micro-calcification magnetic resonance image. (SWI image processing section: A high-pass filter to remove low-spatial frequency components, caused by background field effects, was applied.29 Following filtering corrected phase images were calculated)
Wang show additional support for the capabilities of the system. Column 33 line 9 “ A single image was acquired, and phase unwrapping and high pass filtering were performed. The…filtering algorithms were those described in…The imaged resolution was 93.75 .mu.m isotropic, and the image was acquired using an echo time of 10 ms.”, column 37 line 52 “A method is used to remove motion artifacts in MRI by forcing data consistency…a novel POCS algorithm is developed as an automatic iterative method to remove motion artifacts using high pass phase filtering and convex projections in both k-space and image space.” Column 75 line 14 “ High Pass Filtering: When images from a reference phantom are not available, a high pass filter can remove background field inhomogeneity. A simple high pass filter, described by…was taken to be zero outside of the imaged volume.”)
Accordingly, s person of ordinary skill in the art at the time this invention was effectively filed would have been motivated to apply a high pass filter to the breast micro calcification MRI image because both Ardekani and Wang within the Wang/Jafari/Ardekani system both teach high pass filtering removes unwanted low spatial frequency and background field effects from MRI phase data. This improves the diagnostically useful image contrast of the MRI image. Ardekani applies the high pass filter then corrected phase images are calculated in the breast calcification SWI workflow. Wang similarly teaches high pass filtration on MRI image data. A person of ordinary skill in the art would have found it obvious after generating the breast calcification MRI image to further calculate a filtered image by applying a high pass filter. Doing so predictably suppresses background effects and enhance local phase contrast associated with the targeted calcifications thereby improving calcification identification and localization in the breast MRI image.
As per Claim 13
Claim 13 is the machine executable instructions that dictate claim 1 and will be rejected under the same premise.
As per claim 14
Claim 14 is the parallel method claim of claim 1 and will be rejected under the same premise.
Allowable Subject Matter
Claim 3-12 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANE WRENSFORD CODRINGTON whose telephone number is (571)272-8130. The examiner can normally be reached 8:00am-5pm.
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/SHANE WRENSFORD CODRINGTON/ Examiner, Art Unit 2667
/TOM Y LU/ Primary Examiner, Art Unit 2667