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 .
This communication is in response to the Application No. 18/692,567 filed 03/15/2024. Claims 1-15 are pending.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35. U.S.C 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 03/15/2024 has been entered and considered. Initialed copies of the PTO-1449 by the examiner are attached.
Specification
The disclosure is objected to because at paragraphs [0059] and [0157] of current application’s PGPUB contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Objections
Claim 4 is objected to because of the following informalities:
Claim 4 recites, in part, “where the computing module is configured to”. However, the parent claim 1 does not recite any “computing module” and instead is referred to as “computing device”. Applicant is advice to maintain claim terminology consistent to avoid 112(b) clarity rejections.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a “computer program product comprising executable program code” that is non-statutory subject matter.
A “computer program product comprising executable program code” is defined in the specification to include “FIG. 6 shows a schematic block diagram illustrating a computer program product 200 according to an embodiment of the fourth aspect of the present invention. The computer program product 200 comprises executable program code 250 configured to, when executed, perform the method according to any embodiment of the second aspect of the present invention and/or the third aspect of the present invention, in particular as has been described with respect to the preceding figures” disclosed at paragraph [0139] of application’s PGPUB.
The broadest reasonable interpretation of a claim drawn to a computer-readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01.
A claim drawn to such a computer-readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. § 101 by adding the limitation “non-transitory” to the claim.
Examiner note:
If applicant intends to include the limitation of “non-transitory” to claim 14, the Examiner advises cancelling either claim 14 or 15, as both of these will embody identical claim limitations.
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.
Claims 1 and 4 recite limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 1; recites the limitation, “input module configured to ……,”.
Claim 1; recites the limitation, “computing device configured to ……,”.
Claims 1 and 4; recites the limitation, “output module configured to ……,”.
Claim 4; recites the limitation, “computing module is configured to ......,”.
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.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claim 1 and 4:
“input module” (Fig. 1, #110. PGPUB Paragraph [0012]- “an input module (or: input interface)” and paragraph [0027] “The input module and/or the output module may also be integrated into the computing device” thus, have sufficient structure or material wherein input module is an interface integrated into a computing device).
“computing device” (PGPUB Paragraph [0026]- “The computing device may be realized as any device, or any means, for computing, in particular for executing a software, an App or an algorithm. For example, the computing device may comprise at least one processing unit such as at least one central processing unit, CPU, and/or at least one graphics processing unit, GPU, and/or at least one field-programmable gate array, FPGA, and/or at least one application-specific integrated circuit, ASIC, and/or any combination of the foregoing.” thus, have sufficient structure or material wherein computing device is any CPU, GPU, FPGA, ASIC, and/or any combination of the foregoing).
“output module” (Fig. 1, #190. PGPUB Paragraph [0014]- “ an output module (or: output interface)” and paragraph [0027] “The input module and/or the output module may also be integrated into the computing device” thus, have sufficient structure or material wherein input module is an interface integrated into a computing device).
“computing module” (Not mentioned in Specification; No sufficient structure or material found).
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4 and its dependent claim 5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4 recites limitation:
Claim 4; recites the limitation, “computing module is configured to ......,”.
Claim 4 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed functions. The specification does not provide sufficient details such that one of the ordinary skill in the art would understand which structure performed(s) the claimed function.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 4 and its dependent claim 5 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function in the recited limitation.
Claim 4 recites limitation:
Claim 4; recites the limitation, “computing module is configured to ......,”.
The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
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.
Claim(s) 1-2, 6-7, 9-12 and 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kleesiek et al. (“Can virtual contrast enhancement in brain MRI replace gadolinium?: a feasibility study.”, 2019, hereinafter referred to as “Kleesiek”).
Regarding claim 1, Kleesiek discloses an apparatus for generating a perfusion image, comprising (“predicting contrast enhancement from noncontrast multiparametric brain MRI scans using a deep-learning (DL) architecture” Kleesiek, pg. 653 Col 1): an input module configured to receive at least one non-contrast medical diagnostic image, NCMDI, acquired from organic tissue (“a comprehensive multiparametric MRI protocol including T1w, T2w, T2w fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), as well as susceptibility-weighted imaging (SWI), acquired before contrast agent application” Kleesiek, pg. 653-654 Col 2-1); a computing device configured to implement an artificial neural network, ANN, which is trained and configured to receive input data based on at least one of the received at least one non-contrast medical diagnostic image, NCMDI, and to generate, based on the input data, a perfusion image for the organic tissue in the at least one non-contrast medical diagnostic image, NCMDI (“Using these data, we trained a 3-dimensional (3D) Bayesian neural network (BayesUNet) and evaluated the generated vcT1w maps in comparison to ground truth contrast-enhanced T1w scans (ceT1ws)” Kleesiek, pg.654 Col 1; i.e., the ANN is that of a BayesUNet trained to generate “postcontrast T1w images with high sensitivity and specificity” Kleesiek, pg. 656 Col 2); and
an output module configured to output at least the generated perfusion image (Kleesiek, Fig. 1 (top right) description outputs the virtual contrast enhancement map; i.e., a perfusion image).
Regarding claim 2, Kleesiek discloses the apparatus of claim 1, wherein at least one of the at least one non-contrast medical diagnostic image, NCMDI, is a non-contrast magnetic resonance imaging result, NCMRIR (“a comprehensive multiparametric MRI protocol including T1w, T2w, T2w fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), as well as susceptibility-weighted imaging (SWI), acquired before contrast agent application” Kleesiek, pg. 653-654 Col 2-1).
Regarding claim 6, Kleesiek discloses a computer-implemented method for generating a perfusion image (“predicting contrast enhancement from noncontrast multiparametric brain MRI scans using a deep-learning (DL) architecture” Kleesiek, pg. 653 Col 1), comprising steps of: receiving at least one non-contrast medical diagnostic image, NCMDI, acquired from organic tissue (“a comprehensive multiparametric MRI protocol including T1w, T2w, T2w fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), as well as susceptibility-weighted imaging (SWI), acquired before contrast agent application” Kleesiek, pg. 653-654 Col 2-1); generating, using an artificial neural network, ANN, trained and configured to receive input data based on at least one of the received at least one non-contrast medical diagnostic image, NCMDI, based on the input data, at least a perfusion image for the organic tissue shown in the at least one non-contrast medical diagnostic image, NCMDI “Using these data, we trained a 3-dimensional (3D) Bayesian neural network (BayesUNet) and evaluated the generated vcT1w maps in comparison to ground truth contrast-enhanced T1w scans (ceT1ws)” Kleesiek, pg.654 Col 1; i.e., the ANN is that of a BayesUNet trained to generate “postcontrast T1w images with high sensitivity and specificity” Kleesiek, pg. 656 Col 2); and
outputting at least the generated perfusion image (Kleesiek, Fig. 1 description outputs the virtual contrast enhancement map; i.e., a perfusion image).
Regarding claim 7, Kleesiek discloses the method of claim 6, wherein the non-contrast medical diagnostic image, NCMDI, is a non-contrast magnetic resonance imaging result, MCMRIR (“a comprehensive multiparametric MRI protocol including T1w, T2w, T2w fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), as well as susceptibility-weighted imaging (SWI), acquired before contrast agent application” Kleesiek, pg. 653-654 Col 2-1).
Regarding claim 9, Kleesiek discloses the method of claim 6, further comprising generating based on the input data, perfusion dynamics data (“a DL architecture is able to predict postcontrast T1w images with high sensitivity and specificity. We refer to the predictions as virtual contrast enhancement. Next to a qualitative evaluation, additional quantitative measures, comparing the predicted vcT1w to the ground truth ceT1w scans, confirm our results.” Kleesiek, pg. 656-657 Col 2-1; i.e., the perfusion dynamics is that of a qualitative valuation and quantitative measures of the tissue (in this case a brain) for tumors (see table 1)).
Regarding claim 10, Kleesiek discloses a computer-implemented method for training an artificial neural network for generating a perfusion image (“trained a 3-dimensional (3D) Bayesian neural network (BayesUNet) and evaluated the generated vcT1w maps in comparison to ground truth contrast-enhanced T1w scans (ceT1ws)” Kleesiek, pg. 654, Col 1), comprising steps of: providing a training set of medical diagnostic training image groups, MDTIG, wherein each medical diagnostic training image group, MDTIG, comprises at least (imaging data from a total of 82 patients in this study. Some patients were examined several times (up to 5 times), usually while undergoing therapy, leading to a total of 116 data sets used for training and evaluating the model” Kleesiek, pg. 654 Col 1): a non-contrast medical diagnostic image, NCMDI, at least one subtraction image based on the NCMDI (“Multiparametric imaging data with 10 channels (see aforementioned data) was used as input to the model. All 10 channels were acquired before GBCA administration. The training signal was given by the subT1w maps. The model output vcSub is akin to a subtraction map. It was added to the nT1w to obtain a vcT1w image” Kleesiek, pg. 654 Col 2; where nT1w is a native T1-weighted map as the non-contrast input data); providing an artificial neural network, ANN, configured to receive, as input data, a non-contrast medical diagnostic image, NCMDI, and to generate, based on the input data, at least one perfusion image (“Using these data, we trained a 3-dimensional (3D) Bayesian neural network (BayesUNet) and evaluated the generated vcT1w maps in comparison to ground truth contrast-enhanced T1w scans (ceT1ws)” Kleesiek, pg. 654 Col 1; i.e., the ANN is that of a BayesUNet trained to generate “postcontrast T1w images with high sensitivity and specificity” Kleesiek, pg. 656 Col 2; Additionally, see under Imaging Data section); training the provided artificial neural network, ANN, using the provided training set of medical diagnostic training image groups, MDTIG, using supervised learning while penalizing differences between the generated perfusion image and at least one of the at least one subtraction image (“During training, the model parameters were optimized by minimizing a loss function that compares the model's prediction vcSub with the ground truth subT1w” Kleesiek, pg. 654 Col 2 under Training of the Model).
Regarding claim 11, Kleesiek discloses the method of claim 10, wherein each medical data training image group, MDTIG, further comprises at least one contrast-enhanced medical diagnostic image, CEMDI; and wherein the providing of the training set of MDTIGs comprises calculating the at least one subtraction image of the MDTIG based on the non-contrast medical diagnostic image, NCMDI, and on the at least one contrast-enhanced medical diagnostic image, CEMDI, of the MDTIG (“Thus, in total, 10 channels were used for the model input. For training, we computed the T1w subtraction map (subT1w) using the contrast enhanced T1w (ceT1w) image, which was obtained using intravenous gadoterate meglumine” Kleesiek, pg.654 Col 1; “Subtraction maps (subT1w) were created by subtracting the nT1w from the ceT1w data” Kleesiek, pg. 654 Col 2).
Regarding claim 12, Kleesiek discloses the method of claim 11, wherein each medical data training image group, MDTIG, comprises a plurality of non-contrast medical diagnostic images, NCMDIs (“Multiparametric imaging data with 10 channels (see aforementioned data) was used as input to the model. All 10 channels were acquired before GBCA administration” Kleesiek, pg. 654 Col 2; “Using unenhanced multiparametric MRI data, comprising anatomical (nT1w, T2w, FLAIR) and functional scans (DWI, SWI)” Kleesiek, pg. 656 Col 2); and wherein the artificial neural network, ANN, is configured to receive, as input data, the plurality of NCMDIs and to generate the perfusion image for the supervised learning based on these input data (Using unenhanced multiparametric MRI data, comprising anatomical (nT1w, T2w, FLAIR) and functional scans (DWI, SWI), we demonstrate that a DL architecture is able to predict postcontrast T1w images with high sensitivity and specificity” Kleesiek, pg. 656 Col 2).
Regarding claim 14, Kleesiek discloses a computer program product comprising executable program code configured to, when executed, perform the method according to claim 6 (Kleesiek, pg. 655 Col 1 under Applying the DL Model).
Regarding claim 15, Kleesiek discloses a non-transitory computer-readable data storage medium comprising executable program code configured to, when executed, perform the method according to claim 6 (Kleesiek, pg. 655 Col 1 under Applying the DL Model).
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) 3 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Kleesiek in view of Mann et al. (“Breast MRI: guidelines from the European society of breast imaging”, 2008, hereinafter referred to as “Mann”).
Regarding claim 3, Kleesiek discloses all of the subject matter as described above except for specifically teaching wherein the organic tissue is breast tissue. However, Mann in the same field of endeavor teaches wherein the organic tissue is breast tissue (Mann discloses use of breast MRI for surgical or medical preoperative stages, pg. 1307).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Kleesiek and Mann before the effective filing date of the claimed invention. The motivation for this combination of references would have been to use the sensitivity of a breast MRI, for example, to detect cancer in early stages of asymptomatic women (Mann, pg. 1307). This motivation for the combination of Kleesiek and Mann is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 8, Kleesiek and Mann disclose the method of claim 6, wherein the organic tissue is breast tissue (Mann discloses use of breast MRI for surgical or medical preoperative stages, pg. 1307).
Therefore, combining Kleesiek and Mann would meet the claim limitations for the same reasons as previously discussed in claim 3.
Allowable Subject Matter
Claims 4-5 and 13 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jensen (US 20190122348 A1) discloses A system and method for generating simulated post-contrast T1-weighted magnetic resonance (MR) images without the use of exogenous contrast material using machine learning.
Odry et al. (US 20190049540 A1) discloses systems and methods for synthesizing image data from input MR data using a GAN.
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/EMMANUEL SILVA-AVINA/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673