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 Objections
Claim 1 objected to because of the following informalities: a “;” should be used in place of “,” to separate the claim limitations such as in line 11 and line 17. Appropriate correction is required.
Claims 8-10 are objected for the same.
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 limitations are: first segmentation unit, second segmentation unit, classification unit in claims 1, 5, 6, and 8-10.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they 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 § 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.
Claim 1 recites the limitation "the prostatic cancer lesions and extra-prostatic cancer lesions" in line 16. There is insufficient antecedent basis for this limitation in the claim.
Claims 8, 9 and 10 contain similar language and therefore are rejected for the same reason.
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.
Claims 1-4, 6-7 and 9-10are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Madabhushi et al. (US 2018/0276498 A1 hereinafter Madabhushi).
As to claim 1, Madabhushi teaches a computer-implemented method comprising:
providing a trained machine learning model (machine learning classifier C.sub.R and machine learning classifier C.sub.S are trained; [0038]);
receiving patient data, the patient data comprising a multi-parametric MRI image set of an examination region (a surface of interest (SOI) of a region of tissue demonstrating PCa represented in multi-parametric MRI imagery, and a set of radiomic features from the region of tissue; [0018]) comprising a prostate region of a male human patient (Radiomic features extracted from prostate tissue images; [0020]);
inputting the patient data into the trained machine learning mode, wherein the trained machine learning model comprises a first segmentation unit, a second segmentation unit, and a classification unit (Fig. 7);
wherein the first segmentation unit is configured to receive the multi- parametric MRI image set of the examination region comprising the prostate region of the male human patient and to generate one or more first segmented images based on the multi-parametric MRI image set one or more received images and model parameters (generate first segmented prostate; 720 Fig. 7),
wherein the second segmentation unit is configured to receive the one or more first segmented images and the multi-parametric MRI image set of the examination region and to generate one or more second segmented images based on the one or more first segmented images (generate second segmented prostate; 722 Fig. 7), the multi-parametric MRI image set and the model parameters, wherein the prostatic cancer lesions and extra-prostatic cancer lesions, if present, are segmented in the one or more second segmented images, and
wherein the classification unit is configured to assign the one or more second segmented images to one of at least two classes based on the model parameters, each class corresponding to a prostate cancer local stage (receive probability from first and second classifiers; 760 762 Fig. 7);
receiving from the trained machine learning model a predicted prostate cancer local stage and optionally the one or more first and/or second segmented images (determine if cancer is present; 780 786 784; Fig. 7), and
outputting the predicted prostate cancer local stage and optionally the one or more first and/or second segmented images, and/or storing the predicted prostate cancer local stage and optionally the one or more first and/or second segmented images on a data storage, and/or transmitting the predicted prostate cancer local stage and optionally the one or more first and/or second segmented images to a remote computer system (display classification; 790 Fig. 7).
As to claim 2, Madabhushi teaches the method of claim 1, wherein the multi-parametric MRI image set comprises one or more T2-weighted images and/or one or more apparent diffusion coefficient maps (These changes in the shape and volume of the prostate may be observed in T2 weighted (T2w) MRI images; [0003]).
As to claim 3, Madabhushi teaches the method of claim 1, wherein the multi-parametric MRI image set consists of one or more T2-weighted images and/or one or more apparent diffusion coefficient maps (These changes in the shape and volume of the prostate may be observed in T2 weighted (T2w) MRI images; [0003]).
As to claim 4, Madabhushi teaches the method of claim 1, wherein each class corresponds to a prostate cancer local stage according to the tumor, nodes, and metastases staging system developed by the American Joint Committee on Cancer (predicted survival time between the two classes of patients (BCR−, BCR+); [0018]).
As to claim 6, Madabhushi teaches the method of claim 1, wherein each of the first segmentation unit, second segmentation unit, and classification unit comprises an artificial neural network (a convolutional neural network (CNN); [0062]).
As to claim 7, Madabhushi teaches the method of claim 1, wherein the trained machine learning model was trained on training data, the training data comprising, for each reference patient of a multitude of reference patients, input data and target data, the input data comprising a multi-parametric MRI image set of an examination region comprising a prostate region of the reference patient, and the target data comprising one or more target images in which, if present, prostate gland, prostatic cancer lesions and extra-prostatic cancer lesions are segmented, and a prostate cancer local stage for the reference patient (A set of pre-treatment images of a region of tissue demonstrating PCa is accessed. Spatially contextual SOI of the prostate capsule are uniquely identified from statistically significant shape differences between BCR+ and BCR− atlases created from the training images. To create subpopulation atlases of each of the groups, prostates inside a given subpopulation (i.e. BCR+ or BCR−) are registered to a representative template.; [0033]).
As to claim 9, it is the system claim of claim 1 and therefore is mapped similarly. Please see above for details.
As to claim 10, it is the computer readable medium claim of claim 1 and therefore is mapped similarly. Please see above for details.
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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Madabhushi.
As to claim 5, Madabhushi does not explicitly teach wherein each of the first segmentation unit second segmentation unit, and classification unit is trained separately. However, within the field of invention, the training of the different components would be either together or separate, therefore having limited selection within the art and it would have been obvious to try. It would have been obvious for one ordinary skilled in the art before the effective filing date to have training the first segmentation unit second segmentation unit, and classification unit is trained separately because there is limited options and would have been obvious to try.
Allowable Subject Matter
Claim 8 is 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.
Lay et al. (US11200667 B2) teach Detection of Prostate Cancer in Multi-parametric MRI Using Random Forest with Instance Weighting and MR Prostate Segmentation by Deep Learning with Holistically nested Networks.
Yu et al. (US 20210312615 A1) teach a method for training artificial intelligence entities (AIE) for abnormality detection.
Reaungamornrat et al. (US 20230289984 A1) teach Systems and methods for automatically registering a first input medical image and a second input medical image are provided. The first input medical image in a first modality and the second input medical image in a second modality are received. One or more objects of interest are segmented from the first input medical image to generate a first segmentation map and one or more objects of interest are segmented from the second input medical image to generate a second segmentation map. A first point cloud is extracted from the first segmentation map and a second point cloud is extracted from the second segmentation map. A transformation for aligning the first point cloud and the second point cloud is determined to register the first input medical image and the second input medical image. The transformation is output.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CLAIRE X WANG whose telephone number is (571)270-1051. The examiner can normally be reached M-F 9am-5pm.
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CLAIRE X. WANG
Supervisory Patent Examiner
Art Unit 1774
/CLAIRE X WANG/Supervisory Patent Examiner, Art Unit 1774
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