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 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(s) is/are:
a medical image acquisition unit, an auxiliary information acquisition unit, a diagnosis information inference unit in claim 1.
a first reference region acquisition unit and a second reference region acquisition unit in claim 6.
a first region-related diagnosis information inference unit and a second region-related diagnosis information inference unit in claim 7.
an input image acquisition unit in claim 8.
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(s) 1, 9-13, 15 and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, and opinion). Under Step 2A, Prong One, the claims recite the abstract mental process of evaluating a medical image together with image finding information or prior diagnosis information to infer further diagnosis information concerning a tumor. Under Prong Two, the medical image acquisition, auxiliary information acquisition, and diagnosis inference limitations merely gather information, limit the content and medical field of the evaluation, and perform the diagnostic judgment using generic computer components. Claim 1 does not require a particular image generation algorithm, inference architecture, or improvement to computer or imaging device functionality. Claims 9-12 merely further specify the diagnostic or image finding information being evaluated, while claim 13 adds acquisition of a tumor region as preliminary data gathering. Claims 15 and 16 recite substantially the same abstract process in method and computer readable medium form. Accordingly, the claims do not integrate the abstract idea into a practical application. Under Step 2B, the additional elements, considered individually and as an ordered combination, do not provide an inventive concept. The specification describes implementation using a CPU, memory, magnetic disk, software, and optionally a GPU or FPGA, without requiring specialized computing hardware. These generic components merely automate the recited collection and diagnostic evaluation of medical information. Therefore, claims 1, 9-13, 15, and 16 do not recite significantly more than the judicial exception and are ineligible under 101.
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(s) 1, 10-13 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 20220208355) in view of Wang ("The uncertainty of boundary can improve the classification accuracy of BI‐RADS 4A ultrasound image." Medical Physics 49, no. 5 (2022): 3314-3324. (provided in the IDS filed on 10/01/2024)).
Regarding claim 1:
Li teaches: a medical image processing device (FIG. 1, and ¶¶ para [0035], [0076], [0080], and [0265],) comprising:
a medical image acquisition unit that acquires a medical image including at least a region of a tumor mass (Li discloses receiving an MR image acquired by an MR scanner and inputs the MR image in to a generator network (Li ¶¶ [0026] – [0028]). Li’s tumor detection embodiment concerns liver images containing hemangioma or hepatocellular carcinoma and synthesizes liver contrast enhanced equivalent MRI for tumor detection (Li ¶¶ [0192] – [0193]). Li’s detector locates a tumor region of interest corresponding to hemangioma or hepatocellular carcinoma (Li ¶¶ [0197], [0206] and FIGS. 17, 22, and 23). Also see ¶ [0079]).
an auxiliary information acquisition unit that acquires auxiliary information (Li’s generator obtains “an attention map of tumor specific features” from the received MR image and supplies the attention map to the detector together with the generated CA-free AI-enhanced image (Li ¶¶ [0028] – [0030). Li further teaches an attention aware generator having minutious and global attention modules that generate tumor specific attention information (Li ¶¶ [0193] , [0197] – [0200]; FIGS. 17-20). Li also teaches using those attention maps in the detector for tumor information extraction and classification (Li ¶¶ [0206], [0218]; FIGS. 22 and 29))
a diagnosis information inference unit that infers second diagnosis information, which is information about diagnosis of the tumor mass (Li’s processor implements an R-CNN based detector that obtains a tumor location and tumor classification (Li ¶ [0031]. The detector is a customized Faster R-CNN that proposes candidate tumor bounding boxes, obtains the tumor region of interest, and performs classification and bounding box regression (Li ¶¶ [0197] , [0206] – [0207]; FIGS 17 and 22). Li expressly teaches that the detector determines whether the tumor is benign or malignant (Li ¶ [0206]). The benign versus malignant classification is similar to diagnosis information concerning the tumor mass). ,
in response to input of an image generated based on the medical image and on the auxiliary information (Li inputs the NCEMRI medical image into its generator, synthesizes tumor specific CEMRI from the NCEMRI and supplies the synthetic CEMRI to the detector for tumor localization and classification (Li ¶¶ [0028] – [0030], [0197] – [0199], [0206]; FIGS. 17, 18, and 22)).
Li does not specifically teach: auxiliary information including at least any one of
A) image finding information, which is based on the medical image and is information about a predetermined image finding representing a nature of the tumor mass, and B) first diagnosis information, which is based on the medical image and is information about diagnosis of the tumor mass.
However, in the same field of endeavor, Wang teaches: auxiliary information including at least any one of A) image finding information, which is based on the medical image and is information about a predetermined image finding representing a nature of the tumor mass, and B) first diagnosis information, which is based on the medical image and is information about diagnosis of the tumor mass (Wang teaches acquiring tumor boundary information derived from a medical ultrasound image and using that information as auxiliary information for benign vs malignant tumor classification (Wang abstract); Wang predetermines uncertainty of the tumor boundary as the tumor image characteristic supplied to the classification network. Wang defines the boundary uncertainty using voting based and variance based techniques and embeds the resulting boundary information into the network input (Wang abstract, FIG. 2 and its captions). Wang’s FIG. 2C and 2F depict the boundary derived image used as auxiliary inputs to the classification network. Wangs tumor boundary uncertainty is similar to the claimed image finding information. Also see FIG. 5 and its captions).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Li to incorporate the teachings of Wang by including: auxiliary information including at least any one of A) image finding information, which is based on the medical image and is information about a predetermined image finding representing a nature of the tumor mass in order to improve a similar medical image classification system in the same manner.
Regarding claim 10:
Wang further teaches: wherein the auxiliary information includes at least A) the image finding information, and wherein the image finding information includes information about an image finding region that is a region in the medical image, the image finding being present in the region (Wang teaches acquiring tumor boundary information derived from a medical ultrasound image and using that information as auxiliary information for benign vs malignant tumor classification (Wang abstract); Wang predetermines uncertainty of the tumor boundary as the tumor image characteristic supplied to the classification network. Wang defines the boundary uncertainty using voting based and variance based techniques and embeds the resulting boundary information into the network input (Wang abstract, FIG. 2 and its captions). Wang’s FIG. 2C and 2F depict the boundary derived image used as auxiliary inputs to the classification network. Wangs tumor boundary uncertainty is similar to the claimed image finding information. Also see FIG. 5 and its captions).
Regarding claim 11:
Wang further teaches: wherein the auxiliary information includes at least A) the image finding information, and wherein the image finding information includes information about the presence or absence, a type, or a degree of the image finding (Wang defines tumor boundary uncertainty using voting based and variance based measures and distinguishes high confidence tumor regions from low confidence uncertain boundary regions (Wang abstract, FIG. 2 and its captions). Wang’s FIG. 2C and 2F depict the boundary derived image used as auxiliary inputs to the classification network).
Regarding claim 12:
Wang further teaches: wherein the auxiliary information includes at least A) the image finding information, and wherein the image finding information includes at least any one of information about distinctness of a margin of the tumor mass, information about roughness of a margin of the tumor mass, and information about the presence or absence of a linear opacity of a margin of the tumor mass (Wang generates tumor boundary information distinguishing high confidence tumor regions from low confidence uncertain boundary regions and supplies the boundary uncertainty information to the tumor classification network (Wang abstract, FIG. 2 and its captions). The degree of confidence or uncertainty associated with the depicted tumor boundary teaches information about distinctness of a tumor margin).
Regarding claim 13:
Li further teaches: further comprising a tumor mass region acquisition unit that acquires a region of a tumor mass included in a target image based on the medical image (Li’s R-CNN detector locates and acquires the tumor ROI from the image before classification (Li ¶¶ [0031], [0197], and [0206] and FIGS. 17 and 22)).
Regarding claims 15-16: the claim limitations are similar to those of claim 1; therefore, rejected in the same manner as applied above. Li discloses a CRM in the Abstract.
Allowable Subject Matter
Claims 2-8, 14, and 17-20 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.
Palma (US 20220270254) teaches classifying a lesion as benign, malignant or indeterminate from an image path augmented with a lesion mask or contour.
Kawagishi (US 20210027465) teaches: defining finding specific region in a medical image and modifying or masking pixel values in those regions before neural network analysis.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WASSIM MAHROUKA whose telephone number is (571)272-2945. The examiner can normally be reached Monday-Thursday 8:00-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WASSIM MAHROUKA/Primary Examiner, Art Unit 2665
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