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: "module for generating" in claim 1.
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 § 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 12-13, 15-17 is/are rejected under 35 U.S.C. 103 as being obvious over Wang et. al. (United States Patent Application Publication US 2022/0156929 A1) in view of Preuhs et. al. (United States Patent Application Publication US 2023/0274439 A1).
The applied reference has a common assignee (National Taiwan University) and Inventors (Wei-Chung Wang, Wei-Chih Liao, Kao-Lang Liu, Po-Ting Chen, Po-Chuan Wang, and Da-Wei Chang) with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2).
This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02.
Regarding claim 1 and claim 12, Wang et. al. discloses a medical image analysis system and method, comprising: a database for storing a first medical image data indicating a target medical image (Wang et. al., Figure 1, [0005], [0020], [0040]: medical imaging analyzing system and method, the present disclosure further comprises the step of: interlinking a plurality of images, organ position and range markers and tumor position and range markers to use as a first training set via a database stored with the plurality of images, the organ position and range markers and the tumor positions and range markers.); and a server for accessing the database, the server comprising (Wang et. al. Figure 1, [0038]: cloud server): a first analysis module for generating a first determination data according to the first medical image data (Wang et. al. [0004]: a first analysis module having a second model and configured to input the result of the determined positions and ranges of the organ and the tumor of the patient image into the second model to obtain at least one first prediction value corresponding to the patient image, [0008]: the first analysis module first performs 3D feature analysis on the second training set using an algorithm of radiomics to obtain a plurality of 3D feature values, and then trains a machine learning algorithm of a gradient boosting decision tree using the plurality of 3D feature values to obtain the second model, [0041]: neural network module can be trained to obtain the first model based on the first training set), the first determination data indicating whether the target medical image comprises a cancerous tissue image or indicating a chance of the target medical image comprising a cancerous tissue image (Wang et. al. [0017]: the determining module uses one or both of the first prediction value and the second prediction value representing having cancer as the determined result); a second analysis module for generating a second determination data according to the first medical image data, the second determination data indicating whether the target medical image comprises a cancerous tissue image or indicating a chance of the target medical image comprising a cancerous tissue image (Wang et. al. [0004]: a second analysis module having a third model and configured to input the result of the determined positions and ranges of the organ and the tumor of the patient image into the third model to obtain at least one second prediction value corresponding to the patient image); and an ensemble module communicatively connected with the first analysis module and the second analysis module and generating a third determination data according to the first determination data and the second determination data, the third determination data indicating whether the target medical image comprises a cancerous tissue image or indicating a chance of the target medical image comprising a cancerous tissue image (Wang et. al.: [0004]: a determining module configured to output a determined result based on the first prediction value and the second prediction value); wherein the server trains the first analysis module with a plurality of first image training data, a plurality of second image training data and a plurality of third image training data to allow the first analysis module to generate the first determination data according to the first medical image data, wherein the server trains the second analysis module with the plurality of first image training data, the plurality of second image training data and the plurality of third image training data to allow the second analysis module to generate the second determination data according to the first medical image data (Wang et. al. Figure 1, [0038]).
However, Wang et. al. fails to disclose wherein the plurality of first image training data each indicate a medical image containing normal tissue, the plurality of second image training data each indicate a medical image containing cancerous tissue, and the plurality of third image training data each indicate a medical image containing non-cancerous, abnormal tissue.
Preuhs et. al. teaches wherein the plurality of first image training data each indicate a medical image containing normal tissue, the plurality of second image training data each indicate a medical image containing cancerous tissue, and the plurality of third image training data each indicate a medical image containing non-cancerous, abnormal tissue (Preuhs et. al. [0037]: the normal image may have the same size as the medical image it has been extracted from and/or the corresponding abnormality image. [0028]: an abnormality can be a neoplasm (also denoted as “tumor”), in particular, a benign neoplasm, an in-situ neoplasm, a malignant neoplasms and/or a neoplasms of uncertain/unknown behavior.).
Including a normal image as part of the training data is important to the claimed invention can help the practitioner determine if a change in tissue composition has been calculated correctly, and help avoid false diagnosis. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Wang et. al. and Preuhs et. al. so that a normal image is included in the training data set.
Regarding claim 2 and claim 13, Wang et. al. and Preuhs et. al. disclose the medical image analysis system of claim 1 and method of claim 12, and Wang et. al. further discloses wherein the first analysis module comprises a deep learning model (Wang et. al. [0041]-[0042]: The neural network is a deep learning model architecture based on SegNet or U-Net), and the second analysis module comprises a radiomic module and a machine learning model (Wang et. al. [0010]: the second analysis module first performs 2D feature analysis on the second training set using the algorithm of radiomics to obtain a plurality of 2D feature values, and then trains a machine learning algorithm of a gradient boosting decision tree using the plurality of 2D feature values to obtain the third model.), wherein the first analysis module generates the first determination data according to the first medical image data with the deep learning model (Wang et. al. [0017]: the determining module uses one or both of the first prediction value and the second prediction value representing having cancer as the determined result), wherein the second analysis module generates the second determination data according to the first medical image data with the radiomic module and the machine learning model (Wang et. al. [0004]: a second analysis module having a third model and configured to input the result of the determined positions and ranges of the organ and the tumor of the patient image into the third model to obtain at least one second prediction value corresponding to the patient image).
Regarding claim 4 and claim 15, Wang et. al. further discloses the medical image analysis system of claim 3 and method of claim 14, wherein the radiomic module generates a feature data according to the target image data, and the machine learning model generates the second determination data according to the feature data (Wang et. al. [0010]: the second analysis module first performs 2D feature analysis on the second training set using the algorithm of radiomics to obtain a plurality of 2D feature values, and then trains a machine learning algorithm of a gradient boosting decision tree using the plurality of 2D feature values to obtain the third model. [0004]: a second analysis module having a third model and configured to input the result of the determined positions and ranges of the organ and the tumor of the patient image into the third model to obtain at least one second prediction value corresponding to the patient image).
Regarding claim 5 and claim 16, Wang et. al. and Preuhs et. al. disclose the medical image analysis system of claim 1 and method of claim 12, and Wang et. al. further discloses wherein the third determination data comprises a first risk data indicating a chance of the target medical image comprising a cancerous tissue image (Wang et. al. [0004]: a second analysis module having a third model and configured to input the result of the determined positions and ranges of the organ and the tumor of the patient image into the third model to obtain at least one second prediction value corresponding to the patient image; and a determining module configured to output a determined result based on the first prediction value and the second prediction value).
Regarding claim 6 and claim 17, Wang et. al. and Preuhs et. al. disclose the medical image analysis system of claim 1 and method of claim 12, and Wang et. al. further discloses wherein the database stores a plurality of second medical image data and a plurality of third medical image data (Wang et. al., Figure 1, [0005], [0020], [0040]: medical imaging analyzing system and method, the present disclosure further comprises the step of: interlinking a plurality of images, organ position and range markers and tumor position and range markers to use as a first training set via a database stored with the plurality of images, the organ position and range markers and the tumor positions and range markers.), the plurality of second medical image data each indicate a specific non-cancerous tissue medical image, and the plurality of third medical image data each indicate a specific cancerous tissue medical image, wherein the server generates a fourth determination data according to each of the plurality of second medical image data, the plurality of fourth determination data each comprising a second risk data, wherein the server generates a fifth determination data according to each of the plurality of third medical image data, the plurality of fifth determination data each comprising a third risk data, wherein the server generates a range data according to the plurality of second risk data and the plurality of third risk data, the range data indicating a plurality of ranges (Wang et. al. Figure 1, [0038]: cloud server; it is also well known in the arts that a plurality of datasets can be stored and generated).
Claim(s) 7-11, 18-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et. al. (United States Patent Application Publication US 2022/0156929 A1) in view of Preuhs et. al. (United States Patent Application Publication US 2023/0274439 A1) as applied to claim 6 and claim 17 above, and further in view of Giger et. al. (United States Patent Application Publication US 2004/0101181 A1).
Regarding claims 7-11, and claims 18-22 the rejection analysis is substantially incorporated in the rejection of claim 6 and claim 17, except for the feature of “likelihood ratio data”. Giger et. al. teaches “likelihood ratio data” in the training process for the ANN, the output of an ANN in the limit of large sample sizes approximates a mapping function that is a monotonic transformation of the likelihood ratio (Giger et. al. [0137]). It is obvious to one skilled in the art to incorporate a plurality of data sets, given the size and storage capability of the database and server. This iterative process can continue until the seventh risk data set.
Claim(s) 3 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et. al. (United States Patent Application Publication US 2022/0156929 A1) in view of Preuhs et. al. (United States Patent Application Publication US 2023/0274439 A1) as applied to claim 2 and claim 13 above, and further in view of Kaufman et. al. (International Patent Publication WO 2019/005722 A1).
Regarding claim 3 and claim 14, Wang et. al. and Preuhs et. al. disclose the medical image analysis system of claim 2 and the method of claim 13, with the target image data correlating with the first medical image data and indicating a target organ image in the target medical image (Wang et. al. [0004]: a first analysis module having a second model and configured to input the result of the determined positions and ranges of the organ and the tumor of the patient image into the second model to obtain at least one first prediction value corresponding to the patient image, [0008]: the first analysis module first performs 3D feature analysis on the second training set using an algorithm of radiomics to obtain a plurality of 3D feature values, and then trains a machine learning algorithm of a gradient boosting decision tree using the plurality of 3D feature values to obtain the second model, [0041]: neural network module can be trained to obtain the first model based on the first training set), wherein the first analysis module generates the first determination data according to the target image data correlating with the first medical image data, wherein the second analysis module generates the second determination data according to the target image data correlating with the first medical image data (Wang et. al. [0017]: the determining module uses one or both of the first prediction value and the second prediction value representing having cancer as the determined result).
However, Wang et. al. and Preuhs et. al. fail to disclose wherein the server comprises a segmentation module communicatively connected with the first analysis module and the second analysis module, wherein the segmentation module generates a target image data according to the first medical image data.
Kaufman et. al. teaches wherein the server comprises a segmentation module communicatively connected with the first analysis module and the second analysis module, wherein the segmentation module generates a target image data according to the first medical image data (Kaufman et. al. [0012]: The magnetic resonance imaging information and the computed tomography imaging information can be segments using a segmentation procedure for visualization by a doctor. The segmented cystic lesion can also be classified using one or more classifiers.).
Segmentation is an important feature of the claimed invention because it aids with visualization and diagnosis. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Wang et. al., Preuhs et. al. and Kaufman et. al. so that segmentation is included as part of the solution to the claimed invention.
Conclusion
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 July 14, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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