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 07/01/2024 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 09/19/2023 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
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, 3-5, 7, 10, 15, 17 and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by He et al (“He” hereinafter, CN 111383210A, a copy of translation is attached).
As per claim 1, He discloses a learning device (figure 1) comprising: a first processor; a memory (paragraph [0022]: He’s system includes at least one processor and memory) that stores a data set for learning consisting of a plurality of first medical use images each having a disease label (paragraph [0034]: training data comprises fundus images and corresponding disease type); a first learning model (paragraph [0035]: “the trained first machine learning model 11”) that has been trained and performs extraction of an organ region from the first medical use image (paragraph [0037]: “feature extraction” is performed on the fundus image) and estimation of uncertainty for the extraction of the organ region in a case in which the first medical use image is input (paragraph [0037]: “confidence vector” or “confidence degree” is the claimed “estimation of uncertainty”); and a second learning model (paragraph [0038]: “second machine learning model 12” is to be trained with “fundus image” and “first confidence degree information”) that has not been trained and detects a disease from any second medical use image, wherein the first processor performs processing of reading out the first medical use image from the memory and inputting the read out first medical use image to the first learning model (as shown in figure 1, “fundus image” is inputted to “first machine learning model 11”), processing of normalizing the first medical use image to be input to the second learning model based on the organ region extracted by the first learning model or setting information indicating the organ region with respect to the first medical use image (paragraph [0049]: the input fundus images are to be image processed for image enhancement), processing of calculating a value of uncertainty of the first medical use image input to the first learning model based on the uncertainty estimated by the first learning model (paragraph [0037], the confidence vector (p11, p12, … p1n) is the claimed “value of uncertainty”), and processing of training the second learning model by using the normalized first medical use image, or the first medical use image and the information indicating the organ region as input data, and the disease label of the first medical use image as a correct answer label (as shown in figure 1, the second machine learning model 12 are inputted with fundus image and loss resulted from second confidence value and disease labeling information), in which the second learning model is trained by reflecting the calculated value of uncertainty of the first medical use image (the second machine learning model 12 is trained based on confidence values of (p11, p12, …p1n) as shown in figure 1).
As per claim 3, He discloses wherein, in the processing of training the second learning model, an error between an estimate result of the second learning model and the disease label is weighted according to the calculated value of uncertainty of the first medical use image, and the second learning model is trained based on the weighted error (the loss2 is the claimed “error” between the second confidence value/vector (p21, p22, …p2n) and the disease label as shown in figure 1, and loss2 is weighted in paragraph [0042] in paragraphs [0041] & [0042]).
As per claim 4, He discloses wherein the processing of normalizing the first medical use image includes processing of cutting the estimate organ region from the first medical use image (paragraph [0037]: “feature extraction”).
As per claim 5, as explained above image features extraction is the claimed “normalizing the first medical use image”.
As per claim 7, He discloses wherein the second learning model is learning model consisting of densely connected convolutional networks (DensNet) (paragraph [0035]: “the second machine learning model 12” may be a “Densnet”)
As per claim 10, He in paragraph [0022] teaches the system can employ more than one processor, and a second processor may be used to repeat the above processes in claim 1 for a second fundus image.
As per claim 15, see explanation in claim 1.
As per claim 17, see explanation in claim 3.
As per claim 18, see explanation in claim 1, the examiner notes He’s system is a computer-like system, which inherently includes a non-transitory computer-readable medium.
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 6, 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over He in view of Wickesberg (U.S. Publication No. 2019/0332892 A1).
As per claim 6, He in paragraph [0035] teaches the first machine learning model may be one of a plurality of neural network models. However, He does not explicitly teach the first machine learning model may be a Bayesian neural network.
Wickesberg teaches a system 100 including a machine learning component 108, and the machine learning component 108 may be Baysesian model based neural network for detecting lung disease with input medical images from x-ray device (figures 1-2 and paragraphs [0023] & [0029]).
He and Wickesberg are combinable because they are from the same field of endeavor, ie. medical image analysis using a machine learning mode.
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to modify He in light of Wickesberg’s teaching to employ Bayesian based neural network as an alternative to other machine learning algorithms. One would be motivate do to so because the system for detecting a disease from medical images “can additionally or alternatively employ any suitable machine-learning based techniques, statistical-based techniques and/or probabilistic-based techniques” (Wickesberg: paragraph [0029]).
For claims 8 and 9, Wickesberg teaches the input data may be medical images from an X-ray device and the medical images can include a lung nodule for lung disease detection (paragraphs [0023] & [0029])
Allowable Subject Matter
Claims 2, 11-14 and 16 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.
Vlasimsky, U.S. Publication No. 2022/0180514 A1, teaches a system for subjecting x-ray chest images preprocessing and then input the preprocessed images into first and second neural networks for feature extraction and classification. See figures 1, 3 and 12-16.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TOM Y LU whose telephone number is (571)272-7393. The examiner can normally be reached Monday - Friday, 9AM - 5PM.
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/TOM Y LU/Primary Examiner, Art Unit 2667