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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Examiner Note
The claims have been evaluated under 35 U.S.C. §101 and are directed to patent eligible subject matter. While the claims appear to involve mental steps, the claims are integrated into a practical application because they recite steps/elements directed to a specific improvement such as determining image attributes more effectively and improving the quality of determining the attribute (See page 1 line 26 and page 3 lines 32-34). Accordingly, the claims are not directed to an abstract idea and no rejection under §101 is made.
MPEP 2106.05(a) states:
“After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification. However, the claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel").”
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 (1-14) 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.
First, the limitation of “(indicating (im)proper field” is not clear. For example, it is not clear what applicant is trying to convey with this limitation. Second, the limitation of “from each of the least one image quantification neural network” is not clear. For example, it seems that applicant omitted the noise NN since the limitation call for appending the noise estimate and the image attribute. For the purpose of art consideration on the merits, these limitations will be given its broadest reasonable interpretation.
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) (1-5, 7-11, 13-14) are rejected under 35 U.S.C. 103 as being unpatentable over BAO et al. (hereinafter BAO) (US Publication 2023/0063828 A1)
Re claim 1, BAO discloses a medical system (See fig. 1: 100; ¶ 32 where it teaches a medical image system) comprising: a memory (See fig. 1: 150; ¶ 38 where it teaches a storage device configured to store instructions used by the processing device.) configured to store machine executable instructions and multiple neural networks (See fig. 4: 404; ¶ 110 where it teaches one or more evaluation models may comprise NNs), wherein the multiple neural networks comprise a noise estimation neural network and at least one image quantification neural network (See fig. 4: 404; ¶ 47, 70-73, 97 where it teaches image noise level, image signal uniformity, an artifact suppression degree.); wherein the noise estimation neural network is configured to output a noise estimate of a medical image in response to receiving the medical image as input (See fig. 4: 404; ¶ 47, 70-73, 97 where it teaches image noise level, image signal uniformity, an artifact suppression degree.); wherein the at least one image quantification neural network is configured to output an image attribute representing any one of resolution, amount of blurring, image uniformity (indicating (im)proper field shimming in magnetic resonance imaging, magnetic resonance image contrast weighting or identification of a foreign object of the medical image in response to receiving the medical image as input (See fig. 4: 404; ¶ 47, 70-73, 97 where it teaches image noise level, image signal uniformity, an artifact suppression degree.); a computational system (See fig. 140 where it teaches a processing device), wherein execution of the machine executable instructions causes the computational system to: receive the medical image (See fig. 4: 402; ¶ 88-89 where it teaches obtaining a medical image.); receive the noise estimate in response to inputting the medical image into the noise estimation neural network (See fig. 4: 404; ¶ 47, 70-73, 97 where it teaches image noise level, image signal uniformity, an artifact suppression degree.); receive the image attribute in response to inputting the medical image into the image quantification neural network (See fig. 4: 404; ¶ 47, 70-73, 97 where it teaches image noise level, image signal uniformity, an artifact suppression degree.); append the noise estimate and the image attribute from each of the least one image quantification neural network to an image assessment (See fig. 4: 406; ¶ 111-113 where it teaches obtaining the quality evaluation result of the image based on one or more scores related to the one or more evaluation indexes.); provide a warning signal depending on the image assessment meeting a predetermined criterion. (See ¶ 57-65 where it teaches determining whether the current acquisition parameters of the acquired image need to be re-determined according to the size of the overall score; meeting preset conditions.)
But the reference of BAO fails to explicitly teach wherein determination of the image attribute by the image quantification neural network is dependent upon the noise estimate.
However, the reference of BAO does suggest wherein determination of the image attribute by the image quantification neural network is dependent upon the noise estimate. (See ¶ 92-93 where it teaches the image to be evaluated may be preprocessed; performing gamma correction.)
Therefore, it would have been obvious to one of ordinary skills in the art to incorporate this feature into the system of BAO, in the manner as claimed, for the benefit of improving the efficiency and accuracy of image quality evaluation. (See ¶ 93)
Re claim 2, BAO discloses wherein the medical system further comprises a medical imaging device configured to acquire medical data descriptive of a subject, wherein execution of the machine executable instructions further causes the computational system to: control the medical imaging device to acquire the medical data; reconstruct the medical image from the medical data; and modify the operation of the medical imaging device if the warning signal is provided, wherein modification of the operation of the medical imaging device comprises at least one of the following: provide a user alert, append the warning signal to the medical image, generate a medical imaging device repair request, trigger a reacquisition of the medical data, displaying the resolution estimate, displaying the at least one image attribute, provide operating instructions, or provide repair instructions. (See ¶ 33, 58-61)
Re claim 3, BAO discloses wherein the medial device is a magnetic resonance imaging system. (See ¶ 33)
Re claim 4, BAO discloses wherein the multiple neural networks further comprise a uniformity estimation neural network, wherein the uniformity estimation neural network is configured to output an image uniformity estimation in response to inputting the medical image into the uniformity estimation neural network, wherein execution of the machine executable instructions further causes the computational system to: receive the uniformity estimation in response to inputting the medical image into the uniformity estimation neural network; and appending the uniformity estimation to the image assessment. (See fig. 4: 404; ¶ 47, 70-73, 97, 111-113)
Re claim 5, BAO discloses wherein the uniformity estimation is received before inputting the medical image into the noise estimation neural network, wherein execution of the machine executable instructions further causes the computational system to perform at least one of the following: apply an image uniformity correction algorithm to the medical image before inputting the medical image into the noise estimation neural network, wherein the image uniformity correction algorithm is controlled by the uniformity estimation; trigger a reacquisition of the medical image data if the uniformity estimation meets a predetermined uniformity criterion; or request a magnetic shim readjustment of a magnet of the medical device. (See fig. 4: 404; ¶ 47, 70-73, 97, 111-113)
Re claim 7, BAO discloses wherein the medical imaging device is a computed tomography system, a C-arm computed tomography system, a planar X-ray system, a fluoroscopy system, a positron emission tomography system, a single-photon emission computed tomography system, or an ultrasound system. (See ¶ 33)
Re claim 8, BAO discloses wherein execution of the machine executable instructions further causes the computational system to apply a noise removal algorithm to the medical image after receiving the noise estimate and before inputting the medical image into the at least one image quantification neural network. (See ¶ 92-97)
Re claim 9, BAO discloses wherein the at least one image quantification neural network is further configured to receive the noise estimate as input, wherein execution of the machine executable instructions further causes the computational system to input the noise estimate into the image quantification neural network before receiving the image attribute. (See ¶ 92-97)
Re claim 10, BAO discloses wherein each of the at least one image quantification neural network is selected from a group of image attribute specific image quantification neural networks using the noise estimate. (See ¶ 70-73, 97)
Re claim 11, BAO discloses wherein the at least one image quantification neural network comprises an artifact estimation neural network configured to output an image artifact quantification in the medical image in response to receiving the medical image as input. wherein the at least one image quantification neural network comprises at least one of the following: a resolution estimation neural network configured to output a resolution estimate of the medical image in response to receiving the medical image as input, or an artifact estimation neural network configured to output an image artifact quantification in the medical image in response to receiving the medical image as input. (See ¶ 70-73, 97)
Re claim 13, BAO discloses wherein the memory further contains an evaluation neural network configured to output the warning signal if the image assessment meets the predetermined criterion, and wherein execution of the machine executable instructions further causes the computational system to: input the image assessment into the evaluation neural network; and receive the warning signal if the predetermined criterion is met. (See ¶ 33, 58-61)
Claim 14 has been analyzed and rejected w/r to claim 1 above.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over BAO et al (hereinafter BAO) (US Publication 2023/0063828 A1) in view of Lebel et al. (hereinafter Lebel)(US Patent 10,635,943 B1)
Re claim 15, BAO discloses a method of training a neural network (See ¶ 37, 119 where it teaches training a NN.), wherein the method comprises: receiving an untrained neural network, wherein the untrained neural network has an input configured to receive a medical image (See ¶ 98, 108 where it teaches training the model; input may be a medical image.), wherein the untrained neural network has an output configured to output a noise estimate or an image attribute (See fig. 4: 404; ¶ 47, 70-73, 97 where it teaches image noise level, image signal uniformity, an artifact suppression degree.), receiving training data. (See ¶ 37, 119 where it teaches training a NN.)
But the reference of BAO fails to teach wherein the training data comprises pairs of input images and ground truth data, wherein the ground truth data is noise ground truth data or image attribute ground truth data, wherein at least a portion of the pairs of input images are modified optical images wherein the modified optical images are generated by adding noise to optical images or modeling the image attribute in the input images, wherein the modified optical images are further modified by applying an image mask to black out portions of the modified optical images; and provide a noise estimation neural network or an image quantification neural network by training the untrained neural network with the training data.However, Lebel does. (See fig. 5, 8) In the same field of endeavors, the reference of Lebel discloses and fairly suggest wherein the training data comprises pairs of input images and ground truth data, wherein the ground truth data is noise ground truth data or image attribute ground truth data (See fig. 5: 510, 515; col. 12, lines 4-8), wherein at least a portion of the pairs of input images are modified optical images (See col. 8, lines 9-11) wherein the modified optical images are generated by adding noise to optical images or modeling the image attribute in the input images (See col. 11, lines 16-20), wherein the modified optical images are further modified by applying an image mask to black out portions of the modified optical images (See col. 11, lines 55-62); and provide a noise estimation neural network or an image quantification neural network by training the untrained neural network with the training data. (See fig. 5: 515)
Therefore, taking the combined teachings of BAO & Lebel as a whole, it would have been obvious to one of ordinary skills in the art to incorporate these features into the system of BAO, in the manner as claimed and as taught by Lebel, for the benefit of reducing noise and image artifacts in medical images. (See col. 1, lines 45-46)
Allowable Subject Matter
Claims (6, 12) would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEON FLORES whose telephone number is (571)270-1201. The examiner can normally be reached M-F 8am - 6pm.
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/LEON FLORES/Primary Examiner, Art Unit 2676 June 16, 2026
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