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
1. The information disclosure statements (IDS) submitted on 1/29/2025 and is in compliance with the provisions of 37 CFR 1.97. According, the information disclosure statement is being considered by the Examiner.
Claim Rejections - 35 USC § 112
2. 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.
3. Claims 2 and 4-7 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Regarding claim 2, The expression “symmetrified contrast” used in claim 2 does not appear to have a well-recognized meaning in the art, such that it leaves the reader in doubt as to the meaning of the technical feature to which it refers, thereby rendering the definition of the subject-matter of said claim unclear (Article 84 EPC). Claims 4-6 are also rejected as they inherit the deficiencies in claim 2.
Regarding claim 4, the claim refers to “image contrast of the one or more anomalous locations”, which is to “have a predetermined image contrast range”. This feature comprises a discrepancy. as a location may also be a single point, while a contrast range is a property of a plurality of image points, e.g. of an area in the image, as pointed out under point 2 on p.3 of the description, when referring to “a range of gray values in the area in which anomalies are suspected”. Even if the term “location” would be understood as an area in the image, it would still be unclear from the claim, whether this area coincides with the area of the anomaly (e.g. with a lesion in an image) or with an area (potentially) comprising the anomaly (e.g. the lesion), i.e. an area larger than the anomaly (e.g. a lesion). If the anomaly was e.g. a lesion, it would not be clear from such a claim whether the “predetermined contrast range” of claim 4 would apply to contrast within (solely) the lesion, such that e.g. the visibility of details inside the lesion would be enhanced, or to an area which including and surrounding the lesion, such that the visibility of the lesion itself with respect to its surroundings would be enhanced.
Examiner Notes
4. Examiner cites particular paragraphs, columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Rejections - 35 USC § 102
5. 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
6. Claims 1-8, 11-12 and 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cohen et al. (WO-2022093708 or US20230410315 cited from IDS; hereinafter “Cohen”). Regarding claim 1 and similarly claims 14 and 15, Cohen discloses a medical system (see Fig. 1 and paragraph [0003]) comprising: a memory configured to store machine executable instructions and an anomaly detection module (see Figs. 4 and 13 and paragraph [0061]); and a computational system, wherein execution of the machine executable instructions causes the computational system (see paragraphs [0118-121] and Fig. 13) to: receive a set of magnetic resonance images descriptive of a field of view of a subject acquired according to a synthetic magnetic resonance imaging protocol (Receiving the PD/T1 /T2 parametric images/maps shown in Fig.1, referred to also as “MR tissue parameters (PD, Tl, T2, etc.)” in paragraph [0051].); receive an anomaly indicator from the anomaly detection module in response to inputting at least one of the set of magnetic resonance images into the anomaly detection module (Paragraphs [0048]-[0049] and Fig. 1 disclose performing segmentation, such
as “auto-segmentation of tumors and organs at risk”. A tumor is considered anomalous state, such that said "auto-segmentation of tumors" is regarded as “receiving an anomaly indicator from an anomaly detection module”.); determine a set of reconstruction parameters using the anomaly indicator (Producing a set of acquisition parameters (FA, TR,TE,TI. .. ) by the generator, shown in Fig.1, referred to also as "MR parameters (PD, Tl, T2, etc.) in paragraph [0052].); and reconstruct a synthesized magnetic resonance image from the set of magnetic resonance images and the set of reconstruction parameters (Paragraph [0044] discloses “modifying the acquisition parameters governing the pulse sequence (flip angle (FA), repetition time (TR), echo time (TE) etc.)” such that "any desired image contrast can be synthesized", which is also shown as an operation in the workflow shown in Fig. 1. As based on these parameters Bloch simulation is done to synthesize an image having “any desired image
contrast” (see paragraph [0044] and Fig. 1 ), these parameters may be regarded “reconstruction parameters”. Since said operation is part of the same operation loop as the above mentioned "segmentation" operation (see Fig. 1 of D1 ), it is considered that a set of reconstruction parameters is determined using the output of the segmentation, i.e. the above identified “anomaly indicator”, such as required in the claim.). Regarding claim 2, Cohen discloses the medical system of claim 1, wherein the field of view is descriptive of a right hemisphere and a left hemisphere of a brain of the subject, wherein the anomaly indicator comprises one or more anomalous locations in the field of view, and wherein the algorithmic anomaly detection module is further configured to spatially locate the one or more anomaly in the brain of the subject using a symmetrified contrast between the right hemisphere and the left hemisphere of the brain of the subject (the PD/T1 /T2 parametric images/maps shown in Fig.1 correspond to a field of view of an entire brain of a subject, as required in claim 2.).
Regarding claim 3, Cohen discloses the medical system of claim 1, wherein the anomaly indicator comprises one or more anomalous locations in the field of view, wherein the anomaly detection module comprises an autoencoder neural network configured to output an autoencoded image for each of the at least one of the set of magnetic resonance images, wherein receiving an anomaly indicator in response to inputting the at least one of the set of magnetic resonance images into the anomaly detection module comprises: receiving the autoencoded image in response to inputting the at least one of the set of magnetic resonance images into the autoencoder neural network; determine the one or more anomalous locations by comparing the autoencoded image of each of the at least one of the set of magnetic resonance images with the at least one of the set of magnetic resonance images. (the according to paragraph [0052], the “generator” part of the GAN neural network of the framework shown in Fig.1 is implemented as a variational autoencoder. It follows from the notion of the training (and corresponding training "loss" of the generator) in paragraph [0053] that the output of the (generator) autoencoder is, by the nature thereof, compared to the autoencoder input, to generate said loss).
Regarding claim 4, Cohen discloses the medical system of claim 2, wherein execution of the machine executable instructions further causes the computational system to iteratively vary the set of reconstruction parameters and reconstruct the synthesized magnetic resonance image to adjust the image contrast of the one or more anomalous locations relative to its surroundings in the image to have a predetermined image contrast range (An iterative nature of setting the reconstruction parameters and reconstructing the synthesized magnetic resonance image can be seen from Fig.1 (note the arrows indicating the workflow). Moreover, paragraph 0056] refers to "target MRI contrast".).
Regarding claim 5, Cohen discloses the medical system of claim 2, wherein the anomaly indicator indicates multiple anomalous locations, wherein the synthesized magnetic resonance image is reconstructed for each of the multiple anomalous locations resulting in a set of synthesized magnetic resonance images (see paragraph [0044]).
Regarding claim 6, Cohen discloses the medical system of claim 5, wherein execution of the machine executable instructions further causes the computational system to construct a composite image from the set of synthesized magnetic resonance images locations by: including the anomalous location from each of the set of synthesized magnetic resonance images; and blending pixel values between the anomalous location from each of the set of synthesized magnetic resonance images using the set of synthesized magnetic resonance images (see [0030] and Figs. 3-7).
Regarding claim 7, Cohen discloses the medical system of claim 6, wherein execution of the machine executable instructions further causes the computational system to: display the composite image on a graphical user interface receive a selection of an anomalous location within the composite image from the graphical user interface; and display the synthesized magnetic resonance image comprising the selected anomalous location (see [0030] and Figs. 3-7).
Regarding claim 8, Cohen discloses the medical system of claim 1, wherein the anatomical detection module comprises a convolutional neural network configured to output an anomaly classification as the anomaly indicator in response to receiving the at least one of the set of magnetic resonance images (in paragraph [0049] that the segmentation network of
D1 may be realized as a convolutional neural network. The segmentation of
tumors, as done in D1, may also be regarded as a classification into two
classes, namely to a class when a tumor is segmented (i.e. detected) and a
class when no tumor is segmented, i.e. detected.).
Regarding claim 11, Cohen discloses the medical system of claim 1, wherein reconstructing the synthesized magnetic resonance image comprises: determining a T1 dependent value, a T2 dependent value, and a proton density for each voxel of the field of view by performing a voxel wise fit of a chosen signal intensity equation to the set of magnetic resonance images; and reconstruct the synthesized magnetic resonance image by calculating a signal intensity value for each voxel using a reconstruction signal intensity equation that takes the voxel wise T1 dependent value, the voxel wise T2 dependent value, the voxel wise proton density value, and the reconstruction parameters as input (see block “Bloch equation simulation” in Figs. 1,3 and [0045-53]).
Regarding claim 12, Cohen discloses the medical system of claim 1, wherein the set of magnetic resonance images forms a magnetic resonance fingerprint, and wherein the synthesized magnetic resonance image is reconstructed from the set of magnetic resonance image according to a magnetic resonance fingerprinting protocol (see [0042-44]).
7. Claims 1 and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hinks et al. (US2012/0008842; hereinafter “Hinks”). Regarding claim 1 and similarly claims 14 and 15, Hinks discloses a medical system (a MRI system in Fig. 1) comprising: a memory configured to store machine executable instructions and an anomaly detection module (see paragraphs [0023]); and a computational system, wherein execution of the machine executable instructions causes the computational system (see paragraph [0023]) to: receive a set of magnetic resonance images descriptive of a field of view of a subject acquired according to a synthetic magnetic resonance imaging protocol (receiving an array of magnetic resonance image data descriptive of a field of view of a subject; see at least in [0011, 55] and claim 1); receive an anomaly indicator from the anomaly detection module in response to inputting at least one of the set of magnetic resonance images into the anomaly detection module (the computer is also programmed to manipulate the scan data to determine or identify a first plurality of phase errors in the image data responsible for a Nyquist ghost…, see at least in [0011, 55] and claim 1 ); determine a set of reconstruction parameters using the anomaly indicator (wherein the manipulated scan data is free of navigator echo data, remove the first plurality of phase errors from the image data. See at least in [0011, 55] and claim 1); and reconstruct a synthesized magnetic resonance image from the set of magnetic resonance images and the set of reconstruction parameters (reconstruct an image based on the image data having the first plurality of phase errors removed therefrom. See at least in [0011, 55] and claim 1). Regarding claim 13, Hinks discloses the medical system of claim 1, wherein the medical system further comprises a magnetic resonance imaging system, where the memory further contains pulse sequence commands configured to control the magnetic resonance imaging system to acquire k-space data according to the synthetic magnetic resonance imaging protocol, wherein the execution of the machine executable instructions further causes the computational system to: acquire the k-space data by controlling the magnetic resonance imaging system with the pulse sequence commands; and reconstruct the set of magnetic resonance images from the k-space data (see paragraphs [0022-24, 29-30]).
Allowable Subject Matter
8. Claims 9-10 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.
Prior Art of Record
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huang et al. (U.S Pub. 2015/0302616) discloses a medical imaging system, comprising: a memory which stores magnetic resonance k-space data, the magnetic resonance data including non-rigid motion defects; one or more processors configured to: reconstruct a first image from the magnetic resonance data which includes a high signal to noise ratio and motion artifacts; detect and reject portions of k-space which include non-rigid motion defects; and reconstruct a second image from non-rejected portions of k-space and the first image (see specification for more details). Shinoda et al. (U.S Pub. 2012/0226141) discloses a MRI system comprising a structural information acquisition unit is configured to acquire structural information involving a center line and a junction of blood vessels consisting of carotid arteries, said abnormal part detection unit is configured to detect a stenosis region of the carotid arteries as the abnormal region, and said imaging region setting unit is configured to indicate a section normal to the center line of the carotid arteries as the imaging region according to the detection result of the abnormal region, further comprising: an imaging section correction unit configured to display a branch of the carotid arteries together with the imaging region according to the detection result of the abnormal region as a Curved Multiple Planer Reconstruction image or a Stretched Curved Multiple Planer Reconstruction image on a display unit to adjust an imaging section consisting of the imaging region according to the detection result of the abnormal region so as to be normal to the center line of the carotid arteries according to information inputted from an input device, the branch being selected by an operation of the input device or another input device with referring to maximum intensity projection image data or volume rendering image data of the carotid arteries. (see specification for more details).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THANG LE whose telephone number is (571)272-9349. The examiner can normally be reached on Monday thru Friday 7:30AM-5:00PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Huy Phan can be reached on (571) 272-7924. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THANG X LE/Primary Examiner, Art Unit 2858
7/24/2026
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