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) was submitted on 1/17/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1, 7, 12-14, 21, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aslani et al. “Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation” (Aslani) (IDS) in view of Wei et al. (US 20230386032 A1) (Wei).
Regarding claim 1, Aslani discloses a system (See section 3.2.3), comprising:
a processor (See section 3.2.3); and
training a first machine learning model to identify one or more new lesions and/or enlarging lesions that developed within a multitemporal image input between a first timepoint and a second timepoint, the multitemporal image input including a first image acquired at the first timepoint and a second image acquired at the second timepoint (See abstract and section 3.2.3), the first machine learning model being trained to identify the one or more new lesions and/or enlarging lesions (See abstract) by at least:
generating a first representation of the multitemporal image input from the first timepoint to the second timepoint (See Fig. 2; section 2.1);
generating a second representation of the multitemporal image input from the second timepoint to the first timepoint (See Fig. 2; section 2.1);
generating a third representation of the multitemporal image input including a concatenation of the first representation and the second representation (See Fig. 3); and
generating a lesion mask identifying the one or more new lesions and/or enlarging lesions, the lesion mask corresponding to a decoding of the third representation of the multitemporal image input (See Figs. 3 and 4); and
applying the trained machine learning model to generate, for one or more images of a patient, a patient lesion mask identifying at least one of a new lesion and an enlarging lesion present in the one or more images (See sections 2.1, 2.2, 4.2, 4.3).
Aslani discloses a system with processor (See section 3.2.3), however doesn’t explicitly disclose a memory storing instructions which, when executed by the processor, result in operation.
Wei disclose a lesion detection and segmentation system. Wei discloses that the system uses a memory storing instructions which, when executed by the processor, result in operation (See Fig. 9; para. 88-97).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Aslani with the lesion detection system as disclosed by Wei in order to enhance the scanning system (See Wei para. 19).
Regarding claim 7, The system of claim 1, wherein the multitemporal image input includes a reference lesion mask identifying one or more existing lesions present in the first image and/or the second image, and wherein the reference lesion mask is configured to provide attention to the one or more existing lesions (See Aslani section 3.2.3; supervised training).
Regarding claim 12, The system of claim 1, wherein the first image and the second image include T2-weighted images and/or fluid attenuated inversion recovery images (See Aslani section 2.1).
Regarding claim 13, The system of claim 1, wherein the first machine leaning model is trained based one or more lesion masks with ground truth annotations (See Aslani section 3.2.3; supervised training).
Regarding claim 14, The system of claim 1, wherein each of the first representation and the second representation is an encoding of the multitemporal image input (See Aslani section 1.2).
Regarding claim 21, this claim is drawn to a method that is performed by the system of claim 1, wherein claim 21 contains the same limitations as claim 1 and is therefore rejected upon the same basis.
Regarding claim 22, this claim is drawn to a non-transitory computer readable medium storing instructions that is executed by the system of claim 1, wherein claim 22 contains the same limitations as claim 1 and is therefore rejected upon the same basis. Furthermore, Wei discloses a non-transitory computer readable medium storing instructions, which when executed by at least one processor, result in operations (See Wei paras. 34-35).
Claim(s) 2-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aslani et al. “Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation” (Aslani) (IDS) in view of Wei et al. (US 20230386032 A1) (Wei) as applied to claims 1, 7, 12-14, 21, and 22 above, and further in view of Hu et al. “Segmentation of Intracranial Aneurysm Based on U-Net and BiConvGRU”(Hu) (IDS).
Regarding claim 2, Aslani in view of Wei does not disclose wherein encoding blocks of the first machine learning model comprise a first unidirectional convolutional gated recurrent unit (cGRU) configured to generate the first representation of the multitemporal image input and a second unidirectional convolutional gated recurrent unit configured to generate the second representation of the multitemporal image input.
Hu discloses a system for segmentation of intracranial aneurysm. Hu discloses wherein encoding blocks of the first machine learning model comprise a first unidirectional convolutional gated recurrent unit (cGRU) configured to generate the first representation of the multitemporal image input and a second unidirectional convolutional gated recurrent unit configured to generate the second representation of the multitemporal image input (See Hu abstract).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Aslani in view of Wei with the system as disclosed by Hu in order to enhance the capture spatial, temporal, and motion information of the system (See Hu abstract).
Regarding claim 3, The system of claim 1, wherein the first machine learning model comprises one or more convolutional layers configured to decode the third representation of the multitemporal image input (See Hu Fig. 1).
Please see the motivation stated in claim 2.
Regarding claim 4, The system of claim 1, wherein the generating the first representation of the multitemporal image input includes extracting a first set of temporal features present in the multitemporal image input from the first timepoint to the second timepoint, and wherein the generating the second representation of the multitemporal image input includes extracting a second set of temporal features present in the multitemporal image input from the second timepoint to the first timepoint (See Hu abstract).
Please see the motivation stated in claim 2.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aslani et al. “Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation” (Aslani) (IDS) in view of Wei et al. (US 20230386032 A1) (Wei) as applied to claims 1, 7, 12-14, 21, and 22 above, and further in view of Park et al. (US 20240144474 A1) (Park).
Regarding claim 8, Aslani in view of Wei does not disclose wherein the reference lesion mask is generated by applying a second machine learning model.
Park discloses lesion analysis system. Park discloses that the reference lesion mask is generated by applying a second machine learning model (See Park para. 75).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Aslani in view of Wei with the system as disclosed by Park in order to increase performance when detecting and measuring lesions (See Park para. 3).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aslani et al. “Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation” (Aslani) (IDS) in view of Wei et al. (US 20230386032 A1) (Wei) and Park et al. (US 20240144474 A1) (Park) as applied to claim 8 above, and further in view of Bazgir et al. (WO 2021030629 A1) (Bazgir).
Regarding claim 9, Aslani in view of Wei and Park does not disclose wherein the second machine learning model is pre-trained based on a cross-sectional magnetic resonance imaging sequence and an annotation of the cross-sectional magnetic resonance imaging sequence.
Bazgir discloses an object detection system. Bazgir discloses wherein the second machine learning model is pre-trained based on a cross-sectional magnetic resonance imaging sequence and an annotation of the cross-sectional magnetic resonance imaging sequence (See Fig. 6; para. 78).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Aslani in view of Wei and Park with the system as disclosed by Bazgir in order to increase performance when detecting and measuring objects (See Bazgir para. 47-48).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aslani et al. “Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation” (Aslani) (IDS) in view of Wei et al. (US 20230386032 A1) (Wei) as applied to claims 1, 7, 12-14, 21, and 22 above, and further in view of Wang et al. (US 20220139531 A1) (Wang).
Regarding claim 15, Aslani in view of Wei does not disclose wherein the operations further comprise:
determining if a new lesion and/or an enlarging lesion identified in the patient lesion mask is a false positive; and
rejecting the patient lesion mask for one or more downstream clinical tasks in response to determining that the new lesion and/or the enlarging lesion identified in the patient lesion mask is a false positive.
Wang discloses a system for segmentation of medical images. Wang discloses wherein the operations further comprise:
determining if a new lesion and/or an enlarging lesion identified in the patient lesion mask is a false positive (See paras. 45 and 65; identifying a false positive to be removed); and
rejecting the patient lesion mask for one or more downstream clinical tasks in response to determining that the new lesion and/or the enlarging lesion identified in the patient lesion mask is a false positive (See paras. 45 and 65; removing false positives before providing results downstream).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Aslani in view of Wei with the system as disclosed by Wang in order to provide a more efficient and correct analysis of medical images (See Wang para. 44).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aslani et al. “Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation” (Aslani) (IDS) in view of Wei et al. (US 20230386032 A1) (Wei) as applied to claims 1, 7, 12-14, 21, and 22 above, and further in view of Richter et al. (US 20220139531 A1) (Richter).
Regarding claim 20, Aslani in view of Wei does not disclose wherein each voxel in the patient lesion mask is labeled a first value to indicate the voxel as being a part of a new lesion, a second value to indicate the voxel as being a part of an enlarging lesion, or a third value to indicate the voxel as being a background voxel.
Richter discloses a system for segmentation of medical images. Richter discloses wherein each voxel in the patient lesion mask is labeled a first value to indicate the voxel as being a part of a new lesion, a second value to indicate the voxel as being a part of an enlarging lesion, or a third value to indicate the voxel as being a background voxel (See para. 214-217).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Aslani in view of Wei with the system as disclosed by Richter in order to refine and mitigate noise from the medical images (See Richter para. 214).
Richter discloses a system that labels voxels into at least three different categories. The claimed categories is an obvious matter of design choice because the prior art teaches the same general function, and applicant has not shown that the claimed feature is critical or produces an unexpected result.
Allowable Subject Matter
Claims 5-6, 10-11, and 16-19 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph G Ustaris whose telephone number is (571)272-7383. The examiner can normally be reached 9-5pm M-Th.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Colleen A Fauz can be reached at 571-272-1667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSEPH G USTARIS/Supervisory Patent Examiner, Art Unit 2483
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