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 3/20/2024. 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 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.
“by means of a deep learning method” in claims 1 and 9 has been interpreted to correspond to the structure disclosed on [0008], [0038], [0064]-[0065], [0111], and [0124]. See rejections under 35 U.S.C. 112(a) and 112(b) below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-9 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1 and 9, the claims recite the limitation “a deep learning method” in line 9 and lines 10-11, respectively, and appears to positively recite a particular deep learning method that is used in the image segmentation of the image dataset. This limitation appears to correspond to the features disclosed in paragraphs [0008], [0038], [0064]-[0065], [0111], and [0124]. Each recitation of the step only recites a generic deep learning method or applying a generic nnU-Net method. The claims are interpreted under 112(f) to correspond to a computer implemented method for performing stroke lesion image segmentation and the cited paragraphs do not provide sufficient disclosure of the algorithm that not only segments the DWI images, but also obtains lesion masks from the segmented images. One of ordinary skill in the art would not recognize sufficient disclosure within the specification to support the training or use of a particularly claimed deep learning method and this renders the claims as failing to comply with the written description requirement.
Regarding claims 1 and 9, the claims recite the limitation of “to obtain a key improvement network” in lines 24 and 26, respectively. The term “key improvement network” is not a commonly utilized term within the art and is only disclosed within the specification in paragraphs [0014]-[0015], [0044]-[0045], [0048], [0093]-[0095], [0106], [0118]-[0119], and [0130]-[0131]. Each of these recitations only state that the key improvement network is based upon comparing the acute and chronic functional networks, but the scope of “comparing” is so broad that one of ordinary skill in the art would not understand what scope to attribute to the “key improvement network” as claimed. The specification does not support whether the network is based upon mathematical addition or subtraction of images, some sort of correlation analysis, some other mathematical calculation, or just a binary or spatial comparison between the obtained mapping functional networks. This renders the use of the “key improvement network” within the spatial correlation calculation with the whole brain functional connectivity network unclear as well, because the specification does not provide sufficient description as to what this additional “key improvement network” is. This renders the claims as not complying with the written description requirement and are rejected.
Claims dependent upon rejected claims are also rejected. Therefore, dependent claims 2-8 are also rejected.
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 1-9 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.
Regarding claims 1 and 9, the claims recite the limitation “a deep learning method” in line 9 and lines 10-11, respectively, and appears to positively recite a particular deep learning method that is used in the image segmentation of the image dataset. This limitation appears to correspond to the features disclosed in paragraphs [0008], [0038], [0064]-[0065], [0111], and [0124]. Each recitation of the step only recites a generic deep learning method or applying a generic nnU-Net method. The claims are interpreted under 112(f) to correspond to a computer implemented method for performing stroke lesion image segmentation and the cited paragraphs do not provide sufficient disclosure of the algorithm that not only segments the DWI images, but also obtains lesion masks from the segmented images. One of ordinary skill in the art would not recognize sufficient disclosure within the specification to support the use of a particularly claimed deep learning method and this renders the scope of the claim limitation unclear and indefinite. This renders the claim rejected for indefiniteness.
Regarding claims 1, 4-6, and 9, the claims recite the limitation “N” or “M” to describe items among persons, networks voxels, and linear models. The claim does not define the variable “N” or “M” as a number and therefore it is unclear as to what the scope of the limitation should be interpreted as. The claim should particularly define all variables used within the claim and what numbers or range of possible numbers that the variables correspond to. For this reason, the scope of the limitation is unclear and the claims are rejected for indefiniteness.
Regarding claim 1, the claim recites the limitation “constructing, based on each resting-state fMRI image in the first stroke dataset, a first lesion mapping functional network with a lesion mask correspondingly registered to the brain standard space as a region of interest (ROI)” in lines 12-14 and it is unclear as to whether a singular first lesion mapping function network is created based upon all of the resting state first stroke dataset images combined, or whether a number of functional networks are mapped each corresponding to each image. Furthermore, it is unclear as to what “a lesion mask” is used for the functional network and which of the plurality of lesion masks is chosen for this apparent region of interest. If the dataset of images utilized are from a plurality of persons it is unclear as to how a singular region of interest is achieved from patients of different diagnostic states. Finally, it is unclear as to what the region of interest being selected is modifying, if anything, in related to the first lesion mapping functional network. It is unclear if it is limiting values to a region of interest or merely highlighting a region as a region of interest.
Further regarding claim 1, the claim recites the limitation of “N first lesion mapping functional networks” but these networks are not positively recited in the constructing step as previously discussed. It is unclear as to how a plurality of networks are utilized when the previous limitation that sets forth the element is unclear as to constructing a plurality of functional networks. Furthermore, it is unclear as to how a preset cognitive scale of cognitive scores is incorporated into the acute phase mapping functional network as a qualitative cognitive observation is not readily applied to a mapping functional network. It is unclear as to whether it is providing a weighted multiplier of the functional network values or is used to select particular regions of interest based upon known cognitive scores. This renders the scope of the limitation unclear and indefinite.
Further regarding claim 1, the claim recites the limitations in lines 18-22 directed to the second stroke dataset and chronic phase functional network which contain similar deficiencies as the limitations directed to the first stroke dataset and acute phase mapping functional network. For these reasons, the scopes of the limitations are unclear and indefinite.
Further regarding claim 1, the claim recites the limitation “comparing the acute phase cognitive-lesion mapping functional network with the chronic phase cognitive-lesion mapping functional network to obtain a key improvement network” in lines 23-24 and as discussed above in the 112(a) rejections, it is unclear as to what scope to apply to the “comparing” and the “key improvement network” in light of the applicant’s specification. The term “key improvement network” is not a commonly utilized term within the art and is only disclosed within the specification in paragraphs [0014]-[0015], [0044]-[0045], [0048], [0093]-[0095], [0106], [0118]-[0119], and [0130]-[0131]. Each of these recitations only state that the key improvement network is based upon comparing the acute and chronic functional networks, but the scope of “comparing” is so broad that one of ordinary skill in the art would not understand what scope to attribute to the “key improvement network” as claimed. The specification does not support whether the network is based upon mathematical addition or subtraction of images, some sort of correlation analysis, some other mathematical calculation, or just a binary or spatial comparison between the obtained mapping functional networks. This renders the use of the “key improvement network” within the spatial correlation calculation with the whole brain functional connectivity network unclear as well, because the specification does not provide sufficient description as to what this additional “key improvement network” is. This renders the scope of the claim unclear and rejected for indefinite.
Further regarding claim 1, the claim recites “a functional image” in lines 25-26 and it is unclear as to whether this is limited to the first or second datasets of fMRI images, or whether it is to be interpreted to a broader possible functional image unrelated to those previously set forth. Furthermore, the “resting-state fmri image” of line 29 is unclear as to whether it corresponds to the first or second datasets of fMRI images.
Regarding claim 7, the claim recites the limitation “a top set percentage” in line 5 and it is unclear as to what percentage this corresponds to. One of ordinary skill in the art would be unable to determine whether it is a percentage of merely above 50% or whether it corresponds to a much more limited percentage such as the top 5%. This renders the scope of the limitation unclear and indefinite.
Further regarding claim 7, the claim recites the limitation of “taking spatial correlation values of a top set percentage; and taking a brain region whose vertical distance from scalp is less than or equal to 3 cm” but it is unclear as to what is selected as a therapeutic target if the values are greater than 3 cm from the scalp. The claim does not specify how or if a therapeutic target is selected if it does not meet this limited criterion. The claim should specify whether no therapeutic target is made if it does not meet the claimed range. For these reasons, the claim is rejected for indefiniteness.
Regarding claim 9, the claim is a system claim that contains similar limitations to the method steps of claim 1 and is similarly rejected under 35 U.S.C. 112(b).
Claims dependent upon rejected claims are also rejected for indefiniteness. Therefore, dependent claims 2-6 and 8 are also rejected.
Allowable Subject Matter
In light of the 112(a) and 112(b) rejections above, the claims are not currently allowable, and allowable subject matter in light of the rejections above is not indicated at this time. Claims 1-9 would be allowable if rewritten or amended 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chi, N., et al. “Cerebral Motor Functional Connectivity at the Acute Stage: An outcome predictor of Ischemic Stroke,” Scientific Reports. Vol 8, 2018. P. 1-11 teaches to a resting-state fMRI-based analysis of functional connectivity between groups in stroke patients. The reference includes functional connectivity matrices (figure 7) and correlations between regions of interest (figure 8).
Cohen, A., et al. “Lesion network mapping predicts post-stroke behavioral deficits and improves localization,” Brain Journal of Neurology. Vol 144, 2021. P. 1-4 teaches to a lesion network mapping of cognitive regions of the brain in stroke patients using an fMRI BOLD imaging method.
Li, Q., et al., “Dynamic Neural Network Changes Revealed by Voxel-Based Functional Connectivity Strength in Left Basal Ganglia Ischemic Stroke,” frontiers in Neuroscience. Vol 14, 2020. P. 1-11 teaches to a study using resting state functional magnetic resonance imaging to determine a voxel-based brain functional network. It includes mask definition of regions of interest and correlation of brain function between regions of the brain (figure 1).
Moore, M., et al., “Lesion Symptom Mapping of Domain-Specific Cognitive Impairments using Routine Imaging in Stroke,” medRxiv. 2021. P. 1-37 teaches to lesion based stroke assessments using voxel based lesion symptom mapping across the regions of interest.
Pini, L., et al., “A novel stroke lesion network mapping approach: improved accuracy yet still low deficit prediction,” Brain Communications. 2021. P. 1-16 teaches to lesion mapping of functional networks caused by stroke brain lesions. The method includes functional magnetic resonance imaging techniques and functional connectivity analyses between identified regions of the brain.
Pirondini, E., et al., “Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions,” NeuroImage. Vol 255, 2022. P. 1-13 teaches to functional magnetic resonance imaging analyses of temporal features of brain activations in varying regions of the brain (figure 3). The reference further teaches to interplay between functional dynamics and behavioral deficits.
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/SEAN A FRITH/Primary Examiner, Art Unit 3798