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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas of “mental processes” or “concepts relating to data comparisons that can be performed mentally or are analogous to human mental work” without significantly more.
Analyses of the subject matter eligibility tests are performed for each of the independent claims and associated dependent claims below.
Regarding independent claim 1, the claim recites:
The limitation of “taking a highest cerebral blood flow of each preset segmentation unit in a brain area as corrected cerebral blood flow of each preset segmentation unit based on a cerebral blood perfusion image with multiple post-labeling delays” which is considered to be an abstract idea of a mental process and concept relating to data comparisons that can be performed mentally or are analogous to human mental work as a user may merely think and receive within the mind about what the highest cerebral blood flow regions are based upon mentally observed input data. The limitation of “performing threshold gradient segmentation according to the corrected cerebral blood flow of each preset segmentation unit, so as to obtain a gradient segmentation result” is considered to be an abstract idea of a mental process and concept relating to data comparisons that can be performed mentally or are analogous to human mental work as a user may merely think and receive within the mind about the regions of cerebral blood flow and based upon mental knowledge determine a general gradient of the segmented regions. The claim does not provide any limitations that narrow the gradient segmentation process to a particular series of steps that would preclude a human from performing it within the mind. The limitation of “determining the cerebral infarction area according to the gradient segmentation result” is considered to be an abstract idea of a mental process and concept relating to data comparisons that can be performed mentally or are analogous to human mental work as a user may merely think and receive within the mind about which area corresponds to the cerebral infarction area based upon received input data. Therefore, the claim is directed to an abstract idea and a judicial exception.
Step 2A Prong 2 Analysis (Claim 1): This judicial exception is not integrated into a practical application because it does not recite any elements that integrate the abstract idea into a practical application such as improving the operation of the diagnostic device, or effecting a particular treatment or prophylaxis for a disease or medical condition. The claims do not recite any features of components that integrates the judicial exception into a practical application because the additional recited elements of “a cerebral blood perfusion image with multiple post-labeling delays” is considered to be input data from extra-solution activity of mere data gathering. Therefore, all of these claimed elements are not sufficient to improve the functioning of a diagnostic device or form of technology. Furthermore, while directed to activity for medical diagnostics, the claimed steps do not effect a particular treatment or prophylaxis for a disease or medical condition as it is only claiming diagnostic measurement steps and not altering a particular treatment in any way.
Step 2B Analysis (Claim 1): The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional recited elements of “a cerebral blood perfusion image with multiple post-labeling delays” is considered to be input data from extra-solution activity of mere data gathering. The limitations do not include improvements to the functioning of a computer or to any other technology or technical field, and the elements of the claim further do not effect a particular treatment or prophylaxis for a disease or medical condition. Furthermore, there are no claimed features that provide elements to identify improvements to general computing technologies based on the claimed features. As discussed above, limitations form insignificantly extra-solution activity, and link the judicial exception to generic medical diagnostics. Therefore the additional elements do not amount to significantly more.
Dependent claim 2 includes limitations that are directed to “importing a cerebral blood perfusion image to be localized into a structural space to obtain a cerebral blood perfusion model, wherein the cerebral blood perfusion image to be localized is a cerebral blood perfusion image comprising multiple post-labeling delays” is considered to be an abstract idea of a mental process and concept relating to data comparisons that can be performed mentally or are analogous to human mental work as a user may merely think and receive within the mind about a generic model that relates to the cerebral blood perfusion image. The limitation of “registering a brain atlas to the cerebral blood perfusion model to obtain a blood perfusion model of a brain partition” is considered to be an abstract idea of a mental process and concept relating to data comparisons that can be performed mentally or are analogous to human mental work as a user may merely think and receive within the mind about which particular regions from a brain atlas correspond to the mental perfusion model. The limitation of “taking the highest cerebral blood flow in the multiple post-labeling delays corresponding to each preset segmentation unit in each partition in the blood perfusion model of the brain partition as corrected cerebral blood flow of each preset segmentation unit” is considered to be an abstract idea of a mental process and concept relating to data comparisons that can be performed mentally or are analogous to human mental work as a user may merely think and receive within the mind about highest blood flow levels in the partitions of a brain atlas based model. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 3 includes limitations that are directed to “acquiring a cerebral blood perfusion image of an individual brain by using a multi-delay pseudo-continuous arterial spin labeling method” which form extra-solution activity of mere data gathering. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 4 includes limitations that are directed to narrowing the extra-solution activity of mere data gathering. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 5 includes limitations that are directed to narrowing the mental processing to a particular form of atlas and therefore does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 6 includes limitations that are directed to narrowing the mental processing to a range of mentally observable values and therefore does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 7 includes limitations that are directed to narrowing the mental processing to a range of mentally observable colors and therefore does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more. The display of a color image is considered to be extra solution activity of mere data outputting.
Regarding independent claims 8 and 9, the claims recite similar limitations to independent claim 1 and are similarly rejected under 35 U.S.C. 101. The claims also recite limitations of “a memory”, “a processor”, “a computer program”, and “a computer readable storage medium” which form general purpose computer elements that merely enable execution of the judicial exception on a computer.The limitations do not include improvements to the functioning of a computer or to any other technology or technical field. Therefore, these additional limitations do not integrate the judicial exception of each independent claim into a practical application or amount to significantly more.
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 1-9 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.
Claim 1 recites the limitation "each preset segmentation unit" in line 2. There is insufficient antecedent basis for this limitation in the claim. The use of the language “each” appears to be directed to a plurality of segmentation units that were not previously set forth. Therefore, the recitation using the term “each” is indefinite.
Regarding claim 6, the claim includes a set of ranges but includes no units associated with the claimed ranges. This renders the scope of the ranges unclear as to how to interpret the ranges with respect to the cerebral blood flow. One of ordinary skill in the art would not understand whether they refer to pixel region areas, imaging pixel values, actual blood flow velocities, or weighted percentages. This renders the scope of the claim unclear and rejected for indefiniteness.
Claim 8 recites the limitation "each preset segmentation unit" in line 8. There is insufficient antecedent basis for this limitation in the claim. The use of the language “each” appears to be directed to a plurality of segmentation units that were not previously set forth. Therefore, the recitation using the term “each” is indefinite.
Claim 9 recites the limitation "each preset segmentation unit" in line 6. There is insufficient antecedent basis for this limitation in the claim. The use of the language “each” appears to be directed to a plurality of segmentation units that were not previously set forth. Therefore, the recitation using the term “each” is indefinite.
Claims dependent upon rejected claims are also rejected for indefiniteness. Therefore, dependent claims 2-5 and 7 are also rejected.
Claim Rejections - 35 USC § 102
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 and 8-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Harston, G., et al., (“Quantification of Serial Cerebral Blood Flow in Acute Stroke Using Arterial Spin Labeling,” Stroke AHA. Vol 48(1), 2016. P. 1-8) hereinafter Harston (see attached NPL reference for citations).
Regarding claim 1, Harston teaches:
A localization method for a cerebral infarction area (abstract), comprising:
taking a highest cerebral blood flow of each preset segmentation unit in a brain area as corrected cerebral blood flow of each preset segmentation unit based on a cerebral blood perfusion image with multiple post-labeling delays (page 124, Imaging, multiple post labeling delay arterial spin labeling MRI sequence; Image Registration; Definitions and Regions of Interest, analyses of segmented regions of interest for cerebral blood flow amounts are chosen for areas of gray matter with volume estimates of greater than 50%. With gray matter chosen because of significantly higher blood flow than white matter regions, this forms a selection of highest cerebral blood flow within the regions of interest (segmentation unit) for means of CBF to be used for further calculation and analysis; see also pages 125-127, Results; figures 1-3);
performing threshold gradient segmentation according to the corrected cerebral blood flow of each preset segmentation unit, so as to obtain a gradient segmentation result (page 124, Definitions and Regions of Interest, “Tissue segmentation of the presenting structural T1-weighted image using FMRIB’s Automated Segmentation Tool (FAST) defined gray matter partial volume estimates,21 which were registered into perfusion image space. Analyses were performed within gray matter masks with a partial volume estimate of ≥50%, where ASL CBF estimates are more reliable”. The threshold of 50% volume estimate for automated segmented volumes based upon the initial 6 region of interest atlas based areas forms a gradient segmentation based upon only the gray matter areas of the regions of interest; page 124-125, Statistics; see also pages 125-127, Results; figures 1-3); and
determining the cerebral infarction area according to the gradient segmentation result (age 124, Imaging, multiple post labeling delay arterial spin labeling MRI sequence; Image Registration; Definitions and Regions of Interest; pages 125-127, Results, figures 1-3, provide teachings of the infarction areas within the regions of interest based upon the processed CBF data; figures 4-5).
Regarding claim 8, Harston teaches:
A computer system (abstract), comprising:
a memory, a processor, and a computer program stored on the memory and capable of being operated on the processor, wherein the processor, is configured to execute the computer program to achieve a localization method for a cerebral infarction area (page 124, Imaging,imaging scanner and software utilizes memory processor and computer program for operation as understood by one of ordinary skill in the art), said localization method comprising:
taking a highest cerebral blood flow of each preset segmentation unit in a brain area as corrected cerebral blood flow of each preset segmentation unit based on a cerebral blood perfusion image with multiple post-labeling delays (page 124, Imaging, multiple post labeling delay arterial spin labeling MRI sequence; Image Registration; Definitions and Regions of Interest, analyses of segmented regions of interest for cerebral blood flow amounts are chosen for areas of gray matter with volume estimates of greater than 50%. With gray matter chosen because of significantly higher blood flow than white matter regions, this forms a selection of highest cerebral blood flow within the regions of interest (segmentation unit) for means of CBF to be used for further calculation and analysis; see also pages 125-127, Results; figures 1-3);
performing threshold gradient segmentation according to the corrected cerebral blood flow of each preset segmentation unit, so as to obtain a gradient segmentation result (page 124, Definitions and Regions of Interest, “Tissue segmentation of the presenting structural T1-weighted image using FMRIB’s Automated Segmentation Tool (FAST) defined gray matter partial volume estimates,21 which were registered into perfusion image space. Analyses were performed within gray matter masks with a partial volume estimate of ≥50%, where ASL CBF estimates are more reliable”. The threshold of 50% volume estimate for automated segmented volumes based upon the initial 6 region of interest atlas based areas forms a gradient segmentation based upon only the gray matter areas of the regions of interest; page 124-125, Statistics; see also pages 125-127, Results; figures 1-3); and
determining the cerebral infarction area according to the gradient segmentation result (age 124, Imaging, multiple post labeling delay arterial spin labeling MRI sequence; Image Registration; Definitions and Regions of Interest; pages 125-127, Results, figures 1-3, provide teachings of the infarction areas within the regions of interest based upon the processed CBF data; figures 4-5).
Regarding claim 9, Harston teaches:
A non-transitory computer readable storage medium, wherein a computer program is stored on the non-transitory computer readable storage medium, and the computer program, when executed by a processor, is able to achieve a localization method for a cerebral infarction area (page 124, Imaging,imaging scanner and software utilizes memory processor and computer program for operation as understood by one of ordinary skill in the art), said localization method comprising:
taking a highest cerebral blood flow of each preset segmentation unit in a brain area as corrected cerebral blood flow of each preset segmentation unit based on a cerebral blood perfusion image with multiple post-labeling delays (page 124, Imaging, multiple post labeling delay arterial spin labeling MRI sequence; Image Registration; Definitions and Regions of Interest, analyses of segmented regions of interest for cerebral blood flow amounts are chosen for areas of gray matter with volume estimates of greater than 50%. With gray matter chosen because of significantly higher blood flow than white matter regions, this forms a selection of highest cerebral blood flow within the regions of interest (segmentation unit) for means of CBF to be used for further calculation and analysis; see also pages 125-127, Results; figures 1-3);
performing threshold gradient segmentation according to the corrected cerebral blood flow of each preset segmentation unit, so as to obtain a gradient segmentation result (page 124, Definitions and Regions of Interest, “Tissue segmentation of the presenting structural T1-weighted image using FMRIB’s Automated Segmentation Tool (FAST) defined gray matter partial volume estimates,21 which were registered into perfusion image space. Analyses were performed within gray matter masks with a partial volume estimate of ≥50%, where ASL CBF estimates are more reliable”. The threshold of 50% volume estimate for automated segmented volumes based upon the initial 6 region of interest atlas based areas forms a gradient segmentation based upon only the gray matter areas of the regions of interest; page 124-125, Statistics; see also pages 125-127, Results; figures 1-3); and
determining the cerebral infarction area according to the gradient segmentation result (age 124, Imaging, multiple post labeling delay arterial spin labeling MRI sequence; Image Registration; Definitions and Regions of Interest; pages 125-127, Results, figures 1-3, provide teachings of the infarction areas within the regions of interest based upon the processed CBF data; figures 4-5).
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.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Harston as applied to claim 1 above, and further in view of Li et al. (U.S. Pub. No. 20200341099) hereinafter Li.
Regarding claim 2, primary reference Harston teaches all of the limitations of claim 1. Primary reference Harston further fails to teach:
wherein taking the highest cerebral blood flow of each preset segmentation unit in the brain area as corrected cerebral blood flow of each preset segmentation unit based on the cerebral blood perfusion image with multiple post-labeling delays comprises: importing a cerebral blood perfusion image to be localized into a structural space to obtain a cerebral blood perfusion model, wherein the cerebral blood perfusion image to be localized is a cerebral blood perfusion image comprising multiple post-labeling delays
However, the analogous art of Li of an arterial spin labeling magnetic resonance imaging system and method (abstract) teaches:
wherein taking the highest cerebral blood flow of each preset segmentation unit in the brain area as corrected cerebral blood flow of each preset segmentation unit based on the cerebral blood perfusion image with multiple post-labeling delays comprises: importing a cerebral blood perfusion image to be localized into a structural space to obtain a cerebral blood perfusion model, wherein the cerebral blood perfusion image to be localized is a cerebral blood perfusion image comprising multiple post-labeling delays ([0032], “perfusion quantification model may then be applied to the final image to obtain a quantitative mapping of cerebral blood flow (CBF) through tissue in the anatomical area of interest. This mapping may similarly be presented in the aforementioned GUI” is applied to the perfusion image of Harston in the combined invention, wherein Harston teaches to the multiple post-labeling delays perfusion image);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the cerebral blood flow image analysis method of Harston to incorporate the blood perfusion model for quantitative mapping as taught by Li because it enables precise quantitative mapping of cerebral blood flow through the anatomical area of interest (Li, [0032]). This leads to higher accuracy in display of exact blood flow values, leading to better clinical diagnostics of a patient.
Primary reference Harston, in view of Li further teaches:
registering a brain atlas to the cerebral blood perfusion model to obtain a blood perfusion model of a brain partition (In the combined invention with Li, the atlas segmentation of Harston is applied to the modeled CBF quantitative mapping of Li to provide partition delineated values for cerebral blood flow in the combined prior art invention. See Harston: page 124, Imaging, multiple post labeling delay arterial spin labeling MRI sequence; Image Registration; Definitions and Regions of Interest, analyses of segmented regions of interest for cerebral blood flow amounts based upon the Harard-Oxford atlas of brain regions. With gray matter chosen because of significantly higher blood flow than white matter regions, this forms a selection of highest cerebral blood flow within the regions of interest (segmentation unit) for means of CBF to be used for further calculation and analysis); and
taking the highest cerebral blood flow in the multiple post-labeling delays corresponding to each preset segmentation unit in each partition in the blood perfusion model of the brain partition as corrected cerebral blood flow of each preset segmentation unit (In the combined invention with Li, the atlas segmentation of Harston is applied to the modeled CBF quantitative mapping of Li to provide partition delineated values for cerebral blood flow in the combined prior art invention. See Harston: page 124, Imaging, multiple post labeling delay arterial spin labeling MRI sequence; Image Registration; Definitions and Regions of Interest, analyses of segmented regions of interest for cerebral blood flow amounts based upon the Harard-Oxford atlas of brain regions. With gray matter chosen because of significantly higher blood flow than white matter regions, this forms a selection of highest cerebral blood flow within the regions of interest (segmentation unit) for means of CBF to be used for further calculation and analysis).
Regarding claim 3, the combined references of Harston and Li teach all of the limitations of claim 2. Primary reference Harston further teaches:
wherein prior to said importing the cerebral blood perfusion image to be localized into the structural space to obtain the cerebral blood perfusion model, the localization method further comprises: acquiring a cerebral blood perfusion image of an individual brain by using a multi-delay pseudo-continuous arterial spin labeling method (page 124, Imaging, multiple post labeling delay arterial spin labeling MRI sequence “CBF was measured using a multiple PLD vessel-encoded pseudocontinuous ASL (VEPCASL) sequence”).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Harston, in view of Li as applied to claim 2 above, and further in view of Sanchez et al. (U.S. Pub. No. 20220189014) hereinafter Sanchez.
Regarding claim 4, primary reference Harston and Li teach all of the limitations of claim 2. Primary reference Harston further fails to teach:
wherein the structural space is a T2 Flair space
However, the analogous art of Sanchez of a model for identifying an imaging modality (abstract) teaches:
wherein the structural space is a T2 Flair space ([0043]-[0044], datasets include both arterial spin labeling and T2 flair images for modeling, which teaches to the Harston and Li combined prior art model system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the cerebral blood flow image analysis method of Harston and Li to incorporate the T2 Flair space as taught by Sanchez because it enables additional highlighting of regions within the brain while suppressing signal from cerebral spinal fluid. In a combined sequence with multiple imaging types, this provides improved contrast for tissue regions of interest (Sanchez, [0043]-[0044]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Harston, in view of Li as applied to claim 2 above, and further in view of Rolls, E., et al., (“Automated anatomical labelling atlas 3,” NeuroImage. Vol 206, 2020. P. 1-5) hereinafter Rolls (see attached NPL reference for citations), in further view of Yu, Z., et al. (“An automated ASPECTS method with atlas-based segmentation,” Computer Methods and Programs in Biomedicine. Vol 210, 2021. P. 1-7) hereinafter Yu (see attached NPL reference for citations).
Regarding claim 5, primary reference Harston and Li teach all of the limitations of claim 2. Primary reference Harston further fails to teach:
wherein the brain atlas comprises an AAL3 atlas of MNI152 space, left and right brain atlases,
However the analogous art of Rolls of a magnetic resonance imaging atlas system (abstract) teaches:
wherein the brain atlas comprises an AAL3 atlas of MNI152 space, left and right brain atlases (pages 2-4, AAL3 atlas and includes left and right brain atlas regions),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the cerebral blood flow image analysis method of Harston and Li to incorporate the AAL3 atlas as taught by Rolls because it adds a number of brain areas not previously defined, but that are of interest in many neuroimaging investigations including many on reward and memory systems in the brain, and on psychiatric and neurological disorders (Rolls, page 1, Introduction).
Primary reference Harston further fails to teach
and ASPECTS atlas
However, the analogous art of Yu of a automated atlas-based segmentation method (abstract) teaches:
and ASPECTS atlas (pages 2-5, Materials and Methods, aspects based atlas segmentation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the cerebral blood flow image analysis method of Harston, Li, and Rolls to incorporate the ASPECTS atlas as taught by Yu because it can be used for rapid semi-quantitative evaluation of ischemic lesions, which is helpful to select potential candidates for intravenous and intra-arterial therapies, determine the thrombolytic effect and long-term prognosis (Yu, abstract). This leads to better clinical interventions.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
D'esterre et al. (WO2020154807) teaches to a perfusion functional mapping system that includes volumes such as the penumbra, infract and perfusion areas are displayed in different colors to a user ([00144]-[00145]).
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/SEAN A FRITH/Primary Examiner, Art Unit 3798