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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/11/2025 has been entered.
Priority
Applicant is advised of possible benefits under 35 U.S.C. 119(a)-(d) and (f), wherein an application for patent filed in the United States may be entitled to claim priority to an application filed in a foreign country.
Response to Amendment
Claims 118-135, and 137-138 were previously pending and subject to the final action filed on 06/11/2025. In the response filed on 12/11/2025, claims 118, 121, 124-125, 130, 133 were amended, claim 139 newly added claim. Claims 1-117, 120, 123, 126-129, and 136 were canceled. Therefore, claims 118-119, 121-122, 124-125, 130-135, and 137-139 are currently pending and subject to the non-final action below.
Response to Arguments
Applicant's arguments see pages 13-15, filed on 12/11/2025 regarding claims 118-135 and 137-138 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant’s argument: Applicant respectfully submits in traversal that as discussed in the interview, the claims recite a specific, practical application-processing digital brain images to extract specific quantitative metrics from defined anatomical regions, combining them according to a predetermined method, and displaying the result as an overlay to assist clinical decision-making. This is not a generic "apply it" instruction, but a concrete, technical process implemented by a computer. The claims clearly specify which anatomical regions are analyzed, the types of metrics extracted, how the metrics are combined, and that the results are displayed as an overlay. The published application further describes technical improvements and presents empirical results, demonstrating the practical utility and advantages of the claimed method over the prior art (see, e.g., [0109]-[0123], Tables 1-14, and associated ROC analyses).
Applicant respectfully submits that the claims as amended are analogous to those found eligible in cases such as Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016); McRO,Inc. v. BandaiNamco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016); and CardioNet, LLC v. InfoBionic, Inc., 955 F.3d 1358 (Fed. Cir. 2020), where specific, technical improvements to computer functionality or medical diagnostics were found patent-eligible. medical diagnostics were found patent-eligible. CardioNet v. InfoBionic. CardioNet, the Federal Circuit held that claims directed to a specific improvement in cardiac monitoring technology were not abstract ideas, but rather a technological improvement supported by the written description. Similarly, the present claims are directed to a specific, computer- implemented method for diagnosing cognitive impairment using a novel combination of quantitative image metrics from defined brain regions, and displaying the results as an overlay to assist clinical decision-making. As in CardioNet, the written description here details the technical advantages and improved diagnostic accuracy achieved by the claimed method (see, e.g., [0109]-[0123], Tables 9, 10, 13 and associated ROC analyses).
The claims here implement a new and specific diagnostic approach that cannot be performed in the human mind and provides a concrete technological improvement in the field of neuroimaging-based diagnosis.
Examiner Response: After careful consideration and review of applicant’s argument. The examiner respectfully disagrees for the following reason below.
Claims 118-119, 121-122, 124-125, 130-135, and 137-139 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claims 118 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A prong 1: Does the claim recite a judicial exception? Yes, claims 118 recite limitations of: “determining image metrics comprising a minimum fractal dimension of an image region corresponding to the right middle temporal gyrus and at least one of:… an image texture metric of an image region of the right rostral middle frontal, right supramarginal; determining an indicator based on said image metrics to assist a clinician in predicting or diagnosing cognitive impairment state”. The broadest reasonable interpretation these limitations falls under the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion.
The limitation of: “measure of central tendency of pixel an image intensity in an image region” encompass a mathematical concept.
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application? The claim recites additional element “a computer program product or computer apparatus for processing by the computer digital image data, processing by the computer, an indicator and displaying the indicator as an overlay on an image of the human subject's brain to assist a clinician in predicting or diagnosing cognitive impairment state in the subject based on the indicator.”
The additional elements recites using a generic “the computer” to determine image metrics and displaying (by the computer) the indicator as an overlay on an image of human subject’s brain fail to integrate the abstract idea into a practical application. The limitations, recited at a high level of generality, only amount to “apply it” using a generic computer components (MPEP 2106.05(f)). The use of a “the computer” for determining an indicator of metrics and overlaying said indicators on a human subject’s brain to carry out the routine steps is similar to an “off the shelf” component.
The additional elements, alone and in combination, fail to integrate the abstract idea into a practical application. Thus, the claims is directed to an abstract idea.
Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception? No, the claim recites additional element “a computer program product or computer apparatus for processing by the computer digital image data, processing by the computer, an indicator and displaying the indicator as an overlay on an image of the human subject's brain to assist a clinician in predicting or diagnosing cognitive impairment state in the subject based on the indicator.” These additional limitations fail to integrate the abstract idea into a practical application. The limitations, recited at a high level of generality, only amount to “apply it” using a generic computer components (MPEP 2106.05(f)). Thus, claims are not patent eligible.
As the additional limitations discussed in Step 2A, Prong 2 above, only amounts insignificant extra-solution activity and WURC activities to condition the data for input into “the computer”, similar to “receiving or transmitting data over the network, e.g., using the Internet to gather data, Symantec, 832 F.3d at 1362 (utilizing intermediary computer to forward information)” See MPEP 2106.05 (d). These limitations, taken alone or in combination, fail to provide an inventive concept. Thus, the claim is not patent eligible.
The dependent claims the additional limitations (in claims 119, 121-122, 124, 130-135, and 137-139) recites the limitations of “a measure of fractal dimension such as a minimum fractal dimension, computing a weighted sum of the image metrics, using an HLH filter, a measure of correlation, such as the GLCM correlation, a measure of central tendency such as the mean” and these limitations also constitute concepts which fall within the “Mental Processes” groupings of abstract ideas.
This judicial exception is not integrated into a practical application and amount to no more than adding insignificant extra-solution activity/specifications related to data gathering, data input, or data transmittal. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above.
Applicant's arguments see pages 15-16, filed on 12/11/2025 regarding claims 118-124, 126-135 and 137-138 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant’s argument 1: The Office Action contends that Desikan teaches automated labeling and subdivision of brain regions, including the recited gyri (e.g., right middle temporal gyrus, right rostral middle frontal, right supramarginal, right temporal pole), and processing digital image data for metrics. Oliveira is cited for texture analysis using GLCM parameters (e.g., correlation, sum average) on brain regions like the temporal pole, allegedly teaching the image metrics. The Office Action further contends that it would have been obvious to combine these references for identifying brain disease changes using image processing, with Oliveira providing texture metrics and Raj enabling overlay display.
Applicant respectfully submits that the cited references, alone or in combination, fail to teach or suggest the subject matter of the claims, as amended, particularly the specific image metrics applied to the recited anatomical regions for predicting or diagnosing cognitive impairment. Claim 118 is herein amended to recite that the image metrics comprise "a minimum fractal dimension of an image region corresponding to the right middle temporal gyrus" and one other metric. In paragraph 112 of the final rejection, the Office Action alleges that Nicastro teaches measuring "a minimum fractal dimension of an image region corresponding to the right middle temporal gyrus. While Nicastro does recite "Memory impairment was associated with reduced fractal dimension in bilateral insula, left superior temporal, and isthmus cingulate for AD" (Nicastro, p.335 right column, final para.), it does not teach that a minimum fractal dimension is measured, let alone one from the image region corresponding to the right middle temporal gyrus.
Furthermore, none of the cited documents, including Nicastro, provide an indication that the combination of the minimum fractal dimension with one of the other metrics is particularly beneficial, and to allege that the claim is obvious over a combination of more than four documents with no hint or suggestion that would guide the skilled person to consider them is clearly evidence of an impermissible use of hindsight.
Examiner Response 1: After careful consideration and review of applicant’s argument. The examiner respectfully disagrees for the following reason below.
During examination, the claims must be interpreted as broadly as their terms reasonably allow. In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 U.S.P.Q.2d 1827, 1834 (Fed. Cir. 2004). The amendments to claim 118 does not clearly recite what is a “minimum” fractal dimension specifically under the BRI.
Desikan teaches: determining, based on processing by the computer of digital image data obtained from the images, image metrics comprising an image region corresponding to the right middle temporal gyrus and at least one of: (Desikan − [pdf page 6] Construction of cortical atlas; minimizing the metric distortion between the cortical and the spherical representations through automatic segmenting regions of interest in the brain; [pdf page 3-5, 8] MRI image acquisition; the cerebral hemispheres were subdivided into 34 regions; Subdivided Temporal lobe, Middle temporal gyrus; the lateral fissure (and when present, the supramarginal gyrus; Parietal lobe, the supramarginal gyrus; Frontal Pole, the middle frontal gyrus; Table 1 (manual) and Table 2 (automatic))
an image region corresponding to the right middle temporal gyrus; an image region corresponding to the right rostral middle frontal; the image region corresponding to the right supramarginal; the image region corresponding to the right temporal pole; (Desikan − [pdf page 3-5, 8] MRI image acquisition; the cerebral hemispheres were subdivided into 34 regions; Subdivided Temporal lobe, Middle temporal gyrus; the lateral fissure (and when present, the supramarginal gyrus; Parietal lobe, the supramarginal gyrus; Frontal Pole, the middle frontal gyrus; Table 1 (manual) and Table 2 (automatic))
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Oliveira teaches: and at least one of: an image texture metric of an image region corresponding to [the right rostral middle frontal]; an image texture metric in the image region corresponding to [the right supramarginal]; and a measure of central tendency of pixel an image intensity in an image region corresponding to[ the right temporal pole;] (Oliveira − [pdf page 3] The statistical approach adopted here to extract texture parameters from the MR images was padded on the GLCM (gray level co-occurrence matrices); Fig. 3 MR imaging of the temporal structure, Maps of textural parameters to compute the GLCM with distance of pixels 0 degrees, 45 degrees, 90 degrees or 135 degrees, totaling 16 GLCMs and totaling 176 texture parameters for each region of interest. Region of interest of the temporal structure that includes the temporal pole.) Examiner notes: limitation recites “at least one of (i.e., A, B, or C)” therefore teaching one of the limitation meets the limitation of at least one of
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the method further comprising: determining, based on processing by the computer, an indicator based on said image metrics according to a predetermined method; (Oliveira − [pdf page 2, 6] find differences among patients with aMCI (mild cognitive impairment) and mild AD (Alzheimer Disease) and normal-aging subjects (non-Alzheimer disease), by using TA (texture analysis) applied to the MR images of the CC and the thalami of these groups of subjects. The application of TA techniques seeks mathematic parameters that can differentiate normal and lesioned tissues, by using texture parameters extracted from GLCMs.)
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Raj teaches: and displaying the indicator as an overlay on an image of the human subject's brain to assist a clinician in predicting or diagnosing cognitive impairment state in the subject based on the indicator. (The predicted future disease patterns may be output in a representation selected from the group of a) a ball and stick model overlaid on a connectivity map of the human brain;)
Desikan doesn’t teach: a minimum fractal dimension
However, Nicastro teaches: image metrics comprising a minimum fractal dimension of an image region corresponding to the right middle temporal gyrus. (Nicastro − [page 7, Fig. 1] AD showed reduced fractal dimension in the bilateral insula and supramarginal gyrus, left middle frontal, superior temporal, inferior temporal, and right parahippocampal gyri Fig. 2 fractal dimension group comparison between Controls, Ad, and FTD. [page 9, Fig. 4 associate with reduced fractal dimension in left middle temporal,… for AD.)
Examiner notes, that the middle temporal gyrus is a fold within the larger temporal lobe, between the superior and inferior temporal gyri. Middle temporal is an abbreviation and still refers to the middle temporal gyrus.
Desikan recite subdivision of the middle temporal gyrus of the left and right cerebral hemispheres and Nicastro recites determining fractal dimension of left middle temporal. It would have been obvious to one of ordinary skill in that Nicastro would apply the similar fractal measure to right middle temporal of the right side of the brain as shown in Fig. 4.
Furthermore, Desikan recites the right middle temporal gyrus and the rejection of claim 118, is the teaching of the combination of Desikan, Oliveira, Raj and Nicastro. Therefore, the examiner respectfully disagrees and maintains the prior art of record for teaching the limitations recited in the amendments of claim 118.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Applicant's arguments see pages 16-19, filed on 12/11/2025 regarding independent claim 125 under 35 U.S.C. 103 have been fully considered but they are moot because the arguments do not apply to the new combinations of references being used in the current rejection.
Claim Objections
Claim 122 objected to because of the following informalities: Claim 122 recites the limitation of “The computer implemented method of claim 120,” and it should be claim 118.
Appropriate correction is required.
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 118-119, 121-122, 124-125, 130-135, and 137-139 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claims 118 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A prong 1: Does the claim recite a judicial exception? Yes, claims 118 recite limitations of: “determining image metrics comprising a minimum fractal dimension of an image region corresponding to the right middle temporal gyrus and at least one of:… an image texture metric of an image region of the right rostral middle frontal, right supramarginal; determining an indicator based on said image metrics to assist a clinician in predicting or diagnosing cognitive impairment state”. The broadest reasonable interpretation these limitations falls under the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion.
The limitation of: “measure of central tendency of pixel an image intensity in an image region” encompass a mathematical concept.
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application? The claim recites additional element “a computer program product or computer apparatus for processing by the computer digital image data, processing by the computer, an indicator and displaying the indicator as an overlay on an image of the human subject's brain to assist a clinician in predicting or diagnosing cognitive impairment state in the subject based on the indicator.”
The additional elements recites using a generic “the computer” to determine image metrics and displaying (by the computer) the indicator as an overlay on an image of human subject’s brain fail to integrate the abstract idea into a practical application. The limitations, recited at a high level of generality, only amount to “apply it” using a generic computer components (MPEP 2106.05(f)). The use of a “the computer” for determining an indicator of metrics and overlaying said indicators on a human subject’s brain to carry out the routine steps is similar to an “off the shelf” component.
The additional elements, alone and in combination, fail to integrate the abstract idea into a practical application. Thus, the claims is directed to an abstract idea.
Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception? No, the claim recites additional element “a computer program product or computer apparatus for processing by the computer digital image data, processing by the computer, an indicator and displaying the indicator as an overlay on an image of the human subject's brain to assist a clinician in predicting or diagnosing cognitive impairment state in the subject based on the indicator.” These additional limitations fail to integrate the abstract idea into a practical application. The limitations, recited at a high level of generality, only amount to “apply it” using a generic computer components (MPEP 2106.05(f)). Thus, claims are not patent eligible.
As the additional limitations discussed in Step 2A, Prong 2 above, only amounts insignificant extra-solution activity and WURC activities to condition the data for input into “the computer”, similar to “receiving or transmitting data over the network, e.g., using the Internet to gather data, Symantec, 832 F.3d at 1362 (utilizing intermediary computer to forward information)” See MPEP 2106.05 (d). These limitations, taken alone or in combination, fail to provide an inventive concept. Thus, the claim is not patent eligible.
The dependent claims the additional limitations (in claims 119, 121-122, 124, 130-135, and 137-139) recites the limitations of “a measure of fractal dimension such as a minimum fractal dimension, computing a weighted sum of the image metrics, using an HLH filter, a measure of correlation, such as the GLCM correlation, a measure of central tendency such as the mean” and these limitations also constitute concepts which fall within the “Mental Processes” groupings of abstract ideas.
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 118-119, 121-122, 131-132, 134-135, and 137-138 are rejected under 35 U.S.C. 103 as being unpatentable over Desikan (An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest, Pub Date: Mar. 2006) in view of M.S. de Oliveira (MR Imaging Texture Analysis of the Corpus Callosum and Thalamus in Amnestic Mild Cognitive Impairment and Mild Alzheimer Disease, Pub Date: AJNR 32 Jan. 2011, hereinafter “Oliveira”) in view of Raj (US PGPUB: 20160300352 A1, Filed Date: 3/20/2014) in view of Nicolas Nicastro (Cortical complexity analyses and their cognitive correlate in Alzheimer’s disease and frontotemporal dementia, Pub Date: Dec. 04, 2019, hereinafter “Nicastro”).
Regarding independent claim 118, Desikan teaches: A computer implemented method of predicting or diagnosing cognitive impairment in a human subject based on images of the subject's brain, the method comprising: (Desikan − [pdf page 1] Structural magnetic resonance imaging (MRI) of cerebral cortex (brain); tracking the evolution of disease from the degenerative changes associated with dementing illnesses such as Alzheimer’s disease (AD))
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determining, based on processing by the computer of digital image data obtained from the images, image metrics comprising an image region corresponding to the right middle temporal gyrus and at least one of: (Desikan − [pdf page 6] Construction of cortical atlas; minimizing the metric distortion between the cortical and the spherical representations through automatic segmenting regions of interest in the brain;)
an image region corresponding to the right middle temporal gyrus; an image region corresponding to the right rostral middle frontal; the image region corresponding to the right supramarginal; the image region corresponding to the right temporal pole; (Desikan − [pdf page 3-5, 8] MRI image acquisition; the cerebral hemispheres were subdivided into 34 regions; Subdivided Temporal lobe, Middle temporal gyrus; the lateral fissure (and when present, the supramarginal gyrus; Parietal lobe, the supramarginal gyrus; Frontal Pole, the middle frontal gyrus; Table 1 (manual) and Table 2 (automatic))
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Desikan does not explicitly teach: at least one of: an image texture metric
However, Oliveira teaches: and at least one of: an image texture metric of an image region corresponding to [the right rostral middle frontal]; an image texture metric in the image region corresponding to [the right supramarginal]; and a measure of central tendency of pixel an image intensity in an image region corresponding to[ the right temporal pole;] (Oliveira − [pdf page 3] The statistical approach adopted here to extract texture parameters from the MR images was padded on the GLCM (gray level co-occurrence matrices); Fig. 3 MR imaging of the temporal structure, Maps of textural parameters to compute the GLCM with distance of pixels 0 degrees, 45 degrees, 90 degrees or 135 degrees, totaling 16 GLCMs and totaling 176 texture parameters for each region of interest. Region of interest of the temporal structure that includes the temporal pole.) Examiner notes: limitation recites “at least one of (i.e., A, B, or C)” therefore teaching one of the limitation meets the limitation of at least one of
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the method further comprising: determining, based on processing by the computer, an indicator based on said image metrics according to a predetermined method; (Oliveira − [pdf page 2, 6] find differences among patients with aMCI (mild cognitive impairment) and mild AD (Alzheimer Disease) and normal-aging subjects (non-Alzheimer disease), by using TA (texture analysis) applied to the MR images of the CC and the thalami of these groups of subjects. The application of TA techniques seeks mathematic parameters that can differentiate normal and lesioned tissues, by using texture parameters extracted from GLCMs.)
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and predicting or diagnosing cognitive impairment state in the subject based on the indicator. (Oliveira − [pdf page 2-6] how many times gray level co-occurs with gray level determine the level or severity of AMCI, mild AD and normal control patient)
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Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, and Oliveira as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Oliveira provides Desikan with an texture analysis set of metrics for qualifying brain disease within imagery. Therefore, providing the benefit of distinguishing brain disease from normal aging brain.
Desikan does not explicitly teach: and displaying the indicator as an overlay on an image of the human subject's brain to assist a clinician in predicting
However, Raj teaches: and displaying the indicator as an overlay on an image of the human subject's brain to assist a clinician in predicting or diagnosing cognitive impairment state in the subject based on the indicator. (The predicted future disease patterns may be output in a representation selected from the group of a) a ball and stick model overlaid on a connectivity map of the human brain;)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira and Raj as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Raj provides Desikan and Oliveira with an network diffusion model for qualifying brain disease within imagery. Therefore, providing the benefit of predicting or diagnosing the states of neurodegeneration in the brain.
Desikan doesn’t teach: a minimum fractal dimension
However, Nicastro teaches: image metrics comprising a minimum fractal dimension of an image region corresponding to the right middle temporal gyrus. (Nicastro − [page 7, Fig. 1] AD showed reduced fractal dimension in the bilateral insula and supramarginal gyrus, left middle frontal, superior temporal, inferior temporal, and right parahippocampal gyri Fig. 2 fractal dimension group comparison between Controls, Ad, and FTD. [page 9, Fig. 4 associate with reduced fractal dimension in left middle temporal,… for AD.) Nicastro would apply the similar fractal measure to right middle temporal of the right side of the brain
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj and Nicastro as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Nicastro provides Desikan with fractal metric for determining fractal measure of the brain. Therefore, providing the benefit of distinguishing brain disease from normal aging brain.
Regarding dependent claim 119, depends on claim 118, Desikan teaches: Alzheimer’s disease, but does not explicitly teach: wherein predicting or diagnosing cognitive impairment state comprises distinguishing between: (a) Alzheimer's disease; and (b) non-Alzheimer's disease.
However, Oliveira teaches: wherein predicting or diagnosing cognitive impairment state comprises distinguishing between:(a) Alzheimer's disease; and (b) non-Alzheimer's disease. (Oliveira − [pdf page 2-6] how many times gray level co-occurs with gray level determine the level or severity of AMCI, mild AD and normal control patient)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj and Nicastro as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Nicastro provides Desikan with fractal metric for determining fractal measure of the brain. Therefore, providing the benefit of distinguishing brain disease from normal aging brain.
Regarding dependent claim 121, depends on claim 120, Desikan teaches: the image region corresponding to the right middle temporal gyrus; the image region corresponding to the right rostral middle frontal; and the image region corresponding to the right supramarginal (Desikan − [pdf page 3-5, 8] MRI image acquisition; the cerebral hemispheres were subdivided into 34 regions; Subdivided Temporal lobe, Middle temporal gyrus; the lateral fissure (and when present, the supramarginal gyrus; Parietal lobe, the supramarginal gyrus; Frontal Pole, the middle frontal gyrus; Table 1 (manual) and Table 2 (automatic))
Desikan does not explicitly teach: image metrics
However, Oliveira teaches: wherein the image metrics comprise: the image region corresponding to [the right middle temporal gyrus]; the image texture metric of the image region corresponding to [the right rostral middle frontal]; and the image texture metric in the image region corresponding to [the right supramarginal]. (Oliveira − [pdf page 3] The statistical approach adopted here to extract texture parameters from the MR images was padded on the GLCM (gray level co-occurrence matrices); Fig. 3 MR imaging of the temporal structure, Maps of textural parameters to compute the GLCM with distance of pixels 0 degrees, 45 degrees, 90 degrees or 135 degrees, totaling 16 GLCMs and totaling 176 texture parameters for each region of interest. Region of interest of the temporal structure that includes the temporal pole.)
Desikan doesn’t teach: the minimum fractal dimension
However, Nicastro teaches: the minimum fractal dimension of the image region corresponding to the right middle temporal gyrus; (Nicastro − [page 7, Fig. 1] AD showed reduced fractal dimension in the bilateral insula and supramarginal gyrus, left middle frontal, superior temporal, inferior temporal, and right parahippocampal gyri Fig. 2 fractal dimension group comparison between Controls, Ad, and FTD. [page 9, Fig. 4 associate with reduced fractal dimension in left middle temporal,… for AD.) Nicastro would apply the similar fractal measure to right middle temporal of the right side of the brain
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj and Nicastro as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Nicastro provides Desikan with fractal metric for determining fractal measure of the brain. Therefore, providing the benefit of distinguishing brain disease from normal aging brain.
Regarding dependent claim 122, depends on claim 120, Desikan teaches: wherein the image metrics comprise the image intensity metric. (Desikan − [pdf page 3-5, 8] MRI image acquisition; the cerebral hemispheres were subdivided into 34 regions; Subdivided Temporal lobe, Middle temporal gyrus; the lateral fissure (and when present, the supramarginal gyrus; Parietal lobe, the supramarginal gyrus; Frontal Pole, the middle frontal gyrus; Table 1 (manual) and Table 2 (automatic))
Desikan does not explicitly teach: the image intensity metric
However, Oliveira teaches: wherein the image metrics comprise the image intensity metric in the image region corresponding to [the right temporal pole]. (Oliveira − [pdf page 3] The statistical approach adopted here to extract texture parameters from the MR images was padded on the GLCM (gray level co-occurrence matrices); Fig. 3 MR imaging of the temporal structure, Maps of textural parameters to compute the GLCM with distance of pixels 0 degrees, 45 degrees, 90 degrees or 135 degrees, totaling 16 GLCMs and totaling 176 texture parameters for each region of interest. Region of interest of the temporal structure that includes the temporal pole.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj and Nicastro as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Nicastro provides Desikan with fractal metric for determining fractal measure of the brain. Therefore, providing the benefit of distinguishing brain disease from normal aging brain.
Regarding dependent claim 131, depends on claim 118, Desikan teaches: the right supramarginal (Desikan − [pdf page 3-5, 8] MRI image acquisition; the cerebral hemispheres were subdivided into 34 regions; Subdivided Temporal lobe, Middle temporal gyrus; the lateral fissure (and when present, the supramarginal gyrus; Parietal lobe, the supramarginal gyrus; Frontal Pole, the middle frontal gyrus; Table 1 (manual) and Table 2 (automatic))
Desikan does not explicitly teach: wherein the image texture metric comprises a measure of correlation, such as the GLCM correlation.
However, Oliveira teaches: wherein the image texture metric in the right [supramarginal] comprises a measure of correlation, such as the GLCM correlation. (Oliveira − [pdf page 3] The statistical approach adopted here to extract texture parameters from the MR images was padded on the GLCM (gray level co-occurrence matrices); Fig. 3 MR imaging of the temporal structure, Maps of textural parameters to compute the GLCM with distance of pixels 0 degrees, 45 degrees, 90 degrees or 135 degrees, totaling 16 GLCMs and totaling 176 texture parameters for each region of interest. Region of interest of the temporal structure that includes the temporal pole. [pdf page 2-6] how many times gray level co-occurs with gray level determine the level or severity of AMCI, mild AD and normal control patient (level of intensity in region of interest))
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj and Nicastro as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Nicastro provides Desikan with fractal metric for determining fractal measure of the brain. Therefore, providing the benefit of distinguishing brain disease from normal aging brain.
Regarding dependent claim 132, depends on claim 118, Desikan teaches: the right rostral middle frontal (Desikan − [pdf page 3-5, 8] MRI image acquisition; the cerebral hemispheres were subdivided into 34 regions; Subdivided Temporal lobe, Middle temporal gyrus; the lateral fissure (and when present, the supramarginal gyrus; Parietal lobe, the supramarginal gyrus; Frontal Pole, the middle frontal gyrus; Table 1 (manual) and Table 2 (automatic))
Desikan does not explicitly teach: wherein the image texture metric comprises a measure of correlation, such as the GLCM correlation.
However, Oliveira teaches: wherein the image texture metric the right rostral middle frontal comprises a measure of correlation, such as the GLCM correlation. (Oliveira − [pdf page 3] The statistical approach adopted here to extract texture parameters from the MR images was padded on the GLCM (gray level co-occurrence matrices); Fig. 3 MR imaging of the temporal structure, Maps of textural parameters to compute the GLCM with distance of pixels 0 degrees, 45 degrees, 90 degrees or 135 degrees, totaling 16 GLCMs and totaling 176 texture parameters for each region of interest. Region of interest of the temporal structure that includes the temporal pole. [pdf page 2-6] how many times gray level co-occurs with gray level determine the level or severity of AMCI, mild AD and normal control patient (level of intensity in region of interest))
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj and Nicastro as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Nicastro provides Desikan with fractal metric for determining fractal measure of the brain. Therefore, providing the benefit of distinguishing brain disease from normal aging brain.
Regarding dependent claim 134, depends on claim 118, Desikan does not explicitly teach: wherein the predetermined method comprises computing a weighted sum of the image metrics.
However, Oliveira teaches: wherein the predetermined method comprises computing a weighted sum of the image metrics. (Oliveira − [pdf page 3-4] sum averages of GLCMs; a weighted average of the 176 texture parameters )
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj and Nicastro as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Nicastro provides Desikan with fractal metric for determining fractal measure of the brain. Therefore, providing the benefit of distinguishing brain disease from normal aging brain.
Regarding dependent claim 135, depends on claim 118, Desikan does not explicitly teach: comprising obtaining reference data configured to indicate a cognitive impairment state using reference indicators determined according to the predetermined method, and comparing the indicators for the subject to the reference indicators to perform said predicting or diagnosing of the cognitive impairment state in the subject.
However, Oliveira teaches: comprising obtaining reference data configured to indicate a cognitive impairment state using reference indicators determined according to the predetermined method, and comparing the indicators for the subject to the reference indicators to perform said predicting or diagnosing of the cognitive impairment state in the subject. (Oliveira − [pdf page 2, 6] find differences among patients with aMCI (mild cognitive impairment) and mild AD (Alzheimer Disease) and normal-aging subjects (non-Alzheimer disease), by using TA (texture analysis) applied to the MR images of the CC and the thalami of these groups of subjects. The application of TA techniques seeks mathematic parameters that can differentiate normal and lesioned tissues, by using texture parameters extracted from GLCMs. [pdf page 2-6] how many times gray level co-occurs with gray level determine the level or severity of AMCI, mild AD and normal control patient)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj and Nicastro as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Nicastro provides Desikan with fractal metric for determining fractal measure of the brain. Therefore, providing the benefit of distinguishing brain disease from normal aging brain.
Regarding dependent claim 137, depends on claim 118, Desikan teaches: comprising operating a processor to automatically segment the images to provide digital image data corresponding to ROIs in each of the image regions, and determining the image metrics by operating the processor to perform, on the digital data, image processing steps configured to provide said image metrics. (Desikan − [pdf page 1] Structural magnetic resonance imaging (MRI) of cerebral cortex (brain); tracking the evolution of disease from the degenerative changes associated with dementing illnesses such as Alzheimer’s disease (AD)Official Notice, system for segmentation those skill in the art, a computer with a processor would be used to segment MRI images recited in Desikan)
Regarding dependent claim 138, depends on claim 118, Desikan and Oliveira teaches all the limitation of claim 118, Oliveira teaches: A computer program product or computer apparatus configured to perform the method of claim 118 and to provide an output indicating said prediction or diagnosis. (Oliveira – [pdf page 4] weighted average of the 176 texture parameters over the different sections, by using the region-of-interest size as weight, was computed, by using Matlab (MathWorks, Natick, Massachusetts). [Official notice] Matlab is a software that is run on a computer apparatus.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj and Nicastro as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Nicastro provides Desikan with fractal metric for determining fractal measure of the brain. Therefore, providing the benefit of distinguishing brain disease from normal aging brain.
Claim(s) 130 is rejected under 35 U.S.C. 103 as being unpatentable over Desikan, Oliveira, Raj, Nicastro as applied to claim 118 above, and further in view of ANTONIADES (US PGPUB: 20210374951 A1).
Regarding dependent claim 130, depends on claim 118, Desikan teaches:
wherein the image data in the region corresponding to the right inferior lateral ventricle (Desikan − [pdf page 3-5, 8] MRI image acquisition; the cerebral hemispheres were subdivided into 34 regions; Subdivided Temporal lobe, Middle temporal gyrus; the lateral fissure (and when present, the supramarginal gyrus; Parietal lobe, the supramarginal gyrus; Frontal Pole, the middle frontal gyrus; Table 1 (manual) and Table 2 (automatic))
Desikan does not explicitly teach: using a HLH filter
However, ANTONIADES teaches: using an HLH filter. ([0163] a three level filter bank is used, eight wavelet decompositions result, corresponding to HHH, HHL, HLH, HLL, LHH, LHL, LLH and LLL, where H refers to “high-pass”, and L refers to “low-pass”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj, Nicastro and ANTONIADES as each of the image processing of imagery data of the anatomy. Adding the teaching of ANTONIADES provides Desikan with a filter bank of high and low pass filter. Therefore, providing the benefit of filter out signal to noise from imagery data.
Claim(s) 124 and 133 are rejected under 35 U.S.C. 103 as being unpatentable over Desikan, Oliveira, Raj, Nicastro as applied to claim 118 above, and further in view of Murray (US 20220148181 A1, Filed Date: Apr. 17, 2019).
Regarding dependent claim 124, depends on claim 118, Desikan teaches: image region corresponding to the right middle temporal gyrus; image region corresponding to the right rostral middle frontal; the image region corresponding to the right temporal pole. (Desikan − [pdf page 3-5, 8] MRI image acquisition; the cerebral hemispheres were subdivided into 34 regions; Subdivided Temporal lobe, Middle temporal gyrus; the lateral fissure (and when present, the supramarginal gyrus; Parietal lobe, the supramarginal gyrus; Frontal Pole, the middle frontal gyrus; Table 1 (manual) and Table 2 (automatic))
Desikan does not explicitly teach: the image metrics
However, Oliveira teaches: wherein the image metrics comprises: the minimum fractal dimension of an image region corresponding to [the right middle temporal gyrus]; the image texture metric of an image region corresponding to [the right rostral middle frontal];
Desikan doesn’t teach: and the measure of central tendency of pixel intensity
However, Murray teaches: and the measure of central tendency of pixel intensity in the image region corresponding to [the right temporal pole]. (Murray − 0051] The process then proceeds to act 316, where the individual data types (e.g., data for each of the metrics/features of interest) are normalized. Normalization establishes a mean and standard deviation (measure of central tendency and defined distribution) for the values in the data and applies a Z-score (or other statistical measure of difference) to determine how far an individual data point falls from the mean. The mean and standard deviation for a particular voxel is determined from a group (e.g., 20 or more) of comparable, healthy individuals. )
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj, Nicastro and Murray as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Therefore, providing the benefit of distinguishing brain cognitive behavior.
Regarding dependent claim 133, depends on claim 118, Desikan doesn’t teach: central tendency is the mean.
However, Murray teaches: central tendency is the mean. (Murray − 0051] The process then proceeds to act 316, where the individual data types (e.g., data for each of the metrics/features of interest) are normalized. Normalization establishes a mean and standard deviation (measure of central tendency and defined distribution) for the values in the data and applies a Z-score (or other statistical measure of difference) to determine how far an individual data point falls from the mean. The mean and standard deviation for a particular voxel is determined from a group (e.g., 20 or more) of comparable, healthy individuals. )
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj, Nicastro and Murray as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Therefore, providing the benefit of distinguishing brain cognitive behavior.
Claim(s) 125 and 139 are rejected under 35 U.S.C. 103 as being unpatentable over Desikan (An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest, Pub Date: Mar. 2006) in view of M.S. de Oliveira (MR Imaging Texture Analysis of the Corpus Callosum and Thalamus in Amnestic Mild Cognitive Impairment and Mild Alzheimer Disease, Pub Date: AJNR 32 Jan. 2011, hereinafter “Oliveira”) in view of Raj (US PGPUB: 20160300352 A1, Filed Date: 3/20/2014) in view of Murray (US 20220148181 A1, Filed Date: Apr. 17, 2019).
Regarding independent claim 125, Desikan teaches: A computer implemented method of predicting or diagnosing cognitive impairment in a human subject based on images of the subject's brain, the method comprising: (Desikan − [pdf page 1] Structural magnetic resonance imaging (MRI) of cerebral cortex (brain); tracking the evolution of disease from the degenerative changes associated with dementing illnesses such as Alzheimer’s disease (AD) )
determining based on processing by the computer of digital image data obtained from the images an image region corresponding to the right temporal pole, (Desikan − [pdf page 6] Construction of cortical atlas; minimizing the metric distortion between the cortical and the spherical representations through automatic segmenting regions of interest in the brain;)
and at least one of: an image region corresponding to the right rostral middle frontal; an image region corresponding to the right supramarginal; (Desikan − [pdf page 3-5, 8] MRI image acquisition; the cerebral hemispheres were subdivided into 34 regions; Subdivided Temporal lobe, Middle temporal gyrus; the lateral fissure (and when present, the supramarginal gyrus; Parietal lobe, the supramarginal gyrus; Frontal Pole, the middle frontal gyrus; Table 1 (manual) and Table 2 (automatic))
Desikan does not explicitly teach: image metrics
However, Oliveira teaches: and at least one of: an image texture metric of an image region corresponding to [the right rostral middle frontal]; and an image texture metric in an image region corresponding to [the right supramarginal]; Oliveira − [pdf page 3] The statistical approach adopted here to extract texture parameters from the MR images was padded on the GLCM (gray level co-occurrence matrices); Fig. 3 MR imaging of the temporal structure, Maps of textural parameters to compute the GLCM with distance of pixels 0 degrees, 45 degrees, 90 degrees or 135 degrees, totaling 16 GLCMs and totaling 176 texture parameters for each region of interest. Region of interest of the temporal structure that includes the temporal pole.) Examiner notes: limitation recites “at least one of (i.e., A, B, or C)” therefore teaching one of the limitation meets the limitation of at least one of
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the method further comprising: determining, based on processing by the computer, an indicator based on said image metrics according to a predetermined method; (Oliveira − [pdf page 2, 6] find differences among patients with aMCI (mild cognitive impairment) and mild AD (Alzheimer Disease) and normal-aging subjects (non-Alzheimer disease), by using TA (texture analysis) applied to the MR images of the CC and the thalami of these groups of subjects. The application of TA techniques seeks mathematic parameters that can differentiate normal and lesioned tissues, by using texture parameters extracted from GLCMs.)
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in predicting or diagnosing cognitive impairment state in the subject based on the indicator. (Oliveira − [pdf page 2-6] how many times gray level co-occurs with gray level determine the level or severity of AMCI, mild AD and normal control patient)
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Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, and Oliveira as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Oliveira provides Desikan with an texture analysis set of metrics for qualifying brain disease within imagery. Therefore, providing the benefit of distinguishing brain disease from normal aging brain.
Desikan does not explicitly teach: and displaying the indicator as an overlay on an image of the human subject's brain to assist a clinician in predicting
However, Raj teaches: and displaying the indicator as an overlay on an image of the human subject's brain to assist a clinician in predicting or diagnosing cognitive impairment state in the subject based on the indicator. (The predicted future disease patterns may be output in a representation selected from the group of a) a ball and stick model overlaid on a connectivity map of the human brain;)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira and Raj as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Adding the teaching of Raj provides Desikan and Oliveira with an network diffusion model for qualifying brain disease within imagery. Therefore, providing the benefit of predicting or diagnosing the states of neurodegeneration in the brain.
Desikan doesn’t teach: a measure of central tendency of pixel intensity
However, Murray teaches: and the measure of central tendency of pixel intensity in the image region corresponding to [the right temporal pole]. (Murray − 0051] The process then proceeds to act 316, where the individual data types (e.g., data for each of the metrics/features of interest) are normalized. Normalization establishes a mean and standard deviation (measure of central tendency and defined distribution) for the values in the data and applies a Z-score (or other statistical measure of difference) to determine how far an individual data point falls from the mean. The mean and standard deviation for a particular voxel is determined from a group (e.g., 20 or more) of comparable, healthy individuals. )
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj, and Murray as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Therefore, providing the benefit of distinguishing brain cognitive behavior.
Regarding dependent claim 139, depends on claim 125, Desikan and Oliveira teaches all the limitation of claim 125, Oliveira teaches: A computer program product or computer apparatus configured to perform the method of claim 118 and to provide an output indicating said prediction or diagnosis. (Oliveira – [pdf page 4] weighted average of the 176 texture parameters over the different sections, by using the region-of-interest size as weight, was computed, by using Matlab (MathWorks, Natick, Massachusetts). [Official notice] Matlab is a software that is run on a computer apparatus.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Desikan, Oliveira, Raj, and Murray as each of the inventions relates to providing identifying changes over time regarding brain disease using image processing of imagery data. Therefore, providing the benefit of distinguishing brain cognitive behavior.
Conclusion
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/CARL E BARNES JR/Examiner, Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178