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`UNITED STATES DEPARTMENT OF COMMERCE
`United States Patent and Trademark Office
`Address: COMMISSIONER FOR PATENTS
`PO. Box 1450
`Alexandria, Virginia 2231371450
`
`15/987,794
`
`05/23/2018
`
`Padideh KAMALI-ZARE
`
`53242-701301
`
`7219
`
`WILSON, SONSINI, GOODRICH & ROSATI
`650 PAGE MILL ROAD
`PALO ALTO, CA 94304-1050
`
`CWERN JONATHAN
`
`ART UNIT
`
`3793
`
`PAPER NUMBER
`
`NOTIFICATION DATE
`
`DELIVERY MODE
`
`03/14/2019
`
`ELECTRONIC
`
`Please find below and/or attached an Office communication concerning this application or proceeding.
`
`The time period for reply, if any, is set in the attached communication.
`
`Notice of the Office communication was sent electronically on above—indicated "Notification Date" to the
`
`following e—mail address(es):
`
`patentdoeket@ wsgroom
`
`PTOL-90A (Rev. 04/07)
`
`

`

`0/7709 A0170” Summary
`
`Application No.
`15/987,794
`Examiner
`JONATHAN CWERN
`
`Applicant(s)
`KAMALl-ZARE et al.
`Art Unit
`AIA (FITF) Status
`3793
`Yes
`
`- The MAILING DA TE of this communication appears on the cover sheet wit/7 the correspondence address -
`Period for Reply
`
`A SHORTENED STATUTORY PERIOD FOR REPLY IS SET TO EXPIRE g MONTHS FROM THE MAILING
`DATE OF THIS COMMUNICATION.
`Extensions of time may be available under the provisions of 37 CFR 1.136(a). In no event, however, may a reply be timely filed after SIX (6) MONTHS from the mailing
`date of this communication.
`|f NO period for reply is specified above, the maximum statutory period will apply and will expire SIX (6) MONTHS from the mailing date of this communication.
`-
`- Failure to reply within the set or extended period for reply will, by statute, cause the application to become ABANDONED (35 U.S.C. § 133).
`Any reply received by the Office later than three months after the mailing date of this communication, even if timely filed, may reduce any earned patent term
`adjustment. See 37 CFR 1.704(b).
`
`Status
`
`1). Responsive to communication(s) filed on 10/31/18.
`[:1 A declaration(s)/affidavit(s) under 37 CFR 1.130(b) was/were filed on
`
`2a). This action is FINAL.
`
`2b) C] This action is non-final.
`
`3)[:] An election was made by the applicant in response to a restriction requirement set forth during the interview on
`; the restriction requirement and election have been incorporated into this action.
`
`4)[:] Since this application is in condition for allowance except for formal matters, prosecution as to the merits is
`closed in accordance with the practice under Expat/7e Quay/e, 1935 CD. 11, 453 O.G. 213.
`
`Disposition of Claims*
`5)
`Claim(s)
`
`1,3—4,6—11,13,15,17 and 20—36 is/are pending in the application.
`
`5a) Of the above claim(s)
`
`is/are withdrawn from consideration.
`
`E] Claim(s)
`
`is/are allowed.
`
`Claim(s) 1,3—4,6—11,13,15,17 and 20—36 is/are rejected.
`
`[:1 Claim(s)
`
`is/are objected to.
`
`) ) ) )
`
`6 7
`
`8
`
`
`
`are subject to restriction and/or election requirement
`[j Claim(s)
`9
`* If any claims have been determined aflowabie. you may be eligible to benefit from the Patent Prosecution Highway program at a
`
`participating intellectual property office for the corresponding application. For more information, please see
`
`http://www.uspto.gov/patents/init events/pph/index.jsp or send an inquiry to PPeredback@uspto.gov.
`
`Application Papers
`10)[:] The specification is objected to by the Examiner.
`
`11)[:] The drawing(s) filed on
`
`is/are: a)D accepted or b)l:] objected to by the Examiner.
`
`Applicant may not request that any objection to the drawing(s) be held in abeyance. See 37 CFR 1.85(a).
`Replacement drawing sheet(s) including the correction is required if the drawing(s) is objected to. See 37 CFR 1.121 (d).
`
`Priority under 35 U.S.C. § 119
`12):] Acknowledgment is made of a claim for foreign priority under 35 U.S.C. § 119(a)-(d) or (f).
`Certified copies:
`
`a)D All
`
`b)I:l Some**
`
`c)C] None of the:
`
`1.[:] Certified copies of the priority documents have been received.
`
`2.[:] Certified copies of the priority documents have been received in Application No.
`
`3.[:] Copies of the certified copies of the priority documents have been received in this National Stage
`application from the International Bureau (PCT Rule 17.2(a)).
`
`** See the attached detailed Office action for a list of the certified copies not received.
`
`Attachment(s)
`
`1) C] Notice of References Cited (PTO-892)
`
`Information Disclosure Statement(s) (PTO/SB/08a and/or PTO/SB/08b)
`2)
`Paper No(s)/Mail Date_
`U.S. Patent and Trademark Office
`
`3) C] Interview Summary (PTO-413)
`Paper No(s)/Mail Date
`4) CI Other-
`
`PTOL-326 (Rev. 11-13)
`
`Office Action Summary
`
`Part of Paper No./Mai| Date 20190308
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 2
`
`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 § 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. Patentabiiity shall not be
`negated by the manner in which the invention was made.
`
`This application currently names joint inventors. In considering patentability of the
`
`claims the examiner presumes that the subject matter of the various claims was
`
`commonly owned as of the effective filing date of the claimed invention(s) absent any
`
`evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to
`
`point out the inventor and effective filing dates of each claim that was not commonly
`
`owned as of the effective filing date of the later invention in order for the examiner to
`
`consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2)
`
`prior art against the later invention.
`
`Claims 1, 3-4, 6-11, 13, 15, 17, and 20-36 are rejected under 35 U.S.C. 103 as
`
`being unpatentable over Davatzikos et al (US 20160239969 A1) in view of Mazer et al
`
`(US 20150073258 A1 ).
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 3
`
`Regarding claim 1, Davatzikos et aI teaches a method for determining a disorder
`
`state of brain tissue in a brain of a subject (e.g. “detecting abnormalities (e.g.,
`
`pathological regions) in brain magnetic resonance images”, [0029]), comprising:
`
`.
`
`(a) obtaining magnetic resonance imaging (MRI) data comprising at least one
`
`MRI image of the brain, the MRI image comprising a plurality of voxels, a voxel of
`
`the plurality of voxels being associated with the brain tissue of the brain of the
`
`subject and comprising one or more measured MRI parameters in the MRI data
`
`(e.g. “receive a target image, e.g., an MR image of a brain”, [0032]);
`
`o
`
`(b) for the voxel of the plurality of voxels, using one or more computer processors
`
`to process the one or more measured MRI parameters with one or more
`
`simulated MRI parameters for the voxel, the one or more simulated MRI
`
`parameters being generated from one or more microstructural models at the
`
`voxel (e.g. " analyze the target image by using sparse decomposition and a set of
`
`normative images spatially aligned with the target image (e.g., MR images of a
`
`normal or healthy brain)", [0032], and “classifying each voxel of the target image
`
`as normal or abnormal based on results of the sparse decomposition”, [0039]);
`
`.
`
`(c) for the voxel of the plurality of voxels, selecting a diagnostic model from the
`
`one or more microstructural models, the diagnostic model meeting a threshold
`
`congruence between the one or more measured MRI parameters and the one or
`
`more simulated MRI parameters associated with the diagnostic model (e.g.
`
`“identity matrix I is used as the generic dictionary that accounts for the unknown
`
`pathological patterns, though I may be replaced by more specific dictionaries that
`
`can better represent a target pathology”, [0084]); and
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 4
`
`.
`
`(d) using the diagnostic model to determine the disorder state of the brain tissue
`
`associated with at least the voxel (e.g. “a natural decomposition of y into the
`
`normal part and the residual”, [0084]).
`
`Davatzikos fails to show wherein the one or more microstructural models comprise
`
`measured or predicted values of a parameter selected from the group consisting of: cell
`
`density, cell shape, cell geometry, cell size, cell distribution, intercellular spacing,
`
`extracellular matrix homogeneity, interstitial tortuosity.
`
`Mazer discloses methods for detecting abnormalities using magnetic resonance
`
`imaging. Mazer teaches the one or more microstructural models comprise measured or
`
`predicted values of a parameter selected from the group consisting of: cell density, cell
`
`shape, cell geometry, cell size, cell distribution, intercellular spacing, extracellular matrix
`
`homogeneity, interstitial tortuosity (e.g. “non-water tissue volume and volume of
`
`interacting protons are determined to quantify the volume and exposed surface area of
`
`cell membranes and macromolecules in the soft tissue and compared to soft tissue from
`
`a control subject to evaluate for the presence of abnormalities or degenerative
`
`processes. Soft tissues in the various embodiments include cartilage, fatty tissue,
`
`muscle tissue, peripheral as well as central nerve tissue”, [0009]; “the volume and
`
`exposed surface area of cell membranes and macromolecules in the brain gray and
`
`white matter and compared to brain gray and white matter from a control subject to
`
`evaluate for the presence of abnormalities or degenerative processes" [0010]; and
`
`further [0011]—[0015]).
`
`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 determining of disorder states
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 5
`
`of Davatzikos with the comparison of a control subject of Mazer et al. and using
`
`microstructural models comprising measured or predicted values of a parameter
`
`selected from the group consisting of: cell density, cell shape, cell geometry, cell size,
`
`cell distribution, intercellular spacing, extracellular matrix homogeneity, interstitial
`
`tortuosity, as using quantitative tissue properties in neuroimaging and applying them in
`
`biophysical tissue models is important to advancing our ability to study neural
`
`development, to differentiate diseased neuronal tissues from typical, disease-free
`
`tissues, and to understand the structure and function of key pathways in the human
`
`brain (Mazer, [0006]). Furthermore, Davatzikos and Mazer et al are directed to
`
`quantifying MRI data by comparison to a control and determining abnormalities for
`
`detecting and diagnosing diseases including neurological diseases. Further, doing so
`
`would allow for detecting structural abnormalities and degenerative processes in soft
`
`tissues based on biophysical tissue models derived from quantitative tissue properties
`
`as recognized by Mazer et al with quick registration and detection of abnormal states of
`
`Davatzikos et al.
`
`Regarding claim 3 and claim 4, Davatzikos et al teaches diverse characteristics of
`
`imaging modalities for identifying abnormalities, but is silent on:
`
`o
`
`the method of claim 1, wherein the one or more measured MRI parameters are a
`
`plurality of measured MRI parameters; and
`
`o
`
`the method of claim 3, wherein the one or more simulated MRI parameters are a
`
`plurality of simulated MRI parameters.
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 6
`
`Mazer et al teaches wherein the one or more measured MRI parameters are a
`
`plurality of measured MRI parameters; and wherein the one or more simulated MRI
`
`parameters are a plurality of simulated MRI parameters (e.g. “proton density (PD) and
`
`T1 map data” [0008]) for detecting structural abnormalities and degenerative processes
`
`in soft tissues based on biophysical tissue models derived from quantitative tissue
`
`properties ([0008]).
`
`It would have obvious to one of ordinary skill in the art, before the effective filing date
`
`of the claimed invention, to have combined the determining of disorder states of
`
`Davatzikos with the MRI parameters of Mazer et al wherein the one or more measured
`
`MRI parameters are a plurality of measured MRI parameters; and wherein the one or
`
`more simulated MRI parameters are a plurality of simulated MRI parameters.
`
`Davatzikos and Mazer et al are directed to quantifying MRI data by comparison to a
`
`control and determining abnormalities for detecting and diagnosing diseases including
`
`neurological diseases. Further, doing so would allow for detecting structural
`
`abnormalities and degenerative processes in soft tissues based on biophysical tissue
`
`models derived from quantitative tissue properties as recognized by Mazer et al with
`
`quick registration and detection of abnormal states of Davatzikos et al.
`
`Further, Davatzikos et al teaches:
`
`.
`
`regarding claim 6, the method of claim 1, further comprising repeating (b)-(d) for
`
`all other voxels of the plurality of voxels;
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 7
`
`0
`
`regarding claim 7, the method of claim 1, further comprising repeating (b)-(d) for
`
`all voxels associated with a specified region of the brain to determine disorder
`
`states across the brain tissue associated with the specified region of the brain of
`
`the subject; and
`
`.
`
`regarding claim 8, the method of claim 1, further comprising repeating (b)-(d) for
`
`all voxels associated with an entirety of the brain to determine disorder states of
`
`the brain tissue associated with the entirety of the brain of the subject; and
`
`.
`
`regarding claim 9, the method of claim 1, further comprising repeating (a)-(d) for
`
`a plurality of MRI images, each MRI image of the plurality of MRI images
`
`associated with a brain selected from a plurality of brains, each brain of the
`
`plurality of brains associated with a subject selected from a plurality of subjects,
`
`to determine disorder states of the brain tissue associated the plurality of
`
`subjects;
`
`(e.g. “automated detection of abnormalities in medical images”, “decompose the target
`
`image into a normal part plus a residual”, and classify each voxel as normal or
`
`abnormal, [0006]—[0009]; “automatically detecting abnormalities (e.g., pathological
`
`regions) in brain magnetic resonance images”, [0029]).
`
`Further, Davatzikos et aI teaches:
`
`0
`
`regarding claim 10, the method of claim 1, wherein the MRI image is selected
`
`from the group consisting of: a longitudinal relaxation time (T1)-weighted MRI
`
`image, a transverse relaxation time (T2)-weighted MRI image, and a diffusion-
`
`weighted MRI image; and
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 8
`
`0
`
`regarding claim 11, the method of claim 10, wherein the measured MRI
`
`parameter is selected from the group consisting of: a longitudinal relaxation time
`
`(T1), a transverse relaxation time (T2), and a diffusion coefficient;
`
`(T2 and T1, [0064], [0154], [0164]).
`
`Further, regarding claim 13 and claim 15, Davatzikos fails to show, but Mazer
`
`teaches:
`
`0 wherein the one or more microstructural models comprise information regarding
`
`a parameter selected from the group consisting of: intracellular content,
`
`extracellular content, distribution of extracellular content within interstitial space,
`
`distribution of intracellular content within intracellular space, and tissue geometry;
`
`and
`
`0 wherein the one or more microstructural models are selected from a
`
`microstructural model library;
`
`(e.g. “non-water tissue volume and volume of interacting protons are determined to
`
`quantify the volume and exposed surface area of cell membranes and macromolecules
`
`in the soft tissue and compared to soft tissue from a control subject to evaluate for the
`
`presence of abnormalities or degenerative processes. Soft tissues in the various
`
`embodiments include cartilage, fatty tissue, muscle tissue, peripheral as well as central
`
`nerve tissue”, [0009]; “the volume and exposed surface area of cell membranes and
`
`macromolecules in the brain gray and white matter and compared to brain gray and
`
`white matter from a control subject to evaluate for the presence of abnormalities or
`
`degenerative processes" [0010]; and further [0011]—[0015]).
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 9
`
`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 determining of disorder states
`
`of Davatzikos with the comparison of a control subject of Mazer et al. Davatzikos and
`
`Mazer et al are directed to quantifying MRI data by comparison to a control and
`
`determining abnormalities for detecting and diagnosing diseases including neurological
`
`diseases. Further, doing so would allow for detecting structural abnormalities and
`
`degenerative processes in soft tissues based on biophysical tissue models derived from
`
`quantitative tissue properties as recognized by Mazer et al with quick registration and
`
`detection of abnormal states of Davatzikos et al.
`
`Regarding claim 17, Davitzikos et al teaches wherein the microstructural model
`
`library is constructed by:(a) creating a first microstructural model corresponding to a
`
`brain state that is not associated with a disorder; and (b) iteratively subjecting the first
`
`microstructural model to a perturbation, each iteration producing an additional perturbed
`
`microstructural model (e.g. "deformably register to the target image a subset of images
`
`from a plurality of images, wherein the subset of images is associated with a normal
`
`variation of an anatomical feature, to define a dictionary from the registered normative
`
`images, to use the dictionary in a sparse decomposition that attempts to decompose the
`
`target image into a normal part plus a residual, to soft/hard classify each voxel of the
`
`target image as normal or abnormal based on results of the sparse decomposition, and
`
`to re-iterate the procedure of registration and abnormality detection, by progressively
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 10
`
`increasing the degree of flexibility/elasticity in deforming source to target images.”,
`
`[0009D.
`
`Regarding claim 20, Davitzikos fails to show, but Mazer et al teaches wherein the
`
`perturbation comprises an operation selected from the group consisting of: depleting
`
`cells, altering cellular morphology or distribution, altering intracellular or interstitial
`
`physico-chemical composition or distribution, altering extracellular matrix composition or
`
`distribution, and altering intercellular spacing (e.g. “quantify the volume and exposed
`
`surface area of cell membranes and macromolecules in the soft tissue and compared to
`
`soft tissue from a control subject to evaluate for the presence of abnormalities or
`
`degenerative processes”, [0009]; “quantify the volume and exposed surface area of cell
`
`membranes and macromolecules in the brain gray and white matter and compared to
`
`brain gray and white matter from a control subject to evaluate for the presence of
`
`abnormalities or degenerative processes" [0010]; and [0011]—[0014]).
`
`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 determining of disorder states
`
`of Davatzikos with the comparison of a control subject including quantifying volume and
`
`exposed cell surface area of cell membranes and macromolecules of Mazer et al.
`
`Davatzikos and Mazer et al are directed to quantifying MRI data by comparison to a
`
`control and determining abnormalities for detecting and diagnosing diseases including
`
`neurological diseases. Further, doing so would allow for detecting structural
`
`abnormalities and degenerative processes in soft tissues based on biophysical tissue
`
`models derived from quantitative tissue properties as recognized by Mazer et al with
`
`quick registration and detection of abnormal states of Davatzikos et al.
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 11
`
`Regarding claim 21, claim 22, and claim 23, Davitzikos et al teaches:
`
`wherein the perturbation comprises a stochastic procedure;
`
`wherein the threshold congruence is determined by computing an objective
`
`function between the one or more measured MRI parameters and the one or
`
`more simulated MRI parameters; and
`
`wherein the objective function comprises an L1 norm or an L2 norm;
`
`(e.g. “Deformable registration between a normal template image and a patient image
`
`with pathologies and topological changes”, [0072], L1 norm, [0074], [0178], and L2 norm
`
`[0103])
`
`Further, Davatzikos et al teaches:
`
`regarding claim 24, the method of claim 1, wherein determining the disorder
`
`state of the brain tissue associated with the voxel is achieved at an accuracy of
`
`at least 90%;
`
`regarding claim 25, the method of claim 7, wherein determining the disorder
`
`states across the brain tissue associated with the specified region of the brain is
`
`achieved at an accuracy of at least 90%;
`
`regarding claim 26, the method of claim 8, wherein determining the disorder
`
`states of the brain tissue associated with the entirety of the brain of the subject is
`
`achieved at an accuracy of at least 90%; and
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 12
`
`0
`
`regarding claim 27, the method of claim 9, wherein determining the disorder
`
`states of the brain tissue associated with the plurality of subjects is achieved at
`
`an accuracy of at least 90%;
`
`(evaluating accuracy with AUCs of >0.995, [0148], [0150]).
`
`Further, Davatzikos et al teaches:
`
`0
`
`regarding claim 28, the method of claim 1, wherein the disorder is a non-
`
`neurodegenerative disorder; and
`
`0
`
`regarding claim 29, the method of claim 28, wherein the disorder is selected
`
`from the group consisting of: a primary neoplasm, a metastatic neoplasm, a
`
`motor neuron disease, a seizure disorder, a seizure disorder with focal cortical
`
`dysplasia, multiple sclerosis, a non-neurodegenerative encephalopathy, and a
`
`psychological disorder;
`
`(multiple sclerosis, tumors, [0064]-[0065], [0072]).
`
`Further, Davatzikos et al teaches:
`
`.
`
`regarding claim 30, the method of claim 1, method of claim 1, wherein the
`
`disorder is a neurodegenerative disorder; and
`
`Regarding claim 31 and claim 32, Davatzikos fails to show, but Mazer et al teaches:
`
`0 wherein the method enables diagnosis of a neurodegenerative disorder more
`
`than 5 years prior to the development of symptoms associated with the
`
`neurodegenerative disorder; and
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 13
`
`0 wherein the method enables monitoring of the neurodegenerative disorder at a
`
`plurality of time points, the plurality of time points separated by a plurality of time
`
`intervals;
`
`(e.g. "early detection, prognosis and diagnosis“, [0005], [0081], “provide a prognostic
`
`indication of the progression of multiple sclerosis in the years to come” [0084],
`
`“diagnosis of individuals, and the norms can also be used in longitudinal studies of
`
`development or for monitoring interventions”, [0225]).
`
`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 determining of disorder states
`
`of Davatzikos with the diagnosis and monitoring of Mazer et al. Davatzikos and Mazer
`
`et al are directed to quantifying MRI data by comparison to a control and determining
`
`abnormalities for detecting and diagnosing diseases including neurological diseases
`
`that would diagnosis 5 years prior to development of diseases, and further early
`
`detection is stated by Mazer et al. Further, doing so would allow for detecting structural
`
`abnormalities and degenerative processes in soft tissues based on biophysical tissue
`
`models derived from quantitative tissue properties as recognized by Mazer et al with
`
`quick registration and detection of abnormal states of Davatzikos et al.
`
`0
`
`regarding claim 33, the method of claim 30, wherein the neurodegenerative
`
`disorder is selected from the group consisting of: Alzheimer's disease, a non-
`
`Alzheimer's dementia disorder, Parkinson's disease, a Parkinsonism disorder, a
`
`motor neuron disease, Huntington's disease, a Huntington's disease-like
`
`

`

`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 14
`
`syndrome, transmissible spongiform encephalopathy, chronic traumatic
`
`encephalopathy, and a tauopathy;
`
`(Alzheimer's disease, [0123], [0163]-[0164]).
`
`Further, Davatzikos et al teaches:
`
`0
`
`regarding claim 34, the method of claim 1, further comprising constructing a
`
`brain map that, for each voxel of the plurality of voxels, indicates the disorder
`
`state of the brain tissue associated with the voxel;
`
`.
`
`regarding claim 35, the method of claim 34, further comprising displaying the
`
`brain map on a graphical user interface of an electronic device of a user; and
`
`0
`
`regarding claim 36, the method of claim 34, wherein the brain map is selected
`
`from the group consisting of: a qualitative abnormality map, a binary abnormality
`
`map, a quantitative abnormality map, and a percent abnormality map;
`
`(e.g. abnormality map [0039], [0055], [0058], [0107], FIG. 14, [0166])
`
`Response to Arguments
`
`Applicant's arguments filed 10/31/18 have been fully considered but they are not
`
`persuasive.
`
`In response to applicant's arguments against the references individually, one
`
`cannot show nonobviousness by attacking references individually where the rejections
`
`are based on combinations of references. See In re Keller, 642 F.2d 413, 208
`
`USPQ 871 (CCPA 1981); In re Merck & 00., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir.
`
`1986).
`
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`Application/Control Number: 15/987,794
`Art Unit: 3793
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`Page 15
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`It should be noted that while applicant argues that Davatzikos does not teach the
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`amended limitations of claim 1, these limitations were previously in for example claims
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`13 and 14. These limitations were addressed by the Mazer reference. Applicant’s
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`arguments merely conclude that Mazer does not teach these features, but provides no
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`detail as to why applicant believes Mazer does not teach these features, and fails to
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`address those portions of Mazer cited by the examiner. Therefore, the rejection is
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`maintained.
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`Conclusion
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`Applicant's amendment necessitated the new ground(s) of rejection presented in
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`this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP
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`§ 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37
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`CFR1.136(a).
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`A shortened statutory period for reply to this final action is set to expire THREE
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`MONTHS from the mailing date of this action.
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`In the event a first reply is filed within
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`TWO MONTHS of the mailing date of this final action and the advisory action is not
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`mailed until after the end of the THREE-MONTH shortened statutory period, then the
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`shortened statutory period will expire on the date the advisory action is mailed, and any
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`extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of
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`the advisory action.
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`In no event, however, will the statutory period for reply expire later
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`than SIX MONTHS from the date of this final action.
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`

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`Application/Control Number: 15/987,794
`Art Unit: 3793
`
`Page 16
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`Any inquiry concerning this communication or earlier communications from the
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`examiner should be directed to JONATHAN CWERN whose telephone number is
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`(571)270-1560. The examiner can normally be reached on Monday - Friday, 8:00 am -
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`5:00 pm.
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`Examiner interviews are available via telephone, in-person, and video
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`conferencing using a USPTO supplied web-based collaboration tool. To schedule an
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`interview, applicant is encouraged to use the USPTO Automated Interview Request
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`(AIR) at http://www.uspto.gov/interviewpractice.
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`If attempts to reach the examiner by telephone are unsuccessful, the examiner’s
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`supervisor, William Thomson can be reached on 571-272—3718. The fax phone number
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`for the organization where this application or proceeding is assigned is 571-273-8300.
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`Information regarding the status of an application may be obtained from the
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`Patent Application Information Retrieval (PAIR) system. Status information for
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`published applications may be obtained from either Private PAIR or Public PAIR.
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`Status information for unpublished applications is available through Private PAIR only.
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`For more information about the PAIR system, see http://pair-direct.uspto.gov. Should
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`you have questions on access to the Private PAIR system, contact the Electronic
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`Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a
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`

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`Application/Control Number: 15/987,794
`Art Unit: 3793
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`/JONATHAN CWERN/
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`Primary Examiner, Art Unit 3793
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`Page 17
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