`
`UNITED STATES DEPARTMENT OF COMMERCE
`United States Patent and Trademark Office
`Address: COMlVHSSIONER FOR PATENTS
`PO. Box 1450
`Alexandria1 Virginia 22313-1450
`www.uspto.gov
`
`
`
`
`
`15/987,794
`
`05/23/2018
`
`Padideh KAMALl-ZARE
`
`53242-701.301
`
`7219
`
`08/31/2018 —WILSON, SONSINI, GOODRICH & ROSATI «
`7590
`21971
`650 PAGE MILL ROAD
`HO, DON N
`PALO ALTO, CA 94304-1050
`UNITED STATES OF AMERICA
`
`PAPER NUMBER
`
`3737
`
`NOTIFICATION DATE
`
`DELIVERY MODE
`
`08/31/2018
`
`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):
`
`patentdocket @ wsgr.c0m
`
`PTOL—90A (Rev. 04/07)
`
`

`

`
`
`Applicant(s)
`Application No.
` 15/987,794 KAMALI-ZARE ET AL.
`
`Examiner
`Art Unit
`AIA (First Inventor to File)
`Office Action Summary
`
`Don N. Ho $2213 3737
`
`-- The MAILING DA TE of this communication appears on the cover sheet with 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 CFR1. 136( a).
`after SIX () MONTHS from the mailing date of this communication.
`If 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).
`
`In no event, however, may a reply be timely filed
`
`Status
`
`1)IZI Responsive to communication(s) filed on 05/23/2018.
`El A declaration(s)/affidavit(s) under 37 CFR 1.130(b) was/were filed on
`
`2b)|ZI This action is non-final.
`2a)|:l This action is FINAL.
`3)I:I 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 Exparte Quay/e, 1935 CD. 11, 453 O.G. 213.
`
`Disposition of Claims*
`
`5)IZI Claim(s) 13 4 6-15 17and 20-36 is/are pending in the application.
`5a) Of the above claim(s)
`is/are withdrawn from consideration.
`
`6)I:I Claim(s)
`is/are allowed.
`
`7)|Z| Claim(s) 1 3 4 6- 15 17and 20-36 is/are rejected.
`8)|:I Claim(s)_ is/are objected to.
`
`
`are subject to restriction and/or election requirement.
`9)I:I Claim((s)
`* If any claims have been determined allowable, 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
`hit
`:/'/\W¢W.LISI>I‘.0. ovI’ atentS/init events/
`
`
`
`hI/index.‘s or send an inquiry to PPI-iieedback{®usgtc.00v.
`
`Application Papers
`
`10)I:l The specification is objected to by the Examiner.
`11)|Xl The drawing(s) filed on 05/23/2018 is/are: a)IXI 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)I:| Acknowledgment is made of a claim for foreign priority under 35 U.S.C. § 119(a)-(d) or (f).
`Certified copies:
`
`a)I:l All
`
`b)|:l Some” c)I:l None of the:
`
`1.I:I Certified copies of the priority documents have been received.
`2.|:l 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)
`
`
`
`3) D Interview Summary (PTO-413)
`1) E Notice of References Cited (PTO-892)
`Paper No(s)/Mai| Date.
`.
`.
`4) I:I Other'
`2) E InformatIon DIsclosure Statement(s) (PTO/SB/08a and/or PTO/SB/08b)
`Paper No(s)/Mai| Date
`US. Patent and Trademark Office
`PTOL—326 (Rev. 11-13)
`
`Office Action Summary
`
`Part of Paper No./Mai| Date 20180724
`
`

`

`Application/Control Number: 15/987,794
`
`Page 2
`
`Art Unit: 3737
`
`DETAILED ACTION
`
`Notice of Pre-AIA or AIA Status
`
`1.
`
`The present application, filed on or after March 16, 2013, is being examined
`
`under the first inventor to file provisions of the AIA.
`
`Response to Amendment
`
`2.
`
`Amendments filed 05/23/2018 have been entered. Claims 1, 3, 4, 6-11, 13-15,
`
`17, and 20-36; claims 2, 5, 12, 16, 18, 19, and 37-41 are cancelled; and claims 1, 3, 6-
`
`11, 13, 17,20, 24-28, 30, 33, 34, and 36 are amended.
`
`Claim Objections
`
`3.
`
`Claim 9 is objected to because of the following informalities:
`
`0 Claim 9 recites the limitation "to determine disorder states of the brain tissue
`
`associated the plurality of subjects” and it appears to be a typographical error.
`
`The limitation will be read as "to determine disorder states of the brain tissue
`
`associated m the plurality of subjects” (emphasis added).
`
`Appropriate correction is required.
`
`Claim Rejections - 35 USC § 1 12
`
`4.
`
`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:
`
`

`

`Application/Control Number: 15/987,794
`
`Page 3
`
`Art Unit: 3737
`
`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.
`
`5.
`
`Claim 11 is 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 pre-AIA the applicant regards
`
`as the invention.
`
`6.
`
`Claim 11 recites the limitation “the measured MRI parameter” in line 1. There is
`
`insufficient antecedent basis for this limitation in the claim or at the least unclear if the
`
`limitation is referring to the one or more measure MRI parameters. For the purposes of
`
`applying prior art, the limitation will be read as " the one or more measure MRI
`
`parameters”.
`
`Claim Rejections - 35 USC § 101
`
`7.
`
`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.
`
`8.
`
`Claims 1, 3, 4, 6-11, 13-15, 17, and 20-36 are rejected under 35 U.S.C. 101
`
`because the claimed invention is directed to a judicial exception (Le, a law of nature, a
`
`natural phenomenon, or an abstract idea) without significantly more. Claims 1, 3, 4, 6-
`
`11, 13-15, 17, and 20-36 are directed to an abstract idea of a method a method for
`
`determining a disorder state of brain tissue in a brain of a subject comprising obtaining
`
`MRI data, using a computer processor to process measured MRI parameters, selecting
`
`

`

`Application/Control Number: 15/987,794
`
`Page 4
`
`Art Unit: 3737
`
`a diagnostic model, and using the diagnostic model. The claims do not include
`
`additional elements that are sufficient to amount to significantly more than the judicial
`
`exception because organizing and manipulating information through mathematical
`
`correlations has been found to be patent ineligible (see Dig/tech Image Techs, LLC v
`
`Electronics for Imaging, Inc., 758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)).
`
`For future possible amendments to the claims, Examiner notes an algorithm for
`
`calculating parameters indicating an abnormal condition (see in re Grams, 888 F.2d
`
`835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989)) and diagnosing an abnormal condition by
`
`performing clinical tests and thinking about the results (see in re Grams, 888 F.2d 835,
`
`12 U.S.P.Q.2d 1824 (Fed. Cir. 1989) for methods and method steps that are considered
`
`to be directed to an abstract idea.
`
`Claim 1
`
`is directed to an abstract idea of a method for determining a disorder
`
`state of brain tissue in a brain of a subject comprising obtaining MRI data, using a
`
`computer processor to process measured MRl parameters, selecting a diagnostic
`
`model, and using the diagnostic model. Claim 1
`
`is in the statutory category of a process
`
`and is directed to a judicially recognized exception of an abstract idea. The steps of
`
`obtaining MRI data, selecting a diagnostic model, and using the diagnostic model can
`
`be performed by mental steps, such as a physician looking at printouts or data and
`
`performing the steps in his or her mind or for selecting and using a diagnostic model.
`
`Further using a computer processor to process measured MRl parameters (the claim
`
`limitation "(b) for the voxel of the plurality of voxels, using one or more computer
`
`processors to process the one or more measured MRl parameters with one or more
`
`

`

`Application/Control Number: 15/987,794
`
`Page 5
`
`Art Unit: 3737
`
`simulated MRI parameters for the voxel, the one or more simulated MRI parameters
`
`being generated from one or more microstructural models at the voxel”) is well-known in
`
`the art (see below 102 rejection) and does not add significantly more than the abstract
`
`idea of determining a disorder state of brain tissue.
`
`Claims 3, 4, 10, and 11 are directed to the MRI data, MRI parameters, and
`
`simulated MRI parameters and does not add significantly more than the abstract idea of
`
`determining a disorder state of brain tissue. Claims 5-9 are directed to repeating the
`
`steps of claim 1 and does not add significantly more than the abstract idea of
`
`determining a disorder state of brain tissue. Claims 13-16, 17, and 20-23 are directed to
`
`the microstructural models and does not add significantly more than the abstract idea of
`
`determining a disorder state of brain tissue. Claims 24-33 are directed to the disorder
`
`and disorder states and does not add significantly more than the abstract idea of
`
`determining a disorder state of brain tissue. Claims 34-36 are directed to a brain map
`
`and does not add significantly more than the abstract idea of determining a disorder
`
`state of brain tissue and a well-known in the art (see below 102 rejection).
`
`Limitations that would support the significance of additional elements can include
`
`one or more of the following:
`
`o
`
`0
`
`.
`
`improves another technology or technical field;
`
`improves the functioning of a computer itself
`
`applies the exception with, or by use of, a particular machine: not a generic
`
`computer performing generic computer functions, not adding the words “apply
`
`

`

`Application/Control Number: 15/987,794
`
`Page 6
`
`Art Unit: 3737
`
`it” or words equivalent to “apply the exception”, and not mere instructions to
`
`implement an abstract idea on a computer;
`
`.
`
`effects a transformation or reduction of a particular article to a different state
`
`or thing;
`
`.
`
`adds a specific limitation other than what is well-understood, routine and
`
`conventional in the field: not appending well-understood, routine, and
`
`conventional activities previously known to the industry, specified at a high
`
`level of generality, and not a generic computer performing generic computer
`
`functions;
`
`.
`
`adds unconventional steps that confine the claim to a particular useful
`
`application: not adding insignificant extrasolution activity, such as mere data
`
`gathering; and
`
`.
`
`adds meaningful limitations that amount to more than generally linking the
`
`use of the exception to a particular technological environment.
`
`Claim Rejections - 35 USC § 102
`
`9.
`
`In the event the determination of the status of the application as subject to AIA 35
`
`U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any
`
`correction of the statutory basis for the rejection will not be considered a new ground of
`
`rejection if the prior art relied upon, and the rationale supporting the rejection, would be
`
`the same under either status.
`
`

`

`Application/Control Number: 15/987,794
`
`Page 7
`
`Art Unit: 3737
`
`10.
`
`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)(2) the claimed invention was described in a patent issued under section 151, or in an
`application for patent published or deemed published under section 122(b), in which the
`patent or application, as the case may be, names another inventor and was effectively filed
`before the effective filing date of the claimed invention.
`
`11.
`
`Claims 1, 6-11, 24-30, and 33-36 are rejected under 35 U.S.C. 102(a)(2) as
`
`being anticipated by Davatzikos et al (US 20160239969 A1).
`
`12.
`
`Regarding claim 1, Davatzikos et al 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
`
`

`

`Application/Control Number: 15/987,794
`
`Page 8
`
`Art Unit: 3737
`
`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
`
`(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]).
`
`13.
`
`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;
`
`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
`
`

`

`Application/Control Number: 15/987,794
`
`Page 9
`
`Art Unit: 3737
`
`0
`
`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, [0006j-[0009]; “automatically detecting abnormalities (e.g., pathological
`
`regions) in brain magnetic resonance images”, [0029]).
`
`14.
`
`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
`
`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]).
`
`15.
`
`Further, Davatzikos et aI teaches:
`
`

`

`Application/Control Number: 15/987,794
`
`Page 10
`
`Art Unit: 3737
`
`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
`
`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]).
`
`16.
`
`Further, Davatzikos et al teaches:
`
`regarding claim 28, the method of claim 1, wherein the disorder is a non-
`
`neurodegenerative disorder; and
`
`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]).
`
`

`

`Application/Control Number: 15/987,794
`
`Page 11
`
`Art Unit: 3737
`
`17.
`
`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 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
`
`syndrome, transmissible spongiform encephalopathy, chronic traumatic
`
`encephalopathy, and a tauopathy;
`
`(Alzheimer's disease, [0123], [0163]-[0164]).
`
`18.
`
`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])
`
`

`

`Application/Control Number: 15/987,794
`
`Page 12
`
`Art Unit: 3737
`
`Claim Rejections - 35 USC § 103
`
`19.
`
`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.
`
`20.
`
`The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148
`
`USPQ 459 (1966), that are applied for establishing a background for determining
`
`obviousness under 35 U.S.C. 103 are summarized as follows:
`
`1. Determining the scope and contents of the prior art.
`
`2. Ascertaining the differences between the prior art and the claims at issue.
`
`3. Resolving the level of ordinary skill in the pertinent art.
`
`4. Considering objective evidence present in the application indicating
`
`obviousness or nonobviousness.
`
`21.
`
`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.
`
`

`

`Application/Control Number: 15/987,794
`
`Page 13
`
`Art Unit: 3737
`
`22.
`
`Claims 3, 4, 13-15, 17, 20-23, 31, and 32 are rejected under 35 U.S.C. 103 as
`
`being unpatentable over Mazer et al (US 20150073258 A1).
`
`23.
`
`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.
`
`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 been prima facie obvious to one of ordinary skill in the art 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
`
`

`

`Application/Control Number: 15/987,794
`
`Page 14
`
`Art Unit: 3737
`
`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.
`
`24.
`
`Further, regarding claim 13, claim 14, and claim 15, Mazer teaches:
`
`. 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;
`
`0 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, water to protein ratio, water to lipid
`
`ratio, water to carbohydrate ratio, protein to lipid ratio, protein to carbohydrate
`
`ratio, and lipid to carbohydrate ratio; 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
`
`

`

`Application/Control Number: 15/987,794
`
`Page 15
`
`Art Unit: 3737
`
`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 prima facie obvious to one of ordinary skill in the art 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.
`
`25.
`
`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
`
`

`

`Application/Control Number: 15/987,794
`
`Page 16
`
`Art Unit: 3737
`
`to re-iterate the procedure of registration and abnormality detection, by progressively
`
`increasing the degree of flexibility/elasticity in deforming source to target images.”,
`
`[0009D.
`
`26.
`
`Regarding claim 20, 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 prima facie obvious to one of ordinary skill in the art 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
`
`

`

`Application/Control Number: 15/987,794
`
`Page 17
`
`Art Unit: 3737
`
`tissue properties as recognized by Mazer et al with quick registration and detection of
`
`abnormal states of Davatzikos et al.
`
`27.
`
`Regarding claim 21, claim 22, and claim 23, Davitzikos et al teaches:
`
`0 wherein the perturbation comprises a stochastic procedure;
`
`0 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
`
`[0103p
`
`28.
`
`Regarding claim 31 and claim 32, 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
`
`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],
`
`

`

`Application/Control Number: 15/987,794
`
`Page 18
`
`Art Unit: 3737
`
`“diagnosis of individuals, and the norms can also be used in longitudinal studies of
`
`development or for monitoring interventions”, [0225]).
`
`It would have been prima facie obvious to one of ordinary skill in the art 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.
`
`Conclusion
`
`29.
`
`The prior art made of record and not relied upon is considered pertinent to
`
`applicant's disclosure. Eskildsen et al (US 20080170791 A1) (e.g. cortical thickness of
`
`a healthy and demented subject, [0140], FIG. 16), Zaidel et al (US 20100241020 A1),
`
`and Holland et al (US 20100259263 A1) teach analysis of MRI images for brain
`
`abnormalities.
`
`30.
`
`Any inquiry concerning this communication or earlier communications from the
`
`examiner should be directed to Don N. Ho whose telephone number is (571 )270-0427.
`
`The examiner can normally be reached on M-F, 8 am - 5 pm (EST).
`
`

`

`Application/Control Number: 15/987,794
`
`Page 19
`
`Art Unit: 3737
`
`Examiner interviews are available via telephone, in-person, and video
`
`conferencing using a USPTO supplied web-based collaboration tool. To schedule an
`
`interview, applicant is enco