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 .
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.
DETAILED ACTION
Priority
This application claims priority to IT102022000005873, filed on 3/27/22 and is a 371 of PCT EP2023/057150, filed 3/21/2023.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7, 8, 10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Applicant’s use of “and/or” is indefinite because it is unclear as to what would cause infringement of the claim. Does the claim require all the limitations or just one? And some of the and/or are exclusive. For example, in claim 10, b and c cannot be combined as an and. For purposes of examination, the and/or is treated as “or.”
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Note, due to the claim amendments, the claims will be addressed out of numerical order for clarity.
Claims 6-8, 10-11, 15-16 are rejected under 35 USC 102 as being anticipated by Fader Networks for Domain Adaptation on fMRI: ABIDE-II Study, Pominova et al. (hereafter Fader)
15. A method of processing functional magnetic resonance images of an organ or anatomical part of an individual using a machine learning algorithm through a neural network architecture, wherein the method comprises:
training a machine learning algorithm to extract a desired output variable from functional magnetic resonance images of an organ or anatomical part of individuals, that depend at least in part on a set of confounding variables, using an adversarial learning method, in which the dependency of the output information on said set of confounding variables is progressively reduced during training; (Fader page 2-4 describes fMRI images using an adversarial learning method, reduces confounding variables from the image)
the machine learning algorithm comprises:
the calculation of the optical flow of the training images; (Fader page 2 sequence of images processing; see also page 4)
a multi-channel feature extractor module that simultaneously processes the functional magnetic resonance images and their optical flow and extracts a reduced-size vector; (Fader page 3-4 the FC analysis reduces the data to a 116 x 116 matrix; see also transformation into 1 dimensional vectors)
a first processing module that processes the reduced-size vector and predicts the output variable; (Fader page 3-4 learning attribute-invariant representations)
a second processing module that processes the reduced-size vector and predicts the confounding variables; and (Fader page 3-4 learning attribute-invariant representations)
the training of the machine learning algorithm is favoring the learning of the first processing module and opposing the learning of the second processing module; (Note adversarial learning attempts to find the correct variable to predict and thus favors learning the correct variables and reject the confounding variables)
obtaining scan data of a three-dimensional functional magnetic resonance video, which provide information on said organ or anatomical part, wherein the information is a spatio-temporal data, that depends at least in part on the set of confounding variables; (Fader page 1 fMRI scans of the brain)
obtaining data of the optical flow of said three-dimensional video; (Fader page 1 ABIDE II data is data of brain scans)
simultaneously applying the machine learning algorithm to the scan data and to the optical flow data; (Fader page 5 applies the ML algorithm described in the paper to the ABIDE II data)
and obtaining output information on the organ or anatomical part based on the application of the machine learning algorithm to the scan data and the optical flow data; and (Fader page 6 obtains result information)
getting the output information, wherein the dependency of the output information on said set of confounding variables is reduced. (Fader page 6 obtains result information.)
6. The method according to claim 15, wherein the confounding variables are defined by a vector of confounding variables and wherein the method further comprises:
performing a correlation between the vector of confounding variables and the prediction vector; and
measuring a correlation value at the end of the training of the neural network, wherein the output information on the organ or anatomical part depends on the vector of confounding variables in proportion to said correlation value. (Fader page 5 correlates the various connectively features)
7. The method according to claim 15 wherein:
a. the confounding variables are variables that affect the scan data of the functional magnetic resonance three-dimensional video; and/or
b. the confounding variables include at least technical variables related to the equipment and techniques for acquiring the functional magnetic resonance image and biological variables related to the characteristics of the organ or anatomical part analyzed. (Fader page 3 preprocessing of the fMRI images that are related scan data)
8. The method according to claim 15, wherein:
a. the obtained scan data refer to unprocessed functional magnetic resonance images; and/or
b. the obtained scan data refer to functional magnetic resonance images that maintain their original size without any distortion. (Fader page 2 original dataset)
10. The method according to claim 15, wherein:
a. parameters associated with the features extractor and the first processing module are optimized to minimize the error of the first processing module; and/or
b. parameters associated with the second processing module are optimized to minimize the error of the second processing module; and/or
c. parameters associated with the features extractor are optimized to minimize the error of the first processing module and maximize the error of the second processing module. (Fader page 4 optimize predicting the right attributes)
11. An image processing system or data processing apparatus comprising means, in particular a processor, for carrying out the steps of the method according to claim 15.
See the rejection of claim 15.
16. A method of processing brain functional magnetic resonance images of an individual using a machine learning algorithm through a neural network architecture in order to diagnose a behavioral, neurodevelopmental, or neurodegenerative disorder, wherein the method comprises:
training a machine learning algorithm using an adversarial learning, wherein in the training of the learning algorithm a desired output variable (that is useful to diagnose a behavioral, neurodevelopmental, or neurodegenerative disorder) is set and the scan data of the three-dimensional brain magnetic resonance video are reprocessed based on said desired output variable; (Fader page 2-4 describes fMRI images using an adversarial learning method, reduces confounding variables from the image)
obtaining scan data of a three-dimensional brain functional magnetic resonance video wherein the information is a spatio-temporal data; (Fader page 1 fMRI scans of the brain)
obtaining data of the optical flow of said three-dimensional video; (Fader page 1 ABIDE II data is data of brain scans)
simultaneously applying to the scan data and to the optical flow data the trained machine learning algorithm; and (Fader page 5 applies the ML algorithm described in the paper to the ABIDE II data)
obtaining output information on brain based on the application of the machine learning algorithm to the scan data and the optical flow data; and (Fader page 6 obtains result information)
diagnosing one of a behavioral disorder, a neurodevelopmental disorder, or neurodegenerative disorder based on the output information. (Fader page 6 diagnoses autism)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
2021/0145404, disclosing the use of fMRI machine learning to predict issues with a heart.
Training confounder-free deep learning models for medical applications, Qingyu Zhao, Nature Communications , 2020.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ming Shui whose telephone number is (303)297-4247. The examiner can normally be reached on 7-5 Pacific Time, M-Th.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Greg Morse can be reached on 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Ming Shui/
Primary Examiner, Art Unit 2663
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