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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a generative artificial intelligence (“AI”) module” in claims 1 and 16.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over US 12,002,582 B2 to Muehlberg et al (hereinafter ‘Muehlberg’) in view of US 12229920 B2 to Liang et al (hereinafter ‘Liang’).
Regarding claim 16, Muehlberg discloses a method of estimating a saliency probability map of future brain trajectory, including brain aging trajectory and disease biomarker trajectory, of a patient , (column 3, lines 9-18, wherein a computer-implemented method providing a clinical information, comprising receiving input data, wherein the input data comprises a graph representation of a plurality of disease lesions of a patient, applying a trained function to the input data to generate the clinical information, wherein the trained function is based on a graph machine learning model, and providing the clinical information, wherein the clinical information comprises at least one information for the prediction of the disease progression, the survival, or therapy response of the patient, wherein since the limitation of the brain aging is not further referred to in the claim body, it is treated as intended use of the claim and is not given a patentable weight) the method comprising: pre-processing a magnetic resonance image (MRI) of the patient to reconstruct MRI (column 29, lines 56-61, wherein the medical imaging system comprises devices for conducting a medical imaging examination like CT, MRI, PET, SPECT and/or sonography examination) into a generative artificial intelligence (“AI”) module, the generative AI module configured to compute a training objective for a generative artificial intelligence (“AI”) model based on a training data set of brain data (column 12, lines 36-45, wherein a trained function can comprise a neural network, a support vector machine, . . .Furthermore, a neural network can be an adversarial network, a deep adversarial network and/or a generative adversarial network); receiving, via the generative AI module, an estimated saliency map of the patient for future brain trajectory or future brain disease biomarkers (column 26, lines 38-48, wherein after executing the graph machine learning model, the step of providing the results 111 is executed. The results are the clinical information. The clinical information can comprise a classification information for the prediction of the disease progression, the survival and/or the therapy response. Additionally, it can comprise a feature importance information, especially a saliency map, showing which of the provided features of the plurality of disease lesions contributes most to the provided clinical information. Furthermore, the clinical information can comprise information about potential future disease lesions.); and updating, via the generative AI module, the generative AI model based on the training objective (column 12, lines 32-35, wherein representation learning (an alternative term is “feature learning”) can be used. In particular, the parameters of the trained functions can be adapted iteratively by several steps of training, inherently as updating). Muehlberg does not specifically disclose pre-processing a magnetic resonance image (MRI) of the patient to reconstruct and segment the MRI to form a T1-weighted, T2-weighted, diffusion-weighted, fMRI-weighted, or FLAIR-weighted MRI; inputting the T1-weighted, T2-weighted, diffusion-weighted, fMRI-weighted or FLAIR-weighted MRI into a generative artificial intelligence (“AI”) module, the generative AI module configured to compute a training objective for a generative artificial intelligence (“AI”) model based on a training data set of a plurality of cognitively normal brain data sets, each cognitively normal brain data set in the plurality of cognitively normal brain data sets including cognitively normal multi-dimensional brain imaging data corresponding to a cognitively normal brain of a cognitively normal participant; and specifically updating, via the generative AI module, the generative AI model based on the training objective. Liang discloses wherein in response to the inputting the T1-weighted, T2-weighted, diffusion-weighted, fMRI-weighted, or FLAIR-weighted MRI into the generative AI module, the generative AI module determines a saliency map of future brain trajectory or future brain disease biomarkers based on the T1-weighted, T2-weighted, diffusion-weighted, fMRI-weighted, or FLAIR-weighted MRI (column 13, lines 7-15, wherein there are 25 patients with synthetic HG and LG images and 20 patients with real HG and 10 patients with real LG images. For each patient, FLAIR, T1, T2, and post-Gadolinium T1 magnetic resonance (MR) image sequences are available, and to ease the analysis, the input features were kept consistent by using only one MR imaging sequence (FLAIR) for all patients in both HG and LG categories, resulting in a total of 9,050 synthetic MR slices and 5,633 real MR slices.), a training data set of a plurality of cognitively normal brain data sets, each cognitively normal brain data set in the plurality of cognitively normal brain data sets including cognitively normal multi-dimensional brain imaging data corresponding to a cognitively normal brain of a cognitively normal participant (column 19, lines 37-44, wherein the anomalies are identified by feeding a diseased image to the trained autoencoder followed by subtracting the reconstructed diseased image from the input image. Another less preferred approach is to learn a generative model of healthy training data through a GAN, as the normal brain data), and updating, via the generative AI module, the generative AI model based on the training objective (column 21, line 64 through column 22, line 2, wherein at block 1030, processing logic iteratively repeats the performing of the cross-domain translation learning operations and the same-domain translation learning operation for multiple input images and continuously updating the weights of the generator of the GAN with the computed losses until a training threshold is attained). Muehlberg and Liang are combinable because they both disclose disease through image processing. Therefore, before the effective filing data of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the using of only one MR imaging sequence (FLAIR) in order to ease the analysis of the input features (column 13, line 12).
Regarding claim 17, in the combination of Muehlberg and Liang, Muehlberg discloses wherein in response to the inputting the T1-weighted, T2-weighted, diffusion-weighted, fMRI-weighted, or FLAIR-weighted MRI into the generative AI module, the generative AI module determines brain trajectory or brain disease biomarkers based on the T1-weighted, T2-weighted, diffusion-weighted, fMRI-weighted, or FLAIR-weighted MRI (column 13, lines 9-15, wherein For each patient, FLAIR, T1, T2, and post-Gadolinium T1 magnetic resonance (MR) image sequences are available. To ease the analysis, the input features were kept consistent by using only one MR imaging sequence (FLAIR) for all patients in both HG and LG categories, resulting in a total of 9,050 synthetic MR slices and 5,633 real MR slices), and Liang discloses a saliency map of future brain trajectory or future brain disease (column 26, lines 38-48, wherein the clinical information can comprise a classification information for the prediction of the disease progression, the survival and/or the therapy response. Additionally, it can comprise a feature importance information, especially a saliency map, showing which of the provided features of the plurality of disease lesions contributes most to the provided clinical information. Furthermore, the clinical information can comprise information about potential future disease lesions.)
Allowable Subject Matter
Claims 1-15 and 19-20 are allowed. The following is a statement of reasons for the indication of allowable subject matter: the prior art or the prior art of record specifically, Muehlberg, Liang and US 11,282,198 B2 to Lyman et al, does not disclose:
. . . . determining, via the generative AI module, a saliency probability map of future brain trajectory or future brain disease biomarkers for each of the plurality of subjects based on the saliency map of future brain trajectory or future brain disease biomarkers and the generative AI model; of claims 1 and 19 combined with other features and elements of the claims;
Claims 2-15 and 20 depend from an allowable base claim and are thus allowable themselves.
Claim 18 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter: the prior art or the prior art of record specifically, Muehlberg, Liang and US 11,282,198 B2 to Lyman et al, does not disclose:
. . . .wherein determining the saliency map of future brain trajectory or future brain disease biomarkers includes calculating an average saliency probability map of future brain trajectory or future brain disease biomarkers based on a sex of the patient, of claim 18 combined with other features and elements of the claim.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHERVIN K NAKHJAVAN whose telephone number is (571)272-5731. The examiner can normally be reached Monday-Friday 9:00-12:00 PST.
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 encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sue Lefkowitz can be reached at (571)272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SHERVIN K NAKHJAVAN/ Primary Examiner, Art Unit 2672
Prosecution Insights