DETAILED ACTION
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 § 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 1-20 are 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.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claims 1, 11, and 16 recite the broad recitation “prediction indicates one of a patient-level outcome, plaque progression, plaque regression, or stationary plaque”, and dependent claim 2 recites “prediction indicates one of the plaque progression or the plaque regression”, which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea
without significantly more.
Step 1
Claims 1-20 are within the four statutory categories. However, as will be shown below, claims 1-20 are
nonetheless unpatentable under 35 U.S.C. 101.
Claims 1, 11, and 16 are representative of the inventive concept and recite:
Claim 1
A machine-readable medium having machine executable instructions that cause a processor core to execute operations, the operations comprising:
accessing a set of segmented cross-sectional image frames of a blood vessel of a patient;
defining a set of regions of interest (ROls) of the set of segmented cross- sectional image frames;
determining a set of features for a ROI of the set of ROls;
providing the set of features to a trained outcome prediction algorithm;
and receiving a prediction for the ROI from the trained outcome prediction algorithm, wherein the prediction indicates one of a patient-level outcome, plaque progression, plaque regression, or stationary plaque.
*Claims 11 and 16 recite similar limitations as claim 1, but for a system and method, respectively.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of
defining and determining) or using pen and paper. Other than reciting generic computer
components/functions such as “machine-readable medium having machine executable instructions”, “system”, “processor core”, and “trained algorithm”, nothing in the claims preclude the highlighted
portions from practically being performed in the mind. If a claim limitation, under its broadest
reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of
generic computer components/functions, then it falls within “Mental Processes” grouping of abstract
ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation
out of the mental process grouping and thus, the claim recites a mental process. The recitation of
generic computer components/functions such as providing and receiving also covers behavioral or
interactions between people, and/or managing personal behavior or relationships or interactions
between people (i.e. social activities, teaching, and following rules or instructions), hence the claim falls
under “Certain Methods of Organizing Human Activity”. The types of identified abstract ideas are
considered together as a single abstract idea for analysis purposes.
Dependent claims 2-10, 12-15, and 17-20 recite additional subject matter which further narrows or defines the abstract idea embodied in the claim.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the claims
recite the following additional limitations:
Claim 1 recites: “machine-readable medium having machine executable instructions”, “system”, “processor core”, and “trained algorithm”.
In particular, the additional elements do not integrate the abstract idea into a practical application,
other than the abstract idea per se, because the additional elements amount to no more than
limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations are recited as being performed by an “system”, “processor core”, and “trained algorithm” and amounts to no more than mere instructions to apply the exception using a generic computer. The machine learning models are used to generally apply the abstract idea without limiting how it functions.
Dependent claims 3, 12, and 17 recite: “classification algorithm” and “regression algorithm”
Dependent claim 4 recites: “a gradient-boosted decision tree algorithm, a Nearest Neighbors algorithm, a Support Vector Machine, a Gaussian Process, a Fully Connected Neural Network, a Gaussian Naive Bayes algorithm, a Quadratic Discriminant Analysis algorithm, a Gaussian Mixture Model, or a Linear Regression algorithm”
Dependent claims 9, 15, and 20 recite signal
In particular, the additional elements do no integrate the abstract idea into a practical application, other
than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations
are recited as being performed by “classification algorithm”, “regression algorithm”, “a gradient-boosted decision tree algorithm, a Nearest Neighbors algorithm, a Support Vector Machine, a Gaussian Process, a Fully Connected Neural Network, a Gaussian Naive Bayes algorithm, a Quadratic Discriminant Analysis algorithm, a Gaussian Mixture Model, and a Linear Regression algorithm”. The models/algorithms are used to generally apply the abstract idea without limiting how it functions.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of “signal”.
Dependent claims 2, 5-8, 10, 13-14, and 18-19 do not include any additional elements beyond those already recited in independent claims 1, 11, and 16, and dependent claims 3-4, 9, 12, 15, 17, and 20, hence do not integrate the aforementioned abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1, 11, and 16 do not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to discussion of
integration of the abstract idea into a practical application, the additional elements: A method in claim
1; amount to no more than mere instructions to apply an exception to the abstract idea. Additionally,
the additional limitations, other than the abstract idea per se, amount to no more than limitations which
amount to elements that have been recognized as well-understood, routine, and conventional activity in
particular fields as demonstrated by the recitation of:
Signal, which refers to a transmission used to carry information between devices (Para 0007, Cho(US 20090182208 A1) discloses: “Moreover, since the conventional physiological signal measuring sensor can measure only one type of physiological signal, more than one physiological signal measuring sensor is necessary to measure different types of physiological signals. “) in a manner that would be well-understood, routine, and conventional.
Dependent claims 2, 5-8, 10, 13-14, and 18-19 do not include any additional elements beyond those already recited in independent claims 1, 11, and 16, and dependent claims 3-4, 9, 12, 15, 17, and 20. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 11, and 16 hence do not amount to “significantly more” than the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claims 1-20 are rejected under 35 U.S.C. 103 is being unpatentable over Buckler (US20210390689A1)
In view of Choi (US20210153945A1).
Claim 1
Buckley discloses:
A machine-readable medium having machine executable instructions that cause a processor core to execute operations, the operations comprising: accessing a set of segmented cross-sectional image frames of a blood vessel of a patient(Figure 2, #121A, Buckley discloses the acquisition of patient images); defining a set of regions of interest (ROls) of the set of segmented cross- sectional image frames(Para 0070, Buckley discloses the formation of a region of interest), Buckler discloses; determining a set of features for a ROI of the set of ROIs(Para 0151, Figure2, #122, Buckler discloses determination of image features from a region-of-interest); providing the set of features to a trained outcome prediction algorithm(Figure 2, Buckley discloses providing imaging features to an analyzer module, consisting of algorithms or models which predicts outcome); and receiving a prediction for the ROI from the trained outcome prediction algorithm(Figure 2, #125 Buckley discloses a predicted outcome based on imaging features), wherein the prediction indicates one of a patient-level outcome(Para 0136, Buckley discloses the prediction of a clinical outcome),
Buckley does not explicitly disclose:
Plaque progression
Choi discloses:
plaque progression (Para 0029, Choi discloses prediction of plaque rupture)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system for quantitative imaging of plaques to add plaque progression, as taught by Choi. One of ordinary skill would have been so motivated to provide a means to determine the state of vascular plaque to determine the potential outcome and treatment of a patient, but in this case for predicting plaque vulnerability from an image(Para 0006, Choi discloses: “However, a need exists for systems and methods for predicting coronary plaque vulnerability from patient-specific anatomic image data.”).
Claim 2
Buckley does not explicitly disclose:
The machine-readable medium of claim 1, wherein the prediction indicates one of the plaque progression or the plaque regression.
Choi discloses:
The machine-readable medium of claim 1, wherein the prediction indicates one of the plaque progression(Para 0029, Choi discloses prediction of plaque rupture) or the plaque regression.
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system for quantitative imaging of plaques to add plaque progression, as taught by Choi. One of ordinary skill would have been so motivated to provide a means to determine the state of vascular plaque to determine the potential outcome and treatment of a patient, but in this case for predicting plaque vulnerability from an image(Para 0006, Choi discloses: “However, a need exists for systems and methods for predicting coronary plaque vulnerability from patient-specific anatomic image data.”).
Claim 3
Buckley discloses:
The machine-readable medium of claim 1, wherein the trained outcome prediction algorithm is a machine learning algorithm, wherein the machine learning algorithm is one of a classification algorithm(Para 0156, Buckley discloses classification models) or a regression algorithm(Para 0156, Buckley discloses regression models).
Claim 4
Buckley discloses:
The machine-readable medium of claim 3, wherein the machine learning algorithm comprises one of a gradient-boosted decision tree algorithm, a Nearest Neighbors algorithm, a Support Vector Machine, a Gaussian Process(Para 0244, Buckley discloses a multidimensional Gaussian), a Fully Connected Neural Network, a Gaussian Naive Bayes algorithm, a Quadratic Discriminant Analysis algorithm, a Gaussian Mixture Model, or a Linear Regression algorithm.
Claim 5
Buckley discloses:
The machine-readable medium of claim 1, wherein the set of ROls are defined based on one of: a fixed length, a set of plaque burden values determined for the set of segmented cross-sectional image frames, a feature derived from the set of plaque burden values, a set of lumen measurements determined for the set of segmented cross-sectional image frames(Para 0168, Buckley discloses lumen measurements), a feature derived from the set of lumen measurements, a set of external elastic membrane (EEM) measurements determined for the set of segmented cross-sectional image frames, or a feature derived from the set of EEM measurements.
Claim 6
Buckley does not explicitly disclose:
The machine-readable medium of claim 1, wherein the set of features comprises a clinical feature associated with the patient or a feature derived from the clinical feature
Choi discloses:
The machine-readable medium of claim 1, wherein the set of features comprises a clinical feature associated with the patient or a feature derived from the clinical feature(Para 0070, Choi discloses lipid levels as clinical features).
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system for quantitative imaging of plaques to add clinical feature, as taught by Choi. One of ordinary skill would have been so motivated to provide a means to determine the state of vascular plaque from not only images but clinical data from the patient to determine the potential outcome and treatment of a patient, but in this case for predicting plaque vulnerability from an image(Para 0006, Choi discloses: “However, a need exists for systems and methods for predicting coronary plaque vulnerability from patient-specific anatomic image data.”).
Claim 7
Buckley discloses:
The machine-readable medium of claim 6, wherein the clinical feature is a measure of high-density lipoprotein(Para 0169, Buckley discloses lipoproteins).
Claim 8
Buckley discloses:
The machine-readable medium of claim 1, wherein the set of features comprises a condensed feature defined over the ROI(Para 0151, Buckley discloses multiple features can be calculated on a region-of-interest basis).
Claim 9
Buckley discloses:
The machine-readable medium of claim 8, wherein the condensed feature is one of a statistical measure of a frame-wise feature or a signal processing measure of a frame-wise feature(Para 0296, Buckley discloses intensity features).
Claim 10
Buckley discloses:
The machine-readable medium of claim 9, wherein the frame-wise feature is one of an area enclosed by a lumen contour(Para 0330, Figure 24, Buckley discloses lumen contour) or a plaque burden.
Claims 11 and 16
Claim 11 and 16 recite similar limitations as claim 1. See claim 1 analysis.
Claims 12 and 17
Claim 12 and 17 recite similar limitations as claim 3. See claim 3 analysis.
Claims 13 and 18
Claim 13 and 18 recite similar limitations as claim 5. See claim 5 analysis.
Claims 14 and 19
Claim 14 and 19 recite similar limitations as claim 7. See claim 7 analysis.
Claims 15 and 20
Claim 15 and 20 recite similar limitations as claims 9 and 10. See claim 9 and 10 analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Itu(US12109061B2) discloses data-driven plaque determination in medical imaging.
Aoyama(US11694330B2) discloses a medical image processing apparatus to extract disease status.
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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