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 § 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.
Claim(s) 1-5, 10, 12-15, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sharma et al. (US 2019/0354882).
(1) regarding claims 1 and 10:
Sharma ‘882 discloses a system for obtaining quality image data and measurements from a medical imaging device (paragraph [0013]), comprising:
medical scanner configured to obtain image of a patient (medical scanner 30 in Fig. 3 and paragraph [0059]), and
an artificial intelligence (AI) targeting (paragraph [0012], where the artificial intelligence is targeting medical imaging) and image optimization system (paragraph [0012]-[0013], self-optimizing medical scanner) configured to:
receive (paragraph [0017], where a medical scanner images a patient) and analyze the image with any combination of model-based and deep learning Al methods to find one or more target areas of abnormalities (paragraphs [0024], [0028], [0038], where images area analyzed using different models and deep learning AI algorithms to find abnormalities in patients);
analyze one or more target areas of abnormality of the image to determine a set of targeted scan parameters for the medical scanner for visualizing (paragraph [0037]-[--38], and [0055], where target areas are analyzed and a set of parameters are acquired to be used by the medical scanner) and automatically quantitatively measuring the abnormality (paragraph [0057] and [0074], where the abnormality is measured and presented to the user as a way of diagnosis), and
provide information including a target image acquisition data to a user to perform an additional targeted image acquisition or image reconstruction (paragraph [0074]-[0077], where a result is provided to a user and the algorithm is improved by using previous images as training images, using the improved algorithm the future uses are going to output improved results by way of using previously trained images by the same system).
(2) regarding claims 2 and 12:
Sharma ‘882 further discloses wherein the AI targeting and image optimization system is configured to calculate automated measurement of the abnormality with the target image acquisition data (paragraph [0057] and [0074], where the abnormality is measured and presented to the user as a way of diagnosis).
(3) regarding claims 3 and 13:
Sharma ‘882 further discloses wherein the medical scanner is at least one of Computed Tomography (CT), Magnetic Resonance (MR), Positron Emission Tomography (PET), X-ray Radiography (XR), and Ultrasound (US) scanner (paragraph [0017]).
(4) regarding claims 4 and 14:
Sharma ‘882 further discloses wherein the target image acquisition data includes the set of targeted scan parameters, wherein the targeted scan parameters are determined by any combination of a calibration optimized protocol database, model-based analysis methods, deep learning AI analysis methods, simulation methods, and prior clinical guidance information (paragraph [0028], [0031], and [0041], where the parameters are determined using at least a prior clinical information and deep learning AI methods).
(5) regarding claims 5 and 15:
Sharma ‘882 further discloses wherein the target image acquisition data includes a set of targeted scan parameters and automated measurement algorithms, wherein the targeted scan parameters and automated measurement algorithms are determined by any combination of a calibration optimized protocol database, model-based analysis methods, deep learning Al analysis methods, simulation methods, and prior clinical guidance information (paragraph [0028], [0031], and [0041], where the parameters are determined using at least a prior clinical information and deep learning AI methods).
(6) regarding claim 20:
Sharma ‘882 further discloses a method for obtaining quality image data and measurements from a medical imaging device (paragraph [0013]), comprising the steps of:
operating a medical scanner to obtain an image of a subject (medical scanner 30 in Fig. 3 and paragraph [0059]);
analyzing the image with any combination of models and AI methods to find one or more target areas of abnormalities (paragraphs [0024], [0028], [0038], where images area analyzed using different models and deep learning AI algorithms to find abnormalities in patients);
analyzing one or more target areas of abnormality of the image to determine a set of targeted scan parameters for the medical scanner for visualizing and automatically quantitatively measuring the abnormality (paragraph [0037]-[--38], and [0055], where target areas are analyzed and a set of parameters are acquired to be used by the medical scanner);
providing information including target image acquisition data to a user to perform an additional targeted image acquisition or image reconstruction (paragraph [0074]-[0077], where a result is provided to a user and the algorithm is improved by using previous images as training images, using the improved algorithm the future uses are going to output improved results by way of using previously trained images by the same system), wherein the target image acquisition data includes the set of targeted scan parameters, and calculating automated measurement of the abnormality using the target image acquisition data (paragraph [0057] and [0074], where the abnormality is measured and presented to the user as a way of diagnosis).
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.
Claim(s) 6-8, 11, 16-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al. (US 2019/0354882) in view of Avila (US 2018/0096477).
(1) regarding claim 11:
Sharma ‘882 discloses all the subject matter as described above except wherein the medical scanner is monitored and optimized using an automated calibration phantom monitoring and optimization system.
However, Avila ‘477 teaches wherein the medical scanner is monitored and optimized using an automated calibration phantom monitoring and optimization system (paragraph [0039]).
Having a system of Avila ‘477 reference and then given the well-established teaching of Sharma ‘882 reference, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sharma ‘882 to include the limitations as taught by Avila ‘477 because main advantage of this approach is that it translates image quality performance data into the clinical performance data that radiologists can understand and readily interpret (paragraph [0051]).
(2) regarding claims 6 and 16:
Sharma ‘882 discloses all the subject matter as described above except wherein the automated measurement algorithm uses an image formation simulation engine to estimate automated measurement properties including measurement bias and measurement precision.
However, Avila ‘477 teaches wherein the automated measurement algorithm uses an image formation simulation engine to estimate automated measurement properties including measurement bias and measurement precision (paragraph [0040]).
Having a system of Avila ‘477 reference and then given the well-established teaching of Sharma ‘882 reference, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sharma ‘882 to include the limitations as taught by Avila ‘477 because main advantage of this approach is that it translates image quality performance data into the clinical performance data that radiologists can understand and readily interpret (paragraph [0051]).
(3) regarding claim 7
Sharma ‘882 discloses all the subject matter as described above except wherein the analysis of the target areas for assessing the severity of the abnormality is performed based on fundamental image quality characteristics of the image to determine the targeted acquisition data.
However, Avila ‘477 teaches wherein the analysis of the target areas for assessing the severity of the abnormality is performed based on fundamental image quality characteristics of the image to determine the targeted acquisition data (paragraph [0039]-[0041]).
Having a system of Avila ‘477 reference and then given the well-established teaching of Sharma ‘882 reference, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sharma ‘882 to include the limitations as taught by Avila ‘477 because main advantage of this approach is that it translates image quality performance data into the clinical performance data that radiologists can understand and readily interpret (paragraph [0051]).
(4) regarding claim 8:
Sharma ‘882 discloses all the subject matter as described above except wherein the image quality characteristics includes resolution, noise and sampling rate.
However, Avila ‘477 teaches wherein the image quality characteristics includes resolution, noise and sampling rate (paragraph [0041]).
Having a system of Avila ‘477 reference and then given the well-established teaching of Sharma ‘882 reference, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sharma ‘882 to include the limitations as taught by Avila ‘477 because main advantage of this approach is that it translates image quality performance data into the clinical performance data that radiologists can understand and readily interpret (paragraph [0051]).
(5) regarding claim 17:
Sharma ‘882 discloses all the subject matter as described above except wherein the analysis of the target areas for assessing the severity of the abnormality is performed based on fundamental image quality characteristics of the image and determine the targeted acquisition data, wherein the image quality characteristics includes resolution, noise and sampling rate.
However, Avila ‘477 teaches wherein the analysis of the target areas for assessing the severity of the abnormality is performed based on fundamental image quality characteristics of the image (paragraph [0039]-[0041]) and determine the targeted acquisition data, wherein the image quality characteristics includes resolution, noise and sampling rate (paragraph [0041]).
Having a system of Avila ‘477 reference and then given the well-established teaching of Sharma ‘882 reference, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sharma ‘882 to include the limitations as taught by Avila ‘477 because main advantage of this approach is that it translates image quality performance data into the clinical performance data that radiologists can understand and readily interpret (paragraph [0051]).
(6) regarding claim 19:
Sharma ‘882 discloses all the subject matter as described above except wherein the medical scanner is monitored and optimized using an automated calibration phantom monitoring and optimization system.
However, Avila ‘477 teaches wherein the medical scanner is monitored and optimized using an automated calibration phantom monitoring and optimization system (paragraph [0039]).
Having a system of Avila ‘477 reference and then given the well-established teaching of Sharma ‘882 reference, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sharma ‘882 to include the limitations as taught by Avila ‘477 because main advantage of this approach is that it translates image quality performance data into the clinical performance data that radiologists can understand and readily interpret (paragraph [0051]).
Allowable Subject Matter
Claims 9, 18, and 19 are 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LENNIN R RODRIGUEZ whose telephone number is (571)270-1678. The examiner can normally be reached Monday-Thursday 9:00am-7:00pm.
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/LENNIN R RODRIGUEZGONZALEZ/Primary Examiner, Art Unit 2682