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 Objections
Claim 15 is objected to because of the following informalities:
Regarding claim 15, the limitations “an X-ray source”, “an X-ray detector”, and “a C-arm” in lines 5-6 should be changed to “the X-ray source”, “the X-ray detector”, and “the C-arm” in order to correct the antecedence.
Appropriate correction is required.
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.
Claim(s) 1-3, 9-12, and 14-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kruecker (U.S. 2023/0020252).
Regarding claim 1:
Kruecker discloses a computer-implemented method for automatically determining an operation mode for an X-ray imaging system that comprises an X-ray source and an X-ray detector mounted on a C-arm, the computer-implemented method comprising:
receiving angulation data defining an angulation state of the C-arm ([0015], imaging geometry received);
receiving at least one X-ray image depicting an object according to the angulation state ([0015], image received at current imaging geometry); and
selecting the operation mode for the X-ray imaging system as one of two or more predefined operation modes ([0016], Machine learning computes data representing imaging geometry change), the selecting comprising applying a trained machine learning model for classification to input data modes ([0016], Machine learning computes data representing imaging geometry change for next imaging; [0122], classificaiton), the input data comprising the at least one X-ray image and the angulation data ([0018], input data includes image and imaging geometry).
Regarding claim 2:
Kruecker discloses the computer-implemented method of claim 1, further comprising:
generating an information message informing a user about the selected operation mode ([0099], user interface to show IG (imaging geometry) changes);
automatically configurating the X-ray imaging system according to the selected operation mode ([0016], Machine learning computes data representing imaging geometry change for next imaging); or
a combination thereof.
Regarding claim 3:
Kruecker discloses the computer-implemented method of claim 2, wherein automatically configuring the X-ray imaging system according to the selected operation mode comprises setting a value of an X-ray exposure time assigned to the selected operation mode, setting a value of a frame rate assigned to the selected operation mode, setting at least one image processing parameter assigned to the selected operation mode, activating or deactivating a function of the X-ray imaging system assigned to the selected operation mode ([0080], image geometry changes related to the C-arm), or any combination thereof.
Regarding claim 9:
Kruecker discloses the computer-implemented method of claim 1, wherein the angulation data comprises an angular rotation angle ([0080], C-arm geometry) and an orbital rotation angle of the C-arm ([0080], C-arm geometry).
Regarding claim 10:
Kruecker discloses the computer-implemented method of claim 1, wherein the two or more predefined operation modes comprise a first operation mode for imaging right coronary arteries, a second operation mode for imaging left coronary arteries, a third operation mode for imaging a left ventricle, or any combination thereof ([0112], heart).
Regarding claim 11:
Kruecker discloses a method for X-ray imaging using an X-ray imaging system that comprises an X- ray source and an X-ray detector mounted on a C-arm, the method comprising:
determining angulation data defining an angulation state of the C-arm ([0015], imaging geometry received);
configuring the X-ray imaging system according to at least one preliminary setting ([0015], imaging geometry received);
generating at least one X-ray image depicting an object according to the angulation state ([0015], image at imaging geometry) and according to the at least one preliminary setting using the X-ray source and the X-ray detector ([0015], image at imaging geometry);
automatically determining an operation mode for the X-ray imaging system, the automatically determining comprising selecting the operation mode for the X-ray imaging system as one of two or more predefined operation modes ([0016], Machine learning computes data representing imaging geometry change), the selecting comprising applying a trained machine learning model for classification to input data ([0016], Machine learning computes data representing imaging geometry change for next imaging), the input data comprising the at least one X-ray image and the angulation data ([0018], input data includes image and imaging geometry);
configuring the X-ray imaging system according to the selected operation mode for the X-ray imaging system ([0158], C-arm configuration); and
generating a further X-ray image according to the selected operation mode for the X-ray imaging system using the X-ray source and the X-ray detector ([0146], next X-ray image).
Regarding claim 12:
Kruecker discloses a computer-implemented training method for training a machine learning model for classification for use in a computer-implemented method for automatically determining an operation mode for an X-ray imaging system that comprises an X-ray source and an X-ray detector mounted on a C-arm, the computer-implemented training method comprising:
receiving angulation training data defining a training angulation state of the C- arm ([0015], imaging geometry received);
receiving at least one training image depicting an object according to the training angulation state ([0015], image received at current imaging geometry);
receiving a ground truth annotation for the angulation training data and the at least one training image ([0131], ground truth for images);
selecting a predicted operation mode for the X-ray imaging system as one of two or more predefined operation modes ([0016], Machine learning computes data representing imaging geometry change for next imaging), the selecting comprising applying the MLM to input training data that comprises the at least one X-ray training image and the training angulation data ([0016], Machine learning computes data representing imaging geometry change for next imaging);
and updating the MLM depending on the selected predicted operation mode and the ground truth annotation ([0131], training using ground truth).
Regarding claim 14:
Kruecker discloses a data processing apparatus comprising:
at least one computing unit ([0051], computerized element) configured to automatically determine an operation mode for an X-ray imaging system (Fig. 1) that comprises an X-ray source (Fig. 1, XS) and an X-ray detector (Fig. 1, D) mounted on a C-arm (Fig. 1, G), the at least one computing unit being configured to automatically determine the operation mode of the X-ray imaging system ([0016], Machine learning computes data representing imaging geometry change for next imaging) comprising the at least one computing unit being configured to:
receive angulation data defining an angulation state of the C-arm ([0015], imaging geometry received);
receive at least one X-ray image depicting an object according to the angulation state ([0015], image at imaging geometry received);; and
select the operation mode for the X-ray imaging system as one of two or more predefined operation modes ([0016], Machine learning computes data representing imaging geometry change for next imaging), the selection comprising application of a trained machine learning model for classification to input data ([0016], Machine learning computes data representing imaging geometry change for next imaging), the input data comprising the at least one X-ray image and the angulation data ([0016], Machine learning computes data representing imaging geometry change for next imaging);
at least one further computing unit configured to train the MLM, the at least one further computing unit being configured to train the MLM comprising the at least one further computing unit being configured to:
receive angulation training data defining a training angulation state of the C-arm ([0015], imaging geometry received);
receive at least one training image depicting an object according to the training angulation state ([0015], image at imaging geometry received);
receive a ground truth annotation for the angulation training data and the at least one training image ([0131], ground truth);
select a predicted operation mode for the X-ray imaging system as one of two or more predefined operation modes ([0016], Machine learning computes data representing imaging geometry change for next imaging), the selecting comprising applying the MLM to input training data that comprises the at least one X-ray training image and the training angulation data ([0016], Machine learning computes data representing imaging geometry change for next imaging); and
updating the MLM depending on the selected predicted operation mode and the ground truth annotation ([0131], training using ground truth).
Regarding claim 15:
Kruecker discloses an X-ray imaging system comprising:
an X-ray source (Fig. 1, XS); and
an X-ray detector (Fig. 1, D) mounted on a C-arm (Fig. 1, G); and
at least one computing unit configured to automatically determine an operation mode for an X-ray imaging system (Fig. 1) that comprises an X-ray source (Fig. 1, XS) and an X-ray detector (Fig. 1, D) mounted on a C-arm (Fig. 1, G), the at least one computing unit being configured to automatically determine the operation mode of the X-ray imaging system ([0016], Machine learning computes data representing imaging geometry change for next imaging) comprising the at least one computing unit being configured to:
receive angulation data defining an angulation state of the C-arm ([0015], imaging geometry received);
receive at least one X-ray image depicting an object according to the angulation state ([0015], image at imaging geometry received);; and
select the operation mode for the X-ray imaging system as one of two or more predefined operation modes ([0016], Machine learning computes data representing imaging geometry change for next imaging), the selection comprising application of a trained machine learning model for classification to input data ([0016], Machine learning computes data representing imaging geometry change for next imaging), the input data comprising the at least one X-ray image and the angulation data ([0016], Machine learning computes data representing imaging geometry change for next imaging).
Allowable Subject Matter
Claims 4-8, and 13 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.
The following is a statement of reasons for the indication of allowable subject matter:
The closest prior art is Kruecker (U.S. 2023/0020252).
Regarding claim 4:
Kruecker discloses the computer-implemented method of claim 1, further comprising:
generating image features, the generating of the image features comprising applying a first feature extraction module of the MLM to the at least one X-ray image ([0128], extraction of bio-characteristics).
However, Kruecker fails to disclose generating angulation features, the generating of the angulation features comprising applying a second feature extraction module of the MLM to the angulation data; wherein the operation mode for the X-ray imaging system is selected depending on the image features and the angulation features.
Since the prior art of record fails to teach the details above, nor is there any reason to modify or combine prior art elements absent of applicant’s disclosure, the claim is deemed patentable over the prior art of record, if rewritten in independent form to include all of the limitations of the base claim and any intervening claim. Claims 5-8 are allowable by virtue of their dependency.
Regarding claim 13:
Kruecker discloses The computer-implemented training method of claim 12, further comprising:
generating training image features, the generating of the training image features comprising applying a pre-trained first feature extraction module of the MLM to the at least one X-ray training image ([0128], extraction of bio-characteristics);
applying a pre-trained second feature extraction module of the MLM to training data ([0128], extraction);
However, Kruecker fails to disclose generating training angulation features, the generating of the training angulation features comprising applying a pre-trained second feature extraction module of the MLM to the angulation training data; generating training fused features, the generating of the training fused features comprising fusing the training image features with the training angulation features, wherein selecting the predicted operation mode for the X-ray imaging system comprises applying at least one fully connected neural network layer of the MLM to the training fused features; and updating network parameters of the at least one fully connected neural network layer depending on the predicted operation mode and the ground truth annotation.
Since the prior art of record fails to teach the details above, nor is there any reason to modify or combine prior art elements absent of applicant’s disclosure, the claim is deemed patentable over the prior art of record, if rewritten in independent form to include all of the limitations of the base claim and any intervening claim.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOORENA KEFAYATI whose telephone number is (469)295-9078. The examiner can normally be reached M to F, 7:30 am to 4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Makiya can be reached at 571-272-2273. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S.K./Examiner, Art Unit 2884
/DAVID J MAKIYA/Supervisory Patent Examiner, Art Unit 2884
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