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
Claims 1-18 are not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they are all method claims.
Claim 19 is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the recitations of “memory”, “processor” and “instructions” provide sufficient structure to perform all claimed limitations.
Claim 20 is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it is an article of manufacture claim.
Claim Rejections - 35 USC § 102
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 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) 1-4, 10-11, 13 and 18-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shi et al. (U.S. Pat. App. Pub. No. 2020/0184660 A1, referred as Shi hereinafter).
Regarding claim 1 as a representative claim, She teaches a computer-implemented method (see figure 1), comprising:
receiving first imaging data of a subject, the first imaging data acquired using a first imaging modality (see 100 of figure 1 and para. [0039] ((a first image is acquired. The first image is generated by a first medical image modality));
applying the first imaging data to a neural network trained to output pseudo imaging data, the pseudo imaging data associated with a second imaging modality (see figure 2: output of 202 and/or 206 (in view of BRI and spec. para [0054], output of 202 and/or 206 corresponds to the so-called pseudo image); figure 2 is a representation of the neural network per para. [0045]);
receiving second imaging data of the subject, the second imaging data acquired using the second imaging modality (see 102 of figure 1 and para. [0041] (a second image is acquired. The second image is generated by a second medical image modality));
receiving transformation information based at least in part on the pseudo imaging data and the received second imaging data (see 104 of figure 1 (104 includes machine-learned generator that is illustrated in figure 2 per paras [0044]-[0045]; as can be seen from figure 2, such generator comprises generators 202 and 206; generator 202 receives transformation information Zx (appearance code) and Zy (shape code) from 200; generator 206 receives transformation information Zx (appearance code) and Zy (shape code) from 204; the Zy is from the second image data y); and para. [0066]); and
registering first modality imaging data with second modality imaging data by applying the transformation information (see 106 of figure 1; para. [0065]).
Regarding claim 2, Shi further teaches wherein the first modality imaging data is the first imaging data and the second modality imaging data is the second imaging data (see 100 of figure 1 and para. [0039] (a first image is acquired. The first image is generated by a first medical image modality); and 102 of figure 1 and para. [0041] (a second image is acquired. The second image is generated by a second medical image modality)).
Regarding claim 3, Shi further teaches wherein the first imaging modality is positron emission tomography (PET) and the second imaging modality is computed tomography (CT) (see paras. [0003], [0007], [0019], [0039] and [0041] (PET modality and CT modality)).
Regarding claim 4, Shi further teaches wherein the second imaging data is non-contrast computed tomography attenuation correction imaging data (102 of figure 1 and para. [0041] (a second image is acquired. The second image is generated by a second medical image modality; CT image generated by using CT modality is a non-contrast image)).
Regarding claim 10, Shi further teaches wherein receiving the transformation information includes generating the transformation information by applying a diffeomorphic registration algorithm to the pseudo imaging data and the second imaging data (see para. [0034]: diffeomorphic registration network).
Regarding claim 11, Shi further teaches wherein the neural network includes a generator neural network of a generative adversarial network (GAN), the generator neural network trained to receive first training data associated with the first imaging modality as input and output generated imaging data associated with the second imaging modality (see 104 of figure 1 (104 includes machine-learned generator that is illustrated in figure 2 per paras [0044]-[0045]; as can be seen from figure 2, such generator comprises generators 202 and 206; generator 202 receives transformation information Zx (appearance code) and Zy (shape code) from 200; generator 206 receives transformation information Zx (appearance code) and Zy (shape code) from 204; the Zy is from the second image data y); and para. [0072] (generative adversarial network GAN).
Regarding claim 13, Shi further teaches wherein applying the first imaging data to the neural network to output the pseudo imaging data includes individually applying image slices of the first imaging data to the neural network to output corresponding pseudo imaging slices of the pseudo imaging data (see analysis applied to claim 1 above; with regard to “slices”, see para. [0040] (first image is a 2D slice)).
Regarding claim 18, Shi further teaches identifying one or more regions of interest of the subject based at least in part on the second modality imaging data (see para. [0042] (second image represents region of interest of a portion of liver or other region of interest)); and generating a quantification measurement based at least in part on the first modality imaging data and the identified one or more regions (see paras. [0040] & [0042] (2D slice from 3D volume with size 128*128*128 and resolution of 2.5mm)).
Regarding claim 19, the advanced statements as applied to claim 1 above are incorporated hereinafter. Shi further teaches processors and medium containing instructions (see para. [0094] (computer, CPU, GPU and instructions stored media device; para. [0093] (instructions stored on media or memories)).
Regarding claim 20, the advanced statements as applied to claims 1 and 19 are incorporated herein. Shi further teaches a computer program product tangibly embodied in a non-transitory machine-readable storage medium ((see para. [0094] (instructions stored media device; para. [0093] (instructions stored on media or memories; software, firmware, micro code))
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.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi in view of Ahuja et al. (“18F-Sodium Fluoride PET: History, Technical Feasibility, Mechanism of Action, Normal Biodistribution, and Diagnostic Performance in Bone Metastasis Detection Compared with Other Imaging Modalities”, Journal of Nuclear Medicine Technology (JNMT) 2020, 48: 9-16, referred as Ahuja hereinafter).
Regarding claim 5, Shi does not teach claim limitation “wherein the first imaging data is 18F-Na-F positron emission tomography imaging data”.
However, such claim limitations is well known in the art as evidenced by Ahuja.
Ahuja, in the same field of endeavor that of image processing, teaches such claim limitation (see page 9 left column, first full paragraph; page 11, right column, COMPARISON section, first full paragraph).
The motivation for doing so is to obtain better image quality thereby improving object detection as suggest by Ahuji (see page 11, right column, COMPARISON section, first full paragraph).
Therefore, before the effective filing of the claim invention, it would have been obvious to one of ordinary skill in the art to incorporated such claim limitation as taught by Ahuji in combination with Shi for that reasons.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi and Ahuji as applied to claim 5 above, and further in view of Bebbington et al. (“Lesion detection in 18F-sodium fluoride bone imaging: a comparison of attenuation-corrected versus nonattenuation-corrected PET reconstructions from modern PET-CT systems”, Nuclear Medicine Communications, 2022, 43(1), one page, referred as Bebbington hereinafter).
Regarding claim 6, the combination of Shi and Ahuji do not teach claim limitation “wherein the 18F-Na-F positron emission tomography imaging data is non-attenuation-corrected 18F-Na-F positron emission tomography imaging data”.
However, such claim limitations is well known in the art as evidenced by Bebbington.
Bebbington, in the same field of endeavor that of image processing, teaches such claim limitation (see page 1, Objectives: nonattenuation-corrected 18F-NaF images).
The motivation for doing so is to enhance diagnostic accuracy as suggested by Bebbington (see page 1, Results).
Therefore, before the effective filing of the claim invention, it would have been obvious to one of ordinary skill in the art to incorporated such claim limitation as taught by Bebbington in combination with the combination of Shi and Ahuji for that reasons.
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi in view of Bebbington.
Regarding claim 7, Shi does not teach claim limitation “wherein the first modality imaging data is attenuation corrected 18F-Na-F positron emission tomography imaging data”.
However, such claim limitations is well known in the art as evidenced by Bebbington.
Bebbington, in the same field of endeavor that of image processing, teaches such claim limitation (see page 1, Objectives: attenuation-corrected 18F-NaF images).
The motivation for doing so is to enhance lesion detection as suggested by Bebbington (see page 1, Conclusion).
Therefore, before the effective filing of the claim invention, it would have been obvious to one of ordinary skill in the art to incorporated such claim limitation as taught by Bebbington in combination with Shi for that reasons.
Regarding claim 8, Shi further teaches wherein the second modality imaging data is computed tomography angiography imaging data (see para. [0019]: angiogram device).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi in view of Ubelhart et al. (U.S. Pat. App. Pub. No. 2008/0118132 A1, referred as Ubelhart hereinafter).
Regarding claim 9, Shi does not teach claim limitation “wherein the subject includes coronary tissue”.
However, such claim limitations is well known in the art as evidenced by Ubelhart.
Bebbington, in the same field of endeavor that of image processing, teaches such claim limitation (see para. [0001] (heart scans), [0033] (coronal image, heart image)).
The motivation for doing so is to allow other region to be scanned during the same scanning session so that different condition and disease could be evaluated as suggested by Ubelhart (see paras. [0001] – [0002]).
Therefore, before the effective filing of the claim invention, it would have been obvious to one of ordinary skill in the art to incorporated such claim limitation as taught by Ubelhart in combination with Shi for that reasons.
Claim(s) 9 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi in view of Taerum et al. (U.S. Pat. App. Pub. No. 2020/0085382 A1, referred as Taerum hereinafter).
Regarding claim 9, Shi does not teach claim limitation “wherein the subject includes coronary tissue”.
However, such claim limitations is well known in the art as evidenced by Taerum.
Taerum, in the same field of endeavor that of image processing, teaches such claim limitation (see paras. [0334] & [0337] (heart and ventricles).
The motivation for doing so is to allow other region (heart ,ventricles) to be scanned in order to determine its functions as suggested by Taerum (see para. [0334]).
Therefore, before the effective filing of the claim invention, it would have been obvious to one of ordinary skill in the art to incorporated such claim limitation as taught by Taerum in combination with Shi for that reasons.
Regarding claim 12, Shi does not teach claim limitations “wherein the GAN is a conditional GAN having at least one condition, wherein the at least one condition is a slice label associated with the training data, wherein each image slice of the training data is associated with a respective slice label”.
However, such claim limitations is well known in the art as evidenced by Taerum.
Taerum, in the same field of endeavor that of image processing, teaches such claim limitations (see paras. [0296] & [0300] (training data and labels for the image are stored in the training database for CNN/generative adversarial network); para. [0121] (image and label metadata including slice index are stored in database; the “pair” refers to the so-called “associated”)).
The motivation for doing so is to improve speed, require less time and reduce cost as suggested by Taerum (see paras. [0115] – [0118]).
Therefore, before the effective filing of the claim invention, it would have been obvious to one of ordinary skill in the art to incorporated such claim limitation as taught by Taerum in combination with Shi for that reasons.
Allowable Subject Matter
Claims 14-17 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:
Regarding claim 14, the cited prior art does not teach or suggest claim limitations “receiving third imaging data of the subject, the third imaging data acquired using the second imaging modality; and generating additional transformation information based at least in part on a comparison of the second imaging data and the third imaging data wherein registering the first modality imaging data with the second modality imaging data further includes applying the additional transformation information, wherein the second modality imaging data is the third imaging data”.
Claims 15-17 are also allowable as being dependent upon claim 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Morgas et al. (U.S. Pat. App. Pub. No. 2021/0304402 A1) teaches obtaining training data by using a plurality of modalities (para. [0072] (i.e., PET and CT modalities)), neural network (para. [0071]), and generating pseudo image (317 of figures 10A-10D).
Nathan (U.S. Pat. App. Pub. No. 2021/0304402 A1) teaches a first and second modalities for obtaining medical images (abstract; para. [0014] (PET modality and CT modality)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUY M DANG whose telephone number is (571)272-7389. The examiner can normally be reached Monday to Friday from 7:00AM to 3:00PM. 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.
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DMD
1/2026
/DUY M DANG/Primary Examiner, Art Unit 2662