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
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.
Claim(s) 1-10 are rejected under 35 U.S.C. 102(a) as being unpatentable by Ceballows Lentini (US
US 11,393,575)
[claim 1]
In regard to Claim 1, Ceballows Lentini discloses a computer-implemented method, the method comprising:
providing a machine learning model, wherein the machine learning model is configured to generate a synthetic image based on input data and parameters of the machine learning model (Figure 5b);
providing training data, wherein the training data comprises, for each examination object of a plurality of examination objects, (i) input data and (ii) target data, wherein the target data comprises a target image of an examination region of the examination object; for each target image (Figure 3b and described in Column 9 lines 40+):
determining target image features based on the target image (Column 13 Lines 1-15 describes the target image features on the target image);
training the machine learning model, wherein the training comprises, for each examination object of the plurality of examination objects:
inputting the input data into the machine learning model (Column 13 Lines 17-67);
receiving a synthetic image as an output of the machine learning model (Figure 5a and described in Column 13 Lines 17-67);
determining image features based on the synthetic image (Column 13 Lines 17 through Column 13 Lines 1-50);
and reducing deviations (i) between the synthetic image and the target image and (ii) between the image features of the synthetic image and the target image features by modifying parameters of the machine learning model (Column 14 Lines 40+ describe the reducing of the deviations between synthetic image and target image);
and outputting and/or storing the trained machine learning model and/or transferring the trained machine learning model to a separate computer and/or using the trained machine learning model to generate one or more synthetic medical images of one or more new examination objects (column 15 Lines 11-23 describes the output onto one or more data storage devices and various servers for output of data disclosed in Column 5 Lines 10+).
[claim 2]
In regard to Claim 2, Ceballows Lentini discloses a method of claim 1, further comprising:
receiving new input data (Figure 3 and Figure 5c);
inputting the new input data into the trained machine learning model (Figure 5c shows in the input of new data);
receiving a new synthetic image as output from the trained machine learning model (Figure 5c shows the and outputting and/or storing the new synthetic image and/or transmitting the new synthetic image to a separate computer system (Figures 5a-5c show the receiving of synthetic image and output image from the trained machine learning model).
[claim 3]
In regard to Claim 3, Ceballows Lentini discloses a method of claim 1, wherein the image features of the synthetic image and the target image features comprise one or more of the following features: color histogram, greyscale histogram, texture features, edge features, SIFT features, HOG features, convolutional neural network features, low-level features, high-level features, content features, and style features (Column 9 lines 21-67 describe various target image features that are used to provide synthetic image data).
[claim 4]
In regard to Claim 4, Ceballows Lentini discloses a method of claim 1, wherein the image features of the synthetic image and the target image features are determined using a pre-trained convolutional neural network (Column 9 Lines 39+ through Column1 0 Lines 1-30 describe the pre-trained machine learning system that determined images).
[claim 5]
In regard to Claim 5, Ceballows Lentini discloses a method of claim 1, wherein the image features of the synthetic image and the target image features are one or more feature maps generated by a pre-trained convolutional neural network (Column 9 Lines 39+ through Column1 0 Lines 1-30 describe the pre-trained machine learning system that determined images)..
[claim 6]
In regard to Claim 6, Ceballows Lentini discloses a method of claim 1, wherein each examination object is a living being, and the examination region is a part of the examination object (Column 6 Lines 29-31 dsecribes the living being being the examination object).
[claim 7]
In regard to Claim 7, Ceballows Lentini discloses a method of claim 1, wherein the examination region is or comprises a liver, kidney, heart, lung, brain, stomach, bladder, prostate, intestine, thyroid, eye, breast or a part of said parts or another part of the body of a mammal (Column 5 Lines 1-67 describes the use of medical images such as MRI, CT scans etc. It is well known these images are taken of the various regions of the body.
[claim 8]
In regard to Claim 8, Ceballows Lentini discloses a method of claim 1, wherein the target image is a measured medical image and the synthetic image is a synthetic medical image (Column6 Lines 29+).
[claim 9]
In regard to Claim 9, Ceballows Lentini discloses a method of claim 1, wherein the target image is a computer tomography image, an X-ray image, a magnetic resonance imaging image, a positron emission tomography image, a fluorescein angiography image, an optical coherence tomography image, a histological image, an ultrasound image, a fundus images or a microscopic image, and the synthetic image is a synthetic computer tomography image, a synthetic X- ray image, a synthetic magnetic resonance imaging image, a synthetic positron emission tomography image, a synthetic fluorescein angiography image, a synthetic optical coherence tomography image, a synthetic histological image, a synthetic ultrasound image, a synthetic fundus image, or a synthetic microscopic image l (Column 5 Lines 1-67 describes the use of medical images such as MRI, CT scans etc.).
[claim 10]
In regard to Claim 10, Ceballows Lentini discloses a method of claim 1, wherein the input data comprises, for each examination object of the plurality of examination objects, at least one image of the examination region of the examination object (Column 6 Lines 29+describes the examination region).
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) 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over as being unpatentable by Ceballows Lentini (US US 11,393,575) in view of Annangi (US 12,664,762)
[claim 11]
In regard to Claim 11, Ceballows Lentini discloses a method of claim 1, wherein the input data comprises, for each examination object of the plurality of examination objects; however, fails to disclose one or more radiologic images representing the examination region of the examination object before and/or after application of a first amount of a contrast agent, wherein the target image represents the examination region of the examination object after application of a second amount of a contrast agent, wherein the second amount differs from the first amount. Annaangi teaches a system that uses machine learning to refine the synthetic image generation wherein the region of examination has contrast agent being used (Column 9 Lines 10-67 describes the use of contrast agent and the resulting of this on the medical image analysis). Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to use an image feature system, as disclosed by Ceballows Lentini and further incorporate a system to use one or more radiologic images representing the examination region of an examination object before and/or after application of a first amount of a contrast agent, with the target image representing the examination region after application of a second, different amount of contrast agent, in order to improve the accuracy and clinical utility of synthetic image generation in order to utilize images acquired at different contrast agent levels to make it easier to model and predict images that would minimize patient exposure while maximizing diagnostic information, goals.
[claim 12]
In regard to Claim 12, Ceballows Lentini discloses a method wherein the input data comprises, for each examination object of the plurality of examination objects, a first radiologic image representing the examination region of the examination object without a contrast agent, a second radiologic image representing the examination region of the examination object after application of a first amount of a contrast agent, wherein the target image represents the examination region of the examination object after application of a second amount of a contrast agent, wherein the second amount differs from the first amount. Annaangi teaches a system that uses machine learning to refine the synthetic image generation wherein the region of examination has contrast agent being used (Column 9 Lines 10-67 describes the use of contrast agent and the resulting of this on the medical image analysis). Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to use an image feature system, as disclosed by Ceballows Lentini and further incorporate a system to use one or more radiologic images representing the examination region of an examination object before and/or after application of a first amount of a contrast agent, with the target image representing the examination region after application of a second, different amount of contrast agent as taught by Annaangi, in order to improve the accuracy and clinical utility of synthetic image generation in order to utilize images acquired at different contrast agent levels to make it easier to model and predict images that would minimize patient exposure while maximizing diagnostic information, goals.
[claim 13]
In regard to Claim 13, Ceballows Lentini discloses a method of claim 1, wherein the input data comprises, for each examination object of the plurality of examination objects (Figure 3); however fails to disclose one or more radiologic images representing the examination region of the examination object at one or more points in time before or after application of a contrast agent, wherein the target image represents the examination region of the examination object at another point in time before or after application of the contrast agent. Annaangi teaches a system wherein the images have metadata associated with the iamges including attributes of patient identifiers, patient demographics, BMI, medical history and pathology (Column 9 Lines 10-43). Therefore, it would be obvious at the time of the invention to use the system as disclosed be Ceballows Lentini and further incorporate a system hat uses another point of time and information, as taught by Lentini, in order to provide a more effective and clearer medical history of the patient.
[claim 14]
In regard to Claim 14, Ceballows Lentini discloses a method of claim 1, wherein the input data comprises, for each examination object of the plurality of examination objects (Figure 3); however fails to disclose one or more radiologic images representing the examination region of the examination object at one or more points in time before or after application of a contrast agent, wherein the target image represents the examination region of the examination object at another point in time before or after application of the contrast agent. Annaangi teaches a system wherein the images have metadata associated with the iamges including attributes of patient identifiers, patient demographics, BMI, medical history and pathology (Column 9 Lines 10-43). Therefore, it would be obvious at the time of the invention to use the system as disclosed be Ceballows Lentini and further incorporate a system hat uses another point of time and information, as taught by Lentini, in order to provide a more effective and clearer medical history of the patient.
[claims 18, 19, and 20]
In regard to Claims 18, 19, and 20 have been disclosed as Claim 1.
Allowable Subject Matter
[claim 15, 16, and 17}
Claims 15, 16, and 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Montalt (US 2024/0378503) and Danu et al (US 2025/0217629).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMIE JO ATALA whose telephone number is (571)272-7384. The examiner can normally be reached 830am-500pm M-TH.
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If attempts to reach the examiner by telephone are unsuccessful, please contact Dave Czekaj 571-272-7327. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMIE J ATALA/Supervisory Patent Examiner, Art Unit 2486
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