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 .
DETAILED ACTION
This action is in response to the applicant's communication filed on 06/08/2026. In virtue of this communication, claims 1-4, 8-12, 16-17, 20-23, 36-40 as elected by applicant filled on 06/08/2026 are currently pending in the instant application.
Claims 27, 28, 30, 32, 33 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected embodiment, there being no allowable
generic or linking claim. Election was made without traverse in the reply filed on 06/08/2026.
Claims 5-7, 13-15, 24-35 have been cancelled.
New claims 36-40 have been added.
Information Disclosure Statement
The information Disclosure statement (IDS) form PTO-1449, filed on 12/31/2025, 07/30/2025, 05/29/2025, 05/31/2024 are in compliance with the provisions of CFR 1.97. Accordingly, the information disclosed therein was considered by the examiner.
Drawings
The drawings were received on 03/05/2024 have been reviewed by Examiner and they are acceptable.
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 1-4, 8-12, 16-17, 20-23, 36-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent Claims 1, 39, and 40 are directed to obtaining a plurality of dynamic frame images, each of the plurality of dynamic frame images at least including dynamic image information of a target region; and determining the time-activity curve by processing the plurality of dynamic frame images.
Step 1:
With regard to Step 1, the instant claims are directed to an apparatus, a method, and a non-transitory computer-readable medium, all among the statutory categories of invention.
Step 2A — Prong 1:
With regard to Step 2A — Prong 1, for example in method Claim 1, the limitations “obtaining a plurality of dynamic frame images, each of the plurality of dynamic frame images at least including dynamic image information of a target region;” and “determining the time-activity curve by processing the plurality of dynamic frame images.”, as recited, is a method that, under its broadest reasonable interpretation, covers performance of the limitation in the mind/observation of a person evaluating and inspecting an image/picture collected and determining certain result. That is, other than reciting “by a computer" nothing in the claim steps preclude the limitations from practically being performed in the mind or through observation of a person inspecting an image of collected. The recited computer is simply a generic device. If a claim limitation, under its broadest reasonably interpretation covers performance of the limitation in the mind but for the recitation of a generic components, then it falls within the "Mental processes" grouping of the abstract idea, which include concepts performed in the human mind, including an observation, evaluation, judgement, opinion. Accordingly, the claim recites an abstract idea. In addition, the additional components recited in independent Claims 39 and 40, i.e., a memory, a processor, and a non-transitory computer-readable medium are simply generic computing components, accordingly, these independent claims include the above- described abstract idea.
Step 2A — Prong 2:
The 2019 PEG defines the phrase “integration into a practical application’ to require an additional element or a combination of additional elements in the claim
to apply, rely on, or use the judicial exception. In the instant case, the additional elements in the claims do not apply, rely on, or use the judicial exception.
This judicial exception is not integrated into a practical application because the claims only recite additional elements using a computer, a memory, a processor, or a non-transitory Computer-readable medium, for instance, that includes to perform the recited elements/functions/steps. These computing components in all are recited at high-level of generality and there are no other recited additional limitations in the claims. Accordingly, these additional steps/element do not integrate the abstract idea into a practical application because it is a field-of-use limitation that does not impose any meaningful limits on practicing the abstract idea. Therefore, independent Claims 1, 39, and 40 recite an abstract idea.
Step 2B:
Because the claims fail under Step 2A, the claims are further evaluated under Step 2B. The claims herein do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into practical application, the additional element of using a computer, a memory, a processor, or a non-transitory computer- readable medium to execute programming instructions to perform the step amounts to no more than mere instructions to apply the exception using a generic apparatus component. Mere instructions to apply an exception using generic apparatus component cannot provide an inventive concept. The claim is not patent eligible.
Further, with regard to dependent Claims 3-4, 8-12, 16-17, 20-23, 36-38 viewed individually, these additional elements are under their broadest reasonable interpretation, cover performance of the limitation in the mind and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Accordingly, Claims 1-4, 8-12, 16-17, 20-23, 36-40 are rejected under 35 U.S.C. 101.
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.
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.
Claim(s) 1, 3, 36-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Feng (US 2020/0320753).
As per claim 1, A method for processing an image, comprising: “obtaining a plurality of dynamic frame images, each of the plurality of dynamic frame images at least including dynamic image information of a target region;”(Feng, ¶[0069-0070] discloses An image in the image sequence may be a standardized uptake value (SUV) image with voxels or pixels presenting SUV associated with one or more regions of interests (ROIs) of the subject. A SUV associated with an ROI may represent an uptake of a tracer (or radioactive tracer) of the ROI in the subject (e.g., a tumor). ¶[0071] disclose ¶[0072] discloses the one or more consecutive time periods may refer to time periods after a time interval after the injection of a tracer. During each consecutive time period, an image (i.e., a dynamic frame) may be acquired by the scanner 110 scanning the subject. Further see ¶[0084])
“and determining the time-activity curve by processing the plurality of dynamic frame images.” (Feng, ¶[0085] discloses determine a region of interest (ROI) (e.g., a region associated with the heart or arterial blood) from each of the one or more images in the image sequence. The processing device 120 may identify a blood TAC from the one or more images based on the determined ROI and designate the blood time activity curve (TAC) as the plasma TAC. The plasma TAC identified from the image sequence may be also referred to as an image-derived input function.)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to combine the various embodiments of reference Feng, wherein the combination would allow for the system of the claim to include machine learning training process of information and dynamic parameter associated with subject. One skilled in the art would have been motivated to modify Feng in this manner in order to utilize the additional steps of using image sequences and using multiple group of training samples/data. Therefore, one of ordinary skill in the art, would be capable to have combined the elements as claimed by known methods, and that in combination, each element merely performs the same function as it does separately. It is for at least the aforementioned reasons that the Examiner has reached a conclusion of obviousness with respect to claim 1.
As per claim 2, The method of claim 1, “wherein the determining the time-activity curve by processing the plurality of dynamic frame images comprises: determining the time-activity curve by inputting the plurality of dynamic frame images to a trained extraction model.” (Feng, ¶[0086] discloses the image sequence may be inputted into the trained machine learning model. The trained machine learning model may generate and output the plasma TAC. )
As per claim 3, The method of claim 2, “wherein the trained extraction model is obtained by: obtaining a plurality of training samples each of which including a sample dynamic image and a label of the sample dynamic image, the label including a time-activity curve corresponding to the sample dynamic image;” (Feng, ¶[0075] discloses multiple group of training data are obtained each group of the multiple groups of training data may include a corresponding plasma TAC associated with one of the one or more samples. ¶[0085] discloses obtaining a reference plasma time-activity curve associated with each sample and determining the reference plasma TAC from the sample’s image sequence. ¶[0086] discloses training uses multiple groups, each containing an image sequence and reference plasma TAC. Each training image sequence is paired with its corresponding or reference plasma TAC(label).¶[0092] discloses An image sequence of a specific group may include dynamic activity images (e.g., one or more SUV images) associated with one of the one or more samples which acquired over consecutive time period. ¶[0110-0114]. )
“and training an initial extraction model, based on the plurality of training samples, to adjust model parameters of the initial extraction model to obtain the trained extraction model.”(Feng, ¶[0075] discloses training the machine learning model using model training algorithms, including a gradient descent algorithm, a Newton's algorithm, a Quasi-Newton algorithm, a Levenberg-Marquardt algorithm, a conjugate gradient algorithm, or the like ¶[0078] and ¶[0085] discloses generating the trained machine learning model by training a machine learning model using the multiple groups of training data. Such training necessarily adjust model parameter to produce the trained model. See also¶[0100-0102] discloses learning parameters are initialized, altered, adjusted, and updated during iterative training, and trained mode is determined form the updated learning parameters, then ¶[0121-0123] discloses parameters are updated through backpropagation and trained model is stablished using the updated parameter values.)
As per claim 36, The method of claim 2, “wherein the trained extraction model is trained using a plurality of training samples,”(Feng, ¶[0086] discloses generating the trained machine learning ,model by training a machine learning model using multiple groups of training data.)
“each of the plurality of training samples includes a sample dynamic image and a label of the sample dynamic image,” (Feng,¶[0086] discloses Each group of the multiple groups of training data may include an image sequence and a reference plasma TAC of a sample. The model is trained to map the image sequence to the plasma TAC, so the reference plasma TAC functions as the supervised label associated with that sample image sequence.)
“the sample dynamic image being obtained by segmenting a whole-body dynamic” (Feng, ¶[0091] disclose a sample may be the entire volume of a subject, or a portion of the subject, a sample may be a specific organ, such as the heart,…)
“and the label being obtained in a specific blood pool region”(Feng, ¶[0085] disclose Using the PET blood pool scan technique, the plasma TAC of the subject may be determined based on the image sequence. For example, the processing device 120 may determine a region of interest (ROI) (e.g., a region associated with the heart or arterial blood) from each of the one or more images in the image sequence. The processing device 120 may identify a blood TAC from the one or more images based on the determined ROI and designate the blood TAC as the plasma TAC. )
However Feng does not explicitly disclose the following which would have been obvious in view of Kelly “the sample dynamic image being obtained by segmenting a whole-body dynamic image based on an imaging range of the target region,”(Kelly,¶[0045], ¶[0046] discloses An ROI is defined in this data set around a blood vessel using segmentation or bounding box techniques in its whole body, multi bed dynamic PET process.)
“the label being a sample time-activity curve corresponding to the whole-body dynamic image,.” (Kelly, ¶[0042] discloses obtaining measurements from blood pool volumes of interest at the different bed positions of a whole body dynamic scan and stitching those measurements into a single continuous time activity curve(TAC).)
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Kelly technique of measuring activity of a region of interest in medical images into Feng technique to provide the known and expected uses and benefits of Kelly technique over PET image reconstruction technique of Feng. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Kelly to Feng in order to provide accurate continues whole body scanning. (Refer to Kelly paragraph [0007].)
As per claim 37, The method of claim 36, “wherein the target region is a partial region of an object, and the specific blood pool region is the aorta or the heart of the object.” (Feng, ¶[0085] discloses determine a region of interest (ROI) (e.g., a region associated with the heart or arterial blood) from each of the one or more images in the image sequence. ¶[0091] disclose a sample may be the entire volume of a subject, or a portion of the subject, a sample may be a specific organ, such as the heart,…)
As per claim 38,The method of claim 37, “wherein the target region is a head region or an abdomen region.” (Feng, ¶[0091] disclose a sample may be the entire volume of a subject, or a portion of the subject, such as the head, the thorax, the abdomen, or the like. )
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Feng (US 2020/0320753), in view of Zhang, Xuezhu, et al. "Total-body dynamic reconstruction and parametric imaging on the uEXPLORER." Journal of Nuclear Medicine, (2020).
As per claim 4,The method of claim 3, “wherein the sample dynamic image includes a whole-body dynamic image, the whole-body dynamic image is acquired by whole-body simultaneous imaging.”(Zhang, page285, Col. 2 discloses
uEXPLORER a194-cm-long PET/CT system for total-body imaging. Page 286, Col. 1, first paragraph discloses the uEXPLORER provides the first opportunity to obtain dynamic imaging of the entire body of an adult human with simultaneous coverage of all organs, tissues, and cells. )
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Zhang technique of total body dynamic imaging into Feng technique to provide the known and expected uses and benefits of Zhang technique over PET image reconstruction technique of Zhang. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Zhang to Feng in order to provide image reconstructed with good quality and low noise. (Refer to Zhang page 285, Col. 1.)
Allowable Subject Matter
Claims 8-12, 16-17, 20-23 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 and on the pending conditions of the rejected and objected matter set forth in this action.
The following is a statement of reasons for the indication of allowable subject matter: the prior art of record, alone or in combination, fails to teach or suggest the limitations set forth by each of claims 8-12, 16-17, 20-23.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAGHAYEGH AZIMA whose telephone number is (571)272-1459. The examiner can normally be reached Monday-Friday, 9:30-6:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vincent Rudolph can be reached at (571)272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHAGHAYEGH AZIMA/Examiner, Art Unit 2671
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