DETAILED ACTION
[1] Remarks
I. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
II. This Office Action is in response to the RCE filed on 3/4/26.
III. Claims 1-15, 17, and 19-22 are pending and have been examined, where claims 5, 7, 15, and 17 is/are rejected and claim 1-4, 6, 8-14, and 19-22 is/are objected. Explanations will be provided below.
IV. Inventor and/or assignee search were performed and determined no double patenting rejection(s) is/are necessary.
V. Patent eligibility (updated in 2019) shown by the following: Claims 1-15, 17, and 19-22 pass patent eligibility test because there is/are no limitation or a combination of limitations amounting to an abstract idea. Also, the following limitation or the combinations of the limitations:
determining classification information of the tracer by processing the imaging data using a tracer classification model, the tracer classification model being a trained machine learning model, wherein the classification information of the tracer includes at least one of a tracer type or a probability that the tracer belongs to a certain tracer type;
determining classification information of the tracer by processing the imaging data using a tracer classification model, the tracer classification model being a trained machine learning model; and generating a target scan protocol based on the classification information; or determining one or more reconstruction parameters used in a reconstruction of the imaging data based on the classification information
effects a transformation or a reduction of a particular article to a different state or thing / adds a specific limitation(s) other than what is well-understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application and providing improvements to the technical field of medical image analysis, which recite additional elements that integrate the judicial exception into a practical application and amounting significant more.
VI. There are no PCT associated with the current application.
[2] Response to Arguments
The arguments presented by the applicant have been considered and are found convincing. However regarding claim 1, an updated search was performed and found Vija (US 2022/0309650) to be the closest prior art to the amendment to claim 1.
Vija discloses wherein the classification information of the tracer includes at least one of a tracer type or a probability that the tracer belongs to a certain tracer type (see paragraph 26, an identifier of a radioactive tracer used to acquire image 120 and anatomical segmentation information are input to neural network 110 along with activity image 120).
Regarding claim 11, an updated search was performed and found TUNG (US 20170206680) to be the closest prior art to the amendment to claim 11.
TUNG discloses generating a target scan protocol based on the classification information; or determining one or more reconstruction parameters used in a reconstruction of the imaging data based on the classification information (see paragraph 32, the iRecon system 208 recommends a smoothing additive for the patient that results in a better image quality, or suggests a different reconstruction algorithm based on the tracer information).
Prior art rejections will be applied accordingly.
[3] Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function. Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Claim(s) 11-15 and 17, and 19-22 are not interpreted under 35 U.S.C. 112(f) or pre-AIA U.S.C. 112 6th paragraph because of the following reason(s): limitations are modified by sufficient structure or material for performing the claimed function.
Claim(s) 1-10 does not require 35 U.S.C. 112(f) or pre-AIA U.S.C. 112 6th paragraph interpretation because they are method claims and / or they are CRM claims.
Upon examination of the specification and claims, the examiner has determined, under the best understanding of the scope of the claim(s), rejection(s) under 35 U.S.C. 112(a)/(b) is not necessitated because of the following reasons: sufficient support are provided in the written description / drawings of the invention.
[4] Grounds of Rejection
Claim Rejections - 35 USC § 103
1. 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.
2. Claims 1-4, 6, 8-9 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al., Quantification of uptake in pelvis F-18 FLT PET-CT images using a 3D localization and segmentation CNN, Volume 49, Issue 3, March 2022 in view of Vija (US 2022/0309650).
Regarding claim 1, Xiong discloses a method for classifying a tracer, comprising:
obtaining imaging data related to an emission computed tomography (ECT) scan of a target object, the target object being injected with a tracer during the ECT scan (see figure 3, input patch, 2.1 Image data and reference data); and
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determining classification information of the tracer by processing the imaging data using a tracer classification model, the tracer classification model being a trained machine learning model (see figure 3 being the machine learning model, the segmentation process shows whether each pixel belong to a tracer pixel or not tracer pixel, also see figure 1a):
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Xiong is silent in disclosing wherein the classification information of the tracer includes at least one of a tracer type or a probability that the tracer belongs to a certain tracer type.
Vija discloses wherein the classification information of the tracer includes at least one of a tracer type or a probability that the tracer belongs to a certain tracer type (see paragraph 26, an identifier of a radioactive tracer used to acquire image 120 and anatomical segmentation information are input to neural network 110 along with activity image 120).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include wherein the classification information of the tracer includes at least one of a tracer type or a probability that the tracer belongs to a certain tracer type in order to ensure that radiation detected by cameras is correctly interpreted to identify metabolic processes or abnormal tissues which improves accurate identification and precise quantification in medical and industrial tracer studies.
Regarding claim 2, Xiong discloses the method of claim 1, wherein the determining classification information of the tracer by processing the imaging data using a tracer classification model includes: determining at least one feature of the imaging data (see figure 1, each pixel is read as features); and determining the classification information of the tracer by processing the imaging data and the at least one feature using the tracer classification model (see figure 3, the machine learning model is employed to detect tracer pixels).
Regarding claim 3, Xiong discloses the method of claim 1, wherein the determining classification information of the tracer by processing the imaging data using a tracer classification model includes: determining the classification information of the tracer by processing the imaging data using the tracer classification model and an enhancement model (see figure 3, being read as the tracer classification model and an enhancement model, see figure 4, pixel classification are performed and localization are enhanced, see section 2.3a and section 2.3b):
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Wherein the enhancement model is a model for performing at least one of noise reduction or detailing enhancement on the imaging data (see figure 7 illustration below):
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Regarding claim 4, Xiong discloses the method of claim 3, wherein the determining the classification information of the tracer by processing the imaging data using the tracer classification model and an enhancement model includes one or more iterations, an iteration of the one or more iterations including:
determining initial classification information of the tracer by processing initial imaging data of the iteration using the tracer classification model (see Network Training section, LR is the initial learning rate, which is employ on the initial imaging data):
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generating updated imaging data by performing noise reduction processing on the initial imaging data using the enhancement model (see figure 3, the encoder includes convolution filters which inherently filter noise from the images);
determining whether an iteration termination condition is satisfied (see Network Training section); and
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designating the initial classification information as the classification information of the tracer in response to a determination result that the iteration termination condition is satisfied; OR designating the updated imaging data as initial imaging data of a next iteration in response to a determination result that the iteration termination condition is not satisfied (training will be on going until condition is met which is either 50 epochs or when the LR is below 10-8, see previous citation).
Regarding claim 6, Xiong discloses the method of claim 3, wherein the tracer classification model and the enhancement model are generated by joint training (see Section 2.3 Combined localization and segmentation, both the localization and segmentation model are trained at the same time).
Regarding claim 8, Xiong discloses the method of claim 1, wherein the determining classification information of the tracer by processing the imaging data using a tracer classification model includes:
obtaining reference information (see figure 3, the input to the network is read as the reference information);
determining initial classification information of the tracer based on the reference information (see figure 1 illustration below showing classification information); and
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determining the classification information of the tracer by processing the imaging data and the initial classification information using the tracer classification model (see figure 1 illustration above showing classification information, and the initial learning rate is read as the initial classification information).
Regarding claim 9, Xiong discloses the method of claim 1, wherein the determining classification information of the tracer by processing the imaging data using a tracer classification model includes:
determining a region of interest based on the imaging data (see figure 4 illustration below, covered with bounding boxes);
determining a feature map representing the region of interest, wherein the region of interest and other regions in the feature map are displayed in different manners (see figure 2, the bounding boxes are displayed in different colors which is displayed in different manners); and
determining the classification information of the tracer by processing the imaging data and the feature map using the tracer classification model (see figure 3, the U-Net is the tracer classification model):
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Regarding claim 20, see the rationale and rejection for claim 1. In addition, see network training section:
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The GPU requires processors and instructions to perform all the steps of claim 20.
2. Claims 11-14, 19 and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al., Quantification of uptake in pelvis F-18 FLT PET-CT images using a 3D localization and segmentation CNN, Volume 49, Issue 3, March 2022 in view of TUNG (US 20170206680).
Regarding claim 11, see the rationale and rejection for claim 1. In addition, see network training section:
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Xiong is silent in disclosing generating a target scan protocol based on the classification information; or determining one or more reconstruction parameters used in a reconstruction of the imaging data based on the classification information. TUNG discloses generating a target scan protocol based on the classification information; or determining one or more reconstruction parameters used in a reconstruction of the imaging data based on the classification information (see paragraph 32, the iRecon system 208 recommends a smoothing additive for the patient that results in a better image quality, or suggests a different reconstruction algorithm based on the tracer information).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include reconstruction parameters used in a reconstruction of the imaging data based on the classification information to identify the body part or presence of lesion(s) for the system can select the most appropriate reconstruction algorithm to balance noise reduction, spatial resolution, and contrast.
Regarding claim 12 see the rationale and rejection for claims 2 and 11.
Regarding claim 13 see the rationale and rejection for claims 3 and 11.
Regarding claim 14 see the rationale and rejection for claims 4 and 11.
Regarding claim 19 see the rationale and rejection for claims 9 and 11.
Regarding claim 21, Xiong discloses the method of claim 1, wherein the imaging data includes raw data and image data collected based on the ECT scan of the target object (see figure 2, a is read as the image data and b is read as raw data).
Regarding claim 22, Xiong discloses the method of claim 2, wherein the imaging data includes raw data collected based on the ECT scan of the target object, and the at least one feature includes count information of one or more types of events occurring in the ECT scan (see 1. Random sampling and evaluation of CT volume patches, the utilized inclusion level of 1% represents a trade-off between relevance of the volume patch for the center prediction and number of volume patches available for network training, where the features are included in the number of volume patches).
2. Claims 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al., Quantification of uptake in pelvis F-18 FLT PET-CT images using a 3D localization and segmentation CNN, Volume 49, Issue3, March 2022 in view of TUNG (US 20170206680) and Deasy (US 20210383538).
Regarding claim 10, the combination of Xiong TUNG as a whole discloses all the limitations of claim 1, but is silent in disclosing the method of claim 1, the determining classification information of the tracer by processing the imaging data using a tracer classification model includes: generating a pseudo magnetic resonance (MR) image of the target object based on the imaging data; and determining the classification information of the tracer by processing the pseudo MR image using the tracer classification model.
Deasy discloses the method of claim 1, the determining classification information of the tracer by processing the imaging data using a tracer classification model includes: generating a pseudo magnetic resonance image of the target object based on the imaging data (see paragraph 243, Two GAN networks are trained simultaneously to generate pseudo MRI and pseudo CT from CT and MRI, respectively); and determining the classification information of the tracer by processing the pseudo MR image using the tracer classification model (see paragraph 211, quantitative PD-L1 expression only classifier model may be the result of using a single feature whereas all other classifier models).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include generating pseudo-magnetic resonance images from other imaging data in order to perform MRI-only radiotherapy which reduces the need for CT scans and reducing patient radiation dose.
[5] Claim Objections
Claim(s) 5, 7, 15, and 17 is/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.
With regards to claim 5, the examiner cannot find any applicable prior art providing teachings for the following limitation(s): the method of claim 4, wherein the generating updated imaging data by performing noise reduction processing on the initial imaging data using the enhancement model includes: obtaining at least two candidate enhancement models corresponding to at least two tracer types; selecting the enhancement model from the at least two candidate enhancement models based on the initial classification information; and generating the updated imaging data by performing the noise reduction processing on the initial imaging data using the enhancement model; in combination with the rest of the limitations of claims 1 and 4.
Xiong discloses obtaining at least
selecting the enhancement model from the at least
With regards to claim 7, the examiner cannot find any applicable prior art providing teachings for the following limitation(s):
the method of claim 1, wherein the imaging data includes
raw data and image data collected based on the ECT scan of the target object;
the determining classification information of the tracer by processing the imaging data using a tracer classification model includes:
determining first classification information of the tracer by processing the raw data using a tracer classification model corresponding to the raw data;
determining second classification information of the tracer by processing the image data using the tracer classification model corresponding to the image data;
determining a first weight of the first classification information and a second weight of the second classification information by performing quality assessment on the image data; and
determining the classification information of the tracer based on the first classification information, the second classification information, the first weight, and the second weight;
in combination with the rest of the limitations of claims 1.
Jeong et al., Brain tumor segmentation using 3D Mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging, Published 14 September 2020 2020 Institute of Physics and Engineering in Medicine Physics in Medicine & Biology, Volume 65, Number 18, IOP Science
the determining classification information of the tracer by processing the imaging data using a tracer classification model includes:
determining first classification information
determining second classification information of the tracer by processing the image data using the tracer classification model corresponding to the image data (see figure 1, the “classification (tumor or not)” second classification information);
determining a first weight of the first classification information
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determining the classification information of the tracer based on the first classification information
Regarding claim 15 see the rationale for claims 5 and 11.
Regarding claim 17 see the rationale for claims 7 and 11.
CONTACT INFORMATION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX LIEW (duty station is located in New York City) whose telephone number is (571)272-8623 (FAX 571-273-8623), cell (917)763-1192 or email alexa.liew@uspto.gov. Please note the examiner cannot reply through email unless an internet communication authorization is provided by the applicant. The examiner can be reached anytime.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MISTRY ONEAL R, can be reached on (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALEX KOK S LIEW/Primary Examiner, Art Unit 2674 Telephone: 571-272-8623
Date: 4/25/26
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