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 .
Applicant’s Response
In the Applicant’s Response dated 2/24/26, the Applicant amended 1, 10, 15, cancelled Claims 3, 4, 13 and argued Claims 1-20 previously rejected in the Office Action dated 12/3/25. Claims 1, 2, 5-12, 14-20 are pending examination.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/24/26 has been entered.
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.
Claims 10-12 in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitations uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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, 2, 5-12 and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. Claims 1 and 10 recite “receiving a plurality of input medical images of a patient acquired at a plurality of points in time”, “identifying one or more tumors in each of the plurality of input medical images”, “determining a tumor burden of the patient for each of the plurality of points in time based on the one or more identified tumors using one or more machine learning based networks”, “ determining a tumor score for each of the one or more identified tumors identified in the plurality of input medical images”, “performing an assessment of the one or more tumors based on the tumor burden of the patient determined for each of the plurality of points in time” and “outputting results of the assessment of the one or more tumors”.
The limitation “identifying one or more tumors in each of the plurality of input medical images” falls within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, the claimed identifying tumors in the images encompasses observing images and performing an evaluation, such as looking for tumors.
The limitations “determining a tumor burden of the patient for each of the plurality of points in time based on the one or more identified tumors using one or more machine learning based networks”, “determining a tumor score for each of the one or more identified tumors identified in the plurality of input medical images”, “performing an assessment of the one or more tumors based on the tumor burden of the patient determined for each of the plurality of points in time” encompasses mathematical concepts (e.g., calculating burden over points in time) that can be performed mentally.
This judicial exception is not integrated into a practical application because the limitations “receiving a plurality of input medical images of a patient acquired at a plurality of points in time” and “outputting results of the assessment of the one or more tumors” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05..
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are recited as being implements by a computer (claims 1-9). The computer is recited at a high level of generality. The computer is used to perform an abstract idea such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
The recitation of “using one or more machine learning based networks” in limitation” determining a tumor score for each of the one or more identified tumors identified in the plurality of input medical images” and “determining a tumor burden of the patient for each of the plurality of points in time based on the one or more identified tumors using one or more machine learning based networks” merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using one or more machine learning based networks” limits the identified judicial exceptions “determining a tumor burden of the patient for each of the plurality of points in time”, “determining a tumor score for each of the one or more identified tumors identified in the plurality of input medical images” and “performing an assessment of the one or more tumors based on the tumor burden,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
The recitations of “receiving a plurality of input medical images of a patient acquired at a plurality of points in time” and “outputting results of the assessment of the one or more tumors” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The recitation of a computer to perform the limitations “receiving..” and “outputting..” amounts to no more than mere instructions to apply the exception using a generic computer component.
Claims 2, 5-9, 11, 12, 14-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the invention does not provide an inventive concept.
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.
Claims 1-2, 8-12, 15 and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ferl et al., United States Patent Publication 20230005140 (hereinafter “Ferl”).
Claim 1:
Ferl discloses:
A computer-implemented method comprising:
receiving a plurality of input medical images of a patient acquired at a plurality of points in time (see paragraphs [0048] and [0059]). Ferl teaches receiving input medical images of patients at different moments in time;
identifying one or more tumors in each of the plurality of input medical images (see paragraph [0043]). Ferl teaches identifying one or more tumors in the medical images;
determining a tumor burden of the patient for each of the plurality of points in time based on the one or more identified tumors by (see paragraph [0045]). Ferl teaches the trained machine-learning model can identify a level of tumor burden from the image. For example, the trained machine-learning model can predict that a given image of a particular subject includes a tumor and further predict a medium tumor burden in the given image at a point in time.;
for each respective point in time of the plurality of points in time:
determining a tumor score for each of the one or more identified tumors identified in the plurality of input medical images acquired at the respective point in time using a machine learning based tumor score network (see paragraphs [0043]-[0044]). Ferl teaches classification system may process each image object of the extracted image objects to determine a set of structural characteristics. The set of structural characteristics can be processed using a trained machine-learning model (e.g., a support vector machine). Based on outputs from the trained machine-learning model, the classification system can identify, for each of the extracted image objects, information that indicates whether the image object corresponds to a tumor, a lesion. or a normal tissue;
the machine learning based tumor score network extracting tumor attributes from the one or more identified tumors identified in the plurality of input medical images acquired at the respective point in time and generating the tumor score based on the extracted tumor attributes (see paragraphs [0007] and [0043], [0044]). Ferl teaches processing the image using the segmentation algorithm includes determining one or more structural characteristics associated with another image object. Processing the image using the segmentation algorithm can also include processing the one or more structural characteristics of the image object using the trained machine-learning model to generate estimation data corresponding to an estimation of whether the image object corresponds a type of the biological structure;
determining the tumor burden based on the tumor scores using a machine learning based tumor burden network (see paragraphs [0043]-[0045]). Ferl teaches the trained machine-learning model can identify a level of tumor burden from the image. For example, the trained machine-learning model can predict that a given image of a particular subject includes a tumor and further predict a medium tumor burden in the given image.
performing an assessment of the one or more tumors based on the tumor burden of the patient determined for each of the plurality of points in time (see paragraphs [0149]). Ferl teaches performing an assessment of the tumors based on the burden from the images at different points in time; and
outputting results of the assessment of the one or more tumors (see paragraphs [0149]-[0156]). Ferl teaches outputting the results of the analysis of the tumors and the burden.
Claim 2:
Ferl discloses:
wherein identifying one or more tumors in each of the plurality of input medical images comprises:
segmenting organs from the plurality of input medical images using a machine learning based segmentation network (see paragraph [0065]). Ferl teaches using a segmentation algorithm to segment organs from the plurality of input images;
identifying at least one tumor within organs, soft tissue, and bones based on the segmented organs (see paragraph [0134]). Ferl teaches identifying a tumor within the organs and tissue based on the segmentation; and
filtering out benign tumors (see paragraphs [0060] and [0149]). Ferl teaches filtering different sections of the images such as particular parts, the areas with more/less cancer.
Claim 8:
Ferl discloses:
wherein the plurality of input medical images comprises images of a chest, an abdomen, and pelvis of the patient (see paragraph [0052]). Ferl teaches images of lungs in the chest included in the images.
Claim 9:
Ferl discloses:
wherein outputting results of the assessment of the one or more tumors comprises:
displaying the results of the assessment of the one or more tumors on a display device of a computing system (see paragraph [0154]). Ferl teaches rendering the display of the results of the tumors found in the images.
Claims 10-11:
Although Claims 10-11 are apparatus claims, they are interpreted and rejected for the same reasons as the method of Claims 1-2, respectively.
Claim 12:
Ferl discloses:
means for determining a tumor burden of the patient for each of the plurality of points in time based on the one or more identified tumors using one or more machine learning based networks comprises:
means for selecting tumor targets from the one or more identified tumors; and means for determining a sum of longitudinal diameters of the selected tumor targets using the one or more machine learning based networks as the tumor burden (see paragraphs [0006] and [0054-[0056]). Ferl teaches selecting particular areas of the tumors and calculating diameters of the tumor targets.
Claims 15, 19, 20:
Although Claims 15, 19 and 20 are non-transitory computer readable medium claims, they are interpreted and rejected for the same reasons as the method of Claims 1, 8, 9, respectively.
Claim Rejections - 35 USC § 103
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.
Claims 5, 6, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ferl, in view of Kecskemethy et al., United States Patent Publication 2020/0074634 (hereinafter “Kecskemethy”).
Claim 5:
Ferl fails to expressly disclose comparing tumor burdens at different points in time.
Kecskemethy discloses:
wherein performing an assessment of the one or more tumors based on the tumor burden of the patient determined for each of the plurality of points in time comprises:
comparing the tumor burdens of the patient determined for each of the plurality of points in time (see paragraphs [0006] and [0010]). Kecskemethy teaching comparing the tumor burdens of the patient based on different points in time.
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention was made to modify the method disclosed by Ferl to include comparing tumor burdens at various times for the purpose of efficiently perform volumetric measurements of lesions from CT images in acceptable amount of time due to the sheer number of image slices contained in each CT scan, as taught by Kecskemethy.
Claim 6:
Ferl fails to expressly disclose comparing tumor burdens at different points in time.
Kecskemethy discloses:
wherein performing an assessment of the one or more tumors based on the tumor burden of the patient determined for each of the plurality of points in time further comprises:
classifying the one or more tumors as one of complete response, partial response, stable disease, progressive disease based on the comparison. (see paragraphs [0006] and [0010]). Kecskemethy teaching comparing the tumor burdens of the patient based on different points in time to classify the tumor as progressive or regressive.
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention was made to modify the method disclosed by Ferl to include comparing tumor burdens at various times for the purpose of efficiently perform volumetric measurements of lesions from CT images in acceptable amount of time due to the sheer number of image slices contained in each CT scan, as taught by Kecskemethy.
Claims 16 and 17:
Although Claims 16 and 17 are non-transitory computer readable medium claims, they are interpreted and rejected for the same reasons as the method of Claims 5 and 6, respectively.
Claims 7, 14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ferl, in view of Kuang et al., United States Patent Publication 2018/0236267 (hereinafter “Kuang”).
Claim 7:
Ferl fails to expressly disclose the input mages comprising PCCT images.
Kuang discloses:
wherein the plurality of input medical images comprises PCCT (photon-counting computed tomography) images (see paragraphs [0067] and [0072]). Kuang teaching using photon-counting imaging in the medical images.
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention was made to modify the method disclosed by Ferl to include photon-counting CT images for the purpose of precisely defining and more accurately positioning target radiation volumes, which can improve radiotherapy applications to target structures (e.g., lesions, tumors), as taught by Kuang.
Claim 14:
Although Claims 14 is a non-transitory computer readable medium claim, it is interpreted and rejected for the same reasons as the method of Claim 7.
Claim 18:
Although Claims 18 is a non-transitory computer readable medium claim, it is interpreted and rejected for the same reasons as the method of Claim 7.
Response to Arguments
Applicant's arguments filed 2/24/26 have been fully considered but they are not persuasive.
35 USC 101
Applicant argues The two-stage machine learning based approach for evaluating the treatment response of tumors bifurcates the complex problem of determining a tumor burden of a patient into two separate stages, enabling the consideration of a comprehensive set of tumor attributes for evaluating the treatment response of tumors at any anatomical site in the plurality of input medical images with increased accuracy and robustness, as compared with state of the art approaches for evaluating the treatment response of tumors at the time of filing this application.
The Examiner disagrees.
The improvement of this invention is to the “determining of the tumor burden”. The two stage machine learning based approach for evaluation the treatment response is simply inputting images into a machine learning model receiving output, inputting the output into another known machine learning model and receive a second output. The invention does not make improvements to the machine learning technology. The invention is using known machine learning models to input images and receive output. Thus, the 35 USC 101 rejection is maintained and fails to integrate the abstract idea into a practical application under Step 2A, Prong 2.
35 USC 103
Applicant argues the structural-characteristic value in the cited portions of Ferl is not determined "for each of the one or more identified tumors identified in the plurality of input medical images acquired at the respective point in time," as required in amended claim 1. Instead, the structure-characteristic value in the cited portions of Ferl is a value representing an aggregation of all image objects classified as having a tumor, and is not determined for each of the image objects classified as having a tumor. Paragraph [0149] of Ferl explains this as follows: "The level of tumor burden can be determined by aggregating the image objects classified as having a tumor and generating a structural-characteristic value that represents the aggregated image objects (e.g., tumor volume)." The cited portions of Ferl do not teach or suggest at least a "tumor score for each of the one or more identified tumors" as recited in amended claim 1.
The Examiner disagrees.
Although the Examiner did use that rationale in the previous rejection, the Examiner believes Ferl still teaches on the amended claim language. The claim recites “determining a tumor score for each identified tumor using a machine learning based tumor score network”, the art recites “The set of structural characteristics can be processed using a trained machine-learning model (e.g., a support vector machine). Based on outputs from the trained machine-learning model, the classification system can identify, for each of the extracted image objects, information that indicates whether the image object corresponds to a tumor, a lesion. or a normal tissue” (see paragraph [0043]). The score can be an indication if the object is a tumor, lesion or normal tissue. The claims does not explicitly recite exactly what the tumor score represents. Thus, Ferl reads on the amended limitation.
Applicant argues the cited portions of Ferl do not teach or suggest that the structural-characteristic value is determined "using a machine learning based tumor score network" or that that ratio is determined "using a machine learning based tumor burden network," as required in amended claim 1.
The Examiner disagrees.
Ferl does not claim in Claim 1 that the tumor score or tumor burden is related to the “ratio”. It broadly claims a “tumor score” and “tumor burden”. Ferl teaches using multiple machine learning models to determine the tumor burden. Specifically, Ferl teaches the trained machine-learning model can identify a level of tumor burden from the image. For example, the trained machine-learning model can predict that a given image of a particular subject includes a tumor (indicated by a first machine learning) and further predict a medium tumor burden in the given image (see paragraph [0045]). Thus, Ferl teaches the argued limitation.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIONNA M BURKE whose telephone number is (571)270-7259. The examiner can normally be reached M-F 8a-4p.
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/TIONNA M BURKE/Examiner, Art Unit 2178 4/27/26
