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 9/16/25, the Applicant argued Claims 1-20 previously rejected in the Office Action dated 6/17/25.
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 10-13 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 1-20 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”, “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”, “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 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” 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 2-9 and 11-20 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 1-4, 8-13, 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 using one or more machine learning based networks (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.;
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 [0151]-[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 3:
Ferl discloses:
wherein 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:
selecting tumor targets from the one or more identified tumors; and 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.
Claim 4:
Ferl discloses:
wherein 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:
determining a tumor score for each of the one or more identified tumors using a machine learning based tumor score network; and determining the tumor burden based on the tumor scores using a machine learning based tumor burden network (see paragraph [0149]). Ferl teaches generating a structural-characteristic value that represents the aggregated image objects (e.g., tumor volume). The generated structural-characteristic value can then be compared to a reference structural-characteristic value that represents all image objects segmented and identified from the image (e.g., lung volume). Such comparison can identify a proportion and/or ratio of the aggregated image objects relative to all image objects of the image. Thus the generated value is used to determine the tumor burden.
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 10-13:
Although Claims 10-13 are apparatus claims, they are interpreted and rejected for the same reasons as the method of Claims 1-4, respectively.
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 and 14 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.
Response to Arguments
Applicant's arguments filed 9/16/25 have been fully considered but they are not persuasive.
Rejections under 35 USC 101
Applicant argues Claim 1 does not recite mathematical relationships, mathematical formulas or equations, or mathematical calculations and thus claim 1 is not directed to a mathematical concept. For instance, claim 1 does not recite, either expressed in words or using mathematical symbols, mathematical calculations for determining the tumor burden or for performing the assessment. At most, claim 1 is merely based on or involves a mathematical concept.
The Examiner disagrees.
The claims calculate burden in tumors over different points in time. Although the claims does not expressly disclose a calculation or mathematical formula, it does involve a mathematical concept. The claims encompass a mathematical concept that can be performed mentally. Therefore, the claims recite mathematical concepts.
Applicant argues Specifically, claim 1 explicitly requires that the tumor burden be determined “using one or more machine learning based networks.” As understood by one of ordinary skill in the art, determining a tumor burden using one or more machine learning based networks requires execution of such one or more machine learning based networks on a special purpose computing device. Accordingly, the human mind is not equipped for “determining a tumor burden . . . using one or more machine learning based networks” and thus claim 1 cannot practically be performed in the human mind.
The Examiner disagrees.
The limitation 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” 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 invention doesn’t create or train the machine learning network, just utilizes it for the abstract idea, which does not require a special purpose computing device. Thus, the claims remain rejected under 35 USC 101.
Applicant argues The claims are integrated into the practical application of an improvement in the functioning of a computer or other technology. Specifically, the claims are integrated into the practical application of an improved system for the automatic assessment of tumors. Such automatic assessment of tumors in accordance with embodiments of the invention enable an Extended RECIST workflow to analyze an entire PCCT scan (including chest, abdomen, and pelvis) of the patient for each point in time and record the presence and size of all suspected metastatic disease.
The Examiner disagrees.
Inputting images into a machine learning network and determining tumor burden based on the output does not integrate the abstract idea into a practical application. The claims are simply inputting images into a machines learning network and performing an assessment, besides the data gathering steps. Using known machine learning and generic computers to performing steps that can be performed mentally do not integrate the practical application. There is no improvement to the technology, just an improvement on performing the automated functions faster.
Applicant argues The claims recite elements that amount to significantly more than the alleged abstract idea itself. Specifically, claim 1 requires “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” and “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.” The cited reference does not teach or suggest at least these limitations of claim 1. It follows that the claims are not well understood, routine, or conventional.
The Examiner disagrees.
The argued limitations are recited at a high level of generality and can be performed mentally. A user is able to determine burden by evaluating images and performing an evaluation/assessment on the tumors. Thus, the claims remain rejected.
Rejections under 35 USC 102, 103
Applicant argues While the cited portions of Ferl may refer to a level of tumor burden, the cited portions of Ferl do not teach or suggest at least “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” as recited in claim 1. Specifically, the level of tumor burden in the cited portions of Ferl is not identified “for each of the plurality of points in time.” Instead, the cited portions of Ferl merely identifies a level of tumor burden from a single input image. The single input image in the cited portions of Ferl corresponds only to a single point in time and not to a “plurality of points in time.” Accordingly, the cited portions of Ferl disclose a level of tumor burden for a single point in time, and do not teach or suggest “determining a tumor burden of the patient for each of the plurality of points in time” as recited in amended claim 1, particularly under the strict standard of 35 U.S.C. 102.
The Examiner disagrees.
Ferl recites “Each of the input images may depict a three dimensional representation corresponding to the biological structure. Each of the input images may be captured at a specific point in time. For example, an input image of the input images may be captured at a time point at which no lesions or tumors are detected. Another input image of the input images can be captured at another time point at which the lesions or tumors are visible in the image. The input images may or may not be captured using a respiratory gating operation. For example, an imaging system (e.g., the imaging system of FIG. 1) can use the respiratory gating operation to identify a specific phase (e.g., inhale, exhale) of the subject's respiratory cycle. The imaging system may then capture each of the input images during a particular phase that corresponds to the pre-identified phase. In some instances, the input images are images depicting ROIs, in which an image region depicting the biological structure including heart, lung, and tumor is cropped from an original input image. (see paragraph [0059]). Thus, although Ferl teaches a system to can determine tumor burden for a specific point in time, it can also determine tumor burden for each point in time, or during a phase which consists of multiple points in time.
Applicant argues The cited portions of Ferl also do not teach or suggest at least “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” as recited in claim 1. As the cited portions of Ferl do not teach or suggest “the tumor burden of the patient determined for each of the plurality of points in time” for at least the reasons discussed above, the cited portions of Ferl also do not teach or suggest “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” as recited in claim 1.
The Examiner disagrees.
For the reasons above, Ferl does teach determining the tumor burden of the patient for each of the plurality of points in time. Therefore, the limitations remain rejected.
Applicant argues Therefore, for at least the reasons discussed above, independent claim 1 is allowable over Ferl. Independent claims 10 and 15 include elements similar to those in claim 1 and, thus, are allowable over the cited references for at least similar reasons as discussed above with respect to claim 1. All independent claims are allowable over the cited art.
The Examiner disagrees.
For the reasons above, Ferl does teach the argued limitations. Therefore, the limitations remain rejected.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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 11/25/25
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178