DETAILED ACTION
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 .
This office action is in response to communication filed on 3/4/2026. Claims 1-23 are pending on this application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-23 have been considered but are moot in view of the new grounds of rejection.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: an organ segmentation module, a Monte Carlo dose module, a patient-specific organ dose module, and an inverse optimization module in claims 1 and 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 6-10, 13-17, 20 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al (US20190046813) in view of Rapaka et al (US20170165501) and De Man et al (US20070147579).
Regarding claim 1, Zhou teaches a method of optimizing image quality and organ dose (para. [0020]) for computed tomography (para. [0019]), the method comprising:
performing, by operations of modules stored in memory (para. [0025], [0119]), using processor circuitry equipped with one or more graphics processing units (para. [0112]), the operations of:
segmenting, by an organ segmentation module (Contouring Tool in figs. 1B and 1C), at least one organ based, at least in part, on patient image data (para. [0021], In certain embodiments, the accessing step comprises generating contours of tumor target volume and critical organs in the image data via the interface. The generating step may optionally comprise auto-generating the contours using an automatic segmentation software and modifying the auto-generated contours via the interface; para. [0110], the server may be configured to automatically outline critical organs from the provided CT or MRI image data);
determining, by a Monte Carlo dose module (fig. 10), a patient-specific heterogeneous dose based, at least in part, on the patient image data and based, at least in part, on a selected CT scanner data (para. [0023], The method, in some embodiments, further comprises generating, based on the treatment plan, an evaluation index from, e.g., one or more of: two dimensional or three dimensional isodose distribution and/or curve in a region of interest, a dose-volume histogram (DVH) for the tumor target volume and critical organs contoured, Conformality Index (CI), Heterogeneity Index (HI) of the target volume; para. [0064], The information from a prior CT scan of the patient allows more accurate modeling of the behavior of the radiation as it travels through the patient's tissues. Different dose prediction models are available, including pencil beam, convolution-superposition and Monte Carlo simulation; para. [0113], For example, as illustrated in FIG. 10, Monte-Carlo algorithms may be adopted by the server to verify the treatment plan. Monte Carlo modeling is a statistical method that calculates the dose deposited in the region as a whole by simulating the passage of each photon through the region of interest);
determining, by a patient-specific organ dose module (152 in fig. 1C), a patient-specific nominal organ dose for each segmented organ based, at least in part, on the patient-specific heterogeneous dose (para. [0023], The method, in some embodiments, further comprises generating, based on the treatment plan, an evaluation index from, e.g., one or more of: two dimensional or three dimensional isodose distribution and/or curve in a region of interest, a dose-volume histogram (DVH) for the tumor target volume and critical organs contoured, Conformality Index (CI), Heterogeneity Index (HI) of the target volume; para. [0108], To minimize radiation dosage to adjacent normal cells, targeted tumors or volumes must be precisely identified. Tumor contouring attempts to achieve that goal with high accuracy and reliability by utilizing various automated segmentation processes. In most cases, contouring is carried out manually by a specialist Digital images, obtained from modalities such as CT or MRI, are used to view and locate the tumor; para. [0110], The server can have one or more algorithms adapted to recognize tumors and/or critical organs that can self-train via machine learning and/or artificial intelligence; para. [0114], In treatment planning system (TPS), since the optimization of the machine parameters such as beam directions, MLC aperture and monitor unit, requires many iterations of dose calculation, approximation algorithms are usually involved to speed up the computation. In order to verify the correctness of the final dose distribution and that it falls within a reasonable range of computational error, dose verification procedure can be performed); and
determining, by an inverse optimization module (para. [0065]), at least one CT scanner parameter configured to optimize image quality and minimize a selected patient-specific organ dose of at least one selected organ (para. [0008], In order to obtain a precise treatment plan that matches the oncologist's prescription, dosimetrists must iteratively adjust various parameters during optimization; para. [0056], The more formal optimization process is typically referred to as forward planning and inverse planning. Plans are often assessed with the aid of dose-volume histograms, allowing the clinician to evaluate the uniformity of the dose to the diseased tissue (tumor) and sparing of healthy structures; para. [0064], minimizing the dose to healthy tissue; para. [0098], The cloud based platform 100 can further include a Quality Control module 104 for monitoring the quality of the radiation treatment, which can be configured to model accelerator, monitor accelerator performance and quality control imaging equipment; para. [0108], To minimize radiation dosage to adjacent normal cells).
Zhou fails to teach determining a patient-specific heterogenous dose for CT scanning, determining a patient-specific nominal organ dose for CT scanning, and optimizing image quality and minimize a selected patient-specific CT scanning organ dose.
However Rapaka teaches determining patient specific doses for CT scanning (para. [0042], [0046]) and determining CT scanner parameters (para. [0046]) to optimize image quality (para. [0032]. Any adjustment to an image is interpreted to optimize the quality) and minimize a selected patient-specific organ dose (para. [0021], [0054], [0075]).
Therefore taking the combined teachings of Zhou and Rapaka as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Rapaka into the method of Zhou. The motivation to combine Rapaka and Zhou would be to offload the bulk of the computational effort to the offline training process, resulting in nearly instantaneous prediction for unseen data (para. [0047] of Rapaka).
The modified invention of Zhou fails to teach wherein the steps are performed for diagnostic imaging and using the determined at least one CT scanner parameter for diagnostic imaging of at least one selected organ dose.
However De Man teaches determining doses for diagnostic imaging (para. [0026], The attenuation map 54 is also used to derive a segmented component map 60 which together with the absorbed dose map 56 is used to derive an effective dose formula 62. In this regard, the effective dose formula 62 can be used to determine the effective dose for a given set of acquisition parameters 58) and using at least one CT scanner parameter (claim 12, define the irradiation profile as a function of at least one of tube current, tube voltage, x-ray filtration, and focal spot energization time) for diagnostic imaging of at least one selected organ dose (para. [0008], determine an irradiation profile that minimizes effective dose for each organ of the organ map and maximizes image quality for an image of the subject; para. [0009], The method then determines a radiation profile that results in each anatomical structure receiving a minimal radiation dose without exceeding a noise variance for the image of the patient).
Therefore taking the combined teachings of modified Zhou and De Man as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of De Man into the method of modified Zhou. The motivation to combine De Man and Zhou would be to minimize effective dose for each organ of the organ map and maximizes image quality for an image of the subject (para. [0008] of De Man).
Regarding claim 2, the modified invention of Zhou teaches a method wherein the organ segmentation module comprises a trained artificial neural network (para. [0110] of Zhou, The server can have one or more algorithms adapted to recognize tumors and/or critical organs that can self-train via machine learning and/or artificial intelligence).
Regarding claim 3, the modified invention of Zhou teaches a method wherein the patient image data is selected from the group comprising prior three-dimensional (3-D) CT image data and a plurality of pre-scan two-dimensional (2-D) planning radiographs (para. [0019] of Zhou, In certain embodiments, the collecting step comprises collecting the image data from, e.g., one or more of computerized tomography (CT), positron emission tomography (PET), ultrasound, single-photon emission computed tomography (SPECT) or magnetic resonance imaging (MRI) machine; para. [0064] of Zhou, The information from a prior CT scan of the patient allows more accurate modeling of the behavior of the radiation as it travels through the patient's tissues).
Regarding claim 6, the modified invention of Zhou teaches a method wherein the segmenting and the determining the patient-specific heterogeneous organ dose are performed on a computing system comprising a plurality of graphics processing units (fig. 9 of Zhou; para. [0104], [0112] of Zhou).
Regarding claim 7, the modified invention of Zhou teaches a method wherein the selected patient organ dose is a constraint for an optimization cost function (para. [0117] of Zhou, For the single objective optimization, weight parameters may be assigned to each original objective (e.g., dose distribution, region of interest, etc.), and all the weighted objectives can be summed up to form a single cost function for optimization. For multi-objective optimization, multiple cost functions will be considered. After an optimization process has been completed at the server, a dose-volume histogram (DVH) may be produced to provide statistical information to the user).
Regarding claim 8, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 9, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
Regarding claim 10, the claim recites similar subject matter as claim 3 and is rejected for the same reasons as stated above.
Regarding claim 13, the claim recites similar subject matter as claim 6 and is rejected for the same reasons as stated above.
Regarding claim 14, the claim recites similar subject matter as claim 7 and is rejected for the same reasons as stated above.
Regarding claim 15, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 16, the claim recites similar subject matter as claims 2 and 6 and is rejected for the same reasons as stated above.
Regarding claim 17, the claim recites similar subject matter as claim 3 and is rejected for the same reasons as stated above.
Regarding claim 20, the claim recites similar subject matter as claim 7 and is rejected for the same reasons as stated above.
Regarding claim 22, the modified invention of Zhou teaches a method further comprising:
generating a scanning plan based, at least in part, on inverse optimization (para. [0016], [0065] of Zhou; para. [0050] of Rapaka), the scanning plan configured to reduce the selected patient-specific organ dose for CT scanning of the at least one selected organ while maintaining image quality (para. [0008], [0066], [0108] of Zhou; para. [0050] of Rapaka. It is noted that applicant’s specification states that image quality is maintained while optimizing organ dose (page 12 lines 21-23 of applicant’s specification), the scanning plan based at least in part on the at least one scanner parameter (para. [0046] of Rapaka).
Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al (US20190046813) Rapaka et al (US20170165501) and De Man et al (US20070147579) in view of Fahrig et al (US20160193482).
Regarding claim 5, the modified invention of Zhou fails to teach a method wherein the segmenting and the determining the patient-specific heterogeneous organ dose are completed in at most five seconds.
However Fahrig teaches wherein segmenting and determining a patient-specific dose are completed in at most five seconds (para. [0012], rapidly obtaining a treatment image and verifying selection a treatment plan from the treatment plan options within about one second or less, and then rapidly delivering a radiation treatment beam according to the determined and verified selected treatment plan, wherein an entire dose of the treatment is delivered within 10 seconds or less. In one aspect, re-segmentation is performed through deformable image registration and may be automatic or semi-automatic so as to perform re-segmentation rapidly, such as within 10 seconds or less. “Within 10 seconds or less” includes less than 5 seconds).
Therefore taking the combined teachings of modified Zhou with Fahrig as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Fahrig into the method of modified Zhou. The motivation to combine Fahrig and modified Zhou would be to rapidly deliver a radiation treatment sufficiently fast enough to freeze physiologic motion (para. [0006] of Fahrig).
Regarding claim 12, the claim recites similar subject matter as claim 5 and is rejected for the same reasons as stated above.
Regarding claim 19, the claim recites similar subject matter as claim 5 and is rejected for the same reasons as stated above.
Claim(s) 21 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al (US20190046813), Rapaka et al (US20170165501) and De Man et al (US20070147579) in view of Ji et al (US20200000425).
Regarding claim 21, the modified invention of Zhou fails to teach the optimization system wherein the at least one CT scanner parameters are selected from the group consisting of include tube current modulation parameters, voltage current modulation parameters, beam collimation parameters, filtering parameters, gantry angle, and combinations thereof.
However Ji teaches wherein the at least one CT scanner parameters include tube current modulation parameters (para. [0083], A data point of the DOM profile or a regional DOM profile may provide a degree of modulation of a radiation dose (also referred to as a radiation dose modulation, a dose of modulation, or a tube current modulation) in performing a CT scan on the object. The radiation dose modulation may be implemented by adjusting the tube current of the radioactive scanning source 113 based on the DOM profile during a CT scan), voltage current modulation parameters (para. [0084], tube voltage), beam collimation parameters and gantry angle (para. [0084], As used herein, a pitch may refer to the ratio of table translation (table feed in centimeters per 360° gantry rotation) to the total nominal collimated x-ray beam width in the z direction in helical CT).
Therefore taking the combined teachings of modified Zhou with Ji as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Ji into the method of modified Zhou. The motivation to combine Ji and modified Zhou would be to reduce a CT radiation dose while maintaining a needed image quality during a CT scan (para. [0003] of Ji).
Regarding claim 23, the claim recites similar subject matter as claim 21 and is rejected for the same reasons as stated above.
Allowable Subject Matter
Claims 4, 11, and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 LEON VIET Q NGUYEN whose telephone number is (571)270-1185. The examiner can normally be reached Mon-Fri 11AM-7PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LEON VIET Q NGUYEN/Primary Examiner, Art Unit 2663