DETAILED ACTION
This is responsive to application 19/481,625 filed on 11/05/2025 in which claims 48-67 are presented for examination.
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 .
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 48-67 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 48:
Step 1: Is the claim to a process, machine, manufacture or composition of matter?” Yes, it’s a method(process).
Step 2a Prong 1 (judicial exception)
Step 2A (1): “Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes, the claim comes under mental processes.
Claim 1 recites:
“A computer-implemented method of analyzing a radiotherapy dose-sparing opportunity for a subject, the computer-implemented method comprising: determining, using computer processor circuitry, a representation of a first opportunity dose-volume histogram (ODVH), wherein the first ODVH is modelled based on an anatomy of the patient in a first imaging or radiotherapy treatment session, and wherein an ODVH represents an achievable radiation dose-sparing range for an organ at risk (OAR) given a dose prescription for a target; determining, using the computer processor circuitry, a representation of a second ODVH, wherein the second ODVH is modelled based on an anatomy of the patient in a second imaging or radiotherapy treatment session; determining, using the computer processor circuitry, a difference between the first ODVH and the second ODVH, wherein the difference represents the radiotherapy dose-sparing opportunity for the OAR; generating, using the computer processor circuitry, an output that represents the determined difference, wherein the determined difference represents an opportunity cost; and displaying the output to a user.”
All the limitations above are abstract idea related to the mental process (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)) with exception of bold and underlined limitations. Claim language pertains to creating a patient-specific “opportunity” range for each organ at risk (OAR). That opportunity range is shown as an Opportunity Dose-Volume Histogram, or ODVH. The ODVH estimates what dose-sparing is realistically achievable for the patient’s anatomy and treatment setup. The clinician can compare a planned or delivered dose distribution against that patient-specific opportunity range. The system also compares ODVHs from different imaging sessions or treatment fractions to see whether anatomy changes create more or less sparing opportunity.
Step 2A(2): Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. NO
The claim does recite additional elements; however, they don’t integrate the exception into a practical application of the exception.
Computer-implemented (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Computer processor circuitry (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
displaying (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Step 2B: evaluate whether the claim recites additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception? NO
As discussed previously with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Dependent claims 49-60, further narrows the abstract idea described in claim 1.
Regarding claim 61, it is rejected under the same rationale as claim 48, it is a system claim. In addition, it adds the additional elements of “system”.
Under step 2A, prong two, the additional elements don’t integrate the exception into a practical application of the exception as merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
As discussed previously with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Dependent claim 62-67 further defines the idea described in claim 61.
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 48-53, and 57- 66 are rejected under 35 U.S.C. 103 as being unpatentable over OWENS et al. (US 20180369611 A1 ) in view of PRIVALIKHIN et al. (US 20230390585 A1)
Regarding claim 48, a computer-implemented method of analyzing a radiotherapy dose-sparing opportunity for a subject, the computer-implemented method comprising:
determining, using computer processor circuitry, a representation of a first opportunity dose-volume histogram (ODVH) (para, “[0019] Also described herein are methods for calculating bounded dose-volume histograms (DVH) for evaluating a treatment plan. One example of a method may comprise generating a plurality of images X′.sub.1, 2, 3, . . . , j based on an acquired patient image X, where a patient target volume (PTV) is located at j different positions within a radiation-firing zone (RFZ) of a patient, calculating a dose D.sub.j for each of the images X′.sub.1, 2, 3, . . . , j by multiplying a dose calculation matrix A with a radiation-firing matrix P and X′.sub.j (D.sub.j=A.Math.P.Math.X.sub.j.sup.′), plotting a dose-volume histogram (DVH) curve for each dose D.sub.j to generate a family of j DVH curves, where the DVH curve for each dose D.sub.j may represent a volume fraction for each dose value, and generating a minimum boundary curve (min-DVH curve) and a maximum boundary curve (max-DVH curve) of the family of DVH curves. The min-DVH curve may comprise a first series of points that represent a minimum volume fraction for each dose value of the family of DVH curves, and the max-DVH curve may comprise a second series of points that represent a maximum volume fraction for each dose value.......”)
wherein the first ODVH is modelled based on an anatomy of the patient in a first imaging or radiotherapy treatment session (para, “[0098] Alternatively or additionally to evaluating a treatment plan based on PQI values calculated based on real-time acquired biological activity and/or physiological and/or anatomical data, treatment plans may also be evaluated based on a dose-volume histogram or DVH. A DVH is a plot depicting the dose levels delivered to regions or proportions of a target volume. An example of a DVH plot is shown in FIG. 7A (solid line) ......”)
and wherein an ODVH represents an achievable radiation dose-sparing range for an organ at risk (OAR) given a dose prescription for a target (para, “[0083] Alternatively or additionally to calculating a range of acceptable clinical parameters during a treatment planning session, a range of acceptable treatment plan quality parameters may be calculated. Treatment plan quality parameters may be any metric that assesses how well a particular treatment plan provides a desired radiation dose to target regions while sparing non-target regions (e.g., radiation-sensitive regions and/or normal tissue regions). Examples of plan quality scores or plan quality indices (PQI) are depicted in the table in FIG. 3B, and may include dose distribution indices or parameters that represent tumor dose coverage, DV relationships for the target regions or ROIs, conformity indices, homogeneity and dose gradient indices, etc.....”
Also, para “[0080....... The desired radiation dose may be, in some variations, a prescription that is written by a radiation oncologist and may specify dose-volume (DV) constraints for target regions (e.g., irradiation-target regions and/or irradiation-avoidance regions). These may include DV constraints for tumor regions and/or radiation-sensitive regions (e.g., organs at risk). ...”
Also, para “[0065] .... Treatment plans may be updated to increase the delivered dose to a target region if the data indicates that the target region is more radiation-resistant, and/or the irradiation area may be increased or decreased depending on changes in motion of the target region or size of the target region. Alternatively or additionally, a treatment plan may be adjusted to decrease irradiation of OARs.....” )
determining, using the computer processor circuitry, a representation of a second ODVH, wherein the second ODVH is modelled based on an anatomy of the patient in a second imaging or radiotherapy treatment session ( para, “[0019]..... The min-DVH curve may comprise a first series of points that represent a minimum volume fraction for each dose value of the family of DVH curves, and the max-DVH curve may comprise a second series of points that represent a maximum volume fraction for each dose value........... Generating a min-DVH curve may comprise fitting the first series of points to a first curve, and generating a max-DVH curve may comprise fitting the second series of points to a second curve. The dose calculation matrix A and the radiation-firing matrix P may be derived based on the acquired patient image X prior to a treatment session. In one variation, the patient image X may be a first patient image, and the method may further comprise acquiring a second patient image Y, calculating a dose D.sub.Y by multiplying the dose calculation matrix A with the radiation-firing matrix P and Y (D.sub.Y=A.Math.P.Math.Y), and plotting a DVH curve corresponding to second patient image Y. The method may further comprise generating a notification if the DVH curve of the second patient image Y is not bounded between the min-DVH curve and the max-DVH curve......”
Also, para, “[0098] Alternatively or additionally to evaluating a treatment plan based on PQI values calculated based on real-time acquired biological activity and/or physiological and/or anatomical data, treatment plans may also be evaluated based on a dose-volume histogram or DVH. A DVH is a plot depicting the dose levels delivered to regions or proportions of a target volume. An example of a DVH plot is shown in FIG. 7A (solid line) ......”)
determining, using the computer processor circuitry, a difference between the first ODVH and the second ODVH, wherein the difference represents the radiotherapy dose-sparing opportunity for the OAR (para, “[0019].......In one variation, the patient image X may be a first patient image, and the method may further comprise acquiring a second patient image Y, calculating a dose D.sub.Y by multiplying the dose calculation matrix A with the radiation-firing matrix P and Y (D.sub.Y=A.Math.P.Math.Y), and plotting a DVH curve corresponding to second patient image Y. The method may further comprise generating a notification if the DVH curve of the second patient image Y is not bounded between the min-DVH curve and the max-DVH curve. The notification may be a visual notification and/or an audio notification. The DVH curve corresponding to the second patient image Y may represent a volume fraction over the PTV for each dose value and/or the DVH curve corresponding to the second patient image Y may represent a volume fraction over an organ-at-risk (OAR) for each dose value. Methods may also comprise displaying the min-DVH curve and the max-DVH curve on a display or monitor/screen. In some variations, the display may be a display of a radiation therapy system......”
Also, “[0015] Some variations of a method for adapting a radiotherapy treatment plan may alternatively or additionally comprise calculating a dose volume histogram (DVH) for a patient target region based on the radiotherapy treatment plan and the measured biological activity data, comparing the calculated DVH with a pre-determined bounded DVH, and updating the radiotherapy treatment plan if the calculated DVH is not within the pre-determined bounded DVH.”)
generating, using the computer processor circuitry, an output that represents the determined difference, [wherein the determined difference represents an opportunity cost] (para, “[0019] ....... The dose calculation matrix A and the radiation-firing matrix P may be derived based on the acquired patient image X prior to a treatment session. In one variation, the patient image X may be a first patient image, and the method may further comprise acquiring a second patient image Y, calculating a dose D.sub.Y by multiplying the dose calculation matrix A with the radiation-firing matrix P and Y (D.sub.Y=A.Math.P.Math.Y), and plotting a DVH curve corresponding to second patient image Y. The method may further comprise generating a notification if the DVH curve of the second patient image Y is not bounded between the min-DVH curve and the max-DVH curve. The notification may be a visual notification and/or an audio notification. The DVH curve corresponding to the second patient image Y may represent a volume fraction over the PTV for each dose value and/or the DVH curve corresponding to the second patient image Y may represent a volume fraction over an organ-at-risk (OAR) for each dose value. Methods may also comprise displaying the min-DVH curve and the max-DVH curve on a display or monitor/screen......”
Also, para “[0098]......A range of desired DVH profiles may be represented by a bounded DVH, represented by the dotted lines in FIG. 7A. A bounded DVH may be defined by minimum and maximum dose distributions. A treatment plan that results in a dose distribution between the minimum and maximum dose distributions may be considered acceptable for delivery. During a treatment planning session, bounded DVH curves may be generated for each ROI (e.g., volume of interest) or target region, based on biological activity and/or physiological and/or anatomical data acquired before or during the planning session. At the time of treatment, imaging data acquired in real-time may be used to calculate an updated DVH curve for one or more of the target regions. That is, changes in biological activity, and/or physiological and/or anatomical data may also alter the DVH curve for one or more target regions. The updated DVH curve may be compared to the bounded DVH curves. If it is within range, the treatment system controller and/or clinician may determine that radiation delivery may proceed according to the treatment plan. If it is not within range, the treatment system controller and/or clinician may decide to update the treatment plan before proceeding.”)
and displaying the output to a user (para, “[0019] ....... The method may further comprise generating a notification if the DVH curve of the second patient image Y is not bounded between the min-DVH curve and the max-DVH curve. The notification may be a visual notification and/or an audio notification. The DVH curve corresponding to the second patient image Y may represent a volume fraction over the PTV for each dose value and/or the DVH curve corresponding to the second patient image Y may represent a volume fraction over an organ-at-risk (OAR) for each dose value. Methods may also comprise displaying the min-DVH curve and the max-DVH curve on a display or monitor/screen......” Note: Also, see Fig. 7A)
OWENS does not explicitly teach:
[generating, using the computer processor circuitry, an output that represents the determined difference,] wherein the determined difference represents an opportunity cost.
PRIVALIKHIN teaches:
[generating, using the computer processor circuitry, an output that represents the determined difference,] wherein the determined difference represents an opportunity cost (para,“[0011] Also disclosed herein are methods for generating and updating a GUI for radiotherapy planning One variation of a method for generating a GUI may comprise generating a first fluence map that delivers a prescribed dose to a target region using a cost function having a plurality of penalty functions including a treatment time penalty function that is more heavily weighted than the other penalty functions, calculating an amount of time T.sub.min to deliver the first fluence map, generating a second fluence map that delivers a prescribed dose to a target region and optimizes a dose to a radiation-avoidance region, calculating an amount of time T.sub.max to deliver the second fluence map, generating a plurality of fluence maps that have treatment delivery times between T.sub.min and T.sub.max, generating a bounded dose volume histogram (bDVH) for the target region that represents a dose variability across the first fluence map, the second fluence map, and the generated plurality of fluence maps,......”)
Also, “[0052] FIG. 4B depicts a flowchart representation of one variation of a method for generating (306) the second fluence map (i.e., fluence map having a treatment delivery time that may be used as the upper limit of a range of treatment times). Method (410) may comprise defining (412) a cost function having a plurality of penalty functions including an organ-at-risk (OAR) penalty function and no treatment time penalty function, and generating (414) a fluence map comprising a set of beamlet values by iteratively adjusting the beamlet values based on the cost function such that the fluence map delivers a prescribed dose to the target region and plan quality metrics are within an acceptable range..................”)
It would have been obvious for a person of ordinary skill in the art to apply cost function teachings of PRIVALIKHIN into the teachings of OWENS at the time the application was filed in order to guide the optimization and generation of the fluence map.(para , “[0002]......A cost function, which comprises a plurality of penalty functions, is defined during treatment planning to guide the optimization and generation of the fluence map so that the irradiation of non-tumor tissue is kept below a selected threshold. For example, cost functions may comprise one or more penalty functions......”)
Regarding claim 49, OWENS as modified by PRIVALIKHIN teaches the computer-implemented method of claim 48.
OWENS as modified by PRIVALIKHIN does not explicitly teach wherein the output represents a volume-specific opportunity cost.
PRIVALIKHIN further teaches wherein the output represents a volume-specific opportunity cost (para, “[0029] The treatment time and dose information displayed on the GUI may help the user to control or determine the treatment time that provides the appropriate radiation dose to the VOIs. Once the user finalizes the treatment time selection, the radiotherapy planning system may then incorporate the final, selected treatment time into a treatment time penalty function of an optimization cost function, and generate a fluence map by iteratively adjusting the beamlet weights of the fluence map based on the cost function until the resultant fluence map is one that delivers a prescribed dose to a target region within the treatment time selected by the user.......”)
It would have been obvious for a person of ordinary skill in the art to apply volume-specific cost function teachings of PRIVALIKHIN into the teachings of OWENS at the time the application was filed in order to guide the optimization and generation of the fluence map.(para , “[0002]......A cost function, which comprises a plurality of penalty functions, is defined during treatment planning to guide the optimization and generation of the fluence map so that the irradiation of non-tumor tissue is kept below a selected threshold......”)
Regarding claim 50, OWENS as modified by PRIVALIKHIN teaches the computer-implemented method of claim 48.
OWENS as modified by PRIVALIKHIN does not explicitly teach, wherein the output represents a dose-specific opportunity cost.
PRIVALIKHIN further teaches further teaches wherein the output represents a dose-specific opportunity cost (para, “[0044] FIG. 3 depicts a flowchart representation of one variation of a method for generating a radiotherapy planning GUI that depicts radiation dose information in relation to various treatment delivery times. Method (300) may comprise generating (302) a first fluence map that delivers a prescribed dose to a target region using a cost function having a plurality of penalty functions including a treatment time penalty function that is more heavily weighted than the other penalty functions,......”)
It would have been obvious for a person of ordinary skill in the art to apply dose-specific cost function teachings of PRIVALIKHIN into the teachings of OWENS at the time the application was filed in order to guide the optimization and generation of the fluence map.(para , “[0002]......A cost function, which comprises a plurality of penalty functions, is defined during treatment planning to guide the optimization and generation of the fluence map so that the irradiation of non-tumor tissue is kept below a selected threshold......”)
Regarding claim 51, OWENS as modified by PRIVALIKHIN teaches the computer-implemented method of claim 48.
OWENS further teaches wherein the output represents an aggregation of [opportunity costs over] multiple OARs (para, “[0080]......An example of a radiation dose prescription for a lung stereotactic treatment is depicted in FIG. 3A. As described in the table, a clinician may specify different types of ROIs in a patient, for example, one or more planning target volumes (PTVs; which may include a tumor mass and a margin around the tumor mass), spinal cord regions, whole lung, skin regions, ribs, heart, esophagus, liver, and particular vascular regions (e.g., regions occupied by vessels such as aorta, inferior vena cava, superior vena cava, pulmonary arteries or pulmonary vein), including at least a portion of the arteriovenous system. Some of these ROIs are irradiation-target regions (e.g., PTV) while others are irradiation-avoidance regions (e.g., spinal cord, lung, skin, vasculature). For each of ROI type, the clinician may specify a threshold for the proportion of the ROI to be irradiated, the maximum cumulative amount of radiation that can be applied over the entire ROI, and the maximum amount of radiation that can be applied to a portion of the ROI. For example, for an irradiation-target region such as a PTV region, the radiation dose prescription may specify that at least 95% of the PTV region be irradiated, with a maximum cumulative radiation level of about 50 Gy, and that no portion of the PTV may be exposed to radiation greater than 107% of the prescribed radiation level (e.g., 1.07×50 Gy=53.5 Gy)........”)
PRIVALIKHIN further teach [wherein the output represents an aggregation of] opportunity costs [over multiple OARs] (para, “[0010] .......... The initial treatment time may be calculated by generating a fluence map comprising a set of radiation beamlet weights by iteratively adjusting the beamlet weights based on a cost function comprising an OAR penalty function such that the fluence map delivers the prescribed dose to the target region and changes of a cost function value between iterations of the beamlet weights is less than a selected threshold, and calculating an amount of time to deliver the generated fluence map........”)
It would have been obvious for a person of ordinary skill in the art to apply cost function teachings of PRIVALIKHIN into the teachings of OWENS at the time the application was filed in order to guide the optimization and generation of the fluence map.(para , “[0002]......A cost function, which comprises a plurality of penalty functions, is defined during treatment planning to guide the optimization and generation of the fluence map so that the irradiation of non-tumor tissue is kept below a selected threshold......”)
Regarding claim 52, OWENS as modified by PRIVALIKHIN teaches the computer-implemented method of claim 48.
OWENS further teaches weighting the output based on an importance of the OAR (para, “[0080] One or more methods may be used to evaluate a treatment plan to determine whether the treatment plan would provide a desired dose of radiation to a ROI or target region. The desired radiation dose may be, in some variations, a prescription that is written by a radiation oncologist and may specify dose-volume (DV) constraints for target regions (e.g., irradiation-target regions and/or irradiation-avoidance regions). These may include DV constraints for tumor regions and/or radiation-sensitive regions (e.g., organs at risk). .....................Some of these ROIs are irradiation-target regions (e.g., PTV) while others are irradiation-avoidance regions (e.g., spinal cord, lung, skin, vasculature). For each of ROI type, the clinician may specify a threshold for the proportion of the ROI to be irradiated, the maximum cumulative amount of radiation that can be applied over the entire ROI, and the maximum amount of radiation that can be applied to a portion of the ROI.....”)
Regarding claim 53, OWENS as modified by PRIVALIKHIN teaches the computer-implemented method of claim 48.
OWENS further teaches further comprising receiving an input representing a minimum dose of interest (para “[0014] ....... The pre-determined bounded DVH may be calculated based on one or more of the following: dose delivered to the patient target region in one or more previous treatment session, clinician-approved dose delivery uncertainty, maximum dose level and/or minimum dose level.”
Also, para, “[0020] ....Optionally, some methods may comprise generating a notification that is displayed on the monitor if the radiation dose distribution D.sub.x is not within a dose distribution range defined by a minimum dose threshold and a maximum dose threshold. The imaging data may be PET imaging data and/or CT imaging data. Some variations may comprise calculating a plan quality index (PQI) based on the acquired imaging data x, and generating a notification that is displayed on a monitor if the PQI is not within a PQI range defined by a minimum PQI threshold and a maximum PQI threshold.”)
Regarding claim 57, OWENS as modified by PRIVALIKHIN teaches the computer-implemented method of claim 48.
OWENS further teachers:
wherein the first ODVH corresponds to a previous radiotherapy treatment fraction, and wherein the second ODVH corresponds to a current radiotherapy treatment fraction (para, “[0058]..... The net tracer flow rate may be compared with expected tracer flow rate data (e.g., collected in a previous diagnostic or treatment session) to help determine whether treatment plan adaption may be appropriate. For example, if the net tracer flow rate of a region is less than the expected tracer flow rate (e.g., as calculated during a previous treatment session), the treatment plan may be modified to permit a higher dose to be delivered to that region.....”
Also, para “[0050] ....Determining whether a previously-generated treatment plan continues to be clinically appropriate in light of the biological activity and/or physiological data may include comparing those data values with previously-approved data values (which comparison may be performed by a pre-programmed controller or processor) and/or calculating the efficacy of the current treatment plan based on current data values. In some variations, the system may display the extracted biological activity and/or physiological activity, treatment plan quality parameters, and/or related thresholds to a clinician.”)
the computer-implemented method comprising : modifying, based on the displayed output, a radiotherapy treatment plan for the current radiotherapy treatment fraction(para, “[0050]..... Determining whether a previously-generated treatment plan continues to be clinically appropriate in light of the biological activity and/or physiological data may include comparing those data values with previously-approved data values (which comparison may be performed by a pre-programmed controller or processor) and/or calculating the efficacy of the current treatment plan based on current data values. In some variations, the system may display the extracted biological activity and/or physiological activity, treatment plan quality parameters, and/or related thresholds to a clinician”.
Also, para “[0079] .... Modifications to a treatment plan may include absolute dose values for each target region, dose limits for each target region and/or cumulatively for the patient, biological effective dose values, equivalent dose values, and treatment fractionation, as described above with reference to FIG. 2B. Alternatively or additionally, depending on the type(s) of tracer(s) used, treatment plans may be adapted as described above and summarized in Table 2. In some variations, recalculating or adapting a treatment plan to incorporate biological activity and/or physiological data acquired at the time of treatment may comprise simulating one or more testing treatment plans with the acquired biological activity and/or physiological data, and selecting an updated treatment plan from the one or more testing treatment plans that meet clinical objectives and/or provides a desired dose distribution, as described above with reference to FIG. 2B. In some variations, an updated or adapted treatment plan may adhere to specified or approved PQI ranges and/or biological activity and/or physiological data ranges.......”)
Regarding claim 58, OWENS as modified by PRIVALIKHIN teaches the computer-implemented method of claim 48.
OWENS further teachers:
wherein the first ODVH corresponds to an original radiotherapy treatment plan, and wherein the second ODVH corresponds to a current radiotherapy treatment fraction (para, “[0070].... The adaptive radiotherapy treatment planning process may follow a similar methodology as normal treatment planning, generating a treatment plan and related treatment planning artifacts. In some variations, many of these artifacts are automatically generated or generated with the use of tools specifically designed to reduce the amount of time required to perform the operations. In some variations, the adaptations and adaptive treatment planning process is performed on the same software as the original treatment plan. The method 230 may then comprise delivering radiation 236 in accordance with the updated or adapted treatment plan. In some variations, the updated or adapted treatment plan may be reviewed, evaluated, and/or approved by a clinician at the time it is created and/or before radiation is delivered according to the updated plan. .....”
the computer-implemented method comprising modifying, based on the displayed output, the radiotherapy treatment plan for the current radiotherapy treatment fraction ( para, “[0048]......... The GUI may also provide a menu of commands for user selection, as well as a programming interface so that the user may enter a predetermined set of machine instructions and parameters. For example, the display may present one or more visual indicators that represent real-time patient biological activity and/or physiological data, the effect of this data on the efficacy of the current treatment plan (e.g., plan quality index or PQI, dose volume histogram or DVH, if radiation were to be delivered according to the current treatment plan with the current level of biological and/or physiological activity), and allow the clinician to decide whether to proceed with the current treatment plan, modify the treatment plan, or suspend the treatment session. In some variations, the GUI on the display may include bounded DVH curves, PQI values, and/or any treatment plan evaluation metric which have been calculated during treatment planning......”)
Regarding claim 59, OWENS as modified by PRIVALIKHIN teaches the computer-implemented method of claim 48, comprising:
OWENS further teaches receiving a selection, based on the displayed output, between an original radiotherapy treatment plan and a radiotherapy treatment plan generated subsequent to the original radiotherapy treatment plan (para, “[0070] .......The adaptive radiotherapy treatment planning process may follow a similar methodology as normal treatment planning, generating a treatment plan and related treatment planning artifacts. In some variations, many of these artifacts are automatically generated or generated with the use of tools specifically designed to reduce the amount of time required to perform the operations. In some variations, the adaptations and adaptive treatment planning process is performed on the same software as the original treatment plan. The method 230 may then comprise delivering radiation 236 in accordance with the updated or adapted treatment plan. In some variations, the updated or adapted treatment plan may be reviewed, evaluated, and/or approved by a clinician at the time it is created and/or before radiation is delivered according to the updated plan. The updated or adapted treatment plan may optionally be stored in a treatment plan database (e.g., within the memory of a controller and/or a remove server memory). ....”)
Regarding claim 60, OWENS as modified by PRIVALIKHIN teaches the computer-implemented method of claim 59.
OWENS further teaches wherein the radiotherapy treatment plan generated subsequent to the original radiotherapy treatment plan was developed during a radiotherapy treatment fraction prior to a current radiotherapy treatment fraction( para, “[0112] After each subsequent fraction of the treatment, the patient's tumor hypoxia may be re-evaluated and the boost delivered to the patient's tumor hypoxia (both absolute dose values and target boost region) may be adapted using the hypoxia data acquired from this subsequent fraction and previous fractions to further adapt the patient's course of treatment.”)
Regarding claim 61, OWENS teaches a radiotherapy system for analyzing a radiotherapy dose-sparing opportunity for a subject, the radiotherapy system comprising:
a radiation therapy device configured to deliver a dose of radiation to an anatomical region of interest (para, “[0080] One or more methods may be used to evaluate a treatment plan to determine whether the treatment plan would provide a desired dose of radiation to a ROI or target region. The desired radiation dose may be, in some variations, a prescription that is written by a radiation oncologist and may specify dose-volume (DV) constraints for target regions (e.g., irradiation-target regions and/or irradiation-avoidance regions).....”)
and a processor configured to: determine a representation of a first opportunity dose-volume histogram (ODVH) (para, “[0019] Also described herein are methods for calculating bounded dose-volume histograms (DVH) for evaluating a treatment plan. One example of a method may comprise generating a plurality of images X′.sub.1, 2, 3, . . . , j based on an acquired patient image X, where a patient target volume (PTV) is located at j different positions within a radiation-firing zone (RFZ) of a patient, calculating a dose D.sub.j for each of the images X′.sub.1, 2, 3, . . . , j by multiplying a dose calculation matrix A with a radiation-firing matrix P and X′.sub.j (D.sub.j=A.Math.P.Math.X.sub.j.sup.′), plotting a dose-volume histogram (DVH) curve for each dose D.sub.j to generate a family of j DVH curves, where the DVH curve for each dose D.sub.j may represent a volume fraction for each dose value, and generating a minimum boundary curve (min-DVH curve) and a maximum boundary curve (max-DVH curve) of the family of DVH curves. The min-DVH curve may comprise a first series of points that represent a minimum volume fraction for each dose value of the family of DVH curves, and the max-DVH curve may comprise a second series of points that represent a maximum volume fraction for each dose value.......”)
wherein the first ODVH is modelled based on an anatomy of the patient in a first imaging or radiotherapy treatment session (para, “[0098] Alternatively or additionally to evaluating a treatment plan based on PQI values calculated based on real-time acquired biological activity and/or physiological and/or anatomical data, treatment plans may also be evaluated based on a dose-volume histogram or DVH. A DVH is a plot depicting the dose levels delivered to regions or proportions of a target volume. An example of a DVH plot is shown in FIG. 7A (solid line) ......”)
determine a representation of a second ODVH, wherein the second ODVH is modelled based on an anatomy of the patient in a second imaging or radiotherapy treatment session (para, “[0019]..... The min-DVH curve may comprise a first series of points that represent a minimum volume fraction for each dose value of the family of DVH curves, and the max-DVH curve may comprise a second series of points that represent a maximum volume fraction for each dose value........... Generating a min-DVH curve may comprise fitting the first series of points to a first curve, and generating a max-DVH curve may comprise fitting the second series of points to a second curve. The dose calculation matrix A and the radiation-firing matrix P may be derived based on the acquired patient image X prior to a treatment session. In one variation, the patient image X may be a first patient image, and the method may further comprise acquiring a second patient image Y, calculating a dose D.sub.Y by multiplying the dose calculation matrix A with the radiation-firing matrix P and Y (D.sub.Y=A.Math.P.Math.Y), and plotting a DVH curve corresponding to second patient image Y. The method may further comprise generating a notification if the DVH curve of the second patient image Y is not bounded between the min-DVH curve and the max-DVH curve......”
Also, para, “[0098] Alternatively or additionally to evaluating a treatment plan based on PQI values calculated based on real-time acquired biological activity and/or physiological and/or anatomical data, treatment plans may also be evaluated based on a dose-volume histogram or DVH. A DVH is a plot depicting the dose levels delivered to regions or proportions of a target volume. An example of a DVH plot is shown in FIG. 7A (solid line) ......”)
determine a difference between the first ODVH and the second ODVH, wherein the difference represents the radiotherapy dose-sparing opportunity for an organ at risk (OAR)( (para, “[0019].......In one variation, the patient image X may be a first patient image, and the method may further comprise acquiring a second patient image Y, calculating a dose D.sub.Y by multiplying the dose calculation matrix A with the radiation-firing matrix P and Y (D.sub.Y=A.Math.P.Math.Y), and plotting a DVH curve corresponding to second patient image Y. The method may further comprise generating a notification if the DVH curve of the second patient image Y is not bounded between the min-DVH curve and the max-DVH curve. The notification may be a visual notification and/or an audio notification. The DVH curve corresponding to the second patient image Y may represent a volume fraction over the PTV for each dose value and/or the DVH curve corresponding to the second patient image Y may represent a volume fraction over an organ-at-risk (OAR) for each dose value. Methods may also comprise displaying the min-DVH curve and the max-DVH curve on a display or monitor/screen. In some variations, the display may be a display of a radiation therapy system......” Note: notification denotes finding difference.
Also, “[0015] Some variations of a method for adapting a radiotherapy treatment plan may alternatively or additionally comprise calculating a dose volume histogram (DVH) for a patient target region based on the radiotherapy treatment plan and the measured biological activity data, comparing the calculated DVH with a pre-determined bounded DVH, and updating the radiotherapy treatment plan if the calculated DVH is not within the pre-determined bounded DVH.” Note: comparing is done to find difference)
generate an output that represents the determined difference [that represents an opportunity cost] (para, “[0019] ....... The dose calculation matrix A and the radiation-firing matrix P may be derived based on the acquired patient image X prior to a treatment session. In one variation, the patient image X may be a first patient image, and the method may further comprise acquiring a second patient image Y, calculating a dose D.sub.Y by multiplying the dose calculation matrix A with the radiation-firing matrix P and Y (D.sub.Y=A.Math.P.Math.Y), and plotting a DVH curve corresponding to second patient image Y. The method may further comprise generating a notification if the DVH curve of the second patient image Y is not bounded between the min-DVH curve and the max-DVH curve. The notification may be a visual notification and/or an audio notification. The DVH curve corresponding to the second patient image Y may represent a volume fraction over the PTV for each dose value and/or the DVH curve corresponding to the second patient image Y may represent a volume fraction over an organ-at-risk (OAR) for each dose value. Methods may also comprise displaying the min-DVH curve and the max-DVH curve on a display or monitor/screen......”
Also, para “[0098]......A range of desired DVH profiles may be represented by a bounded DVH, represented by the dotted lines in FIG. 7A. A bounded DVH may be defined by minimum and maximum dose distributions. A treatment plan that results in a dose distribution between the minimum and maximum dose distributions may be considered acceptable for delivery. During a treatment planning session, bounded DVH curves may be generated for each ROI (e.g., volume of interest) or target region, based on biological activity and/or physiological and/or anatomical data acquired before or during the planning session. At the time of treatment, imaging data acquired in real-time may be used to calculate an updated DVH curve for one or more of the target regions. That is, changes in biological activity, and/or physiological and/or anatomical data may also alter the DVH curve for one or more target regions. The updated DVH curve may be compared to the bounded DVH curves. If it is within range, the treatment system controller and/or clinician may determine that radiation delivery may proceed according to the treatment plan. If it is not within range, the treatment system controller and/or clinician may decide to update the treatment plan before proceeding.”)
and display the output to a user (para, “[0019] ....... The method may further comprise generating a notification if the DVH curve of the second patient image Y is not bounded between the min-DVH curve and the max-DVH curve. The notification may be a visual notification and/or an audio notification. The DVH curve corresponding to the second patient image Y may represent a volume fraction over the PTV for each dose value and/or the DVH curve corresponding to the second patient image Y may represent a volume fraction over an organ-at-risk (OAR) for each dose value. Methods may also comprise displaying the min-DVH curve and the max-DVH curve on a display or monitor/screen......” Note: Also, see Fig. 7A)
OWENS does not explicitly teach:
[generate an output that represents the determined difference [that represents an] opportunity cost
PRIVALIKHIN teaches:
generate an output that represents the determined difference that represents an] opportunity cost (para,“[0011] Also disclosed herein are methods for generating and updating a GUI for radiotherapy planning One variation of a method for generating a GUI may comprise generating a first fluence map that delivers a prescribed dose to a target region using a cost function having a plurality of penalty functions including a treatment time penalty function that is more heavily weighted than the other penalty functions, calculating an amount of time T.sub.min to deliver the first fluence map, generating a second fluence map that delivers a prescribed dose to a target region and optimizes a dose to a radiation-avoidance region, calculating an amount of time T.sub.max to deliver the second fluence map, generating a plurality of fluence maps that have treatment delivery times between T.sub.min and T.sub.max, generating a bounded dose volume histogram (bDVH) for the target region that represents a dose variability across the first fluence map, the second fluence map, and the generated plurality of fluence maps,......”)
Also, “[0052] FIG. 4B depicts a flowchart representation of one variation of a method for generating (306) the second fluence map (i.e., fluence map having a treatment delivery time that may be used as the upper limit of a range of treatment times). Method (410) may comprise defining (412) a cost function having a plurality of penalty functions including an organ-at-risk (OAR) penalty function and no treatment time penalty function, and generating (414) a fluence map comprising a set of beamlet values by iteratively adjusting the beamlet values based on the cost function such that the fluence map delivers a prescribed dose to the target region and plan quality metrics are within an acceptable range..................”
It would have been obvious for a person of ordinary skill in the art to apply cost function teachings of PRIVALIKHIN into the teachings of OWENS at the time the application was filed in order to guide the optimization and generation of the fluence map.(para , “[0002]......A cost function, which comprises a plurality of penalty functions, is defined during treatment planning to guide the optimization and generation of the fluence map so that the irradiation of non-tumor tissue is kept below a selected threshold. For example, cost functions may comprise one or more penalty functions....).”)
Regarding claim 62, OWENS as modified by PRIVALIKHIN teaches the radiotherapy system of claim 61.
OWENS as modified by PRIVALIKHIN does not explicitly teach wherein the output represents a volume- specific output opportunity cost.
PRIVALIKHIN further teaches wherein the output represents a volume- specific output opportunity cost. para, “[0029] The treatment time and dose information displayed on the GUI may help the user to control or determine the treatment time that provides the appropriate radiation dose to the VOIs. Once the user finalizes the treatment time selection, the radiotherapy planning system may then incorporate the final, selected treatment time into a treatment time penalty function of an optimization cost function, and generate a fluence map by iteratively adjusting the beamlet weights of the fluence map based on the cost function until the resultant fluence map is one that delivers a prescribed dose to a target region within the treatment time selected by the user........”)
It would have been obvious for a person of ordinary skill in the art to apply volume-specific cost function teachings of PRIVALIKHIN into the teachings of OWENS at the time the application was filed in order to guide the optimization and generation of the fluence map.(para , “[0002]......A cost function, which comprises a plurality of penalty functions, is defined during treatment planning to guide the optimization and generation of the fluence map so that the irradiation of non-tumor tissue is kept below a selected threshold. For example, cost functions may comprise one or more penalty functions that “discourage” excessive radiation dose to organs-at-risk (OARs),....”)
Regarding claim 63, OWENS as modified by PRIVALIKHIN teaches the radiotherapy system of claim 62.
PRIVALIKHIN further teaches wherein the output represents a dose- specific opportunity cost(para, “[0044] FIG. 3 depicts a flowchart representation of one variation of a method for generating a radiotherapy planning GUI that depicts radiation dose information in relation to various treatment delivery times. Method (300) may comprise generating (302) a first fluence map that delivers a prescribed dose to a target region using a cost function having a plurality of penalty functions including a treatment time penalty function that is more heavily weighted than the other penalty functions,.......”)
It would have been obvious for a person of ordinary skill in the art to apply dose-specific cost function teachings of PRIVALIKHIN into the teachings of OWENS at the time the application was filed in order to guide the optimization and generation of the fluence map.(para , “[0002]......A cost function, which comprises a plurality of penalty functions, is defined during treatment planning to guide the optimization and generation of the fluence map so that the irradiation of non-tumor tissue is kept below a selected threshold. For example, cost functions may comprise one or more penalty functions......”)
Regarding claim 64, OWENS as modified by PRIVALIKHIN teaches the radiotherapy system of claim 62.
OWENS further teaches wherein the output represents an aggregation of [opportunity costs over] multiple OARs para, “[0080]......An example of a radiation dose prescription for a lung stereotactic treatment is depicted in FIG. 3A. As described in the table, a clinician may specify different types of ROIs in a patient, for example, one or more planning target volumes (PTVs; which may include a tumor mass and a margin around the tumor mass), spinal cord regions, whole lung, skin regions, ribs, heart, esophagus, liver, and particular vascular regions (e.g., regions occupied by vessels such as aorta, inferior vena cava, superior vena cava, pulmonary arteries or pulmonary vein), including at least a portion of the arteriovenous system. Some of these ROIs are irradiation-target regions (e.g., PTV) while others are irradiation-avoidance regions (e.g., spinal cord, lung, skin, vasculature). For each of ROI type, the clinician may specify a threshold for the proportion of the ROI to be irradiated, the maximum cumulative amount of radiation that can be applied over the entire ROI, and the maximum amount of radiation that can be applied to a portion of the ROI. For example, for an irradiation-target region such as a PTV region, the radiation dose prescription may specify that at least 95% of the PTV region be irradiated, with a maximum cumulative radiation level of about 50 Gy, and that no portion of the PTV may be exposed to radiation greater than 107% of the prescribed radiation level (e.g., 1.07×50 Gy=53.5 Gy)........”)
PRIVALIKHIN further teach [wherein the output represents an aggregation of ] opportunity costs [over multiple OARs](para, “[0010] .......... The initial treatment time may be calculated by generating a fluence map comprising a set of radiation beamlet weights by iteratively adjusting the beamlet weights based on a cost function comprising an OAR penalty function such that the fluence map delivers the prescribed dose to the target region and changes of a cost function value between iterations of the beamlet weights is less than a selected threshold, and calculating an amount of time to deliver the generated fluence map........”)
It would have been obvious for a person of ordinary skill in the art to apply cost function teachings of PRIVALIKHIN into the teachings of OWENS at the time the application was filed in order to guide the optimization and generation of the fluence map.(para , “[0002]......A cost function, which comprises a plurality of penalty functions, is defined during treatment planning to guide the optimization and generation of the fluence map so that the irradiation of non-tumor tissue is kept below a selected threshold....”)
Regarding claim 65, OWENS as modified by PRIVALIKHIN teaches the radiotherapy system of claim 62.
OWENS further teaches the processor further configured to: weight the output based on an importance of the OAR ( (para, “[0080] One or more methods may be used to evaluate a treatment plan to determine whether the treatment plan would provide a desired dose of radiation to a ROI or target region. The desired radiation dose may be, in some variations, a prescription that is written by a radiation oncologist and may specify dose-volume (DV) constraints for target regions (e.g., irradiation-target regions and/or irradiation-avoidance regions). These may include DV constraints for tumor regions and/or radiation-sensitive regions (e.g., organs at risk). ...............Some of these ROIs are irradiation-target regions (e.g., PTV) while others are irradiation-avoidance regions (e.g., spinal cord, lung, skin, vasculature). For each of ROI type, the clinician may specify a threshold for the proportion of the ROI to be irradiated, the maximum cumulative amount of radiation that can be applied over the entire ROI, and the maximum amount of radiation that can be applied to a portion of the ROI.....”)
Regarding claim 66, OWENS as modified by PRIVALIKHIN teaches the radiotherapy system of claim 62.
OWENS further teaches , the processor further configured to: receive an input representing a minimum dose of interest (para “[0014] ....... The pre-determined bounded DVH may be calculated based on one or more of the following: dose delivered to the patient target region in one or more previous treatment session, clinician-approved dose delivery uncertainty, maximum dose level and/or minimum dose level.”
Also, para, “[0020] ....Optionally, some methods may comprise generating a notification that is displayed on the monitor if the radiation dose distribution D.sub.x is not within a dose distribution range defined by a minimum dose threshold and a maximum dose threshold. The imaging data may be PET imaging data and/or CT imaging data. Some variations may comprise calculating a plan quality index (PQI) based on the acquired imaging data x, and generating a notification that is displayed on a monitor if the PQI is not within a PQI range defined by a minimum PQI threshold and a maximum PQI threshold.”)
Claims 54-56 and 67 are rejected under 35 U.S.C. 103 as being unpatentable over OWENS as modified by PRIVALIKHIN and in view of Liu et al. (US 20210322788 A1)
Regarding claim 54, OWENS as modified by PRIVALIKHIN teaches the computer-implemented method of claim 48.
OWENS as modified by PRIVALIKHIN does not explicitly teach wherein determining the difference between the first ODVH and the second ODVH includes: determining a difference between a representation of an area under a curve of the first ODVH and a representation of an area under a curve of the second ODVH.
Liu teaches wherein determining the difference between the first ODVH and the second ODVH includes: determining a difference between a representation of an area under a curve of the first ODVH and a representation of an area under a curve of the second ODVH( para, “[0035] The area under the NDIVH curve (AUC) calculates a numerical index summarizing plan robustness in a manner analogous to the equivalent uniform dose summarizing a DVH; a smaller AUC value indicates better plan robustness. This numerical value can be used in a statistical study, such as a student's t-test, to compare plan robustness between two competing treatment planning methods. In another configuration, the DVH band method may also be used with the band width at D.sub.95% of CTV to indicate the plan robustness.” Note: comparison is done to find the difference.
Also, para “[0048] Referring to FIG. 2, a point on a typical NDIVH curve (dash line) 220 is indicated by cross 210. This point indicates that 60% of the selected structure, or target, has the NDI (e.g., dose variation due to uncertainties) of at least 0.02. This gives a straightforward and quantitative idea how the dose variation is distributed within the selected structure. Using the normalized dose interval volume constraint (indicated by the line 250, in which 60% of the selected structure now has the NDI of at least 0.004), the dash line NDIVH curve 220 can be changed to the solid line NDIVH 230. This results in a more robust plan (by comparing the AUC under solid NDIVH curve 230 and the AUC under dashed NDIVH curve 220).” Note: comparing is done for determining the difference.
It would have been obvious for a person of ordinary skill in the art to apply area under curve teachings of Liu into the teachings of OWENS as modified by PRIVALIKHIN at the time the application was filed in order to determine robustness between two competing treatment planning methods. (para,“[0035]The area under the NDIVH curve (AUC) calculates a numerical index summarizing plan robustness in a manner analogous to the equivalent uniform dose summarizing a DVH; a smaller AUC value indicates better plan robustness. This numerical value can be used in a statistical study, such as a student's t-test, to compare plan robustness between two competing treatment planning methods. In another configuration, the DVH band method may also be used with the band width at D.sub.95% of CTV to indicate the plan robustness.”)
Regarding claim 55, OWENS as modified by PRIVALIKHIN and Liu teaches the computer-implemented method of claim 54.
OWENS as modified by PRIVALIKHIN and Liu does not explicitly teach weighting the determined difference using a corresponding radiotherapy dose.
Liu further teaches weighting the determined difference using a corresponding radiotherapy dose( para, “[0009].... . In one configuration, the method includes a radiotherapy plan optimization method where a normalized dose interval (NDI) is calculated as the full dose range variation divided by the maximum dose in all uncertainty scenarios for each voxel (NDI=[max−min dose]/max dose), which was then used to calculate the Normalized Dose Interval Volume Histogram (NDIVH) curves. The area under this NDIVH curve may then be used to quantify plan robustness, and the tradeoff between nominal plan quality and plan robustness can be adjusted by applying a Normalized Dose Interval Volume Constraint to the corresponding structures, such as both normal organs and targets, in treatment planning.”
[0038] When a dose volume constraint (DVC) is used to control the nominal plan quality, adjustment can be made to: (1) the weights of DVCs terms and/or (2) the position of DVCs in the dose volume histogram (DVH) curves. ........... The weights for the normalized dose interval (NDI) can be adjusted in the penalty term to explore the tradeoff between nominal plan quality and plan robustness, but it may be more effective to adjust the position of NDIVCs in the normalized dose interval volume histograms (NDIVH) curves to explore the tradeoff between nominal plan quality and plan robustness compared to simply adjusting the weight of the NDIVC term in the objective function. By assigning a smaller NDI value of a NDIVC constraint point, we can generate a more robust plan at the cost of nominal plan quality.”
It would have been obvious for a person of ordinary skill in the art to incorporate radiotherapy dose teachings of Liu into the teachings of OWENS as modified by PRIVALIKHIN and Liu at the time the application was filed in order to determine robustness between two competing treatment planning methods. (para,“[0035]The area under the NDIVH curve (AUC) calculates a numerical index summarizing plan robustness in a manner analogous to the equivalent uniform dose summarizing a DVH; a smaller AUC value indicates better plan robustness. This numerical value can be used in a statistical study, such as a student's t-test, to compare plan robustness between two competing treatment planning methods. In another configuration, the DVH band method may also be used with the band width at D.sub.95% of CTV to indicate the plan robustness.”)
Regarding claim 56, OWENS as modified by PRIVALIKHIN and Liu teaches the computer-implemented method of claim 55.
Liu further teaches normalizing the weighted determined difference ( para, “[0010] In one configuration, a method is provided for balancing radiation therapy plan robustness and nominal plan quality (without any uncertainties considered). The method includes calculating normalized dose interval for a plurality of voxels in at least one normal tissue region of interest (ROI) and at least one target ROI in a subject. A normalized dose interval volume histogram (NDIVH) may be generated using the normalized dose interval for the plurality of voxels in the at least one normal tissue ROI and the at least one target ROI in the subject and an area under the normalized dose volume histogram may be measured to determine plan robustness for the plan. The method may also include creating the radiotherapy plan using the NDIVH-based robust optimization.”)
It would have been obvious for a person of ordinary skill in the art to incorporate normalizing weighted difference teachings of Liu into the teachings of OWENS as modified by PRIVALIKHIN and Liu at the time the application was filed in order to determine robustness between two competing treatment planning methods. (para,“[0035]The area under the NDIVH curve (AUC) calculates a numerical index summarizing plan robustness in a manner analogous to the equivalent uniform dose summarizing a DVH; a smaller AUC value indicates better plan robustness. This numerical value can be used in a statistical study, such as a student's t-test, to compare plan robustness between two competing treatment planning methods. In another configuration, the DVH band method may also be used with the band width at D.sub.95% of CTV to indicate the plan robustness.”)
Regarding claim 67, OWENS as modified by PRIVALIKHIN teaches the radiotherapy system of claim 62.
OWENS as modified by PRIVALIKHIN does not explicitly teach wherein the processor configured to determine the difference between the first ODVH and the second ODVH is configured to: determine a difference between a representation of an area under a curve of the first ODVH and a representation of an area under a curve of the second ODVH.
Liu teaches determine a difference between a representation of an area under a curve of the first ODVH and a representation of an area under a curve of the second ODVH (para, “[0035] The area under the NDIVH curve (AUC) calculates a numerical index summarizing plan robustness in a manner analogous to the equivalent uniform dose summarizing a DVH; a smaller AUC value indicates better plan robustness. This numerical value can be used in a statistical study, such as a student's t-test, to compare plan robustness between two competing treatment planning methods. In another configuration, the DVH band method may also be used with the band width at D.sub.95% of CTV to indicate the plan robustness.” Note: comparison is done to find the difference.
Also, para “[0048] Referring to FIG. 2, a point on a typical NDIVH curve (dash line) 220 is indicated by cross 210. This point indicates that 60% of the selected structure, or target, has the NDI (e.g., dose variation due to uncertainties) of at least 0.02. This gives a straightforward and quantitative idea how the dose variation is distributed within the selected structure. Using the normalized dose interval volume constraint (indicated by the line 250, in which 60% of the selected structure now has the NDI of at least 0.004), the dash line NDIVH curve 220 can be changed to the solid line NDIVH 230. This results in a more robust plan (by comparing the AUC under solid NDIVH curve 230 and the AUC under dashed NDIVH curve 220).” Note: comparing is done for determining the difference.
It would have been obvious for a person of ordinary skill in the art to apply area under curve teachings of Liu into the teachings of OWENS as modified by PRIVALIKHIN at the time the application was filed in order to determine robustness between two competing treatment planning methods. (para,“[0035]The area under the NDIVH curve (AUC) calculates a numerical index summarizing plan robustness in a manner analogous to the equivalent uniform dose summarizing a DVH; a smaller AUC value indicates better plan robustness. This numerical value can be used in a statistical study, such as a student's t-test, to compare plan robustness between two competing treatment planning methods. In another configuration, the DVH band method may also be used with the band width at D.sub.95% of CTV to indicate the plan robustness.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20160129282 A1 “Disclosed herein are systems and methods for specifying treatment criteria and treatment planning parameters for patient specific radiation therapy planning. According to an aspect, a method includes receiving data about a patient, computing geometric characterization of one or more organs at risk proximate to a target volume of a patient or vice versa, and selecting relevant treatment knowledge and experience. The method also includes generating, based on the received data, computed geometric characterization, and available knowledge and experience, a first set of radiation treatment planning parameters that will lead to a high quality plan for the patient. Further, the method includes model-based prediction, based on the data, a second set or more of radiation treatment planning parameters that will lead to alternative achievable plans with different organ sparing objectives for treating the patient. The multiple sets for parameters can be used separately or in conjunction to generate treatment plans.”
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/HUMA WASEEM/Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
