MONITORING ORGAN MORPHOLOGY FOR RADIATION TREATMENT DELIVERY USING ULTRASOUND

Application No. 19/288,971

• Filed: Aug 01, 2025
• Priority: Feb 03, 2023 — provisional 63/483,252
  • Jun 01, 2023 — provisional 63/505,473
  • Feb 02, 2024 — continuation of PCTUS2024014302
• Examiner: LANNU, JOSHUA DARYL DEANON
• Art Unit: 3791
• Tech Center: 3700 — Mechanical Engineering & Manufacturing
• Assignee: The Medical College of Wisconsin Inc.
• OA Round: 3 (Non-Final)

Office Action

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 .

Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/9/2026 has been entered.

Response to Arguments
Applicant’s arguments, see pages 8-12, filed 6/9/2026, with respect to claim 20 have been fully considered and are persuasive. The prior art rejections of claim 20 have been withdrawn.
Applicant’s arguments, see pages 8-12, filed 6/9/2026, with respect to the rejection(s) of claim(s) 1 and 26 under 35 U.S.C. 103 and 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art.
For claim 1, Applicant argues that Li and Vojan do not disclose the amended limitations, specifically the ultrasound transducer configuration. However, this limitation is disclosed by US 6,359,190 (Ter-Ovanesyan et al., hereinafter Ter), which will be discussed in the rejection below.
Applicant also argues differences in purpose between the applied art and the claimed invention.
Applicant further argues differences in purpose, specifically modification of treatment plan vs generating a notification for the patient when optimal conformance is achieved. While this might be true, the claim is written so broadly that this difference is not reflected in the claim. Both the argued determination when to deliver vs the modification of treatment would use the same steps. Amending the claim in a manner that reflects this difference would overcome the applied art (such as clearly pointing out that the steps do not occur during treatment).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., determination of when to deliver an existing treatment) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).

For claim 26, Applicant argues that Li does not disclose the limitation specified specifically the indication of whether the size or shape of an anatomical target agrees with a reference morphology. However, the new limitation is disclosed by US 2016/0300120 (Haas et al., hereinafter Haas).
Applicant further argues differences in purpose, specifically modification of treatment plan vs generating feedback data indicating organ’s size and shape changes. While this might be true, as noted in Haas in the rejection below, the comparison and feedback are part of the modification of a treatment plan for new patients.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., determination of when to deliver an existing treatment) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).

Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.

The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.

Claims 26-28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 26 recites the limitation “a radiation treatment plan” in line 9. It is not clear if this is a new instance or refers to the radiation treatment plan in line 2 of the claim.
Claims 27-28 inherit the deficiencies of claim 26 and are likewise rejected.

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.

The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-9, 11, 12, 14, 15, and 19, is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0304099 (Li et al., hereinafter Li) in view of US 2022/0203125 (Vojan et al., hereinafter Vojan) in view of US 6,359,190 (Ter-Ovanesyan et al., hereinafter Ter).
Regarding Claim 1, Li discloses a method for analyzing ultrasound data to monitor agreement of an anatomical target morphology with a radiation treatment plan (Fig. 1 shows receiving ultrasound imaging, which is part of the data used to generate a treatment plan for the patient; [0006], 'Adaptive Radiation Therapy (ART) is a state-of-the-art approach that uses a feedback process to account for patient-specific anatomic and/or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include: (1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient-specific spatial morphological and biological changes with consideration of radiation responses, and (3) technologies to precisely deliver the optimized plan to the patient'; [0040], 'For example, as illustrated in FIG. 1, this can include use of computed tomography (CT) devices, magnetic resonance imaging (MRI) devices, positron emission tomography (PET) imaging devices, ultrasound (US) imaging devices, and so forth'; note, when different imaging types like MRI and CT are mentioned, the same principles can be also applied to the ultrasound images), the method comprising:
(a) acquiring ultrasound data from a region-of-interest in a patient containing an anatomical target, wherein the ultrasound data are acquired using at least one ultrasound transducer arranged about the region-of­interest (Fig. 1 shows an ultrasound device, where an ultrasound device includes at least one transducer; [0106], 'These may operate on varying types of CT (e.g., cone beam CT, MVCT), as well as ultrasound (US) images. In particular for US, such approaches may implement a correspondence function designed specifically for ultrasound'; [0040], 'The process 100 typically begins at process block 10, where medical image information is generated from a patient typically using of a variety of imaging approaches, either for diagnostic or treatment purposes. For example, as illustrated in FIG. 1, this can include use of computed tomography (CT) devices, magnetic resonance imaging (MRI) devices, positron emission tomography (PET) imaging devices, ultrasound (US) imaging devices, and so forth ... First, it is used to determine true three-dimensional positions and extent of targeted diseased tissues relative to adjacent critical structures or objects at risk (OARs), which typically have radiation dose toxicity constraints. Second, it is used to localize such targets and OARs, for example, during a daily treatment setup, in order to make any treatment adjustments prior to radiation delivery.'; ultrasound may be used for imaging the region of interest);
(b) accessing with a computer system (operator workstation 102 and connected servers 110, 112, 114, and 116), reference ultrasound data acquired from the patient while the anatomical target was maintained at a specified reference morphology indicated by a radiation treatment plan (Fig. 2; [0041], 'In general, CT images are the standard imaging modality utilized for treatment planning. In a simulation stage, a patient is immobilized and imaged with reference marks that establish specific coordinates, which may subsequently be reproduced in a treatment system during radiation delivery. The acquired images are then utilized in a planning stage to generate a treatment plan. In addition to CT images, other imaging modalities offer improved contrast and other useful information related to anatomical features and biological processes of normal and diseased tissues or structures'; [0047], 'The operator workstation 102 provides the operator interface that enables scan prescriptions to be entered into the MRI system 200. In general, the operator workstation 102 may be coupled to four servers: a pulse sequence server 110; a data acquisition server 112; a data processing server 114; and a data store server 116'; [0097], 'the deformable registration method, as described, may be implemented as a software tool, either integrated or in conjunction with any radiation planning systems, image analysis or processing systems and software. Such tool may include a variety of steps and functionalities, such as, for example, the capability to ( 1) accept input of images of different modalities, (2) convert existing contours on any reference images (e.g., MRI) into delineated volumes and adjust image intensity within volumes to match target images (e.g., CT) intensity distribution for enhanced similarity metric, (3) register reference and target images using an appropriate deformable registration algorithms, as described, (e.g., b-spline, Demons) and generate deformed contours');
(c) comparing the ultrasound data with the reference ultrasound data, generating feedback data that indicate a conformance of the anatomical target morphology to the reference morphology (Fig. 2; [0006], 'Adaptive Radiation Therapy (ART) is a state-of-the-art approach that uses a feedback process to account for patient-specific anatomic and/or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include: (1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient-specific spatial morphological and biological changes with consideration of radiation responses'; [0040], 'For example, as illustrated in FIG. 1, this can include use of computed tomography (CT) devices, magnetic resonance imaging (MRI) devices, positron emission tomography (PET) imaging devices, ultrasound (US) imaging devices, and so forth'; [0097], 'the deformable registration method, as described, may be implemented as a software tool, either integrated or in conjunction with any radiation planning systems, image analysis or processing systems and software. Such tool may include a variety of steps and functionalities, such as, for example, the capability to (1) accept input of images of different modalities, (2) convert existing contours on any reference images (e.g., MRI) into delineated volumes and adjust image intensity within volumes to match target images (e.g., CT) intensity distribution for enhanced similarity metric, (3) register reference and target images using an appropriate deformable registration algorithms, as described, (e.g., b-spline, Demons) and generate deformed contours'; [0114], 'For situations when respiration is substantially different as compared to when the planning images was acquired, an online adaptive 4D planning and delivery may be implemented in the following steps: (1) a reference plan is generated based on a single-phase image (reference phase, e.g., end of inhalation) from the planning image sets; (2) a comprehensive dry-run QA will be performed for the reference plan; (3) at the time of a treatment fraction, the reference plan is modified using the SAM algorithms based on the anatomy change on the reference phase image of the 4D images acquired with patient in the treatment position immediately prior to the treatment delivery; (4) the newly created reference plan (adaptive plan) is populated based on each of the remaining phase images of the day using the SAM algorithms'; feedback related to the comparison of the reference images to the current images is provided to update the treatment plan).
Li fails to explicitly disclose using multiple ultrasound transducers coupled together by a wearable support and arranged around a region of interest and step (d) when the anatomical target morphology is optimally in agreement with the reference morphology, generating a notification and delivering the notification to the patient via a user device, wherein the notification indicates that the anatomical target morphology is optimally in agreement with the reference morphology indicated by the radiation treatment plan.
Vojan is in the field of radiotherapy workflow (Title and Abstract) and teaches (d) when an anatomical target morphology is optimally in agreement with a reference morphology, generating a notification and delivering the notification to a patient via a user device (console monitor(s) 150), wherein the notification indicates that the anatomical target morphology is optimally in agreement with the reference morphology indicated by a radiation treatment plan (Fig. 4L-O show various alignment information; [0105], 'The system administrator computer 160 may be configured to display various analytic metrics where the system administrator can monitor gantry movement, patient information, and modify various thresholds/rules described herein. The system administrator computer 160 may be configurable to display certain analytics metrics when specific thresholds/rules have been exceeded. For example, the system administrator computer 160 may be alerted if a patient has moved a specified amount during treatment'; [0226], 'In the event the patient moves to an unsatisfactory position, ( or exceeds one or more thresholds that take into the patient breathing) if the analytics server is still displaying the Patient Alignment stage (as shown in FIGS. 4L-4M), the flag that was raised that indicated the patient's acceptable position will be removed and the patient will have to get into an acceptable position again. The analytics server may display an alert such that the user is notified that the patient is not in an acceptable position for treatment. Such flagging may also be used to stop the treatment delivery or even be used to guide an auto-correction of the patient position.'; [0494], 'Additionally, or alternatively, the analytics server may generate and transmit a notification to one or more electronic devices described herein (e.g., described in FIG. IA). For instance, the analytics server may notify the end user by displaying a prompt on the page displayed in step 720 or other pages (e.g., page on the console GUis).'; [0628], 'As discussed above, the analytics server may identify one or more alignment attributes (e.g., alignment angles, dimensions, magnitudes, and/or surfaces for the patient) associated with the patient and the patient's treatment. The alignment angles may indicate the patient's optimum alignment/position on the couch, such that the effects of the radiotherapy machine (e.g., radiation emitted from the gantry) is optimized. Using the alignment information provided by the analytics server, such as by displaying on the workflow-oriented series of pages described herein, the technician may align the patient's body on the couch.'; [0629], 'The method 1200 is not limited to identifying internal positioning of target organs. The method 1200 may also be applicable to mutual position of the target organ and the other organs (sometimes referred to as organs at risk or OARs). The organs at risk may be organs near the target organ that may inevitably receive radiation when the target organs receive radiation. Using the method 1200 and the GUis described herein, the user may evaluate how the organs at risk are positioned, such that these organs at risk receive minimal radiation during the patient's treatment. In a non-limiting example, the method 1200 and the GUis described herein can be used, such that the patient is realigned based on the alignment of his/her organs at risk that are near his/her target organ. Therefore, the method 1200 is applicable to any internal target of the patient (e.g., target organ and other targets).'; [0630], 'As used herein, the method 1200 and the page 1240 are described in the context of analyzing and displaying images. It is intended that images includes any medical images received that represents the patient's organs using any medical imaging techniques and protocols, such as x-ray, CT scan, 4D CT scan, fluoroscopy imaging, ultrasound, and the like.'; [0631], 'The page 1240 may be a part of a stage-by-stage or workflow-oriented series of pages. For instance, the analytics server may display the page 1240 as a part of the workflow-oriented series of pages described herein. In a non­limiting example, the analytics server may display the page 1240 after the patient's body has been aligned using the alignment angles (e.g., set up stage), such that a technician or any other medical professional operating the console monitors may determine whether the patient's internal organs are properly aligned for treatment. That is, the analytics server may compare the presentation of the target organ (e.g., image of the target organ) with optimal treatment orientation determined by other medical professionals (e.g., treating oncologist).'; the analytics system may include a notification on a computing device when the target organ [morphology] is aligned with the optimal [reference] organ position for the radiation treatment plan).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Li with the optimal agreement notification of Vojan for the purpose of determining if the organ targets are optimally aligned with the ideal, reference position, thereby providing the radiation treatment, while minimizing damage to the surrounding organs-at-risk (Vojan; [0628]-[063l]).
Li and Vojan do not show using a plurality of ultrasound transducers coupled together by a wearable support arranged around a region of interest. In a related area, Ter discloses a device for measuring the volume of a body cavity (title and abstract). One of the possible sensors used is an ultrasonic transducer (60), and the sensors are coupled to a wearable support (20) such as a diaper (column 2, line 50-column 8, line 53). Ter states that the sensor devices coupled with the support provides the advantage of accurate measurement even if the sensor device is displaced or not optimally aligned (column 1, line 59-column 2, line 26). Thus, it would have been obvious to one of ordinary skill in the art, before the filing date of the claimed invention to modify the method of Li and Vojan to include a plurality of ultrasound transducers coupled to a wearable support as taught by Ter in order to provides the advantage of accurate measurement even if the sensor device is are displaced or not optimally aligned.

Regarding Claim 2, Li, Vojan and Ter disclose the method of claim 1. Li, Vojan, and Ter fail to explicitly disclose controlling a radiation treatment system to deliver radiation to the patient based on the feedback data indicating that the anatomical target morphology is optimally aligned with the reference morphology indicated by the radiation treatment plan. Vojan is in the field of radiotherapy workflow (Title and Abstract) and teaches controlling a radiation treatment system (system 100) to deliver radiation to the patient based on feedback data indicating that the anatomical target morphology is optimally aligned with the reference morphology indicated by the radiation treatment plan (Fig. 4L-O show various alignment information; [0105], 'The system administrator computer 160 may be configured to display various analytic metrics where the system administrator can monitor gantry movement, patient information, and modify various thresholds/rules described herein. The system administrator computer 160 may be configurable to display certain analytics metrics when specific thresholds/rules have been exceeded. For example, the system administrator computer 160 may be alerted if a patient has moved a specified amount during treatment'; [0226], 'In the event the patient moves to an unsatisfactory position, (or exceeds one or more thresholds that take into the patient breathing) if the analytics server is still displaying the Patient Alignment stage (as shown in FIGS. 4L-4M), the flag that was raised that indicated the patient's acceptable position will be removed and the patient will have to get into an acceptable position again. The analytics server may display an alert such that the user is notified that the patient is not in an acceptable position for treatment. Such flagging may also be used to stop the treatment delivery or even be used to guide an auto-correction of the patient position.'; [0494], 'Additionally, or alternatively, the analytics server may generate and transmit a notification to one or more electronic devices described herein (e.g., described in FIG. IA). For instance, the analytics server may notify the end user by displaying a prompt on the page displayed in step 720 or other pages (e.g., page on the console GUIs).'; [0628], 'As discussed above, the analytics server may identify one or more alignment attributes (e.g., alignment angles, dimensions, magnitudes, and/or surfaces for the patient) associated with the patient and the patient's treatment. The alignment angles may indicate the patient's optimum alignment/position on the couch, such that the effects of the radiotherapy machine (e.g., radiation emitted from the gantry) is optimized. Using the alignment information provided by the analytics server, such as by displaying on the workflow-oriented series of pages described herein, the technician may align the patient's body on the couch.'; [0629], 'The method 1200 is not limited to identifying internal positioning of target organs. The method 1200 may also be applicable to mutual position of the target organ and the other organs (sometimes referred to as organs at risk or OARs). The organs at risk may be organs near the target organ that may inevitably receive radiation when the target organs receive radiation. Using the method 1200 and the GUIs described herein, the user may evaluate how the organs at risk are positioned, such that these organs at risk receive minimal radiation during the patient's treatment. In a non-limiting example, the method 1200 and the GUIs described herein can be used, such that the patient is realigned based on the alignment of his/her organs at risk that are near his/ her target organ. Therefore, the method 1200 is applicable to any internal target of the patient (e.g., target organ and other targets).'; [0630], 'As used herein, the method 1200 and the page 1240 are described in the context of analyzing and displaying images. It is intended that images includes any medical images received that represents the patient's organs using any medical imaging techniques and protocols, such as x-ray, CT scan, 4D CT scan, fluoroscopy imaging, ultrasound, and the like.'; [0631], 'The page 1240 may be a part of a stage-by-stage or workflow-oriented series of pages. For instance, the analytics server may display the page 1240 as a part of the workflow-oriented series of pages described herein. In a non­limiting example, the analytics server may display the page 1240 after the patient's body has been aligned using the alignment angles (e.g., set up stage), such that a technician or any other medical professional operating the console monitors may determine whether the patient's internal organs are properly aligned for treatment. That is, the analytics server may compare the presentation of the target organ (e.g., image of the target organ) with optimal treatment orientation determined by other medical professionals (e.g., treating oncologist).'; the analytics system may include a notification on a computing device when the target organ [morphology] is aligned with the optimal [reference] organ position for the radiation treatment plan).
It would have been obvious to one of ordinary skill in the art before the priority date to modify Li, Vojan, and Ter with the optimal alignment notification of Vojan for the purpose of determining if the organ targets are optimally aligned with the ideal, reference position, thereby providing the radiation treatment, while minimizing damage to the surrounding organs-at-risk (Vojan; [0628]-[063l]).

Regarding Claim 3, Li, Vojan, and Ter disclose the method of claim 1. Li further shows wherein the specified reference morphology indicated in the reference ultrasound data is confirmed via at least one of computed tomography (CT) imaging of the patient or magnetic resonance imaging (MRI) of the patient ([0006], 'Adaptive Radiation Therapy (ART) is a state-of-the-art approach that uses a feedback process to account for patient-specific anatomic and/or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include: (1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient-specific spatial morphological and biological changes with consideration of radiation responses, and (3) technologies to precisely deliver the optimized plan to the patient. Interventions of ART may consist of both online and offline approaches.'; [0040], 'For example, as illustrated in FIG. 1, this can include use of computed tomography (CT) devices, magnetic resonance imaging (MRI) devices, positron emission tomography (PET) imaging devices, ultrasound (US) imaging devices, and so forth'; [0088], 'Image registration typically involves inter­modality images, such as CT and MR images, MRI and PET images, PET and CT images, contrast­enhanced CT images and non-contrast-enhanced CT images, ultrasound and CT images, and so on').

Regarding Claim 4, Li, Vojan, and Ter disclose the method of claim 1. Li further shows wherein the ultrasound data are continually acquired from the patient and compared with the reference ultrasound data, thereby continually generating feedback data in real-time (Fig. 2; [0006], 'Adaptive Radiation Therapy (ART) is a state-of-the-art approach that uses a feedback process to account for patient-specific anatomic and/or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include: (1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient-specific spatial morphological and biological changes with consideration of radiation responses, and (3) technologies to precisely deliver the optimized plan to the patient'; [0077], 'For instance, the mean, human respiratory period is approximately five seconds. In order to resolve motion similarly to 4D-CT (in which the respiratory cycle is decimated into 10 phases), the ideal 4D-MRI method would need to acquire an artifact-free, high-contrast, high-resolution 3D volume every 0.5 seconds or less'; [0083], 'This has advantages for MRI-gRT, in that the DC navigator, acquired for each phase encode line, has extremely low latency, and could be used in real-time tumor tracking prediction algorithms to drive the system of multi-leaf collimators (MLCs).'; [0088], 'Image registration typically involves inter-modality images, such as CT and MR images, MRI and PET images, PET and CT images, contrast-enhanced CT images and non-contrast-enhanced CT images, ultrasound and CT images, and so on'; [0097], 'the deformable registration method, as described, may be implemented as a software tool, either integrated or in conjunction with any radiation planning systems, image analysis or processing systems and software. Such tool may include a variety of steps and functionalities, such as, for example, the capability to (1) accept input of images of different modalities, (2) convert existing contours on any reference images ( e.g., MRI) into delineated volumes and adjust image intensity within volumes to match target images (e.g., CT) intensity distribution for enhanced similarity metric, (3) register reference and target images using an appropriate deformable registration algorithms, as described, (e.g., b-spline, Demons) and generate deformed contours'; [0111], 'Software tools for use in multiple-modality image registration, as described, may achieve physically accurate registration with minimized user interference and computation cost, making multi-modality deformable imaging registration fast and accurate, in particular within the context of ART re-planning or other real-time applications.'; [0114], 'the 4D plan is delivered under real-time image guidance ( e.g., orthogonal cine MRI) and its delivery may be interrupted if MLC fails to track the target due to the abrupt changes in patient positioning or respiration motion'; [0126], 'Then, at process block 1606, updated image information is provided, which may include a multi-modality image set. For example, the updated image set may include any number of magnetic resonance images, computed tomography images, ultrasound images, positron emission tomography images, and synthetic electron density images, or any combinations thereof. The updated image information may then be used at process block 1608 to generate contours of any updated target volumes. As described, such contours may be generated autonomously or semi-autonomously.'; feedback can be provided in real-time, with ultrasound contours being generated autonomously [allowing for continuous generation from the images], or semi­autonomously [also for intermittent contour generation based upon input from the user]).

Regarding Claim 5, Li, Vojan, and Ter disclose the method of claim 1. Li further shows wherein the ultrasound data are intermittently acquired from the patient and compared with the reference ultrasound data, thereby continually generating feedback data in real-time according to an intermittent schedule (Fig. 2; [0006], 'Adaptive Radiation Therapy (ART) is a state-of-the-art approach that uses a feedback process to account for patient-specific anatomic and/or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include:
(1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient-specific spatial morphological and biological changes with consideration of radiation responses, and (3) technologies to precisely deliver the optimized plan to the patient'; [0077], 'For instance, the mean, human respiratory period is approximately five seconds. In order to resolve motion similarly to 4D-CT (in which the respiratory cycle is decimated into 10 phases), the ideal 4D-MRI method would need to acquire an artifact-free, high-contrast, high­resolution 3D volume every 0.5 seconds or less'; [0083], 'This has advantages for MRI-gRT, in that the DC navigator, acquired for each phase encode line, has extremely low latency, and could be used in real-time tumor tracking prediction algorithms to drive the system of multi-leaf collimators (MLCs).';
[0088], 'Image registration typically involves inter-modality images, such as CT and MR images, MRI and PET images, PET and CT images, contrast-enhanced CT images and non-contrast-enhanced CT images, ultrasound and CT images, and so on'; [0097], 'the deformable registration method, as described, may be implemented as a software tool, either integrated or in conjunction with any radiation planning systems, image analysis or processing systems and software. Such tool may include a variety of steps and functionalities, such as, for example, the capability to (1) accept input of images of different modalities,
(2) convert existing contours on any reference images (e.g., MRI) into delineated volumes and adjust image intensity within volumes to match target images (e.g., CT) intensity distribution for enhanced similarity metric, (3) register reference and target images using an appropriate deformable registration algorithms, as described, (e.g., b-spline, Demons) and generate deformed contours'; [0111], 'Software tools for use in multiple-modality image registration, as described, may achieve physically accurate registration with minimized user interference and computation cost, making multi-modality deformable imaging registration fast and accurate, in particular within the context of ART re-planning or other real-time applications.'; [0114], 'the 4D plan is delivered under real-time image guidance (e.g., orthogonal cine MRI) and its delivery may be interrupted if MLC fails to track the target due to the abrupt changes in patient positioning or respiration motion'; [0126], 'Then, at process block 1606, updated image information is provided, which may include a multi-modality image set. For example, the updated image set may include any number of magnetic resonance images, computed tomography images, ultrasound images, positron emission tomography images, and synthetic electron density images, or any combinations thereof. The updated image information may then be used at process block 1608 to generate contours of any updated target volumes. As described, such contours may be generated autonomously or semi-autonomously.'; feedback can be provided in real-time, with ultrasound contours being generated autonomously [allowing for continuous generation from the images], or semi-autonomously [also for intermittent contour generation based upon input from the user]).

Regarding Claim 6, Li, Vojan, and Ter disclose the method of claim 1. Li fails to explicitly disclose wherein the anatomical target morphology is optimally in agreement with the reference morphology when a planned target volume (PTV) in the radiation treatment plan is in alignment with a radiation beam path while minimizing exposure of organs­at-risk (OARs) proximate to the anatomical target. Vojan is in the field of radiotherapy workflow (Title and Abstract) and teaches wherein the anatomical target morphology is optimally in agreement with the reference morphology when a planned target volume (PTV) in the radiation treatment plan is in alignment with a radiation beam path while minimizing exposure of organs-at-risk (OARs) proximate to the anatomical target (Fig. 4L-O show various alignment information; [0002], 'Radiation therapy, which is the use of ionizing radiation, is a localized treatment for a specific target tissue, such as a cancerous tumor. Ideally, radiation therapy is performed on target tissue (also referred to as the planning target volume or the target organ) that spares the surrounding normal tissue from receiving doses above specified tolerances, thereby minimizing risk of damage to healthy tissue. So that the prescribed dose is correctly supplied to the planning target volume during radiation therapy, the patient should be precisely positioned relative to the linear accelerator that provides the radiation therapy, typically using a movable treatment couch mounted on a turntable assembly.'; [0105], 'The system administrator computer 160 may be configured to display various analytic metrics where the system administrator can monitor gantry movement, patient information, and modify various thresholds/rules described herein. The system administrator computer 160 may be configurable to display certain analytics metrics when specific thresholds/rules have been exceeded. For example, the system administrator computer 160 may be alerted if a patient has moved a specified amount during treatment'; [0177], 'The RT file may also contain notes from one or more doctors regarding the patient and/or the diagnoses of the patient, and medical diagnoses tools such as x-ray scans, including computed tomography (CT) scans, MRI images, SPECT images, PET images, ultrasound images, and the like. The RT file may also include whether the treatment is a volumetric modulated arc therapy (VMAT) treatment, intensity-modulated radiation therapy (IMRT) treatment, or high dose rate (FLASH) treatment.'; [0226], 'In the event the patient moves to an unsatisfactory position, (or exceeds one or more thresholds that take into the patient breathing) if the analytics server is still displaying the Patient Alignment stage (as shown in FIGS. 4L-4M), the flag that was raised that indicated the patient's acceptable position will be removed and the patient will have to get into an acceptable position again. The analytics server may display an alert such that the user is notified that the patient is not in an acceptable position for treatment. Such flagging may also be used to stop the treatment delivery or even be used to guide an auto-correction of the patient position.'; [0494], 'Additionally, or alternatively, the analytics server may generate and transmit a notification to one or more electronic devices described herein (e.g., described in FIG. lA). For instance, the analytics server may notify the end user by displaying a prompt on the page displayed in step 720 or other pages (e.g., page on the console GUis).'; [0593], 'Graphical indicator l 120D may indicate the treatment beam projection overlaid on the model l 116D. The graphical indicator l 120D may be extracted by the analytics server from user notes, images during patient planning, and the like'; [0628], 'As discussed above, the analytics server may identify one or more alignment attributes (e.g., alignment angles, dimensions, magnitudes, and/or surfaces for the patient) associated with the patient and the patient's treatment. The alignment angles may indicate the patient's optimum alignment/position on the couch, such that the effects of the radiotherapy machine (e.g., radiation emitted from the gantry) is optimized. Using the alignment information provided by the analytics server, such as by displaying on the workflow­oriented series of pages described herein, the technician may align the patient's body on the couch.'; [0629], 'The method 1200 is not limited to identifying internal positioning of target organs. The method 1200 may also be applicable to mutual position of the target organ and the other organs (sometimes referred to as organs at risk or OARs). The organs at risk may be organs near the target organ that may inevitably receive radiation when the target organs receive radiation. Using the method 1200 and the GUis described herein, the user may evaluate how the organs at risk are positioned, such that these organs at risk receive minimal radiation during the patient's treatment. In a non-limiting example, the method 1200 and the GUis described herein can be used, such that the patient is realigned based on the alignment of his/her organs at risk that are near his/her target organ. Therefore, the method 1200 is applicable to any internal target of the patient (e.g., target organ and other targets).'; [0630], 'As used herein, the method 1200 and the page 1240 are described in the context of analyzing and displaying images. It is intended that images includes any medical images received that represents the patient's organs using any medical imaging techniques and protocols, such as x-ray, CT scan, 4D CT scan, fluoroscopy imaging, ultrasound, and the like.'; [0631], 'The page 1240 may be a part of a stage-by-stage or workflow-oriented series of pages. For instance, the analytics server may display the page 1240 as a part of the workflow-oriented series of pages described herein. In a non-limiting example, the analytics server may display the page 1240 after the patient's body has been aligned using the alignment angles (e.g., set up stage), such that a technician or any other medical professional operating the console monitors may determine whether the patient's internal organs are properly aligned for treatment. That is, the analytics server may compare the presentation of the target organ (e.g., image of the target organ) with optimal treatment orientation determined by other medical professionals (e.g., treating oncologist).'; the analytics system may include a notification on a computing device when the target organ [morphology] is aligned with the optimal [reference] organ position for the radiation treatment plan based upon the volume to be treated and the radiation path).
It would have been obvious to one of ordinary skill in the art before the priority date of the claimed invention to modify Li, Vojan, and Ter with the optimal agreement notification of Vojan for the purpose of determining if the organ targets are optimally aligned with the ideal, reference position for applying the radiation beam, thereby providing the radiation treatment, while minimizing damage to the surrounding organs-at-risk (Vojan; [0628]-[063l]).

Regarding Claim 7, Li, Vojan, and Ter disclose the method of claim 1. Li further shows wherein the anatomical target is a bladder ([0104], 'The mean VOi was calculated for bladder, prostate, and rectum with values of 91.9 percent, 68.7 percent, and 78.2 percent, respectively'; [0106], 'These may operate on varying types of CT (e.g., cone beam CT, MVCT), as well as ultrasound (US) images. In particular for US, such approaches may implement a correspondence function designed specifically for ultrasound.'; [0119], 'Contours obtained by organ delineation are a time-consuming part of any online adaptive re-planning, with OARs typically representing the majority contours to be delineated. Specifically, organs around the digestive tract, such as liver, rectum, bowels, and stomach are very problematic, since they are large organs and require many contours'; [0120], 'To overcome the drawbacks of previous online re-planning methods, in another aspect of the present disclosure, systems and methods are provided that make use of a novel Gradient-Maintenance (GM) algorithm that allows for fully automated online ART re-planning without the need of OAR contouring.'; [0121], 'As will be described, the GM algorithm calls for creating or adjusting beam or segment apertures based on a target of the day, and optimizing beam or segment weights using either ring structures generated autonomously or semi-autonomously or isodose contours automatically converted from isodose lines on, for example, an image of the day'; the bladder, stomach, and rectum are organs that can be imaged using ultrasound to help create and modify treatment plans).

Regarding Claim 8, Li, Vojan, and Ter disclose the method of claim 7. Li further shows wherein the specified reference morphology indicates a bladder filling that results in optimally positioning a planned target volume (PTV) proximate the bladder in a radiation beam path while minimizing exposure to organs­at-risk (OARs) proximate the bladder ([0015], 'The method includes providing an initial radiotherapy plan to be adapted according to an updated image set, the initial radiotherapy plan having a radiation dose distribution, determining a plurality of dose gradients using the radiation dose distribution, and defining an optimization objective using the dose gradients. The method also includes receiving the updated image set, generating, using the updated image set, an updated set of contours representative of an updated target volume, and forming a set of partial rings using the updated set of contours, the set of partial rings arranged about the updated set of contours representative of the updated target volume. The method further includes performing a plan optimization using the optimization objective and the set of partial rings, and generating a report representative of an adapted radiotherapy plan obtained using the plan optimization.'; [0104], 'The mean VOi was calculated for bladder, prostate, and rectum with values of 91.9 percent, 68.7 percent, and 78.2 percent, respectively'; [0106], 'These may operate on varying types of CT (e.g., cone beam CT, MVCT), as well as ultrasound (US) images. In particular for US, such approaches may implement a correspondence function designed specifically for ultrasound.'; [0119], 'Contours obtained by organ delineation are a time-consuming part of any online adaptive re-planning, with OARs typically representing the majority contours to be delineated. Specifically, organs around the digestive tract, such as liver, rectum, bowels, and stomach are very problematic, since they are large organs and require many contours. In addition, their size, shape and content changes drastically and unpredictably from day to day, which makes it very hard for the auto-contouring methods to accurately generate contours, typically requiring human delineation/editing.'; [0120], 'To overcome the drawbacks of previous online re-planning methods, in another aspect of the present disclosure, systems and methods are provided that make use of a novel Gradient-Maintenance (GM) algorithm that allows for fully automated online ART re-planning without the need of OAR contouring.'; [0121], 'As will be described, the GM algorithm calls for creating or adjusting beam or segment apertures based on a target of the day, and optimizing beam or segment weights using either ring structures generated autonomously or semi-autonomously or isodose contours automatically converted from isodose lines on, for example, an image of the day'; [0122], 'In this manner, transfer of dose gradients from the original to the modified plan may result in the best plan achievable with respect to allowed physical dose deposition constraints. Such approach affords several advantages over previous technologies. Specifically, generation of organs at risk (OAR), for example, on a daily basis may not require, thus reducing delineation practice to only the treatment target. In addition, traditional optimization algorithms strive to satisfy a group of objectives, specified in terms of dose volume constraints for each of the OAR, which usually requires volumes to be generated, for example, on daily image sets. By contrast, the approach of the present disclosure strive to arrive at certain dose gradients from the surface of the target toward each organ at risk, allowing the optimization to be more reproducible and predictable, and less likely to require multiple trial and error iterations'; the bladder, stomach, and rectum are organs that can be imaged using ultrasound to help create and modify treatment plans; bladder emptying changes the contours used in dose planning around target volumes; the automated system can account for this factor while optimizing the dose to remain within its constraints for the OAR; by keeping the dose within a constraint, unnecessary radiation to the OARs is reduced).

Regarding Claim 9, Li, Vojan, and Ter disclose the method of claim 8. Li further shows wherein the specified reference morphology includes a size and shape of the bladder ([0085], 'Synthetic CT images generated using systems and methods, as described, exhibit several advantageous features for MRI-based radiation therapy compared to other alternatives, including, full compatibility with existing kilovoltage (kV) and megavoltage (MV) CT-based IGRT techniques, full compatibility with rapidly developing MRI-based IGRT technologies, and full compatibility with MRI or CT-based adaptive radiotherapy, that account for daily changes in the position, size, and shape of tumor and critical structures, and permit additional reduction of margin size'; [0119], 'Contours obtained by organ delineation are a time consuming part of any online adaptive re-planning, with OARs typically representing the majority contours to be delineated. Specifically, organs around the digestive tract, such as liver, rectum, bowels, and stomach are very problematic, since they are large organs and require many contours. In addition, their size, shape and content changes drastically and unpredictably from day to day, which makes it very hard for the auto-contouring methods to accurately generate contours, typically requiring human delineation/ editing.'; [0120], 'As described, fast online re-planning methods require efficient approaches to modifying radiotherapy plans, often while a patient lies on the treatment table. To overcome the drawbacks of previous online re-planning methods, in another aspect of the present disclosure, systems and methods are provided that make use of a novel Gradient-Maintenance (GM) algorithm that allows for fully automated online ART re-planning without the need of OAR contouring.'; [0129],'By contrast, the GM algorithm may not require contours to be very accurate since only the relative positions of the structures may utilized, and variations in the organ shapes may not affect the accuracy of the PCR'; organ size and shapes can be determined, including the bladder).

Regarding Claim 11, Li, Vojan, and Ter disclose the method of claim 1. Li further shows wherein the anatomical target is a rectum ([0104], 'The mean VOI was calculated for bladder, prostate, and rectum with values of 91.9 percent, 68.7 percent, and 78.2 percent, respectively'; [0106], 'These may operate on varying types of CT (e.g., cone beam CT, MVCT), as well as ultrasound (US) images. In particular for US, such approaches may implement a correspondence function designed specifically for ultrasound.'; [0119], 'Contours obtained by organ delineation are a time-consuming part of any online adaptive re-planning, with OARs typically representing the majority contours to be delineated. Specifically, organs around the digestive tract, such as liver, rectum, bowels, and stomach are very problematic, since they are large organs and require many contours'; [0120], 'To overcome the drawbacks of previous online re-planning methods, in another aspect of the present disclosure, systems and methods are provided that make use of a novel Gradient-Maintenance (GM) algorithm that allows for fully automated online ART re-planning without the need of OAR contouring.'; [0121], 'As will be described, the GM algorithm calls for creating or adjusting beam or segment apertures based on a target of the day, and optimizing beam or segment weights using either ring structures generated autonomously or semi-autonomously or isodose contours automatically converted from isodose lines on, for example, an image of the day'; the bladder, stomach, and rectum are organs that can be imaged using ultrasound to help create and modify treatment plans).

Regarding Claim 12, Li, Vojan, and Ter disclose the method of claim 11, wherein the specified reference morphology indicates a rectal emptying that results in optimally positioning a planned target volume (PTV) proximate the rectum in a radiation beam path while minimizing exposure to organs-at-risk (OARs) proximate the rectum ([0015], 'The method includes providing an initial radiotherapy plan to be adapted according to an updated image set, the initial radiotherapy plan having a radiation dose distribution, determining a plurality of dose gradients using the radiation dose distribution, and defining an optimization objective using the dose gradients. The method also includes receiving the updated image set, generating, using the updated image set, an updated set of contours representative of an updated target volume, and forming a set of partial rings using the updated set of contours, the set of partial rings arranged about the updated set of contours representative of the updated target volume. The method further includes performing a plan optimization using the optimization objective and the set of
partial rings, and generating a report representative of an adapted radiotherapy plan obtained using the plan optimization.'; [0104], 'The mean VOI was calculated for bladder, prostate, and rectum with values of 91.9 percent, 68.7 percent, and 78.2 percent, respectively'; [0106], 'These may operate on varying types of CT (e.g., cone beam CT, MVCT), as well as ultrasound (US) images. In particular for US, such approaches may implement a correspondence function designed specifically for ultrasound.'; [0119], 'Contours obtained by organ delineation are a time-consuming part of any online adaptive re-planning, with OARs typically representing the majority contours to be delineated. Specifically, organs around the digestive tract, such as liver, rectum, bowels, and stomach are very problematic, since they are large organs and require many contours. In addition, their size, shape and content changes drastically and unpredictably from
day to day, which makes it very hard for the auto-contouring methods to accurately generate contours, typically requiring human delineation/editing.'; [0120], 'To overcome the drawbacks of previous online re-planning methods, in another aspect of the present disclosure, systems and methods are provided that make use of a novel Gradient-Maintenance (GM) algorithm that allows for fully automated online ART re-planning without the need of OAR contouring.'; [0121], 'As will be described, the GM algorithm calls for creating or adjusting beam or segment apertures based on a target of the day, and optimizing beam or segment weights using either ring structures generated autonomously or semi-autonomously or isodose contours automatically converted from isodose lines on, for example, an image of the day'; [0122], 'In this manner, transfer of dose gradients from the original to the modified plan may result in the best plan
achievable with respect to allowed physical dose deposition constraints. Such approach affords several advantages over previous technologies. Specifically, generation of organs at risk (OAR), for example, on a daily basis may not require, thus reducing delineation practice to only the treatment target. In addition, traditional optimization algorithms strive to satisfy a group of objectives, specified in terms of dose volume constraints for each of the OAR, which usually requires volumes to be generated, for example, on daily image sets. By contrast, the approach of the present disclosure strive to arrive at certain dose gradients from the surface of the target toward each organ at risk, allowing the optimization to be more reproducible and predictable, and less likely to require multiple trial and error iterations'; the bladder, stomach, and rectum are organs that can be imaged using ultrasound to help create and modify treatment plans; rectal emptying changes the contours used in dose planning around target volumes; the automated
system can account for this factor while optimizing the dose to remain within its constraints for the OAR; by keeping the dose within a constraint, unnecessary radiation to the OARs is reduced).

Regarding Claim 14, Li, Vojan, Ter disclose the method of claim 1, wherein the anatomical target is a stomach ([0104], 'The mean VOi was calculated for bladder, prostate, and rectum with values of 91.9 percent, 68.7 percent, and 78.2 percent, respectively'; [0106], 'These may operate on varying types of CT (e.g., cone beam CT, MVCT), as well as ultrasound (US) images. In particular for US, such approaches may implement a correspondence function designed specifically for ultrasound.'; [0119], 'Contours obtained by organ delineation are a time-consuming part of any online adaptive re-planning, with OARs typically representing the majority contours to be delineated. Specifically, organs around the digestive tract, such as liver, rectum, bowels, and stomach are very problematic, since they are large organs and require many contours'; [0120], 'To overcome the drawbacks of previous online re-planning methods, in another aspect of the present disclosure, systems and methods are provided that make use of a novel Gradient-Maintenance (GM) algorithm that allows for fully automated online ART re-planning without the need of OAR contouring.'; [0121], 'As will be described, the GM algorithm calls for creating or adjusting beam or segment apertures based on a target of the day, and optimizing beam or segment weights using either ring structures generated autonomously or semi-autonomously or isodose contours automatically converted from isodose lines on, for example, an image of the day'; the bladder, stomach, and rectum are organs that can be imaged using ultrasound to help create and modify treatment plans).

Regarding Claim 15, Li, Vojan, and Ter disclose the method of claim 14. Li shows wherein the specified reference morphology indicates a stomach emptying that results in optimally positioning a planned target volume (PTV) proximate the stomach in a radiation beam path while minimizing exposure to organs-at-risk (OARs) proximate the stomach ([0015], 'The method includes providing an initial radiotherapy plan to be adapted according to an updated image set, the initial radiotherapy plan having a radiation dose distribution, determining a plurality of dose gradients using the radiation dose distribution, and defining an optimization objective using the dose gradients. The method also includes receiving the updated image set, generating, using the updated image set, an updated set of contours representative of an updated target volume, and forming a set of partial rings using the updated set of contours, the set of partial rings arranged about the updated set of contours representative of the updated target volume. The method further includes performing a plan optimization using the optimization objective and the set of partial rings, and generating a report representative of an adapted radiotherapy plan obtained using the plan optimization.'; [0104], 'The mean VOi was calculated for bladder, prostate, and rectum with values of 91.9 percent, 68.7 percent, and 78.2 percent, respectively'; [0106], 'These may operate on varying types of CT (e.g., cone beam CT, MVCT), as well as ultrasound (US) images. In particular for US, such approaches may implement a correspondence function designed specifically for ultrasound.'; [0119], 'Contours obtained by organ delineation are a time-consuming part of any online adaptive re-planning, with OARs typically representing the majority contours to be delineated. Specifically, organs around the digestive tract, such as liver, rectum, bowels, and stomach are very problematic, since they are large organs and require many contours. In addition, their size, shape and content changes drastically and unpredictably from day to day, which makes it very hard for the auto-contouring methods to accurately generate contours, typically requiring human delineation/editing.'; [0120], 'To overcome the drawbacks of previous online re-planning methods, in another aspect of the present disclosure, systems and methods are provided that make use of a novel Gradient-Maintenance (GM) algorithm that allows for fully automated online ART re-planning without the need of OAR contouring.'; [0121], 'As will be described, the GM algorithm calls for creating or adjusting beam or segment apertures based on a target of the day, and optimizing beam or segment weights using either ring structures generated autonomously or semi-autonomously or isodose contours automatically converted from isodose lines on, for example, an image of the day'; [0122], 'In this manner, transfer of dose gradients from the original to the modified plan may result in the best plan achievable with respect to allowed physical dose deposition constraints. Such approach affords several advantages over previous technologies. Specifically, generation of organs at risk (OAR), for example, on a daily basis may not require, thus reducing delineation practice to only the treatment target. In addition, traditional optimization algorithms strive to satisfy a group of objectives, specified in terms of dose volume constraints for each of the OAR, which usually requires volumes to be generated, for example, on daily image sets. By contrast, the approach of the present disclosure strive to arrive at certain dose gradients from the surface of the target toward each organ at risk, allowing the optimization to be more reproducible and predictable, and less likely to require multiple trial and error iterations'; the bladder, stomach, and rectum are organs that can be imaged using ultrasound to help create and modify treatment plans; stomach emptying changes the contours used in dose planning around target volumes; the automated system can account for this factor while optimizing the dose to remain within its constraints for the OAR; by keeping the dose within a constraint, unnecessary radiation to the OARs is reduced).

Regarding Claim 19, Li, Vojan, and Ter discloses the method of claim 1. Li further shows wherein the ultrasound data are acquired from the region-of-interest in the patient using a plurality of ultrasound transducers arranged about the region-of-interest (Fig. 1 shows an ultrasound device, where an ultrasound device includes at least one transducer; [0106], 'These may operate on varying types of CT (e.g., cone beam CT, MVCT), as well as ultrasound (US) images. In particular for US, such approaches may implement a correspondence function designed specifically for ultrasound'; [0040], 'The process 100 typically begins at process block 10, where medical image information is generated from a patient typically using of a variety of imaging approaches, either for diagnostic or treatment purposes. For example, as illustrated in FIG. 1, this can include use of computed tomography (CT) devices, magnetic resonance imaging (MRI) devices, positron emission tomography (PET) imaging devices, ultrasound (US) imaging devices, and so forth ... First, it is used to determine true three-dimensional positions and extent of targeted diseased tissues relative to adjacent critical structures or objects at risk (OARs), which typically have radiation dose toxicity constraints. Second, it is used to localize such targets and OARs, for example, during a daily treatment setup, in order to make any treatment adjustments prior to radiation delivery.'; ultrasound may be used for imaging the region of interest, where receiving 3D images for positional data using ultrasound will require a plurality of transducers).

Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0304099 (Li et al., hereinafter Li) in view of US 2022/0203125 (Vojan et al., hereinafter Vojan) in view of US 6,359,190 (Ter-Ovanesyan et al., hereinafter Ter) as applied to claim 7 above, and further in view of US 2022/0240850 (Howell et al., hereinafter Howell).
Regarding Claim 10, Li, Vojan, and Ter disclose the method of claim 7. Li shows wherein the feedback data include a notification to the patient in order to conform the anatomical target morphology with the reference morphology (Fig. 2 & 16; [0006], 'Adaptive Radiation Therapy (ART) is a state-of­the-art approach that uses a feedback process to account for patient-specific anatomic and/or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include: (1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient­specific spatial morphological and biological changes with consideration of radiation responses, and (3) technologies to precisely deliver the optimized plan to the patient. Interventions of ART may consist of both online and offline approaches.'; [0047], 'The processor 108 may include a commercially available programmable machine running a commercially available operating system. The operator workstation 102 provides the operator interface that enables scan prescriptions to be entered into the MRI system 200'; [0114], 'For situations when respiration is substantially different as compared to when the planning images was acquired, an online adaptive 4D planning and delivery may be implemented in the following steps: (1) a reference plan is generated based on a single-phase image (reference phase, e.g., end of inhalation) from the planning image sets; (2) a comprehensive dry-run QA will be performed for the reference plan;(3) at the time of a treatment fraction, the reference plan is modified using the SAM algorithms based on the anatomy change on the reference phase image of the 4D images acquired with patient in the treatment position immediately prior to the treatment delivery; (4) the newly created reference plan (adaptive plan) is populated based on each of the remaining phase images of the day using the SAM algorithms'; [0128], 'Then, at process block 1614, a report is generated, representative of the adapted radiotherapy plan obtained from the plan optimization process, which may take any shape or form, as desired or required by a treatment plan verification or delivery system.'; the feedback can be in the form of a report, which can take any form on the display, to shown the updated plan with the target data matching the reference data; feedback can include anything that would affect and request adjustment to the positioning of the treatment). Li, Vojan, and Ter fail to explicitly disclose wherein the feedback data include a notification to the patient to drink more liquid to provide further filling of the bladder.
Howell is in the field of hydration sensors (Title and Abstract), solving the same problem of providing feedback to a user based upon an analysis ([0155]), and teaches wherein feedback data include a notification to a patient to drink more liquid to provide further filling of a bladder ([0032], 'The sensor includes a wireless transmitter to send measurements to, for example, a portable device. Based on the Ip Venture is in the field of hydration sensors (Title and Abstract), solving the same problem of providing feedback to a user based upon an analysis ([0155]), and teaches wherein feedback data include a notification to a patient to drink more liquid to provide further filling of a bladder ([0032], 'The sensor includes a wireless transmitter to send measurements to, for example, a portable device. Based on the measurements received, the portable device can alert the user if he needs to drink.'; [0155], 'the portable device after analyzing the measurements, wirelessly transmits an indication to the sensing element (e.g. the user needs to drink), and the sensing element can alert the user'; [0162], 'After the sensor has taken measurements, the transmitter transmits the measurements to, for example, a portable device, which, for example, can be carried by the user or another. The portable device can analyze the measurements received and provide feedback to the user, such as he is fine and does not need to drink yet.'; [0344], 'The medical monitoring device or appliance can monitor various different health or wellness conditions of a user. As an example, the medical monitoring device or appliance can monitor skin or other conditions, chemical analysis for bodily fluids, etc.'; an alert may be provided to a user to drink more fluids, which would provide hydration, as well as fill the bladder with additional fluids; the device can be used for monitoring various conditions and be used in a variety of medical situations).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Li, Vojan, and Ter with the notification to drink liquids of Howell for the purpose of indicating to the user that they should drink more fluids, thereby monitoring a condition of the user where feedback is needed in order to best provide medical service (Howell; [0032], [0344]).

Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/004099 (Li et al., hereinafter Li) in view of US 2022/0203125 (Vojan et al., hereinafter Vojan) in view of US 6,359,190 (Ter-Ovanesyan et al., hereinafter Ter) as applied to claim 11 above, and further in view of US 2018/0064413 (Nakanishi et al., hereinafter Nakanishi).
Regarding Claim 13, Li, Vojan, and Ter disclose the method of claim 11. Li further discloses wherein the feedback data include a notification to the patient in order to conform the anatomical target morphology with the reference morphology (Fig. 2 & 16; [0006], 'Adaptive Radiation Therapy (ART) is a state-of-the-art approach that uses a feedback process to account for patient-specific anatomic and/ or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include: (1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient-specific spatial morphological and biological changes with consideration of radiation responses, and (3) technologies to precisely deliver the optimized plan to the patient. Interventions of ART may consist of both online and offline approaches.'; [0047], 'The processor 108 may include a commercially available programmable machine running a commercially available operating system. The operator workstation 102 provides the operator interface that enables scan prescriptions to be entered into the MRI system 200'; [0114], 'For situations when respiration is substantially different as compared to when the planning images was acquired, an online adaptive 4D planning and delivery may be implemented in the following steps: (1) a reference plan is generated based on a single-phase image (reference phase, e.g., end of inhalation) from the planning image sets; (2) a comprehensive dry-run QA will be performed for the reference plan; (3) at the time of a treatment fraction, the reference plan is modified using the SAM algorithms based on the anatomy change on the reference phase image of the 4D images acquired with patient in the treatment position immediately prior to the treatment delivery; (4) the newly created reference plan (adaptive plan) is populated based on each of the remaining phase images of the day using the SAM algorithms'; [0128], 'Then, at process block 1614, a report is generated, representative of the adapted radiotherapy plan obtained from the plan optimization process, which may take any shape or form, as desired or required by a treatment plan verification or delivery system.'; the feedback can be in the form of a report, which can take any form on the display, to shown the updated plan with the target data matching the reference data; feedback can include anything that would affect and request adjustment to the positioning of the treatment). Li, Vojan, and Ter fail to explicitly disclose wherein the feedback data include a notification to the patient to empty the rectum.
Nakanishi is in the field of bowel movement notifications ([0030]), solving the same problem of providing feedback to a user based upon an analysis (Fig. 1 & 4), teaches wherein feedback data include a notification to a patient to empty their rectum (rectum 67; Fig. 4 & 5; [0054], 'As described above, the stool amount estimating apparatus 100 includes the ultrasonic sensor 1 that detects the position of the wall of the urinary bladder 64 and the estimator 52 that estimates a stool amount accumulated in the rectum 67 based on an output of the ultrasonic sensor l .'; [0058], 'The stool amount estimating apparatus 100 further includes the notifier 12 that issues a notification of arrival of a bowel movement timing. The estimator 52 causes the notifier 12 to operate when the estimator 52 determines that a bowel movement timing arrives.'; [0059], 'With this configuration, when the estimator 52 determines that a bowel movement timing arrives, the notifier 12 issues a notification of this arrival to an external device. In this manner, the subject wearing the ultrasonic sensor 1 or another third party can be notified of a bowel movement timing ... In this manner, the subject is encouraged to prepare for going to the lavatory relatively early').
It would have been obvious to one of ordinary skill in the art before the priority date to modify Li, Vojan, and Ter with the rectal emptying notification of Nakanishi for the purpose of indicating to the user that they empty their rectum, thereby monitoring a condition of the user where feedback is needed in order to best provide medical service ([0058]-[0059]).

Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/004099 (Li et al., hereinafter Li) in view of US 2022/0203125 (Vojan et al., hereinafter Vojan) in view of US 6,359,190 (Ter-Ovanesyan et al., hereinafter Ter) as applied to claim 14 above, and further in view of US 2022/0262483 (Rosenberg et al., hereinafter Rosenberg).
Regarding Claim 16, Li, Vojan, and Ter disclose the method of claim 14. Li further discloses wherein the feedback data include a notification to the patient in order to conform the anatomical target morphology with the reference morphology (Fig. 2 & 16; [0006], 'Adaptive Radiation Therapy (ART) is a state-of-the-art approach that uses a feedback process to account for patient-specific anatomic and/or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include: (1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient-specific spatial morphological and biological changes with consideration of radiation responses, and (3) technologies to precisely deliver the optimized plan to the patient. Interventions of ART may consist of both online and offline approaches.'; [0047], 'The processor 108 may include a commercially available programmable machine running a commercially available operating system. The operator workstation 102 provides the operator interface that enables scan prescriptions to be entered into the MRI system 200'; [0114], 'For situations when respiration is substantially different as compared to when the planning images was acquired, an online adaptive 4D planning and delivery may be implemented in the following steps: (1) a reference plan is generated based on a single-phase image (reference phase, e.g., end of inhalation) from the planning image sets; (2) a comprehensive dry-run QA will be performed for the reference plan; (3) at the time of a treatment fraction, the reference plan is modified using the SAM algorithms based on the anatomy change on the reference phase image of the 4D images acquired with patient in the treatment position immediately prior to the treatment delivery; (4) the newly created reference plan (adaptive plan) is populated based on each of the remaining phase images of the day using the SAM algorithms'; [0128], 'Then, at process block 1614, a report is generated, representative of the adapted radiotherapy plan obtained from the plan optimization process, which may take any shape or form, as desired or required by a treatment plan verification or delivery system.'; the feedback can be in the form of a report, which can take any form on the display, to shown the updated plan with the target data matching the reference data; feedback can include anything that would affect and request adjustment to the positioning of the treatment).
Li, Vojan, and Ter fail to explicitly disclose wherein the feedback data include a notification to the patient to fast.
Rosenberg is in the field of treatment planning (Title and Abstract), solving the same problem of providing feedback based upon an analysis ([0136]), and teaches wherein feedback data include a notification to a patient to fast (Fig. 1; [0035], 'As a further non-limiting example, the removal of an intestinal tumor, the repair of a hernia, open-heart surgery or other procedures performed on internal organs or structures, whether to repair those organs or structures, to excise them or parts of them, to treat them, etc., can require cutting through, dissecting and/or harming numerous muscles and muscle groups in or about, without limitation, the skull or face, the abdomen, the ribs and/or the thoracic cavity, as well as in or about all joints and appendages'; [0044], 'the systems and methods described herein may be configured to use a treatment apparatus configured to be manipulated by an individual while performing a treatment plan'; [0174], 'the first treatment plan may be generated by one or more trained machine learning models. The machine learning models 13 may be trained by training engine 9 ... The attribute data may be received by the processing device and may include an eating or drinking schedule of the user'; an eating schedule shows when to and when not to eat, which can include time for fasting).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Li, Vojan, and Ter with the fasting notification of Rosenberg for the purpose of indicating to the user that they should fast, thereby monitoring a condition of the user where feedback is needed in order to best provide medical service related to the removal of tumors within the body (Rosenberg; [0035], [0044]).

Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/004099 (Li et al., hereinafter Li) in view of US 2022/0203125 (Vojan et al., hereinafter Vojan) in view of US 6,359,190 (Ter-Ovanesyan et al., hereinafter Ter) as applied to claim 1 above, and further in view of US2017/0216625 (Pishdad et al., hereinafter Pishdad).
Regarding Claim 17, Li, Vojan, and Ter disclose the method of claim 1. Li further shows, wherein the feedback data generated by comparing the ultrasound data with the reference ultrasound data include determining the anatomical target morphology will conform with the reference morphology (Fig. 2; [0006], 'Adaptive Radiation Therapy (ART) is a state-of-the-art approach that uses a feedback process to account for patient-specific anatomic and/or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include: (1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient-specific spatial morphological and biological changes with consideration of radiation responses'; [0040], 'For example, as illustrated in FIG. 1, this can include use of computed tomography (CT) devices, magnetic resonance imaging (MRI) devices, positron emission tomography (PET) imaging devices, ultrasound (US) imaging devices, and so forth'; [0097], 'the deformable registration method, as described, may be implemented as a software tool, either integrated or in conjunction with any radiation planning systems, image analysis or processing systems and software. Such tool may include a variety of steps and functionalities, such as, for example, the capability to (1) accept input of images of different modalities, (2) convert existing contours on any reference images (e.g., MRI) into delineated volumes and adjust image intensity within volumes to match target images (e.g., CT) intensity distribution for enhanced similarity metric, (3) register reference and target images using an appropriate deformable registration algorithms, as described, (e.g., b-spline, Demons) and generate deformed contours'; [0114], 'For situations when respiration is substantially different as compared to when the planning images was acquired, an online adaptive 4D planning and delivery may be implemented in the following steps: (1) a reference plan is generated based on a single-phase image (reference phase, e.g., end of inhalation) from the planning image sets; (2) a comprehensive dry-run QA will be performed for the reference plan; (3) at the time of a treatment fraction, the reference plan is modified using the SAM algorithms based on the anatomy change on the reference phase image of the 4D images acquired with patient in the treatment position immediately prior to the treatment delivery; (4) the newly created reference plan (adaptive plan) is populated based on each of the remaining phase images of the day using the SAM algorithms'; feedback related to the comparison of the reference images to the current images is provided to update the treatment plan).
Li, Vojan, and Ter fail to explicitly disclose a timer counting down a predicted time to when the anatomical target morphology will optimally align with the reference morphology.
Pishdad is in the field of therapy control (Title and Abstract) and teaches a timer counting down a predicted time to when an anatomical target morphology will optimally align with a reference morphology (Fig. 3A, 5 & 7; [0031], 'An updated therapy protocol can be generated in an adaptive manner to align the therapy locus with the predicted target locus. A therapy protocol is generally a therapy plan that the therapy delivery system may execute'; [0062], 'FIG. 5 illustrates an exemplary cyclic motion model 500 to predict a target locus using information indicative of an earlier target locus extracted from imaging information. Imaging information can be obtained contemporaneously with radiation therapy delivery, such as to adjust a radiation therapy protocol to adaptively compensate for motion of a radiation therapy target locus. For example, imaging information can be obtained just before therapy delivery to be used in determining a predicted target locus. Information about one or more acquired images can be used to align an instance of an imaging acquisition with a portion of the cyclic motion model, such as including determining a relative time between a reference datum such as a datum 504 (corresponding to a time t0), and a time, t1, corresponding to an acquired image instance'; [0073], 'A therapy locus 620 can then be aligned with the predicted target locus 616C for therapy delivery. In this manner, the therapy locus 620 can be adaptively aligned with a time-varying target locus such as a tumor. The therapy locus 620 refers to a region of tissue to be targeted by a radiation therapy beam provided by a radiation therapy output 104.'; [0078], 'the therapy locus is adaptively aligned with the predicted target locus to one or more of (a) better align the radiation beam with a tissue target such as a tumor for treatment and (b) avoid or minimize damage to tissue or organs adjacent to the tissue target.'; a predicted time [timer] is used to determine when the target locus of therapy delivery aligns with the reference locus of therapy delivery).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Li modified with Vojan with the timer of Pishdad for the purpose of aligning the therapy based upon patient movement, thereby better aligning the radiation beam and avoiding or minimizing damage to surrounding tissue (Pishdad; [0078]).

Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/004099 (Li et al., hereinafter Li) in view of US 2022/0203125 (Vojan et al., hereinafter Vojan) in view of US 6,359,190 (Ter-Ovanesyan et al., hereinafter Ter) as applied to claim 1 above, and further in view of US 2022/0097555 (Marcil).
Regarding Claim 18, Li, Vojan, and Ter disclose the method of claim 1. Li, Vojan, and Ter fail to explicitly disclose receiving patient feedback data via the user device, wherein the patient feedback data indicate a comfort level of the patient based on a current morphology of the anatomical target.
Marcil is in the field of positioning for ultrasound imaging (Title and Abstract) and teaches receiving patient feedback data via a user device (user interface 136), wherein the patient feedback data indicate a comfort level of a patient based on a current morphology of an anatomical target (Fig. 1 & 4; [0003], 'The placement and dose of the radiation beam should be accurately controlled to ensure that the tumor receives the prescribed radiation, and the placement of the beam should be such as to minimize damage to the surrounding healthy tissue, which can be called the organ(s) at risk (OARs)'; [0005], 'Treatment planning is a process involving determination of one or more specific radiotherapy parameters (e.g., radiation beam angles, radiation intensity level at each angle, etc.) for implementing a treatment goal under the constraints. A typical treatment planning process includes delineating one or more targets and one or more OARs from a medical image of the patient, specifying radiation beam angles, or a range of angles in the case of an arc plan, and determining an aperture shape or shapes and radiation intensity levels for each shape at each beam angle. Ultrasound imaging is one type of medical imaging that can be used during treatment planning (e.g., 2D ultrasound, 3D ultrasound, 4D ultrasound). The ultrasound imaging can also be used during the radiation treatment such as to determine in real time if targets or OARs have moved.'; [0033], 'The image processing device 112 can be configured to be used to generate one or more radiation therapy treatment plans 142 to be used by the radiation therapy device 130.'; [0055], 'the radiotherapy system 100 may include a display device 134 and a user interface 136. The display device 134 may include one or more display screens that display medical images, interface information, treatment planning parameters (e.g., contours, dosages, beam angles, etc.) treatment plans, a target, localizing a target and/or tracking a target, or any related information to the user The user interface 136 may be a key board, a keypad, a touch screen or any type of device that a user may input information to radiotherapy system'; [0065], 'The position of the overlay 304 can be adjusted to provide comfort to the patient 328, such as based on contemporaneous or previous feedback from the patient 328. The marked position of the overlay 304 can be recorded and used in a subsequent radiation therapy session, such as to allow for convenient positioning of the overlay 304 without requiring further adjustments ... Such fine adjustment can help bring the ultrasound probe close enough against the patient to obtain a good quality image, while limiting the amount of discomfort felt by the patient by the probe pressing against the patient.'; the patient may provide feedback as to their comfort, which may be received through the user interface 136, while said patient is positioned to receive imaging for the radiation therapy; the patient's position would be their current morphology, and the comfort feedback would then correspond to the morphology in that position).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Li, Vojan, and Ter with the patient feedback data of Marcil for the purpose of determining the comfort of the patient while being positioned for imaging and therapy, thereby providing a positioning during treatment that will better allow for patient comfort to complete said treatment without movement, allowing for minimal damage to surrounding healthy tissue (Marcil; [0003], [0065]).

Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/004099 (Li et al., hereinafter Li) view of US 2016/0300120 (Haas et al., hereinafter Haas).
Regarding claim 26, Li discloses a method for monitoring agreement of an anatomical target morphology with a radiation treatment plan (Fig. 1 shows receiving ultrasound imaging, which is part of the data used to generate a treatment plan for the patient; [0006], 'Adaptive Radiation Therapy (ART) is a state-of-the-art approach that uses a feedback process to account for patient-specific anatomic and/or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include: (1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient-specific spatial morphological and biological changes with consideration of radiation responses, and (3) technologies to precisely deliver the optimized plan to the patient'; [0040], 'For example, as illustrated in FIG. 1, this can include use of computed tomography (CT) devices, magnetic resonance imaging (MRI) devices, positron emission tomography (PET) imaging devices, ultrasound (US) imaging devices, and so forth'; note, when different imaging types like MRI and CT are mentioned, the same principles can be also applied to the ultrasound images), the method comprising:
(a) accessing ultrasound data with a computer system (operator workstation 102 and connected servers 110, 112, 114, and 116), wherein the ultrasound data have been acquired from a region-of-interest in a patient containing an anatomical target (Fig. 1 shows an ultrasound device, where an ultrasound device includes at least one transducer; [0106], 'These may operate on varying types of CT (e.g., cone beam CT, MVCT), as well as ultrasound (US) images. In particular for US, such approaches may implement a correspondence function designed specifically for ultrasound'; [0040], 'The process 100 typically begins at process block 10, where medical image information is generated from a patient typically using of a variety of imaging approaches, either for diagnostic or treatment purposes. For example, as illustrated in FIG. 1, this can include use of computed tomography (CT) devices, magnetic resonance imaging (MRI) devices, positron emission tomography (PET) imaging devices, ultrasound (US) imaging devices, and so forth ... First, it is used to determine true three-dimensional positions and extent of targeted diseased tissues relative to adjacent critical structures or objects at risk (OARs), which typically have radiation dose toxicity constraints. Second, it is used to localize such targets and OARs, for example, during a daily treatment setup, in order to make any treatment adjustments prior to radiation delivery.'; ultrasound may be used for imaging the region of interest);
(b) accessing reference ultrasound data with the computer system (102), wherein the reference ultrasound data have been acquired from the patient while the anatomical target was maintained at a specified reference morphology indicated by a radiation treatment plan (Fig. 2; [0041], 'In general, CT images are the standard imaging modality utilized for treatment planning. In a simulation stage, a patient is immobilized and imaged with reference marks that establish specific coordinates, which may subsequently be reproduced in a treatment system during radiation delivery. The acquired images are then utilized in a planning stage to generate a treatment plan. In addition to CT images, other imaging modalities offer improved contrast and other useful information related to anatomical features and biological processes of normal and diseased tissues or structures'; [0047], 'The operator workstation 102 provides the operator interface that enables scan prescriptions to be entered into the MRI system 200. In general, the operator workstation 102 may be coupled to four servers: a pulse sequence server 110; a data acquisition server 112; a data processing server 114; and a data store server 116'; [0097], 'the deformable registration method, as described, may be implemented as a software tool, either integrated or in conjunction with any radiation planning systems, image analysis or processing systems and software. Such tool may include a variety of steps and functionalities, such as, for example, the capability to (1) accept input of images of different modalities, (2) convert existing contours on any reference images (e.g., MRI) into delineated volumes and adjust image intensity within volumes to match target images (e.g., CT) intensity distribution for enhanced similarity metric, (3) register reference and target images using an appropriate deformable registration algorithms, as described, (e.g., b-spline, Demons) and generate deformed contours');
(c) generating feedback data with the computer system (102) by comparing the ultrasound data with the reference ultrasound data, wherein the feedback data indicate an agreement of the anatomical target morphology to the reference morphology (Fig. 2; [0006], 'Adaptive Radiation Therapy (ART) is a state-of-the-art approach that uses a feedback process to account for patient-specific anatomic and/or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include: (1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient-specific spatial morphological and biological changes with consideration of radiation responses';
[0040], 'For example, as illustrated in FIG. 1, this can include use of computed tomography (CT) devices, magnetic resonance imaging (MRI) devices, positron emission tomography (PET) imaging devices, ultrasound (US) imaging devices, and so forth'; [0097], 'the deformable registration method, as described, may be implemented as a software tool, either integrated or in conjunction with any radiation planning systems, image analysis or processing systems and software. Such tool may include a variety of steps and functionalities, such as, for example, the capability to (1) accept input of images of different modalities,(2) convert existing contours on any reference images (e.g., MRI) into delineated volumes and adjust image intensity within volumes to match target images (e.g., CT) intensity distribution for enhanced similarity metric, (3) register reference and target images using an appropriate deformable registration algorithms, as described, (e.g., b-spline, Demons) and generate deformed contours'; [0114], 'For situations when respiration is substantially different as compared to when the planning images was acquired, an online adaptive 4D planning and delivery may be implemented in the following steps: (1) a reference plan is generated based on a single-phase image (reference phase, e.g., end of inhalation) from the planning image sets; (2) a comprehensive dry-run QA will be performed for the reference plan; (3) at the time of a treatment fraction, the reference plan is modified using the SAM algorithms based on the anatomy change on the reference phase image of the 4D images acquired with patient in the treatment position immediately prior to the treatment delivery; (4) the newly created reference plan (adaptive plan) is populated based
on each of the remaining phase images of the day using the SAM algorithms'; feedback related to the comparison of the reference images to the current images is provided to update the treatment plan); and
(d) outputting the feedback data to a user via the computer system (operator workstation 102 includes display 104 for user viewing; Fig. 2 & 16; [0006], 'Adaptive Radiation Therapy (ART) is a state-of-the-art approach that uses a feedback process to account for patient-specific anatomic and/or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include: (1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient specific spatial morphological and biological changes with consideration of radiation responses, and (3) technologies to precisely deliver the optimized plan to the patient. Interventions of ART may consist of both online and offline approaches.'; [0047], 'The processor 108 may include a commercially available programmable machine running a commercially available operating system. The operator workstation 102 provides the operator interface that enables scan prescriptions to be entered into the MRI system 200';[0114], 'For situations when respiration is substantially different as compared to when the planning images was acquired, an online adaptive 4D planning and delivery may be implemented in the following steps:(1) a reference plan is generated based on a single-phase image (reference phase, e.g., end of inhalation) from the planning image sets; (2) a comprehensive dry-run QA will be performed for the reference plan;(3) at the time of a treatment fraction, the reference plan is modified using the SAM algorithms based on the anatomy change on the reference phase image of the 4D images acquired with patient in the treatment position immediately prior to the treatment delivery; (4) the newly created reference plan (adaptive plan) is populated based on each of the remaining phase images of the day using the SAM algorithms'; [0128], 'Then, at process block 1614, a report is generated, representative of the adapted radiotherapy plan obtained from the plan optimization process, which may take any shape or form, as desired or required by a treatment plan verification or delivery system.'; the feedback can be in the form of a report, which can take any form on the display, to shown the updated plan with the target data matching the reference data).
However, Li does not state feedback that indicates agreement of a size or shape of the anatomical target to the reference morphology. In a related area, Haas discloses an apparatus and method that examines image similarities based on image features (see title and abstract; paragraphs 6-70). Haas describes the training of a machine using a reference image and a variety of images which take into account sizes and shapes of areas of interest and are ranked based on how well the images match a reference image (paragraphs 33, 45, 50, and 62-65). Note that the claim does not specify what degree of agreement has to occur or if has to be an exact match. Haas states that the algorithm can be performed on ultrasound images (paragraph 21). Haas states in paragraph 19 that the method allows for insight into potential dose distributions for new patients based on similar patients. Thus, it would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify the method of Li to look for agreement of a size or shape of an anatomical target to the reference morphology as taught by Haas in order to provide insight into potential dose distributions for new patients based on similar patients.

Claim(s) 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/004099 (Li et al., hereinafter Li) in view of US 2016/0300120 (Haas et al., hereinafter Haas) as applied to claim 26 above, and further in view of US 2022/0097555 (Marcil).
Regarding claim 27, Li and Haas disclose the method of claim 26. Li and Haas fail to explicitly disclose wherein generating the feedback data further includes receiving patient feedback data from the patient via the computer system and updating the feedback data based on the patient feedback data.
Marcil is in the field of positioning for ultrasound imaging (Title and Abstract) and teaches wherein generating feedback data further includes receiving patient feedback data from a patient via a computer system (user interface 136) and updating the feedback data based on the patient feedback data (Fig. 1 & 4; [0003], 'The placement and dose of the radiation beam should be accurately controlled to ensure that the tumor receives the prescribed radiation, and the placement of the beam should be such as to minimize damage to the surrounding healthy tissue, which can be called the organ(s) at risk (OARs)'; [0005], 'Treatment planning is a process involving determination of one or more specific radiotherapy parameters (e.g., radiation beam angles, radiation intensity level at each angle, etc.) for implementing a treatment goal under the constraints. A typical treatment planning process includes delineating one or more targets and one or more OARs from a medical image of the patient, specifying radiation beam angles, or a range of angles in the case of an arc plan, and determining an aperture shape or shapes and radiation intensity levels for each shape at each beam angle. Ultrasound imaging is one type of medical imaging that can be used during treatment planning (e.g., 2D ultrasound, 3D ultrasound, 4D ultrasound). The ultrasound imaging can also be used during the radiation treatment such as to determine in real time if targets or OARs have moved.'; [0033], 'The image processing device 112 can be configured to be used to generate one or more radiation therapy treatment plans 142 to be used by the radiation therapy device 130.'; [0055], 'the radiotherapy system 100 may include a display device 134 and a user interface 136. The display device 134 may include one or more display screens that display medical images, interface information, treatment planning parameters (e.g., contours, dosages, beam angles, etc.) treatment plans, a target, localizing a target and/or tracking a target, or any related information to the user The user interface 136 may be a key board, a keypad, a touch screen or any type of device that a user may input information to radiotherapy system'; [0065], 'The position of the overlay 304 can be adjusted to provide comfort to the patient 328, such as based on contemporaneous or previous feedback from the patient 328. The marked position of the overlay 304 can be recorded and used in a subsequent radiation therapy session, such as to allow for convenient positioning of the overlay 304 without requiring further adjustments ... Such fine adjustment can help bring the ultrasound probe close enough against the patient to obtain a good quality image, while limiting the amount of discomfort felt by the patient by the probe pressing against the patient.'; the patient may provide feedback as to their comfort, which may be received through the user interface 136, while said patient is positioned to receive imaging for the radiation therapy; patient feedback updates the feedback data used to position the radiation therapy device for treatment).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Li and Haas with the patient feedback data of Marcil for the purpose of determining the comfort of the patient while being positioned for imaging and therapy, thereby providing a positioning during treatment that will better allow for patient comfort to complete said treatment without movement, allowing for minimal damage to surrounding healthy tissue (Marcil; [0003], [0065]).

Claim(s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0304099 (Li et al., hereinafter Li) in view of US 2016/0300120 (Haas et al., hereinafter Haas), as applied to claim 26 above, and further in view of US 2022/0203125 (Vojan et al., hereinafter Vojan).
Regarding claim 28, Li and Haas disclose the method of claim 26. Li and Haas fail to explicitly disclose wherein outputting the feedback data to the user comprises generating a notification with the computer system, wherein the notification indicates a measure of agreement between the anatomical target morphology and the reference morphology.
Vojan is in the field of radiotherapy workflow (Title and Abstract) and teaches wherein outputting feedback data to a user comprises generating a notification with a computer system (console monitor(s) 150), wherein the notification indicates a measure of agreement between an anatomical target morphology and a reference morphology (Fig. 4L-O show various alignment information; [0105], 'The system administrator computer 160 may be configured to display various analytic metrics where the system administrator can monitor gantry movement, patient information, and modify various thresholds/rules described herein. The system administrator computer 160 may be configurable to display certain analytics metrics when specific thresholds/rules have been exceeded. For example, the system administrator computer 160 may be alerted if a patient has moved a specified amount during treatment'; [0226], 'In the event the patient moves to an unsatisfactory position, (or exceeds one or more thresholds that take into the patient breathing) if the analytics server is still displaying the Patient Alignment stage (as shown in FIGS. 4L-4M), the flag that was raised that indicated the patient's acceptable position will be removed and the patient will have to get into an acceptable position again. The analytics server may display an alert such that the user is notified that the patient is not in an acceptable position for treatment. Such flagging may also be used to stop the treatment delivery or even be used to guide an auto-correction of the patient position.'; [0494], 'Additionally, or alternatively, the analytics server may generate and transmit a notification to one or more electronic devices described herein (e.g., described in FIG. IA). For instance, the analytics server may notify the end user by displaying a prompt on the page displayed in step 720 or other pages (e.g., page on the console GUis).'; [0628], 'As discussed above, the analytics server may identify one or more alignment attributes (e.g., alignment angles, dimensions, magnitudes, and/or surfaces for the patient) associated with the patient and the patient's treatment. The alignment angles may indicate the patient's optimum alignment/position on the couch, such that the effects of the radiotherapy machine (e.g., radiation emitted from the gantry) is optimized. Using the alignment information provided by the analytics server, such as by displaying on the workflow-oriented series of pages described herein, the technician may align the patient's body on the couch.'; [0629], 'The method 1200 is not limited to identifying internal positioning of target organs. The method 1200 may also be applicable to mutual position of the target organ and the other organs (sometimes referred to as organs at risk or OARs). The organs at risk may be organs near the target organ that may inevitably receive radiation when the target organs receives radiation. Using the method 1200 and the GUis described herein, the user may evaluate how the organs at risk are positioned, such that these organs at risk receive minimal radiation during the patient's treatment. In a non-limiting example, the method 1200 and the GUis described herein can be used, such that the patient is realigned based on the alignment of his/her organs at risk that are near his/ her target organ. Therefore, the method 1200 is applicable to any internal target of the patient (e.g., target organ and other targets).'; [0630], 'As used herein, the method 1200 and the page 1240 are described in the context of analyzing and displaying images. It is intended that images includes any medical images received that represents the patient's organs using any medical imaging techniques and protocols, such as x-ray, CT scan, 4D CT scan, fluoroscopy imaging, ultrasound, and the like.'; [0631], 'The page 1240 may be a part of a stage-by-stage or workflow-oriented series of pages. For instance, the analytics server may display the page 1240 as a part of the workflow-oriented series of pages described herein. In a non­limiting example, the analytics server may display the page 1240 after the patient's body has been aligned using the alignment angles (e.g., set up stage), such that a technician or any other medical professional operating the console monitors may determine whether the patient's internal organs are properly aligned for treatment. That is, the analytics server may compare the presentation of the target organ (e.g., image of the target organ) with optimal treatment orientation determined by other medical professionals (e.g., treating oncologist).'; the analytics system may include a notification on a computing device when the target organ [morphology] is aligned with the optimal [reference] organ position for the radiation treatment plan).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Li and Haas with the optimal agreement notification of Vojan for the purpose of determining if the organ targets are optimally aligned with the ideal, reference position, thereby providing the radiation treatment, while minimizing damage to the surrounding organs-at-risk (Vojan; [0628]-[063l]).

Allowable Subject Matter
Claims 20-25 and 29-30 are allowed.
The following is a statement of reasons for the indication of allowable subject matter:

In regards to claim 20, the prior art of record does not teach or suggest a device, as claimed by Applicant, wherein the user interface is configured to enable the patient to monitor the conformance of the anatomical target morphology.
Claims 21-25 are dependent on allowed matter from claim 20 and are allowed.

In regards to claim 29, the prior art of record does not teach or suggest a method, as claimed by Applicant, that includes the steps of
comparing the ultrasound data with the reference ultrasound data, generating feedback data that include a timer counting down a predicted time to when the anatomical target morphology will optimally align with the reference morphology; and
generating a notification indicating a rate of change in the morphology of the anatomical target based on the feedback data and delivering the notification to the patient via a user device.
Claim 30 is dependent on allowed matter from claim 29 and are allowed.

Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA DARYL DEANON LANNU whose telephone number is [phone]. The examiner can normally be reached Monday-Thursday 8 AM - 5 PM, Friday 8 AM -12 PM.
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/JOSHUA DARYL D LANNU/Examiner, Art Unit 3791
/CARRIE R DORNA/Primary Examiner, Art Unit 3791

Prosecution Timeline

• Aug 01, 2025: Application Filed
• Oct 21, 2025: Non-Final Rejection mailed — §103, §112
• Jan 21, 2026: Response Filed
• Mar 09, 2026: Final Rejection mailed — §103, §112
• Jun 09, 2026: Request for Continued Examination
• Jun 18, 2026: Response after Non-Final Action
• Jul 28, 2026: Non-Final Rejection mailed — §103, §112 (current)