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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/02/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to the newly added limitations of claims 1, 3-6 and 15-18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments, filed 07/02/2026, with respect to claims 7-14 have been fully considered and are persuasive. The claims are now rejected in view of Herbert. Please kindly see the rejection below.
Regarding claim 18, the correction of the claim to state non-transitory is acknowledged.
Claim Rejections - 35 USC § 101
Thank you for kindly correcting this matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-6 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ohishi (US 2019/0001155 A1; January 3, 2019) in view of Herbert (US 2017/0360325 A1; December 21, 2017).
Regarding claim 1, Ohishi discloses a computer-implemented radiation treatment planning method, the method comprising:
obtaining intrabody imaging data and surface imaging data for determining a radiation treatment plan including a plurality of radiation treatment fraction sessions (Paragraph 23-31; Claim 1);
using the intrabody imaging data and the surface imaging data, generating a predictive model relating 1) the intrabody imaging data having a three-dimensional (3D) patient representation to 2) a two- dimensional (2D) surface patient representation (Paragraph 23-31; Claim 1);
obtaining surface camera imaging data during a particular treatment fraction session (Paragraph 23-31, Claim 4); and
using the surface camera imaging data obtained during the particular treatment fraction session and the model, calculating a 3D patient representation during the particular treatment fraction session (Paragraph 23-33, 123; Claims 5-7).
Ohishi does not teach and during delivery of radiation therapy in the particular treatment fraction session:
detecting, based on the calculated 3D patient representation, a change in a location or an orientation of a target region of a patient's anatomy; and
modifying at least one parameter of the radiation treatment plan based on the detected change.
Herbert teaches during delivery of radiation therapy in the particular treatment fraction session:
detecting, based on the calculated 3D patient representation, a change in a location or an orientation of a target region of a patient's anatomy (Claim 1 - A computer-implemented method of controlling real-time image-guided adaptive radiation treatment of at least a portion of a region of a patient, the computer-implemented method comprising: … determining at least one real-time change of at least a portion of the region based on the approximated 3D motion field estimation; and controlling the treatment of at least a portion of the region using the determined at least one change.).; and
modifying at least one parameter of the radiation treatment plan based on the detected change ( Claim 1 - A computer-implemented method of controlling real-time image-guided adaptive radiation treatment of at least a portion of a region of a patient, the computer-implemented method comprising: … determining at least one real-time change of at least a portion of the region based on the approximated 3D motion field estimation; and controlling the treatment of at least a portion of the region using the determined at least one change.).
Therefore, it would have been obvious at the time of filing to specify the abovementioned limitation in order to ensure accurate treatment planning for the patient.
Regarding claim 3, Ohishi in view of Herbert discloses the method of claim 1. Ohishi further discloses wherein obtaining intrabody imaging data and surface imaging data for determining a radiation treatment plan including a plurality of radiation treatment fraction sessions includes using surface imaging data generated from the intrabody imaging data (Paragraph 23-31; Claim 1).
Regarding claim 4, Ohishi in view of Herbert discloses the method of claim 1. Ohishi further discloses wherein obtaining intrabody imaging data and surface imaging data for determining a radiation treatment plan including a plurality of radiation treatment fraction sessions includes using surface imaging data generated from a surface camera (Paragraph 23-31; Claim 1).
Regarding claim 5, Ohishi in view of Herbert discloses the method of claim 1. Ohishi further discloses wherein obtaining intrabody imaging data and surface imaging data is carried out during a treatment fraction session before initiating delivery of radiation therapy during that treatment fraction session (Figure 7; Paragraphs 60-62).
Regarding claim 6, Ohishi in view of Herbert discloses the method of claim 5. Ohishi further discloses wherein obtaining intrabody imaging data comprises obtaining computed tomography (CT) imaging data during a treatment fraction session prior to delivery of radiation therapy or obtaining cone-beam CT (CBCT) imaging during a treatment fraction session prior to delivery of radiation therapy (Figure 7; Paragraphs 60-71).
Regarding claim 17, Ohishi in view of Herbert discloses the method of claim 1. Herbert further discloses radiation treatment system configured to perform the method of claim 1 (Claim 14 - A system for controlling real-time image-guided adaptive radiation treatment of at least a portion of a region of a patient, the system comprising: a treatment adaptation system configured to: obtain a plurality of real-time image data corresponding to 2-dimensional (2D) magnetic resonance imaging (MRI) images including at least a portion of the region; perform 2D motion field estimation on the plurality of image data; approximate a 3-dimensional (3D) motion field estimation, including applying a conversion model to the 2D motion field estimation; determine at least one real-time change of at least a portion of the region based on the approximated 3D motion field estimation; and a therapy controller circuit configured to: control the treatment of at least a portion of the region using the determined at least one change.).
Regarding claim 18, Ohishi in view of Herbert discloses the method of claim 1. Herbert further discloses non-transitory computer readable medium encoded with instructions that, when executed by a processor, cause the processor to perform the method of claim 1 (Paragarph 68 - Example 1 includes subject matter (such as a method, means for performing acts, machine readable medium (such as a computer-readable medium) including instructions that when performed by a machine cause the machine to performs acts, or an apparatus configured to perform) of controlling real-time image-guided adaptive radiation treatment of at least a portion of a region of a patient,.).
Claim(s) 7-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ohishi (US 20190001155 A1; January 3, 2019) in view Of Herbert (US 2017/0360325 A1; December 21, 2017).
Regarding claim 7, Ohishi discloses the method of claim 1. However Ohishi does not disclose comprising binning projections from intrabody imaging data to create a 4D image.
Herbert discloses comprising binning projections from intrabody imaging data to create a 4D image. (Paragraph 51 - Image data can be obtained over a plurality of respiratory cycles, where individual respiratory cycles include a plurality of portions, and the TAS 120 can generate at least two 3D image data volumes using a central tendency of the image data in like-portions. For example, the respiration cycle can be binned and the TAS 120 can generate a 3D image by taking information from the same bins at different respiratory phases. In this manner, the TAS 120 can generate a 4D image averaged over multiple respiratory cycles.)
Therefore from the teaching of Herbert it would have been obvious at the time of filing to specify the abovementioned limitation since it is a known method of modeling patient movement while being imaged.
Regarding claim 8, Ohishi in view of Herbert discloses the method of claim 7. Herbert further discloses wherein the 4D image provides a 3D image over various respiratory phases (Paragraph 51 - In this manner, the TAS 120 can generate a 4D image averaged over multiple respiratory cycles.).
Regarding claim 9, Ohishi in view of Herbert discloses the method of claim 8. Herbert further discloses comprising:
determining a reference external surface representation corresponding to a reference respiratory phase bin (Paragraph 29 - Referring first to the 3D motion field estimation (block 204), to quantify motion in the 4D image data 200, the TAS 120 can extract a first reference 3D image data volume.)
determining a first deformation vector field (DVF) between various respiratory phase bins and the reference respiratory phase bin (Paragraph 29 - As 3D image data volumes are progressing in time, the changes between two image data volumes can be characterized as a deformation defined by a deformation vector field.; and
determining a second DVF corresponding to an external surface associated with the first DVF (Paragraph 29 - The TAS 120 can perform 3D motion field estimation by, for example, calculating deformation vector fields (DVF) to find the deformation between each successive 3D image data volume and the reference 3D image data volume.).
Regarding claim 10, Ohishi in view of Herbert discloses the method of claim 9. Herbert further discloses comprising using a principal component analysis (PCA) to generate the predictive model using at least one of the first DVF or the second DVF (Paragraph 32- As seen in FIG. 2, the dimensionality reduction technique can include applying a principal component analysis (PCA) to the 3D motion field data (block 206).).
Regarding claim 11, Ohishi in view of Herbert discloses the method of claim 10. Herbert further discloses comprising generating, using the model, an intrafractional intrabody image representation of the patient at various times during a particular radiation treatment fraction session (Paragraph 34 - As indicated above, the TAS 120 can extract 2D slices from the 4D image data volume (block 202). As with the 3D image data volumes, the TAS 120 can perform 2D motion field estimation by, for example, calculating DVFs to find the deformation between successive 2D image data (2D slices)(block 210).).
Regarding claim 12, Ohishi in view of Herbert discloses the method of claim 11. Herbert further discloses comprising:
determining, using the predictive model, at least one DVF during the particular radiation treatment fraction session (Paragraph 35 - In some examples, the TAS 120 can select slices from planes in three orthogonal directions and calculate a DVF in each of those planes.).
Regarding claim 13, Ohishi in view of Herbert discloses the method of claim 11. Herbert further discloses comprising using the generated intrafractional intrabody image representation of the patient to modify a radiation treatment parameter during that particular radiation treatment fraction session (Paragraph 27 - As described in more detail below and in accordance with this disclosure, the TAS 120 can estimate 3D motion from a series of 2D slices acquired in real-time, e.g., using an MRI, to adapt a radiation therapy treatment plan in real-time.).
Regarding claim 14, Ohishi in view of Herbert discloses the method of claim 13. Herbert further discloses wherein modifying the radiation treatment parameter during that particular radiation treatment fraction session is to accurate localization and tracking of a tumor (Paragraph 27- As described in more detail below and in accordance with this disclosure, the TAS 120 can estimate 3D motion from a series of 2D slices acquired in real-time, e.g., using an MRI, to adapt a radiation therapy treatment plan in real-time. In a tracking stage, the TAS 120 can perform 3D real-time tracking based on the conversion model built in the learning stage. The TAS 120 can determine whether a region, e.g., a target, has changed position, and then output information to the imaging and control system 112 that can allow the therapy controller circuit 116 to control the therapy in response to a determined change in position.).
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Ohishi (US 2019/0001155 A1; January 3, 2019) in view of Herbert (US 2017/0360325 A1; December 21, 2017) in view of Margo (US 2019/0220986 A1; July 18, 2019).
Regarding claim 15, Ohishi in view of Herbert discloses the method of claim 1. Ohishi in view Herbert does not teach comprising:
acquiring a 4D CBCT image during a particular radiation treatment fraction session;
generating a synthetic 4D CT image representation from the 4D CBCT image; and
generating an intrafractional intrabody image representation of the patient at various times during a particular radiation treatment fraction session using the synthetic 4D CT image representation.
Magro teaches acquiring a 4D CBCT image during a particular radiation treatment fraction session (Paragraph 63 - In an exemplary embodiment, radiotherapy system 100 may include an image acquisition device 32 configured to acquire medical images (e.g., MR images, such as 3D MRI, 2D streaming MRI, or 4D volumetric MRI, CT images, CBCT, PET images, functional MR images (e.g., fMRI, DCE-MRI, and diffusion MRI), X-ray images, fluoroscopic images, ultrasound images, radiotherapy portal images, SPECT images, etc.) of the patient. );
generating a synthetic 4D CT image representation from the 4D CBCT image (Paragraph 47 - In some embodiments, software programs 44 may convert medical images of one format (e.g., MRI) to another format (e.g., CT) by producing synthetic images, such as a pseudo-CT image. ); and
generating an intrafractional intrabody image representation of the patient at various times during a particular radiation treatment fraction session using the synthetic 4D CT image representation (Paragraph 47 - In some embodiments, software programs 44 may convert medical images of one format (e.g., MRI) to another format (e.g., CT) by producing synthetic images, such as a pseudo-CT image. For instance, software programs 44 may include image processing programs to train a predictive model for converting a medial image 46 in one modality (e.g., an MRI image) into a synthetic image of a different modality (e.g., a pseudo CT image); alternatively, the trained predictive model may convert a CT image into an MRI image. In some embodiments, software programs 44 may register one or more medical images and one or more surface images, as discussed in the embodiments herein. Memory 16 may store data, including medical images 46, surface images, patient data 45, and/or other data required to create and/or implement radiation therapy treatment plan 42.).
Therefore, from the teaching of Magro, it would have been obvious at the time of filing to specify the abovementioned limitation as it is a known method for increased detection accuracy in devices for surface motion tracking.
Regarding claim 16, Ohishi in view of Herbert in view of Magro discloses method of claim 15. Magro further teaches comprising using the generated intrafractional intrabody image representation of the patient to modify at least one parameter during that particular radiation treatment fraction session (Paragraph 50 - Further, image processor 14 may utilize software programs 44 (e.g., a treatment planning software) along with medical images 46, surface images, and/or patient data 45 to create and/or modify radiation therapy treatment plan 42.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-- US 20200185119 A1 teaches detect a change of location or shape of a treatment region of the object based on the image(s).
-US 20090253980 A1 teaches a method for determining the effectiveness of an image transformation process includes acquiring a four-dimensional (4D) image data set.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GISSELLE GUTIERREZ whose telephone number is (571)272-4672. The examiner can normally be reached M-F 8-5:00PM.
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/GISSELLE GUTIERREZ/
Examiner
Art Unit 2884
/UZMA ALAM/Supervisory Patent Examiner, Art Unit 2884
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