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 .
Claims status: amended claims: 1, 8, 15; the rest is unchanged.
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A new reference is currently being used in the present rejection to address the newly added limitation.
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.
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.
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.
Claims 1-2, 5, 7-9, 12, 14-16, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Shao et al. “Automatic liver tumor localization using deep learning-bases liver boundary motion estimation and biomechanical modeling (DL-Bio)” American Association of Physicists in medicine, 2021, pg.7790 – 7805 in view of Kuusela et al. (US 2021/0304866 A1; Sep. 30, 2021) and further in view of LaChaine et al. (WO 2022/056630 A1; pub. March 24, 2022).
Regarding claim 1, Shao et al. disclose: A method comprising:
receiving, by a processor, an attribute of a radiation therapy treatment plan for a patient and at least one medical image of the patient (pg.7791 col2 2nd para.);
executing, by the processor, an artificial intelligence model (pg.7790 Abstract) to predict deformation data for at least one internal structure of the patient using the attribute and the at least one medical image (pg.7790 Methods & Results), wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants and their corresponding deformation data (pg.7790 Methods & Results); wherein predicted deformation data comprises deformation vectors representing spatial movement or volumetric change of at least one voxel based on a biomechanical model of the patient's internal anatomical structures (pg.7790 Methods, pg.7796 col.2); and
outputting, by the processor, the predicted deformation data (pg.7790 Methods & Results).
Shao et al. are silent about: fine-tuned for the patient based on the at least one medical image, predicted deformation data comprises deformation vectors representing spatial movement or volumetric change of a plurality of voxels distributed across the at least one medical image based on a biomechanical model of the patient's internal anatomical structures.
In a similar field of endeavor Kuusela et al. disclose: fine-tuned for the patient based on the at least one medical image (para. [0099], [0117]) motivated by the benefits for a treatment that is patient specific and avoids overexposure of healthy tissues (Kuusela et al. para. [0009]).
In light of the benefits for a treatment that is patient specific and avoids overexposure of healthy tissues as taught by Kuusela et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Shao et al. with the teachings of Kuusela et al.
Kuusela et al. are silent about: predicted deformation data comprises deformation vectors representing spatial movement or volumetric change of a plurality of voxels distributed across the at least one medical image based on a biomechanical model of the patient's internal anatomical structures.
In a similar field of endeavor LaChaine et al. disclose: the deformation can be a voxel-to-voxel deformation in time where each voxel can have a deformation vector that defines its movement from one 3D image to the next 3D image, the voxel deformation vectors point in various directions (pg.34 L1-7) motivated by the benefits for applying optimum dose to the tumor while spearing healthy tissues (LaChaine et al. pg.3 L13-14).
In light of the benefits for applying optimum dose to the tumor while spearing healthy tissues as taught by LaChaine et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Shao et al. and Kuusela et al. with the teachings of LaChaine et al.
Regarding claim 2, Shao et al. disclose: the deformation data corresponds to a movement, volume expansion, or volume shrinkage of the at least one internal structure of the patient (pg.7790 Methods & Results).
Regarding claim 5, Shao et al. disclose: transmitting, by the processor, the deformation data to a plan optimizer computer model (pg.7790 Purpose & Methods).
Regarding claim 7, Shao et al. disclose: the deformation data corresponds to one or more deformation vectors (see rejection of claim 1).
Regarding claim 8, Shao et al., Kuusela et al. and LaChaine et al. disclose: A non-transitory machine-readable storage medium having computer-executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receive an attribute of a radiation therapy treatment plan for a patient and at least one medical image of the patient; execute an artificial intelligence model to predict deformation data for at least one internal structure of the patient using the attribute and the at least one medical image, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants and their corresponding deformation data and fine-tuned for the patient based on the at least one medical image, wherein predicted deformation data comprises deformation vectors representing spatial movement or volumetric change of at a plurality of voxels distributed across the at least one medical image based on a biomechanical model of the patient's internal anatomical structures; and output the predicted deformation data (the claim contains the same substantive limitations as claim 1, therefore, the claim is rejected on the same basis).
Regarding claim 9, Shao et al. disclose: the deformation data corresponds to a movement, volume expansion, or volume shrinkage of the at least one internal structure of the patient (pg.7790 Methods & Results).
Regarding claim 12, Shao et al. disclose: the instructions further cause the one or more processors to transmit the deformation data to a plan optimizer computer model (pg.7790 Purpose & Methods).
Regarding claim 14, Shao et al. disclose: the deformation data corresponds to one or more deformation vectors (see rejection of claim 1).
Regarding claim 15, Shao et al., Kuusela et al. and LaChaine et al. disclose: A system comprising a processor configured to: receive an attribute of a radiation therapy treatment plan for a patient and at least one medical image of the patient; execute an artificial intelligence model to predict deformation data for at least one internal structure of the patient using the attribute and the at least one medical image, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants and their corresponding deformation data and fine-tuned for the patient based on the at least one medical image, wherein predicted deformation data comprises deformation vectors representing spatial movement or volumetric change of a plurality of voxels distributed across the at least one medical image based on a biomechanical model of the patient's internal anatomical structures; and output the predicted deformation data (the claim contains the same substantive limitations as claim 1, therefore, the claim is rejected on the same basis).
Regarding claim 16, Shao et al. disclose: the deformation data corresponds to a movement, volume expansion, or volume shrinkage of the at least one internal structure of the patient (pg.7790 Methods & Results).
Regarding claim 19, Shao et al. disclose: the processor is further configured to transmit the deformation data to a plan optimizer computer model (pg.7790 Purpose & Methods).
Claims 3-4, 6, 10-11, 13, 17-18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shao et al. “Automatic liver tumor localization using deep learning-bases liver boundary motion estimation and biomechanical modeling (DL-Bio)” American Association of Physicists in medicine, 2021, pg.7790 – 7805 in view of Kuusela et al. (US 2021/0304866 A1; Sep. 30, 2021) view of LaChaine et al. (WO 2022/056630 A1; pub. March 24, 2022) and further in view Donghoon et al. “Deformation driven Seq2Seq longitudinal tumor and organs-at-risk prediction for radiotherapy2021 American Association of Physicists in Medicine, wileyonlinelibrary.com/journal/mp, pg.4784-4798.
Regarding claim 3, the combined references are silent about: the deformation data is a hyperparameter used by a second model configured to predict a movement, volume expansion, or volume shrinkage of the at least one internal structure of the patient.
In a similar field of endeavor Donghoon et al. disclose: the deformation data is a hyperparameter used by a second model configured to predict a movement, volume expansion, or volume shrinkage of the at least one internal structure of the patient (pg.4786 2.2) motivated by the benefits for best DIR performance across datasets (Donghoon et al. pg.4786 col.2 para. before last).
In light of the benefits for best DIR performance across datasets as taught by Donghoon et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Shao et al., Kuusela et al. and LaChaine et al. with the teachings of Donghoon et al.
Regarding claim 4, the combined references are silent about: transmitting, by the processor, the hyperparameter to the second model.
In a similar field of endeavor Donghoon et al. disclose: transmitting, by the processor, the hyperparameter to the second model (pg.4786 2.2) motivated by the benefits for best DIR performance across datasets (Donghoon et al. pg.4786 col.2 para. before last).
In light of the benefits for best DIR performance across datasets as taught by Donghoon et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Shao et al., Kuusela et al. and LaChaine et al. with the teachings of Donghoon et al.
Regarding claim 6, the combined references are silent about: adjusting, by the processor, at least one attribute of a radiation therapy machine in accordance with the predicted deformation data
In a similar field of endeavor Donghoon et al. disclose: adjusting, by the processor, at least one attribute of a radiation therapy machine in accordance with the predicted deformation data (pg.4787 Abstract, pg.4785 col.2 1., 2., 3.) motivated by the benefits for overcoming blurring issues (Donghoon et al. pg.4785 col.2 2.).
In light of the benefits for overcoming blurring issues as taught by Donghoon et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Shao et al., Kuusela et al. and LaChaine et al. with the teachings of Donghoon et al.
Regarding claim 10, the combined references are silent about: the deformation data is a hyperparameter used by a second model configured to predict a movement, volume expansion, or volume shrinkage of the at least one internal structure of the patient.
In a similar field of endeavor Donghoon et al. disclose: the deformation data is a hyperparameter used by a second model configured to predict a movement, volume expansion, or volume shrinkage of the at least one internal structure of the patient (pg.4786 2.2) motivated by the benefits for best DIR performance across datasets (Donghoon et al. pg.4786 col.2 para. before last).
In light of the benefits for best DIR performance across datasets as taught by Donghoon et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Shao et al., Kuusela et al. and LaChaine et al. with the teachings of Donghoon et al.
Regarding claim 11, the combined references are silent about: the instructions further cause the one or more processors to transmit the hyperparameter to the second model.
In a similar field of endeavor Donghoon et al. disclose: the instructions further cause the one or more processors to transmit the hyperparameter to the second model (pg.4786 2.2) motivated by the benefits for best DIR performance across datasets (Donghoon et al. pg.4786 col.2 para. before last).
In light of the benefits for best DIR performance across datasets as taught by Donghoon et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Shao et al., Kuusela et al. and LaChaine et al. with the teachings of Donghoon et al.
Regarding claim 13, the combined references are silent about: the instructions further cause the one or more processors to adjust at least one attribute of a radiation therapy machine in accordance with the predicted deformation data
In a similar field of endeavor Donghoon et al. disclose: the instructions further cause the one or more processors to adjust at least one attribute of a radiation therapy machine in accordance with the predicted deformation data (pg.4787 Abstract, pg.4785 col.2 1., 2., 3.) motivated by the benefits for overcoming blurring issues (Donghoon et al. pg.4785 col.2 2.).
In light of the benefits for overcoming blurring issues as taught by Donghoon et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Shao et al., Kuusela et al. and LaChaine et al. with the teachings of Donghoon et al.
Regarding claim 17, the combined references are silent about: the deformation data is a hyperparameter used by a second model configured to predict a movement, volume expansion, or volume shrinkage of the at least one internal structure of the patient.
In a similar field of endeavor Donghoon et al. disclose: the deformation data is a hyperparameter used by a second model configured to predict a movement, volume expansion, or volume shrinkage of the at least one internal structure of the patient (pg.4786 2.2) motivated by the benefits for best DIR performance across datasets (Donghoon et al. pg.4786 col.2 para. before last).
In light of the benefits for best DIR performance across datasets as taught by Donghoon et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Shao et al., Kuusela et al. and LaChaine et al. with the teachings of Donghoon et al.
Regarding claim 18, the combined references are silent about: the processor is further configured to transmit the hyperparameter to the second model.
In a similar field of endeavor Donghoon et al. disclose: the processor is further configured to transmit the hyperparameter to the second model (pg.4786 2.2) motivated by the benefits for best DIR performance across datasets (Donghoon et al. pg.4786 col.2 para. before last).
In light of the benefits for best DIR performance across datasets as taught by Donghoon et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Shao et al. and Kuusela et al. with the teachings of Donghoon et al.
Regarding claim 20, the combined references are silent about: the processor is further configured to adjust at least one attribute of a radiation therapy machine in accordance with the predicted deformation data
In a similar field of endeavor Donghoon et al. disclose: the processor is further configured to adjust at least one attribute of a radiation therapy machine in accordance with the predicted deformation data (pg.4787 Abstract, pg.4785 col.2 1., 2., 3.) motivated by the benefits for overcoming blurring issues (Donghoon et al. pg.4785 col.2 2.).
In light of the benefits for overcoming blurring issues as taught by Donghoon et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Shao et al., Kuusela et al. and LaChaine et al. with the teachings of Donghoon et al.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAMADOU FAYE whose telephone number is (571)270-0371. The examiner can normally be reached Mon – Fri 9AM-6PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Uzma Alam can be reached at 571-272-3995. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MAMADOU FAYE/ Examiner, Art Unit 2884
/UZMA ALAM/ Supervisory Patent Examiner, Art Unit 2884
Prosecution Insights