CTFR 17/958,225 CTFR 94128 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Status of the Claims The status of the claims as of the response filed 3/13/2026 is as follows: Claims 6-7, 13-14, and 20 remain cancelled. Claims 1, 8, and 15 are currently amended. Claims 2-5, 9-12, and 16-19 are original. Claims 1-5, 8-12, and 15-19 are currently pending and have been considered below. Applicant’s remarks have been fully considered. Information Disclosure Statement The information disclosure statement (IDS) submitted on 4/10/2026 is in compliance with the provisions of 37 CFR 1.97and is being considered by the examiner. Response to Amendment Rejection Under 35 USC 103 The amendments made to the claims introduce limitations that are not fully addressed in the previous office action, and thus the corresponding 35 USC 103 rejections are withdrawn. However, Examiner will consider the amended claims in light of an updated prior art search and address their patentability with respect to prior art below. 12-261 AIA Response to Argument Rejection Under 35 USC 103 On pages 7-8 of the response filed 3/13/2026 Applicant argues that neither Han nor Yanemoto teach the independent claims as newly amended. Examiner agrees that the combination of Han and Yanemoto does not fully teach or suggest the claims as presently amended; an updated ground of rejection relying on Han, Yanemoto, and Lee is relied upon to fully teach the amended subject matter, as explained in detail below. Claim Rejections - 35 USC § 103 07-20-02-aia AIA 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. 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-5, 8-12, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Han et al. (US 20230347174 A1) in view of Lee (US 20040165696 A1) and Yonemoto et al. (US 20220370833 A1) . Claims 1, 8, and 15 Han teaches a method of increasing efficiency of execution of computer models (Han abstract, [0049], noting computer-implemented methods for radiation treatment planning that improve efficiency; note that “increasing efficiency of execution of computer models” in the preamble is an intended use of the method and is thus not patentably limiting – see MPEP 2111.02(II)) , the method comprising: receiving, by a processor , radiation therapy treatment planning data associated with a radiation therapy treatment of a patient using at least one configuration of a radiation therapy machine (Han [0108]-[0110], noting a processing device obtains a target image of an object for radiation therapy treatment of a patient; see also [0054], noting a radiation therapy machine that performs the radiation therapy treatment on the patient (considered to be achieved by using at least one configuration of the machine set in accordance with a desired therapy plan)); executing, by the processor, a first computer model to identify one or more attributes of a treatable sector for the radiation therapy treatment of the patient, the first computer model configured to ingest radiation therapy treatment planning data and clinical objectives corresponding to an input indicating a dose distribution of at least one planning target volume or organ at risk , and to determine the one or more attributes of the treatable sector, the one or more attributes of the treatable sector identifying a range of beam entry angles of the treatable sector for the radiation therapy treatment of the patient, wherein the range of beam entry angles is determined in accordance with beam-entry geometry identified based on the dose distribution and patient-specific geometry (Han [0111]-[0119], noting machine learning models may ingest the target image (which includes patient-specific geometry like ROI size, position, etc.) to determine attributes of a treatable sector and identify a candidate beam angle range for a treatment plan (i.e. a range of beam entry angles of a treatable sector as explained in [0085]) based on beam entry geometry implications gleaned from treatment plans of previous similar patients); determining, by the processor, that one or more first beam entry angles results in a collision of the radiation therapy machine with the patient or medical equipment associated with treatment of the patient ; executing, by the processor, a second computer model to identify a radiation therapy treatment plan for the patient using the received radiation therapy treatment planning data associated with the patient, wherein the processor limits a search space used by the second computer model using the one or more attributes of the treatable sector identified by the first computer model whereby the processor limits a full range of possible treatment angles via the range of beam entry angles less the one or more first beam entry angles (Han [0154]-[0159], noting machine learning models utilize the candidate angles to determine treatment plan parameters like target radiation field, collimator angles, and lock field parameters for each candidate angle of the range, i.e. the processor limits the outputs of the second model based on the previously determined angle range and thus limits a full range of possible treatment angles via the range of beam entry angles (e.g. those within less than full example ranges like [0, 10] or [5, 10] as in [0085] & [0092])); and adjusting, by the processor, the at least one configuration of the radiation therapy machine in accordance with the radiation therapy treatment plan calculated by the second computer model (Han [0054], noting the radiotherapy device may be used to perform the radiation therapy on the target area; see also [0064], noting the terminal and processing device may be integrated as a control device of the medical device such that it acts as an operation console. These disclosures show that the calculated radiation therapy treatment plan parameters as in [0158]-[0159] may be utilized to control or adjust an operating configuration of the radiotherapy device). In summary, Han teaches a radiation therapy treatment planning system that uses machine learning models to first determine a range of candidate beam angles for a treatable sector and then output treatment parameters that correspond to the range of candidate beam angles. Though Han contemplates adjusting treatment planning parameters based on user input (see [0092]) and complying with clinical constraints or requirements like dose constraints, target area coverage, and avoidance of organs at risk (see [0159]), this reference fails to explicitly disclose that the first computer model ingests clinical objectives corresponding to an input indicating a dose distribution of at least one planning target volume or organ at risk and using the dose distribution as a basis for identifying beam-entry geometry for use in generating the one or more attributes of the treatable sector as required by the claim. Further, though Han indicates that the outputs of the second model would be limited in accordance with the input range of candidate angles, it fails to explicitly disclose determining, by the processor, that one or more first beam entry angles results in a collision of the radiation therapy machine with the patient or medical equipment associated with treatment of the patient or limiting a search space used by the second model by the range of beam entry angles less the one or more first beam entry angles as intended by the claim. However, Lee teaches an analogous radiation treatment planning method in which clinical planning information such as prescription dose and target lower/upper bounds of radiation dose delivered to various target and healthy patient anatomies (i.e. clinical objectives indicating a dose distribution of at least one planning target volume or organ at risk) is used as an input for a candidate arc selection algorithm that identifies beam entry geometries associated with the desired dose distribution (Lee [0013], [0050]-[0052]). 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 clinical constraint-aware beam entry angle selection method of Han to include the specific clinical objective of dose distribution for a target or area to avoid as in Lee in order to constrain the identified range of beam entry angle candidates to those that facilitate a prescribed dose distribution that will be clinically effective and safe for the patient (as suggested by Lee [0004]-[0005] & [0012]). Further, Yonemoto teaches an analogous radiation treatment planning method in which candidate angles are initially determined based on various clinical criteria (Yonemoto Fig. 12, [0061], [0063], [0065], noting a set of candidate beam angles are identified in accordance with treatment goals or other user-input criteria like treatment area, structures to avoid, etc.), the candidate angles are evaluated to determine one or more first beam entry angles predicted to result in a collision of the radiation therapy machine with the patient or medical equipment associated with treatment of the patient (Yonemoto Fig. 12, [0061], [0065]-[0067]), and wherein the search space of a second treatment planning step is limited based on the candidate angles determined in the first step minus any angles determined to result in a collision (Yonemoto Fig. 12, [0061], [0065], [0068], noting the set of candidate beam angles is reduced by those predicted to result in a collision, and the reduced set of candidate beam angles is passed along to the next stage of treatment planning). 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 radiation treatment planning method of the combination to include limiting a search space of a second treatment planning stage to remove angles predicted to result in a collision as in Yonemoto in order to remove harmful angles from the candidate set so that the number of candidate beam angles evaluated in the more granular treatment planning stage is reduced, thereby reducing the complexity and processing requirements of the system in creating a radiation treatment plan while ensuring patient safety (as suggested by Yonemoto [0061] & [0063]). Claims 8 and 15 recite substantially similar subject matter as claim 1, and are also rejected as above. Claims 2, 9, and 16 Han in view of Lee and Yonemoto teaches the method of claim 1, and the combination further teaches wherein the first computer model uses a machine-learning algorithm to predict the one or more attributes of the treatable sector (Han [0113], [0119], noting use of machine learning models to determine the attributes of the treatable sector such as a candidate beam angle range). Claims 9 and 16 recite substantially similar subject matter as claim 2, and are also rejected as above. Claims 3, 10, and 17 Han in view of Lee and Yonemoto teaches the method of claim 2, and the combination further teaches wherein the machine-learning algorithm trains the first computer model using training data comprising data associated with previously implemented treatments (Han [0113], [0119], noting the machine learning models are trained using historical radiotherapy data (i.e. previously implemented treatments) to determine the attributes of the treatable sector such as a candidate beam angle range). Claims 10 and 17 recite substantially similar subject matter as claim 3, and are also rejected as above. Claims 4, 11, and 18 Han in view of Lee and Yonemoto teaches the method of claim 1, and the combination further teaches wherein the one or more attributes of the treatable sector is calculated based on tumor location (Han [0086]-[0090], noting the system determines the attributes of the treatable sector such as the candidate beam angle range based on reference information related to a region of interest such as a position of the target/tumor area). Claims 11 and 18 recite substantially similar subject matter as claim 4, and are also rejected as above. Claims 5, 12, and 19 Han in view of Lee and Yonemoto teaches the method of claim 1, and the combination further teaches wherein the one or more attributes of the treatable sector is calculated based on data associated with an organ at risk (Han [0086]-[0090], noting the system determines the attributes of the treatable sector such as the candidate beam angle range based on reference information related to a region of interest such as a position, size, or other data associated with an organ at risk). Claims 12 and 19 recite substantially similar subject matter as claim 5, and are also rejected as above . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Das et al. (Reference U on the accompanying PTO-892) describes a method of optimizing radiation treatment plans by inputting dose distribution objectives to constrain selection of beam entry orientations . 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 KAREN A HRANEK whose telephone number is (571)272-1679. The examiner can normally be reached M-F 8:00-4:00 ET. 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, Shahid Merchant can be reached on 571-270-1360. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /KAREN A HRANEK/Primary Examiner, Art Unit 3684 Application/Control Number: 17/958,225 Page 2 Art Unit: 3684 Application/Control Number: 17/958,225 Page 3 Art Unit: 3684 Application/Control Number: 17/958,225 Page 4 Art Unit: 3684 Application/Control Number: 17/958,225 Page 5 Art Unit: 3684 Application/Control Number: 17/958,225 Page 6 Art Unit: 3684 Application/Control Number: 17/958,225 Page 7 Art Unit: 3684 Application/Control Number: 17/958,225 Page 8 Art Unit: 3684 Application/Control Number: 17/958,225 Page 9 Art Unit: 3684
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