DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Response to Arguments
Applicant’s arguments, see pages 5-9, filed 3/16/2026, with respect to the rejection(s) of claim(s) 1-15 under 35 U.S.C. 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 Therefore, the rejection has been withdrawn.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 Heilemann et al (“Ultra-fast, one-click radiotherapy treatment planning outside a treatment planning system”). A Final Rejection is made necessitated by amendment to claims 1, 4-15.
Response to Amendment
The amendment submitted 3/16/2026 has been accepted and entered. Claims 1, 4-5 are amended. Claims 2-3 are cancelled. No new claims are added. Thus, claims 1, 4-15 are examined.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
Claim(s) 1, 4-15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Heilemann et al (“Ultra-fast, one-click radiotherapy treatment planning outside a treatment planning system”).
Regarding claim 1, Heilemann et al discloses a computing system for supporting radiation therapy planning (automated radiation oncology treatment planning) (See Abstract), the computing system comprising: an input interface for receiving input radiation treatment plan templates (TP), or types of such plan templates, for plural radiation treatment plans (See Abstract); and a trained machine learning model (Deep learning DL) (page 1, col. 1) configured to compute plural dose maps associated with the received input radiation treatment plan templates or with the types of such plan templates, wherein the input interface is arranged as a user interface configured to allow a user to select the input radiation treatment plan templates or types of such plan templates (one-click radiotherapy treatment plan) (page 1, col. 2 first paragraph), wherein the input interface is configured to allow user-selection of any one or more of the input radiation treatment plan templates (one-click radiotherapy treatment plan) (page 1, col. 2 first paragraph), and wherein the plural dose maps are computed by the trained machine learning model in response to such user-selection (See Fig. 1, page 2 and page 3, col. 1, Section: 2.2 Data and model training).
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Regarding claim 4, Heilemann et al discloses further comprising: an output navigation user interface, configured to allow a user to navigate through the output plural dose maps and select a dose map from among the plural dose maps (page 3, paragraph 7).
Regarding claim 5, Heilemann et al discloses wherein at least one of the output navigation user interface or the input interface is arranged as a graphical user interface (i.e. dose-volume histograms (DVHS)) (page 3, col. 1, paragraph 7).
Regarding claim 6, Heilemann et al discloses wherein the trained machine learning model is of a generative type (artificial intelligence (AI)) (page 1, col. 1, Introduction).
Regarding claim 7, Heilemann et al discloses wherein the input radiation treatment plan templates include data that relates to different clinical goals (i.e. Trained and validated on 276 plans, and tested on 151 datasets, the system produced clinically deliverable plans—complete with all VMAT parameters—in about 38s) (See Abstract).
Regarding claim 8, Heilemann et al discloses wherein the said different clinical goals includes any one or more of: i) different clinical dose upper limits for one or more organs at risks, ii) different dose lower limits for a target, iii) different trade-offs between target coverage and organ-at-risk sparing iv) different treatment parameters (See Abstract).
Regarding claim 9, Heilemann et al discloses wherein further comprising: including a radiation therapy planning module configured to generate a treatment plan based on the dose map selected by the user via the output navigation user interface (See Fig. 1, page 2 and page 3, col. 1, Section: 2.2 Data and model training).
Regarding claim 10, Heilemann et al discloses wherein further comprising: a training system configured to train the machine learning model based on training data to obtain the trained machine learning model (Deep learning DL) (See Abstract, page 1, col. 1, Fig. 1, page 2 and page 3, col. 1, Section: 2.2 Data and model training).
Regarding claim 11, Heilemann et al discloses a radiation treatment, comprising at least one of a planning system or a treatment delivery device (automated radiation oncology treatment planning) (See Abstract), the computing system comprising: an input interface for receiving input radiation treatment plan templates (TP), or types of such plan templates, for plural radiation treatment plans (See Abstract); and a trained machine learning model (Deep learning DL) (page 1, col. 1) configured to compute plural dose maps associated with the received input radiation treatment plan templates or with the types of such plan templates, wherein the input interface is arranged as a user interface configured to allow a user to select the input radiation treatment plan templates or types of such plan templates (one-click radiotherapy treatment plan) (page 1, col. 2 first paragraph), wherein the input interface is configured to allow user-selection of any one or more of the input radiation treatment plan templates (one-click radiotherapy treatment plan) (page 1, col. 2 first paragraph), and wherein the plural dose maps are computed by the trained machine learning model in response to such user-selection (See Fig. 1, page 2 and page 3, col. 1, Section: 2.2 Data and model training).
Regarding claim 12, Heilemann et al discloses a computer-implemented method for supporting radiation therapy planning, the method comprising: a receiving input radiation treatment plan template or types of such plan templates, for plural radiation treatment plans (automated radiation oncology treatment planning) (See Abstract); and computing by a trained machine learning model (Deep learning DL) (page 1, col. 1) plural dose maps (See Fig. 1) associated with the received radiation treatment plan templates or with the types of such plan templates, wherein an input interface is arranged as a user interface configured to allow a user to select the input radiation treatment plan templates or types of such plan templates (one-click radiotherapy treatment plan) (page 1, col. 2 first paragraph), wherein the input interface is configured to allow user-selection of any one or more of the input radiation treatment plan templates (See Fig. 1), and wherein the plural dose maps are computed by the trained machine learning model in response to such user-selection (See Fig. 1, page 2 and page 3, col. 1, Section: 2.2 Data and model training).
Regarding claim 13, Heilemann et al discloses wherein a machine learning model is trained based on training data comprising the input radiation treatment plan templates or types of such templates and associated dose maps to obtain the trained model (See Abstract, page 1, col. 1, Fig. 1, page 2 and page 3, col. 1, Section: 2.2 Data and model training).
Regarding claim 14, Heilemann et al discloses wherein a computer program element, which, when being executed by at least one processing unit, is adapted to cause the at least one processing unit to perform the method (page 3, col. 1, paragraph 1).
Regarding claim 15, Heilemann et al discloses the computer program element is stored on at least one non-transitory computer readable medium (page 3, col. 1, paragraph 7).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sjolund et al (US 11358003 B2) discloses generating a radiotherapy treatment plan are provided. The techniques include receiving an input parameter related to a patient, the input parameter being of a given type; processing the input parameter with a machine learning technique to estimate a realizable plan parameter of a radiotherapy treatment plan, wherein the machine learning technique is trained to establish a relationship between the given type of input parameter and a set of realizable radiotherapy treatment plan parameters to achieve a target radiotherapy dose distribution; and generating the radiotherapy treatment plan based on the estimated realizable plan parameter.
Hibbard et al (US 20220088410 A1) disclose systems and methods for generating fluence maps for a radiotherapy treatment plan that uses machine learning prediction. The systems and methods include identifying image data that indicates treatment constraints for target dose areas and organs at risk areas in an anatomy of the subject, generating anatomy projection images that represent a view of the subject from respective beam angles, using a trained neural network model to generate the computer-simulated fluence map representations based on the anatomy projection images, where the fluence maps indicate a fluence distribution of the radiotherapy treatment at each of the beam angles.
Purdie et al (US 11735309 B2) discloses methods and systems for evaluating a proposed treatment plan for radiation therapy, for evaluating one or more delineated regions of interest for radiation therapy, and/or for generating a proposed treatment plan for radiation therapy. Machine learning based on historical data may be used.
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.
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/F.P.B./Examiner, Art Unit 2884
/UZMA ALAM/Supervisory Patent Examiner, Art Unit 2884
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