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 5-7, filed 6/26/2026, with respect to the rejection(s) of claim(s) 1, 4-15 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 Therefore, the rejection has been withdrawn.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 Wu et al (US 20220241614 A1).
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 –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 4-15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wu et al (US 20220241614 A1).
Regarding claim 1, Wu et al discloses a computing system (120) for supporting radiation therapy planning (200), the computing system comprising: an input interface (1250) (paragraph [0095]) for receiving input radiation treatment plan templates, or types of such plan templates, for plural radiation treatment plans; and a trained machine learning model configured to compute plural dose maps associated with the received input radiation treatment plan templates or with the types of such plan templates (paragraphs [0025]-[0026]), 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 (direct plan generation) (paragraph [0034]), wherein the input interface (paragraphs [0033], is configured to allow user-selection of any one or more of the input radiation treatment plan templates, and wherein the plural dose maps are computed by the trained machine learning model in response to such user-selection (paragraphs [0034]-[0035], [0038]).
Regarding claim 4, Wu et al discloses wherein 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 (paragraphs [0026]-[0028]).
Regarding claim 5, Wu et al discloses wherein at least one of the output navigation user interfaces or the input interface is arranged as a graphical user interface (paragraphs [0095]).
Regarding claim 6, Wu et al discloses wherein the trained machine learning model is a generative type (paragraphs [0025]) (GAN, i.e. generative adversarial network)).
Regarding claim 7, Wu et al discloses wherein the input radiation treatment plan includes data that relates to different clinical goals (paragraph [0031]).
Regarding claim 8, Wu et al discloses wherein the 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 (i.e. targeted dose organ tolerance)(paragraph [0031]).
Regarding claim 9, Wu et al discloses wherein further comprising: 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 (paragraph [0026]).
Regarding claim 10, Wu et al discloses wherein further comprising: a training system (200) configured to train the machine learning model (210) based on training data to obtain the trained machine learning model (paragraph [0034]).
Regarding claim 11, Wu et al discloses wherein a radiation treatment system (200), comprising at least one of: a planning system (paragraph [0034]); or a treatment delivery device; and a computing system (120) (See Fig. 1) for radiation therapy planning, comprising: an input interface (1250) (paragraph [0095]) for receiving input radiation treatment plan templates, or types of such plan templates, for plural radiation treatment plans; and a trained machine learning model configured to compute plural dose maps (245) (paragraph [0034]) associated with the received radiation treatment plan templates or with the types of such plan templates (paragraphs [0025]-[0026]), 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 (direct plan generation) (paragraph [0034]), wherein the input interface (paragraphs [0033] is configured to allow user-selection of any one or more of the input radiation treatment plan templates, and wherein the plural dose maps are computed by the trained machine learning model in response to such user-selection (paragraphs [0034]-[0035], [0038]).
Regarding claim 12, Wu et al discloses wherein a computer-implemented method (1200) (paragraphs [0040], [0092]) for supporting radiation therapy planning (1150) (paragraph [0086]), the method comprising: receiving input radiation treatment plan templates (paragraphs [0025]-[0026]) or types of such plan templates, for plural radiation treatment plans; and computing by a trained machine learning model plural dose maps (245) (paragraph [0034]) associated with the received radiation treatment plan templates or with the types of such plan templates, wherein an input interface (1250) (paragraph [0095]) 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 (direct plan generation) (paragraph [0034]), wherein the input interface is configured to allow user-selection of any one or more of the input radiation treatment plan templates, and wherein the plural dose maps are computed by the trained machine learning model in response to such user-selection (paragraphs [0034]-[0035], [0038]).
Regarding claim 13, Wu et al discloses wherein a machine learning model is trained based on training data comprising the input radiation treatment plan template or types of such templates and associated dose maps (250) to obtain the trained model (210) (paragraph [0035]).
Regarding claim 14, Wu et al discloses wherein a computer program element, which, when being executed by at least one processing unit (1210), is adapted to cause the at least one processing unit to perform the method (paragraph [0091]).
Regarding claim 15, Wu et al discloses wherein the computer program element is stored (1230) on at least one non-transitory computer readable medium (paragraphs [0090]-[0093).
Conclusion
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/F.P.B./Examiner, Art Unit 2884
/UZMA ALAM/Supervisory Patent Examiner, Art Unit 2884
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