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 .
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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Rusanen et al. (US PAP 2022/0409929 A1).
With respect to claim 1, Rusanen et al. teaches a method of training a neural network for radiation dose determination, the method comprising: accessing a training corpus comprising (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19):
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a plurality of different resultant radiation doses; and a plurality of different input items that each correspond to a particular one of the plurality of different resultant radiation doses, wherein the input items comprise at least one of: a patient image; a fluence map; radiation treatment platform geometry information; or target dose volume information; and training the neural network using the training corpus (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19).
With respect to claim 2, Rusanen et al. teaches the method of claim 1 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein the neural network is deep learning model that can include a transformer neural network that uses “self-attention” to process information all at once rather than step-by step (see paragraphs 0015-0019, 0033, 0034 and 0039-0050).
With respect to claim 3, Rusanen et al. teaches the method of claim 1 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein the target dose volume information comprises sparsely- read target dose volume information (see paragraphs 0032, 0039 and 0047).
With respect to claim 4, Rusanen et al. teaches the method of claim 3 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein at least some of the plurality of different resultant radiation doses each comprises sparsely-written resultant radiation doses (see paragraphs 0032, 0039 and 0047).
With respect to claim 5, Rusanen et al. teaches the method of claim 1 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein each of the plurality of different resultant radiation doses corresponds to at least two of a patient image, a fluence map, radiation treatment platform geometry information, and target dose volume information (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19).
With respect to claim 6, Rusanen et al. teaches the method of claim 1 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein each of the plurality of different resultant radiation doses corresponds to at least three of a patient image, a fluence map, radiation treatment platform geometry information, and target dose volume information (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19).
With respect to claim 7, Rusanen et al. teaches the method of claim 1 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein each of the plurality of different resultant radiation doses corresponds to each of a patient image, a fluence map, radiation treatment platform geometry information, and target dose volume information (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19).
With respect to claim 8, Rusanen et al. teaches the method of claim 1 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein the patient image comprises computed tomography imagery.
With respect to claim 9, Rusanen et al. teaches he method of claim 1 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein the patient image comprises Digital Imaging and Communications in Medicine-compatible imagery (see Fig. 1; paragraph 0027).
With respect to claim 10, Rusanen et al. teaches a method of determining a radiation dose for a patient, the method comprising (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19): accessing patient image information for the patient; providing the patient image information as input to a neural network that is trained using a training corpus that comprises: a plurality of different resultant radiation doses; and a plurality of different input items that each correspond to a particular one of the plurality of different resultant radiation doses, wherein the input items comprise at least one of: a patient image; a fluence map; radiation treatment platform geometry information; or target dose volume information; and outputting from the neural network a determined radiation dose for the patient (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051).
With respect to claim 11, Rusanen et al. teaches the method of claim 10 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein each of the plurality of different resultant radiation doses that comprise the training corpus corresponds to at least two of the input items.
With respect to claim 12, Rusanen et al. teaches the method of claim 11 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein the at least two of the input items comprise a patient image and target dose volume information (see paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051).
With respect to claim 13, Rusanen et al. teaches the method of claim 10 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein the target dose volume information comprises sparsely- read target dose volume information (see paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051).
With respect to claim 14, Rusanen et al. teaches the method of claim 10 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein the patient image comprises computed tomography imagery (see Fig. 1; paragraph 0027).
With respect to claim 15, Rusanen et al. teaches the method of claim 10 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein the neural network is deep learning model that can include a transformer neural network (see paragraphs 0015-0019, 0033, 0034 and 0039-0050).
With respect to claim 16, Rusanen et al. teaches the method of claim 10 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein outputting the determined radiation dose for the patient occurs prior to optimizing a radiation treatment plan for the patient (see paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051).
With respect to claim 17, Rusanen et al. teaches the method of claim 10 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein outputting the determined radiation dose for the patient occurs subsequent to optimizing a radiation treatment plan for the patient (see paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051).
With respect to claim 18, Rusanen et al. teaches the method of claim 10 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein providing the patient image information as input to a neural network comprises the neural network selecting a read location (see paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051).
With respect to claim 19, Rusanen et al. teaches the method of claim 10 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein outputting from the neural network a determined radiation dose for the patient comprises the neural network selecting a write location (see paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051).
With respect to claim 20, Rusanen et al. teaches the method of claim 10 (see abstract; Figs. 1-6; paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051; claims 1, 6-11 and 16-19), wherein providing the patient image information as input to the neural network comprises providing patient image information for a plurality of different positions (see paragraphs 0015, 0018, 0019, 0027, 0032-0038, 0043, 0047 and 0051).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Willcut et al. (US PAP 2019/0076671 A1) discloses a system performing radiation treatment planning (see abstract; Figs. 1A-9; paragraphs 0033-0040).
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wherein, the system preforms receiving a reference treatment plan including one or more dose constraints and determining, based on the reference treatment plan, segment information of a plurality of radiation beams; determining a fluence map for each of the plurality of radiation beams based on the one or more dose constraints using a fluence map optimization (see abstract; Figs. 1A-9; paragraphs 0033-0040) algorithm; determining a dose distribution based on the fluence maps of the plurality of radiation beams; and determining at least one beam modulation property of a new treatment plan using a warm-start optimization algorithm based on the segment information and the dose distribution (see abstract; Figs. 1A-9; paragraphs 0033-0040).
Hissoiny (US PAP 2019/0175952 A1) (see abstract; Figs. 1-8; paragraphs 0024-0038)
teaches raining a deep convolutional neural network model to provide a beam model for a radiation machine, such as to deliver a radiation treatment dose to a subject.
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These teachings provide a memory having a fluence map that corresponds to a particular patient stored therein. This memory also has at least one deep learning model stored therein trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map. By one approach, a control circuit operably coupled to that memory and is configured to iteratively optimize a radiation treatment plan to administer therapeutic radiation to that patient by, at least in part, generating a leaf sequence as a function of the at least one deep learning model and the fluence map that corresponds to the patient (see abstract; Figs. 1-8; paragraphs 0024-0038).
Stahl et al. (US PAP 2019/0209864) discloses delivering radiation treatment by defining a preliminary trajectory including a plurality of control points. Each control point may be associated with position parameters of a gantry and a couch. The method may also include generating a treatment plan based on the preliminary trajectory by optimizing an intensity and position parameters of a collimator and MLC leaves for each control point (see abstract; Figs. 1-8B; paragraphs 0033-0051).
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Irakli Kiknadze
/IRAKLI KIKNADZE/
Primary Examiner, Art Unit 2884
/I.K./ August 4, 2026
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