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 Objections
Claim 1 is objected to because of the following informalities: the claim limitation “the acquisition” in line 9 should be amended to read –an acquisition--. Appropriate correction is required.
Claim 1 is objected to because of the following informalities: the claim limitation “the preparation” in line 11 should be amended to read –a preparation--. Appropriate correction is required.
Claim 1 is objected to because of the following informalities: the claim limitation “the retrieval” in line 13 should be amended to read –a retrieval--. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process or product as a computer implemented method or a computer system/product.
Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Regarding claims 1 and 8, the independent claim is directed to a method for obtaining an irradiation map of a patient. The claim limitations of “a learning phase (see fig. 1-2) consisting in submitting to a multilayer neural network a learning set (comprising associations between a first input tensor comprising data of a first radiology image of a patient's intervention area, and first acquisition parameters of an interventional radiology device, and labels corresponding to an irradiation map obtained by simulation by a simulation module from the said first radiology image and the said first acquisition parameters (see par. [0034]); a prediction phase on a given patient, comprising...the preparation of a second input tensor comprising data of a second radiology image of said given patient and of said second acquisition parameters, the submission of said second input vector to said neural network, and the retrieval of an irradiation map prediction” are directed to an abstract because the claim limitations can be performed via mathematical concepts and mental process, with assistance of basic physical aids or with pen and paper.
Furthermore, the claim does not include additional elements which are sufficient to amount to significantly more than the abstract idea. The additional elements of data of the radiology images and “the acquisition of a stream of second acquisition parameters of said interventional radiology device” are directed to extra solution activity of gathering data and do not include additional elements which are sufficient to amount to significantly more than the abstract idea. Furthermore, the additional element “interventional radiology device” does not add significantly more than the abstract idea because the interventional radiology device is not positively recited and is well known and routine.
In consideration of each of the relevant factors and the claim elements both individually and in combination, claim 1 is directed to an abstract idea without sufficient integration into a practical application and without significantly more.
Regarding claims 2-5, the claims further recite claim limitations (e.g., type of neural network and Monte-Carlo method) that are further directed to abstract idea because the claim limitations can be performed via mathematical concepts and mental process, with assistance of basic physical aids or with pen and paper.
Regarding claims 6-7, the claim limits that are additional elements (such as the images are CT tomography scan, etc.) do not add significantly more than the abstract idea because obtaining CT scans are extra solution ideas.
Regarding claim 9, the independent claim is directed to a system for obtaining a prediction for an irradiation map. The claim limitations of “one simulation module and a multilayer neural network module comprising a multilayer neural network, said system being adapted to in a learning phase, submitting to said multilayer neural network a learning set comprising associations between a first input tensor comprising data of a first radiology image of a patient's intervention area, and first acquisition parameters of an interventional radiology device, and labels corresponding to an irradiation map obtained from said simulation module on the basis of said first radiology image and said first acquisition parameters; and in a prediction phase on a given patient...preparing a second input tensor comprising data of a second radiology image of said given patient and said second acquisition parameters, submitting said second input vector to said neural network and retrieving a prediction of irradiation map” are directed to an abstract because the claim limitations can be performed via mathematical concepts and mental process, with assistance of basic physical aids or with pen and paper.
Furthermore, the claim does not include additional elements which are sufficient to amount to significantly more than the abstract idea. The additional elements of data of the radiology images and “acquiring a stream of second acquisition parameters from said interventional radiology device” are directed to extra solution activity of gathering data and does not include additional elements which are sufficient to amount to significantly more than the abstract idea. Furthermore, the additional element “interventional radiology device” does not add significantly more than the abstract idea because the interventional radiology device is not positively recited and is well known and routine.
In consideration of each of the relevant factors and the claim elements both individually and in combination, claim 9 is directed to an abstract idea without sufficient integration into a practical application and without significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the claim is indefinite because the preamble is directed to a method of obtaining an irradiation map, however, the body of the claim does not limit or directed to any obtaining an irradiation map. The body of the claim does limit “the retrieval of an irradiation map prediction” but not the irradiation map.
the claim limitation “an irradiation map” in line 6-7 is indefinite because it is unclear if this irradiation map is the same irradiation map that is already recited in the preamble.
Furthermore, for claim 1, the claim limitation “a given patient” in line 9 is indefinite because it is unclear if this given patient is same the patient that is recited in the preamble.
Furthermore, for claim 1, the claim limitation “the submission of said second input vector” in line 13 is indefinite because it is unclear what submission of second input vector the claim is referring to and it is unclear what is the input vector.
Regarding claim 2, the claim limitation “a first radiology image” in line 3 is indefinite because it is unclear if this first radiology image is different from the first radiology image that is already recited in claim 1 or same.
Regarding claim 5, the claim limitations “the output” in lines 5, 6, 9; “the same size” in line 5; “the corresponding out” in line 6-7 lack antecedent basis.
Furthermore, for claim 5, the claim limitation “whose output” in line 8 is indefinite because it is unclear what output the claim is referring to since claim 5 recites several different outputs.
Regarding claim 6, the claim limitation “a first radiology image” in lines 2-3 is indefinite because it is unclear if this first radiology image is different from the first radiology image that is already recited in claim 1 or same.
Regarding claim 7, the claim limitation “said radiology image” is indefinite because it is unclear if said radiology image is referring to the first or second radiology image from claim 1.
Regarding claim 9, the claim limitation “an irradiation map” in lines 8-9 is indefinite because it is unclear if this irradiation map is the same irradiation map that is already recited in the preamble.
Furthermore, for claim 9, the claim limitation “a given patient” in line 11 is indefinite because it is unclear if this given patient is same the patient that is recited in the preamble.
Furthermore, for claim 9, the claim limitation “submitting of said second input vector” in line 14-15 is indefinite because it is unclear what submission of second input vector the claim is referring to and it is unclear what is the input vector.
Furthermore, for claim 9, the claim limitation “a prediction of irradiation map” in lines 15-16 is indefinite because it is unclear if this prediction of irradiation map is same as the prediction for irradiation map that is recited in preamble.
Claim Rejections - 35 USC § 102
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 1-9 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Xu et al. (US 2023/0065196; hereinafter Xu).
Regarding claims 1 and 8, Xu discloses a patient specific organ dose quantification and inverse optimization for CT. Xu shows method for obtaining an irradiation map of a patient during interventional radiology (see par. [0046] and fig. 1), comprising: a learning phase (see fig. 1-2) consisting in submitting to a multilayer neural network a learning set (see par. [0019], [0034]; fig. 2) comprising associations between a first input tensor comprising data of a first radiology image of a patient's intervention area (see par. [0031], [0033], ]0034]), and first acquisition parameters of an interventional radiology device (see par. [0034]), and labels corresponding to an irradiation map obtained by simulation by a simulation module from the said first radiology image (see par. [0011], [0030], [0032], [0035], [0042], [0045]; see 124 in fig. 1) and the said first acquisition parameters (see par. [0034]); a prediction phase on a given patient, comprising the acquisition of a stream of second acquisition parameters of said interventional radiology device (see par. [0034], [0043], [0045]), the preparation of a second input tensor comprising data of a second radiology image of said given patient ((see par. [0040], [0041], [0045], [0052]; fig. 3)) and of said second acquisition parameters (see par. [0034], [0043], [0045]), the submission of said second input vector to said neural network (as best understood of the indefinite limitation, (see par. [0019], [0034]; fig. 2-3) and the retrieval of an irradiation map prediction (see par. [0045], [0046]-[0048], [0050], [0052], [0054], fig. 3).
Regarding claim 2, Xu shows during the learning phase, a plurality of data of the learning set is generated for a first radiology image by varying said first acquisition parameters among possible parameters (see par. [0034], [0043], [0045]).
Regarding claim 3, Xu shows wherein said simulation is performed by a Monte-Carlo method adapted for a graphics processor (see par. [0011], [0030], [0032], [0035], [0042], [0045]; see 124 in fig. 1).
Regarding claim 4, Xu shows wherein said neural network is of U-Net type (see par. [0037]).
Regarding claim 5, Xu shows wherein said neural network consists of a first sub-network having as input the data of said first or second radiology image (see fig. 1 and 2; par. [0037]-[0042]), and comprising a first succession of convolution and pooling layers and a second succession of deconvolution and convolution layers (see fig. 1 and 2; par. [0037]-[0042]), such that the output of said neural network is of the same size as said input, and wherein the output of each deconvolution layer is concatenated with the corresponding output in said first succession (see fig. 1 and 2; par. [0037]-[0042]); and a second sub-network having as input said first or second acquisition parameters, respectively, and whose output is concatenated with the output of the last pooling layer of said first succession (see fig. 1 and 2; par. [0037]-[0042]).
Regarding claim 6, Xu shows wherein said input tensor is a concatenation of said data of a first radiology image (see par. [0031], [0033], ]0034]) and said first acquisition parameters (see par. [0034]).
Regarding claim 7, Xu shows wherein said radiology image a computerized tomography scan (see abstract).
Regarding claim 9, Xu discloses a patient specific organ dose quantification and inverse optimization for CT. Xu shows system (see fig. 1) for obtaining a prediction for an irradiation map of a patient during interventional radiology (see par. [0046] and fig. 3), comprising at least one simulation module(see par. [0011], [0030], [0032], [0035], [0042], [0045]; see 124 in fig. 1) and a multilayer neural network module comprising a multilayer neural network (see par. [0019], [0034]; fig. 2), said system being adapted to in a learning phase (see par. [0019], [0034]; fig. 1-2), submitting to said multilayer neural network a learning set comprising associations between a first input tensor comprising data of a first radiology image of a patient's intervention area (see par. [0031], [0033], ]0034]), and first acquisition parameters of an interventional radiology device (see par. [0034]), and labels corresponding to an irradiation map obtained from said simulation module on the basis of said first radiology image and said first acquisition parameters (see par. [0011], [0030], [0032], [0035], [0042], [0045]; see 124 in fig. 1); and in a prediction phase on a given patient, acquiring a stream of second acquisition parameters from said interventional radiology device (see par. [0034], [0043], [0045]), preparing a second input tensor comprising data of a second radiology image of said given patient and said second acquisition parameters (see par. [0019], [0034]; fig. 2-3), submitting said second input vector to said neural network and retrieving a prediction of irradiation map (as best understood of the indefinite language, see par. [0045], [0046]-[0048], [0050], [0052], [0054], fig. 3).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHDEEP MOHAMMED whose telephone number is (571)270-3134. The examiner can normally be reached Monday to Friday, 9am to 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne M Kozak can be reached at (571)270-0552. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHAHDEEP MOHAMMED/ Primary Examiner, Art Unit 3797
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