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 § 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, 4, 6-11, 14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Claim 1 - recite(s) “generating therapeutic radiation delivery field geometry information that includes at least one gantry angle for a particular patient”, “access information corresponding to the patient geometry information for the particular patient; receive at least one variable that is unrelated to the particular patient that consists of only images”, “provide as input”, “the information corresponding to the patient geometry information for the particular patient and the at least one variable comprises a vector of random or pseudorandom numerical value”, “process the input”, “to thereby generate the therapeutic radiation delivery field geometry information for the particular patient.” in the broadest reasonable interpretation are directed to mental determinations made by a human.
This judicial exception is not integrated into a practical application because the plan meaning of the claimed structure, a memory, control circuit, field geometry generator and a trained neural network, merely confines the use of the abstract idea to a particular technological environment.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because under the broadest reasonable interpretation the mental process are performed by a generically recited computer/trained neural network.
Regarding step 1, the claim is an article of manufacture or machine/system.
Regarding step 2A, prong one, the claim recites an abstract idea of mental processes performed by a person as set forth above.
Regarding step 2A, prong two, the limitations set forth amount to mere instructions to apply the exception using a generic computer.
Regarding step 2B, the memory, control circuit, and trained neural network(the field geometry generator) taken individually and in combination do not amount to more than the abstract idea. The memory stores data, the control circuit moves data around, and the neural network provides an output of radiation delivery field geometry for the patient. The radiation delivery field geometry is well understood and routine in the medical arts. Beam radiation therapy requires radiation field geometry information, including gantry angle, to ensure the tissue to be treated receives the dose of radiation required to kill the target while the healthy tissue and organs around the tissue to be treated receives a non-lethal dose.
Claim 4, 6, and 7 – define the data but do not provide any additional structure beyond the abstract idea, using a human mental process to determine the delivery field geometry information.
Claim 8 – does not structurally define the control circuit, sets forth preprocessing which is equally capable of being done in the human mind.
Claim 9 – defines the data to be output by the generically recited computer.
Claim 10 – defines the input or output, data, generated by the generically recited computer.
Claims 11, 14, and 16-20 are similarly rejected.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 4, 6-11, 14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hibbard(2019/0333623) in view of Huang et al (10,504,268, hereinafter Huang) in further view of Hibbard (2019/0030370) (hereby known as Hibbard ‘370).
Claim 1 - Hibbard teaches an apparatus to facilitate generating therapeutic radiation delivery field geometry information for a particular patient including a memory -114- having patient geometry information for the particular patient stored therein; a control circuit -120- operably coupled to the memory -114-, see figure 1, and configured to: access information corresponding to the patient geometry information for the particular patient, image data, -152-; receive at least one variable that is unrelated to the particular patient, see data source -160-, providing a variable such as training data and “gold standard” mappings see paragraph [0041] and data related to the facility, a set of patients or type of treatment, see paragraph [0043] lines 15-21; provide as input to a field geometry generator the information corresponding to the patient geometry information for the particular patient and the at least one variable, at -136- and -138-, wherein the field geometry generator comprises a neural network trained in a conditional generative adversarial networks (GAN) framework as a function of previously-developed field geometry solutions for a plurality of different patients, see paragraphs [0041] and [0043], see paragraph [0107] where the conditional GAN is set forth, and wherein the information corresponding to the patient geometry information for the particular patient serves as conditional input to the neural network at -136- and -138-; process the input using the field geometry generator to thereby generate the therapeutic radiation delivery field geometry for the particular patient, in -120- and element -130 to provide a treatment plan see paragraph [0056]. Applicant’s attention is further invited to paragraph [0048] which sets forth the system generating beam angles(gantry angles).
Hibbard teaches an apparatus as claimed but does not teach the variable being a vector of random numeric input.
Huang teaches using numeric vector input for the learning of conditional generative adversarial networks, column 2 lines 54-60.
It would have been obvious to one of ordinary skill in the medical art at the time the invention was effectively filed to use the vector of random numerical input as taught by Huang in the apparatus of Hibbard to optimize the learning of the conditional generative adversarial networks as set forth in Huang.
Such a combination would produce predictable results of the device of Hibbard including teaching the conditional generative adversarial networks with the random vectors as taught by Huang and have a high expectation of success because training conditional generative adversarial networks with random vectors is old and well known in the arts as taught by Huang.
Hibbard and Huang fail to explicitly teach gathering information on the particular patient which consists of only images.
Hibbard ‘370 teaches a similar system in the same field of endeavor that teaches an explicit process of utilizing only that patient images in a deep learning network processing estimates (par. 68, figure 7). Hibbard teaches using patient images (par. 111, figure 4, item 470) but as noted above it does not consist only of patient images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize only patient images as taught by Hibbard ‘370 to the combination of Hibbard and Huang, the motivation being using only a plethora of patient images allows for adequate analysis of the present patient versus relying on potentially a combination of images and other variables. It is noted that multiple forms of processing would only help getting an accurate diagnosis/treatment.
Regarding claim 11, it is addressed in the same way as claim 1.
Claims 4 and 14 - Hibbard teaches the patient geometry information for the particular patient comprises images depicting at least one segmented and contoured organ-at-risk and at least one segmented and contoured planning target volume, see paragraph [0057] through [0059] which sets forth segmented images of the target and organs at risk.
Claims 6 and 16 - Hibbard teaches the therapeutic radiation delivery field geometry that is generated for the particular patient comprises, at least in part, field delivery directions, such as beam angles, number of beams and dose per beam as set forth in paragraph [0048].
Claims 7 and 17 - Hibbard teaches the control circuit -120- is further configured to: preprocess the patient geometry information for the particular patient to thereby provide the information corresponding to the patient geometry information for the particular patient at element -132- prior to the use of the generative adversarial network model.
Claims 8 and 18 - Hibbard teaches the control circuit -120- is configured to preprocess the patient geometry information, at least in part, by reducing dimensionality of the patient geometry information.
Claims 9 and 19 - Hibbard teaches the patient geometry information comprises, at least in part, a multidimensional numerical representation corresponding to an aggregation of different modalities of informational content, “non-image formats (e.g. coordinates, mappings, etc)”, paragraph [0046].
Claims 10 and 20 - Hibbard teaches the aggregation of different modalities of informational content include, but are not limited to, imagery and non-imagery content images and non-image formats as set forth in paragraph [0046].
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 4, 6-11, 14, and 16-20 (regarding the art rejections) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments filed 12/4/2025 regarding the 35USC 101 rejection have been fully considered but they are not persuasive.
Applicant argues that the limitation of “comprises a neural network…” results in processing that could not be accomplished in the human mind. The Examiner traverses this position and points to example 47 claim 2 (July 2024 Subject Matter Eligibility Examples) issued by the USPTO which maps analogously to the claims at hand where in analysis is performed by an artificial neural network. The generic limitations of the analysis, like claim 2, are able to be performed in the human mind and/or are accomplished by a mathematical algorithm. The Examiner suggests using claim 3 as a means to overcome the 101 rejection.
Conclusion
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 ALEX M VALVIS whose telephone number is (571)272-4233. The examiner can normally be reached 9:00-5:00 M-F.
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ALEX M. VALVIS
Supervisory Patent Examiner
Art Unit 3791
/ALEX M VALVIS/ Supervisory Patent Examiner, Art Unit 3791
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