CTNF 18/086,374 CTNF 100652 DETAILED ACTION This Office Action is in response to the claims filed on 12/21/2022. Claims 1-20 are pending. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Examiner Notes Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. Examiner may also include cited interpretations encompassed within parenthesis, e.g. ( Examiner’s interpretation ), for clarity. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution. 07-06 AIA 15-10-15 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 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. Information Disclosure Statement The information disclosure statements (IDS) submitted on 12/21/2022 and 05/08/2024 were filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception (an abstract idea), as it has not been integrated into a practical application and the claim(s) further do/does not recite significantly more than the judicial exception. Examiner has evaluated the claim(s) under the framework provided in MPEP 2106 and has provided such analysis below. To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires: Step 1 . Determining if the claim falls within a statutory category of a Process, Machine, Manufacture, or a Composition of Matter ( see MPEP 2106.03 ); Step 2A . Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea ( MPEP 2106.04 ); Step 2A is a two-prong inquiry. MPEP 2106.04(II)(A) . Under the first prong , examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2106.04(a)(2) . The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d) . Step 2B . If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106 ). Step 1 : Claims 1-8 are directed to a method , as such these claims fall within the statutory category of a process . Claims 9-20 are directed to a system, as such these claims fall within the statutory category of manufacture . Step 2A, Prong 1 : The examiner submits that the foregoing claim limitations constitute abstract ideas, as the claim is directed towards an abstract idea, i.e . Mental Processes per MPEP 2106.04(a)(2)(III), given the broadest reasonable interpretation. In order to apply Step 2A, a recitation of claims is copied below. The limitations of those claims which describe an abstract idea are bolded . As per claim 1 , the claim recites the limitations of: executing, by the processor, an artificial intelligence model using the value to predict a radiation dosage for the first organ at risk, the second organ at risk, and a target structure (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) which are defined as concepts that can practically be performed in the human mind (e.g. observations, evaluations, judgments, opinions), or by a human using pen and paper as a physical aid. For instance, a person can reasonably determine (i.e. predict) organ radiation dosage using a previously established value with/without the aid of pen and paper. Note: Per MPEP 2106.04(a)(2)(III)(C), claims can recite a mental process even if they are claimed as being performed on a computer (i.e. executed by a processor).) Step 2A, Prong 2 : As per claim 1 , this judicial exception is not integrated into a practical application because the additional claim limitations outside the abstract idea only present Mere Instructions To Apply An Exception and/or Insignificant Extra Solution Activity per MPEP 2106.05(f)/(g), given the broadest reasonable interpretation. In particular, the claim recites the additional limitations: receiving, by a processor, a value indicating a prioritization between a first organ at risk of a patient and a second organ at risk of the patient receiving radiation dosage ; (The additional element amounts to Insignificant Extra-solution Activity (mere data gathering, pre-solution activity) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process.) executing, by the processor, an artificial intelligence model using the value to predict a radiation dosage for the first organ at risk, the second organ at risk, and a target structure , (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, this limitation is directed towards mere instructions to implement an abstract idea (i.e. mental process) on a computer. Note: Use of a computer or other machinery in its ordinary capacity for economic or other tasks ( e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea ( e.g., mental processes) does not integrate a judicial exception into a practical application or provide significantly more.) wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants, dosage administered to the participant's target structure, first organ at risk, and second organ at risk; (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, the limitation recites only the idea of a solution or outcome, i.e . fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".) and outputting, by the processor, the predicted radiation dosage for at least one of the first organ at risk, the second organ at risk, or a target structure. (The additional element amounts to Insignificant Extra-solution Activity (mere data outputting, post-solution activity) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity.) Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole. Step 2B : For step 2B of the analysis, the Examiner must consider whether each claim limitation individually or as an ordered combination amounts to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same. The additional elements as described in Step 2A Prong 2 are not sufficient to amount to significantly more than the judicial exception because the additional limitations are considered directed towards Mere Instructions To Apply An Exception and/or Insignificant Extra-Solution Activity. Note: Per MPEP 2106.05(f)(2), use of a computer or other machinery in its ordinary capacity for economic or other tasks ( e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea ( e.g., mental processes) does not integrate a judicial exception into a practical application or provide significantly more. Also, per MPEP 2106.05(f)(1), the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". Per 2106.05(d), another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. The courts have recognized the following applicable computer functions as well ‐ understood, routine, and conventional functions when they are claimed in a merely generic manner ( e.g., at a high level of generality) or as insignificant extra-solution activity: i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory. Additionally, per the following disclosure by Applicant, the claim as a whole is WURC. Specification [P.0003] discloses “Currently, many software solutions use algorithmic methods to calculate a predicted dose distribution for a patient structure, such as a planning target volume (PTV) or an organ at risk (OAR). For instance, many software solutions use computer models that utilize artificial intelligence (Al) to predict the dosage that could or would be delivered to a structure (e.g., anatomical structure or a patient organ).” For the foregoing reasons, claim 1 is directed to an abstract idea without significantly more and is rejected as not patent eligible under 35 U.S.C. 101. Independent claims 9 and 17 recite substantially the same subject matter as claim 1 and are rejected as not patent eligible under 35 U.S.C. 101. Note: Per MPEP 2106.05(f), use of a computer or other machinery in its ordinary capacity for economic or other tasks ( e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea ( e.g., mental processes) does not integrate a judicial exception into a practical application or provide significantly more. Claim 2 recites wherein outputting the predicted dosage comprises displaying a dose-volume histogram depicting the predicted radiation dosage for at least one of the first organ at risk, the second organ at risk, or a target structure . The additional element elaborates on the outputted data, thus further amounts to Insignificant Extra-solution Activity (mere data outputting, post-solution activity) per MPEP 2106.05(g). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. Claim 3 recites wherein the value indicating the prioritization between the first organ at risk of the patient and the second organ at risk of the patient is received via a sliding scale input element . The additional element elaborates on the gathered data, thus further amounts to Insignificant Extra-solution Activity (mere data gathering, pre-solution activity) per MPEP 2106.05(g). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. Claim 4 recites wherein the processor receives a plurality of values indicating a plurality of prioritizations between the first organ at risk of the patient and the second organ at risk of the patient receiving radiation dosage and outputs a plurality of predicted radiation dosages . The additional element elaborates on the gathered/outputted data, thus further amounts to Insignificant Extra-solution Activity (mere data gathering/outputting, pre/post-solution activity) per MPEP 2106.05(g). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. Claim 5 recites transmitting, by the processor, the predicted radiation dosage to a plan optimizer software solution. The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Use of a computer or other machinery in its ordinary capacity for economic or other tasks ( e.g., to receive, store, or transmit data ) or simply adding a general purpose computer or computer components after the fact to an abstract idea ( e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. Claim 6 recites adjusting, by the processor, at least one attribute of a radiotherapy machine in accordance with the predicted radiation dosage . The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. Claim 7 recites wherein the artificial intelligence model is trained using a set of weighted tensors corresponding to the training dataset . The additional element elaborates on the AI model training, thus further amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. Claim 8 recites wherein the artificial intelligence model is trained using a generative artificial intelligence model corresponding to a variational auto-encoder or a conditional variational auto-encoder . The additional element elaborates on the AI model training, thus further amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. Claim 10 recites substantially the same subject matter as claim 2 and is rejected under similar rationale and further failure to add significantly more. Clam 11 recites substantially the same subject matter as claim 3 and is rejected under similar rationale and further failure to add significantly more. Claim 12 recites substantially the same subject matter as claim 4 and is rejected under similar rationale and further failure to add significantly more. Clam 13 recites substantially the same subject matter as claim 5 and is rejected under similar rationale and further failure to add significantly more. Clam 14 recites substantially the same subject matter as claim 6 and is rejected under similar rationale and further failure to add significantly more. Claim 15 recites substantially the same subject matter as claim 7 and is rejected under similar rationale and further failure to add significantly more. Clam 16 recites substantially the same subject matter as claim 8 and is rejected under similar rationale and further failure to add significantly more. Claim 18 recites substantially the same subject matter as claim 2 and is rejected under similar rationale and further failure to add significantly more. Clam 19 recites substantially the same subject matter as claim 3 and is rejected under similar rationale and further failure to add significantly more. Claim 20 recites substantially the same subject matter as claim 4 and is rejected under similar rationale and further failure to add significantly more. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23 AIA The factual inquiries set forth in Graham V. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-06 AIA 15-10-15 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 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. 07-21-aia AIA Claim s 1-7, 9-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Peltola et al. US Patent No. 11,278,737 B2 (hereinafter referred to as “ Peltola ”) in view of Harrer et al. US Patent No. 12,285,628 B2 (hereinafter referred to as “ Harrer ”) . Regarding claim 1 , Peltola discloses A method comprising: receiving, by a processor, a value indicating a prioritization between a first organ at risk of a patient and a second organ at risk of the patient receiving radiation dosage (“Radiation therapy requires that the physician prescribe suitable goals of radiation doses for the treatment of the patient. These clinical goals (CG) can be given for example in the form of mean dose of radiation (in Gray) to a target structure and the dose that certain volume of an organ, such as an organ at risk (OAR), must not exceed [ ] Each of the given goals can further be ordered in priority describing the importance of meeting a goal in comparison to another goal. Such a set is referred to as a prioritized set of clinical goals (prioritized CG). Each clinical goal can be expressed as a quality metric Q and its associated goal value. An exemplary prioritized set of clinical goals is: GOAL 1: Target (PTV) must receive 50 Gy: Priority 1 GOAL 2: Organ at risk X (OARx) must receive less than 25 Gy: Priority 2 GOAL 3: Organ at risk Y (OARy) must receive a mean dose of less than 30 Gy: Priority 3” Peltola [Col.7 Ln.65]) ; executing, by the processor, an artificial intelligence model using the value to predict a radiation dosage for the first organ at risk, the second organ at risk, and a target structure, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants, dosage administered to the participant's target structure, first organ at risk, and second organ at risk; (“The second approach (i.e., knowledge-based approach) is to employ a library of clinically approved and delivered plans of previously treated patients with similar medical characteristics in order to find a set of parameters for a new patient that produces a clinically desirable plan. In this approach, an algorithm (i.e., a Dose Volume Histogram (DVH) model, for example) that has been trained from historical patient data (i.e., structures and dose distributions) is used as a starting point to predict the achievable dose distributions for a new set of patient structures [ ] These DVH histograms can then be used as objectives (i.e., line objectives, for example) for each structure. A cost function C K for a knowledge-based approach can therefore exemplary be expressed” Peltola [Col.8 Ln.48]. The cost function C k is interpreted to include “the value” because “ In a situation where both a template of prioritized clinical goals (CGs) as well as knowledge-based models are present as input metrics to the optimization algorithm, in order to generate a treatment plan that satisfies both inputs, an appropriate cost function needs to be determined.” Peltola [Col.9 Ln.4]) and outputting, by the processor, the predicted radiation dosage for at least one of the first organ at risk, the second organ at risk, or a target structure (“The system 100 can further include a radiation dose prediction module operable to predict a dose to be delivered to the patient 110 before commencement of the radiation treatment therapy,” Peltola [Col.6 Ln.27], “The scheduled plan can include radiation dosage information [ ] the radiation dose to be applied to the session target volume according to the scheduled plan [ ] The scheduled dose matrix so generated can be sent together with the generated scheduled plan and scheduled isodose values to a display device of system 100 to be displayed for the user in S509.” Peltola [Col.26 Ln.21]) . Peltola fails to specifically disclose an artificial intelligence model . However, Harrer discloses an artificial intelligence model (“This is achieved by applying an Artificial Intelligence (AI) module, which has been trained to predict the specific behaviour of the dose optimization algorithm, i.e. the optimizer, with respect to geometric patient data.” Harrer [Col.3 Ln.1]) Peltola and Harrer are analogous art as both patents address radiotherapy treatment planning and involve computer-implemented methods and systems. Each uses multiple data sources (patient/clinical data) to inform planning, and both ultimately aim to optimize treatment parameters for improved outcomes. Both patents involve generating or suggesting treatment plans that can be executed by a treatment device, and both utilize advanced algorithms (AI or optimization) to process input data and produce actionable outputs. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Peltola to include an artificial intelligence model, as Harrer discloses, in order “to provide for an improved RT treatment planning, e.g. allowing to automatically and precisely predict whether changes in particular RT planning parameters [ ] are likely to have a significant impact on one or more quality parameters [ ] of the treatment plan” Harrer [Col.2 Ln.24]. Regarding claim 2 , Peltola in view of Harrer disclose the method of claim 1, Peltola further discloses wherein outputting the predicted dosage comprises displaying a dose-volume histogram depicting the predicted radiation dosage for at least one of the first organ at risk, the second organ at risk, or a target structure. (“In this approach, an algorithm (i.e., a Dose Volume Histogram (DVH) model, for example) that has been trained from historical patient data (i.e., structures and dose distributions) is used as a starting point to predict the achievable dose distributions for a new set of patient structures. The achievable dose distributions are presented as a pair of Dose Volume Histograms (DVHs) representing the lower and upper bounds of the 95% confidence interval of the prediction. These DVH histograms can then be used as objectives (i.e., line objectives, for example) for each structure.” Peltola [Col.8 Ln.52]) Regarding claim 3 , Peltola in view of Harrer disclose the method of claim 1, although Peltola fails to specifically disclose wherein the value indicating the prioritization between the first organ at risk of the patient and the second organ at risk of the patient is received via a sliding scale input element. However, Harrer further discloses wherein the value indicating the prioritization between the first organ at risk of the patient and the second organ at risk of the patient is received via a sliding scale input element. (“The AI module described herein has been trained with a high number of optimizations from other patients and can predict whether changes in particular parameters p i / sliders are likely to have a significant impact on important characteristics C i of the optimization results such as PTV coverage percentage, maximum dose in most critical organ at risk (OAR) etc.” Harrer [Col.7 Ln.30]) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Peltola to include sliding scale input elements, as Harrer discloses, in order “to streamline the decision making process in RT treatment plan optimization” Harrer [Col.2 Ln.18]. Regarding claim 4 , Peltola in view of Harrer disclose the method of claim 1, Peltola further discloses wherein the processor receives a plurality of values indicating a plurality of prioritizations between the first organ at risk of the patient and the second organ at risk of the patient receiving radiation dosage (“Each of the given goals can further be ordered in priority describing the importance of meeting a goal in comparison to another goal. Such a set is referred to as a prioritized set of clinical goals (prioritized CG). Each clinical goal can be expressed as a quality metric Q and its associated goal value. An exemplary prioritized set of clinical goals is: GOAL 1: Target (PTV) must receive 50 Gy: Priority 1 GOAL 2: Organ at risk X (OARx) must receive less than 25 Gy: Priority 2 GOAL 3: Organ at risk Y (OARy) must receive a mean dose of less than 30 Gy: Priority 3” Peltola [Col.8 Ln.7]) and outputs a plurality of predicted radiation dosages (“in an automatic treatment planning process such as described herein, a method is employed to automatically derive helping objectives from the set of clinical goals, in order to guide the automatic clinical goal-based dose optimization to output clinically acceptable plans” Peltola [Col.17 Ln.55]) . Regarding claim 5 , Peltola in view of Harrer disclose the method of claim 1, Peltola further discloses transmitting, by the processor, the predicted radiation dosage to a plan optimizer software solution. (“The DVH ( i.e. Dose Volume Histogram ) estimates may be presented in the form of bands that mark the upper and lower bounds to be achieved by the optimizer 329 during optimization.” Peltola [Col.12 Ln.17]. Optimizer 329 is interpreted as a “plan optimizer” because “the optimizer 329 may perform treatment planning optimization to determine a plurality of treatment plan candidates” Peltola [Col.12 Ln.56]) Regarding claim 6 , Peltola in view of Harrer disclose the method of claim 1, Peltola further discloses adjusting, by the processor, at least one attribute of a radiotherapy machine in accordance with the predicted radiation dosage. (“Adjusting the control points changes the dose distribution” Peltola [Col.27 Ln.22]. The control points are interpreted as attributes of a radiotherapy machine because “Each control point (CP) is associated with a set of treatment parameters, including but not limited to, a set of (MLC) leaf positions, (MLC) shape, gantry rotation speed, gantry position, dose rate, and/or any other parameters” Peltola [Col.13 Ln.49] and Applicant’s disclosure “the analytics server may revise one or more attributes of the patient's radiotherapy treatment using the data predicted by the AI model. For instance, the analytics server may revise an attribute of a multi-leaf collimator (MLC)” Spec. [P.0103]) Regarding claim 7 , Peltola in view of Harrer disclose the method of claim 1, although Peltola fails to specifically disclose wherein the artificial intelligence model is trained using a set of weighted tensors corresponding to the training dataset . However, Harrer further discloses wherein the artificial intelligence model is trained using a set of weighted tensors corresponding to the training dataset. (“the input training data for the AI module resulting from various other patients and their treatment plans were provided to the AI module in the form of a m-dimensional patient vector.” Harrer [Col.12 Ln.64]. The m-dimensional patient vector is interpreted as a “tensor” due to Applicant’s disclosure “A tensor, as used herein, may refer to any multi- dimensional array of data (e.g., vectors and/or matrices).” Spec. [P.0081]. The input training data is also interpreted to be weighted due to Harrer “neural network” disclosure [Col.6 Ln.54 – Col.7 Ln.7]) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Peltola to include training an AI model using weighted tensors, as Harrer discloses, in order to “identify a correlation with corresponding optimization results for “similar patients” that were contained in the training data.” Harrer [Col.4 Ln.57]. Independent claims 9 and 17 recite substantially the same subject matter as claim 1 and are rejected under similar rationale. Additionally, Peltola discloses a server comprising a processor and a non-transitory computer-readable medium containing instructions (“a computer processing system that executes the sequence of programmed instructions embodied on the computer-readable storage medium” Peltola [Col.3 Ln.31]) a computer in communication with a server and configured to display a graphical user interface (“The treatment planning system 300 includes at least one processor 310 having an input/user interface 311” Peltola [Col.9 Ln.15]) ; a radiotherapy machine in communication with the server; (“the radiation therapy system 100 can include a radiation treatment device 101” Peltola [Col.4 Ln.54]) Claim 10 recites substantially the same subject matter as claim 2 and is rejected under similar rationale. Claim 11 recites substantially the same subject matter as claim 3 and is rejected under similar rationale. Claim 12 recites substantially the same subject matter as claim 4 and is rejected under similar rationale. Claim 13 recites substantially the same subject matter as claim 5 and is rejected under similar rationale. Claim 14 recites substantially the same subject matter as claim 6 and is rejected under similar rationale. Claim 15 recites substantially the same subject matter as claim 7 and is rejected under similar rationale. Claim 18 recites substantially the same subject matter as claim 2 and is rejected under similar rationale. Claim 19 recites substantially the same subject matter as claim 3 and is rejected under similar rationale. Claim 20 recites substantially the same subject matter as claim 4 and is rejected under similar rationale . 07-21-aia AIA Claim s 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Peltola et al. US Patent No. 11,278,737 B2 (hereinafter referred to as “ Peltola ”) in view of Harrer et al. US Patent No. 12,285,628 B2 (hereinafter referred to as “ Harrer ”), in further view of Bonder et al. US Pub. No. 2025/0025719 A1 (hereinafter referred to as “ Bonder ”) . Regarding claim 8 , Peltola in view of Harrer disclose the method of claim 7, but fail to specifically disclose wherein the artificial intelligence model is trained using a generative artificial intelligence model corresponding to a variational auto-encoder or a conditional variational auto-encoder. However, Bondar discloses wherein the artificial intelligence model is trained using a generative artificial intelligence model corresponding to a variational auto-encoder or a conditional variational auto-encoder. (“In particular, regression type networks are envisaged herein such as certain neural network architecture with suitably configured output layers, autoencoders, variational autoencoders [ ] machine learning models of the generative type are envisaged herein in particular” Bondar [P.0105-0106]) Bonder is analogous art as it relates to the technical field of radiation therapy planning, specifically to systems and methods that support planning by predicting multiple dose distributions based on different radiation treatment plan templates or template types using a trained machine learning model. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention the have modified Peltola to include an AI model trained using a generative artificial intelligence model corresponding to a variational auto-encoder, as Bonder discloses, since training “experience helps improve performance if the training data well represents a distribution of examples over which the final system performance is measured.” Bonder [P.0039]. Claim 16 recites substantially the same subject matter as claim 8 and is rejected under similar rationale. Conclusion 07-96 The prior art made of record, listed on form PTO-892, and not relied upon is considered pertinent to applicant's disclosure: Peltola et al. (Artificial Intelligence Modeling For Radiation Therapy Dose Distribution Analysis – US Pub. No 20220296923 A1). “Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model.” [Abstract] Peltola et al. (Artificial Intelligence Modeling For Radiation Therapy Dose Distribution Analysis – US Pub. No 20220296924 A1). “Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model.” [Abstract] Wu et al. (Fluence Map Prediction And Treatment Plan Generation For Automatic Radiation Treatment Planning – US Patent No 12128250 B2). “A radiation treatment planning system can include a machine learning system that receives patient data, including an image scan (e.g., CT scan) and contour(s), a physician prescription, including planning target and dose, and device (radiation beam) data and outputs predicted fluence maps.” [Abstract] Nguyen et al. (Deep Learning Based Dosed Prediction For Treatment Planning And Quality Assurance In Radiation Therapy – US Patent No 11615873 B2). “The computing system generates a volumetric dose prediction model using a neural network by learning, by the neural network, a relationship between a plurality of dose volume histograms for the plurality of patients and the corresponding plurality of volumetric dose distributions.” [Abstract] Kierkels, Roel GJ, et al. "Multicriteria optimization enables less experienced planners to efficiently produce high quality treatment plans in head and neck cancer radiotherapy." Radiation oncology 10.1 (2015): 87. “The final dose distribution was selected by navigation across the Pareto surface using slider bars on clinical objectives” [Pg.3 Col.2 Ln.8] Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anthony Chavez whose telephone number is (571) 272-1036. The examiner can normally be reached Monday - Thursday, 8 a.m. - 5 p.m. ET. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez can be reached at (571) 270-1104 The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov . Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ANTHONY CHAVEZ/Examiner, Art Unit 2186 /RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186 Application/Control Number: 18/086,374 Page 2 Art Unit: 2186 Application/Control Number: 18/086,374 Page 3 Art Unit: 2186 Application/Control Number: 18/086,374 Page 4 Art Unit: 2186 Application/Control Number: 18/086,374 Page 5 Art Unit: 2186 Application/Control Number: 18/086,374 Page 6 Art Unit: 2186 Application/Control Number: 18/086,374 Page 7 Art Unit: 2186 Application/Control Number: 18/086,374 Page 8 Art Unit: 2186 Application/Control Number: 18/086,374 Page 9 Art Unit: 2186 Application/Control Number: 18/086,374 Page 10 Art Unit: 2186 Application/Control Number: 18/086,374 Page 11 Art Unit: 2186 Application/Control Number: 18/086,374 Page 12 Art Unit: 2186 Application/Control Number: 18/086,374 Page 13 Art Unit: 2186 Application/Control Number: 18/086,374 Page 14 Art Unit: 2186 Application/Control Number: 18/086,374 Page 15 Art Unit: 2186 Application/Control Number: 18/086,374 Page 16 Art Unit: 2186 Application/Control Number: 18/086,374 Page 17 Art Unit: 2186 Application/Control Number: 18/086,374 Page 18 Art Unit: 2186 Application/Control Number: 18/086,374 Page 19 Art Unit: 2186 Application/Control Number: 18/086,374 Page 20 Art Unit: 2186 Application/Control Number: 18/086,374 Page 21 Art Unit: 2186 Application/Control Number: 18/086,374 Page 22 Art Unit: 2186