DETAILED ACTION
Receipt of Applicant’s amendment filed 06/30/2026 is acknowledged.
Claims 1, 8-9, and 16-17 have been amended.
Claims 7 and 15 are canceled.
Claims 1-6, 8-14, and 16-20 are pending.
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 .
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.
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.
Response to Arguments
Claim Rejections under 35 U.S.C. § 101:
Acknowledgement is made of amended claims 1, 8-9, and 16-17. Applicants’ arguments have been fully considered, but are not persuasive. Rejections are maintained.
Applicant argues the claims are not directed toward an abstract idea under Step 2A (Prong 1) since the amended claims “recite at least one element that cannot be practically performed in the human mind, or by a human using pen and paper.” Applicant also argues the claims integrate the alleged abstract idea into a practical application under Step 2A (Prong 2) since the claimed invention “may reduce the amount of training data used”, which “improves the efficiency of model training”. After careful re-evaluation, the Examiner respectfully disagrees per the following:
The steps of the subject matter eligibility analysis for products and processes that are to be used during examination for evaluating whether a claim is drawn to patent-eligible subject matter is the following:
Step 1: Determine if the claim is directed to a process, machine, manufacture, or composition of matter. Claims 1-6 and 8 are directed towards a method, therefore fall within the statutory category of a process. Claims 9-14 and 16-20 are directed towards a system, therefore fall within the statutory category of machine.
Step 2A (Prong 1): Determine if the claim is directed to a law of nature, a natural phenomenon (product of nature), or an abstract idea. As shown in Claim Rejections - 35 USC §101 section below, independent claims 1, 9, and 17 are all directed towards an abstract idea (mental processes) since the human mind can reasonably predict (i.e. evaluate, opinion) a radiation dosage for at risk organs using a predefined value. Per MPEP 2106.04(a)(2)(III), the courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer.
Step 2A (Prong 2)/Step 2B: Determine if the claim recites additional elements that amount to significantly more than the judicial exception. As shown in 35 USC §101 analysis section below, 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 amount to Mere Instructions To Apply An Exception and/or Insignificant Extra Solution Activity and/or Field of Use and Technological Environment per MPEP 2106.05(f)/(g)/(h), given the broadest reasonable interpretation. 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 MPEP 2106.05(g), “another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception [ ] As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional.” Per MPEP 2106.05(d), “[t]he 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).” Thus, Applicants’ arguments not persuasive.
Applicant also argues claim 6 does not amount to mere instructions to apply an exception since it “recites processor-controlled adjustment of physical radiotherapy hardware based on the AI-predicted dose.” After re-evaluation the Examiner respectfully disagrees. As shown in 35 USC §101 analysis section below, per MPEP 2106.05(f), “[t]he 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". [Additionally,] simply adding a general purpose computer or computer components after the fact to an abstract idea (i.e. mental processes) does not integrate a judicial exception into a practical application or provide significantly more”. Thus, Applicant’s argument not persuasive.
Claim Rejections under 35 U.S.C. § 103:
Acknowledgement is made of amended claims 1, 8-9, 16-17 and the cancelation of claims 7 and 15. Applicants’ arguments have been considered, but not persuasive. Significant amendments warrant new grounds of rejection. Rejections to claims are maintained.
Applicants’ arguments regarding Harrer and Peltola (Office Action dated 3/30/2026 cited references) not teaching claim 1 amended limitations are moot given new grounds of rejection necessitated by amendment. See Claim Rejections - 35 U.S.C. § 103 section below.
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 1-6, 8-14, and 16-20 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 1-6 and 8 are directed to a method, as such these claims fall within the statutory category of a process.
Claims 9-14 and 16-20 are directed to a system, as such these claims fall within the statutory category of machine.
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:
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)). The "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. 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. The limitation amounts to using a computer as a tool to perform a mental process. Additionally, per MPEP 2106.04(a)(2)(III)(A) “[e]xamples of claims that recite mental processes include: a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind”.)
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 and/or Field of Use and Technological Environment per MPEP 2106.05(f)/(g)/(h), 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.)
dynamically adapting, by the processor, execution of an artificial
intelligence model, (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f) and/or Field of Use and Technological Environment per MPEP 2106.05(h). 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. Additionally, the limitation fails to recite details of how a solution (i.e. dynamically adapting) to a problem (i.e. execution of an AI model) is accomplished. Per MPEP 2106.05(h), “[a]nother consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use [ ] limitations that amount to merely indicating a field of use or technological environment (i.e. an artificial intelligence model) in which to apply a judicial exception (i.e. mental processes) do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.”)
wherein the artificial intelligence model is trained in accordance with a training dataset comprising for each participant in a set of participants, a plurality of weighted anatomy tensors and a corresponding plurality of radiation dose distributions, each weighted anatomy tensor encoding a different value indicating a prioritization between a first training organ at risk and a second training organ at risk of the participant, and each radiation dose distribution corresponding to the different value encoded in the respective weighted anatomy tensor, (The additional limitation amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f) and/or Insignificant Extra-Solution Activity (mere data gathering) per MPEP 2106.05(g). The limitation invokes computers or other machinery merely as a tool to perform an existing process. Also, per MPEP 2106.05(f) “examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: Requiring the use of software (i.e. artificial intelligence model) to tailor information and provide it to the user on a generic computer”. 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. [ ] examples of activities that the courts have found to be insignificant extra-solution activity: Mere Data Gathering [ ] Selecting a particular data source or type of data to be manipulated”.)
wherein the execution of the artificial intelligence model is adapted based on generating, at execution time, a tensor corresponding to the value received, such that the artificial intelligence model adapts prioritization between the first organ at risk of the patient and the second organ at risk of the patient receiving radiation dosage based on the generated tensor (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). The limitation fails to recite details of how the execution of the artificial intelligence model is adapted based on generating, at execution time, a tensor or how the artificial intelligence model adapts prioritization between the organs at risk.)
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 the 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 and/or Field of Use and Technological Environment.
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 MPEP 2106.05(g), “another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception [ ] As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional.”
Per MPEP 2106.05(d), “[t]he 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), “[u]se 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" [Additionally,] 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 7 has been canceled.
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 has been canceled.
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
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.
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.
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.
Claims 1-6, 9-14, 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 for dynamically adapting execution of artificial intelligence models, the 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; using the value to predict a radiation dosage for the first organ at risk, the second organ at risk, and 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” Peltola [Col.6 Ln.27], “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]. See Figs.4, 5, and 6 for clarification.), wherein the model is trained in accordance with a training dataset (“A DVH model is generated from the knowledge-based information in Step S105, and the DVH model is trained in Step S106” Peltola [Col.14 Ln.18]), for each participant in a set of participants (“The second approach (i.e., knowledge-based approach) is to employ a library of clinically approved and delivered plans of previously treated patients (i.e. set of participants) with similar medical characteristics in order to find a set of parameters for a new patient (i.e. each participant) 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” Peltola [Col.9 Ln.4]), a plurality of weighted anatomy tensors and a corresponding plurality of radiation dose distributions (“after a treatment plan has been developed in the treatment planning system 300, [ ] the physician can develop a set of adaptive directives, which is a list of parameters / directives / information that describes the intent of the adaptive treatment, namely, the 4D description of the planned treatment for the patient. The set of adaptive directives can include information regarding the planned dose specification (i.e. radiation dose distribution)” Peltola [Col.14 Ln.61]. The 4D description of the planned treatment is interpreted as anatomy tensors due to Applicant’s disclosure “After generating the plan, the analytics server may generate weighted anatomy tensors corresponding to the generated plans. A tensor, as used herein, may refer to any multi-dimensional array of data (e.g., vectors and/or matrices)” Spec. [P.0081]. The treatment plans are also interpreted to be weighted because “A first treatment plan candidate includes a weighting factor (m) of 10%, for example, and a second treatment plan candidate includes a weighting factor (m) of 90%, for example.” Peltola [Col.14 Ln.46])
each weighted anatomy tensor encoding a different value indicating a prioritization between a first training organ at risk and a second training organ at risk of the participant (“Exemplary treatment plan candidates are illustrated in FIG. 5. During dose preview and plan selection, a user may review the dose distribution and treatment plan candidates obtained for different weighting factors (m) and select the treatment plan that best represents the desired treatment outcome.” Peltola [Col.14 Ln.53]), and each radiation dose distribution corresponding to the different value encoded in the respective weighted anatomy tensor (“treatment planning (i.e. weighted anatomy tensor) follows [ ] The first approach (i.e., goal-based approach) is to develop an automatic optimization algorithm to automatically adjust optimization model parameters [ ] the starting point for the optimization algorithm specifying a preferred dose distribution is a set or a template of clinical goals (CG) [ ] An exemplary prioritized set of clinical goals is (i.e. different values encoded): 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.45 – Col.8 Ln.19]), and wherein the execution of the model is adapted based on generating, at execution time, a tensor corresponding to the value received, such that the model adapts prioritization between the first organ at risk of the patient and the second organ at risk of the patient receiving radiation dosage based on the generated tensor (“By using a plan quality metric that takes into account the prioritized list of clinical and converted goals as an input to the optimization process, both the clinical goal list and the converted goal list can be dynamically re-ordered by the user in order to explore the dose distributions from other priorities, as illustrated in FIGS. 7B and 9.” Peltola [Col.17 Ln.9] See Figs. 7B and 9 below.);
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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 the 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 dynamically adapting, by the processor, execution of an artificial intelligence model.
However, Harrer discloses dynamically adapting, by the processor, execution of an artificial intelligence model (Note: The Examiner interprets dynamically adapting to mean adapting, i.e. changing, input parameters into the AI model. Harrer discloses “FIG. 5 (see below) schematically shows a method of predicting with a trained AI module the dependency Ci (pi) and of determining a plurality of anchor points based on the predicted dependency Ci (pi) of the RT quality criterion Ci according to an exemplary embodiment of the invention” Harrer [Col.19 Ln.29])
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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 and/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 a dynamically optimized 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 pi/sliders are likely to have a significant impact on important characteristics Ci 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])
Claim 7 has been canceled.
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 has been canceled.
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.
Claims 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 1, 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
The prior art made of record, listed on form PTO-892, and not relied upon is
considered pertinent to applicant's disclosure:
Joe Anto et al. (Radiation Therapy Interactive Planning – US Pat. No. 11083911 B2). “A radiation therapy system (100) includes a radiation therapy (RT) optimizer unit (102) and an interactive planning interface unit (120). The RT optimizer unit (102) receives at least one target structure and at least one organ-at-risk (OAR) structure segmented from a volumetric image (108), and generates an optimized RT plan (140) based on dose objectives (200-204, 210-222, 320), at least one dose objective of the dose objectives corresponding to each of the at least one target structure (210-222) and the at least one OAR structure (200-204). The optimized RT plan includes a planned radiation dose for each voxel of the volumetric image using external beam radiation therapy, wherein the RT optimizer unit operates iteratively. The interactive planning interface unit (120) interactively controls each of the dose objectives through controls (300) displayed on a single display (126) of a display device (124), operates the RT optimizer unit to iteratively compute the planned radiation dose according to the controls, and provide visual feedback (310, 134) on the single display according to progress of the RT optimizer unit after each trial.” [Abstract]
Peltola et al. (Artificial Intelligence Modeling For Radiation Therapy Dose Distribution Analysis – US Pat. No 11679274 B2). “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 Pat. No 12121747 B2). “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]
Applicants’ amendment necessitated the new ground(s) of rejection presented in this Office Action. Accordingly, THIS ACTION IS MADE FINAL. 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.
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/ANTHONY CHAVEZ/ Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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