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 .
Response to Amendment
In the amendment filed on August 7, 2026, the following has occurred: claim(s) 1 and 11 have been amended. Now, claim(s) 1-4, 6-14, and 16-20 are pending.
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.
Claim(s) 1-4, 6-14, and 16-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-4, 6-14, and 16-20: Step 2A Prong One
Claims 1 and 11 recite, with claim 1 being representative, claim 1 recite(s):
accessing a plurality of previously-optimized radiation treatment plans;
accessing a plurality of optimization precursor information items, wherein at least some of the plurality of optimization precursor information items comprise clinical goals, and wherein each of the optimization precursor information items corresponds to one of the plurality of previously-optimized radiation treatment plans;
optimizing a new radiation treatment plan, wherein the new radiation treatment plan comprises specified values for each of a variety of treatment-platform parameters during each of a plurality of sequential exposure fields for a particular energy-based treatment platform to be used to deliver therapeutic energy to a corresponding patient; and
administering radiation treatment therapy to the patient as a function of the new radiation treatment plan
These limitations, as drafted given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the recitation of “a control circuit”, the limitations encompass a person following instructions to access a plurality of previously-optimized radiation treatment plans and a plurality of optimization precursor information items, a person following instructions to optimize a new radiation treatment plan, and a person following instructions to administer a radiation treatment therapy to a patient. But for the recitation of generic computer components, such steps encompass Certain Methods of Organizing Human Activity, and could be performed by a radiologist following instructions.
Claims 2-4, 6-10, 12-14, and 16-20 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, claims 2-4, 6 and 12-14, 16 include the abstract identified above and further expand on the optimization precursor information items. Similarly, claims 7-9 and 17-19 include the abstract idea identified above and describe the training corpus and emphasized features. Finally, claims 10 and 20 include the abstract identified above and describe optimizing a radiation treatment plan. Therefore, these claims merely further define information that could be observed by a user and follow rules or instructions to optimize a radiation treatment plan. Therefore, these claims recite limitations fall into the Certain Methods of Organizing Human Activity grouping of abstract ideas.
Claims 1-4, 6-14, and 16-20: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea along with generally linking the abstract idea to a particular technological environment.
Claims 1-4, 6-14, and 16-20, directly or indirectly, recite the following generic computer components configured to implement the abstract idea: "a control circuit" in claim 1, "a memory", and "a control circuit operably coupled to the memory" in claim 11. The written description discloses that the recited computer components encompass generic computer components including "Such a control circuitl0l can comprise a fixed-purpose hard-wired hardware platform (including but not limited to an application-specific integrated circuit (ASIC) (which is an integrated circuit that is customized by design for a particular use, rather than intended for general-purpose use), a field-programmable gate array (FPGA), and the like) or can comprise a partially or wholly- programmable hardware platform (including but not limited to microcontrollers, microprocessors, and the like). These architectural options for such structures are well known and understood in the art and require no further description here. This control circuit 101 is configured (for example, by using corresponding programming as will be well understood by those skilled in the art) to carry out one or more of the steps, actions, and/or functions described herein." (See Specification in Paragraph [0021]), "In addition to information such as radiation dosing information, previously- optimized radiation treatment plans, and a plurality of optimization precursor information items, this memory102 can serve, for example, to non-transitorily store the computer instructions that, when executed by the control circuitl0l, cause the control circuitl0l to behave as described herein. (As used herein, this reference to "non-transitorily" will be understood to refer to a non- ephemeral state for the stored contents (and hence excludes when the stored contents merely constitute signals or waves) rather than volatility of the storage media itself and hence includes both non-volatile memory (such as read- only memory (ROM) as well as volatile memory (such as a dynamic random access memory (DRAM).)" (See Specification in Paragraph [0023]). As set forth in the MPEP 2106.04(d) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claims recite “…generating a machine learning model using the plurality of previously-optimized radiation treatment plans and the plurality of optimization precursor information items as a training corpus”, “…as a function, at least in part, of the generated machine learning model”, “using the particular energy- based treatment platform” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words "apply it" to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words "apply it" or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-4, 6-14, and 16-20: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.")
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2106.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 for establishing a background for determining obviousness under 35 U.S.C. 103 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.
Claims 1-4, 6-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Adler et al. (U.S. Patent Pre-Grant Publication No. 2021/0020297) in view of De Bruin et al. (U.S. Patent Pre-Grant Publication No. 2011/0119212) in view of Sjölund et al. (U.S. Patent Pre-Grant Publication No. 2020/0289847).
As per independent claim 1, Adler teaches a method comprising:
by a control circuit (See Paragraphs [0055]-[0058], [0119]: The radiotherapy processing computing system can collect and obtain data, and communicate with other systems, via a network using one or more communication interfaces, which are communicatively coupled to the processing circuitry and the memory):
accessing a plurality of previously-optimized radiation treatment plans (See Paragraphs [0038], [0077]-[0078], [0099]: The storage device may store transitory or non-transitory computer-executable instructions, such as an operating system, radiation therapy treatment plans, training data, software programs, and any other computer-executable instructions to be executed by the processing circuitry, which the Examiner is interpreting radiation therapy treatment plans to encompass a plurality of previously-optimized radiation treatment plans);
optimizing a new radiation treatment plan as a function, at least in part, of the generated machine learning model (See Paragraphs [0048]-[0050]: Software programs may utilize the treatment processing logic to produce new or updated treatment plan parameters for deployment to the treatment data source and/or presentation on output device, which the Examiner is interpreting the treatment processing logic to produce new or updated treatment plan parameters when combined with the machine learning of De Bruin and Sjölund), wherein the new radiation treatment plan comprises specified values for each of a variety of treatment-platform parameters during each of a plurality of sequential exposure fields for a particular energy-based treatment platform to be used to deliver therapeutic energy to a corresponding patient; and
administering radiation treatment therapy to the patient using the particular energy- based treatment platform as a function of the new radiation treatment plan (See Paragraphs [0048]-[0050], [0103]-[0107]: The radiation therapy plan will be used to treat a patient with radiation via the treatment device, consistent with results of the trained ML model implemented by the treatment processing logic, which the Examiner is interpreting to encompass the claimed portion as the treatment processing logic generates a solution to the radiotherapy treatment plan optimization problem based on the estimated one or more optimization variables of the radiotherapy treatment plan optimization problem.)
While Adler teaches a method comprising: accessing a plurality of previously-optimized radiation treatment plans; and
optimizing a new radiation treatment plan,
Adler may not explicitly teach a method comprising:
accessing a plurality of optimization precursor information items, wherein at least some of the plurality of optimization precursor information items comprise clinical goals, and wherein each of the optimization precursor information items corresponds to one of the plurality of previously-optimized radiation treatment plans;
generating a machine learning model using the plurality of previously-optimized radiation treatment plans and the plurality of optimization precursor information items as a training corpus.
De Bruin teaches a method comprising:
accessing a plurality of optimization precursor information items, wherein at least some of the plurality of optimization precursor information items comprise clinical goals (See Paragraphs [0069], [0072], [0108]: In semisupervised machine learning, in addition to labeled training data where the corresponding target variables are known, the information inherent in unlabeled clinical and laboratory information is used to construct a more efficient decision, estimation and prediction models and improve performance, which the Examiner is interpreting the target corresponding target variables to encompass at least some of the plurality of optimization precursor information items comprise clinical goals as the feedback consists of both qualitative and quantitative data describing the patient's response to the prescribed treatment interpreted within the context of an estimate of the patient's reliability as a historian, adherence to treatment and adequacy of prescribed therapy (e.g. drug dose and duration of administration)), and wherein each of the optimization precursor information items corresponds to one of the plurality of previously-optimized radiation treatment plans (See Paragraphs [0082]-[0083]: The Examiner is interpreting the determined treatment and a valid outcome to encompass each of the optimization precursor information items corresponds to one of the plurality of previously-optimized radiation treatment plans);
generating a machine learning model using the plurality of previously-optimized radiation treatment plans and the plurality of optimization precursor information items as a training corpus (See Paragraph [0082]: This subsystem uses the training data containing information about the therapies used and clinical response in patients previously treated for this condition, employing machine learning and inference methodologies to find a list of the best treatment options that can be sent to the clinician/user, which the Examiner is interpreting the training data containing information about the therapies used and clinical response in patients previously treated for this condition to encompass the plurality of previously-optimized radiation treatment plans and the plurality of optimization precursor information items as a training corpus.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Adler to include accessing a plurality of optimization precursor information items, wherein at least some of the plurality of optimization precursor information items comprise clinical goals, and wherein each of the optimization precursor information items corresponds to one of the plurality of previously-optimized radiation treatment plans; generating a machine learning model using the plurality of previously-optimized radiation treatment plans and the plurality of optimization precursor information items as a training corpus as taught by De Bruin. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Adler with De Bruin with the motivation of improving accuracy and efficiency of the prediction and estimation models (See Background of the Invention of De Bruin in Paragraph [0015]).
While Adler/De Bruin describes a method optimizing a new radiation treatment plan as a function, at least in part, of the generated machine learning model, Adler/De Bruin may not explicitly teach wherein the new radiation treatment plan comprises specified values for each of a variety of treatment-platform parameters during each of a plurality of sequential exposure fields for a particular energy-based treatment platform to be used to deliver therapeutic energy to a corresponding patient.
Sjölund teaches a method optimizing a new radiation treatment plan as a function, at least in part, of the generated machine learning model (See [0019]-[0020]: The ML technique may infer a treatment goal and obtain training data (including paired data for the inferred treatment goal and the estimated and realizable plan parameters) associated with the inferred treatment goal, the ML technique is trained to estimate the realizable parameters based on the training data associated with the inferred treatment goal, which the Examiner is interpreting the ML technique to encompass the generated machine learning model when combined with Adler/De Bruin as the training data includes paired data for the inferred treatment goal and the estimated and realizable plan parameters, and interpreting treatment goals to encompass clinical goals), wherein the new radiation treatment plan comprises specified values for each of a variety of treatment-platform parameters during each of a plurality of sequential exposure fields for a particular energy-based treatment platform to be used to deliver therapeutic energy to a corresponding patient (See [0019]-[0023], [0052]-[0053]: The radiotherapy plan processing operations are performed to enable the radiotherapy system to provide radiation therapy to a patient based on specific aspects of captured medical imaging data and therapy dose calculations or radiotherapy machine configuration parameters, which the Examiner is interpreting specific aspects of captured medical imaging data and therapy dose calculations or radiotherapy machine configuration parameters to encompass specified values for each of a variety of treatment-platform parameters during each of a plurality of sequential exposure fields for a particular energy-based treatment platform to be used to deliver therapeutic energy to a corresponding patient as a sequence of therapy deliveries can be specified according to a radiation therapy treatment plan, such as using one or more different orientations or locations of the gantry, couch, or therapy output.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Adler/De Bruin to include the new radiation treatment plan comprises specified values for each of a variety of treatment-platform parameters during each of a plurality of sequential exposure fields for a particular energy-based treatment platform to be used to deliver therapeutic energy to a corresponding patient as taught by Sjölund as the ML technique may infer a treatment goal and obtain training data (including paired data for the inferred treatment goal and the estimated and realizable plan parameters). One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Adler/De Bruin with Sjölund with the motivation of providing a realizable radiation therapy treatment plan, which may provide an optimized dose to be delivered to treat the tumor while minimizing exposure to the one or more OARs (See Background of Sjölund in Paragraph [0004]).
Claim(s) 11 mirrors claim 1 only within a different statutory category, and is rejected for the same reasons as claim 1. The addition of "a memory having stored... " and "a control circuit operably coupled to the memory ... " is encompassed by Adler in Paragraphs [0055]-[0058], [0119] as the radiotherapy processing computing system can collect and obtain data, and communicate with other systems, via a network using one or more communication interfaces, which are communicatively coupled to the processing circuitry and the memory.
As per claim 2, Adler/De Bruin/Sjölund discloses the method of claim 1 as described above. Adler further teaches wherein at least one of the optimization precursor information items corresponds to at least two of the plurality of previously-optimized radiation treatment plans (See Paragraphs [0082]-[0084]: A plurality of training optimization problems previously solved iteratively for other patients are retrieved together with their corresponding iterative training parameters (e.g., optimization variables and solutions for each iteration or the final set of parameters corresponding to the solution), which the Examiner is interpreting a plurality of training optimization problems previously solved iteratively for other patients are retrieved together with their corresponding iterative training parameters to encompass at least one of the optimization precursor information items corresponds to at least two of the plurality of previously-optimized radiation treatment plans.)
Claim(s) 12 mirrors claim 2 only within a different statutory category, and is rejected for the same reasons as claim 2.
As per claim 3, Adler/De Bruin/Sjölund discloses the method of claim 1 as described above. Adler further teaches wherein at least a majority of the plurality of optimization precursor information items originated with a given radiation treatment facility (See Paragraphs [0070]-[0075]: Training data includes constraints which may define the physical constraints of a given radiotherapy device, and some components of training input may be stored separately at a different off-site facility or facilities than other components, which the Examiner is interpreting training data includes constraints which may define the physical constraints of a given radiotherapy device to encompass the plurality of optimization precursor information items originated with a given radiation treatment facility, and that only some components of training input may be stored separately to encompass at least a majority originated with a given radiation treatment facility.)
Claim(s) 13 mirrors claim 3 only within a different statutory category, and is rejected for the same reasons as claim 3.
As per claim 4, Adler/De Bruin/Sjölund discloses the method of claims 1 and 3 as described above. Adler further teaches wherein at least substantially all of the plurality of optimization precursor information items originated with the given radiation treatment facility (See Paragraphs [0070]-[0075]: Training data includes constraints which may define the physical constraints of a given radiotherapy device, and some components of training input may be stored separately at a different off-site facility or facilities than other components, which the Examiner is interpreting training data includes constraints which may define the physical constraints of a given radiotherapy device to encompass the plurality of optimization precursor information items originated with a given radiation treatment facility, and that only some components of training input may be stored separately to encompass at least substantially all of the plurality of optimization precursor information items the given radiation treatment facility.)
Claim(s) 14 mirrors claim 4 only within a different statutory category, and is rejected for the same reasons as claim 4.
As per claim 6, Adler/De Bruin/Sjölund discloses the method of claim 1 as described above. Adler further teaches wherein at least some of the plurality of optimization precursor information items comprise optimization objectives (See Paragraphs [0079], [0093]: The objective function of a radiotherapy treatment plan optimization problem can be decomposed into individual parts, some parts include different objectives relating to how well the tumor is targeted and other parts focus on sparing healthy tissue, which the Examiner is interpreting a radiotherapy treatment plan optimization problem can be decomposed into individual part of different objectives to encompass optimization objectives.)
Claim(s) 16 mirrors claim 6 only within a different statutory category, and is rejected for the same reasons as claim 6.
As per claim 7, Adler/De Bruin/Sjölund discloses the method of claim 1 as described above. Adler further teaches wherein generating the machine learning model using the plurality of previously-optimized radiation treatment plans and the plurality of optimization precursor information items as a training corpus comprises, at least in part, evaluating dose distributions in the plurality of previously-optimized radiation treatment plans as a function of the plurality of optimization precursor information items to identify emphasized features (See Paragraphs [0028], [0032], [0053], [0068]-[0071], [0085], [0103]: The new radiotherapy treatment plan optimization problem (that includes at least a dose kernel, a dose volume histogram constraint, or a dose constraint) can be solved using the estimated optimization variables that are provided by the ML model, which the Examiner is interpreting the new radiotherapy treatment plan optimization problem (that includes at least a dose kernel, a dose volume histogram constraint, or a dose constraint) to encompass evaluating dose distributions in the plurality of previously-optimized radiation treatment plans as a function of the plurality of optimization precursor information items to identify emphasized features.)
Claim(s) 17 mirrors claim 7 only within a different statutory category, and is rejected for the same reasons as claim 7.
As per claim 8, Adler/De Bruin/Sjölund discloses the method of claims 1 and 7 as described above. Adler further teaches wherein the emphasized features include at least one of: a feature corresponding to an organ-at-risk protection compromise; a feature corresponding to a compromise between target coverage and organ-at-risk protection (See Paragraphs [0071]: An output element may include a dose to be applied to a voxel of a particular organ at risk (OAR), the feature element may include a signed distance indicating the distance between a voxel in an OAR and the closest boundary voxel in a target for the radiation therapy); a feature corresponding to at least one spatially restricted area that has particular weight in achieving or failing a precursor specification.
Claim(s) 18 mirrors claim 8 only within a different statutory category, and is rejected for the same reasons as claim 8.
As per claim 9, Adler/De Bruin/Sjölund discloses the method of claim 1 as described above. Adler further teaches wherein generating the machine learning model using the plurality of previously-optimized radiation treatment plans and the plurality of optimization precursor information items as a training corpus comprises, at least in part, selecting a loss function to be minimized during training of the machine learning model (See Paragraphs [0075]-[0076]: Machine learning model(s) training trains one or more machine learning techniques based on the sets of input-output pairs of paired training data sets, the model training may train the ML model parameters by minimizing a first loss function based on one or more training optimization variables and the corresponding training parameters of a corresponding one of the plurality of training radiotherapy treatment plan optimization problems.)
Claim(s) 19 mirrors claim 9 only within a different statutory category, and is rejected for the same reasons as claim 9.
As per claim 10, Adler/De Bruin/Sjölund discloses the method of claim 1 as described above. Adler further teaches further comprising: optimizing a new radiation treatment plan as a function, at least in part, of the machine learning model (See Paragraphs [0099]-[0103]: Treatment processing logic utilizes the trained model to generate results, after each of the machine learning models is trained, new data, including one or more patient input parameters, may be received, and the new radiotherapy treatment plan optimization problem can be solved using the estimated optimization variables that are provided by the ML model, which the Examiner is interpreting the new radiotherapy treatment plan optimization problem can be solved to encompass optimizing a new radiation treatment plan as a function.)
Claim(s) 20 mirrors claim 10 only within a different statutory category, and is rejected for the same reasons as claim 10.
Response to Arguments
In the Remarks filed on August 7, 2026, the Applicant argues that the newly amended and/or added claims overcome the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s). The Examiner does not acknowledge that the newly added and/or amended claims overcome the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s).
The Applicant argues that:
(1) MPEP 2106.04(d)(2) addresses the patent eligibility of medical treatments during the Step 2A Prong Two analysis. '' A claim reciting a judicial exception is not directed to the judicial exception if it also recites additional element(s) demonstrating that the claim as a whole integrates the exception into a practical application. One way to demonstrate such integration is when the additional elements apply or use the recited judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition." The applicant respectfully submits that the foregoing added features and specifics of administering radiation treatment therapy to a patient clearly serves to establish patent eligibility per that analysis;
(2) the claims specify accessing clinical goals, and using those clinical goals in the training corpus for a machine learning model. The claims then specify, ''optimizing a new radiation treatment plan as a function, at least in part, of the generated machine learning model." Accordingly, the claims specify optimizing a radiation treatment plan as a function of a machine learning model that itself was generated using clinical goals as part of the training corpus. The applicant agrees with the Examiner that Adler does not teach any of the foregoing. The Examiner seeks to characterize De Bruin as teaching the foregoing features, and then suggests that it would be obvious to combine De Bruin's teachings with Adler to yield a result that would read on the independent claims. The applicant respectfully submits that the Examiner errs in those regards. ''Clinical goals'' and ''training'' a machine learning model with ''clinical goals'' and ''optimizing'' a radiation treatment plan with that machine learning model The ordinarily-skilled person who reads De Bruin will find no explicit reference to ''clinical goals." The Examiner suggests that ''clinical goals'' and other features can be found at paragraphs 0069, 0072, 0108, 0082, and 0083 of De Bruin. That, however, is literally not the case. The relevant part of paragraph 0069 lists a number of information types, but none are ''clinical goals'' nor synonymous with that expression. In addition, this same paragraph makes clear that the aforementioned information is not used to ''train'' a machine learning model. Accordingly, even if the list of information items provided in paragraph 0069 is read generously enough to include ''clinical goals," those clinical goals would not be used to train a machine learning model as required by the applicant's claims. Instead, those clinical goals would be fed into a (presumably already) trained machine learning model that would output a report regarding diagnostic possibilities and possible corresponding treatments. Therefore, no combination of these teachings from De Bruin with Adler would yield a result that reads on the independent claims. The ordinarily-skilled person who reads paragraph 0072 of De Bruin will only find the idea of training a model using information that describes a patient's response to their respective treatment and/or information regarding the accuracy of the initial diagnosis. ''Clinical goals'' are categorically not such information, nor is the information in this paragraph used to train a machine learning model that is used to optimize a radiation treatment plan. Therefore, no combination of these teachings from De Bruin with Adler would yield a result that reads on the independent claims. The ordinarily-skilled person will know, without any doubt, that the idea of providing information in a setting where the patient's diagnosis is not known is utterly distinguished from a setting where a radiation treatment plan is being optimized for a particular patient. Without the diagnosis, and a certain one at that, there would be nothing to optimize. And certainly it would be clear that ''clinical goals'' corresponding to such a treatment would not qualify as the kind of ''unlabeled clinical and laboratory information'' that would correspond to De Bruin's case example that focuses on medical diagnosis. And furthermore, the ordinarily-skilled person would not conflate (1) the idea of training a machine learning model with clinical goals in order to then optimize a radiation treatment plan with that machine learning model with (2) treatment selection or efficacy where the final treatment outcome is unavailable for whatever reason. This overly qualifies as an example of the old cliche, comparing apples with oranges; it's really more a case of comparing apples with spark plugs. Therefore, no combination of these teachings from De Bruin with Adler would yield a result that reads on the independent claims. This paragraph speaks generally about using ''the training data containing information about the therapies used and clinical response in patients previously treated for this condition'' to train a machine learning model and to use the latter to ''[provide] a list of diagnostic possibilities and [predict] the treatment options," and in particular to ''to find a list of the best treatment options that can be sent to the clinician/user." It would be an untenable stretch to include ''clinical goals'' within the words ''the therapies used and clinical responses." But even if one were given to accept such a generous and unsupported interpretation, the ordinarily-skilled person will understand that providing a list of diagnostic possibilities and treatment options has nothing to do with optimizing a radiation treatment plan. Accordingly, there is nothing in this paragraph that teaches accessing a clinical goal and using that goal to train a machine learning model that is then used to optimize a radiation treatment plan. There is nothing in De Bruin to suggest that the ''pretreatment data'' in this paragraph might possibly comprise ''clinical data," and there is absolutely nothing in this paragraph to suggest using that pretreatment data to train a machine learning machine that is then used to optimize a radiation treatment plan. Accordingly, the applicant respectfully submits that none of the paragraphs in De Bruin that are relied upon by the Examiner suffice to teach accessing clinical goals that are used to train a machine learning model that is itself used to optimize a radiation treatment plan. The applicant also notes that the purposes and aims of these two prior art references are very distinct, and the Examiner has offered no evidence to support the idea that an ordinarily-skilled person would have an expectation that a combination of their respective teachings would have a reasonable expectation of success. The applicant respectfully submits that the independent claims are not obvious in view of any reasonable combination of these two prior art references. The remaining claims are all ultimately dependent upon one of the independent claims shown above to be allowable. While the applicant believes that other arguments are available to highlight the allowable subject matter presented in various ones of these dependent claims, the applicant also believes that the comments set forth herein regarding allowability of the independent claims are sufficiently compelling to warrant present exclusion of such additional points for the sake of brevity and expedited consideration.
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. The Applicant’s claimed “administering” step is not a particular treatment, the step of “administering radiation treatment therapy to the patient using the particular energy- based treatment platform as a function of the new radiation treatment plan” is similar to reciting “administering a suitable medication to a patient” (See MPEP 2106.04(d)(2)(a)), thus the abstract idea is not integrated into a practical application. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (2), Applicant' s arguments, see Argument (2), filed August 7, 2026, with respect to the rejection(s) of claim(s) 1-4, 6-14, and 16-20 under 35 U.S.C. 103 rejection(s) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of the newly found prior art reference of Sjölund et al. (U.S. Patent Pre-Grant Publication No. 2020/0289847). De Bruin discloses in Paragraphs [0017]: “generating a first level training dataset comprising a plurality of records comprising patient related clinical, symptomatic and laboratory data from a large number of patients on a system database;”, [0018]: processing the first level training dataset in a series of processes including preprocessing, feature extraction, and dimensionality reduction processes to select a reduced-dimension feature data subset. The reduced feature data subset being derived by means of processing the data to compute features appearing to be discriminative and relevant for a useful assessment of the patient status, [0019]: “processing the reduced feature data to derive a data processor scheme or model relating feature data and an assessment status. The processor/model performs the modeling and analysis tasks including classification, regression, estimation, prediction, and detection. A collection of various preliminary processors/models is used in parallel, and the data fusion procedure combines these preliminary processors to find the final optimal results. Fusing the results of several processors/models provides better performance than an individual processor/mode”, [0020]: “generating and collecting treatment-planning and diagnosis datasets including the features derived for the related feature data subset and the corresponding target values, such as reference diagnosis values, true values of attribute of interest, or treatment outcomes;”, and [0021]: “using the treatment-planning and diagnosis datasets in a learning or training procedure to construct the processors and models and find their unknown parameters which are used for assessment of the patient, medical/clinical diagnosis and treatment planning models”. De Bruin further teaches in Paragraph [0071]: “The system and methodology of the present invention is based on advanced "signal/information processing" and "machine learning and inference" techniques. This invention includes a digital automated medical expert system capable of integrating diverse sets of neurological, psychological, psychiatric, biological, demographic and other clinical data to enhance the effectiveness of the physician by using machine learning and inference methods to estimate the probability of response to a range of treatment possibilities appropriate for the illness diagnosed, and, optionally, to provide a list of diagnostic possibilities rank-ordered by likelihood/probability. The signal/information processing method includes several stages including pre-processing, filtering, feature extraction and feature selection, low-dimensional representation, data clustering, statistical and Bayesian modeling and analysis, geometrical analysis, decision/estimation networks, building and learning predictive and estimator models using training data, and incorporating established and tested treatment guidelines and diagnostic classification systems. The rules and models will be improved by learning, combining and fusing different machine learning methods to build a hierarchical, multi-level and structured system and model that processes and compiles the data in different levels and handles missed attributes.” De Bruin teaches “building and learning predictive and estimator models using training data” ([0071]), and the “training dataset” ([0017], [0022]-[0023]) can include “a plurality of records comprising measured patient related clinical and/or laboratory data feature information from a large number of patients, the data including data relating to patient treatment response” the “features” are described in Paragraph [0097]: “ Features are a set of measurements taken from the object under test that allow us to discriminate which class the object belongs to. Features can be raw/direct measurements or can be processed information derived from measurements. Features are alternatively described as raw or processed versions of clinical and medical indicators, symptoms, numerical attributes such as laboratory test results and other measures of brain and body functioning, personality attributes, cognitive functioning, genetic markers and parameters quantized and encoded in numerical or categorical form such that they are understandable by computers and processing equipment for further analysis. In one example, in the case of mental and psychiatric illnesses and disorders, features are extracted from the patient's EEG measurements and other variables, after pre-processing. Features also include neurological, psychological, biological and clinical measurements as indicated earlier, as well as other personality indicators. The EEG feature set may include (but is not limited to) statistical attributes, random process parameters, coherency measures, correlation, higher-order statistics, wavelet analysis coefficients, autoregressive modeling coefficients, absolute and relative spectrum power levels in various frequency bands, the ratio of left-to-right hemisphere power in various frequencies, and the anterior/posterior power gradient in various frequencies, mutual information between sensors, spatial and temporal data model attributes, time-series model attributes, linear and non-linear dynamic process modeling features, features representing and extracting geometrical information of high-dimensional data, attributes representing the data in transformed and mapped space, and clustering and low-dimensional representation attributes, and combinations and transformations thereof. Those skilled in the art may be able to identify further features, without limiting the scope of this patent”. The Applicant’s Specification in Paragraphs [0046] describes the use of “clinical goals” as “By including such things as clinical goals in the training corpus, the dose distributions in the machine learning can be effectively evaluated to determine those features that were likely intentionally emphasized. So configured, these teachings can leverage the efforts of clinicians for a particular facility to meet local facility protocols.” De Bruin describes using “clinical data” in Paragraphs [0027] and [0071]. The Examiner has supplemented the combination of Adler/De Bruin with Sjölund to teach the newly amended claimed portion of “wherein the new radiation treatment plan comprises specified values for each of a variety of treatment-platform parameters during each of a plurality of sequential exposure fields for a particular energy-based treatment platform to be used to deliver therapeutic energy to a corresponding patient” as described above in the 35 U.S.C. 103 rejection(s), and the Examiner has pointed to Paragraphs [0019]-[0020] of Sjölund to be combined with De Bruin’s teaching of training of the machine learning and inference techniques ([0071]) to meet the Applicant’s use of clinical goals by using raining data (including paired data for the inferred treatment goal and the estimated and realizable plan parameters) associated with the inferred treatment goal. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Adler with De Bruin with the motivation of improving accuracy and efficiency of the prediction and estimation models (See Background of the Invention of De Bruin in Paragraph [0015]). The 35 U.S.C. 103 rejection(s) stand.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Holmstrom et al. (U.S. Patent Pre-Grant Publication No. 2024/0033539), describes an estimated or predicted dose for radiotherapy treatment may be generated based on a partial dose map including dose information only for one or more regions of interest within a treatment site, by use of a properly trained machine learning system such as a U-Net or a V-Net, and said partial dose map typically set to fulfil clinical goals.
Douglas et al. (U.S. Patent Pre-Grant Publication No. 2021/0065900), describes a computerized medical diagnostic system uses a training dataset that is updated based on reports generated by a radiologist. AI and/or CAD is used to make an initial determination of no finding, finding, or diagnosis based on the training dataset.
Nguyen et al. (“A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning”), describes guiding clinical plan optimization to save time and maintain high quality plans.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bennett S Erickson whose telephone number is (571)270-3690. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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, Robert Morgan can be reached at (571) 272-6773. 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.
/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683
Prosecution Insights