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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114 was filed in this application after a decision by the Patent Trial and Appeal Board, but before the filing of a Notice of Appeal to the Court of Appeals for the Federal Circuit or the commencement of a civil action. Since this application is eligible for continued examination under 37 CFR 1.114 and the fee set forth in 37 CFR 1.17(e) has been timely paid, the appeal has been withdrawn pursuant to 37 CFR 1.114 and prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant’s submission filed on March 20, 2026 has been entered.
Response to Amendment
In the amendment filed on March 20, 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; and
administering radiation treatment therapy to a 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 describes the training corpus and emphasized features. Finally, claims 10 and 20 include the abstract identified above and describes 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” 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 2016.05(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).
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
administering radiation treatment therapy to a patient as a function of the new radiation treatment plan (See Paragraphs [0048]-[0050]: 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.)
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]).
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 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 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 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 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 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 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 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 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 March 20, 2026, the Applicant argues that the newly amended and/or added claims overcome the 35 U.S.C. 101 rejection(s). The decision by the Patent Trial and Appeal Board on January 20, 2026 reversed the Examiner’s rejection under 35 U.S.C. 102. 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 added feature of administering radiation treatment therapy to a patient as a function of the new radiation treatment plan serves to establish patent eligibility per that analysis.
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. MPEP 2106.04(d)(2) states “Examiners should keep in mind that in order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of "administering amazonic acid to a patient" or a step of "administering a course of plasmapheresis to a patient." If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a "pharmaceutical composition" or that a "feed dispenser is operable to dispense a mineral supplement" are not affirmative limitations because they are merely indicating how the claimed invention might be used.” The Examiner maintains that the Applicant’s claimed step of “administering radiation treatment therapy to a patient as a function of the new radiation treatment plan” is recited at a level of generality that does not identify the administration step as particular, and is instead merely instructions to "apply" the exception in a generic way. Thus, the administration step does not integrate the step into a practical application. The Examiner maintains that the Applicant’s administration step is similar to “administering a suitable medication to a patient”, and this level of generality is not considered to integrate the abstract idea into a practical application. The 35 U.S.C. 101 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.
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.
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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.
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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683
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