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, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03 September 2025 has been entered.
Response to Amendment
Claims 1, 4-10, 13-17, & 20-21 were previously pending in this application. The amendment filed 03 September 2025 has been entered and the following has occurred: Claims 1, 10, 17, & 20 have been amended. Claims 5, 14, & 21 have been cancelled.
Claims 1, 4, 6-10, 13, 15-17, & 20 remain pending in the application
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4, 6-10, 13, 15-17, & 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claims recite subject matter within a statutory category as a process (claims 1, 4, 6-9), machine (claims 10, 13, 15-16), and manufacture (claims 17 & 20) which recite steps of:
inputting a plurality of patient data for a patient into a multi-model framework, wherein the patient data includes the patient’s clinical data and geographic information, wherein the multi-model framework comprises a Bayesian statistical model configured to analyze a plurality of predictions for each of a plurality of treatment response models in a context of at least one target patient outcome, and wherein a prior state of knowledge is encoded into a prior probability distribution as a parameter set for each model;
predicting, for each of the plurality of treatment response models and using the multi-model framework to maximize a patient-specific fitness function, a probability of a given treatment corresponding with each of the plurality of treatment response models producing the at least one target patient outcome, wherein the predictions for each of the plurality of treatment response models is continuously updated in response to receiving new data relating to the state of prior knowledge, and wherein the patient-specific fitness function is maximized according to:
Pr
S
D
,
I
=
Pr
s
I
P
r
(
D
|
S
,
I
)
P
r
(
D
|
I
)
where:
S is a clinical hypothesis of interest;
D represents patient-specific data;
I represents prior acquired information; and
Pr (D|I) is a normalization constant);
responsive to receiving a request, outputting an assessment for each given treatment;
determining a treatment with a highest likelihood of success for the patient; and
directing treatment for the patient in accordance with the determined treatment with the highest likelihood of success, wherein the determined treatment comprises surgery, radiotherapy, chemotherapy, immunotherapy, psychological support, or combinations thereof.
These steps of inputting a plurality of patient data for a patient into a multi-model framework containing a Bayesian statistical model for analyzing predictions for treatment response models corresponding to a target patient outcome, predicting, using the multi-model framework, a probability of a given treatment producing a given outcome for the patient and maximizing a patient-specific fitness function according to the function recited in the independent claims, outputting an assessment for each given treatment option, determining a treatment with a highest likelihood of success for the patient from the outputted given treatment options, directing treatment according to various forms of treatment such as surgery, radiotherapy, etc., as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. MPEP 2106.04(a)(2)(II) describes certain methods of organizing human activity, such as fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people. In particular, managing personal behavior or relationships or interactions between people can specifically include social activities, teaching, and following rules or instructions. The enumerated steps above amount to managing personal behavior or relationships or interactions between people. For instance, the typical interaction that occurs between a patient receiving a treatment and a doctor or medical entity for assigning said treatment is effectively being managed by the performance of the steps, such as on a computer or via a computerized means with a multi-model, statistical framework calculating treatment effects. In particular, the system outputs an assessment, i.e. recommendation for certain rules or instructions to follow for implementing a treatment for the patient, wherein the outputted treatment with the highest likelihood of success is used to direct treatment for the patient. Therefore, the steps recited in the claims amount to methods of organizing human activity, under broadest reasonable interpretation.
The claims recite a mathematical formula. Therefore, the claims are also abstract under Mathematical Concepts grouping of abstract ideas under broadest reasonable interpretation.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 4, 6-9, 13, 15-16, & 20, reciting particular aspects of how assessing the given treatment, receiving data, and/or recommending a treatment may be performed in the mind but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of a multi-model framework, a processor, a memory, a computer program code for at least one program, a network interface, a non-transitory computer-readable storage medium, a computer/computing entity, amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification [0032] & [0072]-[0076] for a multi-model framework, [0061] for a processor/processing unit, [0061] for a memory, [0064] for a computer program code for at least one program, [0060] for a network interface, [0063] for a non-transitory computer-readable storage medium, [0060] for a computer/computing entity, see MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea (such as recitation of inputting a plurality of patient data for a patient into a multi-model framework, wherein the patient data includes the patient’s clinical data and geographic information amounts to mere data gathering, recitation of predicting a probability of a given treatment corresponding with each of the plurality of treatment response models producing the at least one target patient outcome, wherein the predictions for each of the plurality of treatment response models is continuously updated in response to receiving new data relating to the state of prior knowledge and producing an assessment for the given treatment amounts to selecting a particular data source or type of data to be manipulated, recitation of outputting an assessment for the given treatment, determining a treatment with the highest likelihood of success for the patient and using said treatment to direct treatment for the patient (i.e. it should be further noted that while the independent claims specify “directing treatment”, this directed treatment is not actually ever effectuated under BRI of the steps recited, and could potentially constitute a practical application if the claims were amended to recite effectuation/executing the actual treatments by the system, such as via a treatment device/component, versus merely “directing treatment” and/or “administering the given treatment to the patient” as in dependent claim 9, depending on specific claim language/wording and if Applicant has support for such amendments in Applicant’s Specification), maximizing a patient-specific fitness function maximized according to the formula recited in the independent claims amounts to insignificant application, see MPEP 2106.05(g));
generally link the abstract idea to a particular technological environment or field of use (such as recitation of the methods for tumor forecasting in particular, and/or reciting varying fields of uses such as the determined treatments comprising surgery, radiotherapy, chemotherapy, immunotherapy, psychological support, or combinations thereof, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 4, 6-9, 13, 15-16, & 20, which recite limitations relating to a multi-model framework, i.e. Bayesian statistical model, and/or a cloud-computing service/configuration, additional limitations which amount to invoking computers as a tool to perform the abstract idea; claims 4, 6, 13, & 20, which recite limitations relating to specifying forms of patient data received and/or treatment types/parameters, and/or data output types such as tumor burden, tumor local control, etc., additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering; claim 8, which recite limitations relating to analyzing respective predictions of a plurality of models of the multi-model framework and/or recommending a given treatment based on the analysis performed, additional limitations which add insignificant extra-solution activity to the abstract idea by selecting a particular data source or type of data to be manipulated; claims 7, 9, 16, & 20, which recite limitations relating to different types of treatments relating to cancer, the model framework being implemented as a cloud-computing service, and/or recommending/administering varying types of cancer treatments additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as inputting a plurality of patient data for a patient into a multi-model framework, wherein the patient data includes the patient’s clinical data and geographic information, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); predicting a probability of a given treatment corresponding with each of the plurality of treatment response models producing the at least one target patient outcome, wherein the predictions for each of the plurality of treatment response models is continuously updated in response to receiving new data relating to the state of prior knowledge and producing an assessment for the given treatment, determining a treatment with the highest likelihood of success for the patient and using said treatment to direct treatment for the patient, maximizing a patient-specific fitness function maximized according to the formula recited in the independent claims, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); maintaining records of patient data for collection/input into the multi-model framework, maintaining one or more parameters or models of the Bayesian statistical model or other models in the multi-model framework, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); storing patient data, storing a multi-model framework, storing a prediction result and/or assessment for a given treatment/patient, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); outputting the results of the assessment for the given treatment and/or instructions for directing treatment according to the given treatment and/or instructing for a type of treatment such as surgery, radiotherapy, chemotherapy, immunotherapy, psychological support, or combinations thereof, limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to related data, Electric Power Group, LLC v. Alstom S.A., MPEP 2106.05(h); applying a multi-model framework comprising a Bayesian statistical model, i.e. applying one or more computational models, such as Bayesian network or other statistical computational model, for determination of cancer treatment responsiveness, see Rico Table 2 and Par [0204], see Hall Par [0259]).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 4, 6-9, 13, 15-16, & 20, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 4, 6, 13, & 20, which recite limitations relating to specifying forms of patient data received and/or treatment types/parameters, and/or data output types such as tumor burden, tumor local control, etc., e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claim 8, which recite limitations relating to analyzing respective predictions of a plurality of models of the multi-model framework and/or recommending a given treatment based on the analysis performed, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); claims 7, 9, 16, & 20, which recite limitations relating to maintaining and updating parameters for different types of treatments that are recommended/administered relating to cancer, maintaining one or more communication networks, i.e. as a cloud-computing service, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); claims 4, 6-9, 13, 15-16, & 20, which generally recite limitations relating to storing computerized instructions in memory to perform the methods recited, storing one or more model frameworks or patient-specific fitness functions, storing one or more patient data, etc., e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Response to Arguments
Applicant's arguments filed 03 September 2025 have been fully considered but they are not persuasive:
Regarding 35 U.S.C. 101 rejections of claims 1, 4, 6-10, 13, 15-17, & 20, Applicant argues on p. 8-10 of Arguments/Remarks that independent claim 1 includes additional elements demonstrating that the claim as a whole integrates the alleged abstract idea into a practical application. More specifically, Applicant argues in view of Applicant’s Specification [0007] setting forth deficiencies of existing technology and Specification [0078]-[0082] & claim 1 setting forth an associated practical application/technical improvement, because claim 1 provides the described solution of using a multi-model framework to simultaneously evaluate models of different complexities in a manner that balances data fit and model complexity while limiting or eliminating the need for model retraining which is computationally expensive. Examiner respectfully disagrees with Applicant’s arguments. Examiner notes that these described aspects seem to relate to shortcomings in data gathering, data analysis and/or repetitive calculations, and/or outputting of data, e.g. being “computationally expensive”, which are part of the already-characterized abstraction. That is, improving these aspects merely further limit or improve an abstraction or abstract steps, and therefore do not amount to a technological improvement, such as of a system of component that is implementing the abstraction, because this is improving the abstraction itself, not the computerized system or components performing the abstraction. However, assuming arguendo that the purported improvements were directed towards non-abstract subject matter, Examiner also contends that Applicant is not necessarily creating a new model for performing tumor forecasting. Rather, Applicant’s claims recite merely applying generally known model frameworks and/or combinations of known/existing models to specific and/or new data/parameters. That is, if a new, improved computational/learning model was being created by Applicant, then this may result in a technological improvement. However, it is understood by Examiner that known models in the prior art are merely being applied to new data and/or parameters. And therefore, the purported improvement of the models not being “computationally expensive” is a known result of said applied models, but merely recited for use on new data/parameters/field of use. Therefore, these aspects do not amount to a technological improvement and/or practical application, as purported by Applicant. As such, claims 1, 4, 6-10, 13, 15-17, & 20 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of claims 1, 4, 6-10, 13, 15-17, & 20, Applicant argues on p. 11 of Arguments/Remarks that independent claim 1 applies or uses a judicial exception to effect a particular treatment of prophylaxis for a disease or medical condition which integrates the exception into a practical application in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance. Examiner respectfully disagrees with Applicant’s arguments. The requirements for particular treatment or prophylaxis as a practical application of an abstract idea requires a particular treatment to be elected in the claims instead of the model arriving at one or more treatments that are most effective. For example, independent claims elect the potential treatments of surgery, radiotherapy, chemotherapy, immunotherapy, psychological support, OR combinations thereof rather than a singular treatment to be effectuated administered to the patient. By way of example, Examiner points to Example 49 of the 2024 Subject Matter Eligibility Examples, where Claim 1 of Example 49 "does not provide any information as to how the patient is to be treated or what the treatment is, but instead covers any possible treatment that a medical professional decides to administer to the patient” as a result of applied computational modeling, which is similar to the newly amended limitations found in the independent claims. However, if a specific treatment was elected and administered, similar to claim 2 of Example 49, this may support the argument of particular treatment or prophylaxis. However, in their current state, the independent claims do not represent a particular treatment or prophylaxis since any possible treatment of the potential, elected treatments is covered by the verbiage of the claims. As such, claims 1, 4, 6-10, 13, 15-17, & 20 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of claims 1, 4, 6-10, 13, 15-17, & 20, Applicant argues on p. 11 of Arguments/Remarks that independent claims 10 & 17 recite similar subject matter to independent claim 1 and therefore recite eligible subject matter for the same reasons as claim 1. Applicant additionally argues that dependent claims 4, 6-9, 13, 15-16, & 20 are dependent from independent claims 1, 10 & 17 and therefore also recite eligible subject matter by virtue of dependency. Examiner respectfully disagrees with Applicant’s arguments. As discussed above, independent claim 1 does not represent or recite eligible subject matter. Therefore, Applicant’s arguments regarding independent claims 10 & 17 reciting similar subject matter to independent claim 1 and/or dependent claims 4-9, 13-16, & 20-21 being dependent from independent claims 1, 10 & 17 reciting eligible subject matter are rendered moot, because independent claim 1 does not represent or recite eligible subject matter. As such, claims 1, 4, 6-10, 13, 15-17, & 20 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 102 rejections of claims 1-20, Applicant argues on p. 11-12 of Arguments/Remarks that the amendments overcome previous 35 U.S.C. 102 rejections made in view of Hall. More specifically, Applicant argues that subject matter from dependent claim 21 which was previously identified as allowable over the prior art has been amended into independent claims 1, 10, & 17, and therefore the independent claims are allowable over the prior art. Examiner agrees with Applicant’s arguments. More specifically, the prior art does not fairly suggest or disclose the “prior” or likelihood” datasets that are specifically elected by Applicant in the formula found in independent claims 1, 10 & 17, i.e. “Pr(S|I)” Pr(D|S,I) terms found in the numerator of the patient-specific fitness function recited. Therefore the art-based rejections for claims 1, 10, & 17 and claims dependent therefrom (claims 4, 6-9, 13, 15-16, & 20) have been withdrawn. However, each of these claims remains rejected under 35 U.S.C. 101, as discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rico et al. (U.S. Patent Publication No. 2022/0002807) discloses supervised learning methods for prediction of tumor radiosensitivity for potential radiochemotherapy treatment plans/regimens;
Vogelstein et al. (U.S. Patent Publication No. 2019/0256924) discloses systems for detecting and/or treating subject having cancer, given certain biomarkers, and providing increased sensitivity and/or specificity in the detection of cancer in a subject;
Colley et al. (U.S. Patent Publication No. 2021/0090694) discloses a system for predicting cancer types using gene expression data and treatments for said cancer types by utilizing one or more computational models;
Gross et al. (U.S. Patent Publication No. 2021/0313006) discloses a system for training and deploying a cancer classifier for generating a cancer prediction for a test sample, such that the cancer classifier may be a machine-learned model trained with machine-learning algorithms.
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/H.R./Examiner, Art Unit 3684 /Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684