DETAILED ACTION
Claims 4-9, 11-15, & 17-21 are currently pending and have been examined.
This action is in response to the amendment filed on 1/16/2026.
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 .
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 4-8, 11-15, & 17-21 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Criteria - Step 1:
Claims 4-8 are directed to a method (i.e., a process); Claims 11-15 are directed to a system (i.e., a machine); and Claims 17-21 are directed to a CRM (i.e., a manufacture). Accordingly, claims 4-8, 11-15, & 17-21 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 4 includes limitations that recite at least one abstract idea. Specifically, independent claim 4 recites:
4. A method for improved machine learning modelling for predicting relative benefit of therapy options for a condition, wherein the method is implemented by one or more therapy prediction devices and comprises:
generating training cohorts and validation cohorts for each of first and second therapy options for a plurality of patients based on training and validation data for the patients obtained via one or more communication networks from one or more data server devices and comprising at least first molecular and clinical data;
generating an ensemble machine learning model, comprising:
sampling training and cross-validation subsets from each of the training cohorts and validation subsets from each of the validation cohorts comprising excluding from the training cohorts and the validation cohorts a set of other patients that received treatment including one or more agents aligned to both the first and second therapy options training machine learning models using the training and cross-validation subsets, wherein the machine learning models comprise random survival forest (RSF) models trained to predict treatment response data for each of the first and second therapy options,
validating the machine learning models using valid treatment-specific response data in the validation subsets to determine that the machine learning models exceed an accuracy threshold, wherein the treatment-specific response data comprises first progression-free survival data; and
combining first and second subsets of the machine learning models into the ensemble machine learning model, wherein at least a subset of models in each of the first and second subsets of the machine learning models vary by tree depth and parameter set as a result of the training;
applying the machine learning model to patient data for a patient to generate first and second treatment response data, each corresponding to one of a plurality of categories for each of the first and second therapy options, wherein the patient data is obtained via the communication networks from a client device; and
generating, and outputting via the communication networks for display via a display device of the client device, a graphical user interface comprising a representation of the first and second treatment response data with respect to the categories for the first and second therapy options, respectively, to thereby inform treatment of the patient for the condition.
The Examiner submits that the foregoing underlined limitations constitute “methods of organizing human activity” because generating patient cohorts, generating treatment response data corresponding to treatment options, and generating representations of the treatment options to inform a patient of their treatment options are associated with managing personal behavior or relationships or interactions between people. For example, but for the system, this claim encompasses a person facilitating data access, receiving data, and outputting data in the manner described in the identified abstract idea. The Examiner notes that “method of organizing human activity” includes a person’s interaction with a computer – see MPEP 2106.04(a)(2)(II)(C). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Furthermore, the underlined limitations of generating an ensemble machine learning model by sampling data and training the model using random survival forest models, and validating the models to determine if the models exceed an accuracy threshold, combining the models amounts to mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations). When given their broadest reasonable interpretation in light of the background, these limitations amount to mathematical calculations. The plain meaning of these terms are optimization algorithms, which compute neural network parameters using a series of mathematical calculations. As explained in the MPEP, when a claim recites multiple abstract ideas that fall in the same or different groupings, examiners should consider the limitations together as a single abstract idea, rather than as a plurality of separate abstract ideas to be analyzed individually. Accordingly, the claim recites an abstract idea.
Accordingly, independent claim 4 and analogous independent claims 11 & 17 recite at least one abstract idea.
Furthermore, dependent claims 5-10, 12-15, & 18-22 further narrow the abstract idea described in the independent claims. Claims 5, 7, 12, 14, 18, & 20 recite clinical and molecular data; Claims 6, 13, & 18 recite various therapy options; Claim 8, 15, & 21 recite associating patients with reference populations and categories. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 4 and analogous independent claims 11 & 17, even when considered individually and as an ordered combination.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
4. A method for improved machine learning modelling for predicting relative benefit of therapy options for a condition, wherein the method is implemented by one or more therapy prediction devices and comprises:
generating training cohorts and validation cohorts for each of first and second therapy options for a plurality of patients based on training and validation data for the patients obtained via one or more communication networks from one or more data server devices and comprising at least first molecular and clinical data;
generating an ensemble machine learning model, comprising:
sampling training and cross-validation subsets from each of the training cohorts and validation subsets from each of the validation cohorts comprising excluding from the training cohorts and the validation cohorts a set of other patients that received treatment including one or more agents aligned to both the first and second therapy options training machine learning models using the training and cross-validation subsets, wherein the machine learning models comprise random survival forest (RSF) models trained to predict treatment response data for each of the first and second therapy options,
validating the machine learning models using valid treatment-specific response data in the validation subsets to determine that the machine learning models exceed an accuracy threshold, wherein the treatment-specific response data comprises first progression-free survival data; and
combining first and second subsets of the machine learning models into the ensemble machine learning model, wherein at least a subset of models in each of the first and second subsets of the machine learning models vary by tree depth and parameter set as a result of the training;
applying the machine learning model to patient data for a patient to generate first and second treatment response data, each corresponding to one of a plurality of categories for each of the first and second therapy options, wherein the patient data is obtained via the communication networks from a client device; and
generating, and outputting via the communication networks for display via a display device of the client device, a graphical user interface comprising a representation of the first and second treatment response data with respect to the categories for the first and second therapy options, respectively, to thereby inform treatment of the patient for the condition.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the therapy prediction device, graphical user interface, communication network, client device, display device; the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of applying the machine learning models to generate treatment response patient data; the Examiner submits that these additional limitations amount to no more than a recitation of the words “apply it” because they attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result.
Regarding the additional limitation of obtaining patient data, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)) and is conventional as it merely consists of transmitting data over a network (see MPEP § 2106.05(d)(II)).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 1 and analogous independent claim 8 & 15 do not recite additional elements that integrate the judicial exception into a practical application.
Accordingly, the claims recites at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 15 & 21: These claims recite treating the patient in accordance with a therapy option and amounts to merely instructions to “apply” the exception in a generic way. The administration step is not particular because it encompasses all applications of the judicial exception and does not integrate the abstract idea into a practical application (see MPEP 2106.04(d)(2)).
Thus, taken alone, any additional elements do not integrate the at least one abstract idea into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 4 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above, regarding the additional limitations of the therapy prediction device, graphical user interface, communication network, client device, display device; the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of training machine learning models using training and cross-validation subsets, validating the machine learning models using validation subsets, and applying the machine learning models to patient data; the Examiner submits that these additional limitations amount to no more than a recitation of the words “apply it” because they attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result.
Regarding the additional limitation of obtaining patient data, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)) and is conventional as it merely consists of transmitting data over a network (see MPEP § 2106.05(d)(II)). The Examiner has reevaluated such limitation and determined it to not be unconventional as it merely consists of transmitting data over a network. See MPEP 2106.05(d)(II).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 4-8, 11-15, & 17-21 are ineligible under 35 USC §101.
Allowable Subject Matter
Claim 9 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Prior Art Rejection
All of the cited references fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims. In particular, the cited prior art of record fails to expressly teach or suggest the combination of:
The most relevant prior art of record includes:
Issler (US20230049979) teaches to receiving, for each of a plurality of subjects having a specified type of disease and receiving a specified therapy for treating the disease, a first biological signature obtained pre-treatment and a second biological signature obtained on-treatment; calculating, for each of the plurality of subjects, a set of values representing a ratio between the first and second biological signatures associated with the respective subject; at a training stage, training a machine learning model on a training set comprising: (i) the calculated sets of values, and (ii) labels associated with an outcome of the specified therapy in each of the subjects; to generate a classifier suitable for predicting a response in a target patient to said specified therapy.
Madhavan (US20190385740) teaches to a method and system for determining a recommendation for drug treatment are described herein. For example, the method includes determining drug scores based upon network-based distances for one or more target drug nodes, modeling one or more outputs based upon input data, wherein the input data comprises at least a portion of the drug scores, selecting an algorithmic output from the one or more modeling outputs based upon at least one performance criteria, determining if the selected algorithmic output of the modeling satisfies a threshold, and if the selected algorithmic output satisfies the threshold, generating the recommendation for drug treatment.
Curtis (US20230047712) teaches to diagnostics and treatments of breast cancer based on molecular response to targeted treatment. In various embodiments, the cancer's molecular response to a targeted treatment is determined by measuring expression of particular tumor-related or immune-related biomolecules. In various embodiments, a linear model utilized biomolecule expression to determine the likelihood of achieving complete pathologic response to a targeted treatment. In various embodiments, particular treatment regimens are performed based on the likelihood of achieving complete pathologic response.
Response to Arguments
Applicant’s arguments on pages 8-14 regarding claims 4-8, 11-15, & 17-21 being rejected under 35 USC § 101 have been fully considered but they are not persuasive. Applicant claims that:
The claims are not directed to an abstract idea of managing interactions between people.
See updated 101 rejection above.
The claims provide a technical improvement similar to Example 39.
The Examiner, however, asserts that Example 39's abridged background provides the technical solution "minimization of these false positives by performing an iterative training algorithm, in which the system is retrained with an updated training set containing the false positives produced after face detection has been performed on a set of non-facial images. This combination of features provides a robust face detection model that can detect faces in distorted images while limiting the number of false positives" wherein the technical solution is reflected in the claim language. The instant application, however, fails to provide any description of a technical problem and a technical solution. Applicant’s specification presents a non-technical problem - evaluating patient treatment options and generating treatment recommendations and, more particularly, to methods and device for predicting relative benefit of therapy options (e.g., for patients with metastatic pancreatic cancer) (see the instant Specification in para. 2). The solution to the problem is rooted in an improvement to the abstract idea itself and not a technical failure of a computer system. The additional elements can best be characterized as tools to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the "Other examples., v.").
Similar to Desjardins, the claims provide an improvement to how the machine learning model itself operates.
The Examiner, however, asserts that Desjardins decision cited to specific recitations of a technical problem and a technical solution where the Specification recited that the claimed improvement allows artificial intelligence (AI) systems to ‘us[e] less of their storage capacity’ and enables ‘reduced system complexity.’… The same cannot be said here. The instant application fails to provide any description of a technical problem and a technical solution. Applicant’s specification presents a non-technical problem - evaluating patient treatment options and generating treatment recommendations and, more particularly, to methods and device for predicting relative benefit of therapy options (e.g., for patients with metastatic pancreatic cancer) (see the instant Specification in para. 2).
Applicant’s arguments on pages 11-12 regarding claims 9 being rejected under 35 USC § 101 have been fully considered and is persuasive. The 101 rejection applied to claim 9 has been withdrawn.
Conclusion
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 Jonathan K Ng whose telephone number is (571)270-7941. The examiner can normally be reached M-F 8 AM - 5 PM.
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/Jonathan Ng/Primary Examiner, Art Unit 3619