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 ("RCE"), 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 November 10, 2025, has been entered.
Status of Claims
Claims 1, 3-11, and 13-22 were previously pending and subject to a Final Office Action having a notification date of June 18, 2025 (“Final Office Action”). Following the Final Office Action, Applicant filed the RCE and an amendment on November 10, 2025 (“Amendment”), amending claims 1, 4, 6, 7, 9-11, 14, 16, 17, 19, and 20 and adding new claims 23-26
The present non-final Office Action addresses pending claims 1, 3-11, and 13-26 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
On page 10 of the Amendment, Applicant appears to take the position that Ex parte Desjardins, Appeal 2024-000567 (PTAB Sept. 26, 2025) (designated precedential on Nov. 4, 2025) stands for the notion that any recited training of an ML model provides an improvement to other technology or technical field that confers patent-eligibility to the claims. The Examiner disagrees with Applicant's overbroad reading of Desjardins.
In contrast, Desjardins finds that improvements in training the ML model itself can provide a practical application of the abstract idea recited in the claims rather than improvements in the abstract idea. For instance, page 9 of Desjardins notes how the specification describes improvements in the training of the ML model itself such as "effectively [learning] new tasks in succession whilst protecting knowledge about previous tasks" and "[allowing] artificial intelligence (AI) systems to 'us[e] less of their storage capacity' and [enabling] 'reduced system complexity'." It is then noted how at least "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task" as recited in the independent claim reflects the above-noted improvement described in the specification. Id. Notably, the PTAB indicates they "are persuaded that [the above claim limitation] constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation [(i.e., the abstract idea)]." (Emphasis added). Id.
On page 11 of the Amendment, Applicant makes reference to [0022] and [0023] of the published application (respectively corresponding to [0009] and [0010] of the present specification) which are reproduced below:
In some situations, potential interventions must be filtered prior to deployment. For example, a potential intervention, in some, may need to be approved by an authority prior to deployment. Conventionally, filtering may include trying to match a set of criteria governing the filtering process, with an individual intervention prior to deployment. Continuing with the example, circumstances surrounding a particular potential intervention may need to be reconciled with a set of rules determining approval criteria for an intervention. If the filtering process gates deployment of an intervention, as is the case with an approval filter, deployment of an intervention is delayed.
Aspects of the present disclosure allow for fast automatic filtering of potential interventions to minimize time before deployment and inconsistent performance. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples
Applicant also notes how present independent claim 1 recites "training a machine-learning model using training data including prior authorizations and associated probabilistic outputs" which is then used for "generating ... a probabilistic output as a function of the prior authorization request and the plurality of specific interventions." Page 10 of the Amendment.
However, improving the manner in which potential interventions are filtered to minimize time before deployment and inconsistent performance is an improvement to the "mental processes" and "certain methods of organizing human activities" abstract ideas recited in the claims rather than to the ML model recited in the claims. In fact, the above paragraphs of the present application do not even mention training of the ML model in the first place, much less that such training confers the above-noted "improvements" (even assuming that improvements were to other technology or other technical field (such as how an ML model is trained) rather than to the abstract ideas recited in the claims which they are not).
On page 12 of the Amendment, Applicant appears to take the position that the Examiner has "hand-waved" many of the present claim elements as "general" and "routine and conventional" which the PTAB in Desjardins cautioned against doing. The Examiner disagrees. As set forth in the rejection below, the Examiner has considered each claim limitation individually and as an ordered combination in arriving at the decision that the present claims are ineligible under 35 USC 101. With respect to the independent claims, it is only the recited processor and memory with instructions that the Examiner has indicated amount to using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)). With respect to the additional limitations of training an ML model using prior authorizations and associated probabilistic outputs and then performing the (mentally determinable) step of generating the probabilistic output using the trained ML model, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Finally, the additional limitation of transmitting the selected intervention data and the probabilistic output to an intervention evaluator device amounts to insignificant extra-solution activity (transmitting data) (see MPEP § 2106.05(g)) and is not unconventional as it merely consists of receiving/transmitting data over a network. See Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); See MPEP 2106.05(d)(II). The remaining limitations of the independent claims (including (1) "determining one or more intervention classes based on the prior authorization request" and (2) "specifying one or more values for the one or more intervention classes" as asserted by Applicant on page 12 of the Amendment )are all directed to the abstract ideas as discussed in detail in the rejection below.
Finally, in relation to "claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible" per Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14 as cited in the Final Office Action and again hereinbelow, Applicant appears to insinuate that Desjardins is controlling over Recentive. However, as the Examiner is unaware of (and Applicant has not provided) any indication that the teachings and holdings in Recentive have been overturned or even revised, such teachings and holdings are still valid.
The rejection is maintained.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §102/103
On page 12 of the Amendment, Applicant takes the position that Williams does not disclose "intervention classes" as now recited in the present claims. The Examiner disagrees because [0079] of Williams discloses how medical service treatment details, diagnosis/clinical information, patient information, etc. ("intervention classes") are extracted from the prior authorization request. The Examiner also disagrees with Applicant's position that Williams does not disclose "one or more values for the one or more intervention classes" because [0080] of Williams discloses gender (i.e. male/female)(a value) in the patient information (intervention class), [0083] discloses a CPT code (value) for the medical service/treatment/procedure (intervention class), and [0086] discloses an NPI/DEA number (values) for the medication (intervention class)).
The rejections are maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 25 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 25 recites "wherein the one or more values for the one or more intervention classes include continuous values or categorical values." While Applicant does not specify, Applicant's alleged support for this limitation might be from [0015] which states "[a]nalytical constraints, in some cases, may be continuous values or categorical values." However, and as noted in the 35 USC 112(b) rejection below, "analytical constraints" map to the "specific interventions" in the present claims rather than to the recited "one or more values for the one or more intervention classes." Furthermore, the Examiner cannot identify any other portion of the present specification supporting this limitation.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-11, and 13-26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Each of independent claims 1 and 11 has been amended to recite, inter alia,
determining [[,]] one or more intervention classes based on the prior authorization request
specifying one or more values for the one or more intervention classes
identifying a plurality of specific interventions, wherein each specific intervention of the plurality of specific interventions corresponds to an intervention class of the one or more intervention classes.
However, the difference between the recited "one or more values for the one or more intervention classes" and the "plurality of specific interventions…[corresponding] to… the one or more intervention classes" is not understood. For instance, [0093] of the present specification describes how "a criterion may include one or more of an intervention class, such as without limitation a medication class, a diagnosis, or a medical requirement" while "an analytical constraint may include one or more of a specific intervention, such as without limitation a specific medication, a medical condition, or a diagnostic attribute." (Emphasis added). Accordingly, the recited "intervention class" maps to the "criterion" while the recited "specific interventions" maps to the "analytical constraints."
However, a specific medication, specific medical condition, or specific diagnostic attribute would appear to read on both a "value" for an intervention class as well as a "specific intervention" for an intervention class thus leading to uncertainty in the difference between the recited "values" and "specific interventions." For purposes of examination, the Examiner will assume a "value" for an intervention class is some numerical code/alphanumeric representation or the like related to the intervention class while a "specific intervention" corresponding to an intervention class is a detailed description regarding a particular medical treatment/procedure, medication, etc. corresponding to the intervention class.
The remaining claims are rejected based on their dependency from the above claims.
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, 3-11, and 13-26 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 1, 3-10, 21, and 23-26 are directed to a method (i.e., a process) and claims 11, 13-20, and 22 are directed to an apparatus (i.e., a machine). Accordingly, claims 1, 3-11, and 13-26 are all within at least one of the four statutory categories. 35 USC §101.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 and July 2024 updates issued by the USPTO as incorporated into the MPEP, as supported by relevant case law), 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 11 includes limitations that recite at least one abstract idea. Specifically, independent claim 11 recites:
An apparatus for processing candidate intervention representations, the apparatus comprising:
at least one processor; and
a memory communicatively connected to the at least one processor, wherein the memory contains instructions configuring the processor to:
train a machine-learning model using training data including prior authorizations and associated probabilistic outputs;
receive a prior authorization request for a medical intervention;
determine one or more intervention classes based on the prior authorization request;
specify one or more values for the one or more intervention classes;
identify a plurality of specific interventions, wherein each specific intervention of the plurality of specific interventions corresponds to an intervention classes of the one or more intervention classes;
select intervention data from the prior authorization request based on the plurality of specific interventions;
generate, using the trained machine-learning model, a probabilistic output as a function of the prior authorization request and the plurality of specific interventions; and
transmit the selected intervention data and the probabilistic output to an intervention evaluator device.
The Examiner submits that the foregoing underlined limitations constitute “mental processes” because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). As an example, an insurance representative could receive/review a prior authorization request for a medical intervention (e.g., for a proposed surgery on a patient’s ankle), determine "intervention classes" (e.g., medication class, diagnosis, medical requirement, etc.) based on the prior authorization request, specify values for the one or more intervention classes (e.g., alphanumeric value representative of severe spiral fracture of fibula, alphanumeric value indicative of pain reliever, medication identifier, etc.), identify a plurality of specific interventions that each corresponds to one of the intervention classes (e.g., setting moderate to severe fibular spiral fracture with plate and screws for the diagnosis class, taking 50mg Tramadol every 4-6 hours for up to 3 days for the medication class, etc.), select intervention data from the prior authorization request based on the plurality of specific interventions (e.g., choosing/analyzing notes regarding the severity/location of spiral fracture based on the setting spiral fracture with plate and screws intervention, reviewing physician's recommendation regarding medication for the taking Tramadol intervention, obtaining relevant images of the fracture, etc.); and generate a probabilistic output as a function of the prior authorization request and the plurality of specific interventions (e.g., high likelihood of approval when physician recommends taking 50mg Tramadol every 4-6 hours for 4 days and the severity/location/type of spiral fracture corresponds to a moderate fibular fracture, low likelihood of approval when the physician recommends taking 100mg Tramadol every 2 hours for 3 weeks and the severity/location/type of spiral fracture corresponds to a mild fracture of the tibia, etc.).
These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
Furthermore, the Examiner submits that the foregoing underlined limitations constitute “certain methods of organizing human activity” because they relate to fundamental economic practices or principles (e.g., mitigating risk, insurance, or hedging). Specifically, the steps relating to receiving the prior authorization request; analyzing the request to determine intervention classes, specific interventions, intervention data; and generating a probabilistic output regarding the request and the specific interventions relate to how insurance companies control costs by not paying for unnecessary procedures/medications, etc. Also, the steps relate to how patients and medical providers can mitigate risk by seeking prior authorization of medical procedures/treatments.
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 3, 5, 7, 9, 13, 15, 17, and 19 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below:
-Claims 3 and 13 call for determining a confidence output based on the probabilistic output which is practically performable in the human mind with pen and paper (“mental processes”) (e.g., high confidence of a low probability that the prior authorization request would be accepted).
-Claims 5 and 15 recite how selecting the intervention data includes doing so using an intervention data language processing model which is practically performable in the human mind with pen and paper (“mental processes”), such as by applying any appropriate natural language text extraction rules to the candidate intervention representation to select the intervention data.
-Claims 7 and 17 call for pre-processing the prior authorization using language pre-processing which is practically performable in the human mind with pen and paper (“mental processes”), such as by applying any natural language processing rules to the prior authorization such as lemmatization, stop word removal, etc.
-Claims 9 and 19 recite how selecting the intervention data using the image processing module includes generating an image category for each of a plurality of images of the prior authorization using an image classifier, and selecting the intervention data from the plurality of images as a function of the image category and the plurality of specific interventions, all of which is practically performable in the human mind with pen and paper (“mental processes”). For instance, a person could readily compare each image to a list of reference images and corresponding categories (an image classifier) to determine a category for each image and then choose selected intervention data from the plurality of images as a function of the image category and the plurality of analytical constraints (e.g., based on a constraint requiring a zoomed-in image of the fracture in the case of an ankle surgery, the person could easily choose one of the images that clearly displays the fracture.
-Claim 23 recites how the one or more intervention classes include at least one of a medication class, a diagnosis, or a medical requirement which just further defines the at least one abstract idea discussed above.
-Claim 24 recites how the plurality of specific interventions include at least one of a specific medication, a medical condition, or a diagnostic attribute which just further defines the at least one abstract idea discussed above.
-Claim 25 recites how the one or more values for the one or more intervention classes include continuous values or categorical values which just further defines the at least one abstract idea discussed above.
-Claim 26 recites how the prior authorization request does not include patient identification which just further defines the at least one abstract idea discussed above.
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 such as 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”):
An apparatus for processing candidate intervention representations, the apparatus comprising:
at least one processor (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)); and
a memory communicatively connected to the at least one processor, wherein the memory contains instructions configuring the processor to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)):
train a machine-learning model using training data including prior authorizations and associated probabilistic outputs (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f));
receive a prior authorization request for a medical intervention;
determine one or more intervention classes based on the prior authorization request;
specify one or more values for the one or more intervention classes;
identify a plurality of specific interventions, wherein each specific intervention of the plurality of specific interventions corresponds to an intervention classes of the one or more intervention classes;
select intervention data from the prior authorization request based on the plurality of specific interventions;
generate, using the trained machine-learning model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), a probabilistic output as a function of the prior authorization request and the plurality of specific interventions; and
transmit the selected intervention data and the probabilistic output to an intervention evaluator device (extra-solution activity (transmitting data) as noted below, see MPEP § 2106.05(g)).
For the following reasons, the Examiner submits that the above-identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the apparatus including processor and memory with instructions, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of training an ML model using prior authorizations and associated probabilistic outputs and then performing the (mentally determinable) step of generating the probabilistic output using the trained ML model, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Specifically, as the ML model is used to somehow process the prior authorization request and the specific interventions to generate a probabilistic output, then reciting how the ML is trained using training prior authorizations and associated training probabilistic outputs (i.e., the same type of data configured to be respectively input into and output from the ML model) does not provide any details regarding how such ML model is trained to solve a technical problem in the art. These additional limitations provide only a result-oriented solution and lack details as to how training and use of the ML model actually occurs.
Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12.
Regarding the additional limitation of transmitting the selected intervention data and the probabilistic output to an intervention evaluator device, the Examiner asserts that this limitation amounts to insignificant extra-solution activity (transmitting data) (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 11 and analogous independent claim 1 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 11 and analogous independent claim 1 are directed to 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 4 and 14 recite how selecting the intervention data includes processing an image using OCR to generate machine-encoded text which just amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Claims 6 and 16 call for training the intervention data language processing model using intervention training data including prior authorizations and specific interventions correlated to a plurality of selected intervention data which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12. These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs.
Claims 8 and 18 recite how selecting the intervention data uses an image processing module which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)).
Claims 10 and 20 call for training the image classifier using image classifier training data including a plurality of prior candidate intervention representation images correlated to a plurality of image categories which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). Specifically, as claims 9 and 19 already recite the mental process of selecting the intervention data from the images based on the image category of the images in the prior authorization, then specifying that the classifier is trained based on training sets of prior authorization images and image categories (the same type of data configured to be input into and output from the model) does not recite any specific details regarding how the training is accomplished. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12. These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs.
Claims 21 and 22 call for retraining the ML model using the probabilistic output which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12.
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated 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 11 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.
Regarding the additional limitations of the apparatus including processor and memory with instructions, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of training an ML model using prior authorizations and associated probabilistic outputs and then performing the (mentally determinable) step of generating the probabilistic output using the trained ML model, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Specifically, as the ML model is used to somehow process the prior authorization request and the specific interventions to generate a probabilistic output, then reciting how the ML is trained using training prior authorizations and associated training probabilistic outputs (i.e., the same type of data configured to be respectively input into and output from the ML model) does not provide any details regarding how such ML model is trained to solve a technical problem in the art. These additional limitations provide only a result-oriented solution and lack details as to how training and use of the ML model actually occurs.
Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12.
Regarding the additional limitations directed to transmitting the selected intervention data and the probabilistic output to an intervention evaluator device which the Examiner submits merely adds insignificant extra-solution activity to the abstract idea (see MPEP § 2106.05(g)), the Examiner has reevaluated such limitations and determined such limitations to not be unconventional as they merely consist of receiving/transmitting data over a network. See Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); 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.
Claims 4 and 14 recite how selecting the intervention data includes processing an image using OCR to generate machine-encoded text which just amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Claims 6 and 16 call for training the intervention data language processing model using intervention training data including prior authorizations and specific interventions correlated to a plurality of selected intervention data which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12. These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs.
Claims 8 and 18 recite how selecting the intervention data uses an image processing module which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)).
Claims 10 and 20 call for training the image classifier using image classifier training data including a plurality of prior candidate intervention representation images correlated to a plurality of image categories which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). Specifically, as claims 9 and 19 already recite the mental process of selecting the intervention data from the images based on the image category of the images in the prior authorization, then specifying that the classifier is trained based on training sets of prior authorization images and image categories (the same type of data configured to be input into and output from the model) does not recite any specific details regarding how the training is accomplished. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12. These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs.
Claims 21 and 22 call for retraining the ML model using the probabilistic output which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12.
Therefore, claims 1, 3-11, and 13-26 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 5, 7, 8, 11, 13, 15, 17, 18, and 21-25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent App. Pub. No. 2023/0402160 to Williams, III et al. (“Williams”):
Regarding claim 1, Williams discloses a method of processing candidate intervention representations ([0007] and [0104] disclose processes for analyzing prior authorization requests for medical interventions), the method comprising:
training a machine-learning model using training data including prior authorizations and associated probabilistic outputs ([0102], [0103], and [0111] discuss using AI-based processes (ML model per [0071]-[0074]) to determine/generate a likelihood of prior authorization approval (probabilistic output) based on extracted data (which is from the documentation related to a prior authorization request per [0079]); furthermore, [0072] discloses how ML can encompass techniques such as supervised learning (learning from labeled examples); in this regard, as [0102], [0103], and [0111] disclose how the AI/ML model is configured to input extracted data related to a prior authorization and generate "associated probabilistic outputs," then such supervised learning of the AI/ML model would necessarily involve training of the AI/ML model using training data including prior authorizations and associated probabilistic outputs; furthermore, [0102] even discloses use of "preexisting verdicts surrounding past prior authorization decisions");
receiving a prior authorization request for a medical intervention ([0075]-[0079], [0105] disclose how software application 300 (which is executed by a processor per [0039]) receives a prior authorization approval request for a medical intervention);
determining, one or more intervention classes based on the prior authorization request ([0079] discloses how medical service treatment details, diagnosis/clinical information, patient information, etc. ("intervention classes") are extracted from the prior authorization request);
specifying one or more values for the one or more intervention classes ([0080] discloses gender (i.e. male/female)(value) in the patient information (intervention class), [0083] discloses a CPT code (value) for the medical service/treatment/procedure (intervention class), and [0086] discloses an NPI/DEA number (values) for the medication (intervention class));
identifying a plurality of specific interventions, wherein each specific intervention of the plurality of specific interventions corresponds to an intervention class of the plurality of intervention classes ([0080]-[0086] describe various details for each of the "intervention classes"; for instance, [0083] discusses medical service or treatment details including a description of the requested medical service, treatment, or procedure while [0085] discusses primary diagnosis reason for requested service/treatment, etc. ("specific interventions" for the "intervention classes"));
selecting intervention data from the prior authorization request based on the plurality of specific interventions ([0087] discusses extracting various types of documentation (e.g., provider notes, pathologist reports, etc.) that indicate the present illness surrounding the disease, the future plan for care of the patient, etc., [0089]-[0091] discuss extracting data from medical images (e.g., radiographic imaging histologic pathology, etc.) to facilitate determination of the extent of a patient's disease to aid in decision-making for the prior authorization, etc. (selecting "intervention data" from the prior authorization requests "based on the plurality of specific interventions"));
generating, using the trained machine-learning model, a probabilistic output as a function of the prior authorization request and the plurality of specific interventions ([0095], [0102], [0103], and [0111] discuss how the software application uses AI-based processes (trained ML model per [0071]-[0074]) to determine/generate a likelihood of prior authorization approval (probabilistic output) based on the data/information extracted from the documentation, images, etc. (which, as noted above, is obtained from the submitted prior authorization request and describes/relates to the "specific interventions" (e.g., description of medical service/treatment/procedure in documentation per [0083], data related to extent of patient's disease in images per [0091], etc.))); and
transmitting the selected intervention data and the probabilistic output to an intervention evaluator device ([0103] and [0112]-[0114] discuss generating prior authorization approval decisions using the same processes described for determining the likelihood (probability) of prior authorization approval which includes considering the specific documentation, imaging, etc. (the selected intervention data); accordingly, generating prior authorization approval decisions is performed by another set of program instructions of the software program and the other set of program instructions and the processor collectively amount to an “intervention evaluator device”; in this regard, the selected intervention data is passed to the "intervention evaluator device" to generate prior authorization approval decisions); furthermore, [0113] notes how the software 300 can make a decision to approve a prior authorization request if the approval exceeds 60%; accordingly, after approval (probabilistic output) is determined, the approval (probabilistic output) is sent to the “intervention evaluator device” (see above) to make a final decision on the prior authorization approval request (approve or deny)).
Regarding claim 3, Williams discloses the method of claim 2, further including determining, based on the probabilistic output, a confidence output ([0103] and [0111] discuss determining a likelihood of prior authorization approval (probabilistic output) which includes considering the specific documentation, imaging, etc. (the selected intervention data) as weighted by the weights; furthermore, when the likelihood/probabilistic output is above a threshold (e.g., 50% per [0112] or 60% per [0113]), there is a high confidence of approval while below such threshold would be a lower confidence of approval).
Regarding claim 5, Williams discloses the method of claim 1, further including wherein selecting the intervention data comprises selecting the intervention data using an intervention data language processing model ([0087]-[0088] disclose use of NLP (intervention data language processing model) as part of extraction/analysis of the “selected intervention data” from the candidate intervention representation.
Regarding claim 7, Williams discloses the method of claim 5, further including pre-processing the prior authorization using language pre-processing ([0079] discloses using NLP on the request which includes preprocessing per [0066]).
Regarding claim 8, Williams discloses the method of claim 1, further including wherein selecting the interv