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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/01/2026 has been entered.
Claims 1-2, 4-19 and 23-25 remain pending in this 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 1-2, 4-19 and 23-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-2, 4-19 and 23-25 are drawn to a method which is within the four statutory categories (i.e. process).
Step 2A, Prong 1:
Claim 1 has been amended to recite:
“processing patient data for a patient using a first machine learning model trained on a set of training data comprising electronic patient data records describing a success of closing a care gap with an action for a historical patient different from the patient, wherein the patient data comprises electronic patient data obtained from a plurality of electronic records describing a health history of the patient;
generating an output using the first machine learning model based on the patient data, wherein the output includes a plurality of identified care gaps for the patient;
determining, based at least in part on the generated output of the first machine learning model, and using a heterogenous treatment effect (HTE) model comprising a doubly robust causal inference model, a treatment effect for each of the plurality of identified care gaps, wherein determining the treatment effect for each identified care gap comprises identifying historical patients having patient profile of the patient and determining, for the historical patients, a reduction in one or more visits during a six-month period after closing the identified care gap;
for each of the identified care gaps, computing, based on the reduction determined for the historical patients, a treatment effect score indicating a predicted reduction in one or more visits by the patient during a six-month period after closing the identified care gap;
prioritizing the identified care gaps based on the respective treatment effect scores;
based on the prioritization of the filtered identified care gaps, generating, using the HTE model, one or more recommended patient actions for the patient, wherein the one or more recommended patient actions include a next best action comprising at least one of a visit to a healthcare provider, a change in a medical treatment, a change in a prescription, a blood test, or a mechanical examination;
automatically generating, using a natural language generation (NLG) model, a recommendation summary that includes a summarized description of the one or more recommended patient actions for the patient;
generating an electronic communication that includes the one or more recommended patient actions from the HTE model for the patient and the recommendation summary from the NLG model; and
transmitting the electronic communication via a communication network to a communication device of a care manager of the patient to cause the communication device to render the electronic communication via a user interface.”.
The limitations of “processing patient data for a patient using a first machine learning model trained on a set of training data comprising electronic patient data records describing a success of closing a care gap with an action for a historical patient different from the patient, wherein the patient data comprises electronic patient data obtained from a plurality of electronic records describing a health history of the patient”, “generating an output using the first machine learning model based on the patient data, wherein the output includes a plurality of identified care gaps for the patient”, “determining, based at least in part on the generated output of the first machine learning model, and using a heterogenous treatment effect (HTE) model comprising a doubly robust causal inference model, a treatment effect for each of the plurality of identified care gaps…”, “automatically generating, using a natural language generation (NLG) model, a recommendation summary that includes a summarized description of the one or more recommended patient actions for the patient;” correspond to performing mathematical calculations/algorithms, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
The limitations of “for each of the identified care gaps, computing, based on the reduction determined for the historical patients, a treatment effect score indicating a predicted reduction in one or more visits by the patient during a six-month period after closing the identified care gap; prioritizing the identified care gaps based on the respective treatment effect scores;” correspond to “certain methods of organizing human activity” grouping of abstract ideas. This is a method of managing interactions between people, such as user following rules and instructions. The mere nominal recitation of a generic communication device and generic communication network does not take the claim out of the methods of organizing human interactions grouping.
The limitations of “generating an electronic communication that includes the one or more recommended patient actions from the HTE model for the patient and the recommendation summary from the NLG model; and transmitting the electronic communication via a communication network to a communication device of a care manager of the patient to cause the communication device to render the electronic communication via a user interface” correspond to insignificant extra-solution activities (see MPEP 2106.05 (g)), which do not provide a practical application for the abstract idea.
The dependent claims also correspond to “mathematical concept”, such as claim 13 recites “receiving clinician feedback for the one or more recommended patient actions; providing the clinician feedback as training data to the first machine learning model; and updating at least one coefficient of the first machine learning model based on providing the training data to the first machine learning model”, since these limitations correspond to performing mathematical calculations/algorithms.
The dependent claims also correspond to “certain methods of organizing human activity”, such as claim 4 recites “wherein the one or more recommended patient actions are determined, at least in part, with reference to a recommendation library that includes a plurality of recommended actions that are selected by the HTE model”, claim 25 recites “determining a social determinant of health (SDoH) for the patient; and generating at least one additional recommended patient action for the patient based on the determined SDoH for the patient, wherein the at least one additional recommended patient action for the patient provides a description of an action for a clinician to address a barrier for the patient using an SDoH resource, wherein the electronic communication describes the at least one additional recommended patient action.”.
“The communication device” and “the communication network” described in the current specification as generic computing devices. The current specification recites “Non-limiting examples of the communication device 105 may include, for example, personal computing devices or mobile computing devices (e.g., laptop computers, mobile phones, smart phones, smart devices, wearable devices, tablets, etc.). In some examples, the communication device 105 may be operable by or carried by a human user. In some aspects, the communication device 105 may perform one or more operations autonomously or in combination with an input by the user.” In [0064] and “The communication network 140 may include any type of known communication medium or collection of communication media and may use any type of protocols to transport messages between endpoints. The communication network 140 may include wired communications technologies, wireless communications technologies, or any combination thereof.” In [0061].
Claims 2, 4-19, 23-25 are ultimately dependent from claim 1 and include all the limitations of claim 1. Therefore, claims 2, 4-19, 23-25 recite the same abstract idea. Claims 2, 4-19, 23-25 describe a further limitation regarding the basis for generating patient care plan. These are all just further describing the abstract idea recited in claim 1, without adding significantly more.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements of “processing patient data…and generating an output using a first machine learning model,…,”, “generating, using a HTE model, one or more recommended patient actions for the patient”, “automatically generating, using a NLG model, a recommendation summary…”, “generating an electronic communication that includes the one or more recommended patient actions from the HTE model for the patient and the recommended summary from the NLG model; and transmitting the electronic communication via a communication network to a communication device”-claim 1, “the electronic communication is transmitted to a communication device of the patient”-claim 2, “…a recommendation library that includes a plurality of recommended actions that are selected by the HTE model”-claim 4, “…the electronic communication is transmitted via a selected communication channel”-claim 7, “electronic medical record (EMR)”-claim 10, “…waiting a predetermined amount of time after transmitting the electronic communication…”, “…updating a recommendation library…”-claim 19, “…automatically generating a recommendation summary …and including the recommendation summary in the electronic communication”-claim 1, “…the HTE model is updated using a reinforcement learning-based formulation that dynamically updates the HTE model based on clinician feedback…”-claim 23, which are hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
Claims also recite other additional limitations beyond abstract idea, including functions such as “transmitting the electronic communication…(transmitting data)” are insignificant extra-solution activities (see MPEP 2106.05 (g)), which do not provide a practical application for the abstract idea.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B:
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 integration of the abstract idea into a practical application, the additional element of using a machine learning model to perform the processing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims are not patent eligible.
Response to Arguments
Applicant's arguments filed 09/01/2026 have been fully considered. Applicant’s arguments will be addressed below in the order in which they appear.
Applicant argues that claim 1 does not merely instruct a generic computer to apply an unspecified machine learning model to patient information, claim 1 recites a particular ordered combination that uses an HTE model comprising a doubly robust causal inference model.
In response, Examiner submits that the current claim 1 and the current specification recite “processing patient data for a patient using a first machine learning model trained on a set of training data comprising electronic patient data records describing a success of closing a care gap with an action for a historical patient different from the patient” in claim 1, “providing data inputs to a machine learning model, where the data inputs include electronic patient data obtained from a plurality of electronic records describing a health history of the patient; receiving an output from the machine learning model, where the output received from the machine learning model is generated based on the machine learning model processing the data inputs and includes a plurality of identified care gaps for the patient;” in specification, [0004], then the second model of HTE model determines a treatment effect for each care gap. Therse features are directed to mere instructions to apply the abstract idea for determining treatment effects for each care gaps. And using the HTE model to generate recommended patient actions based on the scored and prioritized care gaps correspond to mere instructions to apply abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment.
Applicant argues that the rejection does not address the claimed combination of the HTE model and the NLG model.
In response, Examiner submits that the limitations of “automatically generating, using a NLG model, a recommendation summary…”, “generating an electronic communication that includes the one or more recommended patient actions from the HTE model for the patient and the recommended summary from the NLG model; and transmitting the electronic communication via a communication network to a communication device” correspond to mere instructions to apply/implement/automate an abstract idea in a particular technological environment. The current specification recites “In some aspects, the recommendation summary is generated, at least in part, with a natural language generation (NLG) model.” in [0023], and “In some aspects, the recommendation summary is generated, at least in part, with a natural language generation (NLG) model.” in [0178]. Therefore, the system uses these well-kwon models as mere instructions.
Therefore, the arguments are not persuasive and claims are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Applicant’s arguments, see Remarks, filed 09/03/2026, with respect to 35 USC 103 rejection have been fully considered and are persuasive. The rejection has been withdrawn.
Conclusion
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/DILEK B COBANOGLU/Primary Examiner, Art Unit 3687
