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 .
This communication is in response to the amendment received on 02/20/2026. Claims 1-19 and 23-25 remain pending in this application.
The 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph and 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejections have been withdrawn in light of the amendments.
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 1-19, 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 1-19, 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, a treatment effect for each of the plurality of identified care gaps;
for each of the identified care gaps, computing a treatment effect score indicating a predicted reduction in a metric;
prioritizing the identified care gaps based on the treatment effect score;
based on the prioritization of the filtered identified care gaps, generating, using a heterogenous treatment effect (HTE) model, one or more recommended patient actions for the patient;
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.”.
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”,
“based on the prioritization of the filtered identified care gaps, generating, using a heterogenous treatment effect (HTE) model, one or more recommended patient actions for the patient”, “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” correspond to performing mathematical calculations/algorithms, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
The limitations of “determining, based at least in part on the generated output of the first machine learning model, a treatment effect for each of the plurality of identified care gaps” and “for each of the identified care gaps, computing a treatment effect score indicating a predicted reduction in a metric; prioritizing the identified care gaps based on the treatment effect score” 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 recommended summary from the NLG model; and transmitting the electronic communication via a communication network to a communication device” 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 2-19, 23-25 are ultimately dependent from claim 1 and include all the limitations of claim 1. Therefore, claims 2-19, 23-25 recite the same abstract idea. Claims 2-19, 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, “the electronic communication is transmitted to a communication device of a care manager of the patient”-claim 3, “…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.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-19 and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson et al. (hereinafter Johnson) (US 11,574,731 B2), Kast et al. (hereinafter Kast) (US 2023/0290452 A1) and further in view of McNair et al. (hereinafter McNair) (US 11,749,407 B1).
Claim 1 has been amended to recite a method of automatically generating a patient care plan, the method comprising:
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 (Johnson discloses “To identify relevant action items (that may ultimately be reflected as claims data) for closing an identified gap in care reflected by combinations of disparate claims data records within a claims data store, a task-based interventional system executes rule-based models to determine action items eligible for closing a particular gap in care identified based on criteria associated with one or more programs. The task-based interventional system executes one or more machine-learning models to generate care scores associated with relevant entities associated with the identified gaps in care and generates a task data object comprising data indicative of an action item to close the gap in care together with executable jobs for initiating computer-based workflows to provide data indicative of the gap in care to relevant entities.” in abstract, “…The historical data embodied as the intervention data objects may additionally comprise data indicating whether the historical intervention was successful…” in col. 15, line 46 to col. 16, line 4);
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 (Johnson discloses “…generating output from the rule-based criteria, criteria for closing the one or more gaps in care;…” in col. 2, lines 51-53);
determining, based at least in part on the generated output of the first machine learning model, a treatment effect for each of the plurality of identified care gaps; for each of the identified care gaps, computing a treatment effect score indicating a predicted reduction in a metric; prioritizing the identified care gaps based on the treatment effect score (Johnson discloses “As a part of the action item analysis, action items and/or the corresponding gap in care may be assigned a care score indicative of a priority of addressing the gap in care. The care score may be assigned via a rule-based analysis (e.g., by increasing or decreasing a care score based on the results of various analytical steps), an artificial intelligence-based analysis (e.g., determining an appropriate care score by assigning weights established via machine-learning models (e.g., time-series based anomaly detection, regression modelling (e.g., Bayesian modelling, Linear modelling, Neural Network modelling, and/or the like), clustering, and/or the like), which may assign weights based on comparisons with historical data that may be performed within a vector-based domain), and/or a combination of rule-based and artificial intelligence-based models. As just one example, each gap in care identified via the task-based interventional system 65 may be assigned an initial care score (e.g., a care score of 1, however, other scoring methodologies/reference points may be utilized in other embodiments) that may be increased or decreased based on the results of various analyses as discussed herein.” in col. 21, line 62 to col. 22, line 15);
…
generating an electronic communication that includes the one or more recommended patient actions from the HTE model for the patient and the recommendation summary…(Johnson discloses “..The task data object comprises data indicative of the action item necessary for closure of a particular gap in care for a particular patient, and accordingly Block 725 indicates that action item data representing such data may be populated for inclusion within a task data object. Such data may be embodied as text-based data providing a summary of the necessary steps to be performed by a patient (and/or provider or payer) that is presented for consumption by a payer representative or a provider…” in col. 31, lines 15-59); and
transmitting the electronic communication via a communication network to a communication device (Johnson; col. 31, lines 15-59).
Johnson fails to expressly teach “based on the prioritization of the filtered identified care gaps, generating, using heterogenous treatment effect (HTE) model, one or more recommended patient actions for the patient”. However, this feature is well known in the art, as evidenced by Kast.
In particular, Kast discloses “patient classifier system…a machine-learned model…” in [0112] and “…to assess the heterogeneity of treatment effect (HTE) of different levels of PEEP…” in [0375]-Examiner considers that PEEP is a medical condition.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include in the machine learning models of Holub the ability to include t a heterogeneous treatment effect (HTE) model as taught by Kast since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Johnson teaches “automatically generating,… a recommendation summary that includes a summarized description of the one or more recommended patient actions for the patient” in col. 31, lines 15-24 (“…the action item necessary for closure of a particular gap in care for a particular patient, and accordingly Block 725 indicates that action item data representing such data may be populated for inclusion within a task data object. Such data may be embodied as text-based data providing a summary of the necessary steps to be performed by a patient (and/or provider or payer) that is presented for consumption by a payer representative or a provider…), but fails to expressly teach using a natural language generation (NLG) model. However, this feature is well known in the art, as evidenced by McNair.
In particular, McNair discloses “…decision support services including… improved natural language processing services…” in abstract.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include in the machine learning models of Holub the ability to include the natural language processing services as taught by McNair since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 2 recites the method of claim 1, wherein the electronic communication is transmitted to a communication device of the patient (Johnson; col. 11, line 62 to col. 12, line 23).
Claim 3 recites the method of claim 1, wherein the electronic communication is transmitted to a communication device of a care manager of the patient (Johnson; col. 11, line 62 to col. 12, line 23).
Claim 4 has been amended to recite the method of claim 1, 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 (Johnson; col. 26, line 49 to col. 27, line 29).
Claim 5 has been amended to recite the method of claim 1, wherein the information describing the success of closing the care gap with the action comprises a count of a number of patient admissions to a healthcare facility following an identification of the care gap (Johnson; col. 21, line 62 to col. 22, line 15).
Claim 6 recites the method of claim 1, wherein the treatment effect score for each identified care gap is based, at least in part, on a prediction of success associated with closing each identified care gap (Johnson; col. 21, line 62 to col. 22, line 15).
Claim 7 recites the method of claim 1, wherein the electronic communication is transmitted via a selected communication channel (Johnson; col. 27, lines 54-59).
Claim 8 recites the method of claim 7, wherein the selected communication channel is selected based on a probability of closing a care gap having the highest treatment effect score and wherein the selected communication channel comprises at least one of email, direct mail, SMS, and an automated outbound calling campaign (Johnson; col. 27, lines 54-59).
Claim 9 recites the method of claim 1, wherein the electronic patient data comprises claims-based electronic data (Johnson; col. 2, lines 38-40).
Claim 10 recites the method of claim 9, wherein the electronic patient data further comprises electronic medical record (EMR) data (Johnson; abstract, col. 31, line 60 to col. 32, line 17).
Claim 11 recites the method of claim 9, wherein the claims-based electronic data comprises data describing at least one insurance medical and/or insurance claim made by at least one of the patient and a healthcare provider of the patient (Johnson; abstract, col. 31, line 60 to col. 32, line 17).
Claim 12 recites the method of claim 1, wherein the electronic patient data comprises device data obtained from at least one device associated with the patient (Johnson; col. 11, line 62 to col. 12, line 23).
Claim 13 recites the method of claim 1, further comprising: receiving clinician feedback for the one or more recommended patient actions; providing the clinician feedback as training data to the machine learning model; and updating at least one coefficient of the machine learning model based on providing the training data to machine learning model (Johnson; col. 16, lines 28-43).
Claim 14 recites the method of claim 13, further comprising: replacing the first machine learning model with an updated version of the machine learning model, wherein the updated version of the machine learning model comprises the updated at least one coefficient (Johnson; col. 25, lines 21-39).
Claim 15 recites the method of claim 1, wherein the one or more recommended patient actions comprise a next best action for the patient to take in connection with closing a care gap from the plurality of identified care gaps (Johnson; col. 25, line 40 to col. 26, line 23).
Claim 16 recites the method of claim 15, wherein the next best action is associated with closing more than one care gap from the plurality of identified care gaps (Johnson; col. 25, line 40 to col. 26, line 23).
Claim 17 recites the method of claim 15, wherein the next best action corresponds to an action that is predicted most likely to be taken by the patient (Johnson; col. 25, line 40 to col. 26, line 23).
Claim 18 recites the method of claim 15, wherein the next best action corresponds to at least one of the following: a visit to a healthcare provider, a change in a medical treatment, a change in a prescription, a change in diet, a change in activity, a blood test, and a medical examination (Johnson; col. 25, line 40 to col. 26, line 23).
Claim 19 recites the method of claim 15, further comprising: waiting a predetermined amount of time after transmitting the electronic communication; after the predetermined amount of time, performing a patient lookback to determine whether the patient took the next best action within the predetermined amount of time; if the patient took the next best action within the predetermined amount of time, determining whether the next best action resulted in a partial or complete closing of the care gap; and updating a recommendation library based on whether the next best action results in a partial or complete closing of the care gap (Johnson; col. 25, line 40 to col. 26, line 23).
Claim 23 recites the method of claim 22, wherein the HTE model is updated using a reinforcement learning-based formulation that dynamically updates the HTE model based on clinician feedback while the HTE model is being used to generate the one or more recommended patient actions.
Johnson fails to expressly teach “wherein the HTE model is updated using a reinforcement learning-based formulation that dynamically updates the HTE model based on clinician feedback while the HTE model is being used to generate the one or more recommended patient actions”. However, this feature is well known in the art, as evidenced by Kast.
In particular, Kast discloses “patient classifier system…a machine-learned model…” in [0112] and “…to assess the heterogeneity of treatment effect (HTE) of different levels of PEEP…” in [0375]-Examiner considers that PEEP is a medical condition.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include in the machine learning models of Holub the ability to include t a heterogeneous treatment effect (HTE) model as taught by Kast since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 24 recites the method of claim 1, wherein the one or more recommended patient actions comprises a description of where to send the patient to address a care gap from the plurality of identified care gaps (Holub; [0020]).
Claim 25 recites the method of claim 1, further comprising: 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 (Johnson; col. 24, line 60 to col. 25, line 20).
Response to Arguments
Applicant's arguments filed 02/20/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appear.
Arguments about 35 USC 101 rejection:
Applicant argues that claims are directed to a practical application, since they are directed to improving the functioning of the computer by enabling the computer to automatically generate recommended actions to close a care gap for the patient, a recommendation summary for the patient, and generate an electronic communication that includes the recommended actions and the recommendation summary and transmit the electronic communication to a communication device. Applicant argues that the claims improve the functioning to the computer by implementing the alleged judicial exception in conjunction with a particular machine.
In response, Examiner submits that “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].
MPEP recites “…a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine…” in §2106.05(b). Hence the argument is not persuasive.
Applicant argues that claims are patentable according to the recent Ex Parte Desjardins decision, which explained in the USPTO memorandum that has been December 5, 2025. Applicant argues that claims improve the efficiency of providing recommendations to a patient to close a gap in care and overall aid in efficiently reporting actions to improve a patient’s health.
In response, Examiner submits that providing recommendations to the patient to close the gap in care may be directed to an improvement to the outcomes for the health care, however, these features are not directed to an improvement to the technology nor an improvement to the functioning of the computer itself. Claims are directed to processing patient data, determining a treatment effect for an identified care gap and generating a recommendation summary for the patient. Applicant argues that the use of machine learning models improves the efficiency and overall accuracy of a unique action set for a patient. In response, Examiner submits that providing recommendations to the patient to close the gap in care based on the machine learning processing of the patient data may be directed to an improvement to the outcomes for the health care (results), but the steps of processing patient data using a machine learning model and generating recommended patient actions using another machine learning model are directed to mathematical calculations, which is directed to mathematical concepts.
There is no indication in the current claims nor in the current specification that how the machine learning model itself operates, whether the training the machine learning model learn new tasks, while protecting knowledge about new tasks to overcome a catastrophic forgetting, or similar (based on Ex Parte Desjardins decision). Claims recite “outputting, using the first machine learning model, a plurality of identified care gaps for the patient, generating one or more recommended patient actions, using heterogenous treatment effect (HTE model) and generating, using a natural language generation (NLG) model, a recommendation summary that includes summarization of recommended patient actions”. Therefore, the first machine learning model and the HTE model provides different type of outcomes (care gaps and recommended patient actions) and the NLG model provides a summary of the recommended actions for the patient.
Therefore, the arguments are not persuasive and claims are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Arguments about 35 USC 103 rejection:
Applicant’s arguments with respect to 35 USAC 103 rejection of claims 1-19, 23-25 have been considered but are moot because the new ground of rejection does not rely on the new combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 DILEK B COBANOGLU whose telephone number is (571)272-8295. The examiner can normally be reached 8:30-5:00 ET.
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/DILEK B COBANOGLU/Primary Examiner, Art Unit 3687
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