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 .
DETAILED ACTION
Response to Amendment
In the amendment dated 07/27/2026, the following occurred: Claims 1, 4, 8, 11 and 15 have been amended.
Claims 1-20 are currently pending.
Drawings
The drawings are further objected to as failing to comply with 37 CFR 1.84(I) because the following figure(s) is/are unreadable and/or are unsatisfactory for reproduction:
Fig. 10A, 10B
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1 and 8 are objected to for the following informality: “…presenting the user interface including the generated text output for display on a client device associated with the patient;” should read “…presenting the user interface including the generated text output for display on the client device associated with the patient;”
Claims 4 and 11 are objected to for the following informality: “…generating a notification at a client device associated with a care provider assigned to the patient at the facility, the notification including the medication data; and responsive to selection of the notification, presenting a medication reconciliation interface for display on a client device associated with the care provider…” should read “…generating a notification at the client device associated with a care provider assigned to the patient at the facility, the notification including the medication data; and responsive to selection of the notification, presenting a medication reconciliation interface for display on the client device associated with the care provider…”
Appropriate action is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 8 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method and a system for medication review reconciliation, which are within a statutory category.
Regarding claims 1, 8 and 15, the limitation of (claim 1 being representative) retrieving a plurality of data attributes associated with a visit for a patient, the data attributes including at least one appointment time, a visit location, and a care provider identifier; collecting medication data and care provider information associated with the patient, the medication data including a plurality of previously prescribed medications and one or more patient reported medications; prior to the visit, presenting a conversational medication-review that, for each medication in the medication data: displays an identifier of the medication together with an image of the medication; and presents a plurality of mutually exclusive selectable input controls configured to force the patient to select only one adherence state from a set comprising "taking" and "not taking" for the medication; receiving one or more patient responses indicating the selected adherence state for each medication and, for at least one medication, an optional indication that the patient wants to discuss the medication with a care provider; performing text classification and entity recognition on the one or more patient responses to determine a patient health condition and current medication usage; synthesizing one or more care optimizations based on the collected medication data, the care provider information, and the determined patient health condition and current medication usage as input, wherein the one or more care optimizations include at least one candidate alternative medication or dosage adjustments associated with a medication for which the patient indicated the optimal indication to discuss; automatically updating the medication data stored based on the synthesized one or more care optimizations; generating text output that includes the synthesized one or more care optimizations based on the collected medication data and the care provider information; presenting including the generated text output for display; receiving a selection of a care optimization; detecting arrival of the patient at a facility associated with the visit; responsive to detecting the arrival, generating a notification to a care provider assigned to the patient, the notification including the updated medication data and the selected care optimization; and causing the care optimization to be performed and regarding claim 15- the limitation of identifying a care provider at a facility based on an indication of arrival of the patient at the facility as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e. managing personal behavior including following rules or instructions) but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to retrieve a plurality of data attributes, collect medication data and care provider information, display an identifier and an image of medication, present options for selection, receive responses, determine patient health condition and medication usage, synthesize one or more care optimizations, update the stored medication data, generate text output, present the generated text output for display, receive a selection of a care optimization, detect patient arrival, generate a notification and cause the care optimization to be performed in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “retrieving… collecting… presenting… receiving…using… synthesizing… updating…generating… presenting… receiving… detecting…generating…and causing care optimization” as indicated supra.
Other than reciting generic computer components i.e. the one or more processors and memory in claim 8, the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). The Examiner notes that Claims 1 and 15 are not tied to any particular technological environment. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II))]. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. Claims 1 and 15 are not tied to any particular technological environment. Claim 8 recites the additional elements of one or more processors and memory. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computer components for enabling access to medical information or for performing generic computer functions, see Spec. at para. [0069]-[0071]) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 1, 8 and 15 further recite the additional elements of a client device associated with a care provider and a client device associated with the patient. These additional elements are recited at a high level of generality (i.e. a general means to generate/present/display/receive data) and amount to extra solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Claims 1 and 8 further recite the additional elements of a user interface and a conversational medication-review user interface. Claim 15 further recite the additional elements of a user interface and a conversational interface application. These additional elements merely generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Claims 1, 8 and 15 also recites the additional element of using one or more machine learning models comprising a natural language understanding (NLU) model augmented with a knowledge base including a medical dictionary and index search terms with associated dispositions to perform text classification and entity recognition and synthesize one or more care optimizations. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the one or more machine learning models comprising a natural language understanding (NLU) model augmented with a knowledge base including a medical dictionary and index search terms with associated dispositions to perform text classification and entity recognition and to synthesize one or more care optimizations merely confines the use of the abstract idea (i.e., the machine learning model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
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 elements of the one or more processors and memory to perform the noted 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 (“significantly more”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Also as discussed with respect to integration of the abstract idea into a practical application, the additional elements a client device associated with a care provider and a client device associated with the patient were considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. MPEP 2016.05(d)(II) indicates that receiving and/or transmitting data over a network has been held by the courts to be well-understood, routine, conventional activity (citing Symantec, TLI Communications, OIP Techs., and buySAFE). Well-understood, routine and conventional activity cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
Also as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a user interface, a conversational medication-review user interface and a conversational interface application were determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, these additional elements do not provide significantly more. As such the claim is not patent eligible.
Also as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using one or more machine learning models comprising a natural language understanding (NLU) model augmented with a knowledge base including a medical dictionary and index search terms with associated dispositions to perform text classification and entity recognition and synthesize one or more care optimizations was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the machine learning model) to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 2-7, 9-14 and 16-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 and 9 further merely describe(s) determining clinical data, selecting a machine learning model, determining a portion of the generated text output and presenting the user interface, which further define the abstract idea. Claim(s) 3 and 10 further merely describe(s) generating and sending a notification and receiving an acceptance. Claim(s) 4 and 11 further merely describe(s) detecting arrival, generating a notification and presenting a medication reconciliation interface. Claim(s) 4 and 11 also include the additional element of “a medication reconciliation interface” which is interpreted the same as the user interface and does not provide a practical application nor significantly more for the same reasons. Claim(s) 5 and 12 further merely describe(s) analyzing the collected medication data, generating additional text output and presenting the additional text output. Claim(s) 6 and 13 further merely describe(s) the one or more care optimizations. Claim(s) 7, 14 and 17-20 further merely describe(s) the care optimization to be performed. Claim(s) 16 further merely describe(s) generating and prioritizing the one or more care optimizations. Claims 2-7, 9-14 and 16-20 further define the abstract idea and are rejected for the same reason presented above with respect to claims 1, 8 and 15.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Owen (US 2019/0051381), in view of Moturu US (2017/0235912), in view of De Vries (US 2023/0170065) and in further view of Johnson (US 2012/0101847).
REGARDING CLAIM 1
Owen discloses a method comprising: retrieving, from a data storage associated with a healthcare management system, a plurality of data attributes associated with a visit for a patient, the data attributes including at least one appointment time, a visit location, and a care provider identifier ([0071] teaches collection and processing of clinical encounter information (interpreted by examiner as the plurality of data attributes associated with a visit for a patient) associated with the patient encounter (e.g., the visit to the doctor's office) and teaches a medical professional involved with a patient encounter (interpreted by Examiner as a care provider identifier)); detecting arrival of the patient at a facility associated with the visit ([0085] teaches upon arriving for the patient encounter (e.g., encounter participant 228 visiting the doctor's office), the patient (e.g., encounter participant 228) may be directed to a “check-in” area within the monitored space (e.g., monitored space 130) of the clinical environment (interpreted by examiner as detecting arrival of the patient at a facility associated with the visit)); responsive to detecting the arrival, generating a notification on a client device associated with a care provider assigned to the patient, the notification including the updated medication data and the selected care optimization ([0109] teaches notification to medical professional concerning potential medical situation and [0106] teaches medical situation such as potential medication issue for example (interpreted by examiner as generating a notification including medication data));
Owen does not explicitly disclose, however Moturu discloses:
using one or more machine learning models comprising a natural language understanding (NLU) model augmented with a knowledge base including a medical dictionary and index search terms with associated dispositions, performing text classification and entity recognition on the one or more patient responses to determine a patient health condition and current medication usage (Moturu at [0033] teaches a digital survey can be presented to a user, asking for information regarding: demographic information (e.g., gender, ethnicity, age, name), medical history (e.g., previous diagnoses, previous treatments), current medical status (e.g., current medications, current symptoms, current conditions), future plans (e.g., health plans, exercise plans, habits, life plans, etc.), mental condition (e.g., mental thoughts, anxiety, stress, mood, emotions, etc.), and/or other suitable information. However, collecting a survey dataset can be performed in any suitable manner. [0037] teaches collecting and processing electronic health records and receiving electronic health records and/or associated data from care providers, etc.), which can be subsequently processed (e.g., through natural language processing; extracting features; etc.) [0048] teaches generating patient medical status analysis includes employing a machine learning model (interpreted by Examiner as performing text classification and entity recognition on the one or more patient responses to determine a patient health condition and current medication usage)); receiving a selection of a care optimization from the client device; and causing the care optimization to be performed (Moturu at [0058] teaches selecting an initial list of potential therapeutic interventions, narrowing the initial list based on patient digital behavior information, symptoms, drug information, and/or other suitable information, and presenting the narrowed list of potential therapies to a care provider for providing decision support. Additionally or alternatively, a prioritized list of therapeutic interventions (e.g., prioritized based on strength of model recommendation) can be presented to the care provider (interpreted by examiner as receiving a selection of a care optimization from the client device). User response to the therapeutic interventions can be monitored (e.g., through data collected etc.) and used in updating therapeutic interventions, which can include one or more of: modifying dosage (e.g., increasing or decreasing dosage, changing frequency, etc.), augmenting the medication regimen (e.g., adding medication types, removing medication types, etc.), switching a medication type, and/or any other suitable modifications (interpreted by examiner as causing the care optimization to be performed)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the patient visits of Owen to incorporate the care optimization as taught by Moturu, with the motivation of improving system processing ability for supporting care providers. (Moturu at [0017]).
Owen and Moturu do not explicitly disclose, however De Vries discloses:
collecting medication data and care provider information associated with the patient, the medication data including a plurality of previously prescribed medications and one or more patient reported medications (De Vries at [0021] teaches other provider reported data can include medications prescribed for specific patient types and diagnoses and [0023] teaches the received data can include historical information on the patient’s medications (interpreted by examiner as the medication data including a plurality of previously prescribed medications and one or more patient reported medications) [0030] teaches a subset of the received healthcare information data is provider data that characterizes provider actions (e.g., prescribing behaviors, quality metrics, intervention types, and intervention frequencies) (interpreted by examiner as the care provider information)); synthesizing, using one or more machine learning models, one or more care optimizations based on the collected medication data, the care provider information, and the determined patient health condition and current medication usage as input to the one or more machine learning models, wherein the one or more care optimizations include at least one candidate alternative medication or dosage adjustments associated with a medication for which the patient indicated the optimal indication to discuss (De Vries at [0049] teaches using a recommendation rules engine (interpreted by examiner as the one or more machine learning models) to generate the treatment recommendation (interpreted by examiner as the one or more care optimizations) and that the recommendation rules engine can analyze inputs (interpreted by examiner to be the collected medication data, the care provider information , and the determined patient health condition and current medication usage) and execute the rules on the inputs to determine a treatment recommendation. [0044] teaches optimizing health outcome, [0050] teaches optimizing recommendation, [0061] teaches optimizing treatment intervention and [0070] teaches optimizing the dose of a patient’s medications based on their clinical reaction to the medication and the results that medication is achieving (interpreted by Examiner as wherein the one or more care optimizations include a dosage adjustments associated with a medication) and that each module can be customized to meet the most pressing needs of the system users, such as specific programs, health conditions, disease areas, medication classes, therapeutic areas, formularies, or education issues that are of particular focus. [0060]-[0061] teaches the use of machine learning models.); automatically updating the medication data stored in the data storage based on the synthesized one or more care optimizations (De Vries at [0074] teaches updating treatment profile); generating text output at a user interface that includes the synthesized one or more care optimizations based on the collected medication data and the care provider information; presenting the user interface including the generated text output for display on a client device associated with the patient (De Vries at [0032] teaches that characterize patients health. [0007] teaches presentation recommendation (interpreted by examiner as the generated text output that includes the synthesized one or more care optimizations based on the collected medication data and the care provider information) on a graphical user interface of a client device (interpreted by examiner as presenting the user interface including the generated text output for display on a client device associated with the patient));
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the patient visits of Owen and the care optimization of Moturu to incorporate collecting medication data and care provider information, using one or more machine learning models, synthesizing one or more care optimizations, generating text output at a user interface and presenting the user interface for display on a client device as taught by De Vries, with the motivation of providing patients access to highly needed treatment education and counseling over the course of their care, thereby improving a sustained rate of medication errors. (De Vries at [0004]).
Owen, Moturu and De Vries do not explicitly disclose, however Johnson discloses:
prior to the visit, presenting on a client device associated with the patient a conversational medication-review user interface that, for each medication in the medication data: displays an identifier of the medication together with an image of the medication; and presents a plurality of mutually exclusive selectable input controls configured to force the patient to select only one adherence state from a set comprising "taking" and "not taking" for the medication (Johnson at [0140] teaches the present invention provides a custom-designed user-friendly graphical interface (interpreted by examiner as a conversational medication-review user interface) [0174] teaches generating a medication details screen for a specific medication, which contains all the details of the medications that the patient is currently prescribed/recommended to take. It lists the brand name of the selected medication, the generic name of the selected medication and displays an image (preferably a photo) of the specific medication (interpreted by Examiner as displays an identifier of the medication together with an image of the medication) [0180] teaches by selecting (tapping, when using a touchscreen, or selecting by some other input mechanism for the user) on a compartment, check box 258 is provided for each of the displayed medications. Upon selecting on the checkbox, the display in the checkbox can either show a checkmark, or can indicate "Taken" as shown in Fig. 16 (interpreted by examiner as means to presents a plurality of mutually exclusive selectable input controls configured to force the patient to select only one adherence state from a set comprising "taking" and "not taking" for the medication)); receiving, via the conversational medication-review user interface, one or more patient responses indicating the selected adherence state for each medication and, for at least one medication, an optional indication that the patient wants to discuss the medication with a care provider (Johnson at [0180] teaches display some other indicator that the user has checked the box 258 off to indicate that the medication was taken. In the example shown in FIG. 16, the compartment 254 for Sunday morning has been selected, resulting in a display of Augmentin, Zyrtec and Mucinex, and boxes 256 indicate that all of these doses have been taken by the patient (interpreted by Examiner as receiving, via the conversational medication-review user interface, one or more patient responses indicating the selected adherence state for each medication) [00183] teaches providing "Find a Doctor" feature that identifies providers that are approved by the system and which the user can choose to interact with (interpreted by Examiner as means if a patient wants to discuss the medication with a care provider));
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the patient visits of Owen, the care optimization of Moturu and the treatment recommendation of De Vries, to incorporate presenting on a client device associated with the patient a conversational medication-review user interface that, for each medication in the medication data: displays an identifier of the medication together with an image of the medication; and presents a plurality of mutually exclusive selectable input controls configured to force the patient to select only one adherence state from a set comprising "taking" and "not taking" for the medication receiving, via the conversational medication-review user interface, one or more patient responses indicating the selected adherence state for each medication and, for at least one medication, an optional indication that the patient wants to discuss the medication with a care provider as taught by Johnson the motivation of digitizing and integrating patient medical records to make them more complete and reliable. (Johnson at [0009]).
REGARDING CLAIM 2
Claim 2 is analogous to Claim 1 Claim 2 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
De Vries further discloses determining clinical data associated with the patient, the clinical data including one or more diseases associated with the patient, one or more treatments for the one or more diseases, one or more symptoms associated with the one or more diseases and one or more drug protocol regimens for treatment of the one or more diseases associated with the patient (De Vries [0007] teaches a clinical patient profile can be determined for the patient based on the received healthcare information data and the determined health outcome evaluation. [0028] teaches determining disease profile (e.g., diagnoses, duration, utilization by disease) and [0034] teaches patient reported symptoms. [0060] teaches that if a patient has uncontrolled type 2 diabetes and concurrent cardiovascular disease, the initial recommendation is to recommend a glucagon-like peptide-1 agonist or sodium-glucose co-transporter-2 inhibitors (interpreted by examiner as one or more treatments for the one or more diseases and one or more drug protocol regimens for treatment)).
The obviousness of combining the teachings of De Vries within the systems taught by Owen, Moturu and Johnson are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIM 3
Owen, Moturu, De Vries and Johnson disclose the limitation of claim 1.
Owen, Moturu and Johnson do not explicitly disclose, however De Vries further discloses:
The method of claim 1, further comprising: generating a notification regarding a suggested action based on priority; sending the notification to the client device associated with the patient; and receiving acceptance of the suggested action from the client device (De Vries at [0018] teaches generate prioritized treatment recommendations for patients. [0057] teaches the suggested treatment recommendations can drive the creation of a task associated with the patient. These tasks can in turn be prioritized in a list format such that platform users can view suggested actions (interpreted by examiner as generating notifications regarding the suggested action based on priority and sending to the client device) aimed at reducing the patient risk. A task can be created to follow-up with the provider if they did not respond to the treatment recommendation as evidenced by a lack of medication change in the health outcome evaluation data and [0058] teaches the implementation of the suggested treatment recommendations by a patient and/or a provider can be continuously measured and recorded (interpreted by examiner as means receiving acceptance of the suggested action from the client device)).
The obviousness of combining the teachings of De Vries within the systems taught by Owen, Moturu and Johnson are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIM 4
Owen, Moturu, De Vries and Johnson disclose the limitation of claim 1.
Owen further discloses:
The method of claim 1, further comprising: detecting arrival of the patient at a facility ([0085] teaches upon arriving for the patient encounter (e.g., encounter participant 228 visiting the doctor's office), the patient (e.g., encounter participant 228) may be directed to a “check-in” area within the monitored space (e.g., monitored space 130) of the clinical environment (interpreted by examiner as detecting arrival)); generating a notification at a client device associated with a care provider assigned to the patient at the facility, the notification including the medication data ([0109] teaches notification to medical professional concerning potential medical situation and [0106] teaches medical situation such as potential medication issue for example (interpreted by examiner as generating a notification including medication data)); and responsive to selection of the notification, presenting a medication reconciliation interface for display on a client device associated with the care provider assigned to the patient at the facility, the medication reconciliation interface including the selected care optimization by the patient ([0109] teaches if a potential medical situation is identified, automated clinical documentation process may initiate an inquiry concerning the potential medical situation and provide a notification to the medical professional concerning the potential medical situation. Example of such an inquiry may be asking one or more questions, such as “Does this patient have diabetes?”, “Should we arrange home healthcare for this patient?” of “Would you like to substitute Medication Y for Medication X?”. [0056] teaches a display rendering system 108 may include one or more of each of a tablet computer, a computer monitor, and a smart television and [0110] teaches a private display device (interpreted by examiner as presenting the medication reconciliation interface for display on a client device including the selected care optimization by the patient)).
REGARDING CLAIM 5
Claim 5 is analogous to Claim 1 thus Claim 5 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
REGARDING CLAIM 6
Owen, Moturu, De Vries and Johnson disclose the limitation of claim 1.
Owen, De Vries and Johnson do not explicitly disclose, however Moturu further discloses:
The method of claim 5, wherein the one or more care optimizations are prioritized by the analyzed medication data (Moturu at [0058] teaches a prioritized list of therapeutic interventions (e.g., prioritized based on strength of model recommendation) can be presented to the care provider. User response to the therapeutic interventions can be monitored (e.g., through data collected) and used in updating therapeutic interventions, which can include one or more of: modifying dosage (e.g., increasing or decreasing dosage, changing frequency, etc.), augmenting the medication regimen (e.g., adding medication types, removing medication types, etc.), switching a medication type, and/or any other suitable modifications (interpreted by examiner as the one or more care optimizations are prioritized by the analyzed medication data)).
The obviousness of combining the teachings of Moturu within the systems taught by Owen, De Vries and Johnson are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIM 7
Owen, Moturu, De Vries and Johnson disclose the limitation of claim 1.
Owen, De Vries and Johnson do not explicitly disclose, however Moturu further discloses:
The method of claim 1, wherein the care optimization to be performed is prompting a care provider assigned to the patient to inform the patient of one or more alternative medications during the visit ([0058] teaches a therapy recommendation modification of switching from medication “A” to medication “B” can be generated and presented to the care provider (interpreted by examiner as prompting a care provider assigned to the patient to inform the patient of one or more alternative medications)).
The obviousness of combining the teachings of Moturu within the systems taught by Owen, De Vries and Johnson are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIMS 8-14 and 16
Claims 8-14 and 16 are analogous to Claims 1-7 thus Claims 8-14 and 16 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 1-7.
REGARDING CLAIM 15
Claim 15 is analogous to Claims 1-14 thus Claim 15 is similarly analyzed and rejected in a manner consistent with the rejection of Claims 1-14.
Owen discloses identifying a care provider at a facility based on an indication of arrival of the patient at the facility ([0085] teaches upon arriving for the patient encounter (e.g., encounter participant 228 visiting the doctor's office), the patient (e.g., encounter participant) may be directed to a “check-in” area within the monitored space (of the clinical environment. An example of this “check-in” area may include a booth into which the patient enters. Upon entering this “check-in” area, the pre-visit portion (e.g., the patient intake portion) of the patient encounter may begin and [0142] teaches assign a first role to the first encounter participant, a role for encounter participant may be assigned if that identity defined is associated with a role (e.g., the identity defined for encounter participant is Doctor Susan Jones) (interpreted by examiner as identifying a care provider at a facility based on an indication of arrival of the patient at the facility));
Johnson further discloses responsive to identifying the care provider, generating a user interface at a client device associated with the care provider, the user interface including a notification banner indicating that one or more medications from outside sources need attention, and a selectable control that, when selected, presents a medication reconciliation table listing, for each of the plurality of medications, an adherence status reported by the patient and a source of the report; presenting the one or more care optimizations within the medication reconciliation table for display on the client device associated with the care provider (Johnson at [0175] teaches a pop-up message (interpreted by Examiner as the notification banner) that can be displayed to a user. [0211] teaches trigger a message that a treatment plan need to be reviewed (interpreted by Examiner as the user interface including a notification banner indicating that one or more medications from outside sources need attention) [0143] teaches the graphical interface may include a "current pillbox" and an "updated pillbox" graphical interface, each of which may include images of tablets, capsules and other dosage forms, to alleviate issues with drug reconciliation by the patient by simplifying identification of drugs and drug forms and [0184] teaches adherence to treatment plans (interpreted by Examiner as a selectable control that, when selected, presents a medication reconciliation table listing, for each of the plurality of medications, an adherence status reported by the patient and presenting the one or more care optimizations within the medication reconciliation table for display on the client device associated with the care provider)); receiving, from the client device associated with the care provider, user input associated with a care optimization, the user input comprising at least one of a removal of a medication from the medication data, a modification of a dosage, or an acceptance of the candidate alternative medication from the client device ([0170] teaches in the Treatment Plan box 172 (FIG. 10A), the editing feature provides radio buttons, check boxes, or other selectable features that can be selected or deselected to display or remove any of the listed over-the-counter medications, prescription medications and home instructions (interpreted by Examiner as receiving user input associated with a care optimization, the user input comprising at least one of a removal of a medication from the medication data));
REGARDING CLAIM 17
Owen, Moturu, De Vries and Johnson disclose the limitation of claim 15.
Owen, De Vries and Johnson do not explicitly disclose, however Moturu further discloses:
The method of claim 15, wherein the care optimization to be performed is confirming the medication data with the patient after the visit (Moturu at [0040] teaches therapeutic interventions prescribed to patients (e.g., prescribed and/or verified as efficacious for patients sharing the condition, patients with shared medical parameters; etc.) (interpreted by examiner as the care optimization to be performed is confirming the medication data with the patient after the visit)).
The obviousness of combining the teachings of Moturu within the systems taught by Owen, De Vries and Johnson are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIM 18
Owen, Moturu, De Vries and Johnson disclose the limitation of claim 15.
Owen, De Vries and Johnson do not explicitly disclose, however Moturu further discloses:
The method of claim 15, wherein the care optimization to be performed is updating the medication data with the patient during the visit ([0056] teaches diagnostic analysis can guide further diagnostic steps that can be taken by the care provider (e.g., a series of questions to ask the patient). The care provider can input patient data (e.g., patient answers to care provider questions) into a computing device (e.g., an application executing on the smartphone of the care provider), where generating medical status analysis can include generating an updated diagnostic analysis based on the care provider-inputted data and [0058] teaches user response to the therapeutic interventions can be monitored (e.g., through data collected in Blocks S110-S125, etc.) and used in updating therapeutic interventions, which can include one or more of: modifying dosage (e.g., increasing or decreasing dosage, changing frequency, etc.), augmenting the medication regimen (e.g., adding medication types, removing medication types, etc.), switching a medication type, and/or any other suitable modifications (interpreted by examiner as the care optimization to be performed is updating the medication data with the patient during the visit)).
The obviousness of combining the teachings of Moturu within the systems taught by Owen, De Vries and Johnson are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIM 19
Owen, Moturu, De Vries and Johnson disclose the limitation of claim 15.
Owen, Moturu and Johnson do not explicitly disclose, however De Vries further discloses:
The method of claim 15, wherein the care optimization to be performed is generating a new machine learning model based on the received medication data (De Vries at [0061] teaches the machine learning model can identify the predictors of diabetes control via measurement of hemoglobin A1c as an indicator of treatment success. Using the identified predictors of success, the system can generate additional logic (e.g., modifications to a rule, a new rule, a deletion of a rule, and the like) that can be inserted into the recommendation rules engine (interpreted by examiner as means for generating a new machine learning model based on the received medication data)).
The obviousness of combining the teachings of De Vries within the systems taught by Owen, Moturu and Johnson are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIM 20
Owen, Moturu, De Vries and Johnson disclose the limitation of claim 1.
Owen, Moturu and Johnson do not explicitly disclose, however De Vries further discloses:
The method of claim 15, wherein the care optimization to be performed is generating a determination of sentiment of user feedback received (De Vries at [0007] teaches the treatment recommendation rules engine can be modified by a predictive model that identifies a predictor variable characterizing a likelihood of success of an intervention characterized by the treatment recommendation, the identifying based on received feedback data that indicates a level of success of the intervention, determines a modification to the recommendation rule based on the identified predictor variable, and modifies the recommendation rule based on the determined modification and the behavioral risk parameter can be dynamically updated based on received feedback data characterizing the patient (interpreted by examiner as the care optimization to be performed is generating a determination of sentiment of user feedback received)).
The obviousness of combining the teachings of De Vries within the systems taught by Owen, Moturu and Johnson are discussed in the rejection of claim 1, and incorporated herein.
Response to Arguments
Drawing Objections
Regarding the drawing objection(s), the Applicant has amended Figs. 10A and 10B. However, they do not overcome the basis/bases of objection because they are still unreadable.
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 1-20, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
On page 4, the Office Action characterized original claim 1 as reciting "a series of rules or instructions for a person or persons to follow" corresponding to "Certain Methods of Organizing Human Activity." Applicant respectfully submits that the amended claim 1 can no longer be reasonably characterized this way, because a human mind, with or without pen and paper, cannot practically perform several of the newly added limitations.
Regarding 1, The Examiner respectfully submits that claims were not classified under mental process but under certain methods of organizing human activity, i.e., following a set of rules or instruction with or without the aid of a computer.
These limitations are not merely "generic computer components" performing generic functions layered onto an otherwise-abstract series of instructions. Rather, the amended claim 1 as a whole recites a specific technical architecture: a forced-choice, image-augmented data capture interface, coupled to a knowledge-base-augmented NLU model, coupled to an automatic, event- triggered synchronization mechanism between two distinct client devices. A human physician manually reconciling medication lists on paper does not "force" a patient to select from mutually exclusive UI controls, does not apply a knowledge-base-augmented NLU pipeline to classify natural language responses, and does not automatically synchronize an update to a second person's device upon a GPS/sensor-detected arrival event. Accordingly, when the claim is considered as a whole, Applicant submits that the amended claim 1 is not directed to the "Certain Methods of Organizing Human Activity" grouping identified in the Office Action.
Regarding 2, The Examiner respectfully disagrees. Claims 1 and 15 recite a method and are not tied to any particular technological environment. Claim 8 recites the additional elements of one or more processors and memory, which are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computer components for enabling access to medical information or for performing generic computer functions, see Spec. at para. [0069]-[0071]) such that they amounts to no more than mere instructions to apply the exception using a generic computer components. Moreover, the claim as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e. managing personal behavior including following rules or instructions) but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to retrieve a plurality of data attributes, collect medication data and care provider information, display an identifier and an image of medication, present options for selection, receive responses, determine patient health condition and medication usage, synthesize one or more care optimizations, update the stored medication data, generate text output, present the generated text output for display, receive a selection of a care optimization, detect patient arrival, generate a notification and cause the care optimization to be performed in the manner described in the identified abstract idea, supra. These are all steps a human can perform by following a set of rules or instruction with or without the use of a computer.
Even assuming, arguendo, that the claim recites an abstract idea, the additional elements integrate that idea into a practical application. The Specification identifies specific technical problems with conventional management of patient medication: the process is "time-consuming and can be prone to errors, particularly when a patient is taking multiple medications" (see [0025]). Amended claim 1's forced-choice, image-augmented UI directly addresses the error-proneness problem by structuring data capture so that ambiguity is eliminated at the point of entry (only one adherence state may be selected per medication), while the knowledge-base-augmented NLU model addresses the accuracy problem by grounding entity recognition and text classification in a curated medical ontology rather than an unconstrained general-purpose language model improves the functioning of the computer… The amended claim 1 recites a specific, non-generic architecture… Applicant respectfully submits that meaningful limits from a technological environment can support integration into a practical application when the claim is limited to a specific application of the exception rather than merely reciting the idea at a high level of generality.
Regarding 3, The Examiner respectfully disagrees. This judicial exception is not integrated into a practical application. Claims 1 and 15 are not tied to any particular technological environment. Claim 8 recites the additional elements of one or more processors and memory, which are not exclusively defined by the applicant and are recited at a high-level of generality such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. The additional elements of a client device associated with a care provider and a client device associated with the patient are recited at a high level of generality (i.e. a general means to generate/present/display/receive data) and amount to extra solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. The additional elements of a user interface and a conversational medication-review user interface and a conversational interface application merely generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. The additional element of using one or more machine learning models comprising a natural language understanding (NLU) model augmented with a knowledge base including a medical dictionary and index search terms with associated dispositions to perform text classification and entity recognition and synthesize one or more care optimizations represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the one or more machine learning models comprising a natural language understanding (NLU) model augmented with a knowledge base including a medical dictionary and index search terms with associated dispositions to perform text classification and entity recognition and to synthesize one or more care optimizations merely confines the use of the abstract idea (i.e., the machine learning model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Moreover, the problem of the process is "time-consuming and can be prone to errors, particularly when a patient is taking multiple medications" is not a technical problem but a healthcare/administrative problem. The claim allows a user to select an option to indicate their compliance to medication regimen using an interface. This does not improve upon a functioning of a computer. There is nothing nongeneric in the claim. The Applicant specification at para. [0069]-[0071] states that the processor is a generic computer components for performing generic computer functions.
The Office Action's reliance on Recentive Analytics, Inc. v. Fox Corp. is inapplicable to the amended claim 1. In Recentive, the claims recited applying "known machine learning techniques" to a new data environment without any further specificity as to the model's architecture or its integration with other claimed elements. By contrast, the amended claim 1 specifies the type of model (NLU), a specific architectural augmentation of that model (a knowledge base comprising a medical dictionary and index search terms with associated dispositions), and the specific, structured nature of the input to that model (patient responses captured via a forced-choice, mutually-exclusive-control UI). This multi-step, structurally- specific integration is precisely the type of "additional elements" that MPEP 2106.04(d) and the USPTO's 2019 Revised Patent Subject Matter Eligibility Guidance identify as sufficient to integrate a judicial exception into a practical application, because the claim does not merely recite the idea "apply it" with a generic computer; it recites a specific manner of applying it that improves a specific technical process.
Regarding 4, The Examiner respectfully disagrees. As explained above, The additional element of using one or more machine learning models fails to add an inventive concept to the claims and cannot provide a practical application. The claim uses generic and known machine learning methods to process data. Machine learning technology is not being improved upon.
Rejection under 35 U.S.C. § 103
Regarding the rejection of claims 1-20, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
However, Owen does not teach or suggest that prior to the visit, a conversational medication-review user interface that, for each medication, "displays an identifier of the medication together with an image of the medication" and "presents a plurality of mutually exclusive selectable input controls configured to force the patient to select only one adherence state from a set comprising "taking" and "not taking "for the medication" or "using one or more machine learning models comprising a natural language understanding (NLU) model augmented with a knowledge base including a medical dictionary and index search terms with associated dispositions, performing text classification and entity recognition on the one or more patient responses to determine a patient health condition and current medication usage" as now recited by amended Claim 1… Furthermore, the cited portions of Moturu do not remedy the deficiencies of Owen…Moreover, the cited portions of De Vries do not remedy the deficiencies of Owen and Moturu… Furthermore, the cited portions of Moturu and De Vries do not remedy the deficiencies of Owen and fail to suggest using "one or more machine learning models comprising a natural language understanding (NLU) model augmented with a knowledge base including a medical dictionary and index search terms with associated dispositions" to "identify one or more medications from among the received medication data that are associated with outside sources and require attention" and generating, responsive to identifying the care provider, a user interface comprising "a notification banner indicating that one or more medications from outside sources need attention" and "a selectable control that, when selected, presents a medication reconciliation table listing, for each of the plurality of medications, an adherence status reported by the patient and a source of the report" or "generating a post-visit notification to the client device associated with the patient to confirm the updated medication data through the conversational interface application" as now recited by amended claim 15.
Regarding 1, The Examiner respectfully submits that new reference Johnson has been cited to teach this limitation. Please refer to the new rejection under 35 U.S.C. § 103. Given the broadest reasonable interpretation, the cited references in combination teach the claimed feature.
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this Office action. 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.
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Hanina (US 2011/0153360) discloses method and apparatus for verification of clinical trial adherence.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIZA TONY KANAAN whose telephone number is (571)272-4664. The examiner can normally be reached on Mon-Thu 9:00am-6:00pm ET.
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/L.T.K./Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683
