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 .
Status of the Claims
Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 7, Claim 7 recites “the updated treatment plan.” There is insufficient antecedent basis for this limitation in the claim. Claim 1 (from which Claim 7 depends), recites “updating…at least one subsequent exposure assignment or ritual resistance task” but does not recite, for example, “updating the treatment plan” or “generating an updated treatment plan.” In the interest of compact prosecution, Examiner will interpret Claim 7 as reciting “the treatment plan being updated to include new exposure tasks….” Appropriate correction 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.
Step 1
Claims 1-20 are within the four statutory categories. Claims 1-10 are drawn to a method for generating a patient treatment plan, which is within the four statutory categories (i.e. process). Claims 11-20 are drawn to a system for generating a patient treatment plan, which is within the four statutory categories (i.e. machine).
Prong 1 of Step 2A
Claim 1, which is representative of the inventive concept, recites: A computer-implemented method for managing exposure and response prevention (ERP) treatment healthcare workflows of a patient, the computer-implemented method comprising instructions stored on a non-transitory computer-readable storage medium and executed on a computing device provided with a hardware processor and a memory, the method comprising:
receiving, by the computing device, healthcare data from a plurality of sources, wherein the healthcare data includes at least: symptoms reported by the patient, treatment history, and clinician-provided instructions;
processing the healthcare data to generate a treatment plan, wherein the treatment plan includes exposure assignments tailored to the patient, a sequence of ritual resistance tasks, and anxiety level monitoring parameters;
presenting, via a graphical user interface (GUI) rendered on the computing device, at least one interactive display to the patient, wherein the GUI is configured to:
display progress metrics related to completion of the exposure assignments and ritual resistance tasks;
highlight upcoming tasks aligned with predicted anxiety windows; and
provide clinician feedback generated in part by analyzing patient interaction data using a trained machine learning model;
receiving, by the computing device, user input from the patient or a clinician indicating completion, modification, or deferral of specific tasks within the treatment plan; and
updating, by the hardware processor according to predefined ERP treatment guidelines, at least one subsequent exposure assignment or ritual resistance task in response to the received user input.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the limitations of receiving healthcare data, processing the healthcare data to generate a treatment plan, displaying progress metrics, upcoming tasks, and clinician feedback regarding the treatment plan, receiving user input regarding the tasks of the treatment plan, and updating subsequent tasks of the treatment plan in response to the user input recites following rules or instructions for evaluating and treating a patient, and monitoring the progress of the treatment of a patient), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea(s) are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claim 11 is identical as the abstract idea for Claim 1, because the only difference between Claims 1 and 11 is that Claim 1 recites a method, whereas Claim 11 recites a system.
Dependent Claims 2-10 and 12-20 include other limitations, for example Claims 2 and 12 recite limitations more narrowly defining the process of analyzing the healthcare data and generating the treatment plan, Claims 3 and 13 recite using real-time input and adjusting a schedule of the treatment plan, Claim 4 recites correlating patient anxiety with historical treatment data, Claim 5 recites providing different views for different users, Claim 6 recites receiving user input regarding an exposure assignment, Claims 7 and 15 recite deriving new tasks from a patient assessment, Claims 8 and 16 recite data regarding the patient progress, Claim 9 recites providing automated alerts to a clinician when the patient does not complete an assignment within a timeframe, Claim 10 recites obtaining the healthcare data from external sources, Claim 14 recites the contents of a database, Claim 17 recites storing timestamped resistance data, Claim 18 recites notifying clinicians when the patient task completion rate falls below a threshold, Claim 19 recites recommending additional assignments based on patient progress and clinician parameters, and Claim 20 recites the contents of an administrator dashboard, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04, and/or do not further narrow the abstract idea and instead only recite additional elements, which will be further addressed below. Hence dependent Claims 2-10 and 12-20 are nonetheless directed towards fundamentally the same abstract idea as independent Claim 1.
Hence Claims 1-20 are directed towards the aforementioned abstract idea.
Prong 2 of Step 2A
Claims 1 and 11 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the computing device including the processor and memory, non-transitory computer-readable storage medium, the GUI, and the trained machine learning model) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the computer hardware and its display of the interactive GUI, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see [0089] of the as-filed Specification, and see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language of the trained machine learning model, which amounts to limiting the abstract idea to the field of machine learning, e.g. see MPEP 2106.05(h).
Additionally, dependent Claims 2-10 and 12-20 include other limitations, but these limitations also amount to no more than generally linking the abstract idea to a particular technological environment or field of use (e.g. the limitations defining the types of data processed recited in dependent Claims 2-8, 12-17, and 19-20), and/or adding insignificant extra-solution activity to the abstract idea (e.g. the limitations of the sources for the obtained data recited in dependent Claim 10), and/or do not include any additional elements beyond those already recited in independent Claims 1 and 11, and hence also do not integrate the aforementioned abstract idea into a practical application.
Hence Claims 1-20 do not include additional elements that integrate the judicial exception into a practical application.
Step 2B
Claims 1 and 11 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the computing device including the processor and memory, non-transitory computer-readable storage medium, the GUI, and the trained machine learning model), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the additional elements comprise limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The present Specification expressly disclosing that the structural additional elements are well-understood, routine, and conventional in nature:
[0089] of the as-filed Specification discloses that the additional elements (i.e. the computing device including the processor and memory, and non-transitory computer-readable storage medium) comprise a plurality of different types of generic computing systems;
Relevant court decisions: The functional limitations interpreted as additional elements are analogized to the following examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
A web browser’s back and forward button functionality, e.g. see Internet Patent Corp. v. Active Network, Inc. – similarly, the additional elements recite an interactive GUI rendered on the computing device.
Dependent Claims 2-10 and 12-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly amount to receiving or transmitting data over a network (e.g. the sources of the data recited in dependent Claim 10), and/or the limitations recited by the dependent claims do not recite any additional elements not already recited in independent Claims 1 and 11, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1-20 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 are rejected under 35 U.S.C. 103 as being unpatentable over Zakim (US 2008/0177578) in view of Longmire (US 2021/0358627), further in view of Hoogerwerf (“Personalized exposure and experience sampling method feedback versus exposure as usual for obsessive-compulsive disorder: a study protocol for a randomized controlled trial,” BMC (2024) 25:43, published January 12, 2024) and Garriga Calleja (US 2023/0104450).
Regarding Claim 1, Zakim teaches the following: A computer-implemented method for managing treatment healthcare workflows of a patient, the computer-implemented method comprising instructions stored on a non-transitory computer-readable storage medium and executed on a computing device provided with a hardware processor and a memory, the method comprising:
receiving, by the computing device, healthcare data from a plurality of sources (The system receives patient data from a patient input, e.g. see Zakim [0109], a historical database, e.g. see Zakim [0110] and [0118], and a number of expert analytic systems, e.g. see Zakim [0103].), wherein the healthcare data includes at least: symptoms reported by the patient, treatment history, and clinician-provided instructions (The received patient data includes a chief complaint (i.e. symptoms), e.g. see Zakim [0109], a complete patient medical history including physical examinations, imaging data, and laboratory data (i.e. any of the aforementioned data elements may be interpreted as treatment history), and tracks patient compliance with physician instructions, e.g. see Zakim [0107]-[0108], [0211], and [0214].);
processing the healthcare data to generate a treatment plan (The system utilizes the patient data to generate a treatment plan for the patient, e.g. see Zakim [0184]-[0185].);
receiving, by the computing device, user input from the patient or a clinician indicating completion, modification, or deferral of specific tasks within the treatment plan (The system receives physician input regarding the progression (i.e. completion) of the patient, e.g. see Zakim [0184], [0188], and [0190]-[0191].); and
updating, by the hardware processor according to predefined treatment guidelines, at least one subsequent task in response to the received user input (The system evaluates the obtained data using appropriate guidelines to obtain a definitive diagnosis and a corresponding treatment, e.g. see Zakim [0134], [0137], [0163], [0184]-[0186], and further generates a new set of guidelines for monitoring the patient’s progress during treatment when the physician enters a new prescription (i.e. treatment), e.g. see Zakim [0190].).
But Zakim does not teach and Longmire teaches the following:
presenting, via a graphical user interface (GUI) rendered on the computing device, at least one interactive display to the patient (The system displays an interface for a patient that receives patient input, e.g. see Longmire [0042], Figs. 4, 15, and 17.), wherein the GUI is configured to:
display progress metrics (The system displays available tasks and completed tasks, e.g. see Longmire [0042], Fig. 4.);
highlight upcoming tasks (The system displays available (i.e. upcoming) tasks, e.g. see Longmire [0042], Fig. 4.); and
provide clinician feedback generated in part by analyzing patient interaction data using a trained machine learning model (The system includes a machine learning model that enables users to perform risk predictions for patients, e.g. see Longmire [0045]-[0047], Figs. 7A-7B, wherein the predicted risks are displayed to the patient in the form of various alerts that may be shared among caregivers and patients, e.g. see Longmire [0042], [0050]-[0052], and [0064], Fig. 4.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify Zakim to incorporate the patient GUI displaying the metrics as taught by Longmire in order to enable improved patient care and communication, e.g. see Longmire [0003]-[0004] and [0008]-[0009].
But the combination of Zakim and Longmire does not teach and Hoogerwerf teaches the following:
wherein the treatment healthcare workflows are for exposure and response prevention (ERP) (The system includes a treatment for OCD that is tailored to an individual patient, wherein the treatment includes exposure and response prevention (ERP) as a first step, e.g. see Hoogerwerf Section “Introduction,” pgs. 1-2.);
wherein the treatment plan includes exposure assignments tailored to the patient, a sequence of ritual resistance tasks, and anxiety level monitoring parameters (The ERP treatment is tailored to a patient, wherein the treatment includes planned and repeated systematic confrontation (i.e. a sequence of ritual resistance tasks) with internal and external fear-provoking cues (i.e. anxiety level monitoring parameters), e.g. see Hoogerwerf Section “Interventions” pgs. 4-5.);
wherein the predefined treatment guidelines are ERP treatment guidelines, and wherein the at least one subsequent task is an exposure assignment or ritual resistance task (The treatment includes planned and repeated systematic confrontation (i.e. a sequence of ritual resistance tasks) with internal and external fear-provoking cues, e.g. see Hoogerwerf Section “Interventions” pgs. 4-5. Furthermore, patients who received the tailored ERP treatments are compared to patients who receive ERP treatments according to current national guidelines for OCD, e.g. see Hoogerwerf Section “Control condition: exposure with response prevention as usual,” pg. 4.);
wherein the progress metrics relate to completion of the exposure assignments and ritual resistance tasks (The system tracks patients as they perform/engage with the treatment sessions, e.g. see Hoogerwerf Section “Experimental condition: exposure with response prevention with NiceDay,” pgs. 4-5.);
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Zakim and Longmire to incorporate the personalized ERP treatments as taught by Hoogerwerf in order to improve patient care, e.g. see Hoogerwerf Section “Introduction,” pgs. 2-3.
But the combination of Zakim, Longmire, and Hoogerwerf does not teach and Garriga Calleja teaches the following:
wherein the upcoming tasks are aligned with predicted anxiety windows (The system predicts that a user will have an anxiety status in a future time, and modifies content presented to the user (i.e. tasks aligned with predicted anxiety windows) in response, e.g. see Garriga Callega [0027].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Zakim, Longmire, and Hoogerwerf to incorporate the tasks aligned with predicted anxiety as taught by Garriga Calleja in order to improve the patient’s mental state, e.g. see Garriga Calleja [0021] and [0033].
Regarding Claim 2, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 1, and Garriga Calleja and Hoogerwerf further teach the following: The method of claim 1, wherein processing the healthcare data to generate a treatment plan includes:
applying, by the hardware processor, the trained machine learning model configured to analyze the healthcare data to:
detect patterns indicative of anxiety triggers of the patient (The system includes an analysis unit that is realized as a machine learning system, wherein the analysis unit detects changes in patient anxiety over time based on various data such as reduced physical activity or sleep (i.e. triggers), e.g. see Garriga Calleja [0027]-[0029].) relevant to exposure and response prevention therapy (The patients evaluated include patients suffering from obsessive-compulsive disorders (OCD), e.g. see Garriga Calleja [0049], wherein ERP is a treatment for patients suffering from OCD, e.g. see Hoogerwerf Section “Introduction,” pg. 2.);
predict changes in an anxiety level of the patient over a defined timeframe (The system includes an analysis unit that is realized as a machine learning system, wherein the analysis unit predicts changes in data over time, e.g. see Garriga Calleja [0029], wherein the data includes changes in anxiety over time, e.g. see Garriga Calleja [0027].); and
determine personalized modifications (The system includes an analysis unit that is realized as a machine learning system, wherein the analysis unit predicts changes in data over time, wherein the system changes the content presented to the user based on the predicted change, e.g. see Garriga Calleja [0027]-[0029].) to an ERP treatment plan based on probabilistic inferences drawn from the detected patterns (The patients evaluated include patients suffering from obsessive-compulsive disorders (OCD), e.g. see Garriga Calleja [0049], wherein ERP is a treatment for patients suffering from OCD, e.g. see Hoogerwerf Section “Introduction,” pg. 2.); and
generating, based on an output of the trained machine learning model, a treatment plan that is specific to ERP therapy (The system modifies the content that should be provided to patients based on the determinations made by the machine learning system, e.g. see Garriga Calleja [0027]-[0029], wherein the content provided to patients suffering from OCD includes an ERP therapy, e.g. see Hoogerwerf Section “Introduction,” pg. 2.), wherein the treatment plan comprises:
exposure assignments tailored to the patient's identified anxiety triggers (The ERP treatment is tailored to a patient, wherein the treatment includes exposure to patient triggers, e.g. see Hoogerwerf Section “Interventions” pgs. 4-5.);
a sequence of ritual resistance tasks configured to gradually reduce compulsive responses (The ERP treatment is tailored to a patient, wherein the treatment includes planned and repeated systematic confrontation (i.e. a sequence of ritual resistance tasks), e.g. see Hoogerwerf Section “Interventions” pgs. 4-5.); and
anxiety level monitoring parameters that adapt in real time according to predictive outputs of the machine learning model (The ERP treatment is tailored to a patient, wherein the treatment includes planned and repeated systematic confrontation (i.e. a sequence of ritual resistance tasks) with internal and external fear-provoking cues (i.e. anxiety level monitoring parameters), e.g. see Hoogerwerf Section “Interventions” pgs. 4-5, wherein the treatment is provided in real time, e.g. see Hoogerwerf Section “Introduction,” pg. 3.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Zakim and Longmire to incorporate utilizing the machine learning system to determine and modify the ERP treatment as taught by Hoogerwerf and Garriga Calleja in order to improve the patient’s care, e.g. Hoogerwerf Section “Introduction,” pgs. 2-3, and to improve the patient’s mental state, e.g. see Garriga Calleja [0021] and [0033].
Regarding Claim 3, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 1, and Garriga Calleja further teaches the following: The method of claim 1, wherein the trained machine learning model is further invoked to:
incorporate real-time input as additional data points to refine predicted anxiety trajectories (The system acquires patient data in real-time to train (i.e. refine) a machine learning model such as a convolutional neural network, e.g. see Garriga Calleja [0053]-[0054], wherein the machine learning model is used to predict future patient anxiety states, e.g. see Garriga Calleja [0027].); and
dynamically adjust a schedule and an intensity of the treatment plan for future exposure tasks (The machine learning model predicts patient anxiety states in the future, for example predicting that a patient will have a “step up” in terms of their symptoms in the next two weeks, wherein the system further adjusts the contents presented to the patient based on the prediction, e.g. see Garriga Calleja [0027]. That is, the system determines when to change the type of content provided to the patient based on the machine learning prediction.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Zakim, Longmire, and Hoogerwerf to incorporate utilizing the machine learning system to determine and modify the treatment as taught by Garriga Calleja in order to improve the patient’s mental state, e.g. see Garriga Calleja [0021] and [0033].
Regarding Claim 4, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 1, and Garriga Calleja further teaches the following:
The method of claim 1, wherein processing the healthcare data includes correlating patient- reported anxiety levels with historical treatment data to determine an optimal exposure intensity (The training of the machine learning model includes coupling patient historical data with ground-truth information regarding symptom severity, e.g. see Garriga Calleja [0057], wherein the machine learning model determines the type of content to provide to the patient, e.g. see Garriga Calleja [0027].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Zakim, Longmire, and Hoogerwerf to incorporate utilizing historical patient and treatment data to determine and modify the treatment as taught by Garriga Calleja in order to improve the patient’s mental state, e.g. see Garriga Calleja [0021] and [0033].
Regarding Claim 5, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 1, and Longmire further teaches the following:
The method of claim 1, wherein the GUI includes separate views optimized for patients, clinicians, and administrators, each tailored to display role-specific information (The system includes easily configurable role-based dashboards, e.g. see Longmire [0068], wherein the system includes modules for different users including a caregiver module, a physician module, and a patient module, e.g. see Longmire [0033].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Zakim, Hoogerwerf, and Garriga Calleja to incorporate the role-based displays and modules as taught by Longmire in order to improve care team communication and coordination, e.g. see Longmire [0068].
Regarding Claim 6, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 1, and Hoogerwerf further teaches the following:
The method of claim 1, wherein the received input includes a user-selected indication of ritual resistance or submission during a specific exposure assignment (The interventions for the ERP are agreed upon (i.e. selected) in advance and tailored to the particular patient, e.g. see Hoogerwerf Section “Interventions” and “Experimental condition: exposure with response prevention with NiceDay,” pgs. 4-5.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Zakim, Longmire, and Garriga Calleja to incorporate the agreed upon interventions as taught by Hoogerwerf in order to improve the patient’s care, e.g. Hoogerwerf Section “Introduction,” pgs. 2-3.
Regarding Claim 7, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 1, and Hoogerwerf further teaches the following:
The method of claim 1, wherein the updated treatment plan includes new exposure tasks derived from a hierarchy of fears established during an initial patient assessment (The system enables changes (i.e. updates) to be made to the patient interventions, e.g. see Hoogerwerf Section “Interventions” and “Criteria for discontinuing or modifying allocated interventions,” pg. 5, wherein the interventions include planned and repeated systematic confrontation with internal and external fear-provoking cues, e.g. see Hoogerwerf Section “Interventions” and “Exposure with response prevention,” pg. 4.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Zakim, Longmire, and Garriga Calleja to incorporate the interventions based on patient fears as taught by Hoogerwerf in order to improve the patient’s care, e.g. Hoogerwerf Section “Introduction,” pgs. 2-3.
Regarding Claim 8, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 1, and Longmire further teaches the following:
The method of claim 1, wherein the GUI includes a visual progress tracker that graphically represents the patient's completion of assigned tasks over time (The system displays a graphic showing available tasks and completed tasks over time, e.g. see Longmire [0042], Fig. 4.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Zakim, Hoogerwerf, and Garriga Calleja to incorporate patient progress display as taught by Longmire in order to improve care team communication and coordination, e.g. see Longmire [0068].
Regarding Claim 9, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 1, and Zakim further teaches the following:
The method of claim 1, further comprising providing automated alerts to a clinician when a patient fails to complete an assigned task within a designated timeframe (The system tracks patient compliance and sends an alert to a physician if the patient is not in compliance within an interval, for example the system alerts a physician that the patient has not provided testing results within the proper interval for treatment, e.g. see Zakim [0211].).
Regarding Claim 10, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 1, and Zakim further teaches the following:
The method of claim 1, wherein the healthcare data is supplemented by external sources, including medication records, clinician notes, and appointment schedules (The system receives patient medication data, e.g. see Zakim [0131], physician inputs and findings, e.g. see Zakim [0177], and patient compliance with actions including follow-up visits, e.g. see Zakim [0210]-[0211].).
Regarding Claims 11-13, the limitations of Claims 11-13 are substantially similar to those claimed in Claims 1-3, with the sole difference being that Claims 1-3 recite a method, whereas Claims 11-13 recite a system. Specifically pertaining to Claims 11-13, Examiner notes that Zakim teaches a method and a system, e.g. see Zakim [0001], and hence the grounds of rejection provided above for Claims 1-3 are similarly applied to Claims 11-13.
Regarding Claim 14, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 11, and Zakim and Longmire further teach the following:
The system of claim 11, wherein the database includes structured data fields for patient anxiety metrics, treatment goals, and task progress tracking (The system tracks various data including emotional reactions including anxiety, e.g. see Longmire [0043], patient goals, e.g. see Zakim [0190] and [0228], and progress, e.g. see Zakim [0184], [0190], and [0199].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Zakim, Hoogerwerf, and Garriga Calleja to incorporate the anxiety tracking as taught by Longmire in order to improve patient outcomes, e.g. see Longmire [0004].
Regarding Claims 15-16, the limitations of Claims 15-16 are substantially similar to those claimed in Claims 7-8, with the sole difference being that Claims 7-8 recite a method, whereas Claims 15-16 recite a system. Specifically pertaining to Claims 15-16, Examiner notes that Zakim teaches a method and a system, e.g. see Zakim [0001], and hence the grounds of rejection provided above for Claims 7-8 are similarly applied to Claims 15-16.
Regarding Claim 17, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 11, and Hoogerwerf further teaches the following:
The system of claim 11, wherein the computing device stores ritual resistance data as timestamped records, enabling clinicians to analyze trends over time (The system obtains patient measurements at various times including at baseline, and at least 5, 10, 15, and 20 weeks of treatment, e.g. see Hoogerwerf Section “Introduction” and “Trial design,” pg. 3.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Zakim, Longmire, and Garriga Calleja to incorporate the data being obtained at specific times as taught by Hoogerwerf in order to investigate the effectiveness of EPR treatments over time, e.g. Hoogerwerf Section “Introduction” and “Objectives,” pg. 3.
Regarding Claim 18, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 11, and Zakim further teaches the following:
The system of claim 11, wherein the computing device generates notifications for clinicians when patient task completion rates fall below a predefined threshold (The system tracks patient compliance and sends an alert to a physician if the patient is not in compliance within an interval, for example the system alerts a physician that the patient has not provided testing results within the proper interval for treatment, e.g. see Zakim [0211]. That is, the predefined threshold for the completion rate is interpreted as “not completed within an interval.”).
Regarding Claim 19, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 11, and Hoogerwerf further teaches the following:
The system of claim 11, wherein the treatment plan updates include algorithmically generated recommendations for new exposure assignments based on patient progress and clinician-defined parameters (The system enables changes to be made to the patient interventions (i.e. new exposure assignments), for example if the interventions are a risk for patient health or safety (i.e. clinician-defined parameters), and/or wherein the study may be suspended (i.e. based on patient progress through the study), e.g. see Hoogerwerf Section “Interventions” and “Criteria for discontinuing or modifying allocated interventions,” pg. 5, wherein the interventions include planned and repeated systematic confrontation with internal and external fear-provoking cues, e.g. see Hoogerwerf Section “Interventions” and “Exposure with response prevention,” pg. 4.).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja in view of Albert (US 2011/0246220).
Regarding Claim 20, the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja teaches the limitations of Claim 1, but does not teach and Albert teaches the following:
The system of claim 11, wherein the GUI includes an administrator dashboard for managing user roles, clinician assignments, and audit trails of treatment modifications (The system generates displays (i.e. GUIs) for case administrators and care managers, wherein a care manager can grant access rights and permissions for a patient care plan (i.e. managing user roles), add a new user to a care team (i.e. clinician assignments), and further can view and edit a patient care plan (i.e. treatment modifications), e.g. see Albert [0221]-[0223].).
Furthermore, before the effective filing date, it would have been obvious to modify the combination of Zakim, Longmire, Hoogerwerf, and Garriga Calleja to incorporate the administrator dashboard functions as taught by Albert in order to strengthen the communication between care team members, reduce healthcare costs, and improve patient outcomes, e.g. see Albert [0090].
Conclusion
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/JOHN P GO/Examiner, Art Unit 3681