DETAILED ACTION
The present office action represents a nonfinal action on the merits.
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 .
Priority
This application claims the priority date of provisional application 63/413,142 of date October 4, 2022, a 371 of PCT /US2023/076010 of date October 4, 2023.
Status of Claims
Claims 1-20 are pending.
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 1-6 are drawn to a method, which is within the four statutory categories (i.e., manufacture.). Claims 7-13 are drawn to a method, which is within the four statutory categories (i.e., manufacture.). Claims 14-20 are drawn to a non-transitory computer-readable medium, which is within the four statutory categories (i.e., machine).
Claims 1-6 recite a method, comprising:
receiving user data describing a user associated with a therapy plan;
determining an achieved stage, from a defined set of stages associated with the therapy plan, based on the user data;
labeling the user data based on the achieved stage;
training a machine learning model to predict therapy progress based on the user data and the achieved stage; and
storing a data structure representing the machine learning model for runtime deployment.
Claims 7-13 recite a method, comprising:
receiving user data describing a user associated with a therapy plan;
generating a set of features by applying one or more preprocessing transformations to the user data;
generating a predicted therapy progress for the user by processing the set of features using a trained machine learning model, wherein the predicted therapy progress comprises a stage, of a defined set of stages, associated with the therapy plan; and
facilitating enrollment of the user in the therapy plan based on the predicted therapy progress.
Claims 14-20 recite a non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform an operation comprising:
receiving user data describing a user associated with a therapy plan;
generating a set of features by applying one or more preprocessing transformations to the user data;
generating a predicted therapy progress for the user by processing the set of features using a trained machine learning model, wherein the predicted therapy progress comprises a stage, of a defined set of stages, associated with the therapy plan; and
facilitating enrollment of the user in the therapy plan based on the predicted therapy progress.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity (e.g., gathering user information; managing user information, in this case receiving user data associated with a therapy plan.) managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP 2106.04(a)(2) and mathematical concepts (e.g., the machine learning model). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2-6, 8-13, and 15-20 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 an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
The dependent claims include additional limitations, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 7, and 14.
The additional elements from claim 14 include:
storing (apply it, MPEP 2106.05(f)).
The additional elements from claim 14 include:
a non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform an operation comprising (apply it, MPEP 2106.05(f)).
Claims 1-20 are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of “a non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform an operation comprising”, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g. see Specification Paragraphs [0024] and [0115]. (See MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea – for example, the recitation of storing data, which amounts to mere data gathering, (claims 1, 5, 6, 8, and 15), which amounts to insignificant extra-solution activity. (See MPEP 2106.05(g));
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., Paragraphs [0024] and [0115], of the Specification discloses that the additional elements (i.e., computer, processor, system) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g., MPEP 2106.05(d)(II):
Receiving data over a network, e.g., see Intellectual Ventures v. Symantec – similarly, the current invention receives user data associated with a therapy plan;
Storing and retrieving information in memory, e.g., see Versata Dev. Group, Inc., v. SAP Am., Inc. – the current invention recites storing a data structure.
Dependent claims 2-6, 8-13, and 15-20 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than those found in the independent claims.
Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. 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 machine learning to predict therapy progression 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 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shelly (U.S. Pub. No. 2022/00163690 A1) in view of Qureshi (U.S. Pub. No. 2015/0019259 A1).
Regarding claim 1, Shelly discloses a method, comprising:
receiving user data describing a user associated with a therapy plan (Paragraph [0007] discusses receiving patient information associated with the patient being provided a personalized PAP therapy recommendation.);
determining protocols associated with the therapy plan, based on the user data (Paragraphs [0027], [0036], and [0048] discuss providing the patient with personalized PAP therapy recommendations and PAP therapy analysis can be achieved using a variety of techniques including, but not limited to, monitoring by means of symptoms, questionnaires, overnight polysomnography (PSG) tests examining the Apnea-Hypopnea Index (AHI), home sleep tests, and other portable sleep apnea monitoring instruments, the system learns how users interact and completes tasks.);
labeling the user data based on the achieved stage (Paragraphs [0036]-[0046], [0048]-[0054], and FIG. 3 discuss obtain inputs provided by the user the predictive AI system will analyze the various information regarding the patient received as inputs from one or more of input devices and provide as output personalized PAP therapy recommendations for the patient, building PAP Personalization engine is done in series of steps that include: from data ingestion to, data preparation such as pre-processing stage for data segregation (Examiner is interpreting “pre-processing stage for data segregation” as labeling data based on the stage.); the data collected goes through a transformation process involving data pre-processing, feature engineering, feature selection, filter methods, wrapper/embedded methods and used in training the ML model, evaluating the model.);
training a machine learning model to predict therapy progress based on the user data and the achieved stage (Paragraph [0017] discusses a predictive artificial intelligence system is provided that is trained to: receive patient information obtained passively from one or more electronic devices associated with a patient; analyze the patient information; and determine from analyzing the patient information a personalized PAP therapy recommendation.); and
storing a data structure representing the machine learning model for runtime deployment (Paragraph [0046] discusses data collected actively from users and passively from devices is migrated to cloud/servers in a secured streaming channel via internet and stored in cloud/servers tech stack includes machine learning libraries, databases to store data and cloud infrastructure for running machine learning algorithms.).
Shelly does not explicitly disclose:
determining an achieved stage (Examiner notes that the prior art does not explicitly state “stage”, however, Paragraphs [0020], [0022], and [0032] of the Specification defines “stage”, the PAP therapy enrollment process can involve stages such as asking the user to respond to a questionnaire or survey (e.g., discussing self-reported symptoms of apnea), receiving a response, identifying and engaging with users who may benefit from PAP therapy (based on the survey responses), selecting a device, provisioning the device, and/or actually beginning therapy.), completing enrollment, completing a sleep study, etc.), from a defined set of stages.
Qureshi teaches:
determining an achieved stage, from a defined set of stages (Paragraphs [0022] and [0045]-[0063] discuss healthcare workflow and clinical pathways that create actions and protocols for care providers, for example, the subject is first recruited and enrolled into the system, care coordinator can then attempt to contact the subject. If the subject is reached successfully, a screening process can occur whereby the subject fills out a questionnaire, if the subject is determined to be eligible for the program, the care coordinator can inform the subject about the benefits and expectations about a suitable care program. If the subject agrees for enrollment into the program, the care coordinator can verify the subject's insurance eligibility and begin the enrollment process. If the subject declines enrollment into the program, the care coordinator can discuss primary care physicians and other care strategies for the subject, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelly to include, determining an achieved stage, from a defined set of stages, as taught by Qureshi, in order to address gaps in care and stratify patient populations to project costs of healthcare services to a healthcare institution. (Qureshi Abstract, Paragraph [0019]).
Regarding claims 2, 9, and 16 Shelly discloses wherein:
the therapy plan comprises a positive airway pressure (PAP) therapy, the defined set of protocols comprise a protocol for the PAP therapy, and the protocol indicates whether the user has begun the PAP therapy (Paragraphs [0002], [0032], [0036], [0048], [0055], and [0062] discuss providing positive airway pressure (PAP) therapy to a patient, and providing personalized PAP therapy recommendations to a patient based on subjective and objective inputs and the personalized dose-response and timing profile will be continuously updated as the user makes use of PAP system and more data is available to build an accurate profile and the predictive AI system provides for continuous adaptation as the user changes their health status (e.g. weight loss/gain, etc.), for example, subjective inputs from the user are pre-processed using Natural Language Processing models, as necessary, and then combined with the sensor and device data to feed into the machine learning algorithms, the model is deployed and integrated into system and the Predictive AI system learns how users interact with system and completes tasks, using user feedback to adjust the model and customize the person's future recommendations, it computes the benefit that PAP therapy received had on the user's sleep, e.g., decrease in AHI from predicted, arousal index, desaturation index, cardiac risks (e.g. sleep apnea and ejection fraction, nocturnal blood pressure oscillation, etc.), sleep quality, etc. so as to demonstrate to the patient the benefit(s) of a particular PAP therapy session.).
Shelly does not explicitly disclose:
the defined set of stages comprise a sequence of enrollment phases for the care, and the achieved stage indicates whether the user has begun the care.
Qureshi teaches:
the defined set of stages comprise a sequence of enrollment phases for the care, and the achieved stage indicates whether the user has begun the care (Paragraphs [0022] and [0045]-[0063] discuss healthcare workflow and clinical pathways that create actions and protocols for care providers, for example, the subject is first recruited and enrolled into the system, care coordinator can then attempt to contact the subject. If the subject is reached successfully, a screening process can occur whereby the subject fills out a questionnaire, if the subject is determined to be eligible for the program, the care coordinator can inform the subject about the benefits and expectations about a suitable care program. If the subject agrees for enrollment into the program, the care coordinator can verify the subject's insurance eligibility and begin the enrollment process. If the subject declines enrollment into the program, the care coordinator can discuss primary care physicians and other care strategies for the subject, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelly to include, the defined set of stages comprise a sequence of enrollment phases for the care, and the achieved stage indicates whether the user has begun the care, as taught by Qureshi, in order to address gaps in care and stratify patient populations to project costs of healthcare services to a healthcare institution. (Qureshi Abstract, Paragraph [0019]).
Regarding claim 3, Shelly does not explicitly disclose wherein labeling the user data based on the achieved stage comprises:
identifying a minimum enrollment phase defined as a positive result; and
labeling the user data based on whether the achieved stage satisfies the minimum enrollment phase.
Qureshi teaches:
wherein labeling the user data based on the achieved stage comprises:
identifying a minimum enrollment phase defined as a positive result (Paragraphs [0022] and [0045]-[0063] discuss healthcare workflow and clinical pathways that create actions and protocols for care providers, for example, the subject is first recruited and enrolled into the system, care coordinator can then attempt to contact the subject. If the subject is reached successfully, a screening process can occur whereby the subject fills out a questionnaire, if the subject is determined to be eligible for the program, the care coordinator can inform the subject about the benefits and expectations about a suitable care program. If the subject agrees for enrollment into the program, the care coordinator can verify the subject's insurance eligibility and begin the enrollment process. If the subject declines enrollment into the program, the care coordinator can discuss primary care physicians and other care strategies for the subject, etc.); and
labeling the user data based on whether the achieved stage satisfies the minimum enrollment phase (Paragraphs [0047]-[0049] discuss if the subject is determined to be eligible for the program, the care coordinator can inform the subject about the benefits and expectations about a suitable care program. If the subject agrees for enrollment into the program, the care coordinator can verify the subject's insurance eligibility and begin the enrollment process and the enrollment process of the system comprises providing the subject with educational materials and a health risk assessment and intake of a subject can be facilitated by a data mapping engine that can review subject data taken from any source in any terminology and map the subject data to a standard terminology used by the hosting clinic.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelly to include, wherein labeling the user data based on the achieved stage comprises: identifying a minimum enrollment phase defined as a positive result; and labeling the user data based on whether the achieved stage satisfies the minimum enrollment phase, as taught by Qureshi, in order to address gaps in care and stratify patient populations to project costs of healthcare services to a healthcare institution. (Qureshi Abstract, Paragraph [0019]).
Regarding claims 4, 11, and 18, Shelly discloses wherein the user data comprises one or more of:
(i) demographics of the user (Paragraph [0036] discusses subjective inputs provided by the user are related to demographics.),
(ii) an indication of sleep quality of the user (Paragraph [0038] discusses user input include sleep debt (subjective, based on daytime activity, including physical and cognitive stress, as well as the amount of sleep over the prior nights, if objective sleep and daytime tracking is not available).),
(iii) an indication of motivation of the user with respect to the therapy plan (Paragraphs [0062] discuss personalized dose-response and timing profile will be continuously updated as the user make use of system and more data is available to build an accurate profile.), or
(iv) one or more symptoms experienced by the user (Paragraph [0062] discusses the system provides for continuous adaptation as the user inputs information and changes their health status (e.g. weight loss/gain, etc.).).
Regarding claim 5, Shelly discloses further comprising:
applying one or more preprocessing transformations to the user data, prior to training the machine learning model (Paragraphs [0046]-[0054] discuss data collected from the user goes through a transformation process involving data pre-processing, feature engineering, feature selection, filter methods, wrapper/embedded methods and used in training the ML model.); and
storing transformation metadata defining the one or more preprocessing transformations along with the data structure for runtime deployment (Paragraphs [0046]-[0047] discuss data collected is stored in cloud/servers and includes machine learning libraries, databases to store data that goes through a transformation process involving data pre-processing, feature engineering, feature selection, filter methods for data segregation, training the model, improving the model, candidate model evaluation, model deployment, and performance monitoring.).
Regarding claim 6, Shelly discloses further comprising deploying the model based on the stored data structure and transformation metadata (Paragraphs [0046]-[0053] discuss predictive AI system is “built” in a series of steps: from data ingestion to, data preparation such as pre-processing stage for data segregation, training the model, improving the model, candidate model evaluation, model deployment, and performance monitoring.).
Regarding claim 7, Shelly discloses a method, comprising:
receiving user data describing a user associated with a therapy plan (Paragraph [0007] discusses receiving patient information associated with the patient being provided a personalized PAP therapy recommendation.);
generating a set of features by applying one or more preprocessing transformations to the user data (Paragraphs [0046]-[0052] discuss data collected goes through a transformation process involving data pre-processing, feature engineering, feature selection, filter methods.);
generating a predicted therapy progress for the user by processing the set of features using a trained machine learning model, wherein the predicted therapy progress comprises a protocol, associated with the therapy plan (Paragraphs [0032]-[0035], [0046]-[0052] discuss data collected goes through a transformation process involving data pre-processing, feature engineering, feature selection, filter methods.) inputs from the user are pre-processed using Natural Language Processing models, as necessary, and then combined with the sensor and device data to feed into the machine learning algorithms, for example, data from the PAP device (e.g. AutoCPAP) provides information about the objective AHI as well as the pressures at which the patient was treated by the PAP device which is important for the continued system learning, as you need to continue to generate severity data when the user is using the PAP device and the predictive AI system will provide recommendations to the users.); and
facilitating the user in the therapy plan based on the predicted therapy progress (Paragraph [0006] discusses improve adherence to PAP therapy by providing personalized PAP therapy recommendations to the patient. By personalizing the recommended PAP therapy for a given night, the overall comfort of the device is improved (especially usage-fatigue related discomforts, such as skin red marks, etc.) and users are able to achieve maximum benefit from the PAP therapy while minimizing usage time. This leads to an increased adherence to the PAP therapy by saving the users from avoidable side effects and improved outcomes based on optimizing the times when the PAP therapy is used.).
Shelly does not explicitly disclose:
progress comprises a stage, of a defined set of stages; and
facilitating enrollment of the user in the therapy plan.
Qureshi teaches:
progress comprises a stage, of a defined set of stages (Paragraphs [0022], [0045]-[0063], and [0072], [0075], and [0105] discuss healthcare workflow and clinical pathways that create actions and protocols for care providers, the enrollment process; the system can score and rank patients by assigning scores to patients based on, for example, the gravity of a subject's condition, the risk of a subject in developing a condition, and where a subject falls on the progression of a care protocol which can allow for the prevention of redundant or extraneous interventions, the system determines corresponding interventions between the regimen of interventions the subject is undergoing and the interventions provided by the system.).
facilitating enrollment of the user in the therapy plan (Paragraphs [0022] and [0045]-[0063] discuss healthcare workflow and clinical pathways that create actions and protocols for care providers, for example, the subject is first recruited and enrolled into the system, care coordinator can then attempt to contact the subject. If the subject is reached successfully, a screening process can occur whereby the subject fills out a questionnaire, if the subject is determined to be eligible for the program, the care coordinator can inform the subject about the benefits and expectations about a suitable care program. If the subject agrees for enrollment into the program, the care coordinator can verify the subject's insurance eligibility and begin the enrollment process.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelly to include, progress comprises a stage, of a defined set of stages; and facilitating enrollment of the user in the therapy plan, as taught by Qureshi, in order to address gaps in care and stratify patient populations to project costs of healthcare services to a healthcare institution. (Qureshi Abstract, Paragraph [0019]).
Regarding claims 8 and 15, Shelly discloses further comprising accessing the trained machine learning model based on a stored data structure and transformation metadata defining the one or more preprocessing transformations (Paragraphs [0051]-[0052] discuss data collected goes through a transformation process involving data pre-processing, feature engineering, feature selection, filter methods, wrapper/embedded methods and used in training the ML model, evaluating the model. PAP Personalization engine model: Subjective inputs from the user are pre-processed using Natural Language Processing models, as necessary, and then combined with the sensor and device data to feed into the machine learning algorithms.).
Regarding claims 10 and 17, Shelly discloses wherein:
the defined set of protocols, and the predicted therapy progress indicates whether the user is predicted to reach a protocol (Paragraphs [0027], [0036], and [0048] discuss providing the patient with personalized PAP therapy recommendations and PAP therapy analysis can be achieved using a variety of techniques including, but not limited to, monitoring by means of symptoms, questionnaires, overnight polysomnography (PSG) tests examining the Apnea-Hypopnea Index (AHI), home sleep tests, and other portable sleep apnea monitoring instruments, the system learns how users interact and completes tasks.).
Shelly does not explicitly disclose:
the defined set of stages comprise a sequence of enrollment phases, and indicates whether the user is predicted to reach a minimum enrollment phase defined as a positive result.
Qureshi teaches:
the defined set of stages comprise a sequence of enrollment phases, and indicates whether the user is predicted to reach a minimum enrollment phase defined as a positive result (Paragraphs [0022] and [0045]-[0063] discuss healthcare workflow and clinical pathways that create actions and protocols for care providers, for example, the subject is first recruited and enrolled into the system, care coordinator can then attempt to contact the subject. If the subject is reached successfully, a screening process can occur whereby the subject fills out a questionnaire, if the subject is determined to be eligible for the program, the care coordinator can inform the subject about the benefits and expectations about a suitable care program. If the subject agrees for enrollment into the program, the care coordinator can verify the subject's insurance eligibility and begin the enrollment process.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelly to include, the defined set of stages comprise a sequence of enrollment phases, and indicates whether the user is predicted to reach a minimum enrollment phase defined as a positive result, as taught by Qureshi, in order to address gaps in care and stratify patient populations to project costs of healthcare services to a healthcare institution. (Qureshi Abstract, Paragraph [0019]).
Regarding claims 12 and 19, Shelly discloses further comprising:
refining the trained machine learning model based on whether the user has completed protocols in the therapy plan.
Shelly does explicitly disclose further comprising:
determining whether the user has completed enrollment in the therapy plan; and
updating the protocol based on whether the user has completed enrollment in the therapy plan.
Qureshi teaches:
determining whether the user has completed enrollment in the therapy plan (Paragraphs [0022] and [0045]-[0063] discuss healthcare workflow and clinical pathways that create actions and protocols and after a subject has been enrolled into the system, a subject-specific care protocol can be generated to manage the subject's treatment.);
updating the protocol based on whether the user has completed enrollment in the therapy plan (Paragraphs [0050] and [0056] discuss after a subject has been enrolled into the system, a subject-specific care protocol can be generated to manage the subject's treatment. The care protocol can be generated by analyzing subject data as the data are taken in by the system and the system can additionally update a set of rules within the system based on a clinical outcome obtained for the subject by the care protocol.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelly to include, determining whether the user has completed enrollment in the therapy plan; and updating the protocol based on whether the user has completed enrollment in the therapy plan, as taught by Qureshi, in order to address gaps in care and stratify patient populations to project costs of healthcare services to a healthcare institution. (Qureshi Abstract, Paragraph [0019]).
Regarding claims 13 and 20, Shelly discloses wherein facilitating the user in the therapy plan based on the predicted therapy progress comprises, in response to determining that the predicted therapy progress satisfies defined criteria, outputting the predicted therapy progress to a subject matter expert (Paragraphs [0019] and [0053]-[0055] discuss personalized PAP therapy recommendations and the system learns how users interact with system and completes tasks, using user feedback to adjust the model and customize the person's future recommendations, the predictive AI System computes the benefit that PAP therapy received had on the user's sleep, e.g., decrease in AHI from predicted, arousal index, desaturation index, cardiac risks (e.g. sleep apnea and ejection fraction, nocturnal blood pressure oscillation, etc.), sleep quality, etc. and may provide such information as output which is displayed to the user so as to demonstrate to the patient the benefit(s) of a particular PAP therapy session.).
Shelly does not explicitly disclose:
wherein facilitating enrollment of the user in the therapy plan based on the predicted progress.
Qureshi teaches:
wherein facilitating enrollment of the user in the therapy plan based on the predicted therapy progress (Paragraphs [0022], [0045]-[0063], and [0072] discuss healthcare workflow and clinical pathways that create actions and protocols and if the subject is reached successfully, a screening process can occur whereby the subject fills out a questionnaire, if the subject is determined to be eligible for the program, the care coordinator can inform the subject about the benefits and expectations about a suitable care program and enrollment into the program; the system can modify care protocols based on changes in a subject's condition over time. This allows for optimization of therapy and avoidance of unnecessary therapeutic interventions. Similarly, the ability of the system to recognize where a subject falls in the progression of a care protocol can allow for the prevention of redundant or extraneous interventions.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelly to include, wherein facilitating enrollment of the user in the therapy plan based on the predicted therapy progress, as taught by Qureshi, in order to address gaps in care and stratify patient populations to project costs of healthcare services to a healthcare institution. (Qureshi Abstract, Paragraph [0019]).
Regarding claim 14, Shelly discloses the same limitations as claim 7 with the addition of a processor and a non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform an operation comprising (Paragraph [0026] discusses a non-transitory machine readable medium, for data and program code storage such as in the fashion of an internal storage area of a computer, and can be volatile memory or nonvolatile memory.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAWN TRINAH HAYNES whose telephone number is (571)270-5994. The examiner can normally be reached M-F 7:30-5:15PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham can be reached on (571)272-8109. The fax phone number for the organization where this application or proceeding is assigned is (571)273-8300.
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/DAWN T. HAYNES/
Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
