DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Acknowledgements
This communication is in response to Remarks filed 02/25/2026.
Claims 1, 8, 15 are amended.
Claims 1-20 are currently pending and have been examined.
Claims 1-20 have been rejected as follows.
Drawings
The drawings are objected to Fig.9 text is unclear. 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. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. 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 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, 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 an apparatus, system and, and method for predicting likelihood of non-adherence and initiating an action.
The limitations of receiving, real-time compliance data associated with a user profile identifier, wherein the real-time recommendation compliance data (i) originates at least in part from […] and (ii) comprises temporal sequence data that indicates (a) a set of one or more action data items indicating a recommended course of action for the user profile identifier, and (b) a classification associated with the set of one or more action data items; generating […], and based at least in part on (i) a duration of elapsed […] time since provision of the recommendation set of one or more action data items and (ii) the content type classification associated with the set of one or more action data items recommendation, an adaptive temporal-based prediction score representative of a likelihood of non-compliance with respect to the set of one or more action data items recommendation, wherein the (i) the adaptive temporal-based prediction score is configured to incrementally update based on the duration of elapsed […] time since provision of the set of one or more action data items, and (ii) […] based at least in part on: one or more temporal features associated with historical compliance data and the set of one or more action data items, (a) associated with historical transaction data and (b) associated with the recommendation, or (ii) actions performed in accordance with the historical transaction data associated with the user profile identifier; progressing, […] and based at least in part on the duration of elapsed […] time since provision of the set of one or more action data items, the one or more temporal features; generating, […], to modify the adaptive temporal-based prediction score by an increment associated with a defined interval, and responsive to determining that the duration of elapsed […] time since provision of the set of one or more action data items exceeds a defined interval, a new instance of the adaptive temporal-based prediction score, responsive to determining that the new instance of the adaptive temporal-based prediction score exceeds a threshold, initiating, […], performance of one or more prediction-based actions, and adjusting […] and based on real-time data indicating one or more compliance results associated with user behaviors corresponding to the one or more prediction-based actions and the progressed one or more temporal features, one or more parameters […] 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 recitation of generic computer components. That is, other than reciting a system implemented by computing entity with a processor and a memory (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the processor and memory, this claim encompasses a person looking at recommendation data and patient data, determining a likelihood of non-adherence and initiating an action based on the score in the manner described in the identified abstract idea, supra. The Examiner notes that certain “methods 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” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of (claim 1, 8, 15) a remote computing entity, a network, and (claim 8 and 15) a processor and a memory that implements the identified abstract idea. The processor with a memory, computing entity and network is not described by the applicant and is recited at a high-level of generality (i.e., a generic computer and network performing a generic computer functions of computing, determining, and selecting) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does 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.
The claim further recites the additional element of using the trained machine learning model to predict scores representative of likelihood of non-adherence. 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.
The claim further recites “predictive machine learning model has been trained” When given its broadest reasonable interpretation in light of the disclosure, the training of a machine learning model represents the creation of mathematical interrelationships between data (see, e.g., Spec. Para. 0033) “The training data set may be used to identify features in the training data set and to determine optimal coefficients representing adjustment or weights to apply with respect to the identified features (e.g., for past recommendations) in order to produce a target probability reflected in the training data set based on positive and/or negative correlations between the features and the recommendations. […] The training dataset may (e.g., supervised learning via labeled data) or may not (e.g., unsupervised learning via unlabeled data) include classification labels that characterize data in the training dataset (e.g., indications of compliance or non-compliance results associated with past recommendations).”. As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computing entity, network, processor and a memory which is a general-purpose computer (or components thereof) 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”). Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained machine learning model to predict scores was found to represent mere instructions to implement the abstract idea on a generic computer. 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.
Claims 2-7, 9-14, 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 2, 9, 16 merely describe the actions in the historical data. Claim 3, 10, 17 merely describe the increase in non-compliance based on elapsed time. Claim 5, 12, 19 merely describe selecting prediction-based actions based on a threshold. Claim 7, 14 merely describe temporal feature set progression based on changes over elapsed time.
Claim 6, 13, 20 also includes the additional element of “the predictive machine learning model” which is analyzed the same as in the independent claim and does not provide a practical application or significantly more for the same reasons. Claim 6, 13, 20 merely describe updating the prediction score using temporal feature progression.
Claim 4, 11, 18 includes the additional element of “a display device.” The display device merely generally links 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. Utilization of the display device equates to saying “apply it.” MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application. 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, this additional element does not provide significantly more. As such the claim is not patent eligible. Claim 4, 11, 18 merely describe the prediction-based actions like rendering an interface element or transmitting communications.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 1-2, 4-9, 11-16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kapaldo (US 20210241873) in view of McNair (US 11429885) further in view of Ramasubramanian (US 20080201174)
CLAIM 1, 8, 15
A computer-implemented method for configuring a predictive machine learning model for improved accuracy when generating compliance predictions, the computer-implemented method comprising: (Kapaldo para 27 teaches a processor connected to memories in order to implement computer instructions to perform the method. )
receiving, by one or more processors, real-time compliance data associated with a user profile identifier, (Kapaldo Para 49 for collecting patient data and “tracking data and information regarding medical adherence by patient(s) based on correspondence of the electronic medical adherence intervention communications”. Para 44 teaches correspondence of electronic medical adherence intervention communications including administration of one or more medication treatments specific to patients. Para 28 teaches profile information of the user.)
wherein the real-time compliance data (i) originates at least in part from a remote computing entity and (Kapaldo Para 44 teaches intervention servers that initiate correspondence of an intervention to a medical device such as a mobile phone (i.e., real-time))
(ii) comprises temporal sequence data that indicates (Kapaldo Para 38 teaches feature data which includes time/geo features. Para 43 teaches recording a time period over which a medication has been prescribed. )
(a) a set of one or more action data items indicating a recommended course of action for the user profile identifier, and (Kapaldo Para 44 teaches treatment specific for a patient. Para 28 teaches patient profiles)
(b) a classification associated with the set of one or more action data items; (Kapaldo Para 35 teaches intervention templates that correspond to an intervention channel such as a type of communication such as a telephone communication, text message, in-app, or email. See also Para 73 for an example.)
generating, by the one or more processors, using the predictive machine learning model, and (Kapaldo para 8 teaches using AI based (machine learning based) framework for identifying patients who are likely to be non-adherent.)
based at least in part on (i) a duration of elapsed network time since provision of the set of one or more action data items and (Kapaldo Para 92 teaches the AI based system testing intervention communication including when and what interval to intervene. Examiner notes determining when and what interval would include the time features of the intervention as discussed in para 38 and para 98 which teaches data and features includes time features.)
(ii) the classification associated with the set of one or more action data items, (Kapaldo Para 35 teaches intervention templates that correspond to an intervention channel such as a type of communication such as a telephone communication, text message, in-app, or email. See also Para 73 for an example)
an adaptive temporal-based prediction […] representative of a likelihood of non-compliance with respect to the set of one or more action data items, (Kapaldo para 8 teaches using AI based (machine learning based) framework for identifying patients who are likely to be non-adherent.)
wherein the predictive machine learning model is trained based at least in part on: one or more temporal features associated with historical compliance data and the set of one or more action data items, (Kapaldo Para 10 teaches training the AI model. Kapaldo Para 10 teaches training an AI model based on patient data and intervention communication data. Kapaldo Para 10 teaches training an AI model based intervention communication data including the intervention channel. Para 36 teaches training the artificial intelligence model using patient data and intervention communication data.)
progressing, by the one or more processors, using the machine learned model, and based at least in part on the duration of elapsed network time since provision of the set of one or more action data items, the one or more temporal features; (Kapaldo para 46 teaches training or updating the AI model with data to enhance predictive accuracy of the AI model. Para 38 teaches time/geo features are included as feature data. Para 92 teaches the AI system automatically and continuously creates and tests new intervention correspondence and communications including when for interact modes and intervals to intervene, over time through multiple iterations. Para 92 teaches the AI based system testing intervention communication including when and what interval to intervene. Examiner notes determining when and what interval would include the time features of the intervention as discussed in para 38, 98.)
generating, by the one or more processors, using the predictive machine learning model, to modify the adaptive temporal-based prediction score by an increment associated with a defined interval, and (Kapaldo para 8 teaches using AI based (machine learning based) framework for identifying patients who are likely to be non-adherent. Para 98 teaches features include time features. Para 75 teaches retraining/updating the AI model to provide improved predictions regarding an expected PDC)
responsive to determining that the duration of elapsed network time since provision of the set of one or more action data items […], (Kapaldo Para 38, 98 teaches data and features includes time/geo features. Para 92 teaches the AI based system testing intervention communication including when and what interval to intervene. Examiner notes determining when and what interval would include the time features of the intervention as discussed in para 38, 98.)
a new instance of the adaptive temporal-based prediction […] based on the progression of the one or more temporal features (Kapaldo para 36 teaches training the artificial intelligence model using patient data and intervention communication data. Para 98 teaches features include time features. Para 75 teaches retraining/updating the AI model to provide improved predictions regarding an expected PDC. )
responsive to determining that the new instance of the adaptive temporal-based prediction […], initiating, by the one or more processors, performance of one or more prediction-based actions; and (Kapaldo Para 10 teaches initiating a new communication correspondence to a patient based on adherence intervention prediction)
adjusting, by the one or more processors and based on real-time data indicating one or more compliance results associated with user behaviors corresponding to the one or more prediction-based actions and the progressed one or more temporal features, the predictive machine learning model. (Kapaldo para 36 teaches training the artificial intelligence model using patient data and intervention communication data. Para 98 teaches features include time features. Para 75 teaches retraining/updating the AI model to provide improved predictions regarding an expected PDC.)
Kapaldo does not teach
an adaptive temporal-based prediction score representative of a likelihood of non-compliance with respect to the set of one or more action data items
generating, by the one or more processors, using the predictive machine learning model, and responsive to determining that the duration of elapsed network time since provision of the set of one or more action data items […], a new instance of the adaptive temporal-based prediction score that is incrementally higher than the adaptive temporal-based prediction score; and
responsive to determining that the new instance of the adaptive temporal-based prediction score exceeds a threshold
McNair does teach
an adaptive temporal-based prediction score representative of a likelihood of non-compliance with respect to the set of one or more action data items (Col 6, line 6-12 teaches a comparing a threshold parameter P and a matrix factor vector (i.e., score) in order to determine if is likely that an individual will not adhere to a program)
generating, by the one or more processors, using the predictive machine learning model, and responsive to determining that the duration of elapsed network time since provision of the set of one or more action data items […], a new instance of the adaptive temporal-based prediction score that is incrementally higher than the adaptive temporal-based prediction score; and (Col 6, line 6-12 teaches a comparing a threshold parameter P and a matrix factor vector (i.e., score) in order to determine if is likely that an individual will not adhere to a program)
responsive to determining that the new instance of the adaptive temporal-based prediction score exceeds a threshold (Col 6, line 6-12 teaches a comparing a threshold parameter P and a matrix factor vector (i.e., score) in order to determine if is likely that an individual will not adhere to a program)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of McNair with the teaching of Kapaldo since the combination of the two references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A); see MPEP 2143(I)(A)). It can be seen that each element claimed is present in either Kapaldo in view of McNair or McNair. A prediction as a score and a score satisfying a threshold as taught by McNair does not change or affect the normal prediction based actions. Prediction based actions would be performed the same way even with the addition of the prediction as a score and the score satisfying a threshold. Since the functionalities of the elements in Kapaldo and McNair do not interfere with each other, the results of the combination would be predictable.
Kapaldo in view of McNair do not teach
wherein (i) the adaptive temporal-based prediction score is configured to incrementally update based on the duration of elapsed network time since provision of the set of one or more action data items, and (ii) the predictive machine learning model is trained based at least in part on one or more temporal features associated with historical compliance data and the set of one or more action data items;
generating, by the one or more processors, using the predictive machine learning model, and responsive to determining that the duration of elapsed network time since provision of the set of one or more action data items exceeds a defined interval,
a new instance of the adaptive temporal-based prediction score based on the progression of the one or more temporal features that is higher than the adaptive temporal-based prediction score by an increment associated with the defined interval;
Ramasubramanian does teach
wherein (i) the adaptive temporal-based prediction score is configured to incrementally update based on the duration of elapsed network time since provision of the set of one or more action data items, and (ii) the predictive machine learning model is trained based at least in part on one or more temporal features associated with historical compliance data and the set of one or more action data items; (Ramasubramanian para 88 teaches monitoring elapsed time. Para 89 teaches an elapsed time since a intervention and escalating tone if the time exceeds a parameter)
generating, by the one or more processors, using the predictive machine learning model, and responsive to determining that the duration of elapsed network time since provision of the set of one or more action data items exceeds a defined interval, (Ramasubramanian para 89 teaches an elapsed time since a intervention and escalating tone if the time exceeds a parameter)
a new instance of the adaptive temporal-based prediction score based on the progression of the one or more temporal features that is higher than the adaptive temporal-based prediction score by an increment associated with the defined interval; (Ramasubramanian para 89 teaches an elapsed time since a intervention and escalating tone if the time exceeds a parameter)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the duration of time and score as taught by Kapaldo with the time exceeding a defined interval and incrementally update based on duration of elapsed time as taught by Ramasubramanian. It would be beneficial to account for time exceeding a defined interval and increment a score based on duration of elapsed time as taught by Ramasubramanian because it would increase the urgency or awareness of severity in an attempt to improve responsiveness as taught in Ramasubramanian para 21.
CLAIM 2, 9, 16
Kapaldo teaches wherein the one or more prediction-based actions performed in accordance with the historical compliance data (Kapaldo Para 61 teaches training and updating a model in response to patient responding or reacting or not responding or not reacting (i.e., actions performed and stored as historical data). Kapaldo Para 44 teaches correspondence of electronic medical adherence intervention communications including administration of one or more medication treatments specific to patients.)
associated with the user profile identifier (Kapaldo para 44 teaches interventions are patient-specific and para 28 teaches patients have profiles therefore Examiner notes that an identifier is present in order to be patient specific and a profile is an identifier)
comprise one or more of: compliance with past recommendations, (Kapaldo Para 61 teaches training and updating a model in response to patient responding or reacting (i.e., compliance) or not responding or not reacting)
non-compliance with past recommendations […] (Kapaldo para 61 teaches training and updating a model in response to patient responding or reacting or not responding or not reacting (i.e., compliance) Examiner interprets additional limitations as optional due to claim language " one or more of … ")
CLAIM 4, 11, 18
Kapaldo teaches wherein the one or more prediction-based actions comprise one or more of […] transmission of a non-compliance risk communication (Kapaldo para 70 teach an intervention communication being transmitted such as a text message, in-app communication formatted to be alarmist or gentle)
or transmission of a renewed recommendation communication. (Kapaldo para 70 teach an intervention communication being transmitted such as a text message, in-app communication formatted to be alarmist or gentle. Para 83 teaches interventions may be fine-tuned over time according to patient types, behaviors and preferences Examiner interprets this limitation as optional due to claim language "one or more of …")
CLAIM 5, 12, 19
Kapaldo teaches wherein the one or more prediction-based actions are selected based at least in part on the adaptive temporal-based prediction […]. (Kapaldo para 10 teaches initiating a new communication correspondence to a patient based on adherence intervention prediction. See also Kapaldo para 65 which teaches selecting an intervention that maximizes an expected increase in PDC)
Kapaldo does not teach wherein the one or more prediction-based actions are selected based at least in part on the adaptive temporal-based prediction score satisfying a particular threshold
McNair does teach wherein the one or more prediction-based actions are selected based at least in part on the adaptive temporal-based prediction score satisfying a particular threshold. (Col 19 line 34 - Col 20 line 11 teaches a comparing a threshold parameter P and a matrix factor vector (i.e., score) in order to determine if is likely that an individual will not adhere to a program and if so then an action is invoked)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of McNair with the teaching of Kapaldo in view of McNair since the combination of the two references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A); see MPEP 2143(I)(A)). It can be seen that each element claimed is present in either Kapaldo in view of McNair or McNair. Score satisfying a threshold as taught by McNair does not change or affect the normal prediction based actions. Prediction based actions would be performed the same way even with the addition of satisfying a threshold. Since the functionalities of the elements in Kapaldo and McNair do not interfere with each other, the results of the combination would be predictable.
CLAIM 6, 13, 20
Kapaldo teaches updating, by the one or more processors, using the predictive machine learning model, and based at least in part on temporal feature set progression of the one or more temporal features, the adaptive temporal-based prediction […]. (Para 61 teaches an AI model updating predictions based on new medication features and how the patients react or do not react to specific intervention templates, channels, and/or communications. Kapaldo Para 38, 98 teaches data and features includes time/geo features. Para 92 teaches the AI based system testing intervention communication including when and what interval to intervene. Examiner notes determining when and what interval would include the time features of the intervention as discussed in para 38, 98.)
Kapaldo does not teach updating, by the one or more processors, using the predictive machine learning model, and based at least in part on temporal feature set progression of the one or more temporal features, the adaptive temporal-based prediction score.
McNair does teach updating, by the one or more processors, using the predictive machine learning model, and based at least in part on temporal feature set progression of the one or more temporal features, the adaptive temporal-based prediction score. (Col 6, line 6-12 teaches a matrix factor vector (i.e., score) that represents the risk an individual will not adhere to a program)
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of a score with teaching of Kapaldo since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the prediction score of the secondary reference for just the prediction of the primary reference. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
CLAIM 7, 14
Kapaldo teaches wherein the temporal feature set progression comprises a temporal representation of changes in the one or more features associated with the set of one or more action data items during the duration of elapsed network time. (Para 43 teaches a prescribed medication (i.e., recommendation) and assessing patient behavior or lack thereof (i.e., changes in behavior) over the prescribed times (i.e., temporal representation of changes). Para 61 also teaches " AI based system 200 would have both the new medication features (312) of the new medication and also data regarding how patients responded or reacted (or did not respond or react) to specific intervention templates, channels, and/or communications.")
Claims 3, 10, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kapaldo in view of McNair further in view of Kusayanagi (US 20230050142) referred to hereinafter as Kusayanagi.
CLAIM 3, 10, 17
Kapaldo in view of McNair teach […] the duration of elapsed network time (Kapaldo Para 38, 98 teaches data and features includes time/geo features. Para 92 teaches the AI based system testing intervention communication including when and what interval to intervene. Examiner notes determining when and what interval would include the time features of the intervention as discussed in para 38, 98.)
Kapaldo in view of McNair do not teach wherein the likelihood of non-compliance increases based at least in part on an increase in the duration of elapsed network time
Kusayanagi does teach wherein the likelihood of non-compliance increases based at least in part on an increase in the duration of elapsed network time (Kusanagi para 55 teaches that the probability of a user to forget an action (i.e., not comply with an action) increases as the time elapsed increases)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of Kusayanagi with the teaching of Kapaldo in view of McNair since the combination of the two references is merely combining prior art elements according to known methods to yield predictable results (KSR rationale A); see MPEP 2143(I)(A)). It can be seen that each element claimed is present in either Kapaldo in view of McNair or Kusayanagi. Incorporating the concept of increasing non-compliance likelihood based on the duration of elapsed time, as taught by Kusayanagi, does not change or affect the normal functioning of the system in predicting non-compliance. The system would predict non-compliance the same way even with the addition of accounting that an increase in elapsed time increases the non-compliance likelihood. Since the functionalities of the elements in Kapaldo in view of McNair, and Kusayanagi do not interfere with each other, the results of the combination would be predictable.
Prior art cited but not relied upon
US 20130253975 MANGALAM
Claim 1, a method for determining risk by increasing a risk score based on elapsed time since deficiency identified.
US 20220309168 Coulter
[0095] In an embodiment, the compliance logic 140 can determine a compliance score based on a data broker's 80 compliance or non-compliance to a request within a predetermined time window, or in response to an action by the data broker 80, or the user 90. In an embodiment, the compliance logic 140 can analyze one or more of i) a time elapsed between a request to delete the PII is sent to the data broker 80, and the PII being deleted by the data broker 80, and ii) […]
US 20220028511 Neumann
[Abstract]A system for initiating an updated user ameliorative plan includes a processor configured to identify a user ameliorative plan as a function of a user identifier from a user client device, obtain a periodic longevity factor, determine a user adherence factor, wherein determining further comprises identifying a progression locus as a function of the user ameliorative plan and the periodic longevity factor, receiving a user response, and determining the user adherence factor as a function of the progression locus and the adherence correlator, generate an updated user ameliorative plan as a function of the user adherence factor, and initiate the updated user ameliorative plan.
the algorithm can predict the likelihood of a patient becoming non-adherent to their medications, and the expected timing of the start of that non-adherence event.
US 20230170065 De Vries
[0063] In addition, in some implementations, the aforementioned risk prediction parameters can be assessed on a continuous basis to create a dynamic risk score made up of a composite of all these parameters, related to how they affect a patient’s health and wellbeing. The software platform can be configured to prompt an intervention (e.g., by creating a “just in time” recommendation) before the patient becomes non-adherent. These models can rely on a feedback loop based on the success or failure of previous and similar interventions and/or recommendation timings to further hone the timing and type of intervention required to successfully address non-adherence.
Response to Arguments Regarding U.S.C. 101 Rejection
Applicant argues pg. 11
The Office Action rejects claims 1-20 under 35 U.S.C. § 101 for allegedly being directed to "a judicial exception . .. without significantly more." Office Action 3. The Office Action's analysis did not consider and is inconsistent with the viewpoints expressed by the Appeals Review Panel in Ex Parte Desjardins.1 Applicant respectfully requests reconsideration of the rejections in view of Ex Parte Desjardins.
Specifically, in response to Applicant's previous arguments, the Office Action states that "Applicant's identified problem is a training problem. Because no technological problem is present, the claims do not provide a practical application." Office Action, p. 28; see also Office Action, pp. 29-30. This assessment is expressly rejected in Ex Parte Desjardins, where the Appeals Review Panel acknowledged that "improvements in training the machine learning model itself' were sufficient integrate an abstract idea into a practical application. Ex Parte Desjardins, p. 8.
Examiner responds:
Examiner disagrees. Examiner’s analysis considers and is consistent with the viewpoints expressed by the Appeals Review Panel in Ex Parte Desjardins. Applicant asserts "improvements in training the machine learning model itself' were sufficient integrate an abstract idea into a practical application which is incorrect. Desjardins Pg. 8 recites “Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement.”
Thus such Applicant’s assertion insufficient to support a patent eligibility determination. Examiner notes Applicant has not identified and Examiner cannot find evidence in Applicant specification for an improvement in training a machine learning model. Further, Examiner has not determined the claim itself reflects a disclosed improvement. Moreover, Applicant has not identified nor can Examiner find a technical problem nor a subsequent technical solution.
Examiner notes Desjardins recites claims that integrate the abstract idea into a practical application. Desjardins Pg. 7 recites, “In particular, the Appellant identifies certain limitations of independent claim 1 and asserts that "the claimed subject matter provides technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training," citing paragraph 21 of the Specification for support. Id. at 7-9; see also id. at 8 ("This training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems.").”
In contrast, Applicant's does not identify a technical problem. Applicant’s identified problem is at best a training problem. Because no technological problem is present, the claims do not provide a practical application.
Applicant’s argues. Pg. 11
Just like the claims in Ex Parte Desjardins, the present claims recite an improved machine learning technique for providing and adjusting a machine learning model in a manner that enables updated predictions even in the absence of updated temporal features and improved reliability with respect to such predictions. Compare Specification [0018] & [0019] to Ex Parte Desjardins, p. 9. Thus, like the claims in Ex Parte Desjardins, the present claims are directed to patent eligible subject matter under 35 U.S.C. § 101.
Further, the machine learning techniques recited by the claims conform with at least one of the examples provided by the Advance notice of change to the MPEP in light of Ex Parte Desjardins Memorandum, December 5, 2025 (hereinafter Desjardins Memorandum). […]”
Examiner responds:
Applicant specification para 18-19 do not discuss “updated predictions even in the absence of updated temporal features and improved reliability”. Para 18 discusses terms being used interchangeably throughout the specification. Para 19 discusses that non-compliance is a human problem that leads to wasted resources. There is no discussion of “absence of temporal features” problems in a model or “reliability” problems in a model. Examiner can only find that human non-compliance is a problem because it causes wasted resources and may cause harm. There is no indication of a technical problem with a model or computer or any other technical element.
Further, MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. Here, the Applicant’s argued problem is not a technological problem caused by the computer or machine learning model. The problem of absence of updating temporal features and reliability in predicting a likelihood of non-compliance was not a problem cause by the computer/model, it is a problem that existed and/or exists regardless of whether a computer/model is involved in the process. At best, Applicant’s identified problem is a management / business problem. Because no technological problem is present, the claims do not provide a practical application.
The claims do not conform to the example of Desjardins because Applicant does not identify, and Examiner cannot find a technical problem nor a technical solution and therefore the claims do not integrate the abstract idea into a practical application. Examiner has considered the table showing an attempt to relate Applicant’s claims and those of Desjardins and is not persuaded. Examiner notes Applicant cites para 20 as indicating an improvement because the system may avoid wasted resources, however no technical problem has been identified. Wasted resources occur due to human non-compliance and not model or computer operation. Applicant’s asserted improvement in para 20 is to automatically execute actions to improve likelihood of compliance of a human in order to avoid wasted resources. Examiner submits this is explicitly organizing human activity. Applicant has not identified and Examiner cannot find any indication of technical problem with the model or computer that causes wasted resources, reliability issues, or any other technical issue.
Applicant argues pg. 15:
With respect to certain methods of organizing human activity, the Office Action alleges that "other than reciting a system implemented by a computing entity with a processor and a memory (computer), the claimed invention amounts to managing personal behavior or interaction between people. . . this claim encompasses a person looking at recommendation data and patient data, determining a likelihood of non-adherence and initiating an action based on the score in the manner described in the identified abstract idea, supra." Office Action, pp. 4-5. The MPEP defines certain methods of organizing human activity as fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Id. § 2106.04(a)(2)(II). The MPEP does not include machine learning model maintenance or training in its definitions of certain methods of organizing human activity. As noted above, claim 1 recites an improved machine learning technique - not fundamental economic principle or practice, a commercial or legal interaction, or managing personal behavior or interactions between people.
Examiner responds:
Examiner disagrees. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to look at recommendation data and patient data, determine a likelihood of non-adherence and initiating an action based on the score. Applicant points to machine learning model maintenance or training as being outside the definitions of organizing human activity. However, the claims are not directed to machine learning model maintenance or training. The claims are directed to the abstract idea of predicting non-compliance of a human and performing actions to manage personal behavior of increasing compliance. The additional element of a machine learning model is addressed in Step 2A2 and has not been found to integrate the abstract idea into a practical application because the Applicant’s claiming of the model amount to mere instructions to implement the abstract idea on a computer. Applicant’s modifications to the input and output data are improvements to the abstract idea of scoring likelihood of a person’s non-compliance and not improvements to a model operation itself. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to predicting a likelihood of non-compliance and perform actions, the claimed invention is directed to an abstract idea.
Applicant argues pg. 16
Each of the above operations recite a machine learning technique for optimizing performance of a predictive machine learning model to address performance deficiency of the model due to dynamic timing features predictive of the adaptive temporal-based prediction score. See Specification, as published, [0128].
Examiner responds:
Applicant specification para 128 recites “[0128] Example drug data 802 includes the drug name, dosage form, unit price, drug strength, day supply, dispensable drug identifier (DDID), administration route, refills, drug form, and GPI category (e.g., drug group, drug class, or drug sub-class).”
Examiner cannot find support for “a machine learning technique for optimizing performance of a predictive machine learning model to address performance deficiency of the model due to dynamic timing features predictive of the adaptive temporal-based prediction score.”
The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to determining a likelihood of non-compliance of patients based on temporal features. Applicant points to a machine learning model based on temporal data and merely “using” the model to perform the abstract idea which amount to mere instruction to implement the abstract idea on a general purpose computer. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to predicting a likelihood of non-compliance and perform actions, the claimed invention is directed to an abstract idea.
Further, Applicant has not identified and Examiner cannot find a “performance deficiency” of the model due to dynamic timing features. Applicant has only identified and Examiner has only found a problem of predicting likelihood of non-compliance of a human based on temporal features. The problem of healthcare providers determining a likelihood of non-compliance of patients based on temporal features was not a problem caused by the computer/model, it is a problem that existed and/or exists regardless of whether a computer/model is involved in the process. At best, Applicant’s identified problem is a management / training / personnel / business problem. Because no technological problem is present, the claims do not provide a practical application.
Applicant argues pg. 16:
As acknowledged Ex Parte Desjardins, training a machine learning model is not an abstract idea (e.g., adjusting the parameters based on real-time data and progressed temporal features) - let alone a certain method of organizing human activity. See e.g., Ex Parte Desjardins, p. 8 (recognizing that adjusting values of the parameters of a machine learning model reflects an improvement to how a machine learning model itself operates). Adjusting the parameters of a machine learning model, for example, is not a fundamental economic principle or practice, legal obligation, advertising, marketing or sales activity, behavior, legal interaction, or process of managing personal behavior or relationship or interactions between people.
Examiner responds:
Examiner disagrees. If Applicant were correct then Desjardins would have found there to be no abstract idea recited and no Step 2A, Prong Two analysis would be required. However it was found at least an abstract idea was recited. See Desjardins pg. 6-7 “Independent claim 1 recites "computing ... , an approximation of a posterior distribution over possible values of the plurality of parameters." Independent claims 18 and 19 recite similar limitations. Appeal Br. 20-21 (Claims App.). In entering the new ground of rejection, the Board determined that at least this limitation recites a mathematical calculation, which is a mathematical concept, and, thus, an abstract idea. Dec. 20-21. For this limitation, the Appellant neither disputed that the limitation recites an abstract idea, nor identified the limitation as reciting features that confer technical improvements. Req. 7-8. We see no reason to disturb this undisputed finding, and so independent claims 1, 18, and 18 each recite at least one abstract idea, we proceed to the next part of our analysis – MPEP Step 2A, Prong Two.”
Further, in regards to training, when given its broadest reasonable interpretation in light of the disclosure, the training of a machine learning model represents the creation of mathematical interrelationships between data (see, e.g., Spec. Para. 0033) “The training data set may be used to identify features in the training data set and to determine optimal coefficients representing adjustment or weights to apply with respect to the identified features (e.g., for past recommendations) in order to produce a target probability reflected in the training data set based on positive and/or negative correlations between the features and the recommendations. […] The training dataset may (e.g., supervised learning via labeled data) or may not (e.g., unsupervised learning via unlabeled data) include classification labels that characterize data in the training dataset (e.g., indications of compliance or non-compliance results associated with past recommendations).”. As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
In regards to “Desjardins, p. 8 (recognizing that adjusting values of the parameters of a machine learning model reflects an improvement to how a machine learning model itself operates).” Applicant does not reflect an improvement in how a machine learning model itself operates. Applicant’s claims are directed to an abstract idea and the additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Examiner points Applicant to Desjardins as an example of claims directed to patent eligible subject matter under 35 U.S.C. § 101. Desjardins identified a technical problem of catastrophic learning in continual learning systems and identified a technical solution by adjusting parameters while protecting performance of the model on the first task and reflected the improvement in the claims by reciting “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task”. See Desjardins Pg . 9.
In contrast Applicant does not identify a technical problem nor a technical solution and the claim elements do not reflect a technical improvement.
Applicant argues pg. 17:
Claim 1 recites an improved machine learning technique that addresses traditionally difficult predictions and predictive features, namely dynamical timing features. Even if claim 1 were directed to an abstract idea-which, Applicant submits, it is not the claim recites a combination of additional elements that improves a technical field such that the claim as a whole integrates any alleged abstract idea into a practical application that is patent eligible under 35 U.S.C. § 101.
The MPEP states that "[l]imitations that the courts have found to qualify as 'significantly more' when recited in a claim with a judicial exception include . .. improvements to the functioning of a computer [or] improvements to any other technology or technical field." MPEP § 2106.05(c). Claim 1 includes a number of features that provide improvements to the functioning of a computer with respect to the computer's ability to leverage machine learning for dynamic temporal features. […]
Claim 1 recites a prediction technique that enables training a predictive machine learning model to accurately handle temporal features by (1) progressing temporal features, (2) generating, based on the progression of the temporal features, a new instance of a prediction score upon determining that an elapsed network time exceeds a defined interval, and then (3) adjusting parameters of the predictive machine learning model to maintain its performance. By doing so, claim 1 recites an improvement in machine learning itself that improves the performance of the machine learning model.
Examiner responds:
Applicant’s claims are directed to an abstract idea of methods of organizing human activity and encompass predicting likelihood of non-compliance of a human to actions.
The significantly more analysis is not used in Step 2A, Prong Two. This is considered in Step 2B. See 101 rejection above.
Applicant does claim “to maintain its performance” in reference to the model. Examiner notes though that this limitation would be the intended use and result of adjusting parameters and hold no patentable weight.
Applicant claims recite progressing temporal features and generating a score under a condition. These features are modifications to input/output data to the model and are not modifications to the operation of the model itself.
Applicant claims adjusting parameters of the predictive machine learning model. This is mere recitation of how machine learning models operate normally.
Applicant’s recitation of a machine learning model 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.
Applicant argues pg. 18:
For example, as noted in paragraph [0128] of the Specification, as filed: [129] In the training process 700 illustrated in FIG. 7, the pharmaceutical claims 702 are divided into training and validation data 704 (80% of the pharmaceutical claims 702) and testing data 706 (20% of the pharmaceutical claims 702). The training and validation data 704 is further divided, with 80% used for training and 20% used for validation. The predictive machine learning model 616 is then fed the training data ( e.g., in order to determine parameters of interest) and then validated using the validation data (e.g., in order to optimize configuration of the predictive machine learning model 616 with respect to the parameters). The resulting trained and predictive machine learning model 616 may be configured to produce optimal performance with respect to various metrics, such as accuracy and/or AOC, to name a few examples. The predictive machine learning model 616 is then tested with respect to the testing data 706.
Examiner responds:
Applicant’s paragraph 128 is not used. Para 129 describes splitting the data into (training + validation) and (test) and then further splitting (training + validation) into (training) and (validation). Applicant then describes training and validating to provide optimal performance with respect to metrics such as accuracy and AOC and then the model is tested with the test data. Applicant is purporting that a training, validation, testing workflow as an improvement to the function of machine learning model itself. Examiner is unpersuaded because Applicant’s described workflow is the normal operation of a machine learning model.
Applicant argues pg. 18:
As further noted in paragraph [0113] of the Specification, as filed: [0113] The predictive machine learning model 616 may allow live tracking of medications and updating of predictions for tracking, with maximal accuracy, which individuals are likely to miss a medication and continually update the predictions as more time passes since the prescription is written. The tracking computing entity 101 may collect the data from the various data sources 606 in real-time, may provide prediction results in real-time, and may continuously retrain the predictive machine learning model 616 with real-time and/or near real- time data concerning prescriptions issued for the individual and the individual's behavior with respect to the prescriptions. The predictive machine learning model 616 may be configured such that historical data used to train the model may be comparable with real-time data used to generate the predictions. The compliance management computing entity 100 may be configured to continually feed the real-time data back into retraining the predictive machine learning model 616 as new data is collected.
Thus, by continuously retraining (e.g., adjusting the parameters of) the predictive machine learning model with real-time and/or near-real-time data, the training technique recited by claim 1 improves the performance of the machine learning model. Moreover, as explained in paragraphs [0019]-[0020], by progressing the temporal features of an input to the machine learning model before the adjusting operations, the training technique of claim 1 enables adaptive temporal-based prediction scores that maintain the predictive accuracy of the machine learning model between retraining operations. […]
Accordingly, by generating and updating an adaptive temporal-based prediction score based on one or more progressed temporal features and then adjusting parameters of the model based on outcomes corresponding to the one or more prediction-based actions and the progressed one or more temporal features, claim 1 reflects an improved machine learning training technique that "constitutes an improvement to how the machine learning model itself operates, and
Examiner responds:
Applicant’s asserts continuously retraining improves the performance of the model. Retraining does not change the operation of the machine learning model. It is operating as it normally does only repeatedly to consider new data and output updated results. The improvement of accuracy is directed to an improvement of the abstract idea of organizing human activity encompassing determining a likelihood of non-compliance of a human. Further, the progressing temporal features of an input is a modification the models input. Updating the score based on features is modification of the output. The operation of the model remains unchanged.
Applicant argues pg. 20:
The Office Action further asserts that "the implementation of thresholds and timing intervals on the data to improve the operation of a machine learning model is effectively training a machine learning model with new data to result in improved predictions and is not an improvement to the model itself, it is an improvement to the data used by the model." Office Action, p. 27. As recited by claim 1, the predictive machine learning model does not simply utilize received "new data" or improved data, but instead executes operations such as "progressing ...using the predictive machine learning model, and based at least in part on the duration of elapsed network time since provision of the set of one or more action data items, the one or more temporal features" to improve the reliability and accuracy of its generated predictions even prior to adjusting parameters based on real-time data.
Examiner responds:
Examiner notes “using the predictive machine learning model” is merely “apply it” recitation for the process of “progressing, […] and based on elapsed […] time since provision of the set of one or more action data items, the one or more temporal features ”. The machine learning model is merely applied to perform the abstract idea of progressing temporal features based on elapsed time and determine a likelihood of non-compliance of a human. Examiner notes the improved data may result in improved accuracy, however the operation of the machine learning model is the same and the improvement is directed to the abstract idea and not the how the machine learning model operates.
Applicant argues pg. 20:
Step 2B of the Alice/Mayo test focuses on whether the additional limitations present in the claim and their combination is unconventional and provides an inventive concept. See MPEP §2106.05.II. Applicant respectfully submits that the added limitations are not well-understood, or routine within the industry. Applicant further notes that the added limitations are not taught or suggested by the prior art of record. Accordingly, Applicant respectfully submits that the rejections set forth in the Office Action regarding independent claim 1 (and the claims depending therefrom) have been overcome and respectfully requests withdrawal of such rejections.
Examiner responds:
The Examiner disagrees. MPEP 2106.05(d) states: “Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry (emphasis added).” Further, MPEP 2106.05(I) states: “As made clear by the courts, the novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter (internal quotations omitted, emphasis original).” As such, it is only the additional elements identified by the Examiner to not be part of the abstract idea that are analyzed to determine whether they represent well-understood, routine, conventional activities in the field of the invention.
In that regard, MPEP 2106.05(d)(I) indicates that in determining whether the additional elements represent are well-understood, routine, conventional activities, the Examiner should consider whether the additional elements (1) provide an improvement to the technological environment to which the claim is confined, (2) whether the additional elements are mere instructions to apply the judicial exception, or (3) whether the additional elements represent insignificant extra-solution activity. The additional elements of the claims do not provide significantly more based on this inquiry.
Taking these in turn, whether the additional elements of the claim provide an improvement was analyzed/addressed in the 2A2 analysis as no improvement was present. The technological environment to which the claims are confined (a general-purpose computer performing generic computer functions) is recited at a high level of generality and has been found by the courts to be insufficient to provide a practical application (see MPEP 2106.05(d)(II); Alice Corp.). None of the additional elements of the claim were found to represent extra-solution activity and thus no well-understood, routine, conventional analysis is required.
Response to Arguments Regarding U.S.C. 103 Rejection
Applicant argues pg. 21
The Office Action relies on Ramasubramanian to allegedly teach "wherein (i) the adaptive
temporal-based prediction score is configured to incrementally update based on the duration of
elapsed network time since provision of the set of one or more action data items" citing specifically
paragraph [0089]. […]
As recited, Ramasubramanian describes implementing "levels of escalation" for each type of
intervention, wherein each level of escalation corresponds to an interceding action of sorts. With
respect to the tone-escalation as cited, the tone is not an "adaptive temporal-based prediction score
[] configured to incrementally update based on the duration of elapsed network time since
provision of the set of one or more action data items" as recited by the claims. Instead, each level
of escalation is a distinct action, and one action is not determinable as an "incremental[] update"
of the preceding action, for example.
Examiner responds:
Examiner disagrees. Examiner uses the broadest reasonable interpretation of “incrementally update based on the duration of elapsed network time since”.
McNair does not teach the underlined portion:
wherein (i) the adaptive temporal-based prediction score is configured to incrementally update based on the duration of elapsed network time since provision of the set of one or more action data items, and (ii) the predictive machine learning model is trained based at least in part on one or more temporal features associated with historical compliance data and the set of one or more action data items;
Ramasubramanian does teach
wherein (i) the adaptive temporal-based prediction score is configured to incrementally update based on the duration of elapsed network time since provision of the set of one or more action data items, and (ii) the predictive machine learning model is trained based at least in part on one or more temporal features associated with historical compliance data and the set of one or more action data items; (Ramasubramanian para 88 teaches monitoring elapsed time. Para 89 teaches an elapsed time since a intervention and escalating tone if the time exceeds a parameter)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the duration of time as taught by Kapaldo with the time exceeding a defined interval as taught by Ramasubramanian. It would be beneficial for time exceeding a defined interval as taught by Ramasubramanian because it would increase the urgency or awareness of severity in an attempt to improve responsiveness as taught in Ramasubramanian para 21.
Applicant argues pg. 23:
Moreover, at page 34, the Office Action asserts "Ramasubramanian para 88 teaches monitoring elapsed time. Para 89 teaches an elapsed time since a[ n] intervention and escalating tone if the time exceeds a parameter." Ramasubramanian does not teach a tone, nonetheless a score, "generating ... an adaptive temporal-based prediction score configured to incrementally update based on the duration of elapsed network time since provision of the set of one or more action data items" but instead simply teaches triggering a defined action responsive to satisfaction of a temporal condition. As such, Applicant asserts that Ramasubramanian fails to teach or suggest "generating ... the adaptive temporal-based prediction score[] configured to incrementally update based on the duration of elapsed network time since provision of the set of one or more action data items" as asserted.
Examiner responds:
Examiner disagrees. Examiner uses the broadest reasonable interpretation of "generating ... an adaptive temporal-based prediction score configured to incrementally update based on the duration of elapsed network time since provision of the set of one or more action data items" and finds Ramasubramanian to be analogous and an obvious modification. See 103 rejection above. See also:
Ramasubramanian Para 88 “The `Open Intervention` algorithm continues to monitor the elapsed time, count or days as applicable to the intervention. At the appointed time T.sub.1, the system checks the operational database to see if there has been a response to the intervention 381. Similarly if the appointed count is N.sub.1 or appointed days are D.sub.1. If there has been a response, the intervention is closed 383. If there has been no response, the system invokes the escalation process”
Para 89 “For a time-based intervention, when the elapsed time has exceeded the member parameter T.sub.2, the tone of the intervention is escalated to positive 399, then to a `bald` tone 398 when it has exceeded the member parameter T.sub.3, and finally, when it has exceeded the member parameter T.sub.4, the caregiver is notified 397.”
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/A.K.T./Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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