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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10 April 2026 has been entered.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on the following date(s) is/are entered and considered by Examiner:
* 10 April 2026
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.
Claim(s) 1-36 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim 1 recites:
A method of generating times for providing messages to a user over networked environments, comprising:
applying, by one or more processors, an event dataset to a machine learning (ML) model,
wherein the event dataset identifies a plurality of interaction times corresponding to a plurality of interactions with an application, and
wherein the ML model is established using a training dataset including a plurality of examples, each of the plurality of examples including a respective event dataset identifying a respective plurality of interaction times corresponding to a respective plurality of interactions over a respective time window by a respective user with a respective application on a respective user device;
determining, by the one or more processors, based on applying the event dataset to the ML model, a message presentation time based at least on a behavioral pattern in a plurality of prior interactions identified in the event dataset, the message presentation time corresponding to a likelihood of a future user interaction;
providing, by the one or more processors, instructions to cause presentation of a message on a user device at a subsequent time window corresponding with the message presentation time;
receiving, by the one or more processors, subsequent to presentation of the message on the user device, data indicating an interaction by the user;
adding, by the one or more processors, the data to the event dataset;
determining, by the one or more processors, by applying the event dataset to the ML model, a second message presentation time; and
providing, by the one or more processors, instructions to cause presentation of a second message on the user device at a second time window corresponding with the second message presentation time.
Step 1:
The claim as a whole falls within at least one statutory category, i.e. a process, machine, manufacture, or composition of matter.
Step 2A Prong One:
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mathematical concepts” because the broadest reasonable interpretation of using a training dataset requires specific mathematical calculations (see Specification page 12 paragraph 0035 disclosing any machine learning models including a plurality of algorithms), i.e. mathematical relationships, mathematical formulas or equations, mathematical calculations. MPEP § 2106.04(a)(2)(I)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Certain methods of organizing human activity” because the steps of identifying interaction times (including training a model), generating a defined time, and providing instructions to address the condition of a user were traditionally performed by a human being when treating a patient, i.e. managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). MPEP 2106.04(a)(2)(II)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mental processes”.
But for a generic computer recited with a high level of generality (see Specification page 35-36 paragraph 0090 disclosing general-purpose processors), the steps of identifying times and generating a defined time may be practically performed in the human mind either mentally or with pen and paper.
Accordingly, these limitations have been found to be directed towards concepts performed in the human mind (including an observation, evaluation, judgment, opinion). MPEP 2106.04(a)(2)(III)
The different categories of abstract ideas are being considered together as one single abstract idea. MPEP 2106.04(II)(B)
Dependent claim(s) recite(s) additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim(s) 2-11, 25-26, 28-30 reciting limitations further defining the abstract idea, which may be performed in the mind but for recitation of generic computer components, and/or may be a method of managing relationship or interactions between people).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s), if any:
by one or more processors,
providing, by the one or more processors, instructions to cause presentation of a message on a user device at a subsequent time window corresponding with the message presentation time;
receiving, by the one or more processors, subsequent to presentation of the message on the user device, data indicating an interaction by the user;
providing, by the one or more processors, instructions to cause presentation of a second message on the user device at a second time window corresponding with the second message presentation time.
The additional element(s) do(es) not integrate the abstract idea into a practical application, other than the abstract idea per se.
The processor has been recited with a high level of generality in a post hoc manner to implement the abstract idea, as discussed in Step 2A, Prong One above, and incorporated herein, and therefore amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea). MPEP 2106.05(f))
The step of obtaining an event data set amounts to necessary data gathering.
The step of providing data for presentation on a user device amounts to outputting.
Similarly, the step of storing data amounts to data storage on a computer.
These limitations add(s) insignificant extra-solution activity to the abstract idea (mere data gathering, selecting a particular data source or type of data to be manipulated, insignificant application). MPEP 2106.05(g))
Dependent claim(s) recite(s) additional subject matter which amount to limitation(s) consistent with the additional element(s) in the independent claims (such as claim(s) 12 reciting SMS, MMS, in-app message, chat bot, additional limitation(s) which generally link(s) the abstract idea to a particular technological environment or field of use because the messages have not been positively recited as being displayed in any particular manner, also amounts no more than mere instructions to apply the exception using a generic computer, MPEP 2106.05(f)); claim 27 reciting storing data on a computer, additional limitation(s) which add(s) insignificant extra-solution activity to the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, the additional elements do not integrate the judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claim recites an abstract idea.
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 elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
The additional elements, as discussed above and incorporated herein, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use, as discussed above and incorporated herein.
Mere instructions to apply an exception, insignificant extra-solution activity, and linking to a particular technological environment using a generic computer component cannot provide an inventive concept.
The steps of obtaining data and providing data for presentation amount(s) to element(s) that have been recognized as well-understood, routine, and conventional (WURC) activity in particular fields (e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i)). MPEP 2106.05(d)(II)(ii))
The step of storing data amount(s) to element(s) that have been recognized as well-understood, routine, and conventional (WURC) activity in particular fields (e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv)). MPEP 2106.05(d)(II)(ii))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claim(s) 27 reciting storing data with a computer; e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv)). MPEP 2106.05(d)(II)(ii))
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
The claim is not patent eligible.
Claims 13-24, 31-36 recite substantially similar limitations as those of claims 1-12, 25-30 and are also therefore rejected for substantially similar rationale as applied to claims 1-12, 25-30 above, and incorporated herein.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-15, 17-30 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Iyer (20230360754).
Claim 1: Iyer discloses:
A method (Abstract illustrating a method) of generating times (page 1 paragraph 0007 illustrating determining frequency and time) for providing messages (page 3 paragraph 0030 illustrating providing messages) over networked environments (Figure 1 illustrating a network), comprising:
applying, by one or more processors, an event dataset to a machine learning (ML) model (page 9 paragraph 0070 illustrating an ML model),
wherein the event dataset identifies a plurality of interaction times corresponding to a plurality of interactions with an application (page 11 paragraph 0084 illustrating a duration and frequency of engagement at stage N [considered to be the “first plurality of interaction times”]), and
wherein the ML model is established using a training dataset including a plurality of examples, each of the plurality of examples including a respective event dataset identifying a respective plurality of interaction times corresponding to a respective plurality of interactions over a respective time window by a respective user with a respective application on a respective user device (page 12 paragraph 0088-0089 illustrating receiving a large plurality of digital therapeutic data including targets and attributes as indicated in Figure 5);
determining, by the one or more processors, based on applying the event dataset to the ML model, a message presentation time based at least on a behavioral pattern in a plurality of prior interactions identified in the event dataset (page 9 paragraph 0070 illustrating determining a balance ratio for outreaching to the patient such that the patient is not oversaturated but is given enough touch points to comply with the treatment regimen), the message presentation time corresponding to a likelihood of a future user interaction (page 11 paragraph 0084 illustrating a duration and frequency of engagement optimized to promote maximum engagement with the patient);
providing, by the one or more processors, instructions to cause presentation of a message on a user device at a subsequent time window corresponding with the message presentation time (page 3 paragraph 0030 illustration presentation to the user within the indicated time for maximum therapeutic effects);
receiving, by the one or more processors, subsequent to presentation of the message on the user device, data indicating an interaction by the user (page 9 paragraph 0070 illustrating monitoring whether a use responds to the promotional outreach message [considered to be a form of “subsequent to presentation of the message”] by using a given digital therapeutic);
adding, by the one or more processors, the data to the event dataset (page 9 paragraph 0070 illustrating using the user feedback data to adjust the promotional outreach);
determining, by the one or more processors, by applying the event dataset to the ML model, a second message presentation time (page 11 paragraph 0084 illustrating a duration and frequency of engagement at stage N+1 [considered to be a “second message presentation time”]); and
providing, by the one or more processors, instructions to cause presentation of a second message on the user device at a second time window corresponding with the second message presentation time (page 11 paragraph 0084 illustrating a duration and frequency of engagement at stage N+1 for display).
Claim 2: Iyer discloses:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
wherein the ML model comprises a clustering model defining a plurality of clusters established using the plurality of examples (page 12 paragraph 0088 illustrating a clustering algorithm), each cluster of the plurality of clusters corresponding to a respective subset of the plurality of interaction times, each cluster of the plurality of clusters associated with a respective message presentation time at which to provide a respective message of the plurality of one or more messages within a set time window (page 12 paragraph 0088 illustrating applying the clustering algorithm to targets and outreach attributes, the time window has been discussed above with respect to claim 1 and incorporated herein).
Claim 3: Iyer discloses:
The method of claim 2, as discussed above and incorporated herein.
Iyer further discloses:
wherein determining the message presentation time further comprises:
comparing the plurality of interaction times of the event dataset for the user with one or more of the plurality of clusters of the clustering model (page 11 paragraph 0084 illustrating comparing the N+1 stage score with the user’s N stage score),
identifying, from the plurality of clusters, a cluster which corresponds to at least one of the plurality of interaction times (page 11 paragraph 0085 illustrating determining the target and attributes for the time target), and
using the respective image presentation time of the cluster as the message presentation time (page 11-12 paragraph 0087 illustrating adjusting the user’s time based on the result of the clustering comparison).
Claim 5: Iyer discloses:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
wherein determining the message presentation time further comprises identifying, for each message presentation time of a plurality of message presentation times, a respective message type for a corresponding message of the plurality of one or more messages to be provided to the user device (page 8 paragraph 0060 illustrating determining the type of mobile device and account for messaging).
Claim 6: Iyer discloses:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
wherein at least one of the plurality of examples of the training dataset further identifies at least one of: (i) a respective plurality of message presentation times at which a respective plurality of messages is to be provided to the respective user device over a set time window (page 11 paragraph 0084 illustrating providing times of messaging for training), or (ii) a respective indication of whether the respective plurality of message presentation times increased a corresponding response rate of respective plurality of interactions by at least a threshold (page 11 paragraph 0084 illustrating determining if a N+1 stage score is improved over the N stage score and thereby an adjustment should be made based on the improvement identified).
Claim 7: Iyer discloses:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
wherein determining the message presentation time further comprises:
determining, based on applying the event dataset to the ML model, a score indicating a degree of confidence for the message presentation time (page 11 paragraph 0084 illustrating determining if a N+1 stage score [considered to be a form of “confidence”]); and
determining, by the one or more processors, that the message presentation time is to be used to provide the message to the user device, responsive to the score satisfying a threshold (page 11 paragraph 0085 illustrating determining if a N+1 stage score exceeds a ratio [considered to be a form of “threshold”] used to determine if an improvement were made).
Claim 8: Iyer discloses:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
further comprising:
updating, by the one or more processors, the ML model (page 11 paragraph 0084 illustrating updating the ML model based on the improved engagement level at stage N+1).
Claim 9: Iyer discloses:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
further comprising:
selecting, by the one or more processors, prior to obtaining the event dataset, an initiation schedule identifying an initial plurality of message presentation times at which an initial plurality of messages is to be provided to the user device over an initial time window, responsive to identifying a lack of prior event datasets for the user (page 2 paragraph 0021 illustrating the user activating the mHealth app [considered to be “a lack of prior event datasets for the user”]); and
providing, by the one or more processors, for presentation on the user device, the initial plurality of messages in accordance with the initial plurality of message presentation times of the initiation schedule (page 2 paragraph 0021 illustrating presenting initial messages to the user),
wherein obtaining the event dataset further comprises obtaining the event dataset identifying the plurality of interaction times corresponding to the plurality of interactions by the user with the application in response to presentation of at least one of the initial plurality of messages of the initiation schedule page 11 paragraph 0084 illustrating obtaining data from the user interaction to determine the current engagement level and adjusting the N+1 stage parameters to improve outcome for the patient).
Claim 10: Iyer discloses:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
further comprising:
determining, by the one or more processors, from a plurality of categories, a category for the user based on one or more event datasets, each category of the plurality of categories associated with a respective behavioral pattern (page 6 paragraph 0046 illustrating a healthcare provider entering the type of medication and treatment the patient is prescribed); and
identifying, by the one or more processors, an initiation schedule based on the category determined for the user, the initiation schedule identifying an initial plurality of message presentation times at which an initial plurality of messages is to be provided to the user device (page 6 paragraph 0046 illustrating generating the digital therapeutic for the patient based on the healthcare provider’s inputs).
Claim 11: Iyer discloses:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
wherein the event dataset further comprises at least one of: (i) a health metric associated with a condition of the user (page 11 paragraph 0080 illustrating a metric used to determine the effectiveness of the treatment for the patient), (ii) an interaction rate for the plurality of interactions (page 7 paragraph 0051 illustrating an interaction rate), or (iii) trait information associated with the user, at least one of the plurality of interactions corresponding to an interaction with the application independent of provision of any message (page 2 paragraph 0021 illustrating determining a correlation between the user’s response to prompt for exercise and glucose levels, for example).
Claim 12: Iyer discloses:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
wherein the message comprises at least one of a short message service (SMS) message (page 3 paragraph 0030 illustrating SMS text), a multimedia messaging service (MMS) (page 2 paragraph 0020 illustrating a media player), an in-app message (page 10 paragraph 0074 illustrating communications in an application portal), or a chat bot message (page 10 paragraph 0074 illustrating automated chats), wherein the message is to be presented to the user, at least in partial concurrence with the user being on a medication to address a condition of the user (page 10 paragraph 0077 illustrating prompting the user to take medication at an indicated time).
Claims 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24: these claims recite substantially similar limitations as those of claims 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, respectively, and are therefore also rejected for substantially similar rationale as applied above, and incorporated herein.
Claim 25: Iyer discloses:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
wherein the event dataset comprises: (i) first data corresponding to one or more application-initiated actions (as discussed above with respect to the duration period, and incorporated herein); and (ii) second data corresponding to one or more user-initiated actions (page 3 paragraph 0029 illustrating the user entering feedback).
Claim 26: Iyer discloses:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
wherein the event dataset further comprises at least one of electronic patient-reported outcome (ePRO) data (page 3 paragraph 0029 illustrating the user entering feedback), a health metric (page 3 paragraph 0029 illustrating testing data), or a user-reported outcome associated with a condition of the user (page 3 paragraph 0029 illustrating user feedback).
Claim 27: Iyer discloses:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
wherein the event dataset aggregates data from a plurality of data sources associated with the user, wherein the plurality of data sources is associated with at least one of the user device or a device that is not the user device (page 4 paragraph 0033 illustrating a database stored on a server).
Claims 28-30: Iyer discloses that the purpose of the disclosed system is to encourage the user to adhere the digital therapeutic by responding and being treated by the system (page 1 paragraph 0003).
Claims 31, 32, 33, 34, 35, 36: these claims recite substantially similar limitations as those of claims 25, 26, 27, 28, 29, 30, respectively, and are therefore also rejected for substantially similar rationale as applied above, and incorporated herein.
Claim(s) 4, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iyer in in view of Jain (11789837).
Claim 4: Iyer discloses:
The method of claim 2, as discussed above and incorporated herein.
Iyer further discloses:
wherein determining the message presentation time further comprises:
comparing the plurality of interaction times of the event dataset for the user with one or more of the plurality of clusters of the clustering model (page 11 paragraph 0084 illustrating comparing the N+1 stage score with the user’s N stage score),
identifying, from the plurality of clusters,
using the respective message presentation times of each of the subset of clusters as a plurality of message presentation times for presenting messages to the user device (page 11-12 paragraph 0087 illustrating adjusting the user’s time based on the result of the clustering comparison).
Iyer in view of Gao do not disclose:
a subset of clusters.
Jain discloses:
a subset of clusters (column 79 line 4-9 illustrating using a cluster subset).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the cluster subset of Jain within the digital therapeutic management system of Iyer with the motivation of improving patient care by providing the most accurate ML technique to predict the most desirable patient outcome for a digital therapeutic (Jain; column 79 line 25-28).
Claims 16: these claims recite substantially similar limitations as those of claim 4, respectively, and are therefore also rejected for substantially similar rationale as applied above, and incorporated herein.
Response to Arguments
In the Remarks filed on 10 April 2026, Applicant makes numerous arguments. Examiner will address these arguments in the order presented.
On page 13 Applicant declares that the none of the limitations can be practically performed in the human mind.
While Applicant’s declaration has been carefully considered, the argued limitations do not place any limit on the type of data or how the data is processed. Accordingly, Applicant’s declaration lacks nexus with the broad scope of the claim.
On page 13 Applicant declares that the system provides various technical improvements.
While Applicant’s declaration has been carefully considered, the argued limitations are part of the abstract idea; the additional elements do not provide any technical improvement for the reason stated above and incorporated herein.
On page 14-16 Applicant argues that the claim does not recite a mathematical concept.
As discussed in the section above and incorporated herein, the Specification as originally filed discloses any machine learning models including a plurality of algorithms (page 12 paragraph 0035).
Applicant does not dispute that this disclosure is not within the broadest reasonable interpretation of the claim.
Therefore, Examiner maintains that the claim recites a mathematical concept.
On page 16-17 Applicant argues that the claims do not recite a mental process.
In making this argument, Applicant does not distinguish between the abstract step and the computer.
While Applicant’s declaration has been carefully considered, the declaration lacks nexus with the claim because the claim does not put any specific limitation on the type of data. Instead, Examiner maintains that small amounts of data, including simpleton data, are well within the scope of the broadest reasonable interpretation, and could be practically performed in the human mind.
On page 17-19 Applicant argues that the claim is not directed towards Certain Methods of Organizing Human Activity.
While Applicant’s argument has been carefully considered, Examiner maintains that determine a presentation time for a message is directed towards human activity that a person would perform when determining how to treat a patient.
On page 19-23 Applicant argues that the claim provides a technical improvement.
While Applicant’s argument has been carefully considered, Examiner maintains that the argued limitations are part of the abstract idea. Even newly discovered or novel judicial exceptions are still exceptions. MPEP 2106.04(I)
On page 24 Applicant argues that the applied art do not disclose an event dataset identifying a plurality of interaction times.
Iyer discloses a duration and frequency of user engagement at stage N used to monitor the user’s response to such level of engagement (page 11 paragraph 0084)
Based on the evidence presented above, Applicant' s arguments are not found persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Doganata (20050080806) discloses a technique of managing events, including messaging events (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
Priyadarshan (20120042262) discloses a system for managing interaction with a user (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRAN N NGUYEN whose telephone number is (571)272-0259. The examiner can normally be reached Monday-Friday 9AM-5PM Eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KAMBIZ ABDI can be reached on (571)272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.N.N./ Examiner, Art Unit 3685
/KAMBIZ ABDI/ Supervisory Patent Examiner, Art Unit 3685
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