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 .
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) 1-24 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 over networked environments, comprising:
obtaining, by one or more processors, for a user device, an event dataset identifying a plurality of interaction times corresponding to a plurality of interactions over time window by a user with an application on the user device to address a condition of the user;
applying, by the one or more processors, the event dataset to a machine learning (ML) model, 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, at least one of the respective plurality of interactions detected in response to provision of one or more messages;
determining, by the one or more processors, based on applying the event dataset to the ML model, a delivery time based at least on a behavioral pattern in a plurality of prior interactions identified in the event dataset, the delivery time corresponding to a likelihood of a future user interaction;
storing, by the one or more processors, a schedule corresponding to the delivery time and the behavioral pattern;
providing, by the one or more processors, instructions to cause presentation of a message on the user device at a subsequent time window corresponding with the delivery 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; and
adding, by the one or more processors, the data to the event dataset.
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 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:
obtaining, by one or more processors, for a user device, an event dataset identifying a plurality of interaction times corresponding to a plurality of interactions over time window by a user with an application on the user device to address a condition of the user;
applying, by the one or more processors, the event dataset to a machine learning (ML) model;
by the one or more processors, based on applying the event dataset to the ML model;
storing, by the one or more processors, a schedule corresponding to the delivery time and the behavioral pattern;
providing, by the one or more processors, instructions to cause presentation of a message on the user device at a subsequent time window corresponding with the delivery 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; and
by the one or more processors.
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)).
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.
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 recite substantially similar limitations as those of claims 1-12, and are also therefore rejected for substantially similar rationale as applied to claims 1-12 above, and incorporated herein.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 5-15, 17-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iyer (20230360754) in view of Gao (20220131824).
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:
obtaining, by one or more processors, for a user device, an event dataset identifying a plurality of interaction times corresponding to a plurality of interactions over time window by a user with an application (page 6-7 paragraph 0049 illustrating identifying a target window to address the user’s treatment) on the user device to address a condition of the user (Figure 1 label 1, page 6 paragraph 0048 illustrating an mHealth application on the user’s mobile device);
applying, by the one or more processors, the event dataset to a machine learning (ML) model (page 9 paragraph 0070 illustrating an ML model), 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), at least one of the respective plurality of interactions detected in response to provision of one or more messages (page 7 paragraph 0051 illustrating interaction data with the patient);
determining, by the one or more processors, based on applying the event dataset to the ML model, a delivery time (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)
storing, by the one or more processors, a schedule corresponding to the delivery time and the behavioral pattern (page 3-4 paragraph 0031 illustrating storing the data used to determine delivery as well as patient historical data);
providing, by the one or more processors, the message for instructions to cause presentation of a message on the user device at a subsequent time window corresponding with the delivery 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); and
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).
Iyer does not disclose:
based at least on a behavioral pattern in a plurality of prior interactions identified in the event dataset, the delivery time corresponding to a likelihood of a future user interaction at which a message is to be provided to the user device during a subsequent time window.
Gao discloses:
based at least on a behavioral pattern in a plurality of prior interactions identified in the event dataset, the delivery time corresponding to a likelihood of a future user interaction at which a message is to be provided to the user device during a subsequent time window (page 11 paragraph 0094 illustrating learning from a user’s message preferences and responses for future candidate messages to increase the likelihood that the user will receive the message and engage/respond thereto [considered to be a form of “future user interaction”]).
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 message preference selection of Gao within the digital therapeutic management system of Iyer with the motivation of improving patient care by enhancing messages for a digital therapeutic, e.g. smoking cessation (Gao; page 12 paragraph 0104).
Claim 2: Iyer in view of Gao disclose:
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 defined delivery 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 in view of Gao disclose:
The method of claim 2, as discussed above and incorporated herein.
Iyer further discloses:
wherein determining the delivery 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 defined delivery time of the cluster as the defined delivery time (page 11-12 paragraph 0087 illustrating adjusting the user’s time based on the result of the clustering comparison).
Claim 5: Iyer in view of Gao disclose:
The method of claim 2, as discussed above and incorporated herein.
Iyer further discloses:
wherein determining the defined delivery time further comprises determining a schedule identifying, for each defined delivery time of a plurality of defined delivery 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 in view of Gao disclose:
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 schedule identifying a corresponding plurality of delivery 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 schedule 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 in view of Gao disclose:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
wherein determining the defined delivery time further comprises determining, based on applying the event dataset to the ML model, a score indicating a degree of confidence for the defined delivery time (page 11 paragraph 0084 illustrating determining if a N+1 stage score [considered to be a form of “confidence”]);
determining, by the one or more processors, that the defined delivery 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 in view of Gao disclose:
The method of claim 1, as discussed above and incorporated herein.
Iyer further discloses:
further comprising:
receiving, by the one or more processors, a subsequent event dataset identifying a subsequent plurality of interaction times corresponding to a subsequent plurality of interactions over the subsequent time window by the user with the application in response to provision of at least one of the plurality of one or more messages (page 11 paragraph 0084 illustrating obtaining data from the user interaction to determine the current engagement level); and
updating, by the one or more processors, the ML model using the subsequent event dataset and the defined delivery time (page 11 paragraph 0084 illustrating updating the ML model based on the improved engagement level at stage N+1).
Claim 9: Iyer in view of Gao disclose:
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 defined delivery 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 defined delivery times of the initiation schedule (page 2 paragraph 0021 illustrating presenting initial messages to the user), and
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 in view of Gao disclose:
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 defined delivery 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 in view of Gao disclose:
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 the 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 in view of Gao disclose:
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 the condition (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(s) 4, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iyer in view Gao, as applied above to parent claims 2, 14 above, as applicable, and further in view of Jain (11789837).
Claim 4: Iyer in view of Gao disclose:
The method of claim 2, as discussed above and incorporated herein.
Iyer further discloses:
wherein determining the delivery 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 defined delivery times of each of the subset of clusters as a plurality of defined delivery times for the schedule (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 in view of Gao 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 claims 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 20 October 2025, Applicant makes numerous arguments. Examiner will address these arguments in the order presented.
On page 11-12 Applicant asserts that the claims merely involve an abstract concept because the claims recite various steps performed by a computer.
While Applicant’s arguments have been fully and carefully considered, they are not persuasive because the highlighted portions have been identified as being directed towards an abstract idea, as discussed above and incorporated herein.
On page 12 Applicant argues that the claims are not directed towards an enumerated category of abstract idea.
While Applicant’s arguments have been fully and carefully considered, they are not persuasive because the highlighted portions have been identified as being directed towards an abstract idea, as discussed above and incorporated herein.
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.
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 extension fee 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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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