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 .
DETAILED ACTION
This Office Action represents the first action on the merits
Claim(s) 1-16,23-24,27,29-31,34-66,68,77,79-150 are cancelled
Claim(s) 17,19-21,25-26,28,32-33,67,69-76,78 are amended
Claim(s) 151-152 are new
Claim(s) 17-22,25-26, 28, 32,67,69-71,75-76,78,151-152 are pending
Priority
This Application claims priority to PCT Application PCT/US2022/042797 filed 07 September 2022 and Provisional Application 63241434 filed 07 September 2021
Information Disclosure Statement
The Information Disclosure Statement(s) (lDS) submitted on 18 November 2024 is/are in compliance with the provisions of 37 CFR 1.97 and has/have been fully considered by the Examiner.
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 17-22,25-26,32,67,69-70,75-76,78,151-152 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 17, 67 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) computer-implemented method and system. The limitations of:
Claim(s) 17 and 67 (Claim 17 being representative)
providing, a […] therapeutic to the user, the […] therapeutic comprising a treatment plan comprising:
a series of therapy lessons, wherein each therapy lesson addresses one or more maladaptive beliefs relating to dietary and/or lifestyle behaviors of the user;
at least one interactive exercise to reinforce an improvement with respect to the one or more maladaptive beliefs and/or to initiate a new dietary and/or lifestyle behavior;
the method further comprising:
collecting responses from the user during at least one therapy lesson and/or at least one interactive exercise, and/or collecting biometric data from the user during and/or after at least one therapy lesson and/or said at least one interactive exercise;
responsive to the collecting, identifying one or more goals for the user to achieve between a current and a next therapy lesson and/or between a current and a next interactive exercise using one or more algorithms, and wherein the one or more algorithms is trained using responses and/or biometric data from a plurality of users;
and dynamically adjusting the treatment plan based on one or more identified goals.
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 processor, treatment processor and non-transitory computer-readable storage medium, the claimed invention amounts to managing personal behavior or interaction between people. The Examiner notes that certain “method[s] 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 claims recite an abstract idea.
The Examiner notes that the therapeutic being “digital” is a consequence of confining the identified abstract idea to a computer.
The claim further recites “training a machine learning model.” When given its broadest reasonable interpretation in light of the disclosure, the training of a machine learning model using responses and biometric data from a plurality of subjects represents the creation of mathematical interrelationships between data. 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.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a processor, treatment processor and non-transitory computer-readable storage medium that implement the identified abstract idea. The processor, treatment processor and non-transitory computer-readable storage medium are not described by the applicant and is recited at a high-level of generality (i.e., generic computer components performing generic computer functions) 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 a trained machine learning model to identify goals. 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. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to identify goals merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use ([the one or more machine learning algorithms is selected from the group comprising or consisting of Linear Regression, Logistic Regression, Linear Discriminant Analysis, Classification and Regression Trees, Decision Trees, Clustering, Inductive Logic Processing, Stochastic Modeling, Statistical Modeling, Case-Based Reasoning, Bayes and Naive Bayes, K-Nearest Neighbors (KNN), Learning Vector Quantization (LVQ), Support Vector Machines (SVM), Random Forest, Boosting, AdaBoost, or an artificial neural network selected from the group comprising or consisting of convolutional neural networks (CNN), recurrent neural networks (RNN), and recurrent convolutional neural networks (RCNN)][See Para. 0012]) and thus fails to add an inventive concept to the claims.
The claims do 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 processor, treatment processor and non-transitory computer-readable storage medium 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”).
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 identify goals was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use ([the one or more machine learning algorithms is selected from the group comprising or consisting of Linear Regression, Logistic Regression, Linear Discriminant Analysis, Classification and Regression Trees, Decision Trees, Clustering, Inductive Logic Processing, Stochastic Modeling, Statistical Modeling, Case-Based Reasoning, Bayes and Naive Bayes, K-Nearest Neighbors (KNN), Learning Vector Quantization (LVQ), Support Vector Machines (SVM), Random Forest, Boosting, AdaBoost, or an artificial neural network selected from the group comprising or consisting of convolutional neural networks (CNN), recurrent neural networks (RNN), and recurrent convolutional neural networks (RCNN)][See Para. 0012]). 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. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Claims 18-22,25-26,32,69-70,75-76,78,151-152 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(s) 18 merely describe(s) cardiometabolic disorders. Claim(s) 19,69 merely describe(s) adjusting user goals. Claim(s) 20, 70 merely describe(s) modifying therapy lessons. Claim(s) 21, 71 merely describe(s) selecting maladaptive beliefs. Claim(s) 22, 72 merely describe(s) therapy lessons. Claim(s) 25,75 merely describe(s) notifications. Claim(s) 26, 76 merely describe(s) using previously reach set foals. Claim(s) 28, 78 merely describe(s) prompts. Claim(s) 32 merely describe(s) notifications. Claim(s) 33 merely describe(s) changing the treatment. Claim(s) 151-152 merely describe(s) interacting with the user.
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 Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
Claims 17-19,21-22,25-26,28,32,67,69,71,75-76,78,151-152 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over APPELBAUM’838 et al (Foreign Publication WO-2020092838-A1) in view of Jain et al (US Publication No. 20190243944).
Regarding Claim 17
APPELBAUM’838 teaches a computer-implemented method for dynamically adjusting a treatment plan for a user having a cardiometabolic disorder, the method comprising:
providing, by at least one processor, a digital therapeutic to the user, the digital therapeutic comprising a treatment plan comprising [APPELBAUM’838 at Para. 0166 teaches the behavioral invention app helps participants understand the steps they should prioritize by presenting them with a treatment plan that summarizes their daily and weekly goals (behavioral invention app interpreted as digital therapeutic)]:
a series of therapy lessons, wherein each therapy lesson addresses one or more maladaptive beliefs relating to dietary and/or lifestyle behaviors of the user [APPELBAUM’838 at Para. 0159 teaches participants assigned to the behavioral intervention app are asked to engage with one therapy lesson each week during the study, to report plant-based meals and exercise regularly and report their blood glucose values daily; APPELBAUM’838 at Para. 0164 teaches The BT process involves: 1) identifying and measuring maladaptive thoughts based on misinformed or false underlying core beliefs (e.g., those related to macronutrient fears, the hedonic nature of eating, physical exertion, other perceived barriers to changing lifestyle) that lead to disease-promoting behaviors; 2) replacing these maladaptive core beliefs and thought patterns with adaptive ways of thinking developed from rational reflection];
at least one interactive exercise to reinforce an improvement with respect to the one or more maladaptive beliefs and/or to initiate a new dietary and/or lifestyle behavior [APPELBAUM’838 at Para. 0172 teaches the skill exercises are designed to improve dietary, exercise or supportive behavioral patterns];
the method further comprising:
collecting responses from the user during at least one therapy lesson and/or at least one interactive exercise, and/or collecting biometric data from the user during and/or after at least one therapy lesson and/or said at least one interactive exercise [APPELBAUM’838 at Para. 0173 teaches in addition to completing a therapy lesson and one or more skill exercises, the app asks patients to self-report diet and exercise behaviors, medication adherence, and biometrics (e.g., fasting blood glucose, weight, and, if the patient also has hypertension, blood pressure) each day (interpreted as collecting biometric data from the user during and/or after at least one therapy lesson)];
responsive to the collecting, using at least one treatment processor, identifying one or more goals for the user to achieve between a current and a next therapy lesson and/or between a current and a next interactive exercise [APPELBAUM’838 at Para. 0166 teaches each week, the app asks participants to complete a new behavioral module, along with one or more skill-based exercises that are related to that particular week’s module; APPELBAUM’838 at Para. 0173 teaches weekly behavioral goals, including diet and exercise behaviors, are determined through a goal setting exercise, and are advanced in a manner most likely to maintain or increase self- efficacy], wherein the identifying applies one or more machine learning algorithms, and wherein the one or more machine learning algorithms is trained using responses and/or biometric data from a plurality of users [APPELBAUM’838 at Para. 0024 teaches in some embodiments, the biomarker model is a machine learning model, preferably a tree ensemble method, more preferably a random forest model. In some embodiments, the biomarker model is trained on one or more engagement and/or biometric subject-specific data values. Preferably, the biomarker model is trained on both engagement subject-specific data values and biometric subject-specific data values.];
APPELBAUM’838 does not teach and dynamically adjusting the treatment plan based on one or more identified goals.
Jain teaches and dynamically adjusting the treatment plan based on one or more identified goals [Jain at Para. 0044 teaches the server system 110 automatically adjusts each individual care plan according to the unique set of user inputs, sensor data, and behavior detected for each user (interpret to combine with goals of APPELBAUM’838)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine goals of APPELBAUM’838 with the adjustments of Jain with the motivation to improve outcomes for different categories of patients [Jain at Para. 0003].
Regarding Claim 18
APPELBAUM’838/Jain teach the method according to claim 17,
APPELBAUM’838/Jain further teach wherein the cardiometabolic disorder is selected from the group consisting of type 2 diabetes, gestational diabetes, hypertension, obesity, dyslipidemia, hyperlipidemia, hypertriglyceridemia, non-alcoholic fatty liver disease, non- alcoholic steatohepatitis, hypercholesterolemia and familial hypercholesterolemia, heart disease, coronary artery disease, or chronic kidney disease [APPELBAUM’838 at Para. 0012 teaches in some embodiments, the cardiometabolic disorder is selected from diabetes, dyslipidemia, obesity, or hypertension. In one embodiment, the cardiometabolic disorder is diabetes, and the medication comprises one or more of sulfonylureas, meglitinides, biguanides, thiazolidinediones, or alpha-glucosidase inhibitors].
Regarding Claim 19
APPELBAUM’838/Jain teach the method according to claim 17,
APPELBAUM’838/Jain further teach further comprising dynamically adjusting the goals for the user between consecutive therapy lessons in the series using the at least one treatment processor [APPELBAUM’838 at Para. 0159 teaches participants assigned to the behavioral intervention app are asked to engage with one therapy lesson each week during the study, to report plant-based meals and exercise regularly and report their blood glucose values daily; APPELBAUM’838 at Para. 0173 (see Claim 17 for explanation)], wherein the dynamic adjustment applies one or more machine learning algorithms trained using responses and biometric data from a plurality of users [APPELBAUM’838 at Para. 0024 (see Claim 17 for explanation)], preferably wherein the one or more goals comprise one or more of diet, exercise and medication [APPELBAUM’838 at Para. 0173 (see Claim 17 for explanation)].
Regarding Claim 21
APPELBAUM’838/Jain teach the method according to claim 17,
APPELBAUM’838/Jain further teach wherein the maladaptive belief is selected from the group comprising: ability of the user to change and/or control behaviors; beliefs of the user regarding nutrients, optionally including macronutrients, and importance of various food types; or beliefs of the user regarding experiences in eating and/or exercising [APPELBAUM’838 at Para. 0168 teaches personal beliefs and barriers, such as those related to a participant’s ability to change and control his or her behaviors].
Regarding Claim 22
APPELBAUM’838/Jain teach the method according to claim 17,
APPELBAUM’838/Jain further teach wherein the therapy lesson is specific to the particular condition that the digital therapeutic is treating, or to understanding, addressing, and/or controlling particular human physiological attributes, or to understanding, addressing, and/or controlling particular human physiological responses, or developing certain desirable behaviors [APPELBAUM’838 at Para. 0039 teaches when initially registering with the digital therapy or otherwise beginning to use the application, a patient may indicate a particular health condition (e.g., diabetes, dyslipidemia, high blood-pressure) to be addressed and may then provide certain initial data inputs, which the digital therapy may use to prepare an appropriate therapy regimen to address the indicated condition. The patient may then access the features of the software application to begin tasks assigned to them by the therapy regimen, such as a diet or exercise routine, among other possible tasks].
Regarding Claim 25
APPELBAUM’838/Jain teach the method according to claim 17,
APPELBAUM’838/Jain further teach further comprising generating one or more personalized notifications and communicating the one or more personalized notifications to the user, the one or more personalized notifications selected from the group consisting of guidance, progress reporting, treatment score, reminders, nudges, and rewards [APPELBAUM’838 at Para. 0008 teaches in preferred embodiments, the devices of the digital therapy provider calculate a prioritized list of behavioral actions that are predictive of success in achieving a desired health score value or milestone, and transmits personalized feedback based on same to the patient’s device and/or to their provider’s device (interpreted as guidance)].
Regarding Claim 26
APPELBAUM’838/Jain teach the method according to claim 17,
APPELBAUM’838/Jain further teach wherein the identifying relies at least in part on performance by the user in reaching previously set goals [APPELBAUM’838 at Para. 0008 teaches in one aspect, the devices of the digital therapy provider may execute a variety of machine-learning algorithms and predictive analytics that can use historical and ongoing patient- specific data values to calculate a health score for a patient and to provide specific behavioral feedback based on same, with the intent of motivating a change in behavioral pattern(s) that may improve treatment outcomes].
Regarding Claim 28
APPELBAUM’838/Jain teach the method according to claim 17,
APPELBAUM’838/Jain further teach wherein one or more of the therapy lessons are interactive, and the user inputs information into the digital therapeutic either voluntarily or as the digital therapeutic provides a prompt [APPELBAUM’838 at Para. 0039 teaches the software application may provide patients with various graphical user interfaces (GUIs) that allow the patient to, for example, interact with the features of the software application in a human-friendly way, submit data inputs, log journal entries of the patient’s progress, and receive information and/or feedback related to their treatment and therapy regimen].
Regarding Claim 32
APPELBAUM’838/Jain teach the method according to claim 17,
APPELBAUM’838/Jain further teach further comprising providing biometric notifications in response to entry of the biometric data of the user, optionally wherein the biometric notifications indicate danger levels [APPELBAUM’838 at Para. 0185 teaches saved values that are out of the normal clinical range can generate an automated alert within the app, e.g., and sent, via push notification, to the user (saved values out of normal clinical range interpreted as danger levels)].
Regarding Claim 33
APPELBAUM’838/Jain teach the method according to claim 17,
APPELBAUM’838/Jain further teach the method further comprising determining one or more treatment changes and/or behavioral modifications for the user [APPELBAUM’838 at Para. 0011 teaches b) collecting subject-specific data values associated with a medication adjustment threshold for said cardiometabolic disorder; c) determining by way of predictive analytics using said subject-specific data values whether a medication adjustment threshold has been or will be reached; and d) providing to the coach, clinician or other provider for said patient a medication adjustment alert and/or recommendation if said threshold has been or will be reached within a treatment period].
Regarding Claim 67
APPELBAUM’838 teaches a computer system for dynamically adjusting a treatment plan for a user having a cardiometabolic disorder, the system comprising:
a processor for processing a set of instructions [APPELBAUM’838 at Para. 0057 teaches the operations server 103 may be any computing device comprising a processor and machine-readable memory capable of performing various processes described herein];
and a non-transitory computer-readable storage medium for storing the set of instructions, wherein the instructions, when executed by the processor, perform a method comprising [APPELBAUM’838 at Para. 0072 teaches an operations database 105 may be hosted on any computing device comprising non-transitory machine-readable storage and a processor capable of querying, retrieving, and updating data records of the database]:
providing, by at least one processor, a digital therapeutic to the user, the digital therapeutic comprising a treatment plan comprising [APPELBAUM’838 at Para. 0166 (see Claim 1 for explanation)]:
a series of therapy lessons, wherein each therapy lesson addresses one or more of maladaptive beliefs relating to dietary and/or lifestyle behaviors of the user [APPELBAUM’838 at Para. 0159, 0164 (see Claim 1 for explanation)];
at least one interactive exercise to reinforce an improvement with respect to the one or more maladaptive beliefs and/or to initiate a new dietary and/or lifestyle behavior [APPELBAUM’838 at Para. 0172 (see Claim 1 for explanation)];
the method further comprising:
collecting responses from the user during at least one therapy lesson and/or at least one interactive skill-based exercise, and/or collecting biometric data from the user during and/or after each the therapy lesson and/or at least one interactive exercise [APPELBAUM’838 at Para. 0173 (see Claim 1 for explanation)];
responsive to the collecting, using at least one treatment processor, identifying one or more goals for the user to achieve between a current and a next therapy lesson and/or between a current and a next interactive exercise [APPELBAUM’838 at Para. 0166, 0173 (see Claim 1 for explanation)], wherein the identifying applies one or more machine learning algorithms, and wherein the one or more machine learning algorithms is trained using responses and/or biometric data from a plurality of users [APPELBAUM’838 at Para. 0024 (see Claim 1 for explanation)];
APPELBAUM’838 does not teach and dynamically adjusting the treatment plan based on one or more identified goals.
Jain teaches and dynamically adjusting the treatment plan based on one or more identified goals [Jain at Para. 0044 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine goals of APPELBAUM’838 with the adjustments of Jain with the motivation to improve outcomes for different categories of patients [Jain at Para. 0003].
Regarding Claim 69
Claim(s) 69 is/are analogous to Claim(s) 19, thus Claim(s) 69 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 19.
Regarding Claim 71
Claim(s) 71 is/are analogous to Claim(s) 21, thus Claim(s) 71 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 21.
Regarding Claim 75
Claim(s) 75 is/are analogous to Claim(s) 25, thus Claim(s) 75 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 25.
Regarding Claim 76
Claim(s) 76 is/are analogous to Claim(s) 26, thus Claim(s) 76 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 26.
Regarding Claim 78
Claim(s) 78 is/are analogous to Claim(s) 28, thus Claim(s) 78 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 28.
Regarding Claim 151
APPELBAUM’838/Jain teach the method according to claim 17,
APPELBAUM’838/Jain further teach further comprising interacting with the user so that the user either accepts the identified goals to be achieved, or identifies other goals to be achieved [APPELBAUM’838 at Para. 0040 teaches conventional well-being software products are essentially “voluntary” products that are premised on a user’s voluntary participation and cue off the user’s selected goals, so the functionality is broadly open to user changes and at-will interactions, but not premised on adherence to a particular regimen or other set of tasks].
Regarding Claim 152
Claim(s) 152 is/are analogous to Claim(s) 151, thus Claim(s) 152 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 151.
Claims 20, 70 are rejected under 35 U.S.C. 103(a) as being unpatentable over APPELBAUM'838, Jain as applied to claim 17, 67 above, and further in view of APPELBAUM'080 et al (US Publication No. 20190074080).
Regarding Claim 20
APPELBAUM’838/Jain teach the method according to claim 17,
APPELBAUM’838/Jain do not teach further comprising modifying, for the user, a subsequent one of the therapy lessons and/or at least one interactive exercise using the at least one processor, wherein the modifying applies one or more machine learning algorithms.
APPELBAUM’080 teaches further comprising modifying, for the user, a subsequent one of the therapy lessons and/or at least one interactive exercise using the at least one processor, wherein the modifying applies one or more machine learning algorithms [APPELBAUM’080 at Para. 0109 teaches a computing device may execute one or more artificial intelligence and/or machine learning software programs to generate and update health score modeling algorithms, and/or generate and update the health scores of customers; APPELBAUM’080 at Para. 0187 teaches as an example, using a coach computer a coach may review a GUI output of the data of a customer, and determine that therapy regimen should be adjusted, which may include, for example, changing threshold values, adjusting the required tasks for the customer to perform (e.g., diet, exercise, journal entries), and updating the chatbot queue of the customer to add, remove, or replace one or more chatbot identifiers; APPELBAUM’080 at Para. 0190 teaches in an optional step 221, the operations server may adjust features and/or resources provided to the customer and/or the therapeutic software application on the customer device based on the customer' s health score, certain body measurement scores in the customer profile record, and/or achievement milestone comparisons].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of APPELBAUM’838, Jain with the modifications of APPELBAUM’080 with the motivation to improve compliance and outcomes [APPELBAUM’080 at Para. 0007].
Regarding Claim 70
Claim(s) 70 is/are analogous to Claim(s) 20, thus Claim(s) 70 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 20.
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
IYER et al (US Publication No. 20210174924) discloses a system adapted to improve the health of a user.
EASTON et al (US Publication No. 20190074081) discloses a platform and method for administering a digital behavioral health treatment to an individual.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN C EDOUARD whose telephone number is (571)270-0107. The examiner can normally be reached M-F 730 - 430.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on (571) 272 - 6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683