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 .
Status of Claims
This action is in reply to an amendment filed on 06/09/2026. Claims 14, 16-18, 23, and 24 have been amended. Claims 15 and 20 have been cancelled. Claim 35 has been added. Claims 1-13 and 28-34 have been withdrawn. Therefore, claims 14, 16-19, 21-27 and 35 are currently pending and have been examined.
Claim Rejections - 35 USC § 102
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 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)(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.
Claims 14, 18, 21, and 22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Etkin, et al. (US 2025/0268527 A1).
With regards to claim 14, Etkin teaches a method of treating a mental health or substance abuse disorder in a subject, the method comprising: processing, using a machine learning model trained using a training dataset that includes historical data including biological data of a plurality of historical subjects, digital biomarker data of the plurality of historical subjects (see at least ¶ 0004, 0007, training, based at least on training data, the machine learning model (SELSER), the training data including brain signals of a plurality of subjects), and responses to questions associated with digital content by the plurality of historical subjects (see at least figures 17A-B, ¶ 0030, using self-report questionnaires (HAMD-17, QIDS, MADRS, Beck Depression Inventory, Spielberger State-Trait Anxiety Inventory) as clinical outcome/input data, runs machine learning prediction of treatment outcome from symptoms using these questionnaire scores as model input features; ¶ 0259, one or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry), a set of data streams associated with the subject to determine whether a brain plasticity of the subject has increased (see at least ¶ 0057, using machine learning model to classify levels of brain activity; ¶ 0061, using rTMS to increase the brain activity/plasticity of the targeted brain region), the set of data streams including at least one of: biological data of the subject, digital biomarker data of the subject, or responses to questions associated with digital content by the subject (see at least ¶ 0007, brain signals [biological data of subject]); in response to determining that the brain plasticity of the subject has increased, determining a treatment routine for administrating a drug based on historical data associated with the subject and information indicative of a current state of the subject extracted from the set of data streams of the subject, the treatment routine including a recommended timing (see at least ¶ 0071-0080, 0237-0239, generate treatment outcome prediction indicating a patient’s response to treatment including specific medication class selection (SSRI, SNRI TCA, MAOI, etc.)); and administering the drug to the subject based on the treatment routine (see at least ¶ 0088, treatment is initiated with smaller dosages which are less than the optimum dose of the compound. Thereafter, the dosage is increased by small increments until the optimum effect under circumstances is reached).
With regards to claim 18, Etkin teaches the method of claim 14, wherein the responses to the questions associated with the digital content by the plurality of historical subjects and the responses to the questions associated with the digital content by the subject include at least one of: self-reported activity data, self-reported condition data, or patient responses to questionnaires and surveys (see at least ¶ 0092, self-reported questionnaires).
With regards to claim 21, Etkin teaches the method of claim 14, wherein the treatment routine includes gradually increasing an amount or volume of the drug being administered over a predefined period of time (see at least ¶ 0088, treatment is initiated with smaller dosages which are less than the optimum dose of the compound. Thereafter, the dosage is increased by small increments until the optimum effect under circumstances is reached).
With regards to claim 22, Etkin teaches the method of claim 14, wherein the treatment routine includes administering the drug at periodic intervals (see at least ¶ 0088, treatment is initiated with smaller dosages which are less than the optimum dose of the compound. Thereafter, the dosage is increased by small increments until the optimum effect under circumstances is reached. Dosage amounts and intervals can be adjusted individually to provide levels of the administered compound effective for the particular clinical indication being treated).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 16, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Etkin, et al. (US 2025/0268527 A1) in view of Ozen Irmak, et al. (US 2022/0130518 A1).
With regards to claim 16, Etkin fails to teach the method of claim 14, wherein the biological data of the plurality of historical subjects and the biological data of the subject include at least one of: heart beat data, heart rate data, blood pressure data, body temperature, vocal-acoustic data, electrocardiogram data, or sleep data. Ozen Irmak teaches the method of claim 14, wherein the biological data of the plurality of historical subjects and the biological data of the subject include at least one of: heart beat data, heart rate data, blood pressure data, body temperature, vocal-acoustic data, electrocardiogram data, or sleep data (see at least ¶ 0047). It would have been obvious to one of ordinary skill in the art to combine the mental health machine learning method of Ozen Irmak with the machine learning depression treatment system of Etkin with the motivation of effective care of mental disorders (Ozen Irmak, ¶ 0002-0004).
With regards to claim 17, Etkin fails to teach the method of claim 14, wherein the digital biomarker data of the plurality of historical subjects and the digital biomarker data of the subject includes at least one of: activity data, psychomotor data, response time data of responses to questions associated with the digital content, facial expression data, pupillometry, hand gesture data, or sleep data. Ozen Irmak teaches the method of claim 14, wherein the digital biomarker data of the plurality of historical subjects and the digital biomarker data of the subject includes at least one of: activity data, psychomotor data, response time data of responses to questions associated with the digital content, facial expression data, pupillometry, hand gesture data, or sleep data (see at least ¶ 0047). It would have been obvious to one of ordinary skill in the art to combine the mental health machine learning method of Ozen Irmak with the machine learning depression treatment system of Etkin with the motivation of effective care of mental disorders (Ozen Irmak, ¶ 0002-0004).
With regards to claim 19, Etkin fails to teach the method of claim 14, wherein the model includes: a general linear model, a neural network, a support vector machine (SVM), clustering, or combinations thereof. Ozen Irmak teaches the method of claim 14, wherein the model includes: a general linear model, a neural network, a support vector machine (SVM), clustering, or combinations thereof (see at least ¶ 0075). It would have been obvious to one of ordinary skill in the art to combine the mental health machine learning method of Ozen Irmak with the machine learning depression treatment system of Etkin with the motivation of effective care of mental disorders (Ozen Irmak, ¶ 0002-0004).
Claims 23 and 25-27 are rejected under 35 U.S.C. 103 as being unpatentable over Etkin, et al. (US 2025/0268527 A1) in view of Raz, et al. (US 2021/0183519 A1).
With regards to claim 23, Etkin fails to teach the method of claim 14, wherein the mental health or substance abuse disorder is drug abuse, and the treatment routine includes administration of ibogaine or noribogaine. Raz teaches the method of claim 14, wherein the mental health or substance abuse disorder is drug abuse (see at least ¶ 0059), and the treatment routine includes administration of ibogaine or noribogaine (see at least ¶ 0048). It would have been obvious to one of ordinary skill in the art to combine the drug therapy method of Raz with the machine learning depression treatment system of Etkin with the motivation of enhancing the safety and efficacy of treatment for mental health conditions with specialized drugs (Raz, ¶ 0001-0003).
With regards to claim 25, Etkin teaches the method of claim 14, wherein the mental health or substance abuse disorder is a depressive disorder (see at least ¶ 0008).
Etkin fails to teach …and the treatment routine includes administration of psilocybin or psilocin. Raz teaches …and the treatment routine includes administration of psilocybin or psilocin (see at least ¶ 0048). It would have been obvious to one of ordinary skill in the art to combine the drug therapy method of Raz with the machine learning depression treatment system of Etkin with the motivation of enhancing the safety and efficacy of treatment for mental health conditions with specialized drugs (Raz, ¶ 0001-0003).
With regards to claim 26, Etkin fails to teach the method of claim 14, wherein the mental health or substance abuse disorder is posttraumatic stress disorder, and the treatment routine includes administration of 3,4- Methylenedioxymethamphetamine (MDMA). Raz teaches the method of claim 14, wherein the mental health or substance abuse disorder is posttraumatic stress disorder (see at least ¶ 0059), and the treatment routine includes administration of 3,4- Methylenedioxymethamphetamine (MDMA) (see at least ¶ 0050). It would have been obvious to one of ordinary skill in the art to combine the drug therapy method of Raz with the machine learning depression treatment system of Etkin with the motivation of enhancing the safety and efficacy of treatment for mental health conditions with specialized drugs (Raz, ¶ 0001-0003).
With regards to claim 27, Etkin teaches the method of claim 14, wherein the mental health or substance abuse disorder is a depressive disorder (see at least ¶ 0008).
Etkin fails to teach …and the treatment routine includes administration of N, N- dimethyltryptamine (DMT). Raz teaches …and the treatment routine includes administration of N, N- dimethyltryptamine (DMT) (see at least ¶ 0048). It would have been obvious to one of ordinary skill in the art to combine the drug therapy method of Raz with the machine learning depression treatment system of Etkin with the motivation of enhancing the safety and efficacy of treatment for mental health conditions with specialized drugs (Raz, ¶ 0001-0003).
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Etkin, et al. (US 2025/0268527 A1) in view of Anton, et al. (US 2018/0369238 A1).
With regards to claim 24, Etkin fails to teach the method of claim 14, wherein the mental health or substance abuse disorder is drug abuse, and the treatment routine includes administration of salvinorin A. Anton teaches the method of claim 14, wherein the mental health or substance abuse disorder is drug abuse (see at least ¶ 0052, opiate abuse), and the treatment routine includes administration of salvinorin A (see at least ¶ 0065). It would have been obvious to one of ordinary skill in the art to combine the drug therapy method of Anton with the machine learning depression treatment system of Etkin with the motivation of effective treatment for a psychiatric, mental, and/or neurological disorders (Anton, ¶ 0006, 0009).
Claim 35 is rejected under 35 U.S.C. 103 as being unpatentable over Etkin, et al. (US 2025/0268527 A1) in view of Weis, et al. (US 2020/0352956 A1).
With regards to claim 35, Etkin fails to teach the method of claim 14, wherein the mental health or substance abuse disorder is drug abuse, and the treatment routine includes administration of noribogaine. Weis teaches the method of claim 14, wherein the mental health or substance abuse disorder is drug abuse (see at least ¶ 0003, opioid addiction), and the treatment routine includes administration of noribogaine (see at least ¶ 0009). It would have been obvious to one of ordinary skill in the art to combine the drug therapy method of Weis with the machine learning depression treatment system of Etkin with the motivation of personalized treatment (Weis, ¶ 0315-0316).
Response to Arguments
Applicant's arguments with respect to the 35 USC § 112 rejections set forth in the previous office action have been considered, and are withdrawn based on cancellation of claim 20.
Applicant's arguments with respect to the 35 USC § 101 rejections set forth in the previous office action have been considered, and are persuasive. Therefore, these rejections are withdrawn.
Applicant's arguments with respect to the 35 USC § 103 rejections set forth in the previous office action have been considered, but are moot in view of the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Feuerstein (US 2022/0148707 A1) which discloses computer-implemented techniques for delivering and administering adaptive, personalized care to patients suffering from mental disorders and illnesses (including those creating a risk of suicide). Some aspects described herein provide a computer-implemented method for adapting treatment for a patient based on patient data, and administering the adapted treatment to the patient. For example, a patient's device may obtain the patient data and adapt and administer the treatment. Some aspects described herein provide a system for delivering adaptive treatment of mental disorders and illnesses over a communication network to one or more devices. Some aspects described herein provide a computer-implemented method for administering treatment activities to treat a patient who is at risk of dying by suicide. For example, a patient's device (e.g., mobile phone, tablet, computer, etc.) may select and administer one or more treatment activities to reduce the patient's risk of suicide.
Heneghan, et al. (US 11,191,466 B1) which discloses physiological variables, metrics, biomarkers, and other data points can be used, in connection with a non-invasive wearable device, to screen for, and predict, mental health issues and cognitive states. In addition to metrics such as heart rate, sleep data, activity level, gamification data, and the like, information such as text message and email data, as well as vocal data obtained through a phone and/or a microphone, may be analyzed, provided user authorization. Applying predictive modeling, one or more of the monitored metrics can be correlated with mental states and disorders. Identified patterns can be used to update the predictive models, such as via machine learning-trained models, as well as to update individual event predictions. Information about the mental state predictions, and updates thereto, can be surfaced to the user accordingly.
N. F. Zulkifli, Z. C. Cob, A. A. Latif and S. M. Drus, "A Systematic Review of Machine Learning in Substance Addiction," 2020 8th International Conference on Information Technology and Multimedia (ICIMU), Selangor, Malaysia, 2020, pp. 103-107, doi: 10.1109/ICIMU49871.2020.9243581 which discloses substance addiction affects millions of people worldwide and there is no cure for addiction. With the emergence of machine learning, it has open doors for healthcare industry to incorporate technology to help healthcare workforce to make better decision in treating patients. By applying machine learning in understanding patients with substance addiction, it can help in determining their treatment. This paper aims to provide a summary of how effective machine learning method is applied in addiction studies in which 11 studies are included in this paper by using PRISMA methodology to find sources.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joey Burgess whose telephone number is (571)270-5547. The examiner can normally be reached Monday through Friday 9-6.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOSEPH D BURGESS/ Primary Examiner, Art Unit 3685
Prosecution Insights