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 application filed on 11/07/2023. Claims 1-34 are currently pending and have been examined.
Election/Restrictions
Claims 1-13 and 28-34 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to nonelected inventions, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 11/24/2025.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 20 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 20 recites the limitation "the processor” and “the predictive score”. There is insufficient antecedent basis for these limitations in the claim.
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 14-27 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), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept — i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
STEP 1
The claims are directed to a method which is included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims recite the abstract idea (based on claim 1) of:
A method of treating a mental health or substance abuse disorder in a subject, the method comprising: processing, using a learning model, a set of data streams associated with the subject to determine a likelihood of an adverse event, the set of data steams 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; in response to the likelihood of the adverse event being greater than a predefined threshold, 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; and administering the drug to the subject based on the treatment routine.
The claims, as illustrated by the limitations of Claim 1 above, recite an abstract idea within the “certain methods of organizing human activity” grouping — managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
The claims recite determining a treatment routine and administering a drug to a subject based on the current state of subject involving the likelihood of an adverse event determined from received subject data. Determining a treatment routine and administering a drug to a subject based on the current state of subject involving the likelihood of an adverse event determined from received subject data is a process that merely organizes human activity, as it involves following rules and instructions to process data, determine likelihood of adverse event is greater than threshold, determine treatment routine, and administer drug. It also involves an interaction between a person and a computer. Interaction between a person and computer qualifies as interaction under certain methods of organizing human activity. See MPEP 2106.04(a)(2)(II). As such, the claims recite an abstract idea within the categories of certain methods of organizing human activity.
The dependent claims 15, 20-22 recite further abstract ideas within the category of certain methods of organizing human activity, such as 15 the learning model is trained using a training dataset, the training dataset including a historical dataset from a plurality of historical subjects, the historical dataset including: biological data of the plurality of historical subjects, digital biomarker data of the plurality of historical subjects, and responses to questions associated with digital content by the plurality of historical subjects; 20 determine the likelihood of the adverse event by comparing the predictive score to a predefined score; 21 the treatment routine includes gradually increasing an amount or volume of the drug being administered over a predefined period of time; 22 the treatment routine includes administering the drug at periodic intervals.
STEP 2A PRONG TWO
The claims recite additional elements beyond those that encompass the abstract idea above including:
Independent claim 14:
machine
Dependent claim 15:
machine
Dependent claim 20:
the processor is configured to
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with considerations laid out by the Supreme Court or the Federal Circuit. (see MPEP 2106.05 a-c and e) The additional elements integrate the abstract idea into a practical application when they: improve the functioning of a computer or improving any other technology, apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, apply the judicial exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The additional limitations do not integrate the abstract idea into a practical application when they merely serve to link the use of the abstract idea to a particular technological environment or field of use — i.e. merely uses the computer as a tool to perform the abstract idea; or recite insignificant extra-solution activity (see MPEP 2106.05 f - h).
The machine and processor are recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using generic computer components. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment. Nothing in the claim recites specific limitations directed to an improved machine or processor. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception to computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a basic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claims do not integrate the abstract treatment process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract treatment process. The additional structural elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generic computer structure. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting basic computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently straightforward such that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination, the limitations recited in the claims add nothing that is not already present when the steps are considered individually.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. For example, dependent claim limitations 15 the learning model is trained using a training dataset, the training dataset including a historical dataset from a plurality of historical subjects, the historical dataset including: biological data of the plurality of historical subjects, digital biomarker data of the plurality of historical subjects, and responses to questions associated with digital content by the plurality of historical subjects; 20 determine the likelihood of the adverse event by comparing the predictive score to a predefined score; 21 the treatment routine includes gradually increasing an amount or volume of the drug being administered over a predefined period of time; 22 the treatment routine includes administering the drug at periodic intervals are directed to the abstract idea of certain methods of organizing human activity without integrating into a practical application or amounting to significantly more. Dependent claim limitations 15 the historical dataset including: biological data of the plurality of historical subjects, digital biomarker data of the plurality of historical subjects, and responses to questions associated with digital content by the plurality of historical subjects; 16 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; 17 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; 18 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; 19 the model includes: a general linear model, a neural network, a support vector machine (SVM), clustering, or combinations thereof; 23 the mental health or substance abuse disorder is drug abuse or addiction, and the treatment routine includes administration of ibogaine or noribogaine; 24 the mental health or substance abuse disorder is drug abuse or addiction, and the treatment routine includes administration of salvinorin A; 25 the mental health or substance abuse disorder is a depressive disorder, and the treatment routine includes administration of psilocybin or psilocin; 26 the mental health or substance abuse disorder is posttraumatic stress disorder, and the treatment routine includes administration of 3,4- Methylenedioxymethamphetamine (MDMA); 27 the mental health or substance abuse disorder is a depressive disorder, and the treatment routine includes administration of N, N- dimethyltryptamine (DMT) merely serve to further narrow the abstract idea above. As such, the additional elements do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 14-22 are rejected under 35 U.S.C. 103 as being unpatentable over Yovell (US 2014/0235663 A1) in view of Ozen Irmak, et al. (US 2022/0130518 A1).
With regards to claim 14, Yovell teaches a method of treating a mental health or substance abuse disorder in a subject (see at least ¶ 0015, method of treating acute suicidality by administering a therapeutically effective amount of pharmaceutical), the method comprising: …in response to the likelihood of the adverse event being greater than a predefined threshold, 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 (see at least ¶ 0437, patient who scored more than 6 [greater than a predefined threshold] on Beck Suicidal Ideation scale [information indicative of current state] were recruited for treatment with acute suicidality [adverse event] with a drug, Buprenorphine [treatment]; ¶ 0447, patients were to receive 0.2-1.6 mg Buprenorphine/day for 2 weeks [determining treatment routine]; ¶ 0450-0451, patients were interviewed to determine clinical symptoms and filed out demographic questionnaires including basic demographic information such as age, gender, education, income, etc., as well as information regarding clinical history [data stream]); and administering the drug to the subject based on the treatment routine (see at least ¶ 0447, patients received 0.2-1.6 mg Buprenorphine/day for 2 weeks [administering drug]).
Yovell does not explicitly teach …processing, using a machine learning model, a set of data streams associated with the subject to determine a likelihood of an adverse event, the set of data steams 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. Ozen Irmak teaches …processing, using a machine learning model, a set of data streams associated with the subject to determine a likelihood of an adverse event, the set of data steams 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 ¶ 0018, using the machine learning tool to assess an individual mental health status in an individual, the machine learning tool predicts mental status of an individual; ¶ 0088, machine learning model accepts a plurality of input variables to produce one or more output values based on the plurality of input variables, plurality of input variables comprise data attributable to a subject or a plurality of subjects collected automatically or by an inquiry, input variable may comprise a set of data associated with social data, behavioral data, biological data, affective or cognitive (e.g., neurocognitive) data, experiential data, psychomotor activity data, expressive behavioral data, sociodemographic data, medical data or other health marker data). 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 suicidality treatment system of Yovell with the motivation of effective care of mental disorders (Ozen Irmak, ¶ 0002-0004).
With regards to claim 15, Ozen Irmak teaches the method of claim 14, wherein the machine learning model is trained using a training dataset, the training dataset including a historical dataset from a plurality of historical subjects, the historical dataset including: biological data of the plurality of historical subjects, digital biomarker data of the plurality of historical subjects, and responses to questions associated with digital content by the plurality of historical subjects (see at least ¶ 0065, training machine learning model with data sets associated with healthy individuals (e.g., determined by a clinician as mentally healthy) and/or individuals with known mental disorders (e.g., individuals that have been clinically diagnosed with a mental health disorder); ¶ 0088, machine learning model accepts a plurality of input variables to produce one or more output values based on the plurality of input variables, plurality of input variables comprise data attributable to a subject or a plurality of subjects collected automatically or by an inquiry, input variable may comprise a set of data associated with social data, behavioral data, biological data, affective or cognitive (e.g., neurocognitive) data, experiential data, psychomotor activity data, expressive behavioral data, sociodemographic data, medical data or other health marker data). 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 suicidality treatment system of Yovell with the motivation of effective care of mental disorders (Ozen Irmak, ¶ 0002-0004).
With regards to claim 16, Ozen Irmak teaches the method of claim 15, 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 suicidality treatment system of Yovell with the motivation of effective care of mental disorders (Ozen Irmak, ¶ 0002-0004).
With regards to claim 17, Ozen Irmak teaches the method of claim 15, 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 suicidality treatment system of Yovell with the motivation of effective care of mental disorders (Ozen Irmak, ¶ 0002-0004).
With regards to claim 18, Yovell teaches the method of claim 15, 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 ¶ 0450-0451, patients were interviewed to determine clinical symptoms and filed out demographic questionnaires including basic demographic information such as age, gender, education, income, etc., as well as information regarding clinical history).
With regards to claim 19, 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 suicidality treatment system of Yovell with the motivation of effective care of mental disorders (Ozen Irmak, ¶ 0002-0004).
With regards to claim 20, Yovell teaches the method of claim 14, wherein the processor is configured to determine the likelihood of the adverse event by comparing the predictive score to a predefined score (see at least ¶ 0437, patient who scored more than 6 [predictive score, predefined score] on Beck Suicidal Ideation scale were recruited for treatment with acute suicidality).
With regards to claim 21, Yovell 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 ¶ 0123, 0.2-1.6 mg buprenorphine per day, with the starting dose being 0.2 mg per day, followed by a gradual increase up to the maximal dose during the first week).
With regards to claim 22, Yovell teaches the method of claim 14, wherein the treatment routine includes administering the drug at periodic intervals (see at least ¶ 0123, 0.2-1.6 mg buprenorphine per day [periodic interval]).
Claims 23 and 25-27 are rejected under 35 U.S.C. 103 as being unpatentable over Yovell (US 2014/0235663 A1) in view of Ozen Irmak, et al. (US 2022/0130518 A1) in further view of Raz, et al. (US 2021/0183519 A1).
With regards to claim 23, Yovell teaches the method of claim 14, wherein the mental health or substance abuse disorder is drug abuse or addiction (see at least ¶ 0188, subject is afflicted by condition associated with biological effect and/or withdrawal symptoms caused when using or ceasing use of alcohol, amphetamines, opioids (e.g., heroin), cocaine (particularly during withdrawal), nicotine and benzodiazepines [drug abuse or addiction]).
Yovell does not explicitly teach …and the treatment routine includes administration of ibogaine or noribogaine. Raz teaches …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 suicidality treatment system of Yovell 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, Yovell teaches the method of claim 14, wherein the mental health or substance abuse disorder is a depressive disorder (see at least ¶ 0194).
Yovell does not explicitly 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 suicidality treatment system of Yovell 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, Yovell teaches the method of claim 14, wherein the mental health or substance abuse disorder is posttraumatic stress disorder (see at least ¶ 0096).
Yovell does not explicitly teach …and the treatment routine includes administration of 3,4- Methylenedioxymethamphetamine (MDMA). Raz teaches …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 suicidality treatment system of Yovell 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, Yovell teaches the method of claim 14, wherein the mental health or substance abuse disorder is a depressive disorder (see at least ¶ 0194).
Yovell does not explicitly 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 suicidality treatment system of Yovell 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 Yovell (US 2014/0235663 A1) in view of Ozen Irmak, et al. (US 2022/0130518 A1) in further view of Anton, et al. (US 2018/0369238 A1).
With regards to claim 24, Yovell teaches the method of claim 14, wherein the mental health or substance abuse disorder is drug abuse or addiction (see at least ¶ 0188, subject is afflicted by condition associated with biological effect and/or withdrawal symptoms caused when using or ceasing use of alcohol, amphetamines, opioids (e.g., heroin), cocaine (particularly during withdrawal), nicotine and benzodiazepines [drug abuse or addiction]).
Yovell does not explicitly teach …and the treatment routine includes administration of salvinorin A. Anton teaches …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 suicidality treatment system of Yovell with the motivation of effective treatment for a psychiatric, mental, and/or neurological disorders (Anton, ¶ 0006, 0009).
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.
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.
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/JOSEPH D BURGESS/ Primary Examiner, Art Unit 3681