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 response to the application filed on 15 March 2025.
Claims 1-12 are currently pending and have been examined.
Priority
An English translation for KR10-2022-0183164 as well as a statement that the translation is accurate is required. See 37 CFR 1.55 (g)(3)(iii) & 37 CFR 1.55 (g)(4).
Failure to provide a certified translation may result in no benefit being accorded
for the non-English application.
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 18/544,488 fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. For claims 1 and 7, the prior-filed application does not disclose “collecting a real-time neurophysiological EEG data via the EEG sensor and integrating a collected EEG data with AI-based survey assessments”, “adjusting display parameters displayed for the user to predetermined parameters based on AI-driven display adjustments”, “processing user responses through a haptic actuator, including generation of
predetermined adaptive haptic feedback signals based on detected user engagement levels”, “encrypting the result data, wherein the result data is stored in a cloud- database or a local encrypted database”, and “transmitting a control signal to the pharmaceutical mixer of the medication for ADHD based on the result data.” Examiner cannot find disclosure of collecting a real-time neurophysiological EEG data via the EEG sensor and integrating a collected EEG data with AI-based survey assessments, adjusting display parameters displayed for the user to predetermined parameters based on AI-driven display adjustments, processing user responses through a haptic actuator, including generation of predetermined adaptive haptic feedback signals based on detected user engagement levels, encrypting the result data, wherein the result data is stored in a cloud- database or a local encrypted database. and
transmitting a control signal to the pharmaceutical mixer of the medication for ADHD based on the result data. For claims 2 & 8, the prior-filed application does not disclose “acquiring the real-time neurophysiological EEG data from the EEG sensor to complement the collected physical information.” Examiner cannot find disclosure of acquiring the real-time neurophysiological EEG data from the EEG sensor to complement the collected physical information. For claims 3 & 9, the prior-filed application does not disclose “the real-time neurophysiological EEG data.” Examiner cannot find disclosure of the real-time neurophysiological EEG data. For claims 4 & 10, the prior-filed application does not disclose “providing haptic feedback via the haptic actuator to indicate validation, progress, or required adjustments during a survey completion, based on the AI-processed importance values.” Examiner cannot find disclosure of providing haptic feedback via the haptic actuator to indicate validation, progress, or required adjustments during a survey completion, based on the AI-processed importance values. For claims 6 & 12, the prior-filed application does not disclose “EEG readings and haptic interaction patterns.” Examiner cannot find disclosure of EEG readings and haptic interaction patterns. Accordingly, claims 1-12 are not entitled to the benefit of the prior application.
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 1-12 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.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process (claims 1-6) and a machine (claims 7-12).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
A method for controlling a pharmaceutical mixer of a medication for Attention Deficit Hyperactivity Disorder (ADHD) based on an assessment of a mental health state of an adolescent by using a bioelectrical activity data collected by an electroencephalogram (EEG) sensor in response to a survey formed based on an artificial intelligence (AI), wherein a processor and one or more memory devices communicatively coupled to the processor, and the one or more memory devices stores instructions operable when executed by the processor to perform:
collecting a physical information of a user, verifying survey questions regarding a mental health depending on the physical information of the user, and verifying answers to the verified survey questions inputted by the user;
collecting a real-time neurophysiological EEG data via the EEG sensor and integrating a collected EEG data with AI-based survey assessments; and
forming additional survey questions for the user after having the verified answers and calculating a prediction rate of an appearance of symptoms of a mental illness regarding the additional survey questions by using AI models, wherein the
forming of the additional survey question comprises:
selecting an AI model depending on a number of the additional survey questions;
updating AI models, wherein the AI model selection is adjusted based on the real-time user response patterns and the verified answers;
verifying a prediction value of the selected AI model regarding the additional survey questions by using the selected AI model;
verifying the selected AI model according to evaluation indexes preset based on the verified prediction value;
generating a new feature by means of equation 1, which is
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wherein Fnew is a new feature, N is a total number of a plurality of AI models, Pi is an adjusted prediction value of each AI model, pi is the prediction value of each AI model, and Wi is an entropy in a decision tree model and a weighting in other AI models,
outputting a set of feature importance values collected by the new features by means of equation 2, which is
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wherein SI is a set of collected feature importance values, IMP is an importance value, N is a number of a feature, A-M are identification information of AI models A to M, IMPfN is a final importance value of one feature derived from a plurality of AI models, IMPMf1 is importance values of first features of the AI models, and Nmodels is the number of AI models,
wherein the one or more memory devices stores instructions operable when executed by the processor to further perform:
verifying a set of survey questions including a plurality of sub-questions depending on the calculated prediction rate of the appearance of symptoms of the mental illness;
adjusting display parameters displayed for the user to predetermined parameters based on AI-driven display adjustments;
outputting the verified set of survey questions;
processing user responses through a haptic actuator, including generation of predetermined adaptive haptic feedback signals based on detected user engagement levels;
outputting a result data by adjusting the prediction rate of the appearance of symptoms of the mental illness when the set of survey questions is not verified;
encrypting the result data, wherein the result data is stored in a cloud-database or a local encrypted database; and
transmitting a control signal to the pharmaceutical mixer of the medication for ADHD based on the result data.
Claim 7 recites similar limitations as claim 1.
These steps for personalized medication adjustment (titration) using biofeedback for ADHD, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity but for recitation of generic computer components. That is, nothing in the claim element precludes the italicized portions from managing personal behavior or relationships or interactions between people by organizing the activity around managing a patient's treatment regimen for ADHD. This could be analogized to collecting information, analyzing it, and displaying certain results of the collection and analysis. The italicized portions containing the recitations of calculating a prediction rate, updating AI models, generating a new feature by means of equation 1, and outputting a set of feature importance values collected by the new features by means of equation 2 at a high level of generality has been treated as part of the abstract idea, specifically as mathematical calculations which falls within the abstract idea of mathematical concepts, in light of the new 2024 USPTO AI Guidance. The recitations of equations 1 and 2 has been treated as part of the abstract idea, specifically as mathematical equations which falls within the abstract idea of mathematical concepts. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations as human activity and mathematical calculations & equations but for the recitation of generic computer components, then it falls within the “Methods of Organizing Human Activity” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements, non-italicized portions identified above for claims 1 & 7, do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of by an electroencephalogram (EEG) sensor; based on an artificial intelligence (AI), wherein a processor and one or more memory devices communicatively coupled to the processor, and the one or more memory devices stores instructions operable when executed by the processor; via the EEG sensor and integrating a collected EEG data with AI-based survey assessments; by using AI models; by using the selected AI model; wherein the one or more memory devices stores instructions operable when executed by the processor; based on AI-driven display adjustments; and, through a haptic actuator, including generation of predetermined adaptive haptic feedback signals based on detected user engagement levels amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of collecting a physical information of a user; collecting a real-time neurophysiological EEG data; outputting a set of feature importance values; outputting the verified set of survey questions; outputting a result data; wherein the result data is stored in a cloud-database or a local encrypted database; and, transmitting a control signal to the pharmaceutical mixer amounts to mere data gathering, data output, and storage since it does not add meaningful limitations to the collecting, outputting, transmitting, and storing actions performed, see MPEP 2106.05(g))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity, and add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as recitation of by an electroencephalogram (EEG) sensor, wherein a processor and one or more memory devices communicatively coupled to the processor, and the one or more memory devices stores instructions operable when executed by the processor; via the EEG sensor and integrating a collected EEG data with AI-based survey assessments; and, wherein the one or more memory devices stores instructions operable when executed by the processor; e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f);
such as recitation of based on an artificial intelligence (AI); by using AI models; by using the selected AI model, e.g. requiring the use of software to tailor information and provide it to the user on a generic computer; based on AI-driven display adjustments; and, through a haptic actuator, including generation of predetermined adaptive haptic feedback signals based on detected user engagement levels, Intellectual Ventures I LLC v. Capital One Bank (USA), MPEP 2106.05(f);
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of collecting a physical information of a user; collecting a real-time neurophysiological EEG data; outputting a set of feature importance values; outputting the verified set of survey questions; outputting a result data; and, transmitting a control signal to the pharmaceutical mixer, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); such as recitation of wherein the result data is stored in a cloud-database or a local encrypted database, e.g., storing and retrieving information in memory, Versata Dev. Group, Inc., MPEP 2106.05(d)(II)(iv).
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 generic computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-6 and 8-12 reciting particular aspects for personalized medication adjustment (titration) using biofeedback for ADHD such as
[Claims 2 & 8] outputting questions regarding age, height, weight, or waist measurement of the user and collecting the answers as physical information of the user by receiving answers to the outputted questions; and
acquiring the real-time neurophysiological EEG data from the EEG sensor to complement the collected physical information and enhance a predetermined accuracy level of the AI-based mental health predictions;
[Claims 3 & 9] outputting by using the AI models, among the questions regarding the physical information of the user, survey questions tailored based on both the user-inputted physical information and the real-time neurophysiological EEG data to which answers is obtained within a predetermined time;
[Claims 4 & 10] confirming survey questions, corresponding to the feature importance values included in the set of calculated feature importance values, as the additional survey questions; and
providing haptic feedback via the haptic actuator to indicate validation, progress, or required adjustments during a survey completion, based on the AI-processed importance values;
[Claims 5 & 11] outputting the additional questions confirmed in a descending order of feature importance values calculated by equation 2;
[Claims 6 & 12] periodically re-verifying the set of survey questions for the user when the prediction rate of the appearance of symptoms of the mental illness exceeds a predetermined value; and
selecting the AI model from a plurality of trained models based on real-time data streams, including EEG readings and haptic interaction patterns;
these italicized portions are methods of organizing human activity. Additionally, the italicized portions of claims 5 & 11 recite mathematical calculations which falls within the abstract idea of mathematical concepts. These identified limitations merely describe types of data and determinations that can be performed by humans).
Step 2A Prong 2
Dependent claims 2-5 & 8-11 recites additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claims 3 & 9 (by using the AI models) and claims 4 & 10 (via the haptic actuator) amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f)); add insignificant extra-solution activity to the abstract idea such as claims 2 & 8 (outputting questions regarding age, height, weight, or waist measurement of the user and collecting the answers as physical information of the user by receiving answers to the outputted questions; and, acquiring the real-time neurophysiological EEG data), claims 3 & 9 (outputting […] survey questions tailored […] to which answers is obtained within a predetermined time), claims 5 & 11 (outputting the additional questions) amounts to mere data output since it does not add meaningful limitations to the outputting performed, see MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
Dependent claims 3-4 & 9-10 recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f). Also, see [0042] which provides examples of generic computing devices, [0048] which provides examples of generic memory types, and [0049] which provides examples of generic display devices. Dependent claims 2-3, 5, 8-9, and 11 amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i). There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Therefore, in consideration of all the facts, it is evident that the present invention is not a patent-eligible invention under USC 101.
No Prior Rejection
For claims 1 to 12 no prior art rejection is being presented at this time. the references of record are understood to be the closest prior art. For claim 1, while the combination of Coleman et al. (US20200218350A1) in view of Vaughan et al. (US20190019581A1) and further in view of Hunt (US20140243608A1) teaches most of the limitations of the claim, the scope of the claims describe a particular manner by means of equation 2, which is
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wherein SI is a set of collected feature importance values, IMP is an importance value, N is a number of a feature, A-M are identification information of AI models A to M, IMPfN is a final importance value of one feature derived from a plurality of AI models, IMPMf1 is importance values of first features of the AI models, and Nmodels is the number of AI models. This goes beyond any teachings or suggestions in the art.
Prior Art Cited but Not Relied Upon
Manolo E B. Towards A Personalized Medicine Approach for the Titration of Pharmacologic Treatments in Children with ADHD. J of Pharmacol & Clin Res. 2017; 8(3): 555614.
This reference is relevant because is discloses a personalized medicine approach for treatments in children with ADHD.
Conclusion
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/WINSTON R FURTADO/Examiner, Art Unit 3687