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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07 July 2026 has been entered.
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, 7, 9-15, 17, 19, 21-27, 29, and 34-37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Regarding claim 1, the claim recites a series of steps or acts, including obtaining data, dividing the data into segments, extracting features from the segmented data, and determining whether a subject is experiencing delirium based on a combined output. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of determining whether a subject is experiencing delirium based on a combination of extracted single channel features and extracted multichannel features sets forth a judicial exception. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. The determination of whether the subject is experiencing delirium does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the determination, nor does the method use a particular machine to perform the Abstract Idea.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of obtaining EEG signals from a plurality of electrodes incorporated into a headband or headgear placed on a subject, dividing the EEG signals into segments, and extracting single channel features and multichannel features from the EEG signal segments. The obtaining, dividing, and extracting steps are each recited at a high level of generality such that they amount to insignificant presolution activity, e.g., mere data gathering steps necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes them from well-understood, routine, and conventional data gathering activity engaged in by medical professionals prior to Applicant's invention.
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding claim 25, the device recited in the claim is a generic device comprising generic components configured to perform the Abstract Idea. The claim recites modules of computer system configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps/functions pertaining to data gathering (e.g., length of time windows/epochs, location of EEG signal acquisition) or other steps/functions capable of being performed mentally and/or by hand (classifying segments, determining a delirium score, providing a trace of a delirium score over time, determining a trendline of the trace). The obtaining, dividing, and extracting steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
Response to Arguments
Applicant’s arguments filed 07 July 2026 have been fully considered.
The amendments to the claims have overcome the previous rejection of claims 19 and 29 under 35 U.S.C. 112(a).
Regarding the rejection of the claims under 35 U.S.C. 101, Applicant’s arguments are not persuasive. Applicant argues that the claimed method and system provide a rapid and efficient way of detecting delirium, and that the specification states that “Described herein are systems and methods that may detect and monitor delirium in a subject rapidly and accurately using EEG signals and machine learning.” These arguments are not persuasive as they do not explain why or how the claimed invention provides more rapid or more efficient or more accurate results than the existing technology. No evidence regarding improvements is provided. The mere assertion of an improvement to the current technology, without evidence of the improvement, is not persuasive.
It is noted that ambulatory/portable EEG monitoring is well known in the art, as is collecting EEG signals from electrodes incorporated into a headband or headgear.
Examiner’s Note
Regarding claims 1 and 25, none of the prior art teaches or suggests, either alone or in combination, a method for detecting delirium, wherein an output of a single-channel feature extraction module is combined with an output of a multi-channel feature extraction feature module to determine whether a subject is experiencing delirium, in combination with the other claimed steps.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shinozaki’446 (WO 2020/206446 – previously cited) in view of Jain et al.’788 (US Pub No. 2018/0317788 – previously cited), as discussed in the Non-Final rejection mailed out 11 August 2025, is the closest prior art of record. It discloses all elements of claim 1 with the exception of combining an output of a single-channel feature extraction module and an output of a multi-channel feature extraction module to determine whether a subject is experiencing delirium.
Sameni et al.’727 (US Pub No. 2010/0137727 – previously cited) teaches that signal quality for a bioelectrical signal can be determined using either time-domain multichannel features or frequency-domain multichannel features (section [0037]).
Kamousi et al.’809 (USPN 10,743,809 – previously cited) teaches performing classification of EEG signals using any of a k nearest neighbors model, a random forest model, and a support vector machines model (col. 12, line 61 – col. 13, line 42).
Greenhut et al.’220 (US Pub No. 2007/0239220 – previously cited) teaches determining signal quality of a bioelectric signal by extracting multichannel features (section [0057]).
Wang et al.’664 (US Pub No. 2016/0242664 – previously cited) teaches determining signal quality of a bioelectric signal by extracting multichannel features (section [0016]).
Naber et al.’299 (US Pub No. 2021/0030299 – previously cited) teaches removing noise from an EEG data segment based on a multichannel feature (section [0087]).
Fickling et al. (Good data?... – previously cited) teaches different quality metrics for EEG segments.
Faul et al.’401 (US Pub No. 2012/0101401 – previously cited) teaches classifying each of a plurality of EEG segments as condition positive or condition negative (sections [0169-0183]).
Etemadi et al.’695 (US Pub No. 2023/0363695 – previously cited) teaches providing a physiological classification as one of positive, negative, or uncertain (section [0072]).
Remmers et al.’692 (US Pub No. 2017/0181692 – previously cited) teaches providing a physiological classification as one of positive, negative, or uncertain (section [0009]), and further teaches that adding an “uncertain” category as a classification category increases the prediction accuracy of a classifier.
Wall et al.’509 (US Pub No. 2021/0133509 – previously cited) teaches providing a physiological classification as one of positive, negative, or uncertain (section [0181]).
Abbas et al.’030 (US Pub No. 2022/0344030 – previously cited) teaches providing a physiological classification as one of positive, negative, uncertain, or at risk for/having another disorder (section [0050]).
Groth et al.’889 (USPN 6,443,889 – previously cited) teaches providing a physiological classification as one of positive, negative, or uncertain (description of Figure 1b).
Bosl et al.’436 (US Pub No. 2015/0216436 – previously cited) teaches performing classification of EEG signals using any of a k nearest neighbors model, a random forest model, and a support vector machines model (section [0107]).
Abrahami et al.’045 (US Pub No. 2017/0039045 – previously cited) teaches that it is well known in the art to attach EEG electrodes to a subject by using EEG electrodes incorporated into a headband (section [0035]).
Greenwald et al.’821 (US Pub No. 2003/0181821 – previously cited) teaches that EEG signal classification for the purpose of diagnosing delirium may be performed using both extracted time-domain features and extracted frequency-domain features (ABSTRACT and section [0001]).
Hunter et al. (Delirium screening… – previously cited) discloses detecting delirium using QEEG analysis.
Mulkey et al. (Rapid Hand-held Continuous… – previously cited) teaches detecting delirium by analyzing and classifying EEG signals.
van der Kooi et al. (Delirium Detection Using EEG… – previously cited) teaches analyzing delirium by analyzing EEG signals.
Mulkey et al. (Methods of Identifying Neurological Delirium Study… – previously cited) teaches detecting delirium by analyzing and classifying EEG signals.
Sun et al. (Automated tracking of level of consciousness… – previously cited) teaches classifying EEG signals to detect delirium, but makes note that no features are extracted from the EEG signals to perform the classification.
Hasnain et al.’138 (WO 2021/195138 – previously cited) discloses determining a delirium score, level, or probability for a patient.
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/ETSUB D BERHANU/Primary Examiner, Art Unit 3791
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