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 .
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 1-15 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 6 follows.
STEP 1
Regarding Claim 6, the claim recites a series of steps or acts, including obtaining an identification and prognosis model. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of obtaining an identification and prognosis model sets forth a judicial exception. This step describes a mathematical formula and/or a mathematical calculation. Thus, the claim is drawn to a Mathematical Concept, which is an Abstract Idea.
STEP 2A, PRONG TWO
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 obtained model does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on model nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
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 pre-processing the EEG, splitting the EEG, and extracting brain signal features from the EEG. Pre-processing data (EEG), splitting data, and extracting features (brain signal features) from the data is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the performing, splitting, and extracting steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step 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 it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
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 1, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The recited storage and processor are generic components 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 same rationale applies to Claim 11.
The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to data processing. The performing, splitting, and extracting steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 6, 8, 11, and 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Etkin et al. (US 20210353224), hereinafter Etkin.
Regarding claims 1, 6 and 11, Etkin teaches an identification and prognosis system (fig. 19, system 100), comprising: a storage device configured to store at least one instruction; and a processor coupled to the storage device (¶[0005]) and fig. 21), and the processor configured to access and execute the at least one instruction for:
performing a pre-process on an electroencephalogram (EEG) to obtain a pre-processed EEG (¶[0196-7,0270], EEG preprocessing is applied to remove noise artifacts);
splitting the pre-processed EEG into a plurality of different frequency band EEGs (¶[0241], “The band powers in each of the four canonical EEG frequency bands (theta: 4-7 Hz, alpha: 8-12 Hz, beta: 13-30 Hz, gamma: 31-50 Hz; Filtered using zero-phase FIR filters) were employed as the features”);
extracting a variety of brain functional connectivity features and a variety of power features from the plurality of the different frequency band EEGs ([0275,0277], “features extracted by the feature extractor 114 may include band power (e.g., power within a specific oscillatory band), power-envelope connectivity (e.g., correlation coefficients between the power envelopes of two regions of the brain), weighted phase-lag index (e.g., measuring a coherence between the EEG phases of two regions of the brain), imaginary coherence . . .” (emphasis added)); and
using the variety of the brain functional connectivity features and the variety of the power features for a machine learning to obtain an identification and prognosis model (¶[0007,0267,0278,0283], “a diagnosis for the patient indicating the patient as having one or more types of the psychiatric disease” and “the treatment outcome prediction generated by the regression model 116 may include a measure of how much a patient responds to treatments to obtain a beneficial or desired result (e.g., clinical result) in a subject's condition”).
Regarding claims 3, 8 and 13, Etkin teaches filtering the pre-processed EEG to obtain a delta band EEG, a theta band EEG, an alpha band EEG and a beta band EEG (¶[0045,0241], uses filtering to obtain delta through gamma brain signal frequencies. “The band powers in each of the four canonical EEG frequency bands (theta: 4-7 Hz, alpha: 8-12 Hz, beta: 13-30 Hz, gamma: 31-50 Hz; Filtered using zero-phase FIR filters”).
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 (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 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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 2, 4, 7, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Etkin, as applied to claim 1, further in view of Feng et al. (Functional Connectivity for Motor Imaginary Recognition in Brain-computer Interface- 2020), hereinafter Feng.
Regarding claims 2, 7 and 12, Etkin teaches wherein the pre-process executed by the processor comprises: performing a filtering on the EEG to obtain a 0.5-50 Hz band EEG (¶[0045,0241], uses filtering to obtain delta through gamma brain signal frequencies. “The band powers in each of the four canonical EEG frequency bands (theta: 4-7 Hz, alpha: 8-12 Hz, beta: 13-30 Hz, gamma: 31-50 Hz; Filtered using zero-phase FIR filters”);
re-referencing the 0.5-50 Hz frequency band EEG to remove common components between different channels in the 0.5-50 Hz frequency band EEG, thereby obtaining a re-referencing EEG (¶[0197], “The spectrally filtered EEG data were then re-referenced to the common average.” One of ordinary skill in the art understands that common average reference is for the removal of shared noise of the already filtered data. Therefore, it would remove common components between channels within the filtered bands);
and performing an independent component analysis on the re-referencing EEG to remove an eye movement signal from the re-referencing EEG, thereby obtaining the pre-processed EEG (¶[0197], in step 6, independent component analysis (ICA) related to ocular artifacts are removed after re-referencing. Then, “[a]fter artifact rejection, 54 EEG channels common to all four study sites were identified and extracted for each subject”(emphasis added)).
Etkin fails to teach wherein the filtering is performed using a bandpass filter.
Feng teaches a brain-computer interface to detect a subject’s brain activity, characterize brain states, and distinguish various mental tasks (pg. 3678, Introduction). The analysis is performed by exploring the functional connectivity measured by phase-locking value (PLV), phase lag index (PLI), and weighted PLI (wPLI) of the EEG stream (pg. 3678). The method first begins by preprocessing the EEG data with a bandpass filter to split the EEG into different frequencies before analysis (pg. 3679, “EEG data were preprocessed using a standard pipeline, including re-reference by common average reference(CAR) approach, band-pass filtered using a six-order zero-phase ButterWorth filter. After that, nine frequency ranges (1−5, 5−9, 9−13, 13−17, 17−21, 21−25, 25−29, 29−33, and 33−37 Hz) of filtered signals were used for the following analysis.”)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Etkin, such that filtering is performed using a bandpass filter, as taught by Feng to aid in detecting a subject’s brain activity, characterizing brain states, and distinguishing various mental tasks.
Regarding claim 4, Etkin teaches extracting a weighted phase-lag index (¶[0277])but fails to teach extracting a phase locking value between each two channels in each frequency band EEG from the plurality of the different frequency band EEGs; and extracting a phase lag index and a weighted phase lag index between the each two channels in the each frequency band EEG from the plurality of the different frequency band EEGs, wherein the variety of the brain functional connectivity features comprises the phase locking value, the phase lag index and the weighted phase lag index.
Feng teaches the performance of functional connectivity (FC) features in the brain measured by phase lag index (PLI), weighted PLI (wPLI), and phase-locking value on motor imagery classification using EEG (abstract). The functional networks aid in characterizing brain states and distinguishing mental tasks (pg. 3678). The FC was investigated “different frequency bands of all pairs of electrodes and find the best combination for individual. A total frequency band
ranged from 1-40Hz, and 3.5s EEG signals were used to calculate FC instead of short time values” (pg. 3678). Each of PLI, wPLI, and PLV are extracted for each pair in each frequency band (pg. 3679-80). That is, the PLV between each two channels for each frequency band out of a plurality are extracted (see Algorithm 1 and pg. 3679-80, “The overall connectivity feature (take PLI as an example) calculation and selection algorithm is illustrated” and “computing PLI for every pair of channels”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Etkin, such that PLI, wPLI, and PLV are extracted between each two channels in each frequency, as taught by Feng, detecting a subject’s brain activity, characterizing brain states, and distinguishing various mental tasks (pg. 3681).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Etkin, as applied to claim 1, further in view of Causevic et al. (US 20110144520), hereinafter Causevic.
Regarding claim 5, Etkin fail to expressly disclose performing a wavelet transformation on each channel in each frequency band EEG extracted from the plurality of the different frequency band EEGs to obtain an absolute power and a relative power of the each channel, wherein the variety of the power features comprises the absolute power and the relative power. Etkin does teach that power features are obtained, but does not teach the specifics of the power features recited in the claim (¶[0102] of Etkin).
Causevic teaches a method and device for providing an assessment of the neurological state of a patient using EEG (abstract and ¶[0039]). The method requires deconstructing the EEG using a transformation method, such as wavelet transform (¶[0038]). Next, absolute power and relative power features are extracted from each of the brain wave frequency bands for each electrode pair (¶[0039-41, “the algorithm extracts various quantitative features from the brain wave frequency bands” and “The Z-scores are calculated for each feature and for each electrode”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Etkin, such that wavelet transformation is performed for each electrode in each frequency band to obtain power features that include absolute and relative power, as taught by Causevic, to aid in providing an assessment of the neurological state of a patient.
Claims 9 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Etkin, as applied to claim 6, further in view of Feng and Causevic.
Regarding claims 9 and 14, Etkin teaches extracting a weighted phase-lag index (¶[0277])but fails to teach extracting a phase locking value between each two channels in each frequency band EEG from the plurality of the different frequency band EEGs; and extracting a phase lag index and a weighted phase lag index between the each two channels in the each frequency band EEG from the plurality of the different frequency band EEGs, wherein the variety of the brain functional connectivity features comprises the phase locking value, the phase lag index and the weighted phase lag index.
Feng teaches the performance of functional connectivity (FC) features in the brain measured by phase lag index (PLI), weighted PLI (wPLI), and phase-locking value on motor imagery classification using EEG (abstract). The functional networks aid in characterizing brain states and distinguishing mental tasks (pg. 3678). The FC was investigated “different frequency bands of all pairs of electrodes and find the best combination for individual. A total frequency band
ranged from 1-40Hz, and 3.5s EEG signals were used to calculate FC instead of short time values” (pg. 3678). Each of PLI, wPLI, and PLV are extracted for each pair in each frequency band (pg. 3679-80). That is, the PLV between each two channels for each frequency band out of a plurality are extracted (see Algorithm 1 and pg. 3679-80, “The overall connectivity feature (take PLI as an example) calculation and selection algorithm is illustrated” and “computing PLI for every pair of channels”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Etkin, such that PLI, wPLI, and PLV are extracted between each two channels in each frequency, as taught by Feng, detecting a subject’s brain activity, characterizing brain states, and distinguishing various mental tasks (pg. 3681).
Etkin-Feng fail to expressly disclose performing a wavelet transformation on each channel in each frequency band EEG extracted from the plurality of the different frequency band EEGs to obtain an absolute power and a relative power of the each channel, wherein the variety of the power features comprises the absolute power and the relative power. Etkin does teach that power features are obtained, but does not teach the specifics of the power features recited in the claim (¶[0102] of Etkin).
Causevic teaches a method and device for providing an assessment of the neurological state of a patient using EEG (abstract and ¶[0039]). The method requires deconstructing the EEG using a transformation method, such as wavelet transform (¶[0038]). Next, absolute power and relative power features are extracted from each of the brain wave frequency bands for each electrode pair (¶[0039-41, “the algorithm extracts various quantitative features from the brain wave frequency bands” and “The Z-scores are calculated for each feature and for each electrode”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Etkin-Feng, such that wavelet transformation is performed for each electrode in each frequency band to obtain power features that include absolute and relative power, as taught by Causevic, to aid in providing an assessment of the neurological state of a patient.
Claims 10 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Etkin in view of Feng and Causevic, as applied to claim 9 and 14, further in view of Cai et al. (Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector- 2016), hereinafter Avots.
Regarding claims 10 and 15, Etkin-Feng-Causevic teach inputting the functional connectivity features, including power features, into a classifier (¶[0057] of Etkin, Discussion and Conclusion of Feng, ¶[0038-39] of Causevic), but fail to teach that the extracted EEG features were input to a plurality of different classifiers for training and verification of the machine learning, and after the machine learning, selecting a classifier with a highest evaluation index from the plurality of the different classifiers to be the identification and prognosis model.
Cai teaches a device and method for diagnosing depression via EEG data (abstract). The EEG data is analyzed with KNN (k-Nearest Neighbor), SVM (Support Vector Machine), ANN (Artificial Neuro Network) and DBN (Deep Belief Network) classifiers (abstract). The features extracted include functional connectivity measurements such relative power and absolute power (see 4.2 Feature Extraction). Cai found that the DBN classifier has the highest accuracy and that “Our accuracy reached 78.24% using the combination of DBN and the absolute power of the beta wave. Our main work is as following” and “But as we continue to increase our data collection, DBN will truly play its advantage. The accuracy for classification will increase gradually as well. On the other hand, we are also exploring the EEG data of experiment subject's resting state. With the combination model of both resting-state and under-stimulating, we believe the accuracy of the future pervasive system of depression diagnosis is very promising” (emphasis added) indicating that DBN was selected due to having the highest accuracy of all the models to expand their research in early detection of depression for better results of both treatment and recover (Conclusion).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Etkin-Feng-Causevic, such that the extracted EEG features were input to a plurality of different classifiers for training and verification of the machine learning and selecting a classifier with a highest evaluation index to be the identification and prognosis model, as taught by Cai, to aid in early detection of depression for better results of both treatment and recover.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Intrator teaches abnormality in at least one neural network in the brain of the particular individual is used to diagnose the particular individual having a neurological condition. US 20160235351
Lee teaches a method for providing information on a major depressive disorder and a device using the same, the method comprising the steps of: receiving brain wave data of a subject; extracting feature data of at least one of power spectrum densities (PSDs), a functional connectivity, and a network index with respect to the brain wave data; and determining whether the subject has a major depressive disorder on the basis of at least one feature data, by using a classification model trained to output whether a subject has a major depressive disorder on the basis of the at least one feature data as an input, wherein the subject is a subject suspected of suffering from a major depressive disorder without having a history of drug use. WO 2023048546
Etkin teaches a method of identifying antidepressant-responsive depression phenotypes in subjects undergoing treatment of depression with an antidepressant using a machine learning model. US 20210353224
Avot teaches determining the effects of depression based on EEG signals (abstract). Avot compares the accuracy of a plurality of classifiers trained on EEG features using a 10-fold cross-validation to obtain high accuracy classifier ranking (abstract, “all the models were evaluated using a 10-fold cross-validation”). The classifier with the highest accuracy was identified to be the Naïve Bayes with the ReliefF+Weig. (Ensemble Approach for Detection of Depression Using EEG Features- 2022)
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/MARTIN NATHAN ORTEGA/ Examiner, Art Unit 3791 /TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
