DETAILED ACTION
This Office Action is in response to the amendments filed on 12/01/2025.
Claims 1-3, 5, and 10 currently canceled.
Claims 4, 6, and 7 currently amended.
Claims 11-15 newly added.
Claims 4, 6-9, and 11-15 are currently pending in this application and have been examined.
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 .
Examiner’s Note
Due to the way that the claims were amended (i.e. claim dependencies being not in claim number order), the order of rejections will also be not in claim numbered order.
Response to Arguments
In reference to Applicant’s arguments on page(s) 1 regarding the rejections made under 35 U.S.C. 112:
Applicant canceled Claims 1-3, 5 and 10. Claims 4, 6, 7 were amended. Applicant added new Claims 11-15 to reformulate and reword the Claims to respond to the outstanding objections and rejections as stated in the outstanding Office Action.
Specifically, new Claim 12 represent reworded canceled Claim 2. Claim 12 makes a clarification between "hand-crafted" --"manually engineered summary features". Applicant believes it is compliant with Section 112(b), as well as distinguishes between Applicant's claimed raw-LEGCNN approach from the feature-engineered approach in sited art.
Examiner’s response:
Applicant’s arguments have been fully considered and are found to be persuasive.
Applicant argues that the change in language from “hand crafted” to “manually engineered” does clarify the claim limitation.
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 112 are withdrawn.
In reference to Applicant’s arguments on page(s) 1 regarding the rejections made under 35 U.S.C. 102 and 103:
New Claims 13-14 (substituting canceled Claims 3-5) are directed to specific classifier & CNN details and tie classification to GAP + single-layer perceptron and SiLU-based CNN blocks with stated kernels/stride. These specific elements aren't taught in the van Putten publication. New Claims 13-14 are in compliance with the requirements of 35 USC 102/103.
Amended Claim 6 is directed to the two-pillar training method (augmentation + channel rolling). No such disclosure could be found in van Putten and nor is there a suggestion by generic cloud ANN references (van Putten + Benjamin). Claim 6 is in compliance with the requirements of 35 USC 102/103.
Claim 8 is directed to more clearly defined augmentation and channel-rolling algorithms. Claim 8 points to rolling by kernel-size and stacking to enlarge the first-layer receptive field on multi-channel LEG. No such teaching or disclosure could be found in in van Putten or Benjamin separately or in combination.
Examiner’s response:
Applicant’s arguments have been fully considered and are found to be persuasive.
Applicant argues that the amended claims include specific classifier and training details that are not found in either of the previously applied prior art references of van Putten and Benjamin. Examiner agrees. The amended claim limitations add specificity that is not accounted for in the previous references.
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 102 and 103 are withdrawn.
In reference to Applicant’s arguments on page(s) 1 regarding the rejections made under 35 U.S.C. 101:
Amended Claim 7 is directed to a module-level implementation and real-time API; Claim 7 is in compliance with 35 USC 101.
Examiner’s response:
Applicant’s arguments have been fully considered but are found to be not persuasive.
Applicant argues that the amendments made on claim 7 remedy any rejections made under 35 U.S.C. 101. Examiner disagrees. All claims still recite abstract ideas of mental processes or mathematical calculations/relationships in some form or another. Not only does amended claim 7 not remedy the 101 rejections levied in the non-final rejection, the newly added claims are reworded forms of the canceled claims, which still recite the abstract ideas of the canceled claims.
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 101 are withdrawn and new grounds for rejection is presented below.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 4, 6-8, and 11-15 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1 analysis:
Independent Claim 6 recites, in part, a neural network training method, therefore falling into the statutory category of process. Independent Claim 11 recite, in part, a computer-implemented method, therefore falling into the statutory category of process.
Regarding Claim 6:
Step 2A: Prong 1 analysis:
Claim 6 recites in part:
“(i) applying to each EEG segment on of a set of EEG-specific data augmentation operations including Gaussian noise injection, random dropout of consecutive time-points in K channels, per-channel random amplification, time-axis shrinking/stretching, and time inversion”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses performing data augmentation operations on a dataset.
“(ii) performing channel rolling to extend the receptive field of the first convolutional layer to all input EEG channels by rolling the channel dimension and stacking the rolled tensors”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses performing channel rolling to ensure that all channels of an input EEG are used.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 8:
Step 2A: Prong 1 analysis:
Claim 8 recites in part:
“The data augmentation method of claim 6 is defined by the following algorithm: with a probability of 50%, apply gaussian noise to the input tensor with random standard deviation drawn from a uniform distribution (0,1] pV. with a probability of 70%, apply random dropout of Bk consequent time-points in K EEG channels the input tensor data, where K and Bk are drawn from uniform distributions [1, 8] and [1, Lenseg * SFreq * 0.9], respectively, where Lenseg is the length of one EEG segment in seconds and SFreq is the sampling frequency of raw EEG data. with a probability of 50%, apply random amplification of the input tensor with a multiplier MCh drawn from a uniform distribution [0.8, 1.2] for each EEG channel ch. With a probability of 50%, shrink or stretch time axis with a factor uniform distribution [0.8, 1.2]. With a probability of 50%, inverse time flow for all EEG channels.”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation/formula.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 9:
Step 2A: Prong 1 analysis:
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Claim 9 recites in part:
As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation/formula.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 11:
Step 2A: Prong 1 analysis:
Claim 11 recites in part:
“preprocessing the raw EEG by computing a bipolar EOG and removing ocular artifacts, demeaning, band-pass filtering between 0.5-100 Hz, and removing 50/60 Hz line noise, and segmenting the EEG into 4-second segments while discarding segments flagged as artifact-containing”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation.
“computing the BSP score as a difference between the session-level predicted brain-sex probability and the patient's genetic sex encoded as {0,1}: BSP = Probas - Ytrue, where Proba, ∈ [0,1] is the predicted sex and Ytrue E {0,1} is the biological sex of a subject,”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation/formula.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“receiving raw multi-channel resting-state EEG data for a patient acquired by the scalp EEG system including eyes-open and eyes-closed tasks”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
“for each valid segment, applying a trained deep convolutional neural network (CNN) directly to the raw EEG samples to generate a predicted brain-sex probability for that segment”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (CNN) (See MPEP 2106.05(f)).
“aggregating the segment-level probabilities across segments and eye states to obtain a session-level predicted brain-sex probability in [0,1]”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
“if BSP score is equal to 0 will indicate that the sex of the brain matches the biological sex of the individual”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (indicating the brain phenotype of a patient) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
“if the BSP score > 0, then the brain sex of a biological male has female phenotype, with that the level of expression depends on the score's proximity to 1”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (indicating the brain phenotype of a patient) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
“wherein, if BSP score < 0, then the biological female has the male brain sex phenotype, the level of expression depends on score's proximity to -1”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (indicating the brain phenotype of a patient) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
“outputting the BSP score via an application programming interface for use by a clinical decision-support or diagnostic system”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. post-solution activity of outputting/displaying data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “receiving raw multi-channel resting-state EEG data for a patient acquired by the scalp EEG system including eyes-open and eyes-closed tasks” and “aggregating the segment-level probabilities across segments and eye states to obtain a session-level predicted brain-sex probability in [0,1]” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As discussed above, the additional element(s) of “for each valid segment, applying a trained deep convolutional neural network (CNN) directly to the raw EEG samples to generate a predicted brain-sex probability for that segment” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
As discussed above, the additional element(s) of “if BSP score is equal to 0 will indicate that the sex of the brain matches the biological sex of the individual”, “if the BSP score > 0, then the brain sex of a biological male has female phenotype, with that the level of expression depends on the score's proximity to 1”, and “wherein, if BSP score < 0, then the biological female has the male brain sex phenotype, the level of expression depends on score's proximity to -1” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome (indicating the brain phenotype of a patient) i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
The additional element(s) of “outputting the BSP score via an application programming interface for use by a clinical decision-support or diagnostic system” is/are recited at a high level of generality and amount(s) to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying/outputting a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 12:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the deep learning comprises applying the CNN directly to the raw EEG sample values without computing manually engineered summary features in the time, frequency, or non-linear domains, such that feature extraction is learned by the CNN”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (CNN) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “wherein the deep learning comprises applying the CNN directly to the raw EEG sample values without computing manually engineered summary features in the time, frequency, or non-linear domains, such that feature extraction is learned by the CNN” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 7:
Due to claim language similar to that of Claims 6, 8, and 11, Claim 7 is rejected for the same reasons as presented above in the rejections of Claims 6, 8, and 11, with the exception of the limitation(s) covered below.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“are implemented by a cloud-based service comprising a preprocessing module implementing the operations of step (b). a run-time trained-model module implementing steps (c)-(e), and a REST API configured to receive raw EEG and return the BSP score in real time”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (cloud computing) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “are implemented by a cloud-based service comprising a preprocessing module implementing the operations of step (b). a run-time trained-model module implementing steps (c)-(e), and a REST API configured to receive raw EEG and return the BSP score in real time” is/are directed to particular field(s) of use (cloud computing) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 13:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein classification comprises applying an artificial neural network to a global-average-pooled output of the CNN to produce the predicted brain-sex probability”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (ANN) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “wherein classification comprises applying an artificial neural network to a global-average-pooled output of the CNN to produce the predicted brain-sex probability” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 4:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein said artificial neural network is Single-layer Perceptron implemented as a linear layer of size 128 followed by a Sigmoid activation function”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (single layer perceptrons) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein said artificial neural network is Single-layer Perceptron implemented as a linear layer of size 128 followed by a Sigmoid activation function” is/are directed to particular field(s) of use (single layer perceptrons) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 14:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the CNN comprises four convolutional blocks each including convolution, batch normalization, and a SiLU (the sigmoid-weighted linear unit) activation function, with convolutional kernels (7,64), (7,32), (7,16), (7,8) and a stride of (1,3) along the time axis, followed by global average pooling”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (convolutional blocks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the CNN comprises four convolutional blocks each including convolution, batch normalization, and a SiLU (the sigmoid-weighted linear unit) activation function, with convolutional kernels (7,64), (7,32), (7,16), (7,8) and a stride of (1,3) along the time axis, followed by global average pooling” is/are directed to particular field(s) of use (convolutional blocks) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 15:
Step 2A: Prong 1 analysis:
Claim 15 recites in part:
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• As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses setting the parameters of a neural network.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
van Putten, M.J.A.M., Olbrich, S. & Arns, M. Predicting sex from brain rhythms with deep learning. Sci Rep 8, 3069 (2018). https://doi.org/10.1038/s41598-018-21495-7 – a deep neural net can predict sex from scalp electroencephalograms with an accuracy of >80% (p < 10−5), revealing that brain rhythms are sex specific.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY M SACKALOSKY whose telephone number is (703)756-1590. The examiner can normally be reached M-F 7:30am-3:30pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128