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 the claims filed on 26 December 2025. Claim 1, 7, 11-12, and 14 were amended. Claims 2, 4, and 8-10 were previously canceled. Claims 1, 3, 5-7, and 11-15 are currently pending and have been examined.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim Rejections - 35 USC § 112(b)
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3, 5-7, and 11-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the probability" in line 21. There is insufficient antecedent basis for this limitation in the claim. For the purposes of examination, this element will be considered to state “a probability.” Appropriate correction is required. Claims 3, 5-7, and 11-13 inherit this deficiency.
Claim 5 recites the limitation “the “Temporal Convolutional Network” type” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. For the purposes of examination, this element will be considered to state “a “Temporal Convolutional Network type.” Appropriate correction is required.
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, 3, 5-7, and 11-15 are rejected under 35 USC § 101
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Claims 1, 3, 5-7, and 11-15 fall within one or more statutory categories. Claims 1, 3, 5-7, and 11-13 fall within the category of a process. Claims 14-15 fall within the category of a machine.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1, 3, 5-7, and 11-15 recite an abstract idea. Representative claim 1 recites a method for detecting a microsleep state of mind of a person, wherein:
microsleep means a physical condition of a person that begins when an upper eyelid of a person is lowered to completely cover a pupil and said coverage is maintained for at least 0.5 seconds, and to warn said person in a positive case,
receiving a set of training EEG traces comprising a plurality of EEG traces acquired on more than one person by at least one electrode placed in a frontopolar position (FP1) or a frontopolar position (FP2) on a scalp of said more than one person;
a pre-processing step of said training EEG traces comprising filtering said training EEG traces by means of a high-pass filter with a cut-off frequency chosen between 0.001 and 0.05 Hz and/or a low-pass filter with a cut-off frequency chosen between 10 and 25 Hz;
wherein said method further comprises a step of classifying at least one EEG trace acquired on a person by at least one electrode arranged in a frontopolar position FP1 or FP2 on the scalp of said person, so as to detect or not said microsleep state of mind in said person, said classification step comprising the following operations:
receiving at least said EEG trace;
a pre-processing step of said EEG trace comprising filtering said EEG trace by means of a high-pass filter with a cut-off frequency chosen between 0.001 and 0.05 Hz and/or a low-pass filter with a cut-off frequency chosen between 10 and 25 Hz;
… and deciding whether or not to detect said microsleep state of mind of said person based on said classification output signals; and
upon deciding that said microsleep state of mind is detected, generating and producing a sensory warning signal so that the sensory warning signal is detected by said person to alert said person.
Therefore, the claim as a whole is directed to “detecting microsleep from EEG” which is an abstract idea because it is a method of organizing human activity. “Detecting microsleep from EEG” is considered to be a method of organizing human activity because it is an example of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The broadest reasonable interpretation of the claims include the include the organization of human EEG activity.
Further, the claims can be considered to be directed to a mental process, because they include concepts capable of being performed in the human mind (including an observation, evaluation, judgment, opinion).
Representative claim 1 also recites:
said convolutional neural network ending with a sigmoid activation function, to obtain a classification output signal in which the probability of the detection of said microsleep state of mind in said input training EEG trace is recorded;
receiving said classification output signal for said EEG trace from said activation function.
Under the broadest reasonable interpretation, these elements are directed to “using a sigmoid function,” which is an abstract idea because it is a mathematical concept. “Using a sigmoid function” is considered to be a mathematical concept because it is an example of a specific mathematical calculation.
The limitations that recite a method of organizing human activity and the limitations that recite mathematical concepts are considered together as a single abstract idea for further analysis.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s):
the method is computer-implemented;
said detection being implemented by processing at least one electroencephalogram (EEG) trace of said person by means of at least one suitably trained convolutional neural network, wherein said method includes a training step comprising the following operations:
receiving, for each of said training EEG traces, an annotation indicating an identification for at least one portion of said training EEG trace of said microsleep state of mind intended to be detected, wherein said annotation is generated based on a time-correlated observation of said physical condition of the eyelid covering the pupil for at least 0.5 seconds;
providing each of said training EEG traces and said relative annotation as input data of said convolutional neural network,
by means of said convolutional neural network, processing each of said training EEG traces and training said convolutional neural network, comparing said classification output signal with the annotation relating to said training EEG trace; and
providing said EEG trace as input to said convolutional neural network, after said convolutional neural network has been trained;
processing said EEG trace by means of said already trained convolutional neural network.
The additional elements individually or in combination do not integrate the exception into a practical application. These additional elements, including the training and use of a convolutional neural network, merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 1 is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claim 1 does not include additional elements, considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s), individually and in combination, including the training and use of a convolutional neural network, merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, claim 1 is ineligible.
Dependent claim 3 recites the method of claim 1, wherein:
said classification step, in particular said decision on whether or not to detect said microsleep state of mind of said person, is based on said classification output signals obtained by processing said EEG trace acquired by two electrodes arranged in a frontopolar position FP1 and in a frontopolar position FP2 on the scalp of said person.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 3 is considered to be ineligible.
Dependent claim 5 recites the method of claim 1, wherein:
said convolutional neural network is a network of the “Temporal Convolutional Network” type.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 5 is ineligible.
Dependent claim 6 recites the method of claim 1, wherein:
said operation of deciding whether or not to detect said microsleep state of mind of a person is implemented by at least one recurrent neural network arranged downstream of said at least one convolutional neural network, so that said recurrent neural network receives as input said classification output signals generated by said convolutional neural network.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 6 is ineligible.
Dependent claim 7 recites the method of claim 1, wherein:
said operation of deciding whether or not to detect said microsleep state of mind of a person in said EEG trace, envisages verifying whether a likelihood of detecting said microsleep state of mind indicated in said classification output signal is greater than a pre-set threshold for a predefined minimum period of time.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 7 is considered to be ineligible.
Dependent claim 11 recites the method of claim 1, wherein:
each of said annotations of said training EEG traces comprises a first vector comprising a number of memory cells equal to a duration of each of said training EEG traces divided by a pre-set sampling range, where in each of said memory cells a presence or absence of said microsleep state of mind of a person is noted for each of a sampling ranges of said training EEG trace.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 11 is ineligible.
Dependent claim 12 recites the method of claim 1, wherein:
said classification output signal comprises a second vector comprising a number of memory cells equal to a duration of each of said EEG traces divided by a pre-set sampling range, where in each of said memory cells a likelihood of identifying said microsleep state of mind of a person at a specific sampling range of said EEG trace is recorded.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 12 is ineligible.
Dependent claim 13 recites the method of claim 12, wherein:
said sampling range is comprised between 5 ms and 50 ms.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 13 is considered to be ineligible.
Dependent claim 14 recites a system that performs a method substantially similar to the method of claim 1. However, claim 14 does recited the following additional elements beyond those of claim 1:
a device wearable by said person so that said wearable device is placed at least at a front part of a scalp of said person, said wearable device being provided with at least one electrode adapted to be placed in contact with the scalp of said person in a frontopolar position (FP1) or a frontopolar (FP2) of the scalp itself of said person, said wearable device being configured to acquire at least said EEG trace of said person by means of said electrode;
warning means configured to generate a sensory warning signal to said person.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 14 is ineligible.
Dependent claim 15 recites the system of claim 14, wherein:
said wearable device is provided with first wireless communication means, said electronic control unit belonging to a device distinct from said wearable device and provided with second wireless communication means, said wearable device being configured, once said at least one EEG trace is acquired, to transfer said at least one EEG trace to said electronic device by said first and second wireless communication means.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 15 is ineligible.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3, 6-7, and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Moore-Ede et al. (U.S. Pat. No. 6,070,098), hereinafter “Moore,” in view of Vayrynen et al. (U.S. 2019/0246927), hereinafter “Vayrynen,” and further in view of Vu et al. (U.S. 2022/0218941), hereinafter “Vu.”
Regarding Claim 1, Moore discloses a computer-implemented method for detecting a microsleep state of mind of a person, wherein:
microsleep means a physical condition of a person that begins when an upper eyelid of a person is lowered to completely cover a pupil and said coverage is maintained for at least 0.5 seconds (See Moore col. 1, lines 31-33; A microsleep event can be defined as a somewhat unexpected short episode of sleep, between 1 and 30 seconds, that occurs in the midst of ongoing wakeful activity. See also col 5, lines 6-8; Characteristic features of fatigue-related events which are either corresponding to microsleep events or to various transitional events like partial and complete prolonged eyelid closure. Examiner notes that the range of 1-30 seconds is at least 0.5 seconds.), and to warn said person in the positive case (See Moore Col. 13, lines 3-7; the final output of the system includes an alertness state. This is understood to be a warning of microsleep state. See also Fig. 8), said detection being implemented by processing at least one electroencephalogram (EEG) trace of said person by means of at least one suitably trained … neural network (See Moore col 2, lines18-21 Neural networks seem to be the perfect tool for the automatic recognition, classification and interpretation of various EEG patterns, such as sleep stages.), wherein said method includes a training step comprising the following operations:
receiving a set of training EEG traces comprising a plurality of EEG traces acquired on more than one person … (See Moore col 5, lines 18-21 the neuro-fuzzy hybrid system is trained with person-specific data, containing examples of microsleep events, non-microsleep events and a number of transitional events.);
a pre-processing step of said training EEG traces … (See Moore Col. 12, lines 56-62; the collected data, which includes the physiological data (i.e. the EEG data) is preprocessed via amplifier, filter, and sampler. See also Fig. 8);
receiving, for each of said training EEG traces, an annotation indicating an identification for at least one portion of said training EEG trace of said microsleep state of mind intended to be detected (See Moore col 5, lines 18-21; the neuro-fuzzy hybrid system is trained with person-specific data, containing examples of microsleep events, non-microsleep events and a number of transitional events. Col 9, lines 66-67; the system creates a training data set and a test data set.), wherein said annotation is generated based on a time-correlated observation of said physical condition of the eyelid covering the pupil for at least 0.5 seconds (See Moore col. 1, lines 31-33; A microsleep event can be defined as a somewhat unexpected short episode of sleep, between 1 and 30 seconds, that occurs in the midst of ongoing wakeful activity. See also col 5, lines 6-8; Characteristic features of fatigue-related events which are either corresponding to microsleep events or to various transitional events like partial and complete prolonged eyelid closure. Examiner notes that the range of 1-30 seconds is at least 0.5 seconds.);
providing each of said training EEG traces and said relative annotation as input data of said … neural network (See Moore col 5, lines 18-21; the neuro-fuzzy hybrid system is trained with person-specific data, containing examples of microsleep events, non-microsleep events and a number of transitional events.);
by means of said … neural network, processing each of said training EEG traces and training said … neural network, comparing said classification output signal with the annotation relating to said training EEG trace (See Moore col 5, lines 18-21; the neuro-fuzzy hybrid system is trained with person-specific data, containing examples of microsleep events, non-microsleep events and a number of transitional events. Col 10, lines 36-47; the system adjusts weights based on errors in the neural networks prediction compared to the training and test data sets.); and
wherein said method further comprises a step of classifying at least one EEG trace …, so as to detect or not said microsleep state of mind in said person (See Moore col 5, lines 29-34; Based on the occurrence of all detected events, alertness parameters such as mean and variability and circadian pattern of alertness, number of alertness lapses per time period, periodicity of alertness lapses, and any combinations thereof are determined and can be used for predicting alertness. Col. 13, lines 3-7; the final output of the system includes an alertness state. This is a detection of a microsleep state of mind. See also Fig. 8.), said classification step comprising the following operations:
receiving at least said EEG trace (See Moore col. 6, lines 7-8; the system can collect physiological data, including EEG data, for use as input into the neural network.);
a pre-processing step of said EEG trace … (See Moore Col. 12, lines 56-62; the collected data, which includes the physiological data (i.e. the EEG data) is preprocessed via amplifier, filter, and sampler. See also Fig. 8);
providing said EEG trace as input to said … neural network, after said … neural network has been trained (See Moore col. 6, lines 7-8; the system can collect physiological data, including EEG data, for use as input into the neural network.);
processing said EEG trace by means of said already trained convolutional neural network (See Moore col. 6, lines 7-8; the system can collect physiological data, including EEG data, for use as input into the neural network.);
deciding whether or not to detect said microsleep state of mind of said person based on said classification output signals (See Moore col 5, lines 29-34; Based on the occurrence of all detected events, alertness parameters such as mean and variability and circadian pattern of alertness, number of alertness lapses per time period, periodicity of alertness lapses, and any combinations thereof are determined and can be used for predicting alertness. Col. 13, lines 3-7; the final output of the system includes an alertness state. This is a detection of a microsleep state of mind. See also Fig. 8.).
Moore does not disclose:
[the neural network is a] convolutional neural network;
[the EEG traces are acquired] by at least one electrode placed in the frontopolar position FP1 or FP2 on the scalp of said more than one person;
[the preprocessing step] comprising filtering said training EEG traces by means of a high-pass filter with a cut-off frequency chosen between 0.001 and 0.05 Hz and/or a low-pass filter with a cut-off frequency chosen between 10 and 25 Hz;
said convolutional neural network ending with a sigmoid activation function, to obtain a classification output signal in which the probability of the detection of said microsleep state of mind in said input training EEG trace is recorded;
receiving said classification output signal for said EEG trace from said activation function;
upon deciding that said microsleep state of mind is detected, generating and producing a sensory warning signal so that the sensory warning signal is detected by said person to alert said person.
Vayrynen teaches:
[the neural network is a] convolutional neural network (See Vayrynen [0117] the system can use Convolutional Neural Network technology for the EEG classification.);
[the EEG traces are acquired] by at least one electrode placed in a frontopolar position (FP1) or a frontopolar position (FP2) on a scalp of said more than one person (See Vayrynen [0053] the EEG data can be collected from electrodes in the FP1 and FP2 positions on the scalp.);
said convolutional neural network ending with a sigmoid activation function, to obtain a classification output signal in which the probability of the detection of said microsleep state of mind in said input training EEG trace is recorded (See Vayrynen [0063] the system can use a sigmoid activation function to scale the results to have a desired range between an upper limit and a lower limit. This includes a 0 to 100 scale that translates to a probability.);
receiving said classification output signal for said EEG trace from said activation function (See Vayrynen [0063] the system can use a sigmoid activation function to scale the results to have a desired range between an upper limit and a lower limit. This includes a 0 to 100 scale that translates to a probability.).
The system of Vayrynen is applicable to the disclosure of Moore as they both share characteristics and capabilities, namely, they are directed to using neural networks to analyze EEG signals (e.g. see Vayrynen [0118]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moore to include the use of specific electrodes and neural networks as taught by Vayrynen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Moore in order to use measured phase-to-phase, phase-to-amplitude and/or amplitude-to-phase coupling information of the EEG signals that may reveal effectively how orderly the examined brain functions with respect to a normal brain (see Vayrynen [0004]).
Vu teaches:
[the preprocessing step] comprising filtering said training EEG traces by means of a high-pass filter with a cut-off frequency chosen between 0.001 and 0.05 Hz and/or a low-pass filter with a cut-off frequency chosen between 10 and 25 Hz (See Vu [0061] the system can extract wakefulness-related EEG bands using 4-8 Hz, 8-12 Hz, 12-35 Hz bandpass filters, respectively. A band pass filter is understood to be made up of a high pass and low pass filter. The 8-12 Hz band includes a low pass filter of 12 HZ, which falls between 10 and 25 Hz. See also [0045].); and
upon deciding that said microsleep state of mind is detected, generating and producing a sensory warning signal so that the sensory warning signal is detected by said person to alert said person (See Vu [0095] system may be used to produce the feedback to the patient by using acoustic signals to warn the user, based on the detection of wakefulness.).
The system of Vu is applicable to the disclosure of Moore in view of Vayrynen as they both share characteristics and capabilities, namely, they are directed to analyzing EEG signals using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moore in view of Vayrynen to include band filters and sampling ranges as taught by Vu. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Moore in view of Vayrynen in order to address the significant challenges of continuously gathering and processing biosignal information as a user goes through their normal daily activities (see Vu [0003]).
Regarding claim 3, Moore in view of Vayrynen and Vu discloses the method of claim 1 as discussed above. Moore does not further disclose a method, wherein:
said classification step, in particular said decision on whether or not to detect said microsleep state of mind of said person, is based on said classification output signals obtained by processing said EEG trace acquired by two electrodes arranged in a frontopolar position FP1 and in a frontopolar position FP2 on the scalp of said person.
Vayrynen teaches:
said classification step, in particular said decision on whether or not to detect said microsleep state of mind of said person, is based on said classification output signals obtained by processing said EEG trace acquired by two electrodes arranged in a frontopolar position FP1 and in a frontopolar position FP2 on the scalp of said person (See Vayrynen [0053] the EEG data can be collected from electrodes in the FP1 and FP2 positions on the scalp. [0069] the system can use at least two EEG signals. Therefore, the system can use the data from both the FP1 and FP2 positions.).
The system of Vayrynen is applicable to the disclosure of Moore as they both share characteristics and capabilities, namely, they are directed to using neural networks to analyze EEG signals (e.g. see Vayrynen [0118]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moore to include the use of specific electrodes and neural networks as taught by Vayrynen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Moore in order to use measured phase-to-phase, phase-to-amplitude and/or amplitude-to-phase coupling information of the EEG signals that may reveal effectively how orderly the examined brain functions with respect to a normal brain (see Vayrynen [0004]).
Regarding claim 6, Moore in view of Vayrynen and Vu discloses the method of claim 1 as discussed above. Moore does not further disclose a method, wherein:
said operation of deciding whether or not to detect said microsleep state of mind of a person is implemented by at least one recurrent neural network arranged downstream of said at least one convolutional neural network, so that said recurrent neural network receives as input said classification output signals generated by said convolutional neural network.
Vayrynen teaches:
said operation of deciding whether or not to detect said microsleep state of mind of a person is implemented by at least one recurrent neural network arranged downstream of said at least one convolutional neural network, so that said recurrent neural network receives as input said classification output signals generated by said convolutional neural network (See Vayrynen [0117] the system can use Recurrent Neural Network technology with selection, transformation, segmentation, and/or classification capabilities technology for the EEG classification.).
The system of Vayrynen is applicable to the disclosure of Moore as they both share characteristics and capabilities, namely, they are directed to using neural networks to analyze EEG signals (e.g. see Vayrynen [0118]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moore to include the use of specific electrodes and neural networks as taught by Vayrynen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Moore in order to use measured phase-to-phase, phase-to-amplitude and/or amplitude-to-phase coupling information of the EEG signals that may reveal effectively how orderly the examined brain functions with respect to a normal brain (see Vayrynen [0004]).
Regarding claim 7, Moore in view of Vayrynen and Vu discloses the method of claim 1 as discussed above. Moore further discloses a method, wherein:
said operation of deciding whether or not to detect said microsleep state of mind of a person in said EEG trace, envisages verifying whether a likelihood of detecting said microsleep state of mind indicated in said classification output signal is greater than a pre-set threshold for a predefined minimum period of time (See Moore col. 1, lines 31-33; A microsleep event can be defined as a somewhat unexpected short episode of sleep, between 1 and 30 seconds, that occurs in the midst of ongoing wakeful activity. See also col 5, lines 6-8; Characteristic features of fatigue-related events which are either corresponding to microsleep events or to various transitional events like partial and complete prolonged eyelid closure. Examiner notes, the language “envisages verifying,” and everything that follows it, is considered to be intended use language, and is not given patentable weight.).
Regarding claim 11, Moore in view of Vayrynen and Vu discloses the method of claim 1 as discussed above. Moore further discloses a method, wherein:
each of said annotations of said training EEG traces comprises a first vector comprising a number of memory cells equal to a duration of each of said training EEG traces divided by a pre-set sampling range (See Moore col 7, lines 50-67; the system uses feature vectors that include different bands and their calculated alertness-characterizing features. Col 6, line 55-56 recorded signals are filtered, amplified and digitized with a certain sampling rate.), where in each of said memory cells a presence or absence of said microsleep state of mind of a person is noted for each of a sampling ranges of said training EEG trace (See Moore col 5, lines 18-21 the neuro-fuzzy hybrid System is trained with person-specific data, containing examples of microsleep events, non-microsleep events and a number of transitional events.).
Regarding claim 12, Moore in view of Vayrynen and Vu discloses the method of claim 1 as discussed above. Moore does not further disclose a method, wherein:
said classification output signal comprises a second vector comprising a number of memory cells equal to a duration of each of said EEG traces divided by a pre-set sampling range, where in each of said memory cells a likelihood of identifying said microsleep state of mind of a person at a specific sampling range of said EEG trace is recorded.
Vu teaches:
said classification output signal comprises a second vector comprising a number of memory cells equal to a duration of each of said EEG traces divided by a pre-set sampling range, where in each of said memory cells a likelihood of identifying said microsleep state of mind of a person at a specific sampling range of said EEG trace is recorded (See Vu [0063] In one example, the wakefulness level may be normalized on a scale from 0 to 1, where 0 indicates a state of sleep or microsleep, and 1 indicates an awake state. In some embodiments, the scale may indicate an estimated probability that the patient is in a sleep and/or microsleep state, with 0 being 100% certainty, and 1 being 0% certainty. Alternatively, the ML model may be configured to classify whether the patient is in an awake state, sleep state, and/or in a state of microsleep.).
The system of Vu is applicable to the disclosure of Moore in view of Vayrynen as they both share characteristics and capabilities, namely, they are directed to analyzing EEG signals using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moore in view of Vayrynen to include band filters and sampling ranges as taught by Vu. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Moore in view of Vayrynen in order to address the significant challenges of continuously gathering and processing biosignal information as a user goes through their normal daily activities (see Vu [0003]).
Regarding claim 13, Moore in view of Vayrynen and Vu discloses the method of claim 12 as discussed above. Moore does not further disclose a method, wherein:
said sampling range is comprised between 5 ms and 50 ms.
Vu teaches:
said sampling range is comprised between 5 ms and 50 ms (See Vu [0061] system can define data ranges. EEG data may be defined at the frequency range of 4-35 Hz, EOG defined at a frequency range of 0.1-10 Hz, and EMG at a frequency range of 10-100 Hz. These fall within the sampling range of 20Hz-200Hz (i.e. 5 ms and 50 ms).).
The system of Vu is applicable to the disclosure of Moore in view of Vayrynen as they both share characteristics and capabilities, namely, they are directed to analyzing EEG signals using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moore in view of Vayrynen to include band filters and sampling ranges as taught by Vu. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Moore in view of Vayrynen in order to address the significant challenges of continuously gathering and processing biosignal information as a user goes through their normal daily activities (see Vu [0003]).
Regarding claim 14, Moore discloses a system for detecting microsleep state of mind of a person, wherein:
microsleep means a physical condition of a person that begins when an upper eyelid of a person is lowered to completely cover a pupil and said coverage is maintained for at least 0.5 seconds (See Moore col. 1, lines 31-33; A microsleep event can be defined as a somewhat unexpected short episode of sleep, between 1 and 30 seconds, that occurs in the midst of ongoing wakeful activity. See also col 5, lines 6-8; Characteristic features of fatigue-related events which are either corresponding to microsleep events or to various transitional events like partial and complete prolonged eyelid closure. Examiner notes that the range of 1-30 seconds is at least 0.5 seconds.), and for warning said person in a positive case (See Moore Col. 13, lines 3-7; the final output of the system includes an alertness state. This is understood to be a warning of microsleep state. See also Fig. 8), said detection being implemented by processing at least one electroencephalogram (EEG) trace of said person by means of at least one suitably trained … neural network (See Moore col 2, lines18-21 Neural networks seem to be the perfect tool for the automatic recognition, classification and interpretation of various EEG patterns, such as sleep stages.), said system comprising:
an electronic control unit comprising storage means in which a convolutional neural network is stored according to any one of the preceding claims and processing means configured to execute said convolutional neural network (See Moore col. 12, lines 65-67 the system has a processing unit that includes hardware and/or software which implements the classification algorithms and context interpretation algorithms.), said convolutional neural network being configured to execute:
a training step (See Moore col 5, lines 18-21 the neuro-fuzzy hybrid system is trained with person-specific data, containing examples of microsleep events, non-microsleep events and a number of transitional events.) comprising the following operations:
receiving a set of training EEG traces comprising a plurality of EEG traces acquired on more than one person … (See Moore col 5, lines 18-21 the neuro-fuzzy hybrid system is trained with person-specific data, containing examples of microsleep events, non-microsleep events and a number of transitional events.);
a pre-processing step of said training EEG traces … (See Moore col. 12, lines 56-62; the collected data, which includes the physiological data (i.e. the EEG data) is preprocessed via amplifier, filter, and sampler. See also Fig. 8);
receiving, for each of said training EEG traces, an annotation indicating the identification for at least one portion of said training EEG trace of said microsleep state of mind intended to be detected (See Moore col 5, lines 18-21; the neuro-fuzzy hybrid system is trained with person-specific data, containing examples of microsleep events, non-microsleep events and a number of transitional events. Col 9, lines 66-67; the system creates a training data set and a test data set.), wherein said annotation is generated based on a time-correlated observation of said physical condition of the eyelid covering the pupil for at least 0.5 seconds (See Moore col. 1, lines 31-33; A microsleep event can be defined as a somewhat unexpected short episode of sleep, between 1 and 30 seconds, that occurs in the midst of ongoing wakeful activity. See also col 5, lines 6-8; Characteristic features of fatigue-related events which are either corresponding to microsleep events or to various transitional events like partial and complete prolonged eyelid closure. Examiner notes that the range of 1-30 seconds is at least 0.5 seconds.);
providing each of said training EEG traces and said relative annotation as input data of said … neural network (See Moore col 5, lines 18-21; the neuro-fuzzy hybrid system is trained with person-specific data, containing examples of microsleep events, non-microsleep events and a number of transitional events.);
by means of said convolutional neural network, processing each of said training EEG traces and training said … neural network, comparing said classification output signal with the annotation relating to said training EEG trace (See Moore col 5, lines 18-21; the neuro-fuzzy hybrid system is trained with person-specific data, containing examples of microsleep events, non-microsleep events and a number of transitional events. Col 10, lines 36-47; the system adjusts weights based on errors in the neural networks prediction compared to the training and test data sets.); and/or
said … neural network being configured to execute:
a classification step, when said convolutional neural network receives as input at least one EEG trace of said person so as to detect or not said specific microsleep state of mind in said person (See Moore col 5, lines 29-34; Based on the occurrence of all detected events, alertness parameters such as mean and variability and circadian pattern of alertness, number of alertness lapses per time period, periodicity of alertness lapses, and any combinations thereof are determined and can be used for predicting alertness. Col. 13, lines 3-7; the final output of the system includes an alertness state. This is a detection of a microsleep state of mind. See also Fig. 8.), said classification step comprising the following operations:
receiving at least said EEG trace (See Moore col. 6, lines 7-8; the system can collect physiological data, including EEG data, for use as input into the neural network.);
a pre-processing step of said EEG trace (See Moore Col. 12, lines 56-62; the collected data, which includes the physiological data (i.e. the EEG data) is preprocessed via amplifier, filter, and sampler. See also Fig. 8);
providing said EEG trace as input to said … neural network, after said … neural network has been trained (See Moore col. 6, lines 7-8; the system can collect physiological data, including EEG data, for use as input into the neural network.);
processing said EEG trace by means of said already trained … neural network (See Moore col. 6, lines 7-8; the system can collect physiological data, including EEG data, for use as input into the neural network.);
…deciding whether or not to detect said microsleep state of mind of said person based on said classification output signals (See Moore col 5, lines 29-34; Based on the occurrence of all detected events, alertness parameters such as mean and variability and circadian pattern of alertness, number of alertness lapses per time period, periodicity of alertness lapses, and any combinations thereof are determined and can be used for predicting alertness. Col. 13, lines 3-7; the final output of the system includes an alertness state. This is a detection of a microsleep state of mind. See also Fig. 8.).
Moore does not disclose:
[the neural network is a] convolutional neural network;
a device wearable by said person so that said wearable device is placed at least at the front part of the scalp of said person, said wearable device being provided with at least one electrode adapted to be placed in contact with the scalp of said person in a frontopolar position (FP1) or a frontopolar (FP2) of the scalp itself of said person, said wearable device being configured to acquire at least said EEG trace of said person by means of said electrode;
warning means configured to generate a sensory warning signal to said person;
[the EEG traces are acquired] by at least one electrode placed in the frontopolar position FP1 or FP2 on the scalp of said more than one person;
[the preprocessing step] comprising filtering said training EEG traces by means of a high-pass filter with a cut-off frequency chosen between 0.001 and 0.05 Hz and/or a low-pass filter with a cut-off frequency chosen between 10 and 25 Hz;
said convolutional neural network ending with a sigmoid activation function, to obtain a classification output signal in which a probability of the detection of said microsleep state of mind in said input training EEG trace is recorded;
receiving said classification output signal for said EEG trace from said activation function;
warning means, configured to warn said person of the detection of said microsleep state of mind when said electronic control unit detects said microsleep state of mind of said person;
cause said warning means to generate and produce said sensory warning signal when said electronic control unit detects said microsleep state of mind of said person so that the sensory warning signal is detected by said person to alert said person.
Vayrynen teaches:
[the neural network is a] convolutional neural network (See Vayrynen [0117] the system can use Convolutional Neural Network technology for the EEG classification.);
a device wearable by said person so that said wearable device is placed at least at the front part of the scalp of said person (See Vayrynen [0033] the electrode system is electrically coupled or in contact with the scalp or the brain of a person. See also Fig. 1),
said wearable device being provided with at least one electrode adapted to be placed in contact with the scalp of said person in a frontopolar position (FP1) or a frontopolar (FP2) of the scalp itself of said person (See Vayrynen [0053] the EEG data can be collected from electrodes in the FP1 and FP2 positions on the scalp.), said wearable device being configured to acquire at least said EEG trace of said person by means of said electrode (See Vayrynen [0033] the electrode system provides the EEG signals for the data processing unit. The EEG signals may be directly fed from the electrode system to the data processing unit.);
[the EEG traces are acquired] by at least one electrode placed in the frontopolar position FP1 or FP2 on the scalp of said more than one person (See Vayrynen [0053] the EEG data can be collected from electrodes in the FP1 and FP2 positions on the scalp.);
said convolutional neural network ending with a sigmoid activation function, to obtain a classification output signal in which a probability of the detection of said microsleep state of mind in said input training EEG trace is recorded (See Vayrynen [0063] the system can use a sigmoid activation function to scale the results to have a desired range between an upper limit and a lower limit. This includes a 0 to 100 scale that translates to a probability.);
receiving said classification output signal for said EEG trace from said activation function (See Vayrynen [0063] the system can use a sigmoid activation function to scale the results to have a desired range between an upper limit and a lower limit. This includes a 0 to 100 scale that translates to a probability.).
The system of Vayrynen is applicable to the disclosure of Moore as they both share characteristics and capabilities, namely, they are directed to using neural networks to analyze EEG signals (e.g. see Vayrynen [0118]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moore to include the use of specific electrodes and neural networks as taught by Vayrynen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Moore in order to use measured phase-to-phase, phase-to-amplitude and/or amplitude-to-phase coupling information of the EEG signals that may reveal effectively how orderly the examined brain functions with respect to a normal brain (see Vayrynen [0004]).
Vu teaches:
warning means configured to generate a sensory warning signal to said person (See Vu [0095] system may be used to produce the feedback to the patient by using acoustic signals to warn the user, based on the detection of wakefulness.);
[the preprocessing step] comprising filtering said training EEG traces by means of a high-pass filter with a cut-off frequency chosen between 0.001 and 0.05 Hz and/or a low-pass filter with a cut-off frequency chosen between 10 and 25 Hz (See Vu [0061] the system can extract wakefulness-related EEG bands using 4-8 Hz, 8-12 Hz, 12-35 Hz bandpass filters, respectively. A band pass filter is understood to be made up of a high pass and low pass filter. The 8-12 Hz band includes a low pass filter of 12 HZ, which falls between 10 and 25 Hz. See also [0045].);
cause said warning means to generate and produce said sensory warning signal when said electronic control unit detects said microsleep state of mind of said person so that the sensory warning signal is detected by said person to alert said person (See Vu [0095] system may be used to produce the feedback to the patient by using acoustic signals to warn the user, based on the detection of wakefulness.).
The system of Vu is applicable to the disclosure of Moore in view of Vayrynen as they both share characteristics and capabilities, namely, they are directed to analyzing EEG signals using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moore in view of Vayrynen to include band filters and sampling ranges as taught by Vu. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Moore in view of Vayrynen in order to address the significant challenges of continuously gathering and processing biosignal information as a user goes through their normal daily activities (see Vu [0003]).
Regarding claim 15, Moore in view of Vayrynen and Vu discloses the system of claim 14 as discussed above. Moore does not further disclose a system, wherein:
said wearable device is provided with first wireless communication means, said electronic control unit belonging to a device distinct from said wearable device and provided with second wireless communication means, said wearable device being configured, once said at least one EEG trace is acquired, to transfer said at least one EEG trace to said electronic device by said first and second wireless communication means.
Vayrynen teaches:
said wearable device is provided with first wireless communication means, said electronic control unit belonging to a device distinct from said wearable device and provided with second wireless communication means, said wearable device being configured, once said at least one EEG trace is acquired, to transfer said at least one EEG trace to said electronic device by said first and second wireless communication means (See Vayrynen [0002] the system can use Modern electroencephalographic (EEG) signal acquisition devices that are wireless. [0132] system can have a wireless connection to a cloud server for transferring the raw EEG signal data. See also [0050].).
The system of Vayrynen is applicable to the disclosure of Moore as they both share characteristics and capabilities, namely, they are directed to using neural networks to analyze EEG signals (e.g. see Vayrynen [0118]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moore to include the use of specific electrodes and neural networks as taught by Vayrynen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Moore in order to use measured phase-to-phase, phase-to-amplitude and/or amplitude-to-phase coupling information of the EEG signals that may reveal effectively how orderly the examined brain functions with respect to a normal brain (see Vayrynen [0004]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Moore-Ede et al. (U.S. Pat. No. 6,070,098), hereinafter “Moore,” in view of Vayrynen et al. (U.S. 2019/0246927), hereinafter “Vayrynen,” and Vu et al. (U.S. 2022/0218941), hereinafter “Vu,” and further in view of Gao et al., "EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation," in IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 9, pp. 2755-2763, Sept. 2019, hereinafter “Gao.”
Regarding claim 5, Moore in view of Vayrynen and Vu discloses the method of claim 1 as discussed above. Moore does not further disclose a method, wherein:
said convolutional neural network is a network of a “Temporal Convolutional Network” type.
Gao teaches:
said convolutional neural network is a network of a “Temporal Convolutional Network” type (See Gao Abstract; the system uses EEG signals as input for a spatial–temporal convolutional neural network (ESTCNN) to detect driver fatigue.).
The system of Gao is applicable to the disclosure of Moore in view of Vayrynen and Vu as they both share characteristics and capabilities, namely, they are directed to analyzing EEG signals using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moore in view of Vayrynen and Vu to include TCN technology as taught by Gao. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Moore in view of Vayrynen and Vu in order to perform a better classification accuracy than comparable models (see Gao Abstract).
Response to Arguments
35 U.S.C. §101
Applicant's arguments filed 26 December 2025, with respect to the 35 U.S.C. §101 rejection of the claims, have been fully considered but they are not persuasive. First, Applicant argues that the claims that the present claims do not recite an abstract idea under Step 2A, Prong One of the analysis (see Applicant Remarks pages 9-11). This is not persuasive. The broadest reasonable interpretation of the claims include the include the organization of human EEG activity. This is an example of a method of organizing human activity. The claims also recite the specific use of a sigmoid activation function, which is an example of a mathematical concept because it is an example of a specific mathematical calculation. These elements are considered together to recite an abstract idea under step 2A, Prong One. Therefore, the analysis must continue to step 2A, Prong Two and Step 2B in order to consider any additional elements recited in the claims.
Next, Applicant argues that the claims include additional elements that integrate the abstract idea into a practical application under Step 2A, Prong Two (see Applicant Remarks pages 11-12). This is not persuasive. The presently recited additional elements, including the training and use of a convolutional neural network, merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). This is not enough to integrate the abstract idea into a practical application. The claims do not pass Step 2A, Prong Two.
Finally, Applicant argues that the claims include significantly more under Step 2B because they require that the claims produce a sensory warning signal (see Applicant Remarks pages 12-13). This is not persuasive. Applicant argues that this is an improvement to technological, but this is just a simple output based on a calculation. As discussed above, the presently recited additional elements, including the training and use of a convolutional neural network, merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). This is not enough to amount to significantly more than the abstract idea. Accordingly, the claims remain rejected as being directed to ineligible subject matter.
35 U.S.C. §103
Applicant's arguments filed 26 December 2025, with respect to the 35 U.S.C. §103 rejection of the claims, have been fully considered but they are not persuasive. First, Applicant argues that the Moore reference fails to disclose that the microsleep feature is defined as “at least 0.5 seconds” (see Applicant Remarks pages 13-14). This is not persuasive. Moore recites that the microsleep is an event of 1-30 seconds (see Moore col. 1, lines 31-33). This range of time is also “at least 0.5 seconds” because it is not less than 0.5 seconds.
Next, Applicant argues that the Vu reference recites “a high-pass filter with a cut-off frequency chosen between 0.001 and 0.05 Hz and/or a low-pass filter with a cut-off frequency chosen between 10 and 25 Hz” (see Applicant Remarks page 14). This is not persuasive. The claim as currently presented only requires the high-pass filter or the low pass filter. Vu teaches the system that can extract wakefulness-related EEG bands using 4-8 Hz, 8-12 Hz, 12-35 Hz bandpass filters, respectively (see Vu [0061). A band pass filter is understood to be made up of a high pass and low pass filter. The 8-12 Hz band includes a low pass filter of 12 HZ, which falls between 10 and 25 Hz.
Finally, Applicant argues that the references teach away because of the treatment of ocular artifacts as noise to be eliminates (See Applicant Remarks page 15). This is not persuasive. There is no recitation in the claims of ocular artifacts or noise filtering. Therefore, their presence or absence in the cited references are not applicable to the present claims. Accordingly, the claims remain rejected as being obvious over the cited prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chen et al. (U.S. 2020/0105398) a system and method for detection and enhancement of sleep spindles in EEG data.
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.
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/B.L.H./Examiner, Art Unit 3684
/KENNETH BARTLEY/Primary Examiner, Art Unit 3684
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