DETAILED ACTION
Claims 1-19 are pending and hereby under examination.
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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters "26” and "323" have both been used to designate “rhythmic component power correction unit” and reference characters “24” and “322” have both been used to designate “noise reduction processing control unit”. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“Body movement noise identification unit” first recited in claim 1;
“Noise reduction processing control unit” first recited in claim 1;
“Residual noise identification unit” first recited in claim 2;
“Rhythmic component power calculation unit” first recited in claim 3;
“Rhythmic component power correction unit” first recited in claim 3;
“Body movement analysis unit” first recited in claim 5; and
“Noise reduction processing unit” first recited in claim 8;
The identified structure for the corresponding claim limitations are as follows:
“Body movement noise identification unit” is identified as the body movement noise identification unit 21 includes an analysis window 101, a time feature calculation unit 102, a frequency feature calculation unit 103, a model accumulation unit 104, and a body movement noise label estimation unit 105.
The analysis window 101 cuts out an electroencephalography signal supplied from the EEG 11 while shifting the analysis window interval in the time axis direction by using the window function. The analysis window 101 supplies the cut-out electroencephalography signal to the time feature calculation unit 102 and the frequency feature calculation unit 103.
The time feature calculation unit 102 calculates a time feature of the electroencephalography signal, the time feature being the same type as the time feature calculated by the time feature calculation unit 63 of the learning apparatus 51. The time feature calculation unit 102 supplies the time feature of the electroencephalography signal to the body movement noise label estimation unit 105. The frequency feature calculation unit 103 calculates a time feature of the electroencephalography signal, the time feature being the same type as the time feature calculated by the frequency feature calculation unit 64 of the learning apparatus 51.
The frequency feature calculation unit 103 supplies the frequency feature of the electroencephalography signal to the body movement noise label estimation unit 105.
The model accumulation unit 104 accumulates the body movement noise identification models learned by the learning apparatus 51.
The body movement noise label estimation unit 105 estimates a body movement noise label with respect to the electroencephalography signal on the basis of the temporal feature and the frequency feature of the electroencephalography signal by using the body movement noise identification models accumulated in the model accumulation unit 104. That is, the body movement noise label estimation unit 105 estimates the presence/absence and the type of body movement noise contained in the electroencephalography signal. The body movement noise label estimation unit 105 supplies body movement noise information including an estimation result for the body movement noise label to the noise reduction processing control unit 24 (Paragraphs 0041-0046).
“Noise reduction processing control unit” is identified as the noise reduction processing control unit 24 sets, on the basis of the singular values of the trajectory matrix, a contact impedance change noise discrimination threshold used for discriminating components of the contact impedance change noise (hereinafter, also simply referred to as noise components) as a discrimination parameter. The noise reduction processing control unit 24 supplies the contact impedance change noise discrimination threshold to the noise component removal unit 153 (Paragraph 0060).
“Residual noise identification unit” is identified as the residual noise identification unit 23 includes an analysis window 121, a time feature calculation unit 122, a frequency feature calculation unit 123, a model accumulation unit 124, and a residual noise label estimation unit 125.
The analysis window 121 cuts out the electroencephalography signal supplied from the noise reduction processing unit 22 while shifting the analysis window interval in the time axis direction by using the window function. The analysis window 121 supplies the cut-out electroencephalography signal to the time feature calculation unit 122 and the frequency feature calculation unit 123.
The time feature calculation unit 122 calculates a time feature of the electroencephalography signal, the time feature being the same type as the time feature calculated by the time feature calculation unit 63 of the learning apparatus 51. The time feature calculation unit 122 supplies the time feature of the electroencephalography signal to the residual noise label estimation unit 125. The frequency feature calculation unit 123 calculates a time feature of the electroencephalography signal, the time feature being the same type as the time feature calculated by the frequency feature calculation unit 64 of the learning apparatus 51.
The frequency feature calculation unit 123 supplies the frequency feature of the electroencephalography signal to the residual noise label estimation unit 125.
The model accumulation unit 124 accumulates the residual noise identification model learned by the learning apparatus 51.
The residual noise label estimation unit 125 estimates a residual noise label with respect to the electroencephalography signal on the basis of the temporal feature and the frequency feature of the electroencephalography signal by using the residual noise identification models accumulated in the model accumulation unit 124. That is, the residual noise label estimation unit 125 estimates the presence/absence and the type of residual noise contained in the electroencephalography signal after the noise reduction processing. The residual noise label estimation unit 125 supplies body movement noise information including an estimation result for the residual noise label to the noise reduction processing control unit 24 and the rhythmic component power correction unit 26 (Paragraphs 0048-0053).
“Rhythmic component power calculation unit” is identified as the rhythmic component power calculation unit 25 calculates rhythmic component power. For example, on the basis of power spectrum associated with short-time Fourier transform or spectral density estimation associated with Welch’s method, the rhythmic component power calculation unit 25 calculates power or the like of θ waves (4 to 7 Hz), α waves (8 to 13 Hz), and β waves (14 to 30 Hz) of the electroencephalography signal as the rhythmic component power. The rhythmic component power calculation unit 25 supplies rhythmic component power information indicating a calculation result for the rhythmic component power to the rhythmic component power correction unit 26 (Paragraph 0077);
“Rhythmic component power correction unit” is identified as the rhythmic component power correction unit 26 invalidates the calculation result for the rhythmic component power in an interval (hereinafter, referred to as a noise remaining interval) including the contact impedance change noise as residual noise on the basis of the residual noise information. Accordingly, the rhythmic component power in the noise remaining interval is lost.
The rhythmic component power correction unit 26 interpolates the rhythmic component power in the noise remaining interval. That is, the rhythmic component power correction unit 26 interpolates the rhythmic component power in the noise remaining interval on the basis of a calculation result for rhythmic component power components in a (past and future) interval with no residual noise (hereinafter, referred to as a non-noise remaining interval) immediately temporally preceding and following the noise remaining interval (Paragraphs 0079-0080).
“Body movement analysis unit” is identified as The body movement analysis unit 321 analyzes the body movement of the user on the basis of the acceleration signal and gyro signal. For example, the body movement analysis unit 321 analyzes the body movement of the user to thereby identify a context of the body movement of the user (hereinafter, referred to as a body movement context), and calculates features relating to the identified body movement context (Paragraph 0159); and
“Noise reduction processing unit” is identified as the noise reduction processing unit 22a reduces contact impedance change noise contained in the electroencephalography signal. The contact impedance change noise is generated when contact impedance between the user’s skin and the electrodes of the EEG 11 changes because of a body movement such as a change in facial expression during communication or video viewing in the user’s daily life, for example. The contact impedance change noise appears as impulse noise including a sharp potential change or a transient phenomenon, for example.
Moreover, the noise reduction processing unit 22a in Fig. 5 reduces the contact impedance change noise by singular spectrum analysis. The singular spectrum analysis is a data-driven method of analyzing the structure itself of the electroencephalography signal without assuming a particular basic function and the like. The singular spectrum analysis is capable of separating unsteady signals like the contact impedance change noise.
The noise reduction processing unit 22a includes a trajectory matrix generation unit 151, a singular value decomposition unit 152, a noise component removal unit 153, and a recomposition unit 154.
The trajectory matrix generation unit 151 generates a trajectory matrix on the basis of the electroencephalography signal supplied from the EEG 11. The trajectory matrix generation unit 151 supplies the generated trajectory matrix to the singular value decomposition unit 152.
The singular value decomposition unit 152 decomposes the trajectory matrix into a plurality of singular vector groups by performing singular value decomposition on the trajectory matrix. The singular value decomposition unit 152 supplies information indicating a result obtained by performing the singular value decomposition on the trajectory matrix to the noise component removal unit 153. Moreover, the singular value decomposition unit 152 supplies singular values of the trajectory matrix obtained by the singular value decomposition to the noise reduction processing control unit 24.
The noise reduction processing control unit 24 sets, on the basis of the singular values of the trajectory matrix, a contact impedance change noise discrimination threshold used for discriminating components of the contact impedance change noise (hereinafter, also simply referred to as noise components) as a discrimination parameter. The noise reduction processing control unit 24 supplies the contact impedance change noise discrimination threshold to the noise component removal unit 153.
The noise component removal unit 153 removes, on the basis of the contact impedance change noise discrimination threshold, singular vectors corresponding to the noise components from the singular vector groups of the trajectory matrix. The noise component removal unit 153 supplies a singular vector groups remaining without being removed to the recomposition unit 154.
The recomposition unit 154 recomposes an electroencephalography signal on the basis of the singular vector group supplied from the noise component removal unit 153. The recomposition unit 154 supplies the recomposed electroencephalography signal to the residual noise identification unit 23 and the rhythmic component power calculation unit 25 (Paragraphs 0055-0062).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
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 1-17 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.
Regarding claims 1-17, the claims are directed to an information processing apparatus; however, the claims recite method steps such as “a body movement noise identification unit that identifies …” (Claim 1, line 2). That claims should recite a structure of the apparatus that “is configured to …” followed by its function.
Claim Rejections - 35 USC § 102
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, 10-13, 18, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kang (KR 20100097952).
Regarding claim 1, Kang teaches an information processing apparatus, comprising:
a body movement noise identification unit that identifies, on a basis of an electroencephalography signal of a user, body movement noise contained in the electroencephalography signal (Page 6, paragraph 6, “First, the EEG signals input continuously in time are checked whether noise is generated while sliding a time window having a constant m number of time intervals and a threshold of a specific amplitude (for example, -0.5, 0.5). Because clinically normal EEG waves have an amplitude of up to -0.6 to 0.7, data within the threshold range are considered to be negligible noise or normal EEG”; Page 4, paragraph 8, “However, when the user blinks an eye or moves his head on the position of the third electrode 130, such movements are noise and are input to the third electrode 130 in a state mixed with brain waves. Such noise is referred to as electrooculography (EOG), electromyograph (EMG), and the like”); and
a noise reduction processing control unit that controls, on a basis of an identification result for the body movement noise, noise reduction processing of reducing the body movement noise contained in the electroencephalography signal (Page 6, paragraph 7, “If data exceeding the threshold value is found during scanning, a ladder vector including the values of the input EEG signals from the corresponding time point to the past mth interval is generated, which becomes a column of the trajectory matrix X (step 2). By repeating this process, ladder vectors exceeding the threshold are generated every time interval to generate a total of n * m (n is a natural number) ladder vectors (Step 3)”; Page 8, paragraphs 11-12, “when the diagonal average is calculated from the last value of the ladder vector to the value entered in the past m-1 time intervals, it can be said that the noise value of the data entered before the m-1 time intervals from the present time. Referring back to FIG. 5, when the average of diagonal values is removed from data input before the current m-1 time interval, the data may be referred to as a value from which noise is removed from an input EEG signal”).
Regarding claim 10, Kang further teaches a noise reduction processing unit that executes noise reduction processing (Page 8, paragraphs 11-12, “when the diagonal average is calculated from the last value of the ladder vector to the value entered in the past m-1 time intervals, it can be said that the noise value of the data entered before the m-1 time intervals from the present time. Referring back to FIG. 5, when the average of diagonal values is removed from data input before the current m-1 time interval, the data may be referred to as a value from which noise is removed from an input EEG signal”).
Regarding claim 11, Kang further teaches wherein the noise reduction processing control unit sets, on a basis of the identification result for the body movement noise, a discrimination parameter used for discriminating components of the body movement noise contained in the electroencephalography signal on a basis of an analysis result for the electroencephalography signal (Page 6, paragraph 6, “First, the EEG signals input continuously in time are checked whether noise is generated while sliding a time window having a constant m number of time intervals and a threshold of a specific amplitude (for example, -0.5, 0.5). Because clinically normal EEG waves have an amplitude of up to -0.6 to 0.7, data within the threshold range are considered to be negligible noise or normal EEG.”), and
the noise reduction processing unit removes components of the body movement noise from the electroencephalography signal by using the discrimination parameter (Page 6, paragraphs 6-9, wherein the trajectory matrix is constructed based on the data exceeding the threshold).
Regarding claim 12, Kang further teaches wherein the noise reduction processing control unit sets the discrimination parameter on a basis of an analysis result for the electroencephalography signal in an interval estimated not to contain the body movement noise by the body movement noise identification unit (Page 6, paragraph 6, “First, the EEG signals input continuously in time are checked whether noise is generated while sliding a time window having a constant m number of time intervals and a threshold of a specific amplitude (for example, -0.5, 0.5). Because clinically normal EEG waves have an amplitude of up to -0.6 to 0.7, data within the threshold range are considered to be negligible noise or normal EEG”; Examiner notes that while Kang does not explicitly disclose the intervals are set during an interval that does not contain body movement noise, the thresholds are set so as to detect noise/movement artifacts. Thus, the thresholds would be set to know the baseline when noise/artifacts are not present and apply them in future measurements to detect them).
Regarding claim 13, Kang further teaches wherein the discrimination parameter is a threshold used for discriminating components of the body movement noise contained in the electroencephalography signal on a basis of singular values of a trajectory matrix in singular spectrum analysis based on the electroencephalography signal (Page 6, paragraphs 6-9).
Regarding claim 18, Kang teaches an information processing method, comprising:
identifying, on a basis of an electroencephalography signal of a user, body movement noise contained in the electroencephalography signal (Page 6, paragraph 6, “First, the EEG signals input continuously in time are checked whether noise is generated while sliding a time window having a constant m number of time intervals and a threshold of a specific amplitude (for example, -0.5, 0.5). Because clinically normal EEG waves have an amplitude of up to -0.6 to 0.7, data within the threshold range are considered to be negligible noise or normal EEG”; Page 4, paragraph 8, “However, when the user blinks an eye or moves his head on the position of the third electrode 130, such movements are noise and are input to the third electrode 130 in a state mixed with brain waves. Such noise is referred to as electrooculography (EOG), electromyograph (EMG), and the like”); and
controlling, on a basis of an identification result for the body movement noise, noise reduction processing of reducing the body movement noise contained in the electroencephalography signal (Page 6, paragraph 7, “If data exceeding the threshold value is found during scanning, a ladder vector including the values of the input EEG signals from the corresponding time point to the past mth interval is generated, which becomes a column of the trajectory matrix X (step 2). By repeating this process, ladder vectors exceeding the threshold are generated every time interval to generate a total of n * m (n is a natural number) ladder vectors (Step 3)”; Page 8, paragraphs 11-12, “when the diagonal average is calculated from the last value of the ladder vector to the value entered in the past m-1 time intervals, it can be said that the noise value of the data entered before the m-1 time intervals from the present time. Referring back to FIG. 5, when the average of diagonal values is removed from data input before the current m-1 time interval, the data may be referred to as a value from which noise is removed from an input EEG signal”).
Regarding claim 19, Kang teaches a program that causes a computer to execute processing of:
identifying, on a basis of an electroencephalography signal of a user, body movement noise contained in the electroencephalography signal (Page 6, paragraph 6, “First, the EEG signals input continuously in time are checked whether noise is generated while sliding a time window having a constant m number of time intervals and a threshold of a specific amplitude (for example, -0.5, 0.5). Because clinically normal EEG waves have an amplitude of up to -0.6 to 0.7, data within the threshold range are considered to be negligible noise or normal EEG”; Page 4, paragraph 8, “However, when the user blinks an eye or moves his head on the position of the third electrode 130, such movements are noise and are input to the third electrode 130 in a state mixed with brain waves. Such noise is referred to as electrooculography (EOG), electromyograph (EMG), and the like”); and
controlling, on a basis of an identification result for the body movement noise, noise reduction processing of reducing the body movement noise contained in the electroencephalography signal (Page 6, paragraph 7, “If data exceeding the threshold value is found during scanning, a ladder vector including the values of the input EEG signals from the corresponding time point to the past mth interval is generated, which becomes a column of the trajectory matrix X (step 2). By repeating this process, ladder vectors exceeding the threshold are generated every time interval to generate a total of n * m (n is a natural number) ladder vectors (Step 3)”; Page 8, paragraphs 11-12, “when the diagonal average is calculated from the last value of the ladder vector to the value entered in the past m-1 time intervals, it can be said that the noise value of the data entered before the m-1 time intervals from the present time. Referring back to FIG. 5, when the average of diagonal values is removed from data input before the current m-1 time interval, the data may be referred to as a value from which noise is removed from an input EEG signal”).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-4 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Kang as applied to claim 1 above, and further in view of Garcia Molina (US 20180306376 – cited by Applicant).
Regarding claim 2, Kang teaches the noise reduction process as described above. However, Kang fails to disclose performing the process on the noise reduced signal to remove residual noise.
However, Garcia Molina teaches a system configured to remove artifacts from an EEG signal wherein the noise reduction process is repeated (Paragraph 0032). A method of enhancing a particular class of devices (methods, or products) has been made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in other situations. One of ordinary skill in the art would have been motivated to repeat a noise reduction process of Garcia Molina to the device of Kang in the prior art and the results of reducing noise and residual noise would have been predictable to one of ordinary skill in the art.
Regarding claims 3 and 4, Kang as modified discloses the apparatus as described above. Kang as modified fails to disclose calculating a rhythmic component power, correcting the rhythmic component power, and setting a reliability for the rhythmic component power.
However, Garcia Molina teaches a system wherein power bands of the EEG signal are measured (Paragraph 0037), the signal is demodulated to accurately calculate the power (Paragraphs 0019-0023), and the power calculation reliability is set (Paragraph 0019, “cardiac artifacts in the form of spikes in the EEG signal may cause artificial increase in one or more power bands of the EEG”; Paragraphs 0019-0023, wherein by demodulating the signals, the power can be accurately calculated; Examiner notes that Garcia Molina teaches that the cardiac artifacts disrupt the power bands of the EEG, thus making them unreliable and requiring demodulation of the signal). Garcia Molina discusses that measuring the power is useful to identify slow wave activity (Paragraph 0025). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kang to incorporate the teachings of Garcia Molina to identify slow wave activity of the brain.
Regarding claim 8, Kang as modified further discloses wherein the noise reduction processing control unit sets, on a basis of the identification result for the body movement noise and the identification result for the residual noise, a discrimination parameter used for discriminating components of the body movement noise contained in the electroencephalography signal on a basis of an analysis result for the electroencephalography signal (Page 6, paragraph 6, “First, the EEG signals input continuously in time are checked whether noise is generated while sliding a time window having a constant m number of time intervals and a threshold of a specific amplitude (for example, -0.5, 0.5). Because clinically normal EEG waves have an amplitude of up to -0.6 to 0.7, data within the threshold range are considered to be negligible noise or normal EEG.”), further comprising
a noise reduction processing unit that removes components of the body movement noise from the electroencephalography signal by using the discrimination parameter (Page 6, paragraphs 6-9, wherein the trajectory matrix is constructed based on the data exceeding the threshold).
Regarding claim 9, Kang as modified discloses the discrimination parameter as described above. However, Kang as modified fails to disclose correcting the discrimination parameter.
However, Garcia Molina teaches a system configured to remove artifacts from an EEG signal wherein a threshold is determined based on various inputs (Paragraph 0031, “The amplitude and duration thresholds are applied (e.g., as described above) by demodulation component 30 … In some embodiments, the threshold levels described herein (e.g., amplitude thresholds, inter-peak interval thresholds, etc.) are determined at manufacture, determined by a user via user interface 24 (FIG. 1), automatically determined by processor 20 (FIG. 1), and/or determined by other methods”; Paragraph 0032, wherein the demodulation steps are iterative and the demodulation component is configured to repeat; thus, noise is demodulated more than once). A method of enhancing a particular class of devices (methods, or products) has been made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in other situations. One of ordinary skill in the art would have been motivated in applying this known method of correcting a threshold value of Garcia Molina to the device of Kang and the results of correcting a threshold value would have been predictable to one of ordinary skill in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kang and Garcia Molina to incorporate the teachings of Garcia Molina.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Kang and Garcia Molina as applied to claims 1 and 4 above, and further in view of Keenan (US 20160183881).
Regarding claims 5 and 6, Kang as modified discloses identifying noise based off of body movement (Page 6, paragraph 9, “in addition to noise signals such as EOG, EMG, ocular artifacts, eye blinks, and head movements” [Kang]), setting a reliability based on the noise (Paragraphs 0019-0023 [Garcia Molina]), and identifying residual noise in the signal (Paragraphs 0019-0023 [Garcia Molina]). Kang as modified fails to explicitly disclose measuring these movements from a body movement analysis unit of acceleration/gyroscope measurements.
However, Keenan teaches a method for analysis of data, such as EEG (Paragraphs 0023-0024), wherein body movement is taken from sensors such as accelerometers (Paragraph 0053 and 0060-0061). Keenan discusses adding other sensors will more accurately remove motion artifacts (Paragraph 0053). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kang and Garcia Molina to incorporate the teachings of Keenan to more accurately remove noise and motion artifacts.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kang and Garcia Molina as applied to claim 3 above, and further in view of Matsuura (US 20190038164 – cited by Applicant).
Regarding claim 7, Kang as modified discloses calculating power as described above but fails to disclose interpolating the power component.
However, Matsuura teaches a biological signal processing apparatus that interpolates data influenced by noise and/or inappropriate data (Paragraphs 0012-0014 and 0049). Interpolation is recognized as part of the ordinary capabilities of one skilled in the art. One of ordinary skill in the art would have been motivated in applying an interpolation method of Matsuura to the known device of Kang and Garcia Molina that was ready for improvement and the results of interpolating data influenced by noise and/or inappropriate data would have been predictable to one of ordinary skill in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kang and Garcia Molina to incorporate the teachings of Matsuura.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Kang as applied to claim 11 above, and further in view of Downey (WO 2022061322).
Regarding claim 14, Kang teaches the limitations of claim 11 as described above, but fails to disclose wherein the threshold is determined based on an autocorrelation value of a common component in canonical correlation analysis.
However, Downey teaches a method for removing artifacts from data signals wherein Canonical Correlation Analysis (CCA) Is used to identify relationships between noise and EEG to remove noise components from the EEG signal by removing components above a threshold value according to their correlation strength (Paragraph 0041). Downey discusses this analysis technique is useful to infer information between two sets of variables and is useful to produce clean EEG signals (Paragraphs 0041-0042). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kang to incorporate the teachings of Downey to produce a clean EEG signal.
Claims 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kang as applied to claim 1 above, and further in view of Keenan.
Regarding claim 15, Kang teaches identifying noise based off of body movement (Page 6, paragraph 9, “in addition to noise signals such as EOG, EMG, ocular artifacts, eye blinks, and head movements”) and processing the noise based on the movement as described above.
However, Keenan teaches a method for analysis of data, such as EEG (Paragraphs 0023-0024), wherein body movement is taken from sensors such as accelerometers (Paragraph 0053 and 0060-0061). Keenan discusses adding other sensors will more accurately remove motion artifacts (Paragraph 0053). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kang to incorporate the teachings of Keenan to more accurately remove noise and motion artifacts
Regarding claim 16, Kang as modified further discloses wherein the noise reduction processing control unit stops the noise reduction processing in a case of determining that the user is in a rest state on a basis of the identification result for the body movement noise and the analysis result for the body movement of the user (Page 6, paragraph 9, “Therefore, if the data expected to be a noise signal (threshold exceeded data) does not occur continuously, the data does not accumulate additionally and all the accumulated data is initialized”).
Regarding claim 17, Kang as modified further discloses wherein the body movement noise identification unit identifies, on a basis of the electroencephalography signal and the analysis result for the body movement of the user, the body movement noise contained in the electroencephalography signal (Page 6, paragraphs 7-8, “If data exceeding the threshold value is found during scanning, a ladder vector including the values of the input EEG signals from the corresponding time point to the past mth interval is generated, which becomes a column of the trajectory matrix X (step 2). By repeating this process, ladder vectors exceeding the threshold are generated every time interval to generate a total of n * m (n is a natural number) ladder vectors (Step 3). If you create a training model using your own data without the initial training data, you can use that data as your initial cluster model if you have experimentally collected about three time window sizes (Step 4). Each of the training data columns thus formed may be guaranteed to have at least one or more data exceeding a threshold”).
Conclusion
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/NOAH M HEALY/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791