DETAILED ACTION
Applicant’s arguments, filed 04/15/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application. Applicant has amended their claims, filed 04/15/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment. Applicant has added claim 20.
Claims 1-20 are the current claims 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 .
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 1-20 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, 18, and 19, it is unclear what is meant to “control” or “controlling” the noise reduction processing. How is it controlled, and how does the identification of the body movement noise control the noise reduction processing? It appears Applicant intends to claim that, once body movement noise is identified, the CPU executes a noise reduction step or processes the signal to remove noise. For examination purposes, that is how the claim will be interpreted. Claims 2-17 and 20 are also rejected due to their dependence on claims 1, 18, and 19.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 10-14 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 10 is dependent on claim 1. Claim 1 recites that the CPU is configured to “control … noise reduction processing”. As interpreted above in the 112(b) rejection, “control” is interpreted to mean that the CPU executes a noise reduction step or processes the signal to remove noise. Thus, the limitation of claim 10 of “wherein the CPU is further configured to execute noise reduction processing” fails to further limit the subject matter of claim 1. Claims 11-14 are also rejected due to their dependence on claim 10.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Analysis of independent claims 1, 18, and 19:
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 1 is directed to a system, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Claim 18 is directed to a computer implemented method, which describes one of the four statutory categories of patentable subject matter, i.e., a method. Claim 19 is directed to a non-transitory computer-program software product, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Therefore, further consideration is necessary regarding claims.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claims 1, 18, and 19 recite an abstract idea. In particular, the claims generally recite
the following:
identify body movement noise contained in an electroencephalography signal of a user on a basis of temporal and frequency features in an analysis window for the electroencephalography signal (claims 1, 18, and 19); and
control, 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 (claims 1, 18, and 19).
These elements recited in claims 1, 18, and 19 are drawn to an abstract idea since they are directed towards mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
“identify body movement noise contained in an electroencephalography signal of a user on a basis of temporal and frequency features in an analysis window for the electroencephalography signal” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper. A person of ordinary skill in the art could reasonably view a time domain and frequency domain EEG signal and identify noise components. There is nothing to suggest an undue level of complexity in “identify body movement noise contained in an electroencephalography signal of a user on a basis of temporal and frequency features in an analysis window for the electroencephalography signal”.
“control, 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” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper. A person of ordinary skill in the art could reasonably apply a filter to an EEG data set to remove identified noise. There is nothing to suggest an undue level of complexity in “control, 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”.
Prong Two: Claims 1, 18, and 19 do not recite additional elements that integrate the exception into a practical application. Therefore, the claims are "directed to" the abstract idea. The additional elements merely:
Recite the words "apply it" or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “a central processing unit” (claim 1) and "a program" (claim 19)).
As a whole, the additional elements merely serve to gather information to be used by the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1, 18, and 19 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above. E.g., all elements are directed to implementing the abstract ideas on generic processing components, the pre-solution activity of using generic data-gathering components, and generic post-solution activities, which merely facilitate the abstract idea.
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example, a “central processing unit” and a “program” as disclosed in the Applicant’s specification, “In a computer 1000, a central processing unit (CPU) 1001, a read only memory (ROM) 1002, and a random access memory (RAM) 1003 are connected to one another through a bus 1004 … the CPU 1001 loads, for example, programs stored in the storage unit 1008 into the RAM 1003 via the input/output interface 1005 and the bus 1004 and executes them … Programs executed by the computer 1000 (CPU 1001) can be, for example, provided recorded on the removable medium 1011 that is a package medium” (Paragraphs 0213-0216).
A CPU and program do not qualify as significantly more because this limitation is simply appending well understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'/, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well understood, routine and conventional activity previously known in the industry (see Electric PowerGroup, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'/, 110 USPQ2d 1976 (2014); SAP Am. v. lnvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements include a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Analysis of the dependent claims:
Claims 2-17 and 20 depend from the independent claims. Dependent claims 2-17 and 20 merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely
Further describe the abstract idea (“identify, on a basis of the electroencephalography signal after the noise reduction processing, residual noise which is the body movement noise remaining in the electroencephalography signal; and control the noise reduction processing on a basis of the identification result for the body movement noise and an identification result for the residual noise” (claim 2), “identify, on a basis of the electroencephalography signal and an analysis result for the body movement of the user, the body movement noise contained in the electroencephalography signal; and identify, on a basis of the electroencephalography signal after the noise reduction processing and the analysis result for the body movement of the user, the residual noise remaining in the electroencephalography signal” (claim 6), “set, 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; and remove components of the body movement noise from the electroencephalography signal by using the discrimination parameter” (claim 8), “wherein the CPU is further configured to execute noise reduction processing” (claim 10), “set, 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; and remove components of the body movement noise from the electroencephalography signal by using the discrimination parameter” (claim 11), “set the discrimination parameter on a basis of an analysis result for the electroencephalography signal in an interval estimated to not contain the body movement noise” (claim 12), “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” (claim 13), “wherein the discrimination parameter is a threshold used for discriminating the body movement noise contained in the electroencephalography signal on a basis of an autocorrelation value of a common component in canonical correlation analysis between a singular mode function of the electroencephalography signal and a delayed singular mode function” (claim 14), “control the noise reduction processing on a basis of the identification result for the body movement noise and an analysis result for the body movement of the user” (claim 15), “stop 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” (claim 16), “identify, 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” (claim 17), and “determine that the temporal and frequency features in the analysis window for the electroencephalography signal are of a same type as temporal and frequency features labeled in analysis windows of electroencephalography signals in a machine learning model with corresponding known body movement noise types” (claim 20)), and
Further describe the pre-solution activity (“wherein the CPU is further configured to analyze a body movement of the user” (claim 5) and “analyze a body movement of the user” (claim 15)), and
Further describe the post-solution activity (“calculate a rhythmic component power of the electroencephalography signal after the noise reduction processing and correct the rhythmic component power on a basis of the identification result for the residual noise” (claim 3), “wherein the CPU is further configured to set a reliability for the rhythmic component power on a basis of the identification result for the residual noise” (claim 4), “CPU is further configured to interpolate the rhythmic component power in a noise remaining interval in which the residual noise is estimated to remain, wherein the CPU interpolates the rhythmic component power on a basis of the rhythmic component power in at least one interval of an interval immediately preceding the noise remaining interval or an interval immediately following the noise remaining interval” (claim 7), and “correct the discrimination parameter so that components of the body movement noise are easily removed in a case where the residual noise is estimated to remain in the electroencephalography signal” (claim 9)).
Taken alone or in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. The additional elements do not add anything significantly more than the abstract idea. The collective functions of the additional elements merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. The result of the abstract idea does not cause the computing device and/or application to perform differently.
Therefore, claims 1-20 are rejected as being directed to non-statutory subject matter.
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 1, 10-13, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kang (KR 20100097952) and Maddirala (“Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis”).
Regarding claim 1, Kang discloses an information processing apparatus, comprising a central processing unit (CPU) configured to:
Identify body movement noise contained in an electroencephalography signal of a user on a basis of temporal features in an analysis window 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”; Page 4, paragraph 8, wherein the noise is eye blinks or head moving); and
control, 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 enter 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”; As Kang discloses creating ladder vectors for noise removal based on EEG values from time points, Kang discloses identifying body movement noise based on temporal features).
Kang fails to disclose identifying body movement on a basis of frequency features.
Maddirala and Kang are in the same field of removing motion artifact from EEG signals. Maddirala teaches removing motion artifact from an EEG signal (Page 79, abstract and introduction, wherein the motion artifacts include ocular and muscle artifacts), wherein frequency components in a given signal represent local mobility, the local mobility being small for low frequency signals and large for high frequency signals. This fact was used to separate low-frequency motion artifact from the EEG signal for removal (Page 80, left column paragraph 3 to right column paragraph 4). As Kang is concerned with removing motion artifact from EEG signals based on temporal features, Maddirala teaches a method of removing motion artifact from EEG signals based on frequency features. Maddirala discusses this method results in an improvement in signal to noise ratio and reduction in artifact (Page 80, paragraph 3). 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 the method of Kang to incorporate noise removal based on frequency features as taught by Maddirala to improve signal to noise ratio and reduce artifact in the signal.
Regarding claim 10, Kang as modified further discloses wherein the CPU is further configured to execute 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 enter 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 as modified further discloses wherein CPU is further configured to:
set, 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 up to -0.6 to 0.7, data within the threshold range are considered to be negligible to noise or normal EEG”); and
remove 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 as modified further discloses wherein the CPU is further configured to set the discrimination parameter on a basis of an analysis result for the electroencephalography signal in an interval estimated to not contain the body movement noise (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 up to -0.6 to 0.7, data within the threshold range are considered to be negligible to 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 threshold 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 as modified further discloses 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 discloses an information processing method, comprising:
identifying body movement noise contained in an electroencephalography signal of a user on a basis of temporal features in an analysis window 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”; Page 4, paragraph 8, wherein the noise is eye blinks or head moving); 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 enter 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”; As Kang discloses creating ladder vectors for noise removal based on EEG values from time points, Kang discloses identifying body movement noise based on temporal features).
Kang fails to disclose identifying body movement on a basis of frequency features.
Maddirala and Kang are in the same field of removing motion artifact from EEG signals. Maddirala teaches removing motion artifact from an EEG signal (Page 79, abstract and introduction, wherein the motion artifacts include ocular and muscle artifacts), wherein frequency components in a given signal represent local mobility, the local mobility being small for low frequency signals and large for high frequency signals. This fact was used to separate low-frequency motion artifact from the EEG signal for removal (Page 80, left column paragraph 3 to right column paragraph 4). As Kang is concerned with removing motion artifact from EEG signals based on temporal features, Maddirala teaches a method of removing motion artifact from EEG signals based on frequency features. Maddirala discusses this method results in an improvement in signal to noise ratio and reduction in artifact (Page 80, paragraph 3). 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 the method of Kang to incorporate noise removal based on frequency features as taught by Maddirala to improve signal to noise ratio and reduce artifact in the signal.
Regarding claim 19, Kang discloses a program that causes a computer to execute processing of:
identifying body movement noise contained in an electroencephalography signal of a user on a basis of temporal features in an analysis window 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”; Page 4, paragraph 8, wherein the noise is eye blinks or head moving); 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 enter 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”; As Kang discloses creating ladder vectors for noise removal based on EEG values from time points, Kang discloses identifying body movement noise based on temporal features).
Kang fails to disclose identifying body movement on a basis of frequency features.
Maddirala and Kang are in the same field of removing motion artifact from EEG signals. Maddirala teaches removing motion artifact from an EEG signal (Page 79, abstract and introduction, wherein the motion artifacts include ocular and muscle artifacts), wherein frequency components in a given signal represent local mobility, the local mobility being small for low frequency signals and large for high frequency signals. This fact was used to separate low-frequency motion artifact from the EEG signal for removal (Page 80, left column paragraph 3 to right column paragraph 4). As Kang is concerned with removing motion artifact from EEG signals based on temporal features, Maddirala teaches a method of removing motion artifact from EEG signals based on frequency features. Maddirala discusses this method results in an improvement in signal to noise ratio and reduction in artifact (Page 80, paragraph 3). 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 the method of Kang to incorporate noise removal based on frequency features as taught by Maddirala to improve signal to noise ratio and reduce artifact in the signal.
Claims 2-4 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Kang (KR 20100097952) and Maddirala (“Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis”) as applied to claim 1 above, and further in view of Garcia Molina (US 20180306376).
Regarding claim 2, Kang as modified by Maddirala discloses the noise reduction process as described above. However, Kang as modified fails to disclose performing the process on the noise reduced signal to remove residual noise.
Kang, Maddirala, and Garcia Molina are in the same field of reducing artifacts from EEG signals. Garcia Molina teaches a system configured to remove artifacts from an EEG signal wherein the noise reduction process is repeated (Paragraph 0032). Garcia Molina teaches a method of enhancing a particular class of devices (i.e., repeating a noise reduction process) and it 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 and Maddirala 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 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 and Maddirala 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 CPU is further configured to:
set, 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.”); and
remove 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). Garcia Molina teaches a method of enhancing a particular class of devices (i.e., correcting a discrimination parameter) and it 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 Maddirala and the results of updating the 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, Maddirala, 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 (KR 20100097952) and Maddirala (“Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis”), and Garcia Molina (US 20180306376) as applied to claim 4 above, and further in view of Keenan (US 20160183881).
Regarding claims 5 and 6, Kang as modified by Maddirala and Garcia Molina discloses identifying noise based off of body movement, as described above. Kang as modified fails to explicitly disclose analyzing body movement of the user.
Kang, Maddirala, Garcia Molina, and Keenan are in the same field of removing motion artifacts from EEG signals. 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 as they are useful as a reference signal (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 the method of Kang, Maddirala, and Garcia Molina to incorporate the teachings of measuring body movement as taught by Keenan to more accurately remove noise and motion artifacts present in the signal.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kang (KR 20100097952) and Maddirala (“Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis”), and Garcia Molina (US 20180306376) as applied to claim 3 above, and further in view of Matsuura (US 20190038164).
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 (KR 20100097952) and Maddirala (“Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis”) as applied to claim 11 above, and further in view of Downey (WO 2022061322).
Regarding claim 14, Kang as modified discloses 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 (KR 20100097952) and Maddirala (“Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis”) as applied to claim 1 above, and further in view of Keenan (US 20160183881).
Regarding claim 15, 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”) and processing the noise based on the movement as described above.
Kang, Maddirala, and Keenan are in the same field of removing motion artifacts from EEG signals. 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 as they are useful as a reference signal (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 the method of Kang and Maddirala to incorporate the teachings of measuring body movement as taught by Keenan to more accurately remove noise and motion artifacts present in the signal.
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”).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Kang (KR 20100097952) and Maddirala (“Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis”) as applied to claim 1 above, and further in view of Pino (US 20230178206).
Regarding claim 20, Kang as modified by Maddirala disclose identifying body movement noise based on temporal and frequency features in an analysis window as described above. Kang as modified fails to disclose determining that the features are of the same type in a machine learning model.
Kang, Maddirala, and Pino are in the same field of removing artifacts from EEG signals. Pino teaches a method for determining brain energy from an EEG signal. Pino uses a machine learning method to remove noise and movement artifacts from the EEG (Abstract). A classifier is trained using supervised machine learning on known examples of brain signal artifacts, such as blinks, eye motion, and noise. The classifier then determines if the collected EEG signal has blink or other noise data (Paragraph 0178). As Kang and Maddirala are concerned with removing temporal and frequency noise and body movement features from an EEG signal, Pino teaches a machine learning method to identify noise and body movement features corresponding to known examples it was trained on. 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 the method of Kang and Maddirala to incorporate the machine learning method of Pino to automatically identify the noise features of raw EEG signals to create clean EEG data.
Response to Arguments
Applicant’s arguments, see page 9, filed 04/15/2026, with respect to the drawings objection have been fully considered and are persuasive. As discussed in the interview held on 04/15/2026, Examiner pointed out that the reference characters “23” and “24” were flipped with regard to their respective elements in Fig. 8. Applicant has corrected the figure accordingly. The objection of the drawings has been withdrawn.
Examiner acknowledges Applicant amending the claims to remove the structural elements interpreted under 112(f). Thus, the claims are no longer interpreted as laid out in the Office Action filed 02/06/2026.
Applicant’s arguments, see page 10, filed 04/15/2026, with respect to the 35 U.S.C. §112(b) rejections have been fully considered and are persuasive. Applicant has amended the claims to add the structure of a central processing unit. The rejection of the claims has been withdrawn.
Applicant’s arguments, see pages 10-13, filed 04/15/2026, with respect to the 35 U.S.C. §102(a)(1) and 35 U.S.C. §103 rejections have been fully considered and are persuasive. With respect to the new claim limitation of “identify body movement noise … on a basis of temporal and frequency features …”, Examiner agrees that Kang does not teach this limitation; therefore, the rejection of the claims has been withdrawn. However, upon further review, a new rejection has been applied over Kang in view of Maddirala.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOAH MICHAEL HEALY whose telephone number is (703)756-5534. The examiner can normally be reached Monday - Friday 8:30am - 5:30pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Sims can be reached at (571)272-7540. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NOAH M HEALY/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791
Prosecution Insights