DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 21 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 21 recites “fitting at least another curve to said retrieved most-recent data segment, wherein said at least another curve has a frequency that differs from that of said at least one curve”, which is new matter. The specification does not teach or suggest that curves having different frequencies are applied to the same most-recent data segment.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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, 4-6, 11-13, 16, 20, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over “Real-Time Brain Oscillation Detection and Phase-Locked Stimulation Using Autoregressive Spectral Estimation and Time-Series Forward Prediction” (Chen) (previously cited) in view of US 2017/0215759 A1 (Dudek), and US 2012/0071947 A1 (Gupta).
With regards to claims 1, 16, and 23, Chen teaches an assembly and method (F. Implementation and G. Patients and Data disclose an assembly for implementing an algorithm) comprising: a recording assembly for recording a time-based brain-related signal from a patient (G. Patients and Data disclose acquisition of iEEG signals using electrodes), a stimulus generator for providing a stimulus (I. Introduction: Paragraph 5 and F. Implementation disclose outputting stimulation pulses which necessarily requires a stimulus generator), and a computer assembly, functionally coupled to said recording assembly and to said stimulus generator (F. Implementation discloses the algorithm being implemented in the LabView 9.0 environment and in MATLAB 7.11, which necessarily require a computer assembly), the computer assembly comprising: a memory (F. Implementation discloses real-time operation which requires storage and/or a memory), and a computer program and a non-transitory computer readable medium having stored thereon computer program instructions that, when executed by a processor in a computer to perform in real-time (F. Implementation discloses the algorithm being implemented in the LabView 9.0 environment for real-time operation), using a most-recent data segment of said stored data segment of said time-based brain-related signal (Fig. 1 discloses (b) analyzing the last 1-second segment of the iEEG); fitting, in real time, at least one curve to said retrieved most-recent data segment (¶ [0048] of the Applicant’s published specification indicates that “Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points”. The claim limitation is being read in light of the above disclosure. Fig. 1 discloses autoregressive spectral estimation in the 1-s segment, bandpass filtering the one-second segment, and using an AR model on the signal from tstart to tstop . D. Time-Series Forward Prediction indicates that the AR model formulated in (1) has an input corresponding to the filtered signal from tstart to tstop. The combination of autoregressive spectral estimation, bandpass filtering, and formulating the AR model amounts to a process of constructing a mathematical model that has the best fit to at least a majority of the last 1-second segment of the iEEG); predicting a future continuation of said most-recent data segment using said at least one curve fitted to said most-recent data segment (Fig. 1 and D. Time-Series Forward Prediction discloses using the AR model as formulated in (1) as a basis for linear forward prediction of a signal of length 2(t0 − tstop )); detecting a predefined pattern in said predicted future continuation (Fig. 1 and F. Implementation discloses determining a time delay which corresponds to a desired phase of the output stimulation, wherein the desired phase is a pattern within the waveform; I. Introduction: Paragraph 5 discloses providing stimulation pulses corresponding to a specific phase in the oscillation, which necessarily requires detection of the specific phase of the oscillation), and defining a predicted event time of said predefined pattern (F. Implementation discloses determining the time delay at which the specific phase will be), said predicted event time being in the future with respect to said most-recent data segment (Fig. 1 indicates that the specific phase is after t0), and actuating said stimulus generator for providing a stimulus at the predicted event time (F. Implementation discloses providing the output stimulation at the time delay).
Chen is silent regarding a memory for storing at least a data segment of said time-based brain-related signal during recording of said time-based brain-related signal, and retrieving the most-recent data segment.
In the same field of endeavor of brain-related signal monitoring, Dudek teaches a memory for storing at least a data segment of said time-based brain-related signal during recording of said time-based brain-related signal (¶ [0060] and Fig. 12 depict a base station 21 that receives an EEG in real time and stores the EEG for later retrieval), and retrieving the most-recent data segment (¶ [0060] discloses later retrieval of the EEG). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Chen to incorporate a memory for storing at least a data segment of said time-based brain-related signal during recording of said time-based brain-related signal, and retrieving the most-recent data segment as taught by Dudek. The motivation would have been to provide the necessary structural components for implementing the method and/or improving the accessibility of the EEG data.
The above combination is silent regarding actuating said stimulus generator for providing a stimulus within a predefined event time window of said predicted event time.
In the same field of endeavor of providing stimulation based on brain signals, Gupta teaches actuating a stimulus generator for providing a stimulus within a predefined event time window of said predicted event time (¶ [0007] discloses delivering electrical stimulation within a window of time that extends no greater than 100 milliseconds from occurrence of an episode or event). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the delivery of the stimulus of the above combination to incorporate that it is within a predefined event time window of said predicted event time as taught by Gupta. The motivation would have been to help selectively potentiating a favorable brain state while avoiding the potentiation of other brain states and/or other brain networks (see ¶ [0034] of Gupta).
With regards to claim 4, the above combination teaches or suggests wherein the most-recent data segment has an end time that is less than 0.1 seconds from a current time of the assembly (Fig. 1 of Chen discloses (b) analyzing the last 1-second segment of the iEEG, which is less than 0.1 seconds from t0).
With regards to claim 5, the above combination teaches or suggests wherein said event time is in the future with respect to a current time of said assembly, allowing said assembly to apply said stimulus within said predefined event time window (see the above combination of Chen in view of Dudek and Gupta; Fig. 1 and F. Implementation of Chen indicates a time delay which provides a stimulation after t0).
With regards to claim 6, the above combination teaches or suggests each step of actuating said stimulus generator is preceded by a step of predicting said future continuation of said most recent data segment and wherein each step of actuating said stimulus generator is based on a result of having executed a step of predicting said future continuation prior to said step of actuating said stimulus generator (Fig. 1 of Chen depicts the stimulus is provided with the time delay after the AR model is used to provide the time-series forward predictions).
With regards to claim 11, the above combination teaches or suggests said most-recent data segment has a data window that which has a width of less than 1 second (Fig. 1 (d) of Chen depicts the most-recent data segment t0 to t0-1 includes a data window tstart to tstop that has a width of less than 1 second).
With regards to claim 12, the above combination teaches or suggests said computer program fits at least one periodic function to said most-recent data segment, said at least one periodic function having a period shorter than 2 seconds (D. Time-Series Forward Prediction of Chen discloses that the modeling is of the length 2(t0 − tstop) starting at tstop, wherein t0 − tstop has the bounds of 0.05 and 0.45 seconds).
With regards to claim 13, the above combination teaches or suggests there exists no step of actuating said stimulus generator that is not preceded by a step of predicting a future continuation of said most-recent data segment (Fig. 1 of Chen indicates that the actuation must be preceded by the prediction).
With regards to claim 20, the above combination teaches or suggests said at least one curve has a fitted frequency (Fig. 1 of Chen depicts the AR model is fitted to the signal based on the optimized frequency passband, which indicates that the AR model is fitted to the optimized frequency).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Dudek, and Gupta, as applied to claim 1 above, and further in view of US 5,241,967 A (Yasushi).
With regards to claim 3, the above combination is silent with regards to whether said stimulus generator is arranged for applying a light stimulus that can be perceived by a human.
In the same field of endeavor of stimulating patients based on brain signals, Yasushi teaches providing a stimulus generator is arranged for applying a light stimulus that can be perceived by a human (Col. 11, lines 43-51 discloses providing a stimulating light via a photic stimulus generator). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have substituted the stimulus generator of the above combination with the stimulus generator arranged for applying a light stimulus that can be perceived by a human as taught by Yasushi. Because both elements are capable of modulating brain activity (Col. 2, lines 9-30 of Yasushi; I. Introduction, paragraph 3 of Chen), it would have been the simple substitution of one known equivalent element for another to obtain predictable results.
Claims 10 and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Dudek, and Gupta, as applied to claim 1 above, and further in view of “Sound Asleep: Processing and Retention of Slow Oscillation Phase-Targeted Stimuli” (Cox) (Cited by Applicant).
With regards to claim 10, the above combination teaches or suggests, for each series of provided stimuli, a total number of times that a step of predicting said future continuation has been executed is equal to or greater than a total number of times that a step of actuating said stimulus generator has been executed (Fig. 1 of Chen depicts the stimulus is provided after each time the AR model is used to provide the time-series forward predictions, which indicates that the number of predicting steps and actuating steps is equal to each other).
The above combination is silent regarding whether said computer program performs actuating said stimulus generator multiple times, thereby forming multiple series of provided stimuli.
In the same field of endeavor of stimuli targeted to phases of brain signals, Cox teaches actuating a stimulus generator multiple times, thereby forming multiple series of provided stimuli (Paragraph 6 of Introduction recites repeatedly presenting sound stimuli directed at up and down states to sleeping subjects; Paragraph 3 of Algorithm teaches presentation of stimuli using a full block of 20 presentations). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the stimulation of the above combination to incorporate that it is provided multiple times, thereby forming multiple series of provided stimuli as taught by Cox. The motivation would have been to engender the gradual buildup of a memory trace (Paragraph 6 of Introduction of Cox) and/or to improve the desired stimulation by providing multiple instances of it.
With regards to claim 14, the above combination teaches that, for theta oscillations, the signal segment analysis rate was 10 Hz (F. Implementation of Chen). The above combination is silent with regards to whether said brain-related signal comprises an EEG that has a time resolution of at least 100 samples per second.
In the same field of endeavor of stimuli targeted to phases of brain signals, Cox brain-related signal comprises an EEG that has a time resolution of at least 100 samples per second (Data acquisition discloses teaches sampling at 512 Hz; Preprocessing: Paragraph 2 discloses the data is down sampled to 100 Hz ). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the said brain-related signal comprises an EEG that has a time resolution of at least 100 samples per second as taught by Cox. The motivation would have been to provide an improved temporal and spectral precision (Cox: Preprocessing: Paragraph 2).
With regards to claim 15, the above combination teaches or suggests said predefined pattern has a predefined oscillatory phase in said brain-related signal (F. Implementation of Chen indicates that the desired phase of the output stimulation corresponds to the waveform peak).
The above combination is silent with regards to whether said predefined pattern has a frequency in the slow-oscillation range.
In the same field of endeavor of stimulating patients based on brain signals, Cox teaches a predefined pattern has a frequency in the slow oscillation (Stimuli teaches providing a stimuli that coincides with a slow oscillation (“SO”) peak or trough). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the frequency of the predefined pattern of the above combination to incorporate that it is in the slow oscillation range as taught by Cox. The motivation would have been to identify and modulate slow oscillation range signals, thereby improving the therapeutic applicability of the device.
With regards to claim 17, the above combination teaches or suggests wherein fitting the at least one curve comprises fitting a cosine curve and wherein the fitted cosine curve is in a predefined frequency range of interest (Chen discloses fitting cosine waveform in the theta range in at least A. Cosine Waveform and paragraphs 1 and 3 of G. Patients and Data).
The above combination is silent with regards to the curve being a sinusoidal curve.
In the same field of endeavor of stimulating patients based on brain signals, Cox teaches fitting a sine wave to the analytic signal (Algorithm, paragraphs 1-2). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the curve of the above combination to be a sine wave. Because both sine waves and cosine waves are capable of being used to model a signal (Cox at Algorithm, paragraphs 1-2; Chen at A. Cosine Waveform and paragraphs 1 and 3 of G. Patients and Data), It would have been the simple substitution of one known equivalent element for another to obtain predictable results.
With regards to claim 18, the above combination teaches or suggests wherein fitting the at least one curve comprises fitting a cosine curve (Chen discloses fitting cosine waveform in the theta range in at least A. Cosine Waveform and paragraphs 1 and 3 of G. Patients and Data).
The above combination is silent with regards to the curve being a sinusoidal curve, wherein the fitted sinusoidal curve fits the retrieved most recent data segment to within a fitting error threshold
In the same field of endeavor of stimulating patients based on brain signals, Cox teaches fitting a sine wave to the analytic signal (Algorithm, paragraphs 1-2), wherein the fitted sinusoidal curve fits the retrieved most recent data segment to within a fitting error threshold (Algorithm, paragraph 2 discloses a fitting threshold parameter which uses a least squares approach).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the curve of the above combination to be a sine wave as taught by Cox. Because both sine waves and cosine waves are capable of being used to model a signal (Cox at Algorithm, paragraphs 1-2; Chen at A. Cosine Waveform and paragraphs 1 and 3 of G. Patients and Data), It would have been the simple substitution of one known equivalent element for another to obtain predictable results.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above combination to incorporate the fitted sinusoidal curve fits the retrieved most recent data segment to within a fitting error threshold as taught by Cox. The motivation would have been to ensure a good fit between the wave and the analytic signal (see Algorithm, paragraph 2 of Cox)
With regards to claim 19, the above combination teaches or suggests wherein fitting the at least one curve comprises fitting a cosine curve (Chen discloses fitting cosine waveform in the theta range in at least A. Cosine Waveform and paragraphs 1 and 3 of G. Patients and Data).
The above combination is silent with regards to the curve being a sinusoidal curve, wherein the fitted sinusoidal curve reaches a power threshold.
In the same field of endeavor of stimulating patients based on brain signals, Cox teaches fitting a sine wave to the analytic signal (Algorithm, paragraphs 1-2), wherein the fitted sinusoidal curve reaches a power threshold (Algorithm, paragraph 2 discloses a power threshold).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the curve of the above combination to be a sine wave as taught by Cox. Because both sine waves and cosine waves are capable of being used to model a signal (Cox at Algorithm, paragraphs 1-2; Chen at A. Cosine Waveform and paragraphs 1 and 3 of G. Patients and Data), It would have been the simple substitution of one known equivalent element for another to obtain predictable results.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above combination to incorporate the fitted sinusoidal curve reaches a power threshold as taught by Cox. The motivation would have been to ensure the acquired signal includes a desired state (see Cox at Algorithm, paragraph 2 regarding the determination of deep sleep).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Dudek and Gupta, as applied to claim 1 above, and further in view of US 2017/0223478 A1 (Jot).
With regards to claim 21, the above combination is silent regarding whether said method further comprises fitting at least another curve to said retrieved most-recent data segment where said at least another curve has a frequency that differs from that of said at least one curve.
In a system relevant to the problem of finding a curve of best fit, Jot teaches fitting at least another curve to said retrieved most-recent data segment where said at least another curve has a frequency that differs from that of said at least one curve (¶ [0056] discloses curve fitting at multiple different frequencies). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above combination to incorporate curve fitting at multiple different frequencies as taught by Jot. The motivation would have been to provide a more complete estimation of the frequency-dependent properties of the brain-related signal.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Cox in view of Chen and Gupta
With regards to claim 22, Cox teaches a method for treating a mammal or providing therapy to the mammal by inducing stimulation of deep sleep in the mammal (Procedure, paragraph 1 discloses that sound stimuli were directed at slow oscillation (“SO”) up state during SWS. ¶ [0044] of the Applicant’s published application indicates that providing a slow oscillation up state locked sound stimulus evokes a train of slow oscillations after stimulation. Because the method of Cox provides a slow oscillation up state locked sound stimuli, it is a method for inducing stimulation of deep sleep), the method comprising providing a series of sensory discernible stimuli to the mammal (Procedure, Paragraph 1 indicates that 20 sounds were directed at SO up state), wherein providing the series of sensory discernible stimuli comprises: for each sensory discernible stimulus in the series of sensory discernible stimuli (Algorithm indicates an algorithm for presenting stimuli in a specific phase of the SO in real-time): retrieving a most-recent data segment of a time-based brain-related signal of the mammal while the mammal is asleep (Algorithm, Paragraph 1 indicates buffering and using the last 5,000 samples); fitting at least one curve to the retrieved most-recent data segment (Algorithm, paragraph 1 indicates applying a FFT to the moving window of the most recent samples, band-pass filtering the signal in the SO band, using a Hilbert transform, phase-shifting the signal, and fitting a sine wave to the analytic signal, the combination of which amounts to fitting at least one curve to the retrieved most-recent data segment); predicting a future continuation of the most-recent data segment using the at least one curve fitted to the most-recent data segment (Algorithm, paragraph 1 indicates extrapolating the fitted sine wave into the future), wherein the at least one curve comprises a curve from a series of periodic functions (Algorithm, paragraph 1 indicates the curve is a sine wave which is derived from the periodic functions of the SO); detecting a predefined pattern in the predicted future continuation (Algorithm, paragraph 4 indicates delivering sounds at any desired phase of the SO), the predefined pattern having a frequency in the slow-oscillation range (Algorithm, paragraph 1 indicates the frequency of the fitted curve being in the 0.6-1.2 Hz range; Algorithm, paragraph 4 indicates delivering sounds at any desired phase of the SO), and providing the sensory discernible stimulus (Algorithm, paragraphs 1 and 4 indicate delivering sounds at a desired phase of the SO)
Cox is silent regarding defining a predicted event time of the predefined pattern, the predicted event time being in the future with respect to the most-recent data segment; wherein providing the sensory discernible stimulus comprises releasing the sound stimulus within a predefined event time window of the predicted event time.
In a system relevant to the problem of providing stimulations based on EEG time-series predictions, Chen teaches defining a predicted event time of said predefined pattern (F. Implementation discloses determining the time delay at which the specific phase will be), said predicted event time being in the future with respect to said most-recent data segment (Fig. 1 indicates that the specific phase is after t0), and actuating said stimulus generator for providing a stimulus at the predicted event time (F. Implementation discloses providing the output stimulation at the time delay). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the algorithm of Cox to incorporate defining a predicted event time of the predefined pattern, the predicted event time being in the future with respect to the most-recent data segment, and actuating said stimulus generator for providing a stimulus at the predicted event time as taught by Chen. The motivation would have been to identify the event time associated with the phase, allowing for a more accurate application of the stimulation.
The above combination is silent regarding providing the stimulus within a predefined event time window of said predicted event time.
In the same field of endeavor of providing stimulation based on brain signals, Gupta teaches providing a stimulus within a predefined event time window of said predicted event time (¶ [0007] discloses delivering electrical stimulation within a window of time that extends no greater than 100 milliseconds from occurrence of an episode or event). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the delivery of the stimulus of the above combination to incorporate that it is within a predefined event time window of said predicted event time as taught by Gupta. The motivation would have been to help selectively potentiating a favorable brain state while avoiding the potentiation of other brain states and/or other brain networks (see ¶ [0034] of Gupta).
Potentially Allowable Subject Matter
Claims 8 and 9 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
With regards to claims 8 and 9, the prior art does not teach or suggest that said predefined pattern has a predefined oscillatory phase in said brain-related signal and wherein said predefined pattern has a frequency in the alpha range or the spindle range, respectively.
Although Chen teaches a predefined pattern having a predefined oscillatory phase in said brain-related signal (F. Implementation of Chen indicates that the desired phase of the output stimulation corresponds to the waveform peak), Chen is completely silent how said phase would have a frequency in the alpha range or the spindle range.
Response to Arguments
Claim Objections
Applicant’s arguments, see pages 8, 9, and 15 of the Remarks filed 10/01/2025, with respect to the objections to claims 8, 9, and 15 have been fully considered and are persuasive. The objections to claims 8, 9, and 15 have been withdrawn.
In view of the amendment to claim 12 filed 10/01/2025, the objection to claim 12 has been withdrawn.
Rejections under 35 U.S.C. §112(a)
In view of the amendments filed 10/01/2025, the rejection of claim 21 under 35 U.S.C. §112(a) has been withdrawn.
Rejections under 35 U.S.C. §112(b)
In view of the amendments filed 10/01/2025, the rejections of claim 6 and 10 under 35 U.S.C. §112(b) have been withdrawn.
Prior Art Rejections
Applicant’s arguments, see pages 21-22 of the Remarks filed 10/01/2025, with respect to respective claims 8 and 9 have been fully considered and are persuasive. The above rejections under 35 U.S.C. §103 have been withdrawn.
Applicant's arguments filed 10/01/2025 have been fully considered but they are not persuasive.
Claim 1
–1 and 2–
Applicant asserts that a “phase” is not a “pattern” because a “phase” is simply a number.
This argument is not persuasive. The Examiner maintains that a pattern reads on a phase.
With regards to the term “pattern”, Merriam-Webster defines “pattern” as “a form or model proposed for imitation: exemplar” (see definition 1 of the attachment) and “a natural or chance configuration” (see definition 4 of the attachment). The Applicant’s specification does not redefine the term. Rather, the specification includes instances which agree with the definitions from Merriam-Webster. Paragraph [0035] of the published application indicates that “[t]he predefined pattern can be any pattern in a brain-relates signal that is or can be linked to a functioning of a subject. Examples of such predefined patterns are for instance rising and/or declining flanks of a brain-related signal.” In this case, a rising and/or declining flank of a brain-related signal amounts to “a form or model proposed for imitation: exemplar” and/or “a natural or chance configuration”.
With regards to the term “phase”, Wikipedia indicates that “a phase of a wave or other periodic function F of some real variable t (such as time) is an angle-like quantity representing the fraction of the cycle covered up to t”. See the attachment. An angle-like quantity representing the fraction of the cycle covered up to t amounts to a “a form or model proposed for imitation: exemplar” and/or “natural or chance configuration”. For example, a phase of a waveform is an identifiable configuration or form within the waveform.
The Examiner further notes that a rising and/or declining flank of a brain-related signal (see ¶ [0035] of the Applicant’s published application) corresponds to different phases of a periodic function. Therefore, he Applicant’s specification appears to indicate that a phase of a waveform is understood to be a pattern.
–3–
Applicant asserts that it is not clear what it would mean for a “phase” to be “in” a particular waveform.
This argument is not persuasive. The phase is a quantity that represents a particular fraction within the cycle of the waveform. Therefore, it is possible to predict a desired phase in a waveform.
–4–
Applicant asserts:
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This argument is not persuasive. In the context of Chen, the desired phase is a predefined phase that is chosen by the user. Paragraph 2 of F. Implementation of Chen indicates that φ is the desired phase of the output stimulation (φ = 0 corresponds to the waveform peak, while φ = π corresponds to the trough). Chen therefore indicates that the phase is a predefined value which corresponds to different patterns in the waveform (e.g., waveform peak or trough) at which point the output stimulation is provided.
–5–
Applicant asserts:
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590
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This argument is not persuasive. In the context of F. Implementation of Chen, the desired phase (φ) is chosen. The desired phase, along with the parameters f(t0) and ϕ(t0), is input into the equation 11 to arrive at the time delay. The Fig. 1 clearly depicts the time delay being used to output a stimulation after t0 (i.e., in the future) in at least Fig. 1 (d) and (f). Therefore, Chen teaches that the time delay corresponds to a predicted event time which will occur.
–6–
Applicant asserts:
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112
574
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The term “specific phase” was used by the Examiner to correspond to the “desired phase” of Chen.
–7 and 8–
Applicant asserts:
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242
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Applicant further asserts
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596
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These arguments are not persuasive.
MPEP 2112 (IV) indicates that “[t]he fact that a certain result or characteristic may occur or be present in the prior art is not sufficient to establish the inherency of that result or characteristic.” The fact that Chen may include all the features required to carry out the method disclosed therein does not establish that Chen must already include claimed memory. Chen does not positively recite each and every element required for implementing their algorithm, including the memory. For instance, Chen discloses, at most, the use of LabView 9.0 in F. Implementation. Chen is completely silent regarding how the data is stored. Therefore, one of ordinary skill in the art would have looked to Dudek to incorporate the memory required for storing the data that is to be used in the algorithm of Chen.
Additionally, MPEP 2143.01 indicates that a "motivation to combine may be found explicitly or implicitly in market forces; design incentives; the ‘interrelated teachings of multiple patents’; ‘any need or problem known in the field of endeavor at the time of invention and addressed by the patent’; and the background knowledge, creativity, and common sense of the person of ordinary skill." Increasing the accessibility of data by using a memory is a motivation that would have been at least common sense.
–9–
Applicant asserts:
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251
582
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This argument is not persuasive.
In response to Applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Chen teaches the defining a predicted event time. See the above rejection under 35 U.S.C. §103. Gupta teaches providing a stimulus within a predefined event time window of an event. See the above rejection under 35 U.S.C. §103. The combination results in the stimulation of Chen (which is provided at the predicted event time) being modified to be within a predefined event time window as taught by Gupta. Therefore, the Applicant’s arguments against Gupta individually is not commensurate with the scope of the rejection, and the arguments are not persuasive.
–9, 10, and 11–
Applicant asserts:
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604
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Applicant further asserts:
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300
592
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These arguments are not persuasive.
B. AR Model of Chen indicates that the linear AR model is applied to brain oscillations. Equation 1 of B. AR Model of Chen depicts an AR model which is capable of taking on the form of at least one curve. The AR model of the brain oscillations results in an estimation of the brain oscillations, wherein the estimation reflects the at least one curve of the brain oscillations.
Additionally, ¶ [0048] of the specification does not indicate that fitting a curve is an example of a mathematical model to fit data. Instead, ¶ [0048] of the Applicant’s specification clearly recites “Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points.” This definition of fitting a curve indicates that constructing a mathematical function that has a best fit to a series of data points amounts to curve fitting. Chen teaches fitting a curve to the extent that constructing the AR model is constructing a mathematical function that has a best fit to a series of data points
Claim 4
Applicant asserts:
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This argument is not persuasive. The plain and ordinary meaning of “end time” is a time corresponding to one of the ends of the time period. The ends of the time period of the last one second signal of Chen coincide with (A) one second before the current time and (B) the same time as the current time. The end point that coincides with the current time is less than 0.5 seconds from the current time.
Claims 5, 6, 10, 13, 23
Claims 5, 6, 10, 13, 23 are not patentable because the applicant’s arguments regarding claim 1 are not persuasive.
Claim 11
Applicant asserts:
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This argument is not persuasive. There is no teaching or suggestion that the interval between t0 and t0-1 is sixteen seconds. The Examiner maintains that the segment t0 to t0-1 corresponds to 1 second. See the description of Fig. 1(b) and D. Time-Series Forward Prediction. Fig. 1 (d) of Chen depicts the most-recent data segment t0 to t0-1 (which corresponds to 1 second) includes a data window tstart to tstop that has a width of less than 1 second).
Claim 12
Applicant asserts:
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This argument is not persuasive.
B. AR Model of Chen indicates that the linear AR model is applied to brain oscillations, which necessarily includes at least one periodic function. Equation 1 of B. AR Model of Chen depicts an AR model which is capable of taking on the form of at least one periodic function having a period shorter than 2 seconds. Fig. 1(d) further depicts the AR forward prediction being periodic. D. Time-series Forward Prediction of Chen indicates that the prediction signal has a length (i.e., period) of 2(t0 – tstop).
Claim 20
Applicant asserts:
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This argument is not persuasive. Fig.1 of Chen depicts the AR model is fitted to the signal based on the optimized frequency passband. For the reasons listed above, the generation of at least the AR model amounts to fitting at least one curve. The use of the optimized frequency passband results in the at least one curve of the autoregressive model being “fitted” to a signal having at least one frequency within the passband. The resulting AR model necessarily has a “fitted frequency”.
Claim 3
Applicant asserts:
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The Examiner provided sufficient evidence that they are equivalents. Specifically, because both elements are capable of modulating brain activity (Col. 2, lines 9-30 of Yasushi; I. Introduction, paragraph 3 of Chen), it would have been the simple substitution of one known equivalent element for another to obtain predictable results. There is no teaching or suggestion within Chen that teaches away from the use of the photic stimulation of Yasushi.
Additionally, ¶ [0036] of the Applicant’s published application indicates that the stimuli can be selected from an electromagnetic stimulus, or a sensory perceivable stimulus. The above disclosure indicates that either may be used as stimuli. See MPEP 2144.06 (II), which indicates that an applicant’s expressed recognition of an art-recognized or obvious equivalent may be used to refute an argument that such equivalency does not exist.
Claim 14
–1 and 2–
Applicant asserts:
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This argument is not persuasive because it is not commensurate with the scope of the rejection. G. Patients and Data of Chen teaches sampling at 500 Hz. Sampling at 100 Hz, as taught by paragraph 2 of Preprocessing of Cox, results in reducing an amount of data that is needed to be processed. Such a reduction may result in the elimination of noisy and unrelated data, which improves temporal and spectral precision.
Claim 15
–1–
Applicant asserts:
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This argument is not persuasive. Stimuli of Cox teaches “Our goal was to have the middle of each sound clip coincide with the SO peak or trough”. Algorithm further teaches presenting stimuli in a specific phase of the SO in real-time. Cox therefore teaches providing stimulation at an event time corresponding to a predefined pattern having a predefined oscillatory phase (e.g., peak or trough) and wherein said predefined pattern has a frequency in the slow-oscillation range (i.e., corresponding to SO peak or trough). The peak or trough occurring at SO frequency reads on the claimed predefined pattern having a frequency in the slow-oscillation range.
–2–
Applicant asserts:
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This argument is not persuasive. Cox provides sufficient evidence of the (1) identification and modulation of slow oscillations and (2) improvement of the therapeutic applicability. For example, paragraph 5 of Introduction of Cox teaches indicates “the ability to present stimuli repeatedly and consistently in a specific SO phase would be highly useful and maximize the opportunity to demonstrate SO phase-dependent learning. A related benefit of such a capability would be that meaningful sound stimuli, which are usually of longer duration, will not end up along different SO phases, thereby preventing a straightforward examination of up and down state-related processing.” The above disclosure indicates that identification and modulation of slow oscillation range signal may result in SO phase-dependent learning.
Claim 17
–1–
Applicant asserts:
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This argument is not persuasive.
In response to Applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). In this case, Chen teaches the most-recent data segment which is fitted to a cosine curve. See the above rejection of claims 1 and 17 under 35 U.S.C. §103. The use of the cosine waveform is substituted with the use of a sine waveform, based on the teachings of Cox. Therefore, the Applicant’s arguments against Cox individually is not commensurate with the scope of the rejection, and the arguments are not persuasive.
–2–
Applicant asserts:
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This argument is not persuasive. Chen teaches that the autoregressive model is configured to fit a brain-related signal to a cosine curve (Chen discloses fitting cosine waveform in the theta range in at least A. Cosine Waveform and paragraphs 1 and 3 of G. Patients and Data). Additionally, the Examiner maintains that the autoregression involves fitting data to a curve for the reasons listed above in the arguments regarding claim 1.
Claim 18
–1 and 2–
Applicant asserts:
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These arguments are not persuasive because the rejection does not rely upon Chen teaching claim 18’s limitation of “fitting a sinusoidal curve”.
Claim 19
Claim 19 is not patentable because the applicant’s arguments regarding claim 17 are not persuasive.
Claim 22
–1–
Applicant asserts:
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This argument is not persuasive because it amounts to mere attorney argument without evidence that the any and all audible stimulation would not induce deep sleep.
–2–
Applicant asserts:
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This argument is not persuasive.
The Examiner notes that the arguments refer to “D3”. However, D3 corresponds to Gupta, which is not relied upon for teaching the sound stimulus being provided during the slow oscillation’s up state. The Examiner will assume that the arguments are made with reference to D6 (Cox).
First, the argument amounts to mere attorney argument without evidence that one cannot induce stimulation in deep sleep in a mammal that is already in a state of deep sleep.
Second, the Applicant’s own specification indicates that “each SO-up state targeted stimulus evokes a train of slow oscillations after stimulation” in ¶ [0044], which is an indication that the application of stimulus during SO-up state results in enhancing deep sleep by evoking a train of slow oscillations. Procedure, paragraph 1 of Cox discloses that sound stimuli were directed at slow oscillation (“SO”) up state during SWS. Because the method of Cox provides a slow oscillation up state locked sound stimuli, it is a method for inducing stimulation of deep sleep.
–3–
Applicant asserts that claim 22 is similar to claim 1, so claim 22 is patentable for at least the same reasons discussed in connection with claim 1.
This argument is not persuasive and amounts to a general allegation of patentability without pointing out how the language of claim 22 patentably distinguishes claim 22 from the combination of Cox in view of Chen and Gupta.
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 SAMUEL C KIM whose telephone number is (571)272-8637. The examiner can normally be reached M-F 8:00 AM - 5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jacqueline Cheng can be reached at (571) 272-5596. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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