DETAILED ACTION
Response to Amendment
This Office Action is responsive to the Amendment filed 10 July 2026. Claims 1-20 are now pending. The Examiner acknowledges the amendments to claims 1-7, 9-10. Claims 11-20 remain withdrawn from consideration.
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 Objections
Claims 3, 5, 6, 7, 9 are objected to because of the following informalities:
-Claim 3 recites “the brainwave data” in lines 3-4 and 6. Examiner recommends amending to –the brainwave data of the time domains—
-Claim 5 recites “the brainwave data” in line 4. Examiner recommends amending to –the brainwave data of the time domains—
-Claim 6 recites “the brainwave data” in line 4. Examiner recommends amending to –the brainwave data of the time domains—
-Claim 7 recites “the brainwave data” in line 2. Examiner recommends amending to –the brainwave data of the time domains—
-Claim 9 recites “the brainwave data” in line 6. Examiner recommends amending to –the brainwave data of the time domains—
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
-Claim 1 recites “a motor hotspot location detector” which is a generic placeholder. There is no sufficient structure for this limitation provided in the claims. The function of this limitation is to detect the location of the motor hotspot. According to the specification the motor hotspot location detecting part includes at least one processor, and a memory that stores a motor hotspot location detection algorithm [53] and equivalents thereof.
-Claim 3 recites “a feature data extractor” which is a generic placeholder. There is no sufficient structure for this limitation provided in the claims. The function of this limitation is to extract the feature data from brainwave data collected for the channels. According to the specification the feature data extracting part includes a fast Fourier transform part, a correlation coefficient calculating part and/or a phase synchronization index calculating part [87] and equivalents thereof.
-Claim 5 recites “a correlation coefficient calculator” which is a generic placeholder. There is no sufficient structure for this limitation provided in the claims. The function of this limitation is to calculate feature data including a correlation coefficient. Based on the specification, there is no disclosure provided to disclose the corresponding structure.
-Claim 6 recites “a phase synchronization index calculator” which is a generic placeholder. There is no sufficient structure for this limitation provided in the claims. The function of this limitation is to calculate feature data that includes a phase synchronization index. Based on the specification, there is no disclosure provided to disclose the corresponding structure.
-Claim 10 recites “an electrode mover” which is a generic placeholder. There is no sufficient structure for this limitation provided in the claims. The function of this limitation is to be provided in the head mounted body. According to the specification the electrode moving part includes a first, second and third driving part [125-126] and equivalents thereof.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
-Claim 1 recites “the location” in line 6. There is insufficient antecedent basis for this limitation in the claim. Should possibly read –the target location—
-Claim 4 recites “the plurality of channels” in lines 8-9. There is insufficient antecedent basis for this limitation in the claim.
-Claim 9 recites “different locations” in line 7. It is unclear whether this is the same or different from the different locations originally referenced in claim 1, line 5. Further clarification should be provided.
Claim limitation “a correlation coefficient calculator” and “a phase synchronization index calculator” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. While the specification and drawings reference these parts, it’s unclear whether these are embodied by actual structure such as a processor, memory or CPU or whether this is referring to algorithms or calculations in general. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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.
Section 33(a) of the America Invents Act reads as follows:
Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism.
Claims 9-10 are rejected under 35 U.S.C. 101 and section 33(a) of the America Invents Act as being directed to or encompassing a human organism. See also Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (indicating that human organisms are excluded from the scope of patentable subject matter under 35 U.S.C. 101).
-Claim 9 recites “data are collected at different locations on the scalp” in lines 6-7. Examiner recommends amending to –data can be collected at different locations on the scalp—
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.
Claim(s) 1, 2, 3, 7, 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rich et al. Reliability of the Location of Primary Motor Cortex Using the International 10/20 Electroencephalogram System (10/20 EEG), Journal of Pediatric Neurology and Neuroscience, 1(1):6-7, 2017 in view of Srinivas (WO 2014205356).
Regarding Claim 1, Rich et al. teaches a motor hotspot location detecting device using brainwaves for detecting a location of a motor hotspot that is a target location for a transcranial electrical stimulation of a scalp of a target object [Pg. 6, paragraph 1; “The TMS M1 location, termed the motor hotspot, is derived by eliciting a motor evoked potential in a muscle corresponding to the area stimulated (e.g. the first dorsal interosseous muscle can be monitored when stimulating the hand knob of M1). In contrast, the 10/20 EEG identifies the location of M1 for the placement of scalp electrodes derived from four key individual anatomical landmarks with the nasion (lowest depression between the forehead and nose), in (lowest point of the skull from the back of the head) and the preauricular points of the right and left ears. EEG electrodes can record temporal brain activity in the form of event-related potentials (ERPs).”] and [Pg. 6, paragraph 1; “One form of interventional NIBS is transcranial direct current stimulation (tDCS),”], the motor hotspot location detecting device comprising:
a plurality of brainwave measuring electrodes configured to measure brainwaves generated in the target object at different locations and collect brainwave data [Pg. 6, paragraph 1, “In contrast…the 10/20 EEG…following a stroke.”]; and
Rich et al. is silent on a motor hotspot location detector configured to detect the location of the motor hotspot based on the brainwave data collected by the plurality of brainwave measuring electrodes. Srinivas teaches a motor hotspot location detector [Pg. 25, lines 30-31]—reference to processor, [Pg. 14, line 6]—reference to memory, [Pg. 15, lines 3-4]—reference to algorithms, configured to detect the location of the motor hotspot based on the brainwave data collected by the plurality of brainwave measuring electrodes [Pg. 13, lines 28- pg. 14, line 24] and [Pg. 17, lines 21-23].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate motor hotspot location detector components as taught by Srinivas to determine the location of a hotspot using EEG as suggested by Rich et al., as Rich et al. discusses 10/20 EEG frequently guiding electrode placement for tDCS [Pg. 7, paragraph 4; “ The 10/20…EEG measurements.”] with Srinivas because Srinivas teaches this method providing functional brain information compared to spatial variation of EEG [Pg. 13, lines 28-32].
Regarding Claim 2, Rich et al. is silent on wherein the motor hotspot location detector includes: an artificial neural network configured to calculate the location of the motor hotspot based on brainwave data of time domains, which are collected for channels allocated to the plurality of brainwave measuring electrodes, or feature data extracted from the brainwave data collected by the plurality of brainwave measuring electrodes. Srinivas teaches wherein the motor hotspot location detector includes:
an artificial neural network configured to calculate the location of the motor hotspot based on brainwave data of time domains [Pg. 25, lines 10-16] and [Pg. 24, lines 24-31], which are collected for channels allocated to the plurality of brainwave measuring electrodes [Pg. 12, lines 26-27] and [Pg. 13, lines 3-5], or feature data extracted from the brainwave data collected by the plurality of brainwave measuring electrodes (not interpreted to be required by the claim).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ a neural network and EEG channels as taught by Srinivas to determine the location of a motor hotspot as suggested by Rich et al., as Rich et al. discusses using additional information, such as 3D measurements to provide error information [Pg. 7, paragraph 3; “The addition…intra-raters.”] with Srinivas because Srinivas teaches using these channels and techniques to improve signal to noise ratio [Pg. 13, line 6].
Regarding Claim 3, Rich et al. is silent on wherein the motor hotspot location detector further includes: a feature data extractor configured to extract the feature data from the brainwave data collected for the channels, and the artificial neural network is configured to calculate the location of the motor hotspot based on the feature data extracted from the brainwave data by the feature data extractor.
Srinivas teaches wherein the motor hotspot location detector further includes:
a feature data extractor configured to extract the feature data from the brainwave data collected for the channels [Pg. 15, lines 3-8] and [Fig. 8], and
the artificial neural network is configured to calculate the location of the motor hotspot based on the feature data extracted from the brainwave data by the feature data extractor [Pg. 25, lines 10-16] and [Fig. 20].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extract and calculate data as taught by Srinivas to determine the location of a motor hotspot as suggested by Rich et al., as Rich et al. discusses the use of ICC, SEM to determine absolute reliability [Pg. 7, paragraph 1; “The relative…and the SEM was 0.34 cm.”] with Srinivas because Srinivas teaches the use of calculations to achieve the highest possible SNR [Pg. 10, lines 25-27].
Regarding Claim 7, Rich et al. further teaches and the location of the motor hotspot measured through a transcranial magnetic stimulation [Pg. 6, paragraph 1; “1) transcranial magnetic stimulation…”]. Rich et al. is silent on wherein the artificial neural network performs learning based on the brainwave data collected for the plurality of channels. Srinivas teaches wherein the artificial neural network performs learning based on the brainwave data collected for the plurality of channels [Pg. 24, lines 25-31].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ a neural network and EEG channels as taught by Srinivas to determine the location of a motor hotspot as suggested by Rich et al., as Rich et al. discusses using additional information, such as 3D measurements to provide error information [Pg. 7, paragraph 3; “The addition…intra-raters.”] with Srinivas because Srinivas teaches using these channels and techniques to improve signal to noise ratio [Pg. 13, line 6].
Regarding Claim 9, Rich et al. is silent on further comprising: a head mounted body provided in a form that is configured to be mounted on the scalp of the target object, wherein the plurality of brainwave measuring electrodes are distributed and disposed on an inner surface of the head mounted body such that the brainwave data are collected at different locations on the scalp of the target object. Srinivas teaches further comprising: a head mounted body provided in a form that is configured to be mounted on the scalp of the target object [Fig. 5A/B, elements 20 (sensor assembly) and 64 (band)], wherein the plurality of brainwave measuring electrodes are distributed and disposed on an inner surface of the head mounted body such that the brainwave data are collected at different locations on the scalp of the target object [Fig. 3, elements 37 (reference electrode/sensor), 30 (sensors) and 34 (electrodes)] and [Pg. 9, lines 18-21].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a head mounted device as taught by Srinivas to measure and identify motor hotspot locations as suggested by Rich et al., as Rich et al. discusses placing electrodes on the scalp [Pg. 6, paragraph 1] with Srinivas because Srinivas teaches the band holding the sensor assembly in place on the scalp [Pg. 8, lines 30-31].
Claim(s) 4, 5, 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rich et al. Reliability of the Location of Primary Motor Cortex Using the International 10/20 Electroencephalogram System (10/20 EEG), Journal of Pediatric Neurology and Neuroscience, 1(1):6-7, 2017 in view of Srinivas (WO 2014205356) and Osvath (WO 2014190414).
Regarding Claim 4, Rich et al. is silent on wherein the feature data extractor is configured to: calculate the feature data including power spectrum densities for the channels through Fourier transform, wavelet transform, or an autoregressive scheme from the brainwave data of the time domains, which are collected for the channels allocated to the plurality of brainwave measuring electrodes, and wherein the artificial neural network is configured to calculate the location of the motor hotspot based on the feature data including the power spectrum densities calculated for the plurality of channels.
Srinivas teaches and wherein the artificial neural network is configured to calculate the location of the motor hotspot based on the feature data [Pg. 25, lines 10-16] and [Fig. 20].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extract and calculate data as taught by Srinivas to determine the location of a motor hotspot as suggested by Rich et al., as Rich et al. discusses the use of ICC, SEM to determine absolute reliability [Pg. 7, paragraph 1; “The relative…and the SEM was 0.34 cm.”] with Srinivas because Srinivas teaches the use of calculations to achieve the highest possible SNR [Pg. 10, lines 25-27].
Srinivas is silent on wherein the feature data extractor is configured to: calculate the feature data including power spectrum densities for the channels through Fourier transform, wavelet transform, or an autoregressive scheme from the brainwave data of the time domains, which are collected for the channels allocated to the plurality of brainwave measuring electrodes, and including the power spectrum densities calculated for the plurality of channels.
Osvath teaches wherein the feature data extractor is configured to: calculate the feature data including power spectrum densities for the channels through Fourier transform, wavelet transform, or an autoregressive scheme from the brainwave data of the time domains [00117-00119] and [00137], which are collected for the channels allocated to the plurality of brainwave measuring electrodes [00174], and including the power spectrum densities calculated for the plurality of channels [00120].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Fourier transforms and power spectrum density data as taught by Osvath to determine the location of a motor hotspot as suggested by Rich et al. and Srinivas, as Rich et al. discusses the use of ICC, SEM to determine absolute reliability [Pg. 7, paragraph 1; “The relative…and the SEM was 0.34 cm.”] and Srinivas which discusses signal to noise ration as a function of sensor density [Pg. 8, line 11] with Osvath because Osvath teaches calculations involved with complex coherency as a ratio of and product of power spectral densities [00120].
Regarding Claim 5, Rich et al. is silent on wherein the artificial neural network is configured to calculate the location of the motor hotspot based on the feature data Srinivas teaches and wherein the artificial neural network is configured to calculate the location of the motor hotspot based on the feature data [Pg. 25, lines 10-16] and [Fig. 20].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extract and calculate data as taught by Srinivas to determine the location of a motor hotspot as suggested by Rich et al., as Rich et al. discusses the use of ICC, SEM to determine absolute reliability [Pg. 7, paragraph 1; “The relative…and the SEM was 0.34 cm.”] with Srinivas because Srinivas teaches the use of calculations to achieve the highest possible SNR [Pg. 10, lines 25-27].
Rich and Srinivas are silent on and wherein the feature data extractor includes: a correlation coefficient calculator configured to calculate the feature data including a correlation coefficient between the brainwave data collected for the plurality of channels, including the correlation coefficient. Osvath teaches and wherein the feature data extractor includes: a correlation coefficient calculator configured to calculate the feature data including a correlation coefficient between the brainwave data collected for the plurality of channels, including the correlation coefficient [00134] and [00322].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use correlation coefficient data as taught by Osvath to determine the location of a motor hotspot as suggested by Rich et al. and Srinivas, as Rich et al. discusses the use of ICC, SEM to determine absolute reliability [Pg. 7, paragraph 1; “The relative…and the SEM was 0.34 cm.”] and Srinivas which discusses signal to noise ration as a function of sensor density [Pg. 8, line 11] with Osvath because Osvath teaches calculations involved with complex coherency and the relationships of coherence to a correlation coefficient [00163].
Regarding Claim 6, Rich et al. is silent on wherein the artificial neural network is configured to calculate the location of the motor hotspot based on the feature data including the phase synchronization index. Srinivas teaches wherein the artificial neural network is configured to calculate the location of the motor hotspot based on the feature data [Pg. 25, lines 10-16] and [Fig. 20].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extract and calculate data as taught by Srinivas to determine the location of a motor hotspot as suggested by Rich et al., as Rich et al. discusses the use of ICC, SEM to determine absolute reliability [Pg. 7, paragraph 1; “The relative…and the SEM was 0.34 cm.”] with Srinivas because Srinivas teaches the use of calculations to achieve the highest possible SNR [Pg. 10, lines 25-27].
Rich et al. and Srinivas are silent on wherein the feature data extractor includes: a phase synchronization index calculator configured to calculate the feature data including a phase synchronization index between the brainwave data collected for the channels, including the phase synchronization index.
Osvath teaches wherein the feature data extractor includes: a phase synchronization index calculator configured to calculate the feature data including a phase synchronization index between the brainwave data collected for the channels, including the phase synchronization index [00173] and [00192].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a phase synchronization index as taught by Osvath to determine the location of a motor hotspot as suggested by Rich et al. and Srinivas, as Rich et al. discusses the use of ICC, SEM to determine absolute reliability [Pg. 7, paragraph 1; “The relative…and the SEM was 0.34 cm.”] and Srinivas which discusses signal to noise ration as a function of sensor density [Pg. 8, line 11] with Osvath because Osvath teaches synchronizing channels of EOG [00193].
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rich et al. Reliability of the Location of Primary Motor Cortex Using the International 10/20 Electroencephalogram System (10/20 EEG), Journal of Pediatric Neurology and Neuroscience, 1(1):6-7, 2017 in view of Srinivas (WO 2014205356) and Qiao et al. Depp Spatial-Temporal Neural Network for Classification of EEG-Based Motor Imagery, AICS, July 12-13, 2019.
Regarding Claim 8, Rich et al. and Srinivas are silent on wherein the artificial neural network includes a convolutional neural network. Qiao et al. teaches wherein the artificial neural network includes a convolutional neural network [Abstract].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a convolutional neural network as taught by Qiao et al. to determine the location of a motor hotspot as suggested by Rich et al. and Srinivas, as Rich et al. discusses the use of additional data to provide information as to directionality of displacement error [Pg. 7, paragraph 3] and Srinivas which discusses the use of Markov Chain modeling to infer neurological states [Fig. 20] with Qiao et al. because Qiao et al. teaches the use of CNN as an effective deep learning model in motor imaging classification tasks [Pg. 2, paragraph 2].
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rich et al. Reliability of the Location of Primary Motor Cortex Using the International 10/20 Electroencephalogram System (10/20 EEG), Journal of Pediatric Neurology and Neuroscience, 1(1):6-7, 2017 in view of Srinivas (WO 2014205356) and Wongsarnpigoon (U.S. 20130204315).
Regarding Claim 10, Rich et al. and Srinivas are silent on further comprising: a transcranial electrical stimulation electrode provided in the head mounted body such that the transcranial electrical stimulation is configured to be applied onto the scalp of the target object; and an electrode mover provided in the head mounted body such that the transcranial electrical stimulation electrode is configured to be moved to the location of the motor hotspot. Wongsarnpigoon teaches further comprising:
a transcranial electrical stimulation electrode provided in the head mounted body such that the transcranial electrical stimulation is configured to be applied onto the scalp of the target object [0055]; and
an electrode mover provided in the head mounted body such that the transcranial electrical stimulation electrode is configured to be moved to the location of the motor hotspot [0055; “more gross changes in electrode position may be made by moving to a different therapeutic target region.”], [0056; “simply and quickly move the electrode”], and [0057]—reference to Figs. 4-6 elements 20, 21, and 22.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use an adjustable means for the electrodes as taught by Wongsarnpigoon to apply electrical stimulation as suggested by Rich et al. and Srinivas, as Rich et al. discusses the use of tDCS [Pg. 6, paragraph 1] and Srinivas which discusses electrodes in electrical contact with the scalp [Pg. 10, lines 6-8] with Wongsarnpigoon because Wongsarnpigoon teaches the use of the repositioning capability to improve the likelihood of determining the correct electrode position [0056].
Response to Arguments
Applicant's arguments filed 10 July 2026 with respect to the specification
and claim objections have been fully considered and are persuasive in light of the
amendments.
Applicant's arguments filed 10 July 2026 with respect to 35 U.S.C. 112(b) rejections have been fully considered however, those rejections for claim 1 under 35 U.S.C. 112(b) in light of claim interpretation under 35 U.S.C. 112(f) are maintained. While the applicant contends that structural relationships among the recited components are recited, the claims merely further recite generic terminology such as calculator which is considered a nonce term, and do not identify what these components are. In view of the foregoing, the 35 U.S.C. 112(b) rejection is maintained for claims 5 and 6. All other amended limitations remain under claim interpretation since detector, mover, and extractor do not further define the corresponding structure of the terms.
Applicant's arguments filed 10 July 2026 with respect to 35 U.S.C. 112(b)
rejections have been fully considered and are persuasive however, new rejections are
presented in light of the amendments for claims 1-10.
Applicant's arguments filed 10 July 2026 with respect to 35 U.S.C. 101 rejections have been fully considered however, claim 9 is rejected for not having the recommended language –data can be collected at different locations on the scalp— in lines 6-7.
Applicant’s arguments filed 10 July 2026 with respect to the rejection of
claims 1-10 under 35 U.S.C.102(a)(1) have been fully considered and are persuasive,
however, new rejections are presented above in light of the amendments for claims 1-10. Applicant contends that Choi constitutes disqualified art under 35 U.S.C. 102(b)(1)(A). The examiner acknowledges this exception and has presented a new grounds of rejection under 35 U.S.C. 103 citing Rich et al. in view of Srinivas for claims 1-3, 7 and 9, citing Rich et al. in view of Srinivas and in further view of Osvath for claims 4-6, citing Rich et al. in view of Srinivas and in further view of Qiao et al. for claim 8, and citing Rich et al. in view of Srinivas and in further view of Wongsarnpigoon for claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Fogel (WO 2020053849)—describes testing a patient’s brain during TMS for enhanced brain mapping
-Moises (U.S. 20150105837)—identifies defective brain mapping using EEG and neural networking
-Wang et al. A Shallow Convolutional Neural Network for Classifying MI-EEG, 2019 Chinese Automation Congress (CAC), 13 February 2020.—employs EEG and convolutional neural networking for brain mapping
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BROOKE NICOLE KOHUTKA whose telephone number is (571)272-5583. The examiner can normally be reached Monday-Friday 7:30am-5:00pm EST.
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/B.N.K./Examiner, Art Unit 3791
/CHRISTINE H MATTHEWS/Primary Examiner, Art Unit 3791
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