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 .
Response to Amendment
The objection to the specification has been withdrawn in light of the amendment to the title filed on 20 July 2026.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 21 July 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-6 & 13-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Galinsky et al. (“Brain Waves: Emergence of Localized, Persistent, Weakly Evanescent Cortical Loops,” (21 July 2020), Cogn Neurosci. 2020 Jul 21;32(11):2178–2202; hereinafter "Galinsky").
With regards to Claim 1, Galinsky discloses a method for determining volumetric distribution of electric field potential within a brain (mapping wave trajectories to MRI data is clearly illustrated in Galinsky FIGS. 3-4; see also pg. 17, ¶ 3 cont. pg. 18, ¶ 2 ), comprising:
acquiring at least two datasets including electroencephalography (EEG) data associated with a volume of the brain and magnetic resonance imaging (MRI) data associated with the volume of the brain (several multimodal EEG and MRI datasets were acquired to estimate actual amplitudes in the human brain for an ensemble of wave packets; see Galinsky pg. 16, ¶ 4);
determining, using an approximation for a volumetric distribution of electrostatic potential in an anisotropic and inhomogeneous medium, a frequency-dependent electrostatic field potential (cortical waves model for modelling wave propagation in a thin dissipative inhomogeneous and anisotropic cortical layer of these averaged properties is sufficient to predict the emergence of coherent, localized, and persistent wave loop patterns in the brain, i.e. wave patterns are frequency dependent; see Galinsky pf 22, ¶ 2) that is based on the EEG data and tissue properties of the brain estimated from the MRI data, the tissue properties including morphological and electrical properties at locations within the volume of the brain (the cortical waves model is based on relatively simple but physically motivated averaged electrostatic properties of human neuronal tissue within realistic data-derived brain tissue distributions {i.e. energy distributions across different frequencies of cortical wave}, geometries {i.e. morphological & volumetric}, and anisotropy; see Galinsky pg. 18, ¶ 3 & 22, ¶ 2);
iteratively constructing an approximate solution for the frequency-dependent electrostatic field potential within the volume of the brain using a brain wave model constrained by the tissue properties (In order to account for geometric variations, we construct a slightly more complex two dimensional model that can be viewed as a very crude approximation of a cortical fold (Fig. 1b). The equation for the ϕ⊥y will again be of a wave type, similar to (7), with the addition of a z component of the conductivity gradient and with a similar wave—like solution that will include an additional term induced by the inhomogeneity in z; see Galinsky pg. 5, ¶ 4; it should be appreciated that equation (7) is solved as x → ϵx rendering it an iterative solution) and based on weakly evanescent transverse cortical brain wave propagation (our model uses actual tissue and geometry properties {i.e. tissue constraints} instead and thus presents the possibility of making quantitative hypotheses testable with actual measurements that elucidate the dependence of the wave parameters (e.g., frequency) on tissue parameters and geometry {i.e. frequency dependent}; see Galinsky pg. 3, ¶ 2);
determining spatiotemporal modes of electrical activity within the volume of the brain by solving the approximation using the approximate solution (equation 44 is solved based on the strength of resonant coupling between modes and 𝑎𝑘𝑛=0 for n < 0 and n > N); and
obtaining the volumetric distribution of the electric field potential within the volume of the brain based on the spatiotemporal modes (FIGS. 3-4 of Galinsky clearly illustrated the reconstructive volumetric field potential trajectories).
Claim 13 recites similar limitations and are rejected under the same rationale as Claim 1 with the addition of a computer inherent based on the Galinsky disclosure of a computation framework (see Galinsky pg. 28, ¶ 4-5).
With regards to Claim 21, Galinsky discloses further comprising:
determining spatial and temporal patterns describing the electrical activity within the volume of the brain based on the spatiotemporal modes (we demonstrate that an excess of power in different bands (from alpha to gamma) at different conditions is what distinguishes the brain activity from randomly statistically forced power decay. Our model provides a clear prediction of power spectrum {i.e. spatiotemporal modes} for this randomly forced waves as well as predicts frequency ranges where this simple single exponent power decay cannot be used and these ranges are also in agreement with the observational EEG data; see Galinsky pg. 27, ¶ 1).
With regards to Claim 32, Galinsky discloses wherein at least some of the spatial and temporal patterns are used to obtain a reconstructed image of the brain (Fig. 4 shows the final persistent wave patterns for 9 simulations that were initialized with wave packets of random parameters (frequency, wave number, location, etc.) as well as with different types of anisotropy (as described in section 2.2); see Galinsky pg. 18, ¶ 2).
With regards to Claim 41, Galinsky discloses wherein at least some of the spatiotemporal modes are correlated to one another (the type of proportionality between the frequency and the wave number in the linear wave dispersion relation is not directly related to the temporal/spatial correlation relationships and it is possible for waves with the inversely proportional dispersion to encounter either directly or inversely proportional relationships between temporal and spatial correlations using different single point wave correlation properties {i.e. spatiotemporal modes} (e.g. different slopes of power spectra, different spectral frequency ranges, etc.); see Galinsky pg. 21, ¶ 1).
Claim 15 recites similar limitations and are rejected under the same rationale as Claim 4.
With regards to Claim 51, Galinsky discloses wherein the volumetric distribution of the electric field potential is displayed in an image (FIGS. 3-4 of Galinsky clearly illustrated the reconstructive volumetric field potential trajectories).
With regards to Claim 61, Galinsky discloses wherein at least some of the tissue properties are frequency-dependent (wherein the different single point wave correlation properties includes e.g. different slopes of power spectra, different spectral frequency ranges {i.e. frequency dependency}, etc.; see Galinsky pg. 21, ¶ 1).
Claim 16 recites similar limitations and are rejected under the same rationale as Claim 5.
With regards to Claim 1413, Galinsky discloses wherein the instructions upon execution by the processor further cause the processor to:
determine spatial and temporal patterns describing the electrical activity within the volume of the brain based on the spatiotemporal modes (we demonstrate that an excess of power in different bands (from alpha to gamma) at different conditions is what distinguishes the brain activity from randomly statistically forced power decay. Our model provides a clear prediction of power spectrum {i.e. spatiotemporal modes} for this randomly forced waves as well as predicts frequency ranges where this simple single exponent power decay cannot be used and these ranges are also in agreement with the observational EEG data; see Galinsky pg. 27, ¶ 1),
wherein at least some of the spatial and temporal patterns are used to obtain a reconstructed image of the brain (Fig. 4 shows the final persistent wave patterns for 9 simulations that were initialized with wave packets of random parameters (frequency, wave number, location, etc.) as well as with different types of anisotropy (as described in section 2.2); see Galinsky pg. 18, ¶ 2).
Allowable Subject Matter
Claims 7-12 & 17-20 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.
The following is a statement of reasons for the indication of allowable subject matter: entropy field decomposition (EFD) is a term of art. It should be appreciated that In the field of EEG source reconstruction, Inventors Galinsky & Frank have pioneered EFD functional tractography analysis in their publication Frank et al. (“Dynamic Multiscale Modes of Resting State Brain Activity Detected by Entropy Field Decomposition,” (1 September 2016), Neural Comput. 2016;28(9):1769–811). Neither the searched nor cited prior art teaches of, explicitly or inherently, EFD within the field of EEG, let alone EEG source reconstructed via fMRI volumes. While the instant inventors pioneered EFD, none of their teachings of EFD with respect to EEG source reconstruction via fMRI imaging meets the effective filing data of the instant application.
Moreover, neither Abreu et al. (“Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors,” (10 February 2022) Brain Topogr; 35(3):282-301) nor Warbrick et al. (“Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?,” (14 March 2022), Sensors, 2022, 22(6), 2262) teach of anisotropic analysis.
Response to Arguments
Applicant's arguments filed 20 July 2026 have been fully considered but they are not persuasive. In particular, Applicant contends that Galinsky does not anticipate independent Claim 1.
In support, Applicant argues that “Galinsky relates to a different technical problem and fails to teach or suggest a method for "determining volumetric distribution of electric field potential" as recited in claim 1” because Galinsky is not related to “to solving the inverse EEG problem (i.e., determining volumetric electric field distributions from measured data).” The Office disagrees. Galinsky’s “persistent wave loop patterns,” as cited, is illustrated in FIGS. 3-4 in which the spherical shell model is utilized to determine emergent loop patterns. The volumetric distribution of electric field potential is clearly illustrated in FIG. 5 {i.e. Examples of electric fields and scalar potentials for two subjects from a set of multimodal EEG and MRI datasets} which are generated from the functional EEG modes from combined EEG and FMRI data (see Galinsky pg. 16, ¶ 4).
Applicant also argues that Galinsky fails to disclose “"iteratively constructing an approximate solution for the frequency-dependent electrostatic field potential" and “Galinsky fails to teach or suggest an approximate solution for a "frequency-dependent electrostatic field potential", where the frequency-dependent electrostatic field potential is "based on the EEG data and tissue properties of the brain estimated from the MRI data" and determined "using an approximation for a volumetric distribution of electrostatic potential in an anisotropic and inhomogeneous medium"” because “merely substituting variables in a mathematical equation is different from "iteratively constructing an approximate solution."” The Office disagrees. Firstly, As cited in the previous action, Equation (7) is solved as x → ϵx rendering it an iterative solution. Regardless, Galinsky teaches that “the model can explain many of the observed properties of the real data using different models of tissue anisotropy and conductivity varying from the simplest fixed position independent anisotropy to the complex anisotropy created by an ensemble of multiple fibers.” In relation to Eq 7, Galinsky iteratively solves for the electrostatic potential at a corresponding brain fiber based on anisotropic inhomogeneous conditions.
It should be appreciated that Galinsky goes into more detail in how the EEG modes are combined with the FMRI data as disclosed when mentioning commonly held Galinsky et al. (2018) {i.e. Galinsky et al. “Joint Estimation of Effective BrainWave Activation Modes using EEG/MEG Sensor Arrays and Multi-modal MRI Volumes,” (13 April 2018) Neural Comput. 2018 Apr 13;30(7):1725–1749; hereinafter "Galinksy2"} (see Galinsky pg. 18, ¶ 4). More specifically, Galinksy2 discusses in § 2.2 that the inverse solution iteratively reconstructs the “potential ϕ across an entire MRI brain volume” where ϕ is the potential at a detected frequency (see Galinksy2 pg. 4, ¶ 4 & Eqs. 4-5). The Office submits that one of ordinary skill in the art would recognize according to Galinsky alone that Galinsky’s linear theory amounts to an iterative solution to a frequency-dependent electrostatic field potential based on both electrostatic field potential in an anisotropic and inhomogeneous medium {i.e. brain fiber tissues}. For at least this reason, Applicant’s arguments are not persuasive.
Applicant also argues that Galinsky fails to teach or suggest “obtaining the volumetric distribution of the electric field potential within the volume of the brain based on the spatiotemporal modes” because ‘[W]hile an image of a brain appears in Figures 3-4 of Galinsky, those figures do not show a "volumetric distribution of the electric field potential within the volume of the brain based on the spatiotemporal modes." Galinsky teaches that a "wave trajectory" or "wave loop" is a path or curve determined by a simulated point-like wave packet as it propagates through space over time. This is distinct from a "volumetric distribution of electric field potential. Moreover, Figures 3-4 in Galinsky are generated from simulations "initialized with wave packets of random parameters," not from actual EEG and MRI measurements acquired from a brain.” The Office disagrees in part. FIGS. 3-4 provide a simulation of the cortical fold model; however, FIG. 5 illustrates the electric field potential based on a multimodal set of EEG and MRI data in which the distribution of the electric fields on the skull is determined across different frequencies for the cortical waves based on said simulations as detailed in § 3.3. For at least this reason, Applicant’s arguments are not persuasive.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHISH S. JASANI whose telephone number is (571) 272-6402. The examiner can normally be reached M-F 8:00 am - 4:00 pm (CST).
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/ASHISH S. JASANI/Examiner, Art Unit 3798
/KEITH RAYMOND/Supervisory Patent Examiner, Art Unit 3798
