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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5 May 2026 has been entered.
Response to Amendment
The rejection under 35 U.S.C. 112(a) has been withdrawn in light of the arguments filed on 5 May 2026.
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 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.
Claims 1-6 & 8-19 are rejected under 35 U.S.C. 103 as being unpatentable over Cranmer et al. (US PGPUB 20190206057; hereinafter "Cranmer") in view of Arya et al. (US PGPUB 20230087736; hereinafter "Arya") and in further view of Power et al. ("Evidence for hubs in human functional brain networks," (2014 Aug 21), Neuron. 2013 Aug 21;79(4); hereinafter "Power").
With regards to Claim 1, a system for performing functional brain mapping (a system for modeling neural architecture; see Cranmer ¶ [0019]), the system comprising:
a memory configured to store first data from a magnetic resonance imaging (MRI) system and second data from one or more electrodes (memory for storing the neural activity data {i.e. MR & ECoC data}; see Cranmer ¶ [0027]); and
a processor operatively coupled to the memory and configured to:
identify first edges in a brain network (identifying ROIs corresponding to critical subnetworks {e.g. Default Mode Network (DMN), the Dorsal Attention Network, and the Frontoparietal Control Network}, i.e. first edges, in the imaging derived neural activity data {i.e. fMRI, DTI, and/or DWI images}; see Cranmer ¶ [0086]) based on the first data from the MRI (acquire first neural activity data via diffusion weighted and/or diffusion tension imaging with an MR scanner; see Cranmer ¶ [00025 & 0084]) and second edges in the brain network (identifying ROIs corresponding to critical subnetworks {e.g. Default Mode Network (DMN), the Dorsal Attention Network, and the Frontoparietal Control Network}, i.e. second edges, in the imaging derived neural activity data {i.e. EEG/ECoG images}; see Cranmer ¶ [0086]) based on the second data from the one or more electrodes (acquire second acquire neural activity data via EEG/ECoG data from electrodes placed directly on the brain; see Cranmer ¶ [0026 & 0084]), wherein an edge is a correlation of activity between nodes in the brain network (computing an endogenous two-star statistic, i.e. a degree of preferential attachment between two nodes {i.e. two-star graph or edges}; see Cranmer ¶ [0057] & Table 1) (claimed in the alternative);
compute, based on the first edges and the second edges, graph metrics for each node in the brain network (computing an endogenous two-star or transitive triads statistic, i.e. a degree of preferential attachment between two nodes {i.e. two-star graph or 1 edges} or three nodes {i.e. triangular graph with 3 edges` }; see Cranmer ¶ [0057] & Table 1),
classify, hubs in the brain network (quantifying the presence of hubs using the two-star or transitive triads inferential testing (see Cranmer ¶ [0057])
While Cranmer discloses “brain networks can demonstrate a “small world” structure: both strong clustering of regions within communities, and integration across these communities. This feature of brain networks can be considered as balancing local and global efficiency, and allowing the brain to minimize wiring costs. The brain can include a prevalence of “hub” regions: regions that are more highly interconnected with a large number of other regions than expected by chance” (see Cranmer ¶ [0087]); it appears that Cranmer may be silent to a graph metric that includes a participation coefficient and classifying nodes as critical or non-critical nodes. However, Power teaches of identifying hubs in functional brain networks based on 1) finding network nodes that participate in multiple sub-networks of the brain, and 2) finding spatial locations where several systems are represented within a small volume (see Power Summary). In particular, Power teaches that “First, using a model of the brain at the level of functional areas we identify nodes that participate in many sub-networks of the brain (e.g., a node that has relationships with members of multiple brain systems, such as visual, default mode, or fronto-parietal control systems). These nodes are candidate brain hubs. We identify these candidate hubs using the established measure of participation coefficients” (see Power pg. 2, ¶ 4 and FIG. 6 which illustrates the participation coefficient for each node).
Cranmer and Power are both considered to be analogous to the claimed invention because they are in the same field of functional brain networks. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Cranmer to incorporate the above teachings of Power to provide at least wherein the graph metric includes a participation coefficient. Doing so would aid in brain testing in terms of lesions (see Power Summary).
While modified Cranmer teaches of delineating subnetworks, it appears that Cranmer may be silent to wherein a critical node comprises a node that, when lesioned or stimulated, causes one or more of speech arrest and language errors. However, However, Arya teaches of functional brain mapping using high gamma modulation from sEEG based on fMRI co-registration, and identifying a functional brain site in the subject that is responsible for performing the task based the collection of output data parameters (see Arya Abstract & ¶ [0027 & 0091]). In particular, Arya teaches of mapping language/speech sites related to seizure-onset zone(s) {i.e. critical nodes} of a subject having epilepsy {i.e. an event known to result in language errors and speech arrest} (see Arya ¶ [0026]).
Cranmer and Arya are both considered to be analogous to the claimed invention because they are in the same field of EEG & MRI informed brain mapping. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Cranmer to incorporate the above teachings of Arya to provide wherein a critical node comprises a node that, when lesioned or stimulated, causes one or more of speech arrest and language errors. Doing so would aid clearly identify regions of brain disorder which may be useful for safe removal (see Arya ¶ [0042]).
Claim 12 recite similar limitations and are rejected under the same rationale as Claim 1.
With regards to Claim 21, modified Cranmer teaches of wherein the MRI data includes diffusion MRI data (acquire first neural activity data via diffusion weighted and/or diffusion tension imaging with an MR scanner; see Cranmer ¶ [00025 & 0084]).
With regards to Claim 31, modified Cranmer teaches of wherein the one or more electrodes comprise stereo- electroencephalography electrodes (functional brain mapping using high gamma modulation from sEEG based on fMRI co-registration, and identifying a functional brain site in the subject that is responsible for performing the task based the collection of output data parameters; see Arya Abstract & ¶ [0027 & 0091]).
With regards to Claim 41, modified Cranmer teaches of wherein the one or more electrodes comprise electrocorticography electrodes (acquire second acquire neural activity data via EEG/ECoG data from electrodes placed directly on the brain; see Cranmer ¶ [0026 & 0084]).
With regards to Claim 51, modified Cranmer teaches of wherein the processor is configured to generate a brain function map, wherein the brain function map depicts the critical nodes and the non-critical nodes (FIG. 6 of Power clearly illustrates a functional brain map which illustrates hubs in the area network identified by high participation coefficients).
Claims 13-14 recite similar limitations and are rejected under the same rationale as Claim 5.
With regards to Claim 61, modified Cranmer teaches of wherein the processor performs tractography on the first data to identify the first edges in the brain network (wherein the DTI imaging includes fiber tracts, i.e. tractography; see Cranmer ¶ [0091]).
Claim 19 recites similar limitations and are rejected under the same rationale as claim 6.
With regards to Claim 81, modified Cranmer teaches of wherein the processor is also configured to determine first graph metrics based on the first data and second graph metrics based on the second data (a joint modeling framework can be providing by combining connectivity matrices derived based on given ROIs, e.g. a combined connectivity matrix can be created by combining the connectivity matrix ρ derived based on an MRI image with the connectivity matrix ρ derived based on an fMRI image; see Cranmer ¶ [0092]; it should be appreciated that combining separate connectivity matrices indicates that each connectivity matrix is based on their respective data, i.e. DTI/DWI connectivity matrix is based on DTI/DWI neural activity data & ECoG connectivity matrix is based on ECoG neural activity data).
With regards to Claim 98, modified Cranmer teaches of wherein the processor is configured to determine third graph metrics based on third data from the MRI system and fourth graph metrics based on fourth data from the electrodes, wherein the first data is diffusion MRI data (image data is DWI/DTI data; see Cranmer ¶ [0023]), the second data is stereo-electroencephalography electrode data (functional brain mapping using high gamma modulation from sEEG based on fMRI co-registration; see Arya Abstract & ¶ [0091]), the third data is functional MRI data (image data is fMRI data; see Cranmer ¶ [0023]), and the fourth data is electrocorticography electrode data (image data also includes ECoG data; see Cranmer ¶ [0026]).
With regards to Claim 101, modified Cranmer teaches of wherein the graph metrics are static, dynamic (network analysis performed on both during resting {i.e. static} and during tasks {i.e. dynamic}; see Cranmer ¶ [0086]), or (claimed in the alternative).
With regards to Claim 111, modified Cranmer teaches of wherein the network connectivity metrics include one or more of local efficiency (descriptive statistic on the network could be a related diameter (e.g., the longest shortest path in the network) {i.e. local efficiency}; see Cranmer ¶ [0039]), and clustering coefficient (descriptive statistic on the network could be a related clustering coefficient; see Cranmer ¶ [0039]).
Claim 18 recites similar limitations and are rejected under the same rationale as Claim 11.
With regards to Claim 1514, modified Cranmer teaches of further comprising identifying, by the processor, the critical nodes as language error (LE) nodes, speech arrest (SA) nodes, motor nodes, somatosensory nodes, nodes critical to memory, or nodes critical to higher cognitive functions (Dorsal Attention Network, a Frontoparietal Control Network, a Ventral Attention Network, a Visual Network, an Auditory Network, a Reward Network, a Subcortical Network, a Salience Network subnetworks are modeled; see Cranmer ¶ [0107]).
With regards to Claim 1614, modified Cranmer teaches of it further comprising calculating high-gamma correlations to identify the nodes as critical nodes (identifying a functional brain site in the subject that is responsible for performing the task based the collection of output data parameters based on high-gamma signals; see Arya ¶ [0027]).
With regards to Claim 1716, modified Cranmer teaches of further comprising recording, by the processor, local field potentials during a task performed by a patient, wherein the high-gamma correlations are calculated based at least in part on the local field potentials (identifying a functional brain site in the subject that is responsible for performing the task based {i.e. local field potentials} the collection of output data parameters based on high-gamma signals; see Arya ¶ [0027]).
Claims 7 & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cranmer in view of Arya, as applied to Claims 1 & 12 above, and in further view of Tandon et al. (US PGPUB 20210264623; hereinafter "Tandon").
With regards to Claim 71, while modified Cranmer teaches of using a common brain atlas for comparative analysis when identifying the ROIs, it appears that modified Cranmer may be silent to wherein the processor performs voxel parcellation and electrode co-registration on the first data.
However Tandon teaches of co-registering imaging modalities to localize sEEG electrodes (see Tandon ¶ [0028 & 0030]). In particular, Tandon teaches of wherein the processor performs voxel parcellation (generate a labelled parcellation dataset; see Tandon ¶ [0052]) and electrode co-registration on the first data (electrodes are co-registered with the MRI dataset; see Tandon ¶ [0103]).
Modified Cranmer and Tandon are both considered to be analogous to the claimed invention because they are in the same field of . Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Cranmer to incorporate the above teachings of Tandon to provide at least voxel parcellation and electrode co-registration on the first data. Doing so would aid in visualizing intracranial EEG with visualized surfaces (see Tandon ¶ [0045]).
Claim 20 recites similar limitations and are rejected under the same rationale as Claim 7.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
In particular, the Office submits Power to remedy the shortcomings of Cranmer & Arya. It should be appreciated that Applicant argues “Cranmer does not consider the use of "graph metrics" to classify brain nodes as critical to brain function if lesioned by a surgeon. This is a different use of the word critical than in Cranmer, where it is talking about whole subnetworks, not nodes.” The Office disagrees, Cranmer’s atlas upon which subnetworks are identified as critical/non-critical is a 20x20 node matrix (see Cranmer ¶ [0091 & 0102-0103]). Accordingly, Cranmer is specifically discussing nodes; however, does mention subnetworks of nodes that correlate to one another establishing a plurality of critical nodes. It should be appreciated that Cranmer is replete with disclosures establishing that their “network” is a network of nodes, as described in ¶ [0032-0033]. Also, Cranmer’s GERGM algorithm is explicitly a graph model, which one of ordinary skill in the art would readily understand relies on graph-metrics (see Cranmer ¶ [0042] & Claim 12). Moreover, Cranmer explicitly discloses determining a connection strength between each of the 20 regions of the 20 node atlas (see Cranmer ¶ [0103]), i.e. node strength which Applicant admits is a graph-metric in ¶ [0052] as published.
With regards to the newly amended limitation of the graph metrics include a participation- coefficient, since Applicant fails to establish a special definition, the Office relies on a plain and ordinary meaning of participation coefficient. In particular, a participation coefficient measures the strength of a node's connections within its community1. Cranmer alludes to this by disclosing that “brain networks can demonstrate a “small world” structure: both strong clustering of regions within communities, and integration across these communities. This feature of brain networks can be considered as balancing local and global efficiency, and allowing the brain to minimize wiring costs. The brain can include a prevalence of “hub” regions: regions that are more highly interconnected with a large number of other regions than expected by chance.” (see Cranmer ¶ [0087]). Moreover, Cranmer teaches of quantifying the presence of hubs using the two-star or transitive triads inferential testing (see Cranmer ¶ [0057]). While Cranmer discloses evaluating strong clustering indicative of integration into communities, Cranmer does not explicitly disclose of measuring a participation coefficient. However, Power teaches of identifying hubs in functional brain networks based on 1) finding network nodes that participate in multiple sub-networks of the brain, and 2) finding spatial locations where several systems are represented within a small volume (see Power Summary). In particular, Power teaches that “[F]irst, using a model of the brain at the level of functional areas we identify nodes that participate in many sub-networks of the brain (e.g., a node that has relationships with members of multiple brain systems, such as visual, default mode, or fronto-parietal control systems). These nodes are candidate brain hubs. We identify these candidate hubs using the established measure of participation coefficients” (see Power pg. 2, ¶ 4).
With regard the functional brain mapping limitation, Power clearly illustrates a functional brain map in FIG. 6 based on hub correlated to high participation coefficient.
Conclusion
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 9:00 am - 5:00 pm (CST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Keith Raymond can be reached on (571) 270-1790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHISH S. JASANI/Examiner, Art Unit 3798
/KEITH M RAYMOND/Supervisory Patent Examiner, Art Unit 3798
1 https://search.r-project.org/CRAN/refmans/NetworkToolbox/html/participation.html
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