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The rejection under 35 U.S.C. 112(a) has been withdrawn in light of the amendment to the claims filed on 10 November 2025.
The rejection under 35 U.S.C. 102(a)(1) has been withdrawn in light of the amendment to the claims filed on 10 November 2025.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63255677, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. In particular, said provisional application fails to explicitly or inherently support the newly recited limitation of “wherein a critical node comprises a node that, when lesioned or stimulated, cause one or more of speech arrest and language errors.” Accordingly, Claims 1-20 do not benefit from the earlier filing data of 63255677.
Claim Rejections - 35 USC § 112(a)
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.
Claims 1-20 are 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.
An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may "express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure." Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340 (Fed. Cir. 2008) (internal citation omitted). This can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015), see MPEP § 2161(I).
With regards to Claim 1, the claim recites “classify, based at least in part on the network connectivity metrics, nodes as critical nodes or non-critical nodes in the brain network, wherein a critical node comprises a node that, when lesioned or stimulated, causes one or more of speech arrest and language errors”; however, the instant specification fails to explain the steps/procedure for performing the classification, i.e. computer function, in sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. More specifically, while ¶ [0032] of the instant specification discloses that “[T]he inventors were able to use this limited set of network features alone to train simple machine learning classifiers to predict which nodes would be critical to speech and language,” ¶ [0054] goes on the further disclose that “[S]upport vector machine (SVM) and k-nearest neighbor (KNN) classifiers were used due to their power, wide recognition, and straightforward implementation as representative machine learning models.” However, the instant specification fails to provide any details regarding the SVM or KNN classifiers were utilized such that one of ordinary skill in the art would understand their intended functionality. For example, the instant specification fails to provide any details regarding an SVM such as hyperparameters, hyperplane, margin kernel, etc. OR any details of k-nearest neighbor such as k value, distance metric, neighbor weighting, etc. One of ordinary skill in the art would not deem the instant specification having sufficient detail so that they could understand how the inventor intended the function to be performed.
Since the instant specification fails to provide a finite sequence of steps for performing the “classify, based at least in part on the network connectivity metrics, nodes as critical nodes or non-critical nodes in the brain network, wherein a critical node comprises a node that, when lesioned or stimulated, causes one or more of speech arrest and language errors,” the aforementioned claim fails to meet the written description requirement under 35 U.S.C. 112(a).
Claim 12 recite similar limitations and are rejected under the same rationale as claim 1. Dependent claims are rejected by virtue of their dependency to abovementioned claims.
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 in further view of Arya et al. (US PGPUB 20230087736; hereinafter "Arya").
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 (exogenous covariates allow theoretical testing of whether properties of nodes or dyads influence the strength of connectivity between those regions… by combining endogenous and exogenous statistics, the neuro-GERGM framework can be used to model a wide range of network interdependencies {i.e. activity correlation between nodes} in weighted networks; see Cranmer ¶ [0059]) (claimed in the alternative);
determine, based on the first edges and the second edges, network connectivity metrics for the brain network (connectivity matrix ρ {i.e. network connectivity metrics} can be constructed based on the neural activity data; see Cranmer ¶ [0091]); and
classify, based at least in part on the network connectivity metrics, nodes as critical nodes or non-critical nodes in the brain network (the connectivity matrix ρ {i.e. network connectivity metrics} can be analyzed by the neural modeling engine {i.e. classifier} to generate a neural model representative of the neural activity data; see Cranmer ¶ [0092]; delineating the subnetworks {i.e. differentiating critical from non-critical nodes} & modelling subnetworks such as 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 {i.e. identifying critical nodes} ; see Cranmer ¶ [0086 & 0107]),
While 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]).
Arya also teaches of
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 with the decoder, wherein the brain function map depicts the critical nodes and the non-critical nodes (wherein the neural model is a 2D map as illustrated in Cranmer FIG. 3; see also ¶ [0044-0045]; wherein the pixels in the 2D map indicate the correlation neural activity data with the structural parameters, i.e. indicating critical & non-critical nodes).
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 network connectivity metrics based on the first data and second connectivity network 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 network connectivity metrics based on third data from the MRI system and fourth connectivity network 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 network connectivity 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]), (claimed in the alternative), 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 regarding the rejection under 35 U.S.C. 112(a) have been fully considered and are not persuasive. However, instead of reciting a decoder Applicant now recites the step of classifying the nodes as either critical or non-critical. However, similar to the decoder, the instant specification fails to provide any details regarding a classifier. The instant specification only mentions examples of classifiers such as SVM’s & KNN’s and they are trained. However, the instant specification fails to provide any details regarding the training set let alone the structure of the learning algorithms such as basic parameters as detailed above. Accordingly, the newly amended claims fail meet the written description standards under 35 U.S.C. 112(a) as laid out above.
Applicant's arguments regarding the rejection under 35 U.S.C. 102(a)(1) of independent Claims 1 and 12 have been fully considered and they are persuasive. More specifically, as detailed above, Cranmer still discloses that “exogenous covariates allow theoretical testing of whether properties of nodes or dyads influence the strength of connectivity between those regions,” i.e. an activity correlation between nodes. However, Cranmer may be silent to nodes associated with one or more of speech arrest and language errors. Therefore, the Office relies on previously cited Arya which teaches of mapping language/speech sites related to seizure-onset zone(s) of a subject having epilepsy (see Arya ¶ [0026]).
With regards to dependent claims, Applicant relies on the virtue of their dependency upon abovementioned independent claims 1 and 12 to argue novelty. Accordingly, said argument is not persuasive for at least the same reasons as Claims 1 and 12 as detailed above.
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 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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Keith M. 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