DETAILED ACTION
Claims 22-33 and 35-38 are currently pending.
Examiner notes there is no claim 34.
Claims 1-21 have been canceled.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/27/024 and 06/24/2026 have been considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 22-33 and 35-38 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 5-8, 20, 12-15, and 17-21 of U.S. Patent No.11, 734, 842 (hereinafter ‘842 and noted in italics), and claims 1-16 of U.S. Patent No. 12, 223,666 (hereinafter ‘666 and noted in bold). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims correspond as discussed below.
Regarding claim 22,
A method, comprising:
obtaining a first imaging scan and a second imaging scan of a single subject brain; (‘842, claim 1, obtaining a first imaging scan and a second imaging scan of a single subject brain) (‘666 claim 1, obtaining a first imaging scan and a second imaging scan of a single subject brain)
converting the first imaging scan to a first dataset, and the second imaging scan to a second dataset; (‘842 claim 1, converting the first imaging scan to a first dataset, and the second imaging scan to a second dataset) (‘666 claim 1, converting the first imaging scan to a first dataset, and the second imaging scan to a second dataset)
applying an algorithm to the first dataset and the second dataset, wherein the algorithm performs tissue classification to generate a first labeled dataset and a second labeled dataset; (‘842 claim 1, applying a sequence adaptive multimodal segmentation algorithm to the first dataset and the second dataset, wherein the sequence adaptive multimodal segmentation algorithm performs automatic intensity-based tissue classification to generate a first labelled data set and a second labelled dataset) (‘666 claim 1, applying an adaptive segmentation algorithm to the first dataset and the second dataset, wherein the adaptive segmentation algorithm performs automatic intensity-based tissue classification to generate a first labeled dataset and a second labeled dataset)
automatically co-registering the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset; (‘842 claim 1, automatically co-registering the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset) (‘666 claim 1, automatically co-registering the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset)
and
applying the transformation matrix to align the first dataset and the second dataset. (‘842 claim 1, applying the transformation matrix to align the first dataset and the second dataset) (‘666 claim 1, applying the transformation matrix to align the first dataset and the second dataset)
Regarding claim 23,
The method of claim 22, wherein:
the single subject brain comprises a cerebrospinal fluid region; (‘842 claim 5, selecting, based on the labeled dataset, voxels of the first dataset identified as belonging to a cerebrospinal fluid region) (’666 claim 2, wherein: the single subject brain comprises a cerebrospinal fluid region)
the first imaging scan is a contrast weighted scan and the first dataset is a contrast weighted dataset; (‘842 claim 5, the first imaging scan is a contrast weighted MRI scan and the first dataset is a contrast weighted dataset) (’666 claim 2, the first imaging scan is a contrast weighted scan and the first dataset is a contrast weighted dataset; and the method further comprises)
and
the method further comprises:
selecting voxels of the first dataset identified as belonging to the cerebrospinal fluid region; (‘842 claim 5, the first imaging scan is a contrast weighted MRI scan and the first dataset is a contrast weighted dataset) (’666 claim 2, selecting voxels of the first dataset identified as belonging to the cerebrospinal fluid region)
applying a filtering algorithm to identify voxels of the first dataset representing blood vessels and assign a vesselness weight value to each voxel; (‘842 claim 5, applying a multi-scale tubular algorithm to identify voxels of the first dataset representing blood vessels and assign a veselness weight value to each voxel) (’666 claim 2, applying a filtering algorithm to identify voxels of the first dataset representing blood vessels and assign a vesselness weight value to each voxel)
integrating the vesselness weight values into the first dataset; and after aligning the first dataset and the second dataset, converting the first dataset to a surface anatomical mesh model. (‘842 claim 5, integrating the vesselness weight values into the first dataset) (’666 claim 2, integrating the vesselness weight values into the first dataset; and after aligning the first dataset and the second dataset, converting the first dataset to a surface anatomical mesh model.)
Regarding claim 24,
The method of claim 22, wherein the adaptive segmentation algorithm assigns a numeric label value to each voxel of the first dataset or the second dataset. (‘842 claim 3, wherein the sequence adaptive multimodal segmentation algorithm assigns a numerical label value to each voxel of the first dataset or the second dataset) (‘666 claim 3, wherein the adaptive segmentation algorithm assigns a numeric label value to each voxel of the first dataset or the second dataset)
Regarding claim 25,
The method of claim 22 wherein the method further comprises:
defining predicted target point coordinates and entry point coordinates for a probe based on target point coordinates and entry point coordinates of previously implanted probes or by user defined target and entry points; (‘842 claim 6, defining predicted target point coordinates and entry point coordinates for a probe based on target point coordinates and entry point coordinates of previously implanted probes or by user defined target and entry points) (‘666 claim 4, defining predicted target point coordinates and entry point coordinates for a probe based on target point coordinates and entry point coordinates of previously implanted probes or by user defined target and entry points)
defining a trajectory for the probe based on a mean target coordinates and mean entry point coordinates;(‘842 claim 6, defining a trajectory for the probe based on a mean target coordinates and mean entry point coordinates) (‘666 claim 4, defining a trajectory for the probe based on a mean target coordinates and mean entry point coordinates)
adjusting the trajectory to intersect with a nearest voxel assigned a label of an anatomical region of interest; (‘842 claim 6, adjusting the trajectory to intersect with a nearest voxel assigned a label of an anatomical region of interest) (‘666 claim 4, adjusting the trajectory to intersect with a nearest voxel assigned a label of an anatomical region of interest)
checking proximity of the trajectory to critical structures based on user defined constraints and/or user defined modification of the trajectory to satisfy the user defined constraints; (‘842 claim 6, checking proximity of the trajectory to critical structures based on user defined constraints and/or user defined modification of the trajectory to satisfy the user defined constraints) (‘666 claim 4, checking proximity of the trajectory to critical structures based on user defined constraints and/or user defined modification of the trajectory to satisfy the user defined constraints)
and
superimposing the trajectory on the second data set to form a planning dataset. (‘842 claim 6, superimposing the trajectory on the second data set to form a planning dataset.) (‘666 claim 4, superimposing the trajectory on the second data set to form a planning dataset)
Regarding claim 26,
The method of claim 22, wherein:
the first imaging scan is an anatomical scan, the second imaging scan is a post- implantation imaging scan, the first dataset is an anatomical dataset, and the second dataset is a post-implantation imaging dataset; and the method further comprises: (‘842 claim 7, the first imaging scan is an anatomical MRI scan, the second imaging scan is a post-implantation CT imaging scan, the first dataset is an anatomical MRI dataset, and the second dataset is a post-implantation CT imaging dataset) (‘666 claim 5, the first imaging scan is an anatomical scan, the second imaging scan is a post-implantation imaging scan, the first dataset is an anatomical dataset, and the second dataset is a post-implantation imaging dataset; and the method further comprises)
obtaining a third imaging scan used to guide electrode implantation during surgery; (‘842 claim 7, obtaining a third imaging scan used to guide electrode implantation during surgery) (‘666 claim 5, obtaining a third imaging scan used to guide electrode implantation during surgery)
converting the third imaging scan to a third dataset;(‘842 claim 7, converting the third imaging scan to a third dataset) (‘666 claim 5, converting the third imaging scan to a third dataset)
aligning a third dataset with the first dataset; (‘842 claim 7, aligning a third dataset with the first dataset) (‘666 claim 5, aligning a third dataset with the first dataset)
obtaining a trajectory implant data file created during the electrode implantation; (‘842 claim 7, obtaining a trajectory implant data file created during the electrode implantation; ) (‘666 claim 5, obtaining a trajectory implant data file created during the electrode implantation)
generating a planned trajectory dataset, based on trajectory implant data file, that includes dummy objects disposed at locations of electrode geometry; (‘842 claim 7, obtaining a trajectory implant data file created during the electrode implantation; generating a planned trajectory dataset, based on trajectory implant data file, that includes dummy objects disposed at locations of electrode geometry) (‘666 claim 5, generating a planned trajectory dataset, based on trajectory implant data file, that includes dummy objects disposed at locations of electrode geometry)
aligning the planned trajectory dataset to the imaging dataset; (‘842 claim 7, aligning the planned trajectory dataset to the CT imaging dataset) (‘666 claim 5, aligning the planned trajectory dataset to the imaging dataset)
and
automatically identifying and labelling electrodes in an electrode dataset based on the dummy objects of the trajectory implant data file. (‘842 claim 7, automatically identifying and labelling electrodes in the CT electrode dataset based on the dummy objects of the trajectory implant data file.) (‘666 claim 5, automatically identifying and labelling electrodes in an electrode dataset based on the dummy objects of the trajectory implant data file)
Regarding claim 27,
A non-transitory computer-readable medium encoded with instructions that are executable by one or more processors to: (‘842 claim 8, A non-transitory computer-readable medium encoded with instructions that are executable by one or more processors to: ) (‘666 claim 6, A non-transitory computer-readable medium encoded with instructions that are executable by one or more processors to)
obtain a first imaging scan and a second imaging scan of a single subject brain; (‘842 claim 8, obtain a first imaging scan and a second imaging scan of a single subject brain) (‘666 claim 6, obtain a first imaging scan and a second imaging scan of a single subject brain)
convert the first imaging scan to a first dataset, and the second imaging scan to a second dataset; (‘842 claim 8, convert the first imaging scan to a first dataset, and the second imaging scan to a second dataset) (‘666 claim 6, convert the first imaging scan to a first dataset, and the second imaging scan to a second dataset)
apply an algorithm to the first dataset and the second dataset, wherein the algorithm performs tissue classification to generate a first labelled dataset and a second labeled dataset; (‘842 claim 8, apply a sequence-adaptive multimodal segmentation algorithm to the first dataset and the second dataset, wherein the sequence-adaptive multimodal segmentation algorithm performs automatic intensity-based tissue classification to generate a first labelled dataset and a second labeled dataset) (‘666 claim 6, apply an adaptive segmentation algorithm to the first dataset and the second dataset, wherein the adaptive segmentation algorithm performs automatic intensity-based tissue classification to generate a first labelled dataset and a second labeled dataset)
automatically co-register the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset; (‘842 claim 8, automatically co-register the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset) (‘666 claim 6, automatically co-register the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset)
and
apply the transformation matrix to align the first dataset and the second dataset. (‘842 claim 8, apply the transformation matrix to align the first dataset and the second dataset.) (‘666 claim 6, apply the transformation matrix to align the first dataset and the second dataset)
Regarding claim 28,
The non-transitory computer-readable medium of claim 27, wherein:
the single subject brain comprises a cerebrospinal fluid region; (‘842 claim 12, select, based on the labeled dataset, voxels of the first dataset identified as belonging to a cerebrospinal fluid region) (‘666 claim 7, the single subject brain comprises a cerebrospinal fluid region)
the first imaging scan is a contrast weighted scan and the first dataset is a contrast weighted dataset; and the instructions are executable by the one or more processors to: (‘842 claim 12, the first imaging scan is a contrast weighted MRI scan and the first dataset is a contrast weighted dataset) (‘666 claim 7, the first imaging scan is a contrast weighted scan and the first dataset is a contrast weighted dataset)
select, based on the labeled dataset, voxels of the first dataset identified as belonging to the cerebrospinal fluid region; (‘842 claim 12, select, based on the labeled dataset, voxels of the first dataset identified as belonging to a cerebrospinal fluid region) (‘666 claim 7, select, based on the labeled dataset, voxels of the first dataset identified as belonging to the cerebrospinal fluid region)
apply a filtering algorithm to identify voxels of the first dataset representing blood vessels and assign a vesselness weight value to each voxel; (‘842 claim 12, apply a multi-scale tubular filtering algorithm to identify voxels of the first dataset representing blood vessels and assign a vesselness weight value to each voxel) (‘666 claim 7, apply a filtering algorithm to identify voxels of the first dataset representing blood vessels and assign a vesselness weight value to each voxel)
integrate the vesselness weight values into the first dataset; (‘842 claim 12, integrate the vesselness weight values into the first dataset ) (‘666 claim 7, integrate the vesselness weight values into the first dataset)
and after aligning the first dataset and the second dataset, convert the first dataset to a surface anatomical mesh model. (‘842 claim 12, after aligning the first dataset and the second dataset, convert the first dataset to a surface anatomical mesh model.) (‘666 claim 7, after aligning the first dataset and the second dataset, convert the first dataset to a surface anatomical mesh model)
Regarding claim 29,
The non-transitory computer-readable medium of claim 27, wherein the adaptive segmentation algorithm assigns a numeric label value to each voxel of the first dataset or the second dataset. (‘842 claim 10, wherein the sequence-adaptive multimodal segmentation algorithm assigns a numeric label value to each voxel of the first dataset or the second dataset.)
(‘666 claim 8, wherein the adaptive segmentation algorithm assigns a numeric label value to each voxel of the first dataset or the second dataset.)
Regarding claim 30,
The non-transitory computer-readable medium of claim 27, wherein:
the first imaging scan is a contrast weighted scan and the second imaging scan is an anatomical scan;(‘842 claim 13, the first imaging scan is a contrast weighted MRI scan and the second imaging scan is an anatomical MRI) (‘666 claim 9, the first imaging scan is a contrast weighted scan and the second imaging scan is an anatomical scan) and
the instructions are executable by the one or more processors to:
define predicted target point coordinates and entry point coordinates for a probe based on target point coordinates and entry point coordinates of previously implanted probes or by user defined target and entry points; (‘842 claim 13, define predicted target point coordinates and entry point coordinates for a probe based on target point coordinates and entry point coordinates of previously implanted probes; or by user defined target and entry points) (‘666 claim 9, define predicted target point coordinates and entry point coordinates for a probe based on target point coordinates and entry point coordinates of previously implanted probes or by user defined target and entry points)
define a trajectory for the probe based on a mean target coordinates and mean entry point coordinates; (‘842 claim 13, define a trajectory for the probe based on a mean target coordinates and mean entry point coordinates) (‘666 claim 9, define a trajectory for the probe based on a mean target coordinates and mean entry point coordinates)
adjust the trajectory to intersect with a nearest voxel assigned a label of an anatomical region of interest; (‘842 claim 13, adjust the trajectory to intersect with a nearest voxel assigned a label of an anatomical region of interest) (‘666 claim 9, adjust the trajectory to intersect with a nearest voxel assigned a label of an anatomical region of interest)
check proximity of the trajectory to critical structures based on user defined constraints and/or user defined modification of the trajectory to satisfy the user defined constraints; (‘842 claim 13, check proximity of the trajectory to critical structures based on user defined constraints and/or user defined modification of the trajectory to satisfy the user defined constraints) (‘666 claim 9, check proximity of the trajectory to critical structures based on user defined constraints and/or user defined modification of the trajectory to satisfy the user defined constraints)
and
superimpose the trajectory on the second data set to form a planning dataset. (‘842 claim 13, superimpose the trajectory on the second data set to form a planning dataset.) (‘666 claim 9, superimpose the trajectory on the second data set to form a planning dataset)
Regarding claim 31,
The non-transitory computer-readable medium of claim 27, wherein:
the first imaging scan is an anatomical scan, the second imaging scan is a post- implantation imaging scan, the first dataset is an anatomical dataset, and the second dataset is a post-implantation dataset; (‘842 claim 14, the first imaging scan is an anatomical MRI scan, the second imaging scan is a post-implantation CT imaging scan, the first dataset is an anatomical MRI dataset, and the second dataset is a post-implantation CT dataset) (‘666 claim 10, the first imaging scan is an anatomical scan, the second imaging scan is a post-implantation imaging scan, the first dataset is an anatomical dataset, and the second dataset is a post-implantation dataset)
and
the instructions are executable by the one or more processors to:
obtain a third imaging scan used to guide electrode implantation during surgery; (‘842 claim 14, obtain a third imaging scan used to guide electrode implantation during surgery) (‘666 claim 10, obtain a third imaging scan used to guide electrode implantation during surgery)
convert the third imaging scan to a third dataset; align a third dataset with the first dataset; (‘842 claim 14, convert the third imaging scan to a third dataset; align a third dataset with the first dataset) (‘666 claim 10, convert the third imaging scan to a third dataset; align a third dataset with the first dataset)
obtain a trajectory implant data file created during the electrode implantation; (‘842 claim 14, obtain a trajectory implant data file created during the electrode implantation) (‘666 claim 10, obtain a trajectory implant data file created during the electrode implantation)
generate a planned trajectory dataset, based on trajectory implant data file, that includes dummy objects disposed at locations of electrode geometry; (‘842 claim 14, generate a planned trajectory dataset, based on trajectory implant data file, that includes dummy objects disposed at locations of electrode geometry;) (‘666 claim 10, generate a planned trajectory dataset, based on trajectory implant data file, that includes dummy objects disposed at locations of electrode geometry)
align the planned trajectory dataset to the post-implantation dataset; (‘842 claim 14, align the planned trajectory dataset to the post-implantation CT dataset) (‘666 claim 10, align the planned trajectory dataset to the post-implantation dataset)
and
automatically identify and label electrodes in an electrode dataset based on the dummy objects of the trajectory implant data file. (‘842 claim 14, automatically identify and label electrodes in the CT electrode dataset based on the dummy objects of the trajectory implant data file.) (‘666 claim 10, automatically identify and label electrodes in an electrode dataset based on the dummy objects of the trajectory implant data file.)
Regarding claim 32,
A system, comprising:
one or more processors; (‘842 claim 15, one or more processors) (‘666 claim 11, one or more processors)
and
a memory coupled to the one or more processors, wherein the memory stores instructions that configure the one or more processors to: (‘842 claim 15, a memory coupled to the one or more processors, wherein the memory stores instructions that configure the one or more processors to) (‘666 claim 11, a memory coupled to the one or more processors, wherein the memory stores instructions that configure the one or more processors to)
obtain a first imaging scan and a second imaging scan of a subject brain; (‘842 claim 15, obtain a first imaging scan and a second imaging scan of a subject brain) (‘666 claim 11, obtain a first imaging scan and a second imaging scan of a subject brain)
convert the first imaging scan to a first dataset, and the second imaging scan to a second dataset; (‘842 claim 15, convert the first imaging scan to a first dataset, and the second imaging scan to a second dataset) (‘666 claim 11, convert the first imaging scan to a first dataset, and the second imaging scan to a second dataset)
apply an algorithm to the first dataset and the second dataset, wherein the algorithm performs tissue classification to generate a first labeled dataset and a second labeled dataset;(‘842 claim 15, apply a sequence-adaptive multimodal segmentation algorithm to the first dataset and the second dataset, wherein the sequence-adaptive multimodal segmentation algorithm performs automatic intensity-based tissue classification to generate a first labelled dataset and a second labeled dataset) (‘666 claim 11, apply an adaptive segmentation algorithm to the first dataset and the second dataset, wherein the adaptive segmentation algorithm performs automatic intensity-based tissue classification to generate a first labeled dataset and a second labeled dataset)
automatically co-register the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset; (‘842 claim 15, automatically co-register the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset) (‘666 claim 11, automatically co-register the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset)
and
apply the transformation matrix to align the first dataset and the second dataset. (‘842 claim 15, apply the transformation matrix to align the first dataset and the second dataset.) (‘666 claim 11, apply the transformation matrix to align the first dataset and the second dataset)
Regarding claim 33,
The system of claim 32, wherein:
the single subject brain comprises a cerebrospinal fluid region; (‘842 claim 19, select, based on the labeled dataset, voxels of the first dataset identified as belonging to a cerebrospinal fluid region) (‘666 claim 12, the single subject brain comprises a cerebrospinal fluid region)
the first imaging scan is a contrast weighted scan and the first dataset is a contrast weighted dataset; (‘842 claim 19, the first imaging scan is a contrast weighted MRI scan and the first dataset is a contrast weighted dataset) (‘666 claim 12, the first imaging scan is a contrast weighted scan and the first dataset is a contrast weighted dataset)
and
the instructions configure the one or more processors to:
select, based on the labeled dataset, voxels of the first dataset identified as belonging to the cerebrospinal fluid region; (‘842 claim 19, select, based on the labeled dataset, voxels of the first dataset identified as belonging to a cerebrospinal fluid region) (‘666 claim 12, select, based on the labeled dataset, voxels of the first dataset identified as belonging to the cerebrospinal fluid region)
apply a filtering algorithm to identify voxels of the first dataset representing blood vessels and assign a vesselness weight value to each voxel; (‘842 claim 19, apply a multi-scale tubular filtering algorithm to identify voxels of the first dataset representing blood vessels and assign a vesselness weight value to each voxel) (‘666 claim 12, apply a filtering algorithm to identify voxels of the first dataset representing blood vessels and assign a vesselness weight value to each voxel)
integrate the vesselness weight values into the first dataset; (‘842 claim 19, integrate the vesselness weight values into the first dataset) (‘666 claim 12, integrate the vesselness weight values into the first dataset)
and
after aligning the first dataset and the second dataset, convert the first dataset to a surface anatomical mesh model. (‘842 claim 19, after aligning the first dataset and the second dataset, convert the first dataset to a surface anatomical mesh model) (‘666 claim 12, after aligning the first dataset and the second dataset, convert the first dataset to a surface anatomical mesh model)
Regarding claim 35,
The system of claim 32, wherein the adaptive segmentation algorithm assigns a numeric label value to each voxel of the first dataset or the second dataset. (‘842 claim 17, wherein the sequence-adaptive multimodal segmentation algorithm assigns a numeric label value to each voxel of the first dataset or the second dataset.) (‘666 claim 13, wherein the adaptive segmentation algorithm assigns a numeric label value to each voxel of the first dataset or the second dataset)
Regarding claim 36,
The system of claim 32, wherein the instructions configure the one or more processors to:
extract voxels from the first dataset having a label corresponding to a subcortical region of interest; (‘842 claim 18, extract voxels from the first dataset having a label corresponding to a subcortical region of interest) (‘666 claim 14, extract voxels from the first dataset having a label corresponding to a subcortical region of interest)
form a third dataset containing the voxels extracted from the first dataset; (‘842 claim 18, form a third dataset containing the voxels extracted from the first dataset;) (‘666 claim 14, form a third dataset containing the voxels extracted from the first dataset)
convert the third dataset into a first subcortical surface mesh model; (‘842 convert the third dataset into a first subcortical surface mesh model) (‘666 claim 14, convert the third dataset into a first subcortical surface mesh model)
compute curvature and sulcal features of the first subcortical surface mesh model; (‘842 claim 18, compute curvature and sulcal features of the first subcortical surface mesh model) (‘666 claim 14, compute curvature and sulcal features of the first subcortical surface mesh model)
align the first subcortical surface mesh model to a subcortical atlas of the region of interest using the curvature and sulcal features; (‘842 claim 18, align the first subcortical surface mesh model to a subcortical atlas of the region of interest using the curvature and sulcal features) (‘666 claim 14, align the first subcortical surface mesh model to a subcortical atlas of the region of interest using the curvature and sulcal features)
and
overlay the first subcortical surface mesh model aligned to an atlas of the subcortical region of interest on a second subcortical surface mesh model, the second subcortical surface mesh model having a standardized number of nodes that enables a one-to-one correspondence between node identity and atlas location; (‘842 claim 18, overlay the first subcortical surface mesh model aligned to an atlas of the subcortical region of interest on a second subcortical surface mesh model, the second subcortical surface mesh model having a standardized number of nodes that enables a one-to-one correspondence between node identity and atlas location) (‘666 claim 14, overlay the first subcortical surface mesh model aligned to an atlas of the subcortical region of interest on a second subcortical surface mesh model, the second subcortical surface mesh model having a standardized number of nodes that enables a one-to-one correspondence between node identity and atlas location)
and
assign coordinates of nodes of the first subcortical surface mesh model to the second subcortical structure surface mesh model such that the second subcortical surface mesh model assumes a topology of the first subcortical structure surface mesh model. (‘842 claim 18, assign coordinates of nodes of the first subcortical surface mesh model to the second subcortical structure surface mesh model such that the second subcortical surface mesh model assumes a topology of the first subcortical structure surface mesh model) (‘666 claim 14, assign coordinates of nodes of the first subcortical surface mesh model to the second subcortical structure surface mesh model such that the second subcortical surface mesh model assumes a topology of the first subcortical structure surface mesh model.)
Regarding claim 37,
The system of claim 32, wherein:
the first imaging scan is a contrast weighted scan and the second imaging scan is an anatomical scan; (‘842 claim 20, the first imaging scan is a contrast weighted MRI scan and the second imaging scan is an anatomical MRI scan) (‘666 claim 15, the first imaging scan is a contrast weighted scan and the second imaging scan is an anatomical scan)
and the instructions configure the one or more processors to:
define predicted target point coordinates and entry point coordinates for a probe based on target point coordinates and entry point coordinates of previously implanted probes or by user defined target and entry points; (‘842 claim 20, define predicted target point coordinates and entry point coordinates for a probe based on target point coordinates and entry point coordinates of previously implanted probes; or by user defined target and entry points) (‘666 claim 15, define predicted target point coordinates and entry point coordinates for a probe based on target point coordinates and entry point coordinates of previously implanted probes or by user defined target and entry points)
define a trajectory for the probe based on a mean target coordinates and mean entry point coordinates; (‘842 claim 20, define a trajectory for the probe based on a mean target coordinates and mean entry point coordinates;) (‘666 claim 15, define a trajectory for the probe based on a mean target coordinates and mean entry point coordinates)
adjust the trajectory to intersect with a nearest voxel assigned a label of an anatomical region of interest; (‘842 claim 20, adjust the trajectory to intersect with a nearest voxel assigned a label of an anatomical region of interest) (‘666 claim 15, adjust the trajectory to intersect with a nearest voxel assigned a label of an anatomical region of interest)
check proximity of the trajectory to critical structures based on user defined constraints and/or user defined modification of the trajectory to satisfy the user defined constraints; (‘842 claim 20, check proximity of the trajectory to critical structures based on user defined constraints and/or user defined modification of the trajectory to satisfy the user defined constraints) (‘666 claim 15, check proximity of the trajectory to critical structures based on user defined constraints and/or user defined modification of the trajectory to satisfy the user defined constraints)
and
superimpose the trajectory on the second data set to form a planning dataset. (‘842 claim 20, superimpose the trajectory on the second data set to form a planning dataset.) (‘666 claim 15, superimpose the trajectory on the second data set to form a planning dataset.)
Regarding claim 38,
The system of claim 32, wherein:
the first imaging scan is an anatomical scan, the second imaging scan is a post- implantation imaging scan, the first dataset is an anatomical dataset, and the second dataset is a post-implantation imaging dataset; (‘842 claim 21, the first imaging scan is an anatomical MRI scan, the second imaging scan is a post-implantation CT imaging scan, the first dataset is an anatomical MRI dataset, and the second dataset is a post-implantation CT imaging dataset )
(‘666 claim 16, the first imaging scan is an anatomical scan, the second imaging scan is a post-implantation imaging scan, the first dataset is an anatomical dataset, and the second dataset is a post-implantation imaging dataset)
and
the instructions configure the one or more processors to:
obtain a third imaging scan used to guide electrode implantation during surgery; (‘842 claim 21, obtain a third imaging scan used to guide electrode implantation during surgery) (‘666 claim 16, obtain a third imaging scan used to guide electrode implantation during surgery)
convert the third imaging scan to a third dataset; (‘842 claim 21, convert the third imaging scan to a third dataset) (‘666 claim 16, convert the third imaging scan to a third dataset)
align a third dataset with the first dataset; (‘842 claim 21, align a third dataset with the first dataset)(‘666 claim 16, align a third dataset with the first dataset)
obtain a trajectory implant data file created during the electrode implantation; (‘842 claim 21, obtain a trajectory implant data file created during the electrode implantation) (‘666 claim 16, obtain a trajectory implant data file created during the electrode implantation)
generate a planned trajectory dataset, (‘842 claim 21, generate a planned trajectory dataset) (‘666 claim 16, generate a planned trajectory dataset, wherein)
wherein:
the planned trajectory dataset is based on trajectory implant data file; (‘842 claim 21, generate a planned trajectory dataset, based on trajectory implant data file) (‘666 claim 16, the planned trajectory dataset is based on trajectory implant data file)
and the planned trajectory dataset includes dummy objects disposed at locations of electrode geometry; (‘842 claim 21, generate a planned trajectory dataset, based on trajectory implant data file, that includes dummy objects disposed at locations of electrode geometry)
(‘666 claim 16, the planned trajectory dataset includes dummy objects disposed at locations of electrode geometry)
align the planned trajectory dataset to the post-implantation imaging dataset; (‘842 claim 21, align the planned trajectory dataset to the post-implantation CT imaging dataset) (‘666 claim 16, align the planned trajectory dataset to the post-implantation imaging dataset)
and
identify electrodes in an electrode dataset based on the dummy objects of the trajectory implant data file. (‘842 claim 21, identify electrodes in the CT electrode dataset based on the dummy objects of the trajectory implant data file.) (‘666 claim 16, identify electrodes in an electrode dataset based on the dummy objects of the trajectory implant data file)
Allowable Subject Matter
Claims 22-33 and 35-38 are not rejected under the prior art and would be in condition for allowance if the above double patenting rejections were overcome.
Regarding claim 22, neither the closest known prior art, nor any reasonable combination thereof, teaches:
obtaining a first imaging scan and a second imaging scan of a single subject brain;
converting the first imaging scan to a first dataset, and the second imaging scan to a second dataset;
applying an algorithm to the first dataset and the second dataset, wherein the algorithm performs tissue classification to generate a first labeled dataset and a second labeled dataset;
automatically co-registering the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset;
applying the transformation matrix to align the first dataset and the second dataset.
Wang (“A co-registration approach for electrocorticogram electrode localization using post-implantation MRI and CT of the head”) teaches co-registering multiple brain images from the same patient. See page 526. However, the registration used in Wang involves only one set of segmented labeled tissue, the second data set is aligned over the segmented MRI brain image. See page 526.
Claims 23-26 depend from claim 22 and would therefore also be allowable.
Claim 27 is similar in scope to claim 22 and would be allowable for the reasons discussed above. Claims 28-31 depend from claim 27 and would therefore also be allowable.
Claim 32 is similar in scope to claim 22 and would therefore also be allowable for the reasons discussed above. Claims 33 and 35-38 depend from claim 32 and would therefore also be allowable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Refer to PTO-892, Notice of References Cited for a listing of analogous art.
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/Molly Wilburn/Primary Examiner, Art Unit 2666
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