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 .
Claim Objections
Claims 12-14 objected to because of the following informalities: the recitation “the process or is” is considered a typographical error in place of “the processor is”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a system comprising a computing device without significantly more. The claim(s) recite(s) the acquisition of fMRI data calculating connectivity between regions of the brain and identifying a target location in the brain to be targeted which can be performed by obtaining print out data and by human observation of the regions performing the mental step of identifying a target location through said observation in the human mind. This judicial exception is not integrated into a practical application because the claims do not require more than the judicial exception since the computer device includes a processor that is a generic computing structure which includes conventional computer functions. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the combination of additional elements fails to integrate the judicial exception into a practical application. The claim is directed to an abstract idea with additional generic computer elements where said elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Each of Claims 1-17 has been analyzed to determine whether it is directed to any judicial exceptions.
Each of Claims 1-17 recites at least one step or instruction for “determine, using the MRI data, functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain; identify a target location in the brain to be targeted by neuromodulation based on the determined functional connectivity to achieve a clinical outcome”, which is grouped as a mental process under the 2019 PEG or a certain method of organizing human activity under the 2019 PEG. The claimed limitations involve managing interactions between people, namely, humans following rules, which is one of certain methods of organizing human activity since the claimed steps only require observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG. Accordingly, each of Claims ______ recites an abstract idea.
Specifically, claim 1 recites:
1. A system comprising: a computing device including a processor programmed to: receive magnetic resonance imaging (MRI) data of a brain of the subject; determine, using the MRI data, functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain; identify a target location in the brain to be targeted by neuromodulation based on the determined functional connectivity to achieve a clinical outcome; and a display configured to display a report indicating the target location.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Further, dependent Claims 2-17 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
The above-identified abstract idea in each of independent Claim 1 (and their respective dependent Claims 2-17) is not integrated into a practical application under 2019 PEG because the additional elements (identified above in independent Claim 1), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of: computing device including a processor and a display are generically recited computer elements in independent Claim 1 (and their respective dependent claims 2-17) which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent Claims ______ (and their respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer (e.g., computing device including a processor as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claim 1 (and their respective dependent claims 2-17) is not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claim 1 (and their respective dependent claims 2-17) are each directed to an abstract idea under 2019 PEG.
None of Claims 1-17 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: computing device including a processor and a display.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. Per Applicant’s specification, According to Specification of the current application, for example in paragraph 0089 describes the computer, processor, and display as components that are well understood, routine and conventional.
Accordingly, in light of Applicant’s specification, the claimed term processor is reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for a processor or computer device. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 1-17 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the system of claims 1-17 are directed to applying an abstract idea (e.g., mental process or certain method of organizing human activity) on a general-purpose computer without (i) improving the performance of the computer itself (as in McRO, Bascom and Enfish), or (ii) providing a technical solution to a problem in a technical field (as in DDR). In other words, none of Claims 1-17 provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent Claim 1 (and their dependent claims 2-17) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. As such, the above-identified additional elements, when viewed as whole, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1-17 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1-17 amounts to significantly more than the abstract idea itself.
Accordingly, claims 1-17 are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas implemented on a generic computer in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al. and 2019 PEG.
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 1-17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-10, 12-13, and 15-20, of U.S. Patent No. 11,733,332 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the current application are directed to the same subject matter as the claims in the US Patent No. 11,733,332 B2.
Claims 1, and 3-14, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14, of U.S. Patent No. 12,158,512 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the current application are directed to the same subject matter as the claims in the US Patent No. 12,158,512 B2.
See concordance of claims below:
Current Application
US 11,733,332 B2
1. A system comprising: a computing device including a processor programmed to: receive magnetic resonance imaging (MRI) data of a brain of the subject; determine, using the MRI data, functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain; identify a target location in the brain to be targeted by neuromodulation based on the determined functional connectivity to achieve a clinical outcome; and a display configured to display a report indicating the target location.
15. A system comprising: a computing device including a processor programmed to: receive functional magnetic resonance imaging (fMRI) data of a brain of the subject; determine functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain, based on the fMRI data; identify a target location in the brain to be targeted by neuromodulation based on the determined functional connectivity; a display configured to display the target location; wherein the processor is further programmed to determine the functional connectivity of the brain by determining a temporal correlation of a neurophysiological index.
2. The system of claim 1, wherein the processor is further programmed to determine the functional connectivity of the brain by determining a temporal correlation of a neurophysiological index.
15. A system comprising: a computing device including a processor programmed to: receive functional magnetic resonance imaging (fMRI) data of a brain of the subject; determine functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain, based on the fMRI data; identify a target location in the brain to be targeted by neuromodulation based on the determined functional connectivity; a display configured to display the target location; wherein the processor is further programmed to determine the functional connectivity of the brain by determining a temporal correlation of a neurophysiological index.
3. The system of claim 1, wherein the processor is further programmed to determine functional connectivity of the brain between the voxel in the subcortical region and each cortical functional network in a plurality of cortical functional networks.
19. The system of claim 15, wherein the processor is configured determine functional connectivity of the brain between the voxel in the subcortical region of the brain and a vertex in a cortical functional network by assessing BOLD activity time-course data from each vertex in the cortical functional network and determining functional connectivity further comprises: averaging the BOLD activity time-course data of the cortical functional network across all vertices in the cortical functional network; extracting BOLD activity time-course data from the voxel in the subcortical region; and determining functional connectivity as a correlation between the BOLD activity time-course data of the voxel in the subcortical region and the BOLD activity time-course data of the cortical functional network.
4. The system of claim 1, wherein the processor is further programmed to identify a winning functional network among the plurality of cortical functional networks as a functional network having the highest functional connectivity with the voxel to identify the target location.
1. A method of performing functional mapping of a subject for interventional planning, comprising: using a computer system, acquiring functional magnetic resonance imaging (fMRI) data of a brain of the subject; using a computer system, determining functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain, based on the fMRI data; identifying a target location in the brain to be targeted by neuromodulation based on the calculated functional connectivity; using a computer system, generating a report indicating the target location; and wherein determining the functional connectivity of the brain includes determining a temporal correlation of a neurophysiological index.
6. The method of claim 3, wherein the target location is in an integrative zone, wherein determining functional connectivity comprises determining functional connectivity of the brain between the voxel in the subcortical region and each cortical functional network in a plurality of cortical functional networks; and identifying a target location further comprises: identifying a winning functional network among the plurality of cortical functional networks as a functional network having the highest functional connectivity with the voxel; including the voxel in the integrative zone when functional connectivity between the voxel and one or more functional networks is above a threshold, wherein the one or more functional networks are among a remaining of the plurality of cortical functional networks minus the winning functional network; and identifying the target location to include the integrative zone.
5. The system of claim 4, wherein the target location is in an integrative zone, and wherein the processor is further programmed to integrate the voxel in the integrative zone when functional connectivity between the voxel and one or more functional networks is above a threshold.
6. The method of claim 3, wherein the target location is in an integrative zone, wherein determining functional connectivity comprises determining functional connectivity of the brain between the voxel in the subcortical region and each cortical functional network in a plurality of cortical functional networks; and identifying a target location further comprises: identifying a winning functional network among the plurality of cortical functional networks as a functional network having the highest functional connectivity with the voxel; including the voxel in the integrative zone when functional connectivity between the voxel and one or more functional networks is above a threshold, wherein the one or more functional networks are among a remaining of the plurality of cortical functional networks minus the winning functional network; and identifying the target location to include the integrative zone.
6. The system of claim 5, wherein the one or more functional networks are among a remaining of the plurality of cortical functional networks minus the winning functional network.
6. The method of claim 3, wherein the target location is in an integrative zone, wherein determining functional connectivity comprises determining functional connectivity of the brain between the voxel in the subcortical region and each cortical functional network in a plurality of cortical functional networks; and identifying a target location further comprises: identifying a winning functional network among the plurality of cortical functional networks as a functional network having the highest functional connectivity with the voxel; including the voxel in the integrative zone when functional connectivity between the voxel and one or more functional networks is above a threshold, wherein the one or more functional networks are among a remaining of the plurality of cortical functional networks minus the winning functional network; and identifying the target location to include the integrative zone.
7. The system of claim 5, wherein the processor is further programmed to identify a given subcortical region as an integrative zone if a correlation between the given subcortical region and one or more functional networks other than the winning functional network is greater than a predetermined threshold
6. The method of claim 3, wherein the target location is in an integrative zone, wherein determining functional connectivity comprises determining functional connectivity of the brain between the voxel in the subcortical region and each cortical functional network in a plurality of cortical functional networks; and identifying a target location further comprises: identifying a winning functional network among the plurality of cortical functional networks as a functional network having the highest functional connectivity with the voxel; including the voxel in the integrative zone when functional connectivity between the voxel and one or more functional networks is above a threshold, wherein the one or more functional networks are among a remaining of the plurality of cortical functional networks minus the winning functional network; and identifying the target location to include the integrative zone.
8. The system of claim 7, wherein the threshold is 66 percent.
8. The method of claim 6, further comprising identifying a given subcortical region as an integrative zone if a correlation between the given subcortical region and one or more functional networks other than the winning functional network is greater than at least 66%.
9. The system of claim 1, wherein the magnetic resonance data includes at least one of functional magnetic resonance imaging (fMRI) data or resting state (rs) fMRI data of the subject.
18. The system of claim 15, wherein processor forms part of a magnetic resonance imaging (MRI) system and the fMRI data is resting state (rs) fMRI data.
10. The method of claim 9, wherein the processor is further programmed to acquire fMRI data as task fMRI data of the subject and determine functional connectivity based on the rs-fMRI data.
18. The system of claim 15, wherein processor forms part of a magnetic resonance imaging (MRI) system and the fMRI data is resting state (rs) fMRI data.
11. The system of claim 10, wherein the processor is further programmed to identify at least one of an activation region and a deactivation region based on the acquired task fMRI data to derive a task fMRI map and validate the identified target location using the derived task fMRI map.
10. The method of claim 9, wherein: acquiring fMRI data further comprises acquiring task fMRI data of the subject; determining functional connectivity further comprises determining functional connectivity based on the rs-fMRI data; and the method further comprises: identifying at least one of an activation region and a deactivation region based on the acquired task fMRI data to derive a task fMRI map; and validating the identified target location using the derived task fMRI map.
12. The system of claim 1, wherein the process or is further programmed to determine functional connectivity by determining functional connectivity between the voxel in the subcortical region and a region of interest (ROI) in the cortical region.
19. The system of claim 15, wherein the processor is configured determine functional connectivity of the brain between the voxel in the subcortical region of the brain and a vertex in a cortical functional network by assessing BOLD activity time-course data from each vertex in the cortical functional network and determining functional connectivity further comprises: averaging the BOLD activity time-course data of the cortical functional network across all vertices in the cortical functional network; extracting BOLD activity time-course data from the voxel in the subcortical region; and determining functional connectivity as a correlation between the BOLD activity time-course data of the voxel in the subcortical region and the BOLD activity time-course data of the cortical functional network.
13. The system of claim 1, wherein the process or is further programmed to determine functional connectivity by calculating timing of the functional connectivity between the voxel in a subcortical region and the voxel in the cortical region based on the magnetic resonance data.
19. The system of claim 15, wherein the processor is configured determine functional connectivity of the brain between the voxel in the subcortical region of the brain and a vertex in a cortical functional network by assessing BOLD activity time-course data from each vertex in the cortical functional network and determining functional connectivity further comprises: averaging the BOLD activity time-course data of the cortical functional network across all vertices in the cortical functional network; extracting BOLD activity time-course data from the voxel in the subcortical region; and determining functional connectivity as a correlation between the BOLD activity time-course data of the voxel in the subcortical region and the BOLD activity time-course data of the cortical functional network.
14. The system of claim 13, wherein the process or is further programmed to identify the target location by identifying a voxel having an abnormal timing compared to a healthy individual as the target location.
14. The method of claim 13, wherein identifying a target location further comprises identifying a voxel having an abnormal timing compared to a healthy individual as the target location.
15. The system of claim 1, wherein the processor is further configured to determine functional connectivity of the brain between the voxel in the subcortical region of the brain and a vertex in a cortical functional network by assessing includes blood oxygenation level dependent (BOLD) activity time-course data from each vertex in the cortical functional network and determining functional connectivity further comprises: averaging the BOLD activity time-course data of the cortical functional network across all vertices in the cortical functional network; extracting BOLD activity time-course data from the voxel in the subcortical region; and determining functional connectivity as a correlation between the BOLD activity time-course data of the voxel in the subcortical region and the BOLD activity time-course data of the cortical functional network.
19. The system of claim 15, wherein the processor is configured determine functional connectivity of the brain between the voxel in the subcortical region of the brain and a vertex in a cortical functional network by assessing BOLD activity time-course data from each vertex in the cortical functional network and determining functional connectivity further comprises: averaging the BOLD activity time-course data of the cortical functional network across all vertices in the cortical functional network; extracting BOLD activity time-course data from the voxel in the subcortical region; and determining functional connectivity as a correlation between the BOLD activity time-course data of the voxel in the subcortical region and the BOLD activity time-course data of the cortical functional network.
16. The system of claim 1, wherein the processor is further configured to determine the functional connectivity of the brain by determining a temporal correlation of a neurophysiological index.
15. A system comprising: a computing device including a processor programmed to: receive functional magnetic resonance imaging (fMRI) data of a brain of the subject; determine functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain, based on the fMRI data; identify a target location in the brain to be targeted by neuromodulation based on the determined functional connectivity; a display configured to display the target location; wherein the processor is further programmed to determine the functional connectivity of the brain by determining a temporal correlation of a neurophysiological index.
17. The system of claim 16, wherein, the neurophysiological index is a measure of low frequency fluctuations of blood flow or oxygenation measured across a plurality of regions in the brain.
16. The system of claim 15, wherein, the neurophysiological index is a measure of low frequency fluctuations of blood flow or oxygenation measured across a plurality of regions in the brain.
Current Application
US 12,158,512 B2
1. A system comprising: a computing device including a processor programmed to: receive magnetic resonance imaging (MRI) data of a brain of the subject; determine, using the MRI data, functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain; identify a target location in the brain to be targeted by neuromodulation based on the determined functional connectivity to achieve a clinical outcome; and a display configured to display a report indicating the target location.
1. A method of surgical planning, comprising: using a computer system, acquiring magnetic resonance imaging data of a brain of a subject; using a computer system and the magnetic resonance data, determining functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain; identifying a target location in the brain to be targeted by neuromodulation based on the calculated functional connectivity; and using a computer system, generating a report indicating the target location.
3. The system of claim 1, wherein the processor is further programmed to determine functional connectivity of the brain between the voxel in the subcortical region and each cortical functional network in a plurality of cortical functional networks.
2. The method of claim 1, wherein determining functional connectivity comprises determining functional connectivity of the brain between the voxel in the subcortical region and each cortical functional network in a plurality of cortical functional networks.
4. The system of claim 1, wherein the processor is further programmed to identify a winning functional network among the plurality of cortical functional networks as a functional network having the highest functional connectivity with the voxel to identify the target location.
3. The method of claim 1, wherein identifying a target location further includes identifying a winning functional network among the plurality of cortical functional networks as a functional network having the highest functional connectivity with the voxel.
5. The system of claim 4, wherein the target location is in an integrative zone, and wherein the processor is further programmed to integrate the voxel in the integrative zone when functional connectivity between the voxel and one or more functional networks is above a threshold.
4. The method of claim 3, wherein identifying a target location further includes integrating the voxel in the integrative zone when functional connectivity between the voxel and one or more functional networks is above a threshold.
6. The system of claim 5, wherein the one or more functional networks are among a remaining of the plurality of cortical functional networks minus the winning functional network.
5. The method of claim 4, wherein the one or more functional networks are among a remaining of the plurality of cortical functional networks minus the winning functional network.
7. The system of claim 5, wherein the processor is further programmed to identify a given subcortical region as an integrative zone if a correlation between the given subcortical region and one or more functional networks other than the winning functional network is greater than a predetermined threshold.
7. The method of claim 6, further comprising identifying a given subcortical region as an integrative zone if a correlation between the given subcortical region and one or more functional networks other than the winning functional network is greater than a predetermined threshold.
8. The system of claim 7, wherein the threshold is 66 percent.
8. The method of claim 7, wherein the threshold is 66 percent.
9. The system of claim 1, wherein the magnetic resonance data includes at least one of functional magnetic resonance imaging (fMRI) data or resting state (rs) fMRI data of the subject.
9. The method of claim 1, wherein the magnetic resonance data includes at least one of functional magnetic resonance imaging (fMRI) data or resting state (rs) fMRI data of the subject.
10. The method of claim 9, wherein the processor is further programmed to acquire fMRI data as task fMRI data of the subject and determine functional connectivity based on the rs-fMRI data.
10. The method of claim 9, wherein acquiring fMRI data further comprises acquiring task fMRI data of the subject and determining functional connectivity further comprises determining functional connectivity based on the rs-fMRI data.
11. The system of claim 10, wherein the processor is further programmed to identify at least one of an activation region and a deactivation region based on the acquired task fMRI data to derive a task fMRI map and validate the identified target location using the derived task fMRI map.
11. The method of claim 10, further comprising identifying at least one of an activation region and a deactivation region based on the acquired task fMRI data to derive a task fMRI map and validating the identified target location using the derived task fMRI map.
12. The system of claim 1, wherein the process or is further programmed to determine functional connectivity by determining functional connectivity between the voxel in the subcortical region and a region of interest (ROI) in the cortical region.
12. The method of claim 1, wherein determining functional connectivity comprises determining functional connectivity between the voxel in the subcortical region and a region of interest (ROI) in the cortical region.
13. The system of claim 1, wherein the process or is further programmed to determine functional connectivity by calculating timing of the functional connectivity between the voxel in a subcortical region and the voxel in the cortical region based on the magnetic resonance data.
13. The method of claim 1, wherein determining functional connectivity further comprises calculating timing of the functional connectivity between the voxel in a subcortical region and the voxel in the cortical region based on the magnetic resonance data.
14. The system of claim 13, wherein the process or is further programmed to identify the target location by identifying a voxel having an abnormal timing compared to a healthy individual as the target location.
14. The method of claim 13, wherein identifying a target location further comprises identifying a voxel having an abnormal timing compared to a healthy individual as the target location.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liston (US 2020/0289044 A1).
With respect to claim 1, Liston discloses a system comprising (see system in Figure 1 provided herein): a computing device including a processor programmed to (see processor #102): receive magnetic resonance imaging (MRI) data of a brain of the subject (see paragraph 0036); determine, using the MRI data, functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain (see paragraphs 0004, 0060-0061 and 0066;
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also, the voxel in the region is described in paragraph 0066 as functional volume of the fMRI data since it is known that functional volume data is made of voxels); identify a target location in the brain to be targeted by neuromodulation based on the determined functional connectivity to achieve a clinical outcome (see paragraphs 0040 and 0089 disclosing the use of rTMS neurostimulator targeting the location dorsomedial prefrontal cortex identified by depression biotype using a classification module #104 as seen on Figure 1 for said identification); and a display configured to display a report indicating the target location (see paragraph 0055 discussing the use of a display with the device shown).
With respect to claims 2 and 16, Liston discloses the processor is further programmed to determine the functional connectivity of the brain by determining a temporal correlation of a neurophysiological index (see paragraph 0059 discussing the BOLD signal reflecting a function of neural activity, blood flow, and changes in blood volume in the brain wherein as neurons are stimulated in the brain, oxygenated blood flow increases, implying low frequency fluctuations of blood flow and oxygenation, in the activated region hence showing fluctuations throughout different regions considered as the claimed neurophysiological index defined in the Specification of the current application in paragraph 0056 as low frequency fluctuations of blood flow and oxygenation, measured in different brain areas).
With respect to claim 3, Liston discloses the processor is further programmed to determine functional connectivity of the brain between the voxel in the subcortical region and each cortical functional network in a plurality of cortical functional networks (see paragraphs 0004, 0060-0061 and 0066; also, the voxel in the region is described in paragraph 0066 as functional volume of the fMRI data since it is known that functional volume data is made of voxels).
With respect to claim 4, Liston discloses the processor is further programmed to identify a winning functional network among the plurality of cortical functional networks as a functional network having the highest functional connectivity with the voxel to identify the target location (see paragraphs 0040 and 0089 disclosing the use of rTMS neurostimulator targeting the location dorsomedial prefrontal cortex identified by depression biotype using a classification module #104 as seen on Figure 1 for said identification wherein the classification module #104 generates the highest biotype likelihood score based on the extracted brain region functional connectivity according to paragraphs 0014, 0069 and 0089).
With respect to claim 5, Liston discloses the target location is in an integrative zone, and wherein the processor is further programmed to integrate the voxel in the integrative zone when functional connectivity between the voxel and one or more functional networks is above a threshold (see paragraphs 0014, 0040, 0069 and 0089).
With respect to claim 6, Liston discloses one or more functional networks are among a remaining of the plurality of cortical functional networks minus the winning functional network (see paragraphs 0004, 0060-0061 and 0066).
With respect to claim 7, Liston discloses the processor is further programmed to identify a given subcortical region as an integrative zone if a correlation between the given subcortical region and one or more functional networks other than the winning functional network is greater than a predetermined threshold (see paragraphs 0004, 0060-0061 and 0066).
With respect to claim 8, Liston discloses the threshold is 66 percent (see paragraph 0105).
With respect to claim 9, Liston discloses the magnetic resonance data includes at least one of functional magnetic resonance imaging (fMRI) data or resting state (rs) fMRI data of the subject (see paragraphs 0035-0036).
With respect to claim 10, Liston discloses the processor is further programmed to acquire fMRI data as task fMRI data of the subject and determine functional connectivity based on the rs-fMRI data (see paragraphs 0035-0036 and 0057).
With respect to claim 11, Liston discloses the processor is further programmed to identify at least one of an activation region and a deactivation region based on the acquired task fMRI data to derive a task fMRI map and validate the identified target location using the derived task fMRI map (see paragraphs 0035-0037 and 0057).
With respect to claim 12, Liston discloses the processor is further programmed to determine functional connectivity by determining functional connectivity between the voxel in the subcortical region and a region of interest (ROI) in the cortical region (see paragraphs 0004, 0060-0061 and 0066).
With respect to claim 13, Liston discloses the processor is further programmed to determine functional connectivity by calculating timing of the functional connectivity between the voxel in a subcortical region and the voxel in the cortical region based on the magnetic resonance data (see paragraphs 0035, 0060 and 0066).
With respect to claim 14, Liston discloses the processor is further programmed to identify the target location by identifying a voxel having an abnormal timing compared to a healthy individual as the target location (Abstract and paragraphs 0035, 0060 and 0066).
With respect to claim 15, Liston discloses the processor is further configured to determine functional connectivity of the brain between the voxel in the subcortical region of the brain and a vertex in a cortical functional network (see paragraphs 0004, 0060-0061 and 0066) by assessing includes blood oxygenation level dependent (BOLD) (see paragraphs 0037-0038) activity time-course data from each vertex in the cortical functional network and determining functional connectivity further comprises (Abstract and paragraphs 0035, 0060 and 0066): averaging the BOLD activity time-course data of the cortical functional network across all vertices in the cortical functional network; extracting BOLD activity time-course data from the voxel in the subcortical region; and determining functional connectivity as a correlation between the BOLD activity time-course data of the voxel in the subcortical region and the BOLD activity time-course data of the cortical functional network (see paragraphs 0045-0046, 0059-0061, 0064-0066 and 0109).
With respect to claim 17, Liston discloses the neurophysiological index is a measure of low frequency fluctuations of blood flow or oxygenation measured across a plurality of regions in the brain (see paragraph 0059 discussing the BOLD signal reflecting a function of neural activity, blood flow, and changes in blood volume in the brain wherein as neurons are stimulated in the brain, oxygenated blood flow increases, implying low frequency fluctuations of blood flow and oxygenation, in the activated region hence showing fluctuations throughout different regions considered as the claimed neurophysiological index defined in the Specification of the current application in paragraph 0056 as low frequency fluctuations of blood flow and oxygenation, measured in different brain areas).
Conclusion
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/DIXOMARA VARGAS/Primary Examiner, Art Unit 3798