DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. According to the Amendment, filed 16 July 2026, the status of the claims is as follows:
Claims 1-4, 7, 11-13, and 18 are currently amended; and
Claims 8-10, and 19 are as originally filed; and
Claims 5, 6, 14-17, and 20 are cancelled.
3. The rejection of claims 2, 13, and 14 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention, is withdrawn in view of the Amendment, filed 16 July 2026.
Response to Arguments
4. Applicant’s arguments, see Remarks, pp. 6-10, filed 16 July 2026, with respect to the rejection of claims 1-20 under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, i.e. abstract idea, without significantly more, have been fully considered, and are persuasive in view of the Amendment, filed 16 July 2026. Therefore, the rejection has been withdrawn.
5. Applicant’s arguments, see Remarks, pp. 11-13, filed 16 July 2026, with respect to the rejection of claims 1-20 under 35 U.S.C. 102(a)(2) as being anticipated by Elias, U.S. Patent Application Publication No. 2023/0018247 A1 (“Elias”), have been fully considered, and are persuasive in view of the Amendment, filed 16 July 2026. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection, which was necessitated by amendment, is discussed below.
Claim Objections
6. Claim 11 is objected to because of the following informalities:
In line 19, “the at least one classical computing …” is typographical error and lacks proper antecedent basis, and should be amended to “the at least one classical computing device …”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
7. 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.
8. 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
9. Claims 1-4, 7, 8, 11-13, 18, and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bach et al., U.S. Patent Application Publication No. 2022/0133194 A1 (“Bach”).
As to Claim 1, Bach teaches the following:
A method (see “According to some embodiments of the present disclosure, a method for improving performance on a conscious activity, and cognitive decision making, is disclosed.” in para. [0009]) including the following steps:
providing, by at least one brain activity sensor (“neurometric interface”) 120, brain activity measurements of a person (see “The neurometric interface 120 can comprise any of or several of the neurophysiological sensors described in the background section of this application. … For example, one neurometric interface 120 comprises both an EEG, which is portable, and a fMRI, which is not. The EEG comprises sensors that detect electrical activity in the brain.” in para. [0150]);
in an amplification and digitization stage amplifying and, then, digitizing the brain activity measurements (see “Digital signal processors (DSPs) 111 digitize any analog information collected by these interfaces 110, 120, and 130, and deliver neurophysiological data 102, physiological data 103, and performance data 101, respectively, to a data interface and logger/recorder 140.” in para. [0143]);
receiving, by at least one classical computing device (“logger/recorder 140” and “decision engine 143”) 140/143, the digitized brain activity measurements (see “The logger/recorder 140 recorder collects and records neurometric data 102 from the neurometric interface 120, physiological data 103 from the physiological interface 130, the performance data 101 from the behavioral task interface 110 or some other source, and survey responses 104 from surveys 140. In one implementation, the collection of data 101, 102, and 103 are done simultaneously.” in para. [0143]; and see “The decision engine 143 uses the data to program a task controller 143, a neurofeedback interface 144, and an intervention planner and evaluator 147.” in para. [0199]);
processing, by the at least one classical computing device 140/143, the digitized brain activity measurements (see “In one implementation, the decision engine 160 identifies changes in the data 101, 102, or 103, or a running average of that data 101, 102, or 103, that exceed a predetermined threshold for a group or team of performers.” in para. [0200]);
providing, by the at least one classical computing device 140/143, the processed digitized measurements to at least one quantum computing device (“quantum computing processor”, not labeled) (see “A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions and the like, including a central processing unit (CPU), a general processing unit (GPU), a logic board, a chip (e.g., a graphics chip, a video processing chip, a data compression chip, or the like), a chipset, a controller, a system-on-chip (e.g., an RF system on chip, an AI system on chip, a video processing system on chip, or others), an integrated circuit, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), an approximate computing processor, a quantum computing processor, …” in para. [0603]);
processing, by the at least one quantum computing device (“quantum computing processor”, not labeled), the processed digitized measurements by inputting the processed digitized measurements into a quantum machine learning pipeline (“data processing pipeline”, not labeled) configured to provide data indicative of commands or thoughts of the person's brain (see “The system comprises a data processor; a plurality of neurocognitive sensors, an assessment program, a program of training tasks, a data processing pipeline, and a reporting program.” in para. [0509]; and see “In another implementation, the data processing pipeline identifies characteristic physiological states by decomposing the physiological data by preprocessing and transforming the physiological data to identify components associated with variances in or sources of the physiological data, bandpassing the components across several frequency bands, finding correlations between envelopes of the bandpassed components, and clustering the correlation data.” in para. [0533]), the quantum machine learning pipeline (“data processing pipeline”) being trained with a training dataset with brain activity measurements of the person and commands or thoughts associated therewith (see “The assessment program is stored on a computer medium and configured for computer execution to visually, audibly and/or tactilely present a plurality of assessment tasks to the company employees and receive responses from the company employees while the neurocognitive sensors collect data about electrical activity in the company employees' brains. The program of training tasks stored on a computer medium and configured for computer execution to provide audibly, visually, and/or tactilely stimulation to employees to direct and aid their performance of the training tasks. The data processing pipeline processes the collected data to generate spatial maps of cognitive workload across the brain. The reporting program stored on a computer medium contrasts the cognitive workload maps generated from the pre-training assessment with the cognitive workload maps generated from the post-training assessment. As used herein, “program” can be a routine or subroutine of a larger program.” in para. [0509]);
processing, by the at least one classical computing device 140/143, outputs of the quantum machine learning pipeline (“data processing pipeline”) to provide a command (“queries”) associated with the outputs (see “The reporting engine 160 queries the database 141 to build or obtain a profile 164 for the subject, generate an assessment of the subject's performance and physiological characteristics from the performance data 101, the neurometric data 102, and the physiological data 103, and produce graphical & textual reports 161 about the subject's neurophysiological and behavioral performance on the tasks. The reporting engine 160 also optionally use the normative signatures 155 of performance as a baseline against which to compare a subject's brain activity and/or functional connectivity.” in para. [0179]); and
the at least one classical computing device 140/143 using the command (“queries”) to control itself or one or more electronic devices (“database”) 141 (see “The reporting engine 160 queries the database 141 to build or obtain a profile 164 for the subject, generate an assessment of the subject's performance and physiological characteristics from the performance data 101, the neurometric data 102, and the physiological data 103, and produce graphical & textual reports 161 about the subject's neurophysiological and behavioral performance on the tasks. The reporting engine 160 also optionally use the normative signatures 155 of performance as a baseline against which to compare a subject's brain activity and/or functional connectivity.” in para. [0179]).
As to Claim 2, Bach teaches the following:
wherein the processing, by the at least one classical computing device 140/143, of the digitized brain activity measurements includes data cleaning and/or data preprocessing (see “The data analysis used the data-conditioning pipeline 850 shown in FIG. 31 began with preprocessing 851 (i.e., “cleaning”) the raw electroencephalogram (EEG) data 852 that was collected.” in para. [0331]).
As to Claim 3, Bach teaches the following:
wherein one or more electronic circuits and analog-to-digital converters are arranged in the amplification and digitization stage (see “The apparatus comprises one or more physiological sensors, analog-to-digital converters, memory, electrical connectors, behavioral interface, and feed of comparative data. The one or more physiological sensors transduce signals received from the head or body of the person. The one or more analog to digital converters convert analog signals from the physiological sensors into digital signals. The memory stores the digital signals as sensor data.” in para. [0530]).
As to Claim 4, Bach teaches the following:
the classical computing device 140/143 providing to a user presenting device (“feedback display interface”) 135 the outputs of the machine learning pipeline (“data processing pipeline”) to produce a feedback loop with the person (see “The 3D model is presented to the feedback display interface 135, which is a monitor, screen, video-containing headset, VR headset 137, game headset, glasses-embedded display, or other display device. The feedback display interface 135 is located within a viewing range of the subject and while the subject performs the tasks. The feedback display interface 135 provides the subject a visualization of the mapped 3D model to the subject while the subject is performing the task. In some implementations, the visualization is live, in real-time, with relatively little lag time. In other implementations, one or more visualizations are provided after the task is completed. In another implementation, the feedback display interface 135 also provides real-time assessment information about the subject's performance and physiological (including neurophysiological) characteristics.” in para. [0172]).
As to Claim 7, Bach teaches the following:
wherein the quantum machine learning pipeline (“data processing pipeline”) comprises one of the following: a quantum support vector machine, a quantum neural network, and a quantum ensemble method-based algorithm (see “In embodiments, the processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platforms. A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions and the like, including a central processing unit (CPU), a general processing unit (GPU), a logic board, a chip (e.g., a graphics chip, a video processing chip, a data compression chip, or the like), a chipset, a controller, a system-on-chip (e.g., an RF system on chip, an AI system on chip, a video processing system on chip, or others), an integrated circuit, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), an approximate computing processor, a quantum computing processor, a parallel computing processor, a neural network processor, or other type of processor.” in para. [0603]).
As to Claim 8, Bach teaches the following:
wherein the at least one brain activity sensor measures the brain activity with at least one of the following: an electroencephalogram, magnetic resonance images, and a magnetoencephalogram (see “For example, one neurometric interface 120 comprises both an EEG, which is portable, and a fMRI, which is not. The EEG comprises sensors that detect electrical activity in the brain. The sensory data is Fourier-transformed to identify brain wave frequencies of different parts of the brain. The fMRI and EEG measurements are taken simultaneously for an initial test audience to find correlations between the relatively more abundant and accurate fMRI data and the relatively sparse EEG data. With an adequate database of fMRI correlation data, EEG data can be interpreted more accurately to indicate activity in various brain regions and pathways. In another implementation, the neurometric interface 120 is simplified, such as being made to operate without the fMRT or with fewer EEG sensors or be distributed among a smaller surface area of the head, after sufficient data is obtained to demonstrate that reasonably accurate measurements of brain activity can still be obtained.” in para. [0150]).
As to Claim 11, Bach teaches the following:
A system (see “To identify physiological states that are predictive of a person's performance, a system provides physiological and behavioral interfaces and a data processing pipeline.” in Abstract) comprising an amplification and digitization stage, at least one classical computing device, and at least one quantum computing device, wherein:
the amplification and digitization stage is configured to amplify and, then, digitize brain activity measurements provided (see “Digital signal processors (DSPs) 111 digitize any analog information collected by these interfaces 110, 120, and 130, and deliver neurophysiological data 102, physiological data 103, and performance data 101, respectively, to a data interface and logger/recorder 140.” in para. [0143]) by at least one brain activity sensor (“neurometric interface”) 120 (see “The neurometric interface 120 can comprise any of or several of the neurophysiological sensors described in the background section of this application. … For example, one neurometric interface 120 comprises both an EEG, which is portable, and a fMRI, which is not. The EEG comprises sensors that detect electrical activity in the brain.” in para. [0150]);
the at least one classical computing device (“logger/recorder 140” and “decision engine 143”) 140/143 is configured to receive and process the digitized brain activity measurements (see “The logger/recorder 140 recorder collects and records neurometric data 102 from the neurometric interface 120, physiological data 103 from the physiological interface 130, the performance data 101 from the behavioral task interface 110 or some other source, and survey responses 104 from surveys 140. In one implementation, the collection of data 101, 102, and 103 are done simultaneously.” in para. [0143]; and see “The decision engine 143 uses the data to program a task controller 143, a neurofeedback interface 144, and an intervention planner and evaluator 147.” in para. [0199]), and to provide the processed digitized measurements to the at least one quantum computing device (“quantum computing processor”, not labeled) (see “A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions and the like, including a central processing unit (CPU), a general processing unit (GPU), a logic board, a chip (e.g., a graphics chip, a video processing chip, a data compression chip, or the like), a chipset, a controller, a system-on-chip (e.g., an RF system on chip, an AI system on chip, a video processing system on chip, or others), an integrated circuit, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), an approximate computing processor, a quantum computing processor, …” in para. [0603]);
the at least one quantum computing device (“quantum computing processor”) is configured to process the processed digitized measurements by inputting the processed digitized measurements into a quantum machine learning pipeline (“data processing pipeline”, not labeled) configured to provide data indicative of commands or thoughts of the person's brain (see “The system comprises a data processor; a plurality of neurocognitive sensors, an assessment program, a program of training tasks, a data processing pipeline, and a reporting program.” in para. [0509]; and see “In another implementation, the data processing pipeline identifies characteristic physiological states by decomposing the physiological data by preprocessing and transforming the physiological data to identify components associated with variances in or sources of the physiological data, bandpassing the components across several frequency bands, finding correlations between envelopes of the bandpassed components, and clustering the correlation data.” in para. [0533]), the quantum machine learning pipeline (“data processing pipeline”) being trained with a training dataset with brain activity measurements of the person and commands or thoughts associated therewith (see “The assessment program is stored on a computer medium and configured for computer execution to visually, audibly and/or tactilely present a plurality of assessment tasks to the company employees and receive responses from the company employees while the neurocognitive sensors collect data about electrical activity in the company employees' brains. The program of training tasks stored on a computer medium and configured for computer execution to provide audibly, visually, and/or tactilely stimulation to employees to direct and aid their performance of the training tasks. The data processing pipeline processes the collected data to generate spatial maps of cognitive workload across the brain. The reporting program stored on a computer medium contrasts the cognitive workload maps generated from the pre-training assessment with the cognitive workload maps generated from the post-training assessment. As used herein, “program” can be a routine or subroutine of a larger program.” in para. [0509]);
the at least one classical computing device 140/143 is configured to process outputs of the quantum machine learning pipeline (“data processing pipeline”) to provide a command (“queries”) associated with the outputs (see “The reporting engine 160 queries the database 141 to build or obtain a profile 164 for the subject, generate an assessment of the subject's performance and physiological characteristics from the performance data 101, the neurometric data 102, and the physiological data 103, and produce graphical & textual reports 161 about the subject's neurophysiological and behavioral performance on the tasks. The reporting engine 160 also optionally use the normative signatures 155 of performance as a baseline against which to compare a subject's brain activity and/or functional connectivity.” in para. [0179]); and
the at least one classical computing [device] 140/143 is configured to use the command (“queries”) to control itself or one or more electronic devices (“database”) 141 (see “The reporting engine 160 queries the database 141 to build or obtain a profile 164 for the subject, generate an assessment of the subject's performance and physiological characteristics from the performance data 101, the neurometric data 102, and the physiological data 103, and produce graphical & textual reports 161 about the subject's neurophysiological and behavioral performance on the tasks. The reporting engine 160 also optionally use the normative signatures 155 of performance as a baseline against which to compare a subject's brain activity and/or functional connectivity.” in para. [0179]).
As to Claim 12, Bach teaches the following:
wherein one or more electronic circuits and analog-to-digital converters are arranged in the amplification and digitization stage (see “The apparatus comprises one or more physiological sensors, analog-to-digital converters, memory, electrical connectors, behavioral interface, and feed of comparative data. The one or more physiological sensors transduce signals received from the head or body of the person. The one or more analog to digital converters convert analog signals from the physiological sensors into digital signals. The memory stores the digital signals as sensor data.” in para. [0530]).
As to Claim 13, Bach teaches the following:
wherein the classical computing device 140/143 is configured to provide to a user presenting device (“feedback display interface”) 135 the outputs of the machine learning pipeline (“data processing pipeline”) to produce a feedback loop with the person (see “The 3D model is presented to the feedback display interface 135, which is a monitor, screen, video-containing headset, VR headset 137, game headset, glasses-embedded display, or other display device. The feedback display interface 135 is located within a viewing range of the subject and while the subject performs the tasks. The feedback display interface 135 provides the subject a visualization of the mapped 3D model to the subject while the subject is performing the task. In some implementations, the visualization is live, in real-time, with relatively little lag time. In other implementations, one or more visualizations are provided after the task is completed. In another implementation, the feedback display interface 135 also provides real-time assessment information about the subject's performance and physiological (including neurophysiological) characteristics.” in para. [0172]).
As to Claim 18, Bach teaches the following:
wherein the quantum machine learning pipeline (“data processing pipeline”) comprises one of the following: a quantum support vector machine, a quantum neural network, and a quantum ensemble method-based algorithm (see “In embodiments, the processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platforms. A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions and the like, including a central processing unit (CPU), a general processing unit (GPU), a logic board, a chip (e.g., a graphics chip, a video processing chip, a data compression chip, or the like), a chipset, a controller, a system-on-chip (e.g., an RF system on chip, an AI system on chip, a video processing system on chip, or others), an integrated circuit, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), an approximate computing processor, a quantum computing processor, a parallel computing processor, a neural network processor, or other type of processor.” in para. [0603]).
As to Claim 19, Bach teaches the following:
the at least one brain activity sensor (“neurometric interface”) 120 (see “The neurometric interface 120 can comprise any of or several of the neurophysiological sensors described in the background section of this application. … For example, one neurometric interface 120 comprises both an EEG, which is portable, and a fMRI, which is not. The EEG comprises sensors that detect electrical activity in the brain.” in para. [0150]).
Claim Rejections - 35 USC § 103
10. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
11. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
12. Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Bach, as applied to claim 1 and to claim 11, respectively, above, and further in view of Elias.
As to Claims 9 and 10, Bach teaches the subject matter of claim 1 above. Bach does not teach the following:
wherein the at least one brain activity sensor comprises at least one quantum brain activity sensor; and
wherein the at least one quantum brain activity sensor comprises one or both of: at least one optically pumped magnetometer, and a diamond magnetometer.
However, Elias teaches the following:
at least one brain activity sensor (“head-worn body”) 510 comprises at least one quantum brain activity sensor (“quantum sensors”) 512 (see “The XR device 500 may include quantum sensors 512 … integrated with the head-worn body that are configured to generate brain-activity signals (e.g., MEG signals) based on magnetic fields in local areas of a brain of a user.” in para. [0048]); and
wherein the at least one quantum brain activity sensor 512 comprises one or both of: at least one optically pumped magnetometer (“optically pumped magnetometers”, not labeled), and a diamond magnetometer (see “quantum sensors 512 (e.g., optically pumped magnetometers, nitrogen-vacancy magnetometers) integrated with the head-worn body …” in para. [0048]).
Thus, it would have been obvious for one of ordinary skill in the art at the time the present application was effectively filed to modify Bach’s brain activity sensor (“neurometric interface”) 120 to include Elias’s at least one quantum brain activity sensor (“quantum sensors”) 512, wherein the at least one quantum brain activity sensor 512 comprises one or both of: at least one optically pumped magnetometer (“optically pumped magnetometers”), because it is a simple substitution of one known element, i.e. Bach’s “EEG sensors”, for another, i.e. Elias’s “quantum sensors 512”, to obtain predictable results, i.e. generate brain-activity signals for analysis.
Conclusion
13. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVIN NATNITHITHADHA whose telephone number is (571)272-4732. The examiner can normally be reached Monday - Friday 8:00 am - 8:00 am - 4:00 pm.
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/NAVIN NATNITHITHADHA/Primary Examiner, Art Unit 3791 09/25/2026
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