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 Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 24 is rejected 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. The scope and meaning of “leading sequences” of functional connectivity patterns is unclear. While paragraph [0017] states that “[i]n some embodiments, the step of training the second machine learning system to identify relationships between the functional connectivity patterns and performance quality may include identifying relationships between leading sequences of the functional connectivity patterns and performance quality”, no further guidance is given on what constitutes a “leading sequence” or how it I would be calculated or determined.
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 1-9, 12. 13. 15-20. and 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bach (US2020/0008725). Bach discloses a method for improving performance on a conscious activity (a method of improving a person's productivity in performing a task; paragraphs [0197]-(0198]), the method comprising: collecting behavioral data and neurophysiological data while a person performs the conscious activity (collecting performance (behavior) data and neurophysical data while a person performs a task; paragraphs [0209]-(0212]); assessing the behavioral data by comparing the behavioral data with reference data to score the person's conscious activity in an assessment (compare the particular subject's performance of each task with a distribution, average, median, or other centralizing statistic of the performances of the population of subjects; construct, from the comparisons above. a functional assessment of neurophysiological functions of the particular subject's brain's systems and pathways; paragraphs [0209]-(0213], [0217]; see claim 1 of Bach); synchronizing the behavioral data with the neurophysiological data (processed signal data synchronized with task performance data for the participants; paragraph (0211]); inputting the behavioral data, neurophysiological data, and the assessment into a machine learning system (signatures are built by inputting the database of task performance and brain activity data into a machine learning apparatus that identifies brain systems and/or pathways between the brain systems that are activated by each of the tasks; the data processing pipeline collects the physiological data from the physiological interface. the performance data from the behavioral interface. and reference data from a population of people performing the same or similar tasks or real-world activities; abstract paragraphs (0149], (0225], [0412]-(0413]. [0507]; see claim 1 of Bach); and training the machine learning system with said inputs to identify a probabilistic relationship between the person's neurophysiological data and the person's performance (the pipeline decomposes the physiological data into frequency-banded components, identifies brain states derived from the decomposed data-for example, clusters of correlations of decomposed data envelopes-grades the performance data, compares the graded performance data to the brain states, and identifies statistical relationships between the brain states and levels of performance; brain signatures are further refined by inputting data relating to several subjects' performances on tasks; abstract; paragraphs [0149], (0225], (0503]; see claim 1 of Bach).
As per claim 2, Bach discloses the method of claim 1. Bach further discloses wherein the neurophysiological data is brain activity data (the neurophysiological data is brain activity data; paragraph [0210]).
As per claim 3, Bach discloses the method of claim 2. Bach further discloses further discloses transforming the neurophysiological data into a sequence of discrete brain states (the neurophysiological data is transformed into different brain states; paragraphs (0120]-[0121], (0151]-(0152], [0154]).
As per claim 4, Bach discloses the method of claim 2. Bach further discloses performing a clustering operation on a large set of functional connectivity matrices (clustering was performed on large data sets of functional connectivity matrices; paragraphs [0317]-(0320]; see claim 20 of Bach).
As per claim 5, Bach discloses the method of claim 2. Bach further discloses transforming the neurophysiological data into a sequence of discrete brain states by performing a clustering operation on a large set of functional connectivity matrices (the neurophysiological data is transformed into brain states by performing clustering on a large set of functional connectivity matrices; paragraphs [0317]-(0320]).
As per claim 6, Bach discloses the method of claim 5. Bach further discloses decomposing the neurophysiological data into a set of characteristic states (the statistical engine is configured to decompose and bandpass sensor data into components that extend across frequency bands and identify a first set of correlations between characteristics of the decomposed and bandpassed data in order to identify a first set of physiological states; paragraphs (0503], (0510]; see claim 16 of Bach), wherein said decomposing comprises identifying brain states from the neurophysiological data through at least one of filtering, clustering and component analysis (brain states are identified via clustering; paragraph (0318]); wherein the step of training a machine learning system with the behavioral data and assessments uses at least one of the identifications, assessments, and derivatives of brain states (the statistical engine 150 processes and analyzes the data 101, 102, 103, and 104 collected from a population of subjects to build normative models of brain activity and correlated performance levels for each of a plurality of task conditions (states); the statistical engine 150 can make use of machine learning. deep learning, and neural networks to identify patterns between the performance data 101 and other data and brain activity; paragraph [0149]).
As per claim 7, Bach discloses the method of claim 6. Bach further discloses subsequently decomposing a new collection of neurophysiological data into a set of functional connectivity state estimation “FCSE” states and matching the newly decomposed FCSE states to the earlier determined characteristic states (a new set of sensor data is collected and decomposed into FCSE states, wherein the new sensor data is compared with the first set of physiological states; paragraph (0503); see claims 16 and 20 of Bach).
As per claim 8, Bach discloses the method of claim 5. Bach further discloses wherein the brain states are differentiated into one of a set of N different brain states, wherein N is at least 2 (at least two brain states; paragraphs [0257]-[0258]).
As per claim 9, Bach discloses the method of claim 8. Bach further discloses wherein each of the N different brain states is represented by a unique identifier and the set of N different brain states corresponds to a set of unique identifiers (the brain states are an objectively discernable and quantifiable pattern of power density, neuronal firing, correlations between brain waves, and/or other dynamic physical characteristics of the brain, wherein each of the brain states is represented by a unique descriptor such as “irritable”, “creative”, “engaged”, “attention”, and “memory retrieval"; paragraphs [0083]-[0084], [0257], [0306], [0313]).
As per claim 12, Bach discloses the method of claim 1. Bach further discloses collecting and training the machine learning system with behavioral and neurophysiological data from a plurality of persons performing the activity (the statistical engine 150 processes and analyzes the performance data 101, neurometric data 102, collected from a population of subjects to build normative models of brain activity and correlated performance levels for each of a plurality of task conditions (i.e., states). The statistical engine 150 can make use of machine learning, deep learning, and neural networks to identify patterns between the performance data 101 and other data and brain activity; paragraph [0149]).
As per claim 13, Bach discloses the method of claim 1. Bach further discloses decomposing the behavioral data and neurophysiological data into spatial and temporal components that reflect a functional connectivity state at an instant of time (the statistical engine 150 compares the spatial-temporal pattern of the physiological indicators across the task conditions (states) to make inferences of the neurophysiological basis of various states (e.g., inattention or overloaded); wherein the pattern reflects the functional connectivity “FC” of the brain state at an instant in time; paragraphs [0083], [0152], [0226], [0323]); repeating said decomposing step for a sequence of instances (identifying a first set of correlations between characteristics of the decomposed and bandpassed data; collecting a new set of sensor data from the one or more physiological sensors during time windows preceding the person measuring the person's performance on a second set of decisions or actions; decomposing and bandpassing the new set of sensor data; see claim 16 of Bach); and using machine learning, clustering a plurality of functional connectivity matrices into a set of discrete steps (a plurality of functional connectivity matrices are clustered using machine learning; paragraphs [0019]-[0020], [0318]-[0320]).
As per claim 15, Bach discloses the method of claim 13. Bach further discloses wherein characteristic neurophysiological states are identified by: decomposing the neurophysiological data (characteristic neurophysiological states are identified by decomposing the neurophysiological data; see claim 16 of Bach); identifying components associated with variances in or sources of the neurophysiological data (decomposing and bandpassing the data into components that extend across frequency bands, identifying a first set of correlations between characteristics of the decomposed and bandpassed data in order to identify a first set of physiological states; see claim 16 of Bach); bandpassing the components across several frequency bands (decomposing and bandpassing the data into components that extend across frequency bands; see claim 16 of Bach)); finding correlations between envelopes of the bandpassed components (identifying envelopes enclosing data signals of each of the principal component and frequency band channels; computing correlation matrices between the envelopes; see claim 20 of Bach); and clustering the correlation data (clustering data of the correlation matrices; see claim 20 of Bach).
As per claim 16, Bach discloses the method of claim 1. Bach further discloses predicting the score of the person's subsequent conscious activity as a function of the person's neurophysiological activity leading up to said subsequent conscious activity (based on data from neurophysiological sensors, generating an expected value of the person's performance on the second set of decisions or actions, before the person makes or performs the second set of decisions or actions; paragraph [0225], [0503]; see claim 16 of Bach).
As per claim 17, Bach discloses the method of claim 1. Bach further discloses wherein: the conscious activity is trading a financial asset (trading a financial asset such as a bond, stock, security, or fund; paragraphs [0301]-[0303]); the behavioral data is transactional data related to trading the financial data (the performance data is transactional data related to trading the financial data; paragraphs [0301]-[0303]); and the reference data is market averages pertinent to trading the financial asset (data about the profitability of the trades, market values, including volume weighted average price or VWAP; paragraph [0303]).
As per claim 18, Bach discloses the method of claim 17. Bach further discloses wherein said financial asset is at least one of a stock, a bond (trading a financial asset such as a bond, stock, security, or fund; paragraphs [0301]-[0303]), an amount of debt, a commodity, an amount of fiat currency, and an amount of cryptocurrency.
As per claim 19, Bach discloses the method of claim 17. Bach further discloses wherein the market averages are the volume weighted average price “VWAP" of securities in a window of time around when the financial assets were traded (the market averages are the volume weighted average price “VWAP" of the securities in a window of time around when the financial assets were traded; paragraph [0303]; see claim 5 of Bach).
As per claim 20, Bach discloses the method of claim 1. Bach further discloses wherein the conscious activity is related to cognitive efficiency in performing a business activity (neurocognitive performance is a corporate environment, paragraphs [0174], [0289]-[0290]).
As per claim 22, Bach discloses the method of claim 1. Bach further discloses wherein the conscious activity is related to cognitive efficiency in performing a sporting activity (sports activity efficiency; paragraphs [0125], [0151], [0158], [0197)).
Claim Rejections - 35 USC § 103
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.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Bach in view of Mavroeidis. Bach discloses the method of claim 10 with the exception of the provision of a LSTM network as recited, This feature is known in the art, as taught for example by Mavroeidis at paragraph [0042], and would have been obvious to one of ordinary skill in the art as an obvious substitution of one known element for another and for the purpose of using a model that is well-suited to classifying, processing, and making predictions based on time series data.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Bach in view of Abrams. Bach discloses the method of claim 10 with the exception of the provision of a logistic regression model as recited. This feature is known in the art, as taught for example by Abrams at paragraph [0090], and would have been obvious to one of ordinary skill in the art as an obvious substitution of one known element for another and for the purpose of using a prediction algorithm that is easy to implement, interpret, and train.
Claims 14, 23 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Bach. Bach discloses the method of claim 1. Bach does not disclose wherein the step of training a machine learning system with the behavioral data and neurophysiological data and assessments involves two machine learning layers or systems, including: a first machine learning layer in which the neurophysiological data is decomposed into neurophysiological states that a person experienced; and a second machine learning layer that receives temporal sequences of neurophysiological states and correlates different sequential patterns of said states with probabilities of performing the activity well. However, since Bach discloses wherein the pipeline (machine learning system) decomposes the physiological data into frequency-banded components, identifies brain states derived from the decomposed data (Bach; abstract; paragraph [0503]), wherein the statistical engine 150 compares the spatial-temporal pattern of the physiological indicators across the task conditions (states) to make inferences of the neurophysiological basis of various states e.g., inattention or overloaded; wherein the method also comprises challenging an individual to complete diagnostic tasks while equipped with the at least one neurophysiological sensor; measuring the individual's performance on the diagnostic tasks while simultaneously collecting brain activity data from the sensors; and constructing a predictive heuristic model of the individual's probable performance on a training set of tasks, based on the individual's screening task performance, the individual's synchronized brain activity data, and the patterns identified between performance on the first set of tasks and brain activity in the population of subjects (Bach; paragraphs [0152], [0226], [0323], [0427]), it would have been obvious to one of ordinary skill in the art, at the time of invention, to have modified the method of Bach, wherein the step of training a machine learning system with the behavioral data and neurophysiological data and assessments involves two machine learning layers, including: a first machine learning layer in which the neurophysiological data is decomposed into neurophysiological states that a person experienced; and a second machine learning layer that receives temporal sequences of neurophysiological states and correlates different sequential patterns of said states with probabilities of performing the activity well, for the advantages of improving the performance of the system in predicting the probable performance in the set of tasks. Further, the provision of two machine learning layers to perform the two tasks, rather than having a single machine learning system configured to perform the tasks, is considered to be an obvious duplication of elements yielding no new or unexpected results under MPEP 2144.04(VI)(B).
With respect to claim 23, the recited steps of using the machine learning systems to estimate and train with functional connectivity patterns and predicting a quality of subsequent performance as recited are suggested at paragraphs [0019], [0317-21] and [0372] of Bach. With respect to claim 24, the recited step of identifying relationships between leading sequences of functional connectivity patterns and performance quality is considered to be an obvious variant on the teachings of Bach as discussed with respect to claim 23, particularly given that the meaning of “leading sequences” is not clear as discussed above.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Bach in view of Siebach. Bach discloses the method of claim 21 with the exception of the activity being performing a role of a business executive as recited. This feature is known in the art, as taught for example by Abrams in the abstract and at paragraph [0027], and would have been obvious to one of ordinary skill in the art as an obvious substitution of one known element for another and for the purpose of using the system in making informed decisions on whether to employ an individual in the role (see paragraph [0006] of Siebach).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KURT FERNSTROM whose telephone number is (571)272-4422. The examiner can normally be reached M-F 10-6.
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/KURT FERNSTROM/Primary Examiner, Art Unit 3715
December 11, 2025