Office Action 2: Final Rejection (2023-06-14)
Rejection types: 103 - Obviousness

CLAIMS:
Amendments to the Claims: The following listing of claims replaces all prior listings of claims in the application.

Listing of claims:

1\. (Currently Amended) A computer system comprising:
a computing device;
a bio-signal sensor in communication with the computing device; and
a computer server in communication with the computing device over a communications network, the computer server configured to:
receive, from the computing device, time-coded bio-signal data acquired using the bio-signal sensor, the time-coded bio-signal data including metadata associated with a user;
acquire time-coded feature event data;
extract feature events from the time-coded feature event data corresponding to feature event time codes;
determine segments of the time-coded bio-signal data that correlate to the feature event time codes to identify a pattern from the time-coded bio-signal data representing a response of the user to the feature events;
generate one or more encryption keys based at least in part on a comparison of the pattern from the time-coded bio-signal data to a model of expected user behaviour associated with the user and expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user, each of the encryption keys associated with a pre-defined data access right; and
encrypt data using one or more of the encryption keys corresponding to a selected data access right to limit access to the data.

2\. (Currently Amended) The system of claim 1, wherein the pre-defined data access right includes at least one of a time-limited access right and a fidelity-limited access right.
3\. (Cancelled)

4\. (Previously Presented) The system of claim 1, wherein the model of expected user behaviour associated with the user includes a brain model associated with the user representing brain state responses of the user to select events.

5\. (Previously Presented) The system of claim 1, wherein the encryption keys are unique to the user.

6\. (Currently Amended) The system of claim 1, wherein the computer server is further configured to:
receive additional time-coded bio-signal data and additional time-coded feature event data associated with an additional user;
extract additional feature events from the additional time-coded feature event data corresponding to additional feature event time codes;
determine segments of the additional time-coded bio-signal data that correlate to the additional feature event time codes to identify a pattern from the additional time-coded bio-signal data representing a response of the additional user to the additional feature events;
generate one or more decryption keys based at least in part on a comparison of the pattern from the additional time-coded bio-signal data to a model of expected user behaviour associated with the additional user and expected variance between the pattern from the additional time-coded bio-signal data and the model associated with the additional user; and
using one or more of the decryption keys, decrypt data that is encrypted by one or more of the encryption keys.

7\. (Currently Amended) The system of claim 6, wherein the computer server is further configured to:
generate one or more additional encryption keys based at least in part on a comparison of the pattern from the additional time-coded bio-signal data to the model of expected user behaviour associated with the additional user and the expected variance between the pattern from the additional time-coded bio- signal data and the model associated with the additional user; and
generate one or more additional decryption keys based at least in part on a comparison of the pattern from the time-coded bio-signal data to the model of expected user behaviour associated with the user and the expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user.

8\. (Previously Presented) The system of claim 6, wherein the computer server is further configured to:
receive periodic bio-signal data associated with the user;
receive periodic bio-signal data associated with the additional user;
identify patterns from the periodic bio-signal data associated with the user to update the model of expected user behaviour associated with the user; and
identify patterns from the periodic bio-signal data associated with the additional user to update the model of expected user behaviour associated with the additional user.

9\. (Previously Presented) The system of claim 1, wherein the computer server is further configured to:
receive multiple user bio-signal data corresponding to multiple users, including the user, in response to a time-coded event stimulus;
identify patterns from the multiple user bio-signal data to identify users; and
initiate a shared user experience based on the identified users.

10\. (Previously Presented) The system of claim 1, wherein the selected data access right is associated with a target brain state to access the data.

11\. (Previously Presented) The system of claim 1, wherein the bio-signal sensor includes an electroencephalography (EEG) sensor, and the time-coded bio-signal data includes time-coded EEG bio-signal data.

12\. (Previously Presented) The system of claim 1, wherein the bio-signal sensor includes a functional Near- Infrared Spectroscopy (fNIRS) sensor, and the time-coded bio-signal data includes time-coded fNIRS bio-signal data.

13\. (Previously Presented) The system of claim 1, further comprising at least one additional computing devices, each of the at least one additional computing devices in communication with at least one additional bio-signal sensor, and the computer server is further configured to:
receive additional time-coded bio-signal data from the at least one additional computing devices; and
update the model of expected user behaviour associated with the user based on the additional time-coded bio-signal data.

14\. (Currently Amended) A computer-implemented method comprising:
receiving time-coded bio-signal data acquired using a bio-signal sensor, the time-coded bio-signal data including metadata associated with a user;
acquiring time-coded feature event data;
extracting feature events from the time-coded feature event data corresponding to feature event time codes;
determining segments of the time-coded bio-signal data that correlate to the feature event time codes to identify a pattern from the time-coded bio-signal data representing a response of the user to the feature events;
generating one or more encryption keys based at least in part on a comparison of the pattern from the time-coded bio-signal data to a model of expected user behaviour associated with the user and expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user, each of the encryption keys associated with a pre-defined data access right; and
encrypting data using one or more of the encryption keys corresponding to a selected data access right to limit access to the data.

15\. (Currently Amended) The method of claim 14, wherein the pre-defined data access right includes at least one of a time-limited access right and a fidelity-limited access right.

16\. (Cancelled)

17\. (Previously Presented) The method of claim 14, wherein the model of expected user behaviour associated with the user includes a brain model associated with the user representing brain state responses of the user to select events.

18\. (Previously Presented) The method of claim 14, wherein the encryption keys are unique to the user.

19\. (Currently Amended) The method of claim 14, further comprising:
receiving additional time-coded bio-signal data and additional time-coded feature event data associated with an additional user; extracting additional feature events from the additional time-coded feature event data corresponding to additional feature event time codes;
determining segments of the additional time-coded bio-signal data that correlate to the additional feature event time codes to identify a pattern from the additional time-coded bio-signal data representing a response of the additional user to the additional feature events;
generating one or more decryption keys based at least in part on a comparison of the pattern from the additional time-coded bio-signal data to a model of expected user behaviour associated with the additional user and expected variance between the pattern from the additional time-coded bio-signal data and the model associated with the additional user; and
using one or more of the decryption keys, decrypting data that is encrypted by one or more of the encryption keys.

20\. (Currently Amended) The method of claim 19, further comprising:
generating one or more additional encryption keys based at least in part on a comparison of the pattern from the additional time-coded bio-signal data to the model of expected user behaviour associated with the additional user and the expected variance between the pattern from the additional time-coded bio- signal data and the model associated with the additional user; and
generating one or more additional decryption keys based at least in part on a comparison of the pattern from the additional time-coded bio-signal data to the model of expected user behaviour associated with the user and the expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user.

21\. (Previously Presented) The method of claim 19, wherein the computer server is further configured to:
receive periodic bio-signal data associated with the user;
receive periodic bio-signal data associated with the additional user;
identify patterns from the periodic bio-signal data associated with the user to update the model of expected user behaviour associated with the user; and
identify patterns from the periodic bio-signal data associated with the additional user to update the model of expected user behaviour associated with the additional user.

22\. (Previously Presented) The method of claim 14, wherein the computer server is further configured to:
receive multiple user bio-signal data corresponding to multiple users, including the user, in response to a time-coded event stimulus;
identify patterns from the multiple user bio-signal data to identify users; and
initiate a shared user experience based on the identified users.

23\. (Previously Presented) The method of claim 14, wherein the selected data access right is associated with a target brain state to access the data.

24\. (Currently Amended) A non-transitory computer readable medium comprising a computer readable memory storing computer executable instructions thereon that when executed by a computer cause the computer to:
 ' receive time-coded bio-signal data acquired using a bio-signal sensor, the time-coded bio-signal data including metadata associated with a user;
acquire time-coded feature event data;
extract feature events from the time-coded feature event data corresponding to feature event time codes;
determine segments of the time-coded bio-signal data that correlate to the feature event time codes to identify a pattern from the time-coded bio-signal data representing a response of the user to the feature events;
generate one or more encryption keys based at least in part on a comparison of the pattern from the time- coded bio-signal data to a model of expected user behaviour associated with the user and expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user, each of the encryption keys associated with a pre-defined data access right; and
encrypt data using one or more of the encryption keys corresponding to a selected data access right to limit access to the data.

25\. (New) The system of claim 1, wherein the expected variance is based on at least one of age of the user, time of day, fatigue, attention, signal quality, and placement of EEG electrodes.

26\. (New) The system of claim 1, wherein:
the model of expected user behaviour associated with the user is based on the user's behaviour during a first task; and
the pattern from the time-coded bio-signal data is based on the user's behaviour during a second task that is different than the first task.

27 (New) The system of claim 1, wherein the generate the one or more encryption keys based at least in part on the comparison of the pattern from the time-coded bio-signal data to the model of expected user behaviour associated with the user and the expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user comprises:
prompting the user to provide a current response to a task;
determining a difference between the current response to the task and an initial response to the task as provided in a prior session; and
modulating the pattern from the time-coded bio-signal data based on the difference between the current response to the task and the initial response to the task.
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OFFICE ACTION:
DETAILED ACTION
Status of Claims
The present application is being examined under the AIA  first to invent provisions.
This action is in reply to a request for continued examination (“RCE”) filed 07 February 2023 on an application that claims priority to an application that claims priority to provisional applications filed 12 September 2012.
Claims 1, 2, 4-6, 8 and 9 have been amended.
Claims 3, 7 and 10 have been canceled.
Claims 11-13 have been added.
Claims 1, 2, 4-6, 8, 9 and 11-13 are currently pending and have been examined.
Response to Amendments
The rejection of claim 14 under 35 USC 112 have been withdrawn in light of the amendments to the claims.
All rejections of claims 3 and 16 have been withdrawn in light of the cancellation of these claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a)  A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains.  Patentability shall not be negatived by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1.	Determining the scope and contents of the prior art.
2.	Ascertaining the differences between the prior art and the claims at issue.
3.	Resolving the level of ordinary skill in the pertinent art.
4.	Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-14 and 17-25 are rejected under 35 U.S.C. 103(a) as being obvious over Peot et al. (U.S. PG-Pub 2010/0185113 A1), hereinafter Peot, further in view of Breuer et al. (U.S. Patent 9,672,760 B1), hereinafter Breuer, further in view of Matthews (U.S. PG-Pub 2011/0015538 A1), hereinafter Matthews.
As per claims 1, 14, 24 and 25, Peot discloses a computer system, a non-transitory computer readable medium comprising a computer readable memory storing computer executable instructions thereon and a computer implemented method (Peot, Figs. 1 and 3) comprising:
1, 14.	a computing device (Peot, Figs. 1 and 3);
a bio-signal sensor in communication with the computing device (Various processors (computing devices) are in communication with plural bio-signal sensors, see Fig. 3 of Peot, and paragraphs 28 and 55. Peot discloses reception of stimulus and cues from multiple users/operators using processors, see paragraphs 11, 26 and 54 and Fig. 8. A cue is derived from captured bio signal data, see paragraphs 4, 9 and 10. Each user has a helmet mounted device [HMD] #12, shown in Figs. 2 #3, and are engaged in activities, such as monitoring their direct environment in a ground based environment, see Figs. 7 and 8. Activities can also include flying planes, see paragraph 8. Each HMD contains one or several processors/computing devices executing applications to monitor the users physiological activities, such as their cognitive response or the visual fixation or position, see Fig. 3 and paragraphs 27-29. Similarly, all of these signals are processed by a decision level classifier #130, which is a respective application that processes all of the inputs from the other applications, including EEG Classifier #86, FNIRS Classifier #114, Pupil Classifier #104 and Dwell Time Classifier #126, see Fig. 4. See also the plural Spatial Classifiers #162-164 of Fig. 6, which are then processed by the temporal classifier #168.); and
... the computer ... configured to:
receive ... time-coded bio-signal data acquired using the bio-signal sensor, the time-coded bio-signal data including metadata associated with a user (Peot, Figs. 3 and 4 discloses reception of bio-signal data from plural sensors.);
acquire time-coded feature event data (Various time-coded feature event data is assembled from the received bio-signal data, such as when the user noticed various stimulus, relevant and not relevant, when the user began fixation, see Fig. 5 and see paragraphs 10, 23-25.);
extract feature events from the time-coded feature event data corresponding to feature event time codes (Feature events are assembled from the feature event data, such as when the eyes (variable) remain focused on a region of less than half a degree (value), see paragraphs 29-30. See also paragraph 31.);
determine segments of the time-coded bio-signal data that correlate to the feature event time codes to identify a pattern from the time-coded bio-signal data representing a response of the user to the feature events (The system of Peot determines whether a particular stimulus at a particular segment is relevant or not relevant based on examining the received data, see Figs. 5 and 6, and paragraphs 29-31, 34 and 43-45.); and
generate data based at least in part on a comparison of the pattern from the time-coded bio-signal data to a model of expected user behaviour associated with the user (User responses are classified as relevant or non-relevant, wherein either could be considered expected user behavior, and the system provides feedback based on that classification, see paragraphs 49-54; wherein the model comprised of the classifier that assembles data from plural user inputs [i.e., not just EEG inputs] to determine whether extracted material, including EEG input, is expected, see paragraphs 51-53.).
Peot fails to explicitly disclose:
a computer server in communication with the computing device over a communications network;
generate one or more encryption keys based at least in part on a comparison of data generated via biosignal measures, each of the encryption keys associated with a pre-defined data access right;
generate data based on an expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user;
encrypt data using one or more of the encryption keys corresponding to a selected data access right to limit access to the data; and
25.	wherein the expected variance is based on at least one of age of the user, time of day, fatigue, attention, signal quality, and placement of EEG electrodes.
However, Breuer discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis at the time of the invention/filing to provide:
a computer server in communication with the computing device over a communications network (Breuer, Figs. 1-3.);
generate one or more encryption keys based at least in part on a comparison of data generated via biosignal measures, each of the encryption keys associated with a pre-defined data access right (Breuer, Fig. 4 discloses generation of a data encryption key associated with an access right based on comparison of patterns to a user model.); and
encrypt data using one or more of the encryption keys corresponding to a selected data access right to limit access to the data (Breuer, Fig. 4 discloses generation of a data encryption key associated with an access right based on comparison of patterns to a user model. It is the Office’s position that an encryption key having any access right associated would comprise a selected data access right.)
in order to provide computers enabled to communicate with other computers over a network wherein a “reproducible electroencephalogram encryption key is defined that is unique to the user as a function of one or both of the trained weighting factors and the first weighted set of electroencephalogram data signals” (Breuer, Abstract.).
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave signal analysis at the time of the invention/filing to modify the brainwave monitoring system of Peot to include a computer server in communication with the computing device over a communications network, generate one or more encryption keys based at least in part on a comparison of data generated via biosignal measures, each of the encryption keys associated with a pre-defined data access right; and encrypt data using one or more of the encryption keys corresponding to a selected data access right to limit access to the data, as taught by Breuer, in order to provide a brainwave monitoring system enabled to communicate with other computers over a network wherein a “reproducible electroencephalogram encryption key is defined that is unique to the user as a function of one or both of the trained weighting factors and the first weighted set of electroencephalogram data signals” (Breuer, Abstract.).
Neither Peot nor Breuer disclose a system that can:
1,14.	generate data based on an expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user; and
25.	wherein the expected variance is based on at least one of age of the user, time of day, fatigue, attention, signal quality, and placement of EEG electrodes.
Matthews discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis at the time of the invention/filing to provide:
1,14.	generate data based on an expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user (Matthews discloses a system that analyzes EEG signals to determine whether they are more then an expected amount of difference [standard deviations] from modeled data based on signal noise deriving from electrode placement, see Abstract and paragraph 82.); and
25.	wherein the expected variance is based on at least one of age of the user, time of day, fatigue, attention, signal quality, and placement of EEG electrodes (Matthews paragraph 82.), in order to provide a “ system that [can] quickly and efficiently determines the most abnormal brain region from EEG” (Matthews, paragraph 6.).
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave signal analysis at the time of the invention/filing to modify the brainwave monitoring system of Peot to include generate data based on an expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user; and wherein the expected variance is based on at least one of age of the user, time of day, fatigue, attention, signal quality, and placement of EEG electrodes, as taught by Matthews, in order to provide a brainwave monitoring system that can “quickly and efficiently determines the most abnormal brain region from EEG” (Matthews, paragraph 6.). Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Peot, Breuer and Matthews are all directed to the processing of biologically generated data.
As per claims 6-9, 11-13, 19-22 and 26, Peot/Breuer/Matthews disclose claims 1 and 14, discussed above. Peot/Breuer/Matthews also disclose:
6, 19. 	receive additional time-coded bio-signal data and additional time-coded feature event data associated with an additional user (Various processors (computing devices) are in communication with plural bio-signal sensors, see Fig. 3 of Peot, and paragraphs 28 and 55. Peot discloses reception of stimulus and cues from multiple users/operators using processors, see paragraphs 11, 26 and 54 and Fig. 8.);
extract additional feature events from the additional time-coded feature event data corresponding to additional feature event time codes (Feature events are assembled from the feature event data, such as when the eyes (variable) remain focused on a region of less than half a degree (value), see paragraphs 29-30. See also paragraph 31.);
determine segments of the additional time-coded bio-signal data that correlate to the additional feature event time codes to identify a pattern from the additional time-coded bio-signal data representing a response of the additional user to the additional feature events (The system of Peot determines whether a particular stimulus at a particular segment is relevant or not relevant based on examining the received data, see Figs. 5 and 6, and paragraphs 29-31, 34 and 43-45.);
generate one or more decryption keys based at least in part on a comparison of the pattern from the additional time-coded bio-signal data to a model of expected user behaviour associated with the additional user and expected variance between the pattern from the additional time-coded bio-signal data and the model of expected user behaviour associated with the additional user (Peot, user responses are classified as relevant or non-relevant, wherein either could be considered expected user behavior, and the system provides feedback based on that classification, see paragraphs 49-54. Same as in the independent claims, Breuer, Fig. 4 discloses generation of a data decryption key associated with an access right based on comparison of patterns to a user model and Matthews discloses expected variance between EEG signals and modeled behavior.); and
using one or more of the decryption keys, decrypt data that is encrypted by one or more of the encryption keys (Breuer, Fig. 4 discloses generation of an data encryption key associated with an access right based on comparison of patterns to a user model. It is the Office’s position that a decryption key having any access right associated would comprise a selected data access right.);
7, 20. 	generate one or more additional encryption keys based at least in part on a comparison of the pattern from the additional time-coded bio-signal data to the model of expected user behaviour associated with the additional user and the expected variance between the pattern from the additional time-coded bio-signal data and the model of expected user behaviour associated with the additional user (Peot, user responses are classified as relevant or non-relevant, wherein either could be considered expected user behavior, and the system provides feedback based on that classification, see paragraphs 49-54. Same as in the independent claims, Breuer, Fig. 4 discloses generation of a data decryption key associated with an access right based on comparison of patterns to a user model and Matthews discloses expected variance between EEG signals and modeled behavior.); and
generate one or more additional decryption keys based at least in part on a comparison of the pattern from the time-coded bio-signal data to the model of expected user behaviour associated with the user and the expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user (Same as in the independent claims, Breuer, Fig. 4 discloses generation of a data decryption key associated with an access right based on comparison of patterns to a user model and Matthews discloses expected variance between EEG signals and modeled behavior.); and
8, 21. 	receive periodic bio-signal data associated with the user (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9.);
receive periodic bio-signal data associated with the additional user (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9.);
identify patterns from the periodic bio-signal data associated with the user to update the model of expected user behaviour associated with the user states (The model of Peot determines whether a particular stimulus is relevant or not relevant based on examining the received data, see Fig. 5 and paragraphs 29-31, 34 and 43-45 and 49-53. Peot describes creation of plural parallel pipelines, classifier pipelines #162-164, which are based on analysis of features extracted from a time window of the response signals; these pipelines are associated with the plurality of applications, such as various classifiers of Fig. 4, which are used to inspect the signal data. Applicant’s specification defines a pipeline as an instance” generated “in order to analyze the user’s private bio-signal data using particular analysis or processing parameters applied during the analysis or processing”, see present specification at paragraph 56; accordingly, the plural parallel spatial classifiers would comprise a pipeline, as would the decision level classifier #130 of Fig. 6.); and
identify patterns from the periodic bio-signal data associated with the additional user to update the model of expected user behaviour associated with the additional user (The model of Peot determines whether a particular stimulus is relevant or not relevant based on examining the received data, see Fig. 5 and paragraphs 29-31, 34 and 43-45 and 49-53. Peot describes creation of plural parallel pipelines, classifier pipelines #162-164, which are based on analysis of features extracted from a time window of the response signals; these pipelines are associated with the plurality of applications, such as various classifiers of Fig. 4, which are used to inspect the signal data. Applicant’s specification defines a pipeline as an instance” generated “in order to analyze the user’s private bio-signal data using particular analysis or processing parameters applied during the analysis or processing”, see present specification at paragraph 56; accordingly, the plural parallel spatial classifiers would comprise a pipeline, as would the decision level classifier #130 of Fig. 6.);
9, 22. 	receive multiple user bio-signal data corresponding to multiple users, including the user, in response to a time-coded event stimulus (Various processors (computing devices) are in communication with plural bio-signal sensors, see Fig. 3 of Peot, and paragraphs 28 and 55. Peot discloses reception of stimulus and cues from multiple users/operators using processors, see paragraphs 11, 26 and 54 and Fig. 8.);
identify patterns from the multiple user bio-signal data to identify users (Peot identifies patterns from biosignal data to determine response relevance and is responsible for maintaining data of all users, see Figs. 1, 3-6 and 8.); and
initiate a shared user experience based on the identified users (System of Peot describes a shared experience for all users, identified or not, see Figs. 1, 3-6 and 8.);
11. 	wherein the bio-signal sensor includes an electroencephalography (EEG) sensor, and the time-coded bio-signal data includes time-coded EEG bio-signal data (Peot, Fig. 4.);
12. 	wherein the bio-signal sensor includes a functional Near-Infrared Spectroscopy (fNIRS) sensor, and the time-coded bio-signal data includes time-coded fNIRS bio-signal data (Peot, Fig. 4.);
13. 	at least one additional computing devices, each of the at least one additional computing devices in communication with at least one additional bio-signal sensor, and the computer server is further configured (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9. Breuer discloses a server, see Figs. 1-3.) to:
receive additional time-coded bio-signal data from the at least one additional computing devices (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9.); and
update the model of expected user behaviour associated with the user based on the additional time-coded bio-signal data (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9.); and
26.	the model of expected user behaviour associated with the user is based on the user's behaviour during a first task (Model is the classifier that assembles data from plural user inputs [i.e., not just EEG inputs] to determine whether extracted material, including EEG input, is expected, see paragraphs 51-53. Model is trained to make decisions based on all inputs, which would comprise a task different then just one input, such as EEG data, see paragraphs 34, 36, 38, 42, 44 and 50-53); and
the pattern from the time-coded bio-signal data is based on the user's behaviour during a second task that is different than the first task (EEG data is examined and classified, user EEG task, see paragraphs 35, 36 and 51.).
As per claims 4, 5, 10, 17, 18 and 23, Peot/Breuer/Matthews disclose claims 1 and 14, discussed above. Peot fails to explicitly disclose:
4, 17. 	wherein the model of expected user behaviour associated with the user includes a brain model associated with the user representing brain state responses of the user to select events;
5, 18. 	wherein the encryption keys are unique to the user; and
10, 23.	wherein the selected data access right is associated with a target brain state to access the data.
However, Breuer discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis at the time of the invention/filing to provide:
4, 17. 	wherein the model of expected user behaviour associated with the user includes a brain model associated with the user representing brain state responses of the user to select events (See the ANN model of Breuer, Fig. 4.);
5, 18. 	wherein the encryption keys are unique to the user (Breuer, Abstract.);
10, 23.	wherein the selected data access right is associated with a target brain state to access the data (Breuer, Fig. 4, the data access right is associated with the encryption key based on the monitored users brain state.);
in order to provide computers enabled to communicate with other computers over a network wherein a “reproducible electroencephalogram encryption key is defined that is unique to the user as a function of one or both of the trained weighting factors and the first weighted set of electroencephalogram data signals” (Breuer, Abstract.).
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave signal analysis at the time of the invention/filing to modify the brainwave monitoring system of Peot to include wherein the model of expected user behaviour associated with the user includes a brain model associated with the user representing brain state responses of the user to select events;  wherein the encryption keys are unique to the user; and wherein the selected data access right is associated with a target brain state to access the data, as taught by Breuer, in order to provide a brainwave monitoring system enabled to communicate with other computers over a network wherein a “reproducible electroencephalogram encryption key is defined that is unique to the user as a function of one or both of the trained weighting factors and the first weighted set of electroencephalogram data signals” (Breuer, Abstract.).
Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Claims 2 and 15 are rejected under 35 U.S.C. 103(a) as being obvious over Peot/Breuer/Matthews, further in view of Ornstein et al. (WIPO 2005/043802 A1), hereinafter Ornstein.
As per claims 2 and 15, Peot/Breuer/Matthews disclose claims 1 and 14, discussed above. Although Breuer discloses a fidelity limited access right, see Figs. 4 and 5, Peot fails to explicitly disclose wherein the pre-defined data access right includes at least one of a time-limited access right and a fidelity-limited access right.
However, Ornstein discloses that it would have been obvious to one of ordinary skill in the art of secure electronic communications at the time of the invention/filing to provide wherein the pre-defined data access right includes a time-limited access right (Ornstein, Fig. 8 #882.) so as to result in “a secure technique of distributing electronic content such as videos, text, data, photos, financial data, sales solicitations, or the like” (Ornstein, Abstract.).
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave signal analysis at the time of the invention/filing to modify the brainwave monitoring system of Peot to include wherein the pre-defined data access right includes a time-limited access right, as taught by Ornstein, in order to provide a brainwave monitoring system that used “a secure technique of distributing electronic content such as videos, text, data, photos, financial data, sales solicitations, or the like” (Ornstein, Abstract.). Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Peot, Breuer, Matthews and Orenstein are all directed to the processing of biologically generated data.
Claim 27 is rejected under 35 U.S.C. 103(a) as being obvious over Peot/Breuer/Matthews, further in view of Gevins et al. (U.S. Patent 4,736,751), hereinafter Gevins.
As per claim 27, Peot/Breuer/Matthews disclose claim 1, discussed above. Peot/Breuer/Matthews discloses wherein the generate the one or more encryption keys based at least in part on the comparison of the pattern from the time-coded bio-signal data to the model of expected user behaviour associated with the user and the expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user (As shown above.).
Peot/Breuer/Matthews fails to explicitly disclose:
prompting the user to provide a current response to a task;
determining a difference between the current response to the task and an initial response to the task as provided in a prior session; and
modulating the pattern from the time-coded bio-signal data based on the difference between the current response to the task and the initial response to the task.
However, Gevins discloses that it would have been obvious to one of ordinary skill in the art of secure electronic communications at the time of the invention/filing to provide:
prompting the user to provide a current response to a task (Gevins discloses a system in which a user performs a task plural times, aka trial of tasks, see C6L37-C79L6
determining a difference between the current response to the task and an initial response to the task as provided in a prior session (Gevins discloses analyzing those plural task performances to identify a difference and remove artifacts, which would comprise modulating the signal based on the response, see C9L9-L58.); and
modulating the pattern from the time-coded bio-signal data based on the difference between the current response to the task and the initial response to the task (Gevins discloses analyzing those plural task performances to identify a difference and remove artifacts, which would comprise modulating the signal based on the response, see C9L9-L58.);
in order to provide additional methods of inspecting patient brainwave data.
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave signal analysis at the time of the invention/filing to modify the brainwave monitoring system of Peot to include generate data based on an prompting the user to provide a current response to a task; determining a difference between the current response to the task and an initial response to the task as provided in a prior session; and modulating the pattern from the time-coded bio-signal data based on the difference between the current response to the task and the initial response to the task, as taught by Gevins, in order to provide a brainwave monitoring system that provide additional methods of inspecting patient brainwave data. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Peot, Breuer, Matthews and Gevins are all directed to the processing of biologically generated data.
Response to Arguments
Applicant’s arguments filed 7 February 2023	 concerning the rejection of all claims under 35 U.S.C.  103 have been fully considered but they are not persuasive.
With regard to the rejection of the claims under 35 USC 103, Applicant argues on pages 9-12 that Peot and Breuer fail to disclose the amendments to the claims.
Applicant's arguments have been fully considered but are moot in view of the new ground(s) of rejection, specifically with reference to the new reference necessitated by amendment, Matthews and Gevins, as detailed above, or because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
In conclusion, all of the limitations which Applicant disputes as missing in the applied references, including the features newly added by amendment, have been fully addressed by the Office as either being fully disclosed or obvious in view of the collective teachings of Peot, Breuer, Matthews, Gevins and Ornstein, based on the logic and sound scientific reasoning of one ordinarily skilled in the art at the time of the invention, as detailed in the remarks and explanations given in the preceding sections of the present Office Action and in the prior Office Action (7 October 2022), and incorporated herein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.  Accordingly, THIS ACTION IS MADE FINAL.  See MPEP § 706.07(a).  Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action.  In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action.  In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382.

APPLICANT REBUTTAL: REMARKS
/
ARGUMENTS
In accordance with the foregoing, claims 1, 14, 24, and 25 have been amended. Support for these amendments can be found at least in the claims as originally filed and in paragraph [0427]. No new matter is being presented. Therefore claims 1-27 are pending in the application and reconsideration is respectfully requested.
Claim rejection - 35 USC 103
) further in view of Matthews (US 2011/0015538Al). The Examiner has further rejected claims 2 and 15 further in view of Ornstein (WO 2005/043802 Al), and claim 27 further in view of Gevins (US 4736751).
Claim 1 and 14
Without conceding to the merits of the rejection and solely to clarify the pending subject matter, Applicant amends claims 1, 14, and 24. Support for these amendments can be found at least in the claims as originally filed and in paragraphs [0427]. Claim 1 has been amended to clarify that (emphasis added):
...encrypt data using one or more of the encryption keys corresponding to a selected data access right
and a tar
g
et brain state
to limit access to the data...
The claimed subject matter involves data that is encrypted "using one or more of the encryption keys corresponding to a selected data access right and a target brain state to limit access to the data". Some embodiments of the pending subject matter can, for example, limit rights to unlock a piece of equipment based on whether the user meets a target brain state (present application, paragraph [0427]).
The Examiner concedes that Peot fails to disclose "encrypt data using one or more of the encryption keys corresponding to a selected data access right to limit access to the data". Applicant further submits that Peot fails to disclose "encrypt data using one or more of the encryption keys corresponding to a selected data access right
and a tar
g
et brain state
to limit access to the data" as now claimed.
Breuer appears to disclose a user-specific, electroencephalogram data-based secure encryption generator that maps artificial neural network neuron elements to electroencephalogram data signals generated from scanning neural activity of a user while the user executes a mental activity (Breuer, abstract). Breuer fails to disclose at least "encrypt data using one or more of the encryption keys corresponding to a selected data access right
and a tar
get
brain state
to limit access to the data" as now claimed.
These deficiencies are not remedied by Matthews, Ornstein, or Gevins.
Applicant asserts that none of the prior art references cited, either alone or in combination, disclose the pending subject matter. Applicant further asserts that the prior art references cited do not teach, suggest, or disclose the advantages arising from the pending subject matter. Applicant asserts that claims 1, 14, and 24, as amended, and claims 2, 4-13, 15, 17-23, and 25-27, by virtue of their dependencies, are novel and inventive over the prior art cited.
Claim 25
Without conceding to the merit of the rejection and solely to clarify the pending subject matter, Applicant has amended claim 25. Support forthese amendments can be found at least in the claims as originally filed. Claim 25, as amended, recites: "the expected variance is based on at least one of age of the user, time of day, fatigue, and attention". Matthews describes excluding electrode data from sites that are "likely to represent artifact contamination" (Matthews, paragraph [0082]). Matthews does not disclose expected variance based on age of the
user, time of day, fatigue, or attention. Applicant asserts that for these reasons and those provided above that claim 25 is novel and inventive over the prior art cited. Reconsideration and withdrawal of the rejection are respectfully requested.
The application is thus believed in condition for immediate allowance, and an early notice to that effect is earnestly solicited. In the event that there remains any question concerning this response or the application in general, the Examiner is invited to communicate with the undersigned so that prosecution of this application may be expedited.
The Director is hereby authorized to charge any additional fees which may be required regarding this application under 37 C.F.R. § 1.16-1.17, or credit any overpayment to Deposit Account No. 19-5113. Should no proper payment be enclosed herewith, as by payment being in the wrong amount, unsigned, post-dated, otherwise improper or even entirely missing, the Director is authorized to charge the unpaid amount to Deposit Account No. 19-5113. If any extensions of time are needed for timely acceptance of papers submitted herewith, applicant hereby petitions for such extension under 37 C.F.R. §1.136 and authorizes payment of any such extension fees to Deposit Account No. 19-5113.
Respectfully submitted,

NEXT OFFICE ACTION: DETAILED ACTION
Status of Claims
The present application is being examined under the AIA  first to invent provisions.
This action is in reply to a request for continued examination (“RCE”) filed 13 October 2023 on an application that claims priority to an application that claims priority to provisional applications filed 12 September 2012.
The nearest
Claims 1, 2, 4, 14, 24 and 25 have been amended.
Claims 1, 2, 4-15 and 17-27 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.  Applicant's submission filed on 13 October 2023 has been entered.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a)  A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains.  Patentability shall not be negatived by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1.	Determining the scope and contents of the prior art.
2.	Ascertaining the differences between the prior art and the claims at issue.
3.	Resolving the level of ordinary skill in the pertinent art.
4.	Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-14 and 17-25 are rejected under 35 U.S.C. 103(a) as being obvious over Peot et al. (U.S. PG-Pub 2010/0185113 A1), hereinafter Peot, further in view of Breuer et al. (U.S. Patent 9,672,760 B1), hereinafter Breuer, further in view of Makeig et al. “Changes in alertness are a principal component of variance in the EEG spectrum” (Neuroreport. 1995 Dec 29;7(1):213-6. PMID: 8742454), hereinafter Makeig.
As per claims 1, 14, 24 and 25, Peot discloses a computer system, a non-transitory computer readable medium comprising a computer readable memory storing computer executable instructions thereon and a computer implemented method (Peot, Figs. 1 and 3) comprising:
1, 14.	a computing device (Peot, Figs. 1 and 3);
a bio-signal sensor in communication with the computing device (Various processors (computing devices) are in communication with plural bio-signal sensors, see Fig. 3 of Peot, and paragraphs 28 and 55. Peot discloses reception of stimulus and cues from multiple users/operators using processors, see paragraphs 11, 26 and 54 and Fig. 8. A cue is derived from captured bio signal data, see paragraphs 4, 9 and 10. Each user has a helmet mounted device [HMD] #12, shown in Figs. 2 #3, and are engaged in activities, such as monitoring their direct environment in a ground based environment, see Figs. 7 and 8. Activities can also include flying planes, see paragraph 8. Each HMD contains one or several processors/computing devices executing applications to monitor the users physiological activities, such as their cognitive response or the visual fixation or position, see Fig. 3 and paragraphs 27-29. Similarly, all of these signals are processed by a decision level classifier #130, which is a respective application that processes all of the inputs from the other applications, including EEG Classifier #86, FNIRS Classifier #114, Pupil Classifier #104 and Dwell Time Classifier #126, see Fig. 4. See also the plural Spatial Classifiers #162-164 of Fig. 6, which are then processed by the temporal classifier #168.); and
... the computer ... configured to:
receive ... time-coded bio-signal data acquired using the bio-signal sensor, the time-coded bio-signal data including metadata associated with a user (Peot, Figs. 3 and 4 discloses reception of bio-signal data from plural sensors.);
acquire time-coded feature event data (Various time-coded feature event data is assembled from the received bio-signal data, such as when the user noticed various stimulus, relevant and not relevant, when the user began fixation, see Fig. 5 and see paragraphs 10, 23-25.);
extract feature events from the time-coded feature event data corresponding to feature event time codes (Feature events are assembled from the feature event data, such as when the eyes (variable) remain focused on a region of less than half a degree (value), see paragraphs 29-30. See also paragraph 31.);
determine segments of the time-coded bio-signal data that correlate to the feature event time codes to identify a pattern from the time-coded bio-signal data representing a response of the user to the feature events (The system of Peot determines whether a particular stimulus at a particular segment is relevant or not relevant based on examining the received data, see Figs. 5 and 6, and paragraphs 29-31, 34 and 43-45.); and
generate data based at least in part on a comparison of the pattern from the time-coded bio-signal data to a model of expected user behaviour associated with the user (User responses are classified as relevant or non-relevant, wherein either could be considered expected user behavior, and the system provides feedback based on that classification, see paragraphs 49-54; wherein the model comprised of the classifier that assembles data from plural user inputs [i.e., not just EEG inputs] to determine whether extracted material, including EEG input, is expected, see paragraphs 51-53.).
Peot fails to explicitly disclose:
a computer server in communication with the computing device over a communications network;
generate one or more encryption keys based at least in part on a comparison of data generated via biosignal measures, each of the encryption keys associated with a pre-defined data access right;
consideration of an expected variance between measured and expected EEG signals for a given measured user; and
encrypt data using one or more of the encryption keys corresponding to a selected data access right and a target brain state to limit access to the data; and
25.	wherein the expected variance is based on at least one of age of the user, time of day, fatigue, and attention.
However, Breuer discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis at the time of the invention/filing to provide:
a computer server in communication with the computing device over a communications network (Breuer, Figs. 1-3.);
generate one or more encryption keys based at least in part on a comparison of data generated via biosignal measures, each of the encryption keys associated with a pre-defined data access right (Breuer, Fig. 4 discloses generation of a data encryption key associated with an access right based on comparison of patterns to a user model.); and
encrypt data using one or more of the encryption keys corresponding to a selected data access right and a target brain state to limit access to the data (Breuer, Fig. 4 discloses generation of a data encryption key associated with an access right based on comparison of patterns to a user model, and generates an encryption key based on a target brain state, that is, the mental activity of the user, see Fig. 4 #112 and C13L20-25.  It is the Office’s position that an encryption key having any access right associated would comprise a selected data access right.),
in order to provide computers enabled to communicate with other computers over a network wherein a “reproducible electroencephalogram encryption key is defined that is unique to the user as a function of one or both of the trained weighting factors and the first weighted set of electroencephalogram data signals” (Breuer, Abstract.).
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave signal analysis at the time of the invention/filing to modify the brainwave monitoring system of Peot to include a computer server in communication with the computing device over a communications network, generate one or more encryption keys based at least in part on a comparison of data generated via biosignal measures, each of the encryption keys associated with a pre-defined data access right; and encrypt data using one or more of the encryption keys corresponding to a selected data access right and a target brain state to limit access to the data, as taught by Breuer, in order to provide a brainwave monitoring system enabled to communicate with other computers over a network wherein a “reproducible electroencephalogram encryption key is defined that is unique to the user as a function of one or both of the trained weighting factors and the first weighted set of electroencephalogram data signals” (Breuer, Abstract.).
Neither Peot nor Breuer disclose a system that can:
1,14.	consideration of an expected variance between measured and expected EEG signals for a given measured user; and
25.	wherein the expected variance is based on at least one of age of the user, time of day, fatigue, and attention.
Makeig however discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis at the time of the invention/filing to provide:
1,14.	consideration of an expected variance between measured and expected EEG signals for a given measured user (Makeig discloses that a user’s alertness is known to show an expected variance based on their alertness, see Abstract, page 213.); and
25.	wherein the expected variance is based on at least one of age of the user, time of day, fatigue, attention, signal quality, and placement of EEG electrodes (Makeig discloses that a user’s alertness is known to show an expected variance based on their alertness, see Abstract, page 213.).
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave signal analysis at the time of the invention/filing to modify the brainwave monitoring system of Peot/Breuer to include consideration of an expected variance between measured and expected EEG signals for a given measured user and wherein the expected variance is based on at least one of age of the user, time of day, fatigue, attention, signal quality, and placement of EEG electrodes, as taught by Makeig, in order to provide a brainwave monitoring system that provides consideration of a user’s state of alertness when measuring their brainwave signals. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Peot, Breuer and Makeig are all directed to the processing of biologically generated data.
A recitation directed to the manner in which a claimed apparatus is intended to be used does not distinguish the claimed apparatus from the prior art, if the prior art has the capability to so perform, see MPEP 2114 (II) and Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987). “Language that suggest or makes optional but does not require steps to be performed or does not limit a claim to a particular structure does not limit the scope of a claim or claim limitation”, see MPEP 2111.04.
The following limitation is interpreted as an intended use of the claimed invention:
1,14,25.	… to limit access to the data.
The prior art is capable of performing the intended use recitation, therefore the prior art meets the limitations.
As per claims 6-9, 11-13, 19-22 and 26, Peot/Breuer/Makeig disclose claims 1 and 14, discussed above. Peot/Breuer/Makeig also disclose:
6, 19. 	receive additional time-coded bio-signal data and additional time-coded feature event data associated with an additional user (Various processors (computing devices) are in communication with plural bio-signal sensors, see Fig. 3 of Peot, and paragraphs 28 and 55. Peot discloses reception of stimulus and cues from multiple users/operators using processors, see paragraphs 11, 26 and 54 and Fig. 8.);
extract additional feature events from the additional time-coded feature event data corresponding to additional feature event time codes (Feature events are assembled from the feature event data, such as when the eyes (variable) remain focused on a region of less than half a degree (value), see paragraphs 29-30. See also paragraph 31.);
determine segments of the additional time-coded bio-signal data that correlate to the additional feature event time codes to identify a pattern from the additional time-coded bio-signal data representing a response of the additional user to the additional feature events (The system of Peot determines whether a particular stimulus at a particular segment is relevant or not relevant based on examining the received data, see Figs. 5 and 6, and paragraphs 29-31, 34 and 43-45.);
generate one or more decryption keys based at least in part on a comparison of the pattern from the additional time-coded bio-signal data to a model of expected user behaviour associated with the additional user and expected variance between the pattern from the additional time-coded bio-signal data and the model of expected user behaviour associated with the additional user (Peot, user responses are classified as relevant or non-relevant, wherein either could be considered expected user behavior, and the system provides feedback based on that classification, see paragraphs 49-54. Same as in the independent claims, Breuer, Fig. 4 discloses generation of a data decryption key associated with an access right based on comparison of patterns to a user model and Matthews discloses expected variance between EEG signals and modeled behavior.); and
using one or more of the decryption keys and the target brain state, decrypt data that is encrypted by one or more of the encryption keys (Same as in the independent claims, Breuer, Fig. 4 discloses generation of an data encryption key associated with an access right based on comparison of patterns to a user model. It is the Office’s position that a decryption key having any access right associated would comprise a selected data access right.);
7, 20. 	generate one or more additional encryption keys based at least in part on a comparison of the pattern from the additional time-coded bio-signal data to the model of expected user behaviour associated with the additional user and the expected variance between the pattern from the additional time-coded bio-signal data and the model of expected user behaviour associated with the additional user (Peot, user responses are classified as relevant or non-relevant, wherein either could be considered expected user behavior, and the system provides feedback based on that classification, see paragraphs 49-54. Same as in the independent claims, Breuer, Fig. 4 discloses generation of a data decryption key associated with an access right based on comparison of patterns to a user model and Matthews discloses expected variance between EEG signals and modeled behavior.); and
generate one or more additional decryption keys based at least in part on a comparison of the pattern from the time-coded bio-signal data to the model of expected user behaviour associated with the user and the expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user (Same as in the independent claims, Breuer, Fig. 4 discloses generation of a data decryption key associated with an access right based on comparison of patterns to a user model and Matthews discloses expected variance between EEG signals and modeled behavior.); and
8, 21. 	receive periodic bio-signal data associated with the user (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9.);
receive periodic bio-signal data associated with the additional user (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9.);
identify patterns from the periodic bio-signal data associated with the user to update the model of expected user behaviour associated with the user states (The model of Peot determines whether a particular stimulus is relevant or not relevant based on examining the received data, see Fig. 5 and paragraphs 29-31, 34 and 43-45 and 49-53. Peot describes creation of plural parallel pipelines, classifier pipelines #162-164, which are based on analysis of features extracted from a time window of the response signals; these pipelines are associated with the plurality of applications, such as various classifiers of Fig. 4, which are used to inspect the signal data. Applicant’s specification defines a pipeline as an instance” generated “in order to analyze the user’s private bio-signal data using particular analysis or processing parameters applied during the analysis or processing”, see present specification at paragraph 56; accordingly, the plural parallel spatial classifiers would comprise a pipeline, as would the decision level classifier #130 of Fig. 6.); and
identify patterns from the periodic bio-signal data associated with the additional user to update the model of expected user behaviour associated with the additional user (The model of Peot determines whether a particular stimulus is relevant or not relevant based on examining the received data, see Fig. 5 and paragraphs 29-31, 34 and 43-45 and 49-53. Peot describes creation of plural parallel pipelines, classifier pipelines #162-164, which are based on analysis of features extracted from a time window of the response signals; these pipelines are associated with the plurality of applications, such as various classifiers of Fig. 4, which are used to inspect the signal data. Applicant’s specification defines a pipeline as an instance” generated “in order to analyze the user’s private bio-signal data using particular analysis or processing parameters applied during the analysis or processing”, see present specification at paragraph 56; accordingly, the plural parallel spatial classifiers would comprise a pipeline, as would the decision level classifier #130 of Fig. 6.);
9, 22. 	receive multiple user bio-signal data corresponding to multiple users, including the user, in response to a time-coded event stimulus (Various processors (computing devices) are in communication with plural bio-signal sensors, see Fig. 3 of Peot, and paragraphs 28 and 55. Peot discloses reception of stimulus and cues from multiple users/operators using processors, see paragraphs 11, 26 and 54 and Fig. 8.);
identify patterns from the multiple user bio-signal data to identify users (Peot identifies patterns from biosignal data to determine response relevance and is responsible for maintaining data of all users, see Figs. 1, 3-6 and 8.); and
initiate a shared user experience based on the identified users (System of Peot describes a shared experience for all users, identified or not, see Figs. 1, 3-6 and 8.);
11. 	wherein the bio-signal sensor includes an electroencephalography (EEG) sensor, and the time-coded bio-signal data includes time-coded EEG bio-signal data (Peot, Fig. 4.);
12. 	wherein the bio-signal sensor includes a functional Near-Infrared Spectroscopy (fNIRS) sensor, and the time-coded bio-signal data includes time-coded fNIRS bio-signal data (Peot, Fig. 4.);
13. 	at least one additional computing devices, each of the at least one additional computing devices in communication with at least one additional bio-signal sensor, and the computer server is further configured (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9. Breuer discloses a server, see Figs. 1-3.) to:
receive additional time-coded bio-signal data from the at least one additional computing devices (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9.); and
update the model of expected user behaviour associated with the user based on the additional time-coded bio-signal data (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9.); and
26.	the model of expected user behaviour associated with the user is based on the user's behaviour during a first task (Model is the classifier that assembles data from plural user inputs [i.e., not just EEG inputs] to determine whether extracted material, including EEG input, is expected, see paragraphs 51-53. Model is trained to make decisions based on all inputs, which would comprise a task different then just one input, such as EEG data, see paragraphs 34, 36, 38, 42, 44 and 50-53); and
the pattern from the time-coded bio-signal data is based on the user's behaviour during a second task that is different than the first task (EEG data is examined and classified, user EEG task, see paragraphs 35, 36 and 51.).
As per claims 4, 5, 10, 17, 18 and 23, Peot/Breuer/Makeig disclose claims 1 and 14, discussed above. Peot fails to explicitly disclose:
4, 17. 	wherein the model of expected user behaviour associated with the user includes a brain model associated with the user representing brain state responses of the user to select events;
5, 18. 	wherein the encryption keys are unique to the user; and
10, 23.	wherein the selected data access right is associated with a target brain state to access the data.
However, Breuer discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis at the time of the invention/filing to provide:
4, 17. 	wherein the model of expected user behaviour associated with the user includes a brain model associated with the user representing brain state responses of the user to select events (See the ANN model of Breuer, Fig. 4.);
5, 18. 	wherein the encryption keys are unique to the user (Breuer, Abstract.);
10, 23.	wherein the selected data access right is associated with a target brain state to access the data (Breuer, Fig. 4, the data access right is associated with the encryption key based on the monitored users brain state.);
in order to provide computers enabled to communicate with other computers over a network wherein a “reproducible electroencephalogram encryption key is defined that is unique to the user as a function of one or both of the trained weighting factors and the first weighted set of electroencephalogram data signals” (Breuer, Abstract.).
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave signal analysis at the time of the invention/filing to modify the brainwave monitoring system of Peot to include wherein the model of expected user behaviour associated with the user includes a brain model associated with the user representing brain state responses of the user to select events;  wherein the encryption keys are unique to the user; and wherein the selected data access right is associated with a target brain state to access the data, as taught by Breuer, in order to provide a brainwave monitoring system enabled to communicate with other computers over a network wherein a “reproducible electroencephalogram encryption key is defined that is unique to the user as a function of one or both of the trained weighting factors and the first weighted set of electroencephalogram data signals” (Breuer, Abstract.).
Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Claims 2 and 15 are rejected under 35 U.S.C. 103(a) as being obvious over Peot/Breuer/Makeig, further in view of Ornstein et al. (WIPO 2005/043802 A1), hereinafter Ornstein.
As per claims 2 and 15, Peot/Breuer/Makeig disclose claims 1 and 14, discussed above. As shown above, Breuer discloses a fidelity limited access right, see Figs. 4 and 5; however Peot fails to explicitly disclose wherein the pre-defined data access right includes at least one of a time-limited access right and a fidelity-limited access right.
However, Ornstein discloses that it would have been obvious to one of ordinary skill in the art of secure electronic communications at the time of the invention/filing to provide wherein the pre-defined data access right includes a time-limited access right (Ornstein, Fig. 8 #882.) so as to result in “a secure technique of distributing electronic content such as videos, text, data, photos, financial data, sales solicitations, or the like” (Ornstein, Abstract.).
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave signal analysis at the time of the invention/filing to modify the brainwave monitoring system of Peot to include wherein the pre-defined data access right includes a time-limited access right, as taught by Ornstein, in order to provide a brainwave monitoring system that used “a secure technique of distributing electronic content such as videos, text, data, photos, financial data, sales solicitations, or the like” (Ornstein, Abstract.). Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Peot, Breuer, Makeig and Orenstein are all directed to the processing of biologically generated data.
Claim 27 is rejected under 35 U.S.C. 103(a) as being obvious over Peot/Breuer/Makeig, further in view of Gevins et al. (U.S. Patent 4,736,751), hereinafter Gevins.
As per claim 27, Peot/Breuer/Makeig disclose claim 1, discussed above. Peot/Breuer/Makeig discloses wherein the generate the one or more encryption keys based at least in part on the comparison of the pattern from the time-coded bio-signal data to the model of expected user behaviour associated with the user and the expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user (As shown above.).
Peot/Breuer/Makeig fails to explicitly disclose:
prompting the user to provide a current response to a task;
determining a difference between the current response to the task and an initial response to the task as provided in a prior session; and
modulating the pattern from the time-coded bio-signal data based on the difference between the current response to the task and the initial response to the task.
However, Gevins discloses that it would have been obvious to one of ordinary skill in the art of secure electronic communications at the time of the invention/filing to provide:
prompting the user to provide a current response to a task (Gevins discloses a system in which a user performs a task plural times, aka trial of tasks, see C6L37-C79L6
determining a difference between the current response to the task and an initial response to the task as provided in a prior session (Gevins discloses analyzing those plural task performances to identify a difference and remove artifacts, which would comprise modulating the signal based on the response, see C9L9-L58.); and
modulating the pattern from the time-coded bio-signal data based on the difference between the current response to the task and the initial response to the task (Gevins discloses analyzing those plural task performances to identify a difference and remove artifacts, which would comprise modulating the signal based on the response, see C9L9-L58.);
in order to provide additional methods of inspecting patient brainwave data.
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave signal analysis at the time of the invention/filing to modify the brainwave monitoring system of Peot to include generate data based on an prompting the user to provide a current response to a task; determining a difference between the current response to the task and an initial response to the task as provided in a prior session; and modulating the pattern from the time-coded bio-signal data based on the difference between the current response to the task and the initial response to the task, as taught by Gevins, in order to provide a brainwave monitoring system that provide additional methods of inspecting patient brainwave data. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Peot, Breuer, Makieg and Gevins are all directed to the processing of biologically generated data.
Response to Arguments
Applicant’s arguments filed 13 October 2023	 concerning the rejection of all claims under 35 U.S.C.  103 have been fully considered but they are not persuasive.
With regard to the rejection of the claims under 35 USC 103, Applicant argues on pages 9-10 that the cited art fails to disclose the amendments to the claims.
Applicant's arguments have been fully considered but are moot in view of the new ground(s) of rejection, specifically with reference to the new reference necessitated by amendment, Makeig, as detailed above, or because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
In conclusion, all of the limitations which Applicant disputes as missing in the applied references, including the features newly added by amendment, have been fully addressed by the Office as either being fully disclosed or obvious in view of the collective teachings of Peot, Breuer, Makeig, Matthews, Gevins and Ornstein, based on the logic and sound scientific reasoning of one ordinarily skilled in the art at the time of the invention, as detailed in the remarks and explanations given in the preceding sections of the present Office Action and in the prior Office Action (14 June 2023 and 7 October 2022), and incorporated herein.
Conclusion
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382.

CITED REFERENCE SUMMARIES: Ref US20110015538A1: Claims:
A diagnostic system analyzing EEG data comprising: single-site data from at least one single-site; a computing device operable to calculate a single-site deviance for the single-site data and a single score for each of a plurality of scalp sites based on the single-site deviance; and an output device ranking the single scores to identify sites showing abnormal EEG.
single-site data from at least one single-site;
a computing device operable to calculate a single-site deviance for the single-site data and a single score for each of a plurality of scalp sites based on the single-site deviance; and
an output device ranking the single scores to identify sites showing abnormal EEG.
The system of claim 1 , further comprising site-pair data from at least one site-pair, wherein the computing device is further operable to calculate a site-pair deviance for the site-pair data and the single score is calculated for each of a plurality of scalp sites based on the single-site deviance and corresponding site-pair deviance.
The system of claim 1 , wherein the computing device is operable to export the single scores as output to EEG acquisition software.
The system of claim 1 , wherein the single-site data is represented by at least one z-score.
The system of claim 1 , wherein the site-pair data is represented by at least one z-score.
The system of claim 1 , wherein deviance is calculated by taking the absolute value of the at least one z-score.
The system of claim 1 , further comprising a deficit input for allowing an operator to identify specific sites where skull deficits exist, wherein the computing device is operable to exclude data from these sites.
The system of claim 1 , wherein the data is static.
The system of claim 1 , where the data is in real-time.
The system of claim 1 , further comprising at least one cross-session report that combines input from multiple types of EEG measures.
The system of claim 10 , wherein the EEG measures comprise Linked Ears.
The method as set forth in claim 10 , wherein the EEG measures comprise Average Reference.
The method as set forth in claim 10 , wherein the EEG measures comprise Laplacian.
The system of claim 1 , wherein the corresponding site-pair deviance includes calculating deviance for each site-pair data that includes as one of the sites in the site-pair a site corresponding to the single-site for which a score is being calculated.
The system of claim 1 , wherein the computing device is operable to export the single scores as output for integration with billing software.
The system of claim 1 , wherein the single scores are operable to measure effects of medications.
The system of claim 1 , wherein the single scores are operable to measure treatment response.
The system of claim 1 , further comprising a display operable to graphically display a representation of the scores.
The system of claim 1 , further comprising a database created and populated with information.
The system of claim 19 , wherein information comprises the data, the single-site deviance, the site-pair deviance, and the single scores.
The system of claim 1 , further comprising an interest input for allowing an operator to specify at least one site of interest, wherein scores from the at least one site of interest facilitate hypothesis testing.
The system of claim 1 , further comprising a trainer output device operable to facilitate operant conditioning of a client's EEG.
The system of claim 1 , further comprising a collection of baseline data.
The system of claim 23 , wherein the collection of baseline data is substantially automatically collected by the computing device.
The system of claim 24 , wherein the collection of baseline data facilitates exclusion of artifact.
The system of claim 24 , wherein the collection of baseline data facilitates exclusion of pauses.
The system of claim 1 , further comprising a chosen threshold deviance.
The system of claim 1 , wherein deviance is calculated by taking the square of the at least one z-score.
The system of claim 1 , wherein deviance is calculated by taking the square of the at least one z-score divided by a chosen threshold.
A method of analyzing EEG data comprising the steps of: loading EEG single-site data; determining a single-site deviance for the single-site data; determining a single score for a plurality of scalp sites; and producing a ranked report identifying sites showing abnormal EEG.
loading EEG single-site data;
determining a single-site deviance for the single-site data;
determining a single score for a plurality of scalp sites; and
producing a ranked report identifying sites showing abnormal EEG.
The method as set forth in claim 30 , further including the step of loading EEG site-pair data.
The method as set forth in claim 31 , wherein the site-pair data is represented by at least one z-score.
The method as set forth in claim 31 , further including the step of determining site-pair deviance.
The method as set forth in claim 33 , wherein the single score for each of a plurality of sites is determined by utilizing the single-site deviance and each site-pair deviance for each site-pair that includes as one of the sites in the site-pair a site corresponding to the single-site for which a single score is being calculated.
The method as set forth in claim 30 , further including the step of exporting the single scores to EEG acquisition software.
The method as set forth in claim 30 , wherein the single-site data is represented by at least one z-score.
The method as set forth in claim 30 , wherein deviance is calculated by taking the absolute value of the at least one z-score.
The method as set forth in claim 30 , further including the step of excluding specific sites where skull deficits exist.
The method as set forth in claim 30 , wherein the data is static.
The method as set forth in claim 30 , wherein the data is in real-time.
The method as set forth in claim 30 , further including the step of producing reports that combine input from multiple types of EEG measures.
The method as set forth in claim 41 , wherein the EEG measures comprise Linked Ears.
The method as set forth in claim 41 , wherein the EEG measures comprise Average Reference.
The method as set forth in claim 41 , wherein the EEG measures comprise Laplacian.
The method as set forth in claim 30 , further including the step of allowing an operator to specify at least one site of interest to facilitate hypothesis testing.
The method as set forth in claim 30 , further including the step of providing feedback to a client to facilitate operant conditioning.
The method as set forth in claim 30 , further including the step of substantially automatically collecting baseline data.
The method as set forth in claim 30 , further including the step of excluding artifact.
The method as set forth in claim 30 , further including the step of excluding pauses.
The method as set forth in claim 30 , further including the step of selecting a chosen threshold deviance.
The method as set forth in claim 30 , wherein deviance is calculated by taking the square of the at least one z-score.
The method as set forth in claim 30 , wherein deviance is calculated by taking the square of the at least one z-score divided by a chosen threshold.
A computer program for analyzing EEG data, with the computer program being stored on a computer-readable medium and executable by a computing device, the computer program comprising instructions for: selecting input variables; loading EEG single-site data; loading EEG site-pair data; determining a single-site deviance for the at least single-site data; determining a site-pair deviance for the site-pair data; determining a single score for a plurality of scalp sites from the single-site deviance and corresponding site-pair deviance; and presenting on a display a ranked report identifying sites showing abnormal EEG.
selecting input variables;
loading EEG site-pair data;
determining a single-site deviance for the at least single-site data;
determining a site-pair deviance for the site-pair data;
determining a single score for a plurality of scalp sites from the single-site deviance and corresponding site-pair deviance; and
presenting on a display a ranked report identifying sites showing abnormal EEG.
The computer program of claim 53 , further including instructions for identifying and excluding from the single score determination deviance scores for sites where skull deficits exist.
The computer program of claim 53 , further including instructions for selecting a chosen threshold deviance.
The computer program of claim 53 , further including instructions for specifying at least one site of interest.
The computer program of claim 53 , further including instructions for requiring that a data point meet or exceed a threshold value before being included in further calculations.
The computer program of claim 53 , further including instructions for collecting baseline data.
The computer program of claim 53 , further including instructions for excluding artifact.
The computer program of claim 53 , further including instructions for excluding pauses.
Ref US4736751A: Claims:
A method to display the location and inter-area temporal relationships of a human subject's brain neural activity, comprising the steps of: (i) determining and recording the three-dimensional location of selected portions of the subject's head and internal portions of the subject's brain and recording said locations using a three-dimensional anatomical scan instrument to provide anatomical location data; (ii) positioning a plurality of at least 32 sensors external to the subject's scalp and proximate to the subject's scalp to detect analog brain neural activity detectable proximate to the surface of the scalp; (iii) determining the three-dimensional positions of the sensors, converting the said sensor position determinations into digital data, and storing the said sensor position digital data in computer storage means; (iv) providing a set of predetermined stimuli to the subject; (v) detecting the subject's analog brain signals at the sensors during delivery of said stimuli and amplifying said analog signals; converting the analog signals into digital data and storing said digital data in computer storage means; (vi) statistically analyzing (a) the said anatomical location data, (b) the said sensor position data, and (c) said stored brain signal digital data, to provide a set of statistical indications which represent generators of brain neural activity at sites within the brain and their temporal interrelationship; and (vii) displaying said statistical indications on a three-dimensional perspective display of at least part of a human brain, the brain portions of said display corresponding in location to the brain portion locations provided by the scan instrument.
(i) determining and recording the three-dimensional location of selected portions of the subject's head and internal portions of the subject's brain and recording said locations using a three-dimensional anatomical scan instrument to provide anatomical location data;
(ii) positioning a plurality of at least 32 sensors external to the subject's scalp and proximate to the subject's scalp to detect analog brain neural activity detectable proximate to the surface of the scalp;
(iii) determining the three-dimensional positions of the sensors, converting the said sensor position determinations into digital data, and storing the said sensor position digital data in computer storage means;
(iv) providing a set of predetermined stimuli to the subject;
(v) detecting the subject's analog brain signals at the sensors during delivery of said stimuli and amplifying said analog signals; converting the analog signals into digital data and storing said digital data in computer storage means;
(vi) statistically analyzing (a) the said anatomical location data, (b) the said sensor position data, and (c) said stored brain signal digital data, to provide a set of statistical indications which represent generators of brain neural activity at sites within the brain and their temporal interrelationship; and
(vii) displaying said statistical indications on a three-dimensional perspective display of at least part of a human brain, the brain portions of said display corresponding in location to the brain portion locations provided by the scan instrument.
The method as in claim 1 wherein the location of the selected portions of the brain is obtained by scanning the brain to provide magnetic resonance images (MRI).
A method as in claim 1 wherein the brain wave signals are improved in their signal-noise ratio to reduce contaminants and reduce brain activity not related to said stimulus by comparing trial sets of said brain wave digital data to statistically determined norms of uncontaminated and non-stimulus related brain wave digital data in a pattern recognition classification to eliminate those data which are contaminated or which do not have strong stimulus-related brain wave signals.
A method as in claim 1 wherein the sensors are electrodes positioned on the internal face of a removable hat.
A method as in claim 1 wherein the sensors are electrodes of an electroencephalograph (EEG) system.
A method as in claim 1 wherein the sensors are detectors of a magnetoencephalograph (MEG) system.
A method as in claim 1 wherein the stimuli are automatically presented under computer program control.
A method as in claim 1 wherein the stimuli include a set of motor tasks to be performed by the subject.
A method as in claim 8 wherein the tasks include a task in which the subject must respond with left or right hands to a visual cue.
A method as in claim 8 wherein the subject must respond with one hand to sound and visual cues.
A method as in claim 1 wherein said display includes color coded lines and areas in which color of lines indicates time delays and color or areas indicates current flow through the scalp.
A method as in claim 1 wherein the display is a model based on the subject's brain and head.
A method as in claim 1 wherein the electrical brain wave digital data is processed to reduce distortion due to passage from the originating sites to the sensors, said processing including the use of a spatial deconvolution algorithm.
A method as in claim 1 wherein the electrical brain digital data is processed to extract source network properties using compartmentalization, subset selection, and linear least squares methods.
A method as in claim 1 wherein the subject performs a predetermined task consisting of a series of trials substantially involving the voluntary movement of a portion of the subject's body in response to the stimulus and the detection of the subject's brain waves continues during each trial.
A method as in claim 1 wherein the display is of the spatial and temporal brain wave properties of stimulus-or response-related signals, including preparation, stimulus encoding, feature-extraction, decision, response preparation, response execution, feedback-updating, and other cognitive processes.
A method as in claim 1 in which the display is a model of the subject's brain and head, the scan is a magnetic resonance image (MRI) and the model is based on the said subject's MRI.
A system to display the location and inter-area temporal relationships of a human subject's brain neural activity, comprising: (i) a plurality of at least 32 sensors adapted to be positioned proximate the subject's scalp to detect analog brain neural activity detectable proximate to the surface of the scalp; (ii) means for determining the three-dimensional positions of the sensors, converting the said position determinations into digital data, and storing the said position digital data in computer storage means; (iii) means connected to said sensors for detecting and amplifying the subject's analog brain signals; converting the analog signals into brain signal digital data and storing said brain signal digital data in computer storage; (iv) means for statistically analyzing the stored brain signal digital data to provide a set of statistical indications of brain activity representing temporal interrelationships between sensor positions; (v) a three-dimensional perspective display means having head and brain portions for displaying said statistical indications as lines between sensor positions; where the said head and brain portions on said display means correspond in location to the sensor positions.
(i) a plurality of at least 32 sensors adapted to be positioned proximate the subject's scalp to detect analog brain neural activity detectable proximate to the surface of the scalp;
(ii) means for determining the three-dimensional positions of the sensors, converting the said position determinations into digital data, and storing the said position digital data in computer storage means;
(iii) means connected to said sensors for detecting and amplifying the subject's analog brain signals; converting the analog signals into brain signal digital data and storing said brain signal digital data in computer storage;
(iv) means for statistically analyzing the stored brain signal digital data to provide a set of statistical indications of brain activity representing temporal interrelationships between sensor positions;
(v) a three-dimensional perspective display means having head and brain portions for displaying said statistical indications as lines between sensor positions; where the said head and brain portions on said display means correspond in location to the sensor positions.
A system as in claim 18 and further including brain scan means to determine and record the three-dimensional location of internal portions of the subject's brain, wherein the brain scan means is a magnetic resonance image (MRI) system.
A system as in claim 18 and including a pattern recognition classification means to eliminate those data set trials which do not have strong stimulus-related brain wave signals.
A system as in claim 18 and including a removable hat on which are mounted at least 32 of said sensors.
A system as in claim 18 and including a computer program control means to automatically present said stimuli.
A system as in claim 18 wherein the display is a model based on the subject's brain.
A system as in claim 18 wherein the sensors are electrodes of an electroencephalograph (EEG) system.
A system as in claim 18 wherein the sensors are detectors of a magnetoencephalograph (MEG) system.
A method as in claim 18 wherein the electrical brain wave digital data is processed to extract source network properites using compartmentalization, subset selection, and linear least squares methods.
A system to display the location and inter-area temporal relationships of a human subject's brain neural activity, comprising: (i) brain scan means to determine and record the three-dimensional location of selected internal portions of the subject's brain and head by viewing said selected brain portions in a three-dimensional scan; (ii) a plurality of at least 32 sensors adapted to be positioned proximate the subject's scalp to detect analog brain neural activity detectable proximate to the surface of the scalp; (iii) means for determining the three-dimensional positions of the sensors, converting the said position determinations into digital data, and storing the said position digital data in computer storage means; (iv) means for providing a set of predetermined stimuli to the subject; (v) means connected to said sensors for detecting and amplifying the subject's analog brain signals which are responsive to said stimuli, converting the analog signals into brain signal digital data and storing said brain signal digital data in computer storage; (vi) means for statistically analyzing the stored brain signal digital data to provide a set of statistical indications of brain activity representing temporal interrelationships between sensor posit
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