Office Action 1: Non-Final Rejection (2022-10-07)
Rejection types: 103 - Obviousness, 112 - Written Description/Enablement

CLAIMS:
CLAIMS

What is claimed is:

1\. 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, 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\. The system of claim 1, wherein the pre-defined data access right includes a time-limited access right.
3\. The system of claim 1, wherein the pre-defined data access right includes a fidelity- limited access right.

4\. 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\. The system of claim 1, wherein the encryption keys are unique to the user.

6\. 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
using one or more of the decryption keys, decrypt data that is encrypted by one or more of the encryption keys.

7\. 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
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.
8\. 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\. 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\. The system of claim 1, wherein the selected data access right is associated with a target brain state to access the data.

11\. 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\. 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\. 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\. 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, 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\. The method of claim 14, wherein the pre-defined data access right includes a time- limited access right.

16\. The method of claim 14, wherein the pre-defined data access right includes a fidelity- limited access right.

17\. 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\. The method of claim 14, wherein the encryption keys are unique to the user.

19\. 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
using one or more of the decryption keys, decrypting data that is encrypted by one or more of the encryption keys.

20\. 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
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.

21\. 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\. 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\. The method of claim 14, wherein the selected data access right is associated with a target brain state to access the data.

24\. 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 perform the method of claim 14. 111 112 113 114 115 116

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 an application filed 13 March 2020, which claims priority to an application that claims priority to provisional applications filed 12 September 2012.
Claims 1-24 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 13 March 2020 (x4), 19 April 2021, 14 October 2021 and 8 March 2022 have been considered by the Office to the extent indicated.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA  35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA  35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 14 is rejected under 35 U.S.C. 112(d) or pre-AIA  35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.  Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim 14 is rejected under 35 U.S.C. 112(d) or pre-AIA  35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends (35 U.S.C. 112, fourth paragraph), or, in other words, that it shall not conceivably be infringed by anything which would not also infringe the basic claim. When, as here, an independent claim recites a particular method, a dependent claim drawn to an apparatus capable of performing the method of the independent claim is not a proper dependent claim, since the dependent claim (the apparatus) could conceivably be infringed by mere possession of the apparatus without performing any particular method steps at all, thereby infringing the dependent claim (the apparatus) without necessarily infringing the independent claim (the method), in violation of the infringement test for proper dependency of claims. See MPEP § 608.01(n)(III). Applicant is required to cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, or rewrite the claim(s) in independent form.
To the extent that other claims rely on claims that are rejected under 35 USC 112 and fail to correct the deficiencies of the claims they rely on, those other claims are rejected for the same reasons as the claims they rely on. Appropriate correction is required.
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 16-24 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.
As per claims 1 and 14, Peot discloses a computer system and a computer implemented method (Peot, Figs. 1 and 3) comprising:
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, each of the encryption keys associated with a pre-defined data access right (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.).
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; and
encrypt data using one or more of the encryption keys corresponding to a selected data access right to limit access to 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:
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.).
As per claims 3-13 and 16-24, Peot/Breuer disclose claims 1 and 14, discussed above. Peot/Breuer also disclose:
3, 16. 	The system of claim 1, wherein the pre-defined data access right includes a fidelity-limited access right (Encryption keys provide access to user who have been proven to be the actual intended target recipient, that is those that have fidelity, see Figs. 4 and 5 of Breuer.);
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.);
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 (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. 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
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 (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. 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
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 (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.);
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.);
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.);
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
24. 	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 perform the method of claim 14 (Peot, Figs. 1 and 3).
Claims 2 and 15 are rejected under 35 U.S.C. 103(a) as being obvious over Peot/Breuer, further in view of Ornstein et al. (WIPO 2005/043802 A1), hereinafter Ornstein.
As per claims 2 and 15, Peot/Breuer disclose claims 1 and 14, discussed above. Peot/Breuer fail to explicitly disclose wherein the pre-defined data access right includes a time-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/Breuer 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).
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.


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 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.

CITED REFERENCE SUMMARIES: Ref US20100185113A1: Claims:
A method of cueing a system response, comprising: monitoring neurophysiological responses of an operator subjected to stimuli; processing the neurophysiological responses to determine if the operator had a significant cognitive response to a stimulus to generate a positive cue; monitoring the operator's eye movement to determine when the operator fixates on the stimulus; determining the position of the stimulus in the operator's field-of view (FOV); outputting the positive cue and the position of the stimulus; and triggering a system response to the positive cue and the position of the stimulus.
monitoring neurophysiological responses of an operator subjected to stimuli;
processing the neurophysiological responses to determine if the operator had a significant cognitive response to a stimulus to generate a positive cue;
monitoring the operator's eye movement to determine when the operator fixates on the stimulus;
determining the position of the stimulus in the operator's field-of view (FOV);
outputting the positive cue and the position of the stimulus; and
triggering a system response to the positive cue and the position of the stimulus.
The method of claim 1 , wherein determining the stimulus position comprises: determining the orientation of the stimulus with respect to the operator; and determining the range of the stimulus with respect to the operator.
determining the orientation of the stimulus with respect to the operator; and
determining the range of the stimulus with respect to the operator.
The method of claim 2 , further comprising: determining the geo-location of the operator; and determining a geo-location of the stimulus from the geo-location of the operator and the position of the stimulus.
determining the geo-location of the operator; and
determining a geo-location of the stimulus from the geo-location of the operator and the position of the stimulus.
The method of claim 1 , wherein the stimulus comprises a non-visual stimulus.
The method of claim 1 , wherein the stimulus comprises a visual stimulus.
The method of claim 5 , wherein the operator fixates on the visual stimulus prior to the operator's cognitive response to the stimulus, further comprising: synchronizing the neurophysiological responses to the operator's fixation on the stimulus for processing; and processing the synchronized neurophysiological responses.
synchronizing the neurophysiological responses to the operator's fixation on the stimulus for processing; and
processing the synchronized neurophysiological responses.
The method of claim 6 , wherein at least one window is positioned relative to the onset of fixation and the neurophysiological responses within the window processed.
The method of claim 7 , wherein one said window is positioned to capture neurophysiological responses both before and after the onset of fixation.
The method of claim 7 , wherein the neurophysiological responses within said at least one window are processed by: extracting features from the neurophysiological responses, said features based on the assumption that the at least one window has a specified position relative to the onset of fixation; and presenting the extracted features to a computer-implemented classifier trained to detect patterns of the extracted features and to generate the positive cue indicative of the occurrence a significant brain response.
extracting features from the neurophysiological responses, said features based on the assumption that the at least one window has a specified position relative to the onset of fixation; and
presenting the extracted features to a computer-implemented classifier trained to detect patterns of the extracted features and to generate the positive cue indicative of the occurrence a significant brain response.
The method of claim 1 , wherein the monitored neurophysiological responses include at least one of EEG signals of brainwave activity, dilation signals of pupillary response, dwell time signals of eye movement and imaging signals of vascular response.
The method of claim 10 , wherein at least two of the EEG, dilation, dwell time and imaging signals are monitored and processed.
The method of claim 1 , wherein the response is triggered in real-time with respect to the operator's cognitive response to the stimulus.
The method of claim 1 , further comprising: wireless transmitting the output of the positive cue and position of the stimulus from the operator to a remote location where the system response is triggered.
wireless transmitting the output of the positive cue and position of the stimulus from the operator to a remote location where the system response is triggered.
The method of claim 13 , wherein the system response is to aim a weapon at the position of the stimulus.
The method of claim 13 , where the system response is to command multiple operators to turn toward the position of the stimulus in a show of force.
The method of claim 13 , wherein a control system at the remote locations receives cues and position data from multiple operators and synthesizes the data to trigger the system response.
The method of claim 1 , wherein the positive cue and position of the stimulus are provided as one of a plurality of inputs to classifier, the output of the classifier triggering the system response.
The method of claim 1 , wherein the triggered system response is to record the positive cue and position of the stimulus and any action taken in response to that stimulus.
The method of claim 1 , wherein a time sequence of cues and fixation measurements are output, further comprising processing the temporal cues and fixation measurements to reinforce or reject the positive cue and refine the stimulus position.
A system comprising: a head-mounted system for an operator, said system comprising: at least one sensor monitoring neurophysiological responses of an operator subjected to stimuli; a classifier processing the neurophysiological responses to determine if the operator had a significant cognitive response to a stimulus to generate a positive cue; an eye-tracking device monitoring the operator's eye movement to determine when the operator fixates on a stimulus; means for determining the position of the stimulus in the operator's field-of view (FOV); and a wireless data link transmitting the positive cue and the position of the stimulus; and a remote system receiving the cue and position and triggering a system response to the positive cue and the position of the stimulus.
a head-mounted system for an operator, said system comprising: at least one sensor monitoring neurophysiological responses of an operator subjected to stimuli; a classifier processing the neurophysiological responses to determine if the operator had a significant cognitive response to a stimulus to generate a positive cue; an eye-tracking device monitoring the operator's eye movement to determine when the operator fixates on a stimulus; means for determining the position of the stimulus in the operator's field-of view (FOV); and a wireless data link transmitting the positive cue and the position of the stimulus; and
at least one sensor monitoring neurophysiological responses of an operator subjected to stimuli;
a classifier processing the neurophysiological responses to determine if the operator had a significant cognitive response to a stimulus to generate a positive cue;
an eye-tracking device monitoring the operator's eye movement to determine when the operator fixates on a stimulus;
means for determining the position of the stimulus in the operator's field-of view (FOV); and
a wireless data link transmitting the positive cue and the position of the stimulus; and
a remote system receiving the cue and position and triggering a system response to the positive cue and the position of the stimulus.
The system of claim 20 , wherein said at least one sensor monitors neurophysiological responses including at least one of EEG signals of brainwave activity, dilation signals of pupillary response, dwell time signals of eye movement and imaging signals of vascular response.
The system of claim 20 , wherein the remote system triggers a system response of aiming a weapon at the position of the stimulus or commanding multiple operators to turn toward the position of the stimulus.
The system of claim 22 , wherein the remote system receives cues and position data from multiple operators and synthesizes the data to trigger the system response.
The system of claim 20 , wherein the operator fixates on the visual stimulus prior to the operator's cognitive response to the stimulus, further comprising a data pre-processing system that windows the monitored neurophysiological responses into at least one time window synchronized to the operator's fixation on the stimulus.
Ref US9672760B1: Claims:
A computer-implemented method for a secure encryption generator based on user-specific electroencephalogram data, the method comprising executing on a computer processor the steps of: mapping each of a plurality of artificial neural network neuron elements to a first set of electroencephalogram data signals that are generated from scanning neural activity of a user while the user executes a current mental activity; training a first set of artificial neural network neuron element weighting factors to transform the first set of electroencephalogram data signals into a first weighted set of electroencephalogram data signals that are different from second weighted set of electroencephalogram data signals that are generated from scanning neural activity of the user while the user executes another activity that is different from the current mental activity, and from a third weighted set of electroencephalogram data signals that are generated from scanning neural activity of another user while the another user executes a mental activity that corresponds to the current mental activity; associating the trained first set of artificial neural network neuron element weighting factors with the first set of electroencephalogram data signals and with the current mental activity; defining a reproducible electroencephalogram encryption key that is unique to the user as a function of at least one of the trained first set of artificial neural network neuron element weighting factors and the first weighted set of electroencephalogram data signals; locking message content via a public key encryption process with the trained first set of artificial neural network neuron element weighting factors functioning as a public key; and sending the locked message to the user as a designated receiver.
mapping each of a plurality of artificial neural network neuron elements to a first set of electroencephalogram data signals that are generated from scanning neural activity of a user while the user executes a current mental activity;
training a first set of artificial neural network neuron element weighting factors to transform the first set of electroencephalogram data signals into a first weighted set of electroencephalogram data signals that are different from second weighted set of electroencephalogram data signals that are generated from scanning neural activity of the user while the user executes another activity that is different from the current mental activity, and from a third weighted set of electroencephalogram data signals that are generated from scanning neural activity of another user while the another user executes a mental activity that corresponds to the current mental activity;
associating the trained first set of artificial neural network neuron element weighting factors with the first set of electroencephalogram data signals and with the current mental activity;
defining a reproducible electroencephalogram encryption key that is unique to the user as a function of at least one of the trained first set of artificial neural network neuron element weighting factors and the first weighted set of electroencephalogram data signals;
locking message content via a public key encryption process with the trained first set of artificial neural network neuron element weighting factors functioning as a public key; and
sending the locked message to the user as a designated receiver.
The method of claim 1 , further comprising: integrating computer-readable program code into a computer system comprising a processor, a computer readable memory in circuit communication with the processor, and a computer readable storage medium in circuit communication with the processor; and wherein the processor executes program code instructions stored on the computer-readable storage medium via the computer readable memory and thereby performs the steps of mapping each of the plurality of artificial neural network neuron elements to the first set of electroencephalogram data signals, training the first set of artificial neural network neuron element weighting factors, associating the trained first set of artificial neural network neuron element weighting factors with the first set of electroencephalogram data signals and with the current mental activity, defining the reproducible electroencephalogram encryption key that is unique to the user as the function of at least one of the trained first set of artificial neural network neuron element weighting factors and the first weighted set of electroencephalogram data signals, locking the message content via the public key encryption process with the trained first set of artificial neural network neuron element weighting factors functioning as the public key, and sending the locked message to the user as the designated receiver.
integrating computer-readable program code into a computer system comprising a processor, a computer readable memory in circuit communication with the processor, and a computer readable storage medium in circuit communication with the processor; and
wherein the processor executes program code instructions stored on the computer-readable storage medium via the computer readable memory and thereby performs the steps of mapping each of the plurality of artificial neural network neuron elements to the first set of electroencephalogram data signals, training the first set of artificial neural network neuron element weighting factors, associating the trained first set of artificial neural network neuron element weighting factors with the first set of electroencephalogram data signals and with the current mental activity, defining the reproducible electroencephalogram encryption key that is unique to the user as the function of at least one of the trained first set of artificial neural network neuron element weighting factors and the first weighted set of electroencephalogram data signals, locking the message content via the public key encryption process with the trained first set of artificial neural network neuron element weighting factors functioning as the public key, and sending the locked message to the user as the designated receiver.
The method of claim 2 , wherein the computer-readable program code is provided as a service in a cloud environment.
The method of claim 1 , further comprising: defining a personal electroencephalogram signature for the user as a function of at least one of the trained first set of artificial neural network neuron element weighting factors and the first weighted set of electroencephalogram data signals; and wherein the user is enabled to unlock the message with the personal electroencephalogram signature defined for the user by using the at least one of the trained first set of artificial neural network neuron element weighting factors and the first weighted set of electroencephalogram data signals.
defining a personal electroencephalogram signature for the user as a function of at least one of the trained first set of artificial neural network neuron element weighting factors and the first weighted set of electroencephalogram data signals; and
wherein the user is enabled to unlock the message with the personal electroencephalogram signature defined for the user by using the at least one of the trained first set of artificial neural network neuron element weighting factors and the first weighted set of electroencephalogram data signals.
The method of claim 1 , wherein the step of training the first set of artificial neural network neuron element weighting factors further comprises: defining a learned model profile of the artificial neural network neuron element weighting factors that corresponds to values of the first weighted set of electroencephalogram data signals that are unique to the user and different from values of the third weighted set of electroencephalogram data signals that are generated from scanning the neural activity of the another user while the user executes the current mental activity.
defining a learned model profile of the artificial neural network neuron element weighting factors that corresponds to values of the first weighted set of electroencephalogram data signals that are unique to the user and different from values of the third weighted set of electroencephalogram data signals that are generated from scanning the neural activity of the another user while the user executes the current mental activity.
The method of claim 1 , wherein the current mental activity comprises at least one of directing muscle movements, tracing out an alphanumeric character, pausing volitional movement activity for a pause period of time, and recalling a specific image.
The method of claim 1 , wherein the step of training the artificial neural network neuron element weighting comprises defining numerically weighted decision connections between the plurality of neuron elements for exchange of messages between each other that are tuned to correspond to neural processes of the user that are represented by electroencephalogram data.
The method of claim 1 , further comprising: defining a personal electroencephalogram signature for the user as a function of weighting the first set of electroencephalogram data signals with the trained first set of artificial neural network neuron element weighting factors; and encrypting a message using a public key of a receiver, wherein the message comprises message content in combination with the defined personal electroencephalogram signature; and wherein the encrypted message may be decrypted by the receiver as a function of a private key of the receiver and the trained first set of artificial neural network neuron element weighting factors.
defining a personal electroencephalogram signature for the user as a function of weighting the first set of electroencephalogram data signals with the trained first set of artificial neural network neuron element weighting factors; and
encrypting a message using a public key of a receiver, wherein the message comprises message content in combination with the defined personal electroencephalogram signature; and
wherein the encrypted message may be decrypted by the receiver as a function of a private key of the receiver and the trained first set of artificial neural network neuron element weighting factors.
The method of claim 8 , wherein decryption of the encrypted message as the function of the private key of the receiver and the trained first set of artificial neural network neuron element weighting factors permanently destroys the personal electroencephalogram signature.
A system, comprising: a processor; a computer readable memory in circuit communication with the processor; and a computer readable storage medium in circuit communication with the processor; wherein the processor executes program instructions stored on the computer-readable storage medium via the computer readable memory and is thereby caused to: map each of a plurality of artificial neural network neuron elements to a first set of electroencephalogram data signals that are generated from scanning neural activity of a user while the user executes 
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