Office Action 5: Notice of Allowance (2024-08-29)

CLAIMS:
Listing of claims:

Amendments to the Claims:

The following listing of claims replaces all prior listings of claims in the application.

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 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, the expected variance based on at least one of heart rate, breathing, age of the user, time of day, temperature, light levels, background sound levels,
signal quality, and placement of the bio-signal sensor; 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.

2\. (Previously Presented) 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\. (Previously Presented) 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 and the target brain state, decrypt data that is encrypted by one or more of the encryption keys.

7\. (Previously Presented) 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, the expected variance based on at least one of heart rate, breathing, age of the user, time of day, temperature, light levels, background sound levels, signal quality, and placement of the bio- signal sensor; and
encrypting 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.

15\. (Previously Presented) 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\. (Previously Presented) 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 and the target brain state, decrypting data that is encrypted by one or more of the encryption keys.

20\. (Previously Presented) 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, the expected variance based on at least one of heart rate, breathing, age of the user, time of day, temperature, light levels, background sound levels, signal quality, and placement of the bio- signal sensor; 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.

25\. (Cancelled)

26\. (Previously Presented) 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\. (Previously Presented) 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.

28\. (New) 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 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, the expected variance based on at least one of age of the user,
time of day, temperature, signal quality, and placement of the bio-signal sensor; 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.

OFFICE ACTION:
DETAILED ACTION
America Invents Act
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner’s Amendment
An examiner’s amendment to the record appears below. Should the changes and/or additions be unacceptable to applicant, an amendment may be filed as provided by 37 CFR 1.312. To ensure consideration of such an amendment, it MUST be submitted no later than the payment of the issue fee.
Authorization for this examiner’s amendment was given by Applicant’s representative Maya Madeiros on 21 August 2024. The application has been amended as follows: Replace the claims filed 21 June 2024 with the following set 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 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, the expected variance based on at least one of heart rate, breathing, age of the user, time of day, temperature, light levels, background sound levels, signal quality, and placement of the bio-signal sensor; 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;
wherein the generation of 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.
2. (Previously Presented) 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. (Previously Presented) 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 and the target brain state, decrypt data that is encrypted by one or more of the encryption keys.
7. (Previously Presented) 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, the expected variance based on at least one of heart rate, breathing, age of the user, time of day, temperature, light levels, background sound levels, signal quality, and placement of the bio-signal sensor; and
encrypting 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;
wherein the generation of 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.
15. (Previously Presented) 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. (Previously Presented) 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 and the target brain state, decrypting data that is encrypted by one or more of the encryption keys.
20. (Previously Presented) 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, the expected variance based on at least one of heart rate, breathing, age of the user, time of day, temperature, light levels, background sound levels, signal quality, and placement of the bio-signal sensor; 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;
wherein the generation of 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.
25. (Cancelled)
26. (Previously Presented) 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. (Cancelled)
28. (New) 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 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, the expected variance based on at least one of age of the user, time of day, temperature, signal quality, and placement of the bio-signal sensor; 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;
wherein the generation of 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.
Reasons for Allowance
The following is an examiner’s statement of reasons for allowance regarding claims 1, 2, 4-15, 17-24, 26 and 28:
The prior art of record includes:
Peot et al. (U.S. PG-Pub 2010/0185113 A1) discloses coordinating system responses based on an operator’s response to cognitive stimulus that teaches the reception of time coded bio-signal data of a plurality of users, that is associated with time-coded feature event data in order to identify bio-signal patterns that represent a user response to the feature events.
Breuer et al. (U.S. Patent 9,672,760 B1) discloses a personalized EEG-based encryptor that teaches the generation of encryption keys with predefined data access rights using biosignal measures, encryption of those keys corresponding to a selected data access right and a target brain state to limit access to the data.
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) discloses a system for analyzing EEG component variance in view of operator alertness that teaches consideration of an expected variance of the data based on user alertness.
However the combination of the cited art fails to explicitly disclose
the reception of time coded bio-signal data of a plurality of users, that is associated with time-coded feature event data in order to identify bio-signal patterns that represent a user response to the feature events with the generation of encryption keys with predefined data access rights using biosignal measures, encryption of those keys corresponding to a selected data access right and a target brain state to limit access to the data that considers an expected variance of the data and modulates the pattern of the biosignal data based on a user response to a task over plural sessions.
Furthermore, in regards to patent-eligibility under 35 U.S.C. §101, Applicant’s claimed invention is patent-eligible because a process of analyzing brain signal data to associate a user’s brain signal and develop encryption keys based thereon that consider signal modulation based on an expected variance is not an abstract idea.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
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 is571.270.1382.

APPLICANT REBUTTAL: REMARKS
/
ARGUMENTS
In accordance with the foregoing, claims 1, 14, and 24 have been amended, and claim 28 has been added. Support for these amendments can be found at least in the claims as originally filed and in paragraphs [00219], [00413], [00415], and [00422]. No new matter is being presented. Therefore claims 1, 2, 4-15, 17-24, 26-28 are pending in the application and reconsideration is respectfully requested.
Claim Rejection - 35 USC 103
), further in view of Makeig ("Changes in alertness are a principalcomponent of variance in the EEG spectrum" (Neuroreport. 1995 Dec 29;7(1):213-6. PMID: 8742454)). The Examiner has further rejected claims 2 and 15 as being allegedly obvious over Peot/Breuer/Makeig, further in view of Ornstein (WIPO 2005/043802 Al) and claim 27 over Peot/Breuer/Makeig, further in view of Gevins (US 4736751).
Claims 1
,
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 [00219], [00413], [00415], and [00422]. Claim 1 has been amended to clarify that (emphasis added):
...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 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, the expected variance based on at least one of
movement,
heart rate, breathing, age of the user, time of day, temperature, light levels, background sound levels, signal quality, and placement of the bio- signal sensor...
The claimed subject matter involves user expected variance between a time-coded bio-signal data pattern and a model of expected user behaviour associated with the user, that is based on "at least one of heart rate, breathing, age of the user, time of day, temperature, light levels, background sound levels, signal quality, and placement of the bio-signal sensor". Some embodiments of the pending subject matter can, for example, generate a first variation at a session at a first timestamp and a second variation at a second timestamp for the same tasks (present application, paragraph [00421]).
The Examiner concedes that Peot and Breuer fail to disclose "consideration of an expected variance between measured and expected EEG signals for a given measured user" and "the expected variance based on at least one of movement, heart rate, breathing, age of the user, time of day, temperature, light levels, background sound levels, signal quality, and placement of the bio-signal sensor" (Examiner's Report, page 8). Applicant further submits that Peot and Breuer fail to disclose "...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 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, the expected variance based on at least one of
movement,
heart rate, breathing, age of the user, time of day, temperature, light levels, background sound levels, signal quality, and placement of the bio-signal sensor..." as now claimed.
Makeig appears to disclose that a user's alertness is known to show an expected variance in EEG spectrum frequencies (Makeig, Introduction, page 213). Makeig is directed to "alertness" which is referenced as being based on "changes in behavioral arousal", where "arousaf' refers primarily to changes in behaviour associated with transitions from slow-wave sleep to waking, and alertness to waking (Makeig, Introduction, page 213). Makeig appears to disclose that "alertness" is measured in a study conducted based on a user's movement, wherein the
user is instructed to press one of two buttons in response to auditory signals, as the user's movement is directly related to the user's fatigue (Makeig, Materials and Methods, page 214). Makeig also appears to disclose that a user's drowsiness and state of hypnagogy are shown to be associated with an expected variance in the EEG spectrum (Makeig, Discussion, page 215), but there is no mention of heart rate, breathing, age of the user, time of day, signal quality, placement of EEG electrodes or any other variables that could show an expected variance. Makeig fails to disclose at least "...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 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, the expected variance based on at least one of
movement,
heart rate, breathing, age of the user, time of day, temperature, light levels, background sound levels, signal quality, and placement of the bio-signal sensor..." as now claimed.
Ornstein and Gevins do not disclose expected variance based on heart rate, breathing, age of the user, time of day, temperature, light levels, background sound levels, signal quality or placement of EEG electrodes. Applicant asserts that for these reasons, these deficiencies are not remedied by Ornstein or Gevins.
For at least these reasons, Applicant respectfully submits that none of the prior art references cited, either alone or in combination, disclose the pending subject matter. Applicant further respectfully submits that the prior art references cited do not teach, suggest or disclose the advantages arising from the pending subject matter. Applicant respectfully submits that claims 1, 14, and 24, as amended, and claims 2, 4-13, 15, 17-23, and 26-27 by virtue of their dependencies, are novel and inventive over the prior art cited.
Claim 28
Applicant has added new claim 28. Support for this amendment can be found at least in the claims as originally filed and in paragraphs [00219], [00413], [00415], and [00422].
New claim 28 recites:
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 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, the expected variance based on at least one of age of the user, time of day, temperature, signal quality, and placement of the bio-signal sensor; 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.
In addition to the reasons described above, Applicant further asserts that the expected variance can be based on at least one of age of the user, time of day, temperature, signal quality, and placement of the bio-signal sensor. None of the prior art references disclose "the expected variance based on at least one of age of the user, time of day, temperature, signal quality, and placement of the bio-signal sensor". Applicant submits that new claim 28 is novel and inventive over the prior art cited for these reasons and those described above.
***
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,


CITED REFERENCE SUMMARIES: 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 a current mental activity; train 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; associate 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; define 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; lock 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 send the locked message to the user as a designated receiver.
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 a current mental activity;
train 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;
associate 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;
define 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;
lock 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
send the locked message to the user as a designated receiver.
The system of claim 10 , wherein the processor executes the program instructions stored on the computer-readable storage medium via the computer readable memory and is thereby caused to: define 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; 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.
define 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;
The system of claim 10 , wherein the processor executes the program instructions stored on the computer-readable storage medium via the computer readable memory and is thereby caused to train the first set of artificial neural network neuron element weighting factors further by 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 system of claim 10 , wherein the processor executes the program instructions stored on the computer-readable storage medium via the computer readable memory and is thereby caused to train the artificial neural network neuron element weighting by 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 system of claim 10 , wherein the processor executes the program instructions stored on the computer-readable storage medium via the computer readable memory and is thereby caused to: define 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 encrypt 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.
define 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
encrypt a message using a public key of a receiver, wherein the message comprises message content in combination with the defined personal electroencephalogram signature; and
The system of claim 10 , wherein the processor executes the program instructions stored on the computer-readable storage medium via the computer readable memory and is thereby caused to permanently destroy the personal electroencephalogram signature in response to 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.
A computer program product for a user-specific, electroencephalogram data-based secure encryption generator, the computer program product comprising: a computer readable storage medium having computer readable program code embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the computer readable program code comprising instructions for execution by a processor that cause the processor 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 a current mental activity; train 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; associate 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; define a reproducible electroencephalogram encryption key that is unique to the user as a function of at least one of the trained first set of artific
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