Office Action 4: Final Rejection (2024-03-22)
Rejection types: 103 - Obviousness

CLAIMS:
Amendments to the Claims:

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

Listing of claims:

1\. (Currently Amended) A computer system comprising:a computing device;
a bio-signal sensor in communication with the computing device; and
a computer server in communication with the computing device over a communications network, the computer server configured to:
receive, from the computing device, time-coded bio-signal data acquired using the bio-signal sensor, the time-coded bio-signal data including metadata associated with a user;
acquire time-coded feature event data;
extract feature events from the time-coded feature event data corresponding to feature event time codes;
determine segments of the time-coded bio-signal data that correlate to the feature event time codes to identify a pattern from the time-coded bio-signal data representing a response of the user to the feature events;
generate one or more encryption keys based at least in part on a comparison of the pattern from the time-coded bio-signal data to a model of expected user behaviour associated with the user 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; 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 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; 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 movement, heart rate, breathing, age of the user, time of day, temperature, liqht levels, backqround 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.

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 response filed 14 March 2024, on a request for continued examination (“RCE”) filed 13 October 2023 on an application that claims priority to an application that claims priority to provisional applications filed 12 September 2012.
The closest art for double patenting issues is U.S. Patent 10,606,353 B2, however this patent does not currently present a double patenting issue, nor does U.S. PG-Pub 2023/0221801 A1.
Claims 1, 14 and 24 have been amended.
Claim 25 has been canceled.
Claims 1, 2, 4-15, 17-24, 26 and 27 are currently pending and have been examined.
Response to Amendments
All rejections of claim 25 have been withdrawn in light of the cancellation of this claim.
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, 4-14 and 17-24 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, further in view of Makeig et al. “Changes in alertness are a principal component of variance in the EEG spectrum” (Neuroreport. 1995 Dec 29;7(1):213-6. PMID: 8742454), hereinafter Makeig.
As per claims 1, 14 and 24, Peot discloses a computer system, a non-transitory computer readable medium comprising a computer readable memory storing computer executable instructions thereon and a computer implemented method (Peot, Figs. 1 and 3) comprising:
1, 14.	a computing device (Peot, Figs. 1 and 3);
a bio-signal sensor in communication with the computing device (Various processors (computing devices) are in communication with plural bio-signal sensors, see Fig. 3 of Peot, and paragraphs 28 and 55. Peot discloses reception of stimulus and cues from multiple users/operators using processors, see paragraphs 11, 26 and 54 and Fig. 8. A cue is derived from captured bio signal data, see paragraphs 4, 9 and 10. Each user has a helmet mounted device [HMD] #12, shown in Figs. 2 #3, and are engaged in activities, such as monitoring their direct environment in a ground based environment, see Figs. 7 and 8. Activities can also include flying planes, see paragraph 8. Each HMD contains one or several processors/computing devices executing applications to monitor the users physiological activities, such as their cognitive response or the visual fixation or position, see Fig. 3 and paragraphs 27-29. Similarly, all of these signals are processed by a decision level classifier #130, which is a respective application that processes all of the inputs from the other applications, including EEG Classifier #86, FNIRS Classifier #114, Pupil Classifier #104 and Dwell Time Classifier #126, see Fig. 4. See also the plural Spatial Classifiers #162-164 of Fig. 6, which are then processed by the temporal classifier #168.); and
... the computer ... configured to:
receive ... time-coded bio-signal data acquired using the bio-signal sensor, the time-coded bio-signal data including metadata associated with a user (Peot, Figs. 3 and 4 discloses reception of bio-signal data from plural sensors.);
acquire time-coded feature event data (Various time-coded feature event data is assembled from the received bio-signal data, such as when the user noticed various stimulus, relevant and not relevant, when the user began fixation, see Fig. 5 and see paragraphs 10, 23-25.);
extract feature events from the time-coded feature event data corresponding to feature event time codes (Feature events are assembled from the feature event data, such as when the eyes (variable) remain focused on a region of less than half a degree (value), see paragraphs 29-30. See also paragraph 31.);
determine segments of the time-coded bio-signal data that correlate to the feature event time codes to identify a pattern from the time-coded bio-signal data representing a response of the user to the feature events (The system of Peot determines whether a particular stimulus at a particular segment is relevant or not relevant based on examining the received data, see Figs. 5 and 6, and paragraphs 29-31, 34 and 43-45.); and
generate data based at least in part on a comparison of the pattern from the time-coded bio-signal data to a model of expected user behaviour associated with the user (User responses are classified as relevant or non-relevant, wherein either could be considered expected user behavior, and the system provides feedback based on that classification, see paragraphs 49-54; wherein the model comprised of the classifier that assembles data from plural user inputs [i.e., not just EEG inputs] to determine whether extracted material, including EEG input, is expected, see paragraphs 51-53.).
Peot fails to explicitly disclose:
a computer server in communication with the computing device over a communications network;
generate one or more encryption keys based at least in part on a comparison of data generated via biosignal measures, each of the encryption keys associated with a pre-defined data access right;
consideration of an expected variance between measured and expected EEG signals for a given measured user; and
encrypt data using one or more of the encryption keys corresponding to a selected data access right and a target brain state to limit access to the data; and
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.
However, Breuer discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis before the effective filing date of the claimed invention to provide:
a computer server in communication with the computing device over a communications network (Breuer, Figs. 1-3.);
generate one or more encryption keys based at least in part on a comparison of data generated via biosignal measures, each of the encryption keys associated with a pre-defined data access right (Breuer, Fig. 4 discloses generation of a data encryption key associated with an access right based on comparison of patterns to a user model.); and
encrypt data using one or more of the encryption keys corresponding to a selected data access right and a target brain state to limit access to the data (Breuer, Fig. 4 discloses generation of a data encryption key associated with an access right based on comparison of patterns to a user model, and generates an encryption key based on a target brain state, that is, the mental activity of the user, see Fig. 4 #112 and C13L20-25.  It is the Office’s position that an encryption key having any access right associated would comprise a selected data access right.),
in order to provide computers enabled to communicate with other computers over a network wherein a “reproducible electroencephalogram encryption key is defined that is unique to the user as a function of one or both of the trained weighting factors and the first weighted set of electroencephalogram data signals” (Breuer, Abstract.).
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave signal analysis before the effective filing date of the claimed invention to modify the brainwave monitoring system of Peot to include a computer server in communication with the computing device over a communications network, generate one or more encryption keys based at least in part on a comparison of data generated via biosignal measures, each of the encryption keys associated with a pre-defined data access right; and encrypt data using one or more of the encryption keys corresponding to a selected data access right and a target brain state to limit access to the data, as taught by Breuer, in order to provide a brainwave monitoring system enabled to communicate with other computers over a network wherein a “reproducible electroencephalogram encryption key is defined that is unique to the user as a function of one or both of the trained weighting factors and the first weighted set of electroencephalogram data signals” (Breuer, Abstract.).
Neither Peot nor Breuer disclose a system that can:
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.
Makeig however discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis before the effective filing date of the claimed invention to provide:
consideration of an expected variance between measured and expected EEG signals for a given measured user (Makeig discloses that a user’s EEG signals are known to show an expected variance based on their alertness, see Abstract, page 213.); 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 (Makeig discloses that a user’s alertness is known to show an expected variance based on their alertness, which is measured in a study conducted based on a user’s movement, wherein the user is instructed to press one of two buttons [movement measurement] in response to auditory signals, as the user’s movement is directly related to the user’s fatigue, see Makeig, left column, page 214.).
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave signal analysis before the effective filing date of the claimed invention to modify the brainwave monitoring system of Peot/Breuer to include consideration of an expected variance between measured and expected EEG signals for a given measured user and 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 taught by Makeig, in order to provide a brainwave monitoring system that provides consideration of a user’s state of alertness when measuring their brainwave signals. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Peot, Breuer and Makeig are all directed to the processing of biologically generated data.
A recitation directed to the manner in which a claimed apparatus is intended to be used does not distinguish the claimed apparatus from the prior art, if the prior art has the capability to so perform, see MPEP 2114 (II) and Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987). “Language that suggest or makes optional but does not require steps to be performed or does not limit a claim to a particular structure does not limit the scope of a claim or claim limitation”, see MPEP 2111.04.
The following limitation is interpreted as an intended use of the claimed invention:
1,14,25.	… to limit access to the data.
The prior art is capable of performing the intended use recitation, therefore the prior art meets the limitations.
As per claims 6-9, 11-13, 19-22 and 26, Peot/Breuer/Makeig disclose claims 1 and 14, discussed above. Peot/Breuer/Makeig also disclose:
6, 19. 	receive additional time-coded bio-signal data and additional time-coded feature event data associated with an additional user (Various processors (computing devices) are in communication with plural bio-signal sensors, see Fig. 3 of Peot, and paragraphs 28 and 55. Peot discloses reception of stimulus and cues from multiple users/operators using processors, see paragraphs 11, 26 and 54 and Fig. 8.);
extract additional feature events from the additional time-coded feature event data corresponding to additional feature event time codes (Feature events are assembled from the feature event data, such as when the eyes (variable) remain focused on a region of less than half a degree (value), see paragraphs 29-30. See also paragraph 31.);
determine segments of the additional time-coded bio-signal data that correlate to the additional feature event time codes to identify a pattern from the additional time-coded bio-signal data representing a response of the additional user to the additional feature events (The system of Peot determines whether a particular stimulus at a particular segment is relevant or not relevant based on examining the received data, see Figs. 5 and 6, and paragraphs 29-31, 34 and 43-45.);
generate one or more decryption keys based at least in part on a comparison of the pattern from the additional time-coded bio-signal data to a model of expected user behaviour associated with the additional user and expected variance between the pattern from the additional time-coded bio-signal data and the model of expected user behaviour associated with the additional user (Peot, user responses are classified as relevant or non-relevant, wherein either could be considered expected user behavior, and the system provides feedback based on that classification, see paragraphs 49-54. Same as in the independent claims, Breuer, Fig. 4 discloses generation of a data decryption key associated with an access right based on comparison of patterns to a user model and Matthews discloses expected variance between EEG signals and modeled behavior.); and
using one or more of the decryption keys and the target brain state, decrypt data that is encrypted by one or more of the encryption keys (Same as in the independent claims, Breuer, Fig. 4 discloses generation of an data encryption key associated with an access right based on comparison of patterns to a user model. It is the Office’s position that a decryption key having any access right associated would comprise a selected data access right.);
7, 20. 	generate one or more additional encryption keys based at least in part on a comparison of the pattern from the additional time-coded bio-signal data to the model of expected user behaviour associated with the additional user and the expected variance between the pattern from the additional time-coded bio-signal data and the model of expected user behaviour associated with the additional user (Peot, user responses are classified as relevant or non-relevant, wherein either could be considered expected user behavior, and the system provides feedback based on that classification, see paragraphs 49-54. Same as in the independent claims, Breuer, Fig. 4 discloses generation of a data decryption key associated with an access right based on comparison of patterns to a user model and Matthews discloses expected variance between EEG signals and modeled behavior.); and
generate one or more additional decryption keys based at least in part on a comparison of the pattern from the time-coded bio-signal data to the model of expected user behaviour associated with the user and the expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user (Same as in the independent claims, Breuer, Fig. 4 discloses generation of a data decryption key associated with an access right based on comparison of patterns to a user model and Matthews discloses expected variance between EEG signals and modeled behavior.); and
8, 21. 	receive periodic bio-signal data associated with the user (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9.);
receive periodic bio-signal data associated with the additional user (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9.);
identify patterns from the periodic bio-signal data associated with the user to update the model of expected user behaviour associated with the user states (The model of Peot determines whether a particular stimulus is relevant or not relevant based on examining the received data, see Fig. 5 and paragraphs 29-31, 34 and 43-45 and 49-53. Peot describes creation of plural parallel pipelines, classifier pipelines #162-164, which are based on analysis of features extracted from a time window of the response signals; these pipelines are associated with the plurality of applications, such as various classifiers of Fig. 4, which are used to inspect the signal data. Applicant’s specification defines a pipeline as an instance” generated “in order to analyze the user’s private bio-signal data using particular analysis or processing parameters applied during the analysis or processing”, see present specification at paragraph 56; accordingly, the plural parallel spatial classifiers would comprise a pipeline, as would the decision level classifier #130 of Fig. 6.); and
identify patterns from the periodic bio-signal data associated with the additional user to update the model of expected user behaviour associated with the additional user (The model of Peot determines whether a particular stimulus is relevant or not relevant based on examining the received data, see Fig. 5 and paragraphs 29-31, 34 and 43-45 and 49-53. Peot describes creation of plural parallel pipelines, classifier pipelines #162-164, which are based on analysis of features extracted from a time window of the response signals; these pipelines are associated with the plurality of applications, such as various classifiers of Fig. 4, which are used to inspect the signal data. Applicant’s specification defines a pipeline as an instance” generated “in order to analyze the user’s private bio-signal data using particular analysis or processing parameters applied during the analysis or processing”, see present specification at paragraph 56; accordingly, the plural parallel spatial classifiers would comprise a pipeline, as would the decision level classifier #130 of Fig. 6.);
9, 22. 	receive multiple user bio-signal data corresponding to multiple users, including the user, in response to a time-coded event stimulus (Various processors (computing devices) are in communication with plural bio-signal sensors, see Fig. 3 of Peot, and paragraphs 28 and 55. Peot discloses reception of stimulus and cues from multiple users/operators using processors, see paragraphs 11, 26 and 54 and Fig. 8.);
identify patterns from the multiple user bio-signal data to identify users (Peot identifies patterns from biosignal data to determine response relevance and is responsible for maintaining data of all users, see Figs. 1, 3-6 and 8.); and
initiate a shared user experience based on the identified users (System of Peot describes a shared experience for all users, identified or not, see Figs. 1, 3-6 and 8.);
11. 	wherein the bio-signal sensor includes an electroencephalography (EEG) sensor, and the time-coded bio-signal data includes time-coded EEG bio-signal data (Peot, Fig. 4.);
12. 	wherein the bio-signal sensor includes a functional Near-Infrared Spectroscopy (fNIRS) sensor, and the time-coded bio-signal data includes time-coded fNIRS bio-signal data (Peot, Fig. 4.);
13. 	at least one additional computing devices, each of the at least one additional computing devices in communication with at least one additional bio-signal sensor, and the computer server is further configured (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9. Breuer discloses a server, see Figs. 1-3.) to:
receive additional time-coded bio-signal data from the at least one additional computing devices (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9.); and
update the model of expected user behaviour associated with the user based on the additional time-coded bio-signal data (The system of Peot is operative to be updated over time and receive data from multiple users, see Figs. 1, 3, 4 and 9.); and
26.	the model of expected user behaviour associated with the user is based on the user's behaviour during a first task (Model is the classifier that assembles data from plural user inputs [i.e., not just EEG inputs] to determine whether extracted material, including EEG input, is expected, see paragraphs 51-53. Model is trained to make decisions based on all inputs, which would comprise a task different then just one input, such as EEG data, see paragraphs 34, 36, 38, 42, 44 and 50-53); and
the pattern from the time-coded bio-signal data is based on the user's behaviour during a second task that is different than the first task (EEG data is examined and classified, user EEG task, see paragraphs 35, 36 and 51.).
As per claims 4, 5, 10, 17, 18 and 23, Peot/Breuer/Makeig disclose claims 1 and 14, discussed above. Peot fails to explicitly disclose:
4, 17. 	wherein the model of expected user behaviour associated with the user includes a brain model associated with the user representing brain state responses of the user to select events;
5, 18. 	wherein the encryption keys are unique to the user; and
10, 23.	wherein the selected data access right is associated with a target brain state to access the data.
However, Breuer discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis before the effective filing date of the claimed invention 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 before the effective filing date of the claimed invention to modify the brainwave monitoring system of Peot to include wherein the model of expected user behaviour associated with the user includes a brain model associated with the user representing brain state responses of the user to select events;  wherein the encryption keys are unique to the user; and wherein the selected data access right is associated with a target brain state to access the data, as taught by Breuer, in order to provide a brainwave monitoring system enabled to communicate with other computers over a network wherein a “reproducible electroencephalogram encryption key is defined that is unique to the user as a function of one or both of the trained weighting factors and the first weighted set of electroencephalogram data signals” (Breuer, Abstract.).
Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Claims 2 and 15 are rejected under 35 U.S.C. 103(a) as being obvious over Peot/Breuer/Makeig, further in view of Ornstein et al. (WIPO 2005/043802 A1), hereinafter Ornstein.
As per claims 2 and 15, Peot/Breuer/Makeig disclose claims 1 and 14, discussed above. As shown above, Breuer discloses a fidelity limited access right, see Figs. 4 and 5; however Peot fails to explicitly disclose wherein the pre-defined data access right includes at least one of a time-limited access right and a fidelity-limited access right.
However, Ornstein discloses that it would have been obvious to one of ordinary skill in the art of secure electronic communications before the effective filing date of the claimed invention 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 before the effective filing date of the claimed invention to modify the brainwave monitoring system of Peot to include wherein the pre-defined data access right includes a time-limited access right, as taught by Ornstein, in order to provide a brainwave monitoring system that used “a secure technique of distributing electronic content such as videos, text, data, photos, financial data, sales solicitations, or the like” (Ornstein, Abstract.). Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Peot, Breuer, Makeig and Orenstein are all directed to the processing of biologically generated data.
Claim 27 is rejected under 35 U.S.C. 103(a) as being obvious over Peot/Breuer/Makeig, further in view of Gevins et al. (U.S. Patent 4,736,751), hereinafter Gevins.
As per claim 27, Peot/Breuer/Makeig disclose claim 1, discussed above. Peot/Breuer/Makeig discloses wherein the generate the one or more encryption keys based at least in part on the comparison of the pattern from the time-coded bio-signal data to the model of expected user behaviour associated with the user and the expected variance between the pattern from the time-coded bio-signal data and the model of expected user behaviour associated with the user (As shown above.).
Peot/Breuer/Makeig fails to explicitly disclose:
prompting the user to provide a current response to a task;
determining a difference between the current response to the task and an initial response to the task as provided in a prior session; and
modulating the pattern from the time-coded bio-signal data based on the difference between the current response to the task and the initial response to the task.
However, Gevins discloses that it would have been obvious to one of ordinary skill in the art of secure electronic communications before the effective filing date of the claimed invention 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 secure electronic communications before the effective filing date of the claimed invention 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, Makeig and Gevins are all directed to the processing of biologically generated data.
Response to Arguments
Applicant’s arguments filed 13 October 2023	 concerning the rejection of all claims under 35 U.S.C.  103 have been fully considered but they are not persuasive.
With regard to the rejection of the claims under 35 USC 103, Applicant argues on pages 8 that Makeig fails to disclose the amendments to the claims.
The Office respectfully disagrees. Makeig, as shown above, discloses 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 ….  Makeig discloses that a user’s EEG signals are known to show an expected variance based on their alertness, see Abstract, page 213. Makeig also discloses that a user’s alertness is known to show an expected variance based on their alertness, which is measured in a study conducted based on a user’s movement, wherein the user is instructed to press one of two buttons [movement measurement] in response to auditory signals, as the user’s movement is directly related to the user’s fatigue, see Makeig, left column, page 214.
Accordingly, the prior art rejection is upheld.
In conclusion, all of the limitations which Applicant disputes as missing in the applied references, including the features newly added by amendment, have been fully addressed by the Office as either being fully disclosed or obvious in view of the collective teachings of Peot, Breuer, Makeig, Matthews, Gevins and Ornstein, based on the logic and sound scientific reasoning of one ordinarily skilled in the art at the time of the invention, as detailed in the remarks and explanations given in the preceding sections of the present Office Action and in the prior Office Actions (15 August 2024, 14 June 2023 and 7 October 2022), and incorporated herein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.  Accordingly, THIS ACTION IS MADE FINAL.  See MPEP § 706.07(a).  Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action.  In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action.  In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382.

APPLICANT REBUTTAL: REMARKS
/
ARGUMENTS
In accordance with the foregoing, claims 1, 14, 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,

NEXT 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.

CITED REFERENCE SUMMARIES: Ref US10606353B2: Claims:
What is claimed is: 1. A brainwave monitoring system comprising: a plurality of client computing devices, each of the plurality of client computing devices in communication with at least one bio-signal sensor including at least one electroencephalography (EEG) bio-signal sensor; and at least one computer server in communication with the plurality of computing devices over a communications network, the at least one computer server configured to: receive time-coded EEG bio-signal data from at least one of the plurality of client computing devices, the time-coded EEG bio-signal data having metadata indicating a user identifier of the plurality of user identifiers; acquire time-coded feature event data; generate a cloud pipeline instance to process the time-coded EEG bio-signal data and the time coded feature event data, the cloud pipeline instance defining pipeline parameters for a classification model; extract, using the cloud pipeline instance, feature events from the time coded feature event data at feature event time codes, each feature event being a set of variables and corresponding values at at least one feature event time code; automatically search the feature events to identify a pattern, the pattern linked to a feature event time code of the at least one feature event time code, the pattern representing user response associated with the feature event data at the feature event time code; using the feature event time code linked to the pattern identified in the feature-events, label segments in the time-coded EEG bio-signal data having EEG bio-signal time-codes being same or similar to the feature event time code linked to the pattern; update the classification model with the EEG bio-signal features extracted from labelled segments of the time-coded EEG bio-signal data, the classification model for predicting brain state based on the pipeline parameters; determine a response classification of the segments of the time-coded EEG bio-signal data using the classification model and pipeline parameters, the response classification being an automatic prediction of a brain state at the EEG bio-signal time codes; update, for the user identifier, a bio-signal interaction profile based on the classification model, the response classification, the time-coded EEG bio-signal data associated with that user identifier, and the time-coded feature event data; receive additional time-coded EEG bio-signal data associated with the user identifier; and generate an encryption key using the bio-signal interaction profile associated with the user identifier and segments of the additional time-coded EEG bio-signal data associated with the user identifier. 2. The system of claim 1, wherein the encryption key is unique to the user identifier. 3. The system of claim 1, wherein the additional time-coded EEG bio-signal data is associated with a set of discrete bins, each of the discrete bins having value boundaries, and the encryption key is generated based at least in part on the association between the additional time-coded EEG bio-signal data and the set of discrete bins. 4. The system of claim 1, wherein the bio-signal interaction profile is a brain model associated with the user identifier. 5. The system of claim 1, wherein the additional time-coded EEG bio-signal data is associated with a task prompting a user response. 6. The system of claim 1, wherein the at least one computer server is further configured to encrypt data using the encryption key. 7. The system of claim 6, wherein the at least one computer server is further configured to discard the encryption after the data is encrypted. 8. The system of claim 1, wherein the additional time-coded EEG bio-signal data is associated with a first point in time, and the at least one computer server is further configured to: receive a second additional time-coded EEG bio-signal data associated with the user identifier at a second point in time; and identify a variance of values of the additional time-coded EEG bio-signal data and the second additional time-coded EEG bio-signal data, generate a second encryption key using the bio-signal interaction profile associated with the user identifier, segments of the second additional time-coded EEG bio-signal data, and the variance. 9. The system of claim 8, wherein the variance represents a change in a user response to a repeated task at the first point in time and the second point in time. 10. The system of claim 1, further comprising at least one non-bio-signal sensor at and in communication with each of the client computing devices, the at least one computer server further configured to: receive time-coded non-bio-signal sensor data from the at least one non-bio-signal sensor; wherein the generated encryption key is based at least partly on the received time-coded non-bio-signal sensor data. 11. A brainwave monitoring system comprising: at least one user effector to provide a real-time biofeedback output; and at least one computer server in communication with a plurality of computing devices over a communications network, each of the plurality of client computing devices in communication with at least one bio-signal sensor including at least one electroencephalography (EEG) bio-signal sensor, the at least one computer server configured to: receive time-coded EEG bio-signal data from each of the plurality of client computing devices, the time-coded EEG bio-signal data associated with a plurality of user identifiers; acquire time-coded feature event data; generate a cloud pipeline instance to process the time-coded EEG bio-signal data and the time coded feature event data, the cloud pipeline instance defining pipeline parameters for a classification model; extract, using the cloud pipeline instance, feature events from the time coded feature event data at feature event time codes, each feature event being a set of variables and corresponding values at at least one feature event time code; automatically search the feature events to identify patterns that are statistically significant, each pattern linked to a feature event time code of the at least one feature event time code, the pattern representing user response associated with the feature event data at the feature event time code; using the feature event time codes linked to the patterns identified in the feature-events, label segments in the time-coded EEG bio-signal data having EEG bio-signal time-codes being same or similar to the feature event time codes linked to the patterns; update the classification model with the EEG bio-signal features extracted from labelled segments of the time-coded EEG bio-signal data, the classification model for predicting brain state based on the pipeline parameters; determine a response classification of the segments of the time-coded EEG bio-signal data using the classification model and pipeline parameters, the response classification being an automatic prediction of a brain state at the EEG bio-signal time codes; generate the real-time biofeedback output based on segments of the time-coded EEG bio-signal data, the classification model and the response classification; the at least one user effector configured to provide the real-time biofeedback output for each of the plurality of user identifiers. 12. The system of claim 11, wherein the real-time biofeedback output is an aggregation of segments of the EEG bio-signal data from each user identifier. 13. The system of claim 11, wherein the real-time biofeedback output is a modulation of some of the EEG bio-signal data from each user identifier. 14. The system of claim 11, wherein the at least one effector is associated with each user identifier and are co-located. 15. The system of claim 11, wherein the at least one effector is associated with each user identifier and are remote from each other. 16. The system of claim 11, wherein the at least one computer server is configured to send the real-time biofeedback output to an audio and visual display that is common to the users associated with the plurality of user identifiers. 17. The system of claim 11, wherein the at least one computer server is configured to generate, for each of the plurality of user identifiers, an individual real-time biofeedback output based on segments of the time-coded EEG bio-signal data, the classification model and the response classification, the at least one user effector configured to provide the individual real-time biofeedback output for that user identifier. 18. The system of claim 11, wherein the real-time biofeedback output comprises at least one of visual data, audio data and tactile data. 19. The system of claim 11 wherein the at least one user effector is part of a augment reality system and the real-time biofeedback output comprises visual elements on a display that is part of the augment reality system. 20. The system of claim 11 wherein the at least one user effector comprises a visual indication on a display.
A brainwave monitoring system comprising: a plurality of client computing devices, each of the plurality of client computing devices in communication with at least one bio-signal sensor including at least one electroencephalography (EEG) bio-signal sensor; and at least one computer server in communication with the plurality of computing devices over a communications network, the at least one computer server configured to: receive time-coded EEG bio-signal data from at least one of the plurality of client computing devices, the time-coded EEG bio-signal data having metadata indicating a user identifier of the plurality of user identifiers; acquire time-coded feature event data; generate a cloud pipeline instance to process the time-coded EEG bio-signal data and the time coded feature event data, the cloud pipeline instance defining pipeline parameters for a classification model; extract, using the cloud pipeline instance, feature events from the time coded feature event data at feature event time codes, each feature event being a set of variables and corresponding values at at least one feature event time code; automatically search the feature events to identify a pattern, the pattern linked to a feature event time code of the at least one feature event time code, the pattern representing user response associated with the feature event data at the feature event time code; using the feature event time code linked to the pattern identified in the feature-events, label segments in the time-coded EEG bio-signal data having EEG bio-signal time-codes being same or similar to the feature event time code linked to the pattern; update the classification model with the EEG bio-signal features extracted from labelled segments of the time-coded EEG bio-signal data, the classification model for predicting brain state based on the pipeline parameters; determine a response classification of the segments of the time-coded EEG bio-signal data using the classification model and pipeline parameters, the response classification being an automatic prediction of a brain state at the EEG bio-signal time codes; update, for the user identifier, a bio-signal interaction profile based on the classification model, the response classification, the time-coded EEG bio-signal data associated with that user identifier, and the time-coded feature event data; receive additional time-coded EEG bio-signal data associated with the user identifier; and generate an encryption key using the bio-signal interaction profile associated with the user identifier and segments of the additional time-coded EEG bio-signal data associated with the user identifier.
The system of claim 1, wherein the encryption key is unique to the user identifier.
The system of claim 1, wherein the additional time-coded EEG bio-signal data is associated with a set of discrete bins, each of the discrete bins having value boundaries, and the encryption key is generated based at least in part on the association between the additional time-coded EEG bio-signal data and the set of discrete bins.
The system of claim 1, wherein the bio-signal interaction profile is a brain model associated with the user identifier.
The system of claim 1, wherein the additional time-coded EEG bio-signal data is associated with a task prompting a user response.
The system of claim 1, wherein the at least one computer server is further configured to encrypt data using the encryption key.
The system of claim 6, wherein the at least one computer server is further configured to discard the encryption after the data is encrypted.
The system of claim 1, wherein the additional time-coded EEG bio-signal data is associated with a first point in time, and the at least one computer server is further configured to: receive a second additional time-coded EEG bio-signal data associated with the user identifier at a second point in time; and identify a variance of values of the additional time-coded EEG bio-signal data and the second additional time-coded EEG bio-signal data, generate a second encryption key using the bio-signal interaction profile associated with the user identifier, segments of the second additional time-coded EEG bio-signal data, and the variance.
The system of claim 8, wherein the variance represents a change in a user response to a repeated task at the first point in time and the second point in time.
The system of claim 1, further comprising at least one non-bio-signal sensor at and in communication with each of the client computing devices, the at least one computer server further configured to: receive time-coded non-bio-signal sensor data from the at least one non-bio-signal sensor; wherein the generated encryption key is based at least partly on the received time-coded non-bio-signal sensor data.
A brainwave monitoring system comprising: at least one user effector to provide a real-time biofeedback output; and at least one computer server in communication with a plurality of computing devices over a communications network, each of the plurality of client computing devices in communication with at least one bio-signal sensor including at least one electroencephalography (EEG) bio-signal sensor, the at least one computer server configured to: receive time-coded EEG bio-signal data from each of the plurality of client computing devices, the time-coded EEG bio-signal data associated with a plurality of user identifiers; acquire time-coded feature event data; generate a cloud pipeline instance to process the time-coded EEG bio-signal data and the time coded feature event data, the cloud pipeline instance defining pipeline parameters for a classification model; extract, using the cloud pipeline instance, feature events from the time coded feature event data at feature event time codes, each feature event being a set of variables and corresponding values at at least one feature event time code; automatically search the feature events to identify patterns that are statistically significant, each pattern linked to a feature event time code of the at least one feature event time code, the pattern representing user response associated with the feature event data at the feature event time code; using the feature event time codes linked to the patterns identified in the feature-events, label segments in the time-coded EEG bio-signal data having EEG bio-signal time-codes being same or similar to the feature event time codes linked to the patterns; update the classification model with the EEG bio-signal features extracted from labelled segments of the time-coded EEG bio-signal data, the classification model for predicting brain state based on the pipeline parameters; determine a response classification of the segments of the time-coded EEG bio-signal data using the classification model and pipeline parameters, the response classification being an automatic prediction of a brain state at the EEG bio-signal time codes; generate the real-time biofeedback output based on segments of the time-coded EEG bio-signal data, the classification model and the response classification; the at least one user effector configured to provide the real-time biofeedback output for each of the plurality of user identifiers.
The system of claim 11, wherein the real-time biofeedback output is an aggregation of segments of the EEG bio-signal data from each user identifier.
The system of claim 11, wherein the real-time biofeedback output is a modulation of some of the EEG bio-signal data from each user identifier.
The system of claim 11, wherein the at least one effector is associated with each user identifier and are co-located.
The system of claim 11, wherein the at least one effector is associated with each user identifier and are remote from each other.
The system of claim 11, wherein the at least one computer server is configured to send the real-time biofeedback output to an audio and visual display that is common to the users associated with the plurality of user identifiers.
The system of claim 11, wherein the at least one computer server is configured to generate, for each of the plurality of user identifiers, an individual real-time biofeedback output based on segments of the time-coded EEG bio-signal data, the classification model and the response classification, the at least one user effector configured to provide the individual real-time biofeedback output for that user identifier.
The system of claim 11, wherein the real-time biofeedback output comprises at least one of visual data, audio data and tactile data.
The system of claim 11 wherein the at least one user effector is part of a augment reality system and the real-time biofeedback output comprises visual elements on a display that is part of the augment reality system.
The system of claim 11 wherein the at least one user effector comprises a visual indication on a display.
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 netw
[Truncated to input limit]