DETAILED ACTION
Status of Claims
The present application is being examined under the pre-AIA first to invent provisions.
This action is in reply to a response filed 6 January 2026, on an application filed 16 September 2013, which claims priority to provisional applications filed 14 September 2012.
Claims 1 and 11 have been amended.
Claims 21 and 22 have been added by amendment.
Claims 1-22 are currently pending and have been examined.
The claims are comprised of a statutory category (system and machine readable medium) and are not directed to an abstract idea and therefore comprise statutory material.
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.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
Claims 1-4, 6-14 and 16-20 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, in view of Dove et al. (U.S. PG-Pub 2012/0090003 A1), hereinafter Dove, in view of Ozdamar et al. (U.S. Patent 5,230,344), hereinafter Ozdamar, further in view of Guan et al. (U.S. PG-Pub 2012/0108997 A1), hereinafter Guan.
As per claims 1 and 11, Peot discloses a computer readable medium storing machine executable instructions to configure a processor to execute a brainwave monitoring process and a brainwave monitoring system (See Peot Figs. 1 and 3.) comprising:
at least one client computing device storing or accessing an application (See Peot Fig. 3 and corresponding text. Peot discloses reception of stimulus and cues from multiple users/operators using processors, see paragraphs 11, 26 and 55 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.);
at least one bio-signal sensor including at least one electroencephalography (EEG) bio-signal sensor and in communication with the at least one client computing device (See Peot Fig. 3 and corresponding text.);
at least one user effector to provide a real-time biofeedback output for the application (System outputs real time biofeedback, significant or non-significant response detected as a result of the determined response classification, see Peot paragraph 54.); and
the at least one client computing device configured (See Peot Fig. 3 and corresponding text.) to:
… a client pipeline instance generated or selected on the at least one computer server, the client pipeline instance based on a prediction model and … user characteristics, wherein the prediction model is a … model trained on aggregated data, the client pipeline instance for predicting brain states, wherein the user characteristics comprises a set of features extracted from EEG bio-signal data using feature extraction … (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, 36 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 signal, see paragraphs 34, 36, 51 and 52; 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. Various signals received from the user would comprise user characteristics, see #s 84,112, 102 and 124 of Figs. 4 and 5.);
receive time-coded EEG bio-signal data of a user from the at least one EEG bio-signal sensor (Peot Figs. 3 and 4 discloses reception of EEG bio-signal data from sensors; it is well known that EEG data is time-coded.);
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 Peot Fig. 5 and see paragraphs 10, 23-25.);
extract feature events from the time coded feature event data at 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 Peot paragraphs 29-30; see also paragraph 31.);
label segments in the time-coded EEG bio-signal data using the feature event time codes using the client pipeline instance (Peot, Figs. 5 and 6 and paragraphs 43-45.);
determine biofeedback output based on the labelled segments of the time-coded EEG bio-signal data, the biofeedback output based in part on a brain state of the user at the EEG bio-signal time codes (Response classification [biofeedback output] of whether a particular stimulus is relevant or not relevant based on examining the received data, see Fig. 6 and paragraphs 49-53.);
output the biofeedback output using the user effector (System outputs real time biofeedback, significant or non significant response detected as a result of the determined response classification, see paragraph 54.); and
transmit data pursuant to the biofeedback output to the at least one computer server to update the aggregated data, … (Peot, paragraph 56 discloses the system recording a user response to feedback: “In yet another example, response cueing could be incorporated into user response systems in which control groups of operators watch movies or advertisements before they are released to assess user reaction and feedback. This approach could supplement or replace other methods of user feedback and would identify the particular stimulus that is evoking a strong response. This information could be aggregated and used to reedit the advertisement or movie.”).
Peot fails to explicitly disclose:
training a machine learning model,
a brain signature …, wherein the brain signature is represented as a multidimensional histogram;
receive from at least one computer server data …;
a user response to biofeedback; and
updating the prediction model, and the brain signature.
However, Dove discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis at the time of the invention/filing to disclose:
receive from at least one computer server data (Dove discloses transmission and reception of data using computers over the internet, at least, see paragraphs 16, 17 and 50.);
a user response to biofeedback (Dove discloses provision of biofeedback to a user, and the user responding to said feedback, see Fig. 4 #404-424, wherein the system directs a user to provide a particular input, the user responds through brain signal patterns, the system parses/classifies the information to extract the command, performs the command, and then outputs feedback requesting confirmation, and user provides a confirmation response.); and
updating the prediction model using the user response (User response is utilized to update the user profile, which is a set of data utilized to classify future responses, aka prediction model, see paragraph 41.).
Therefore it would have been obvious to one of ordinary skill in the art of brainwave analysis at the time of the invention/filing to modify the teachings of Peot directed to acquiring feature event data from bio-signals with the reception of data from a server and the updating of a prediction model using a user response to feedback, as disclosed by Dove, because to do so would result in a system for detection and interaction utilizing user mental states that was further enabled to receive input from a user confirming their mental state, thereby resulting in a verified confirmation of the user’s current mental status through use of user feedback to the system.
Neither Peot nor Dove disclose:
training a machine learning model, and
a brain signature …, wherein the brain signature is represented as a multidimensional histogram.
However, Ozdamar discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis at the time of the invention/filing to disclose a brain signature …, wherein the brain signature is represented as a multidimensional histogram and the updating thereof (Ozdamar discloses a three dimensional plot, aka multidimensional histogram, of a brain stem response at Fig. 10, and then updating that response by applying filtering to the process at Fig. 11, see also C14L1-15.) in order to provide a system that can “utilize a two-dimensional filter to remove noise from the EEG signal streams particularly when the chain of stimuli has increasing levels of intensity and/or frequency” (Ozdamar C4L14-17.).
Therefore it would have been obvious to one of ordinary skill in the art of brainwave analysis at the time of the invention/filing to modify the teachings of Peot/Dove directed to acquiring feature event data from bio-signals with a brain signature …, wherein the brain signature comprises a set of features represented as a multidimensional histogram and the updating thereof, as disclosed by Ozdamar, because to do so would result in a system for detection and interaction utilizing user mental states that can “utilize a two-dimensional filter to remove noise from the EEG signal streams particularly when the chain of stimuli has increasing levels of intensity and/or frequency” (Ozdamar C4L14-17.).
Neither Peot, Dove, nor Ozdamar disclose training a machine learning model.
However, Guan discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis at the time of the invention/filing to disclose training a machine learning model (Guan, Fig. 5 and corresponding text.) in order to provide a system that utilizes advanced artificial intelligence networks to process patient data.
Therefore it would have been obvious to one of ordinary skill in the art of brainwave analysis at the time of the invention/filing to modify the teachings of Peot/Dove/Ozdamar directed to acquiring feature event data from bio-signals with training a machine learning model, as disclosed by Guan, because to do so would result in a system for detection and interaction utilizing user mental states that provide a system that utilizes advanced artificial intelligence networks to process patient data.
Peot, Dove, Ozdamar and Guan are all directed to the processing of patient data and specifically to the monitoring of 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).
As per claims 2 and 12, Peot/Dove/Ozdamar/Guan disclose claims 1 and 11, as shown above. Peot is directed to a system operative to provide monitoring and biofeedback for a plurality of users wearing HMDs, see paragraphs 11, 26, 55 and Fig. 8. The Office notes that the limitations contained in claim 2 are the same as that in claim 1 except that they are provided for additional devices, additional applications and additional users, etc. Accordingly, claims 2 and 12 are rejected over Peot/Dove for at least the same reasons and motivations as claims 1 and 11.
As per claims 3, 4, 6, 8, 10, 13, 14, 16, 18 and 20, Peot/Dove/Ozdamar/Guan discloses claims 1 and 20, discussed above. Peot also discloses:
3,13. wherein the at least one computer server is configured to generate or select the client pipeline instance based on a bio-signal interaction profile corresponding to the user (Classifier is trained based on collected brain signal data, which would comprise a bio-signal interaction profile corresponding to the user, see paragraphs 36 and 52.);
4,14. wherein user information is transmitted to the at least one computer server, and the at least one computer server is configured to generate or select the client pipeline instance based on the user information (Classifier is trained based on collected brain signal data, which would comprise selection based on the user information, see paragraphs 36 and 52.);
6,16. wherein the client pipeline instance is generated or selected based on a criteria regarding a user's current state (Classifier is trained based on collected brain signal data, which would comprise a criteria regarding a user's current state, see paragraphs 36 and 52.);
8,18. wherein the feature events are extracted using the client pipeline instance (Feature extractors rely on classifiers, wherein the nature and number of features is determined during classifier training, see Peot, paragraph 51.); and
10,20. wherein the aggregated data comprises data from other users using other applications, wherein the application and the other applications are different (All data is collected into an archive and used to train the classifiers/pipelines, see paragraphs 26 and 55. Applications are different as they are presented to different users and their processing is performed separately by the HMDs, see paragraphs 11, 26, 55 and Fig. 8.).
As per claims 7, 9, 17 and 19, Peot/Dove/Ozdamar/Guan discloses claims 1 and 20, discussed above. Peot fails to explicitly disclose:
7,17. wherein the biofeedback output comprises a user-response classification and the user response to the biofeedback output comprises confirmation of accuracy of the user-response classification; and
9,19. wherein the at least one client computing device is further configured to tune the client pipeline instance based on the user response to the biofeedback output.
However, Dove discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis at the time of the invention/filing to disclose:
7,17. wherein the biofeedback output comprises a user-response classification and the user response to the biofeedback output comprises confirmation of accuracy of the user-response classification (Dove discloses provision of biofeedback to a user, and the user responding to said feedback, see Fig. 4 #404-424, wherein the system directs a user to provide a particular input, the user responds through brain signal patterns, the system parses/classifies the information to extract the command, performs the command, and then outputs feedback requesting confirmation, and user provides a confirmation response.); and
9,19. wherein the at least one client computing device is further configured to tune the client pipeline instance based on the user response to the biofeedback output (User response is utilized to update the user profile, which is a set of data utilized to classify future responses, aka prediction model, see paragraph 41.).
Therefore it would have been obvious to one of ordinary skill in the art of brainwave analysis at the time of the invention/filing to modify the teachings of Peot/Dove/Ozdamar/Guan directed to acquiring feature event data from bio-signals with the user response confirmation of feedback and the user-response tuning of the classifier, as disclosed by Dove, because to do so would result in a system for detection and interaction utilizing user mental states that was further enabled to receive input from a user confirming their mental state, thereby resulting in a verified confirmation of the user’s current mental status through use of user feedback to the system. 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).
As per claims 21 and 22, Peot/Dove/Ozdamar/Guan discloses claim 1, discussed above. Peot fails to explicitly disclose:
21. wherein the brain signature for a respective user is initialized from a generic brain signature and optimized using features collected from the respective user; and
22. wherein the client computing device and the computer server apply updates to the brain signature.
However, Ozdamar discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis at the time of the invention/filing to disclose:
21. wherein the brain signature for a respective user is initialized from a generic brain signature and optimized using features collected from the respective user (Ozdamar discloses a three dimensional plot, aka multidimensional histogram, of a brain stem response at Fig. 10, which could be considered generic and then updating that response by applying filtering to the process at Fig. 11, see also C14L1-15, which could be optimizing using collected features.); and
22. wherein the client computing device and the computer server apply updates to the brain signature (Ozdamar discloses a three dimensional plot, aka multidimensional histogram, of a brain stem response at Fig. 10, and then updating that response by applying filtering to the process at Fig. 11, see also C14L1-15.) in order to provide a system that can “utilize a two-dimensional filter to remove noise from the EEG signal streams particularly when the chain of stimuli has increasing levels of intensity and/or frequency” (Ozdamar C4L14-17.).
Therefore it would have been obvious to one of ordinary skill in the art of brainwave analysis at the time of the invention/filing to modify the teachings of Peot/Dove/Ozdamar/Guan with the use of generic brain signatures, feature optimization disclosed by Ozdamar, because to do so would result in a system for detection and interaction utilizing user mental states that can “utilize a two-dimensional filter to remove noise from the EEG signal streams particularly when the chain of stimuli has increasing levels of intensity and/or frequency” (Ozdamar C4L14-17.). 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 5 and 15 are rejected under 35 U.S.C. 103(a) as being obvious over Peot/Dove/Ozdamar/Guan further in view of Almog (U.S. Patent 6,024,701 A).
As per claims 5 and 15, Peot/Dove/Ozdamar/Guan discloses claims 1 and 20, discussed above. Peot also discloses wherein the client pipeline instance is generated or selected based on the data available at the client computing device.
Peot/Dove/Ozdamar/Guan fails to explicitly disclose consideration of computational resources available at the client computing device.
However, Almog discloses that it would have been obvious to one of ordinary skill in the art of brain signal analysis at the time of the invention/filing to disclose consideration of computational resources available at the client computing device (Almog, discloses wherein a model is selected based on available computational resources, see C6L10-17.).
Therefore it would have been obvious to one of ordinary skill in the art of brainwave analysis at the time of the invention/filing to modify the teachings of Peot/Dove/Ozdamar/Guan directed to acquiring feature event data from bio-signals with model selection based on consideration of computational resources available at the client computing device, as disclosed by Almog, because to do so would result in a system for detection and interaction utilizing user mental states that was further enabled to tailor model use to those that could be used efficiently. 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).
Response to Arguments
Applicant’s arguments filed 6 January 2026 concerning the rejection of all claims under 35 U.S.C. 103 have been fully considered but they are not persuasive.
The Applicant argues on pages 7-8 that the Ozdamar fails to disclose the amended language directed to feature extraction from EEG signals.
The Office respectfully disagrees. As shown above, Peot is used to disclose feature extraction from EEG signals, and then combined with the other references to disclose the invention as claimed.
Applicant's arguments have been fully considered but are moot in view of the new ground(s) of rejection, specifically with reference to the new reference necessitated by amendment, Guan, as detailed above, or because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
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, Dove, Ozdamar, Guan and Almog, 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 (6 January 2025, 13 June 2025 and 11 February 2025), 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. The Examiner can normally be reached on Monday-Friday (8-5). If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Kambiz Abdi, can be reached at 571.272.6702.
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/MARK HOLCOMB/
Primary Examiner, Art Unit 3685
22 February 2025