DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/1/2026 has been entered.
Status of Claims
Claims 1, 9, and 17 are currently amended.
Claims 18-19 and 21-25 are cancelled.
Response to Arguments
Applicant’s arguments, see pages 10-13, filed 5/1/2026, with respect to 35 U.S.C. 101
rejection have been fully considered, but are not persuasive.
35 U.S.C. 101: Step 2A Prong 1
Regarding claim 1, applicant argues that the subject matter cannot be performed by the human
mind. The applicant submits (specification; paragraph 31) that generating such example data representative of a cognitive state of a user is based on granular or precise tracking of a localized region of a user's head (e.g., region proximal to the user's eye). The examiner respectfully argues that the generation of example data representative of a cognitive state of a user using correlation, determination, and comparison steps are all abstract ideas that can be performed in the human mind. The examiner further argues that although the eye tracker sensors “used to collect such granular or precise tracking of a localized region of a user's head” are additional elements, they merely recite extra-solution activity to the step of data gathering.
35 U.S.C. 101: Step 2A Prong 2
Regarding claim 1, applicant argues that the subject matter of the claim reflects an
improvement to the functioning of the computer or to another technology or technical field, integrating a recited judicial exception into a practical application of the exception. Specifically, applicant argues (specification; paragraph 22) that the subject matter uses specific analysis steps to identify what needs better workflow or made less complicated to improve the changes in user cognitive state. The examiner respectfully disagrees and argues this is an improvement in the abstract idea and not the computer or technological field itself. The determination to increase or decrease specific tasks (visual regions) based on cognitive change can be done in the human mind.
The applicant further argues that (specification; paragraph 22) the multiple tasks requires user
interaction with the system. Specifically, determining whether to include additional training and to display regions that would reduce the amount of cognitive state change. The examiner respectfully disagrees and argues that additional training is recited at a high level of generality and is interpreted as computer implementation to perform the abstract idea of “providing feedback or diagnosis to the system.” The display is recited as extra-solution activity to the step of giving a diagnosis based on cognitive state of the patient.
Applicant is reminded that abstract ideas cannot provide a practical application or significantly
more (e.g., an improvement). Both Step 2A Prong 2 and Step 2B require an additional element, not an abstract idea, to provide a practical application or significantly more (e.g., an improvement). See Genetic Technologies Limited v. Merial LLC (Fed Cir 2016). Here, the additional elements of claims 1, 9, and 17 are merely generically recited computer elements used as tools for executing the abstract ideas or insignificant extra-solution activity.
Applicant’s arguments, see pages 13-15, filed 5/1/2026, with respect to the rejection(s) of
claim(s) 1-17, 20, and 26-28 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Ellison.
35 U.S.C. 103:
Regarding claim 1, applicant argues that Bach, alone or in combination with the prior art, does
not teach “determining, based at least on the correlating and the cognitive workload of the user, a metric representative of an amount of the cognitive state change for the respective two or more visual regions as induced by the system in the user; and determining, based on a relative amount of cognitive state change among the respective two or more visual regions, additional training representing feedback for one of the two or more visual regions for display to reduce the amount of the cognitive state change; and generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback to at least one region for display.” After further search and consideration, the examiner will now refer to Ellison to teach these limitations (paragraph 219 and 270).
determining, based at least on the correlating and the cognitive workload of the user, a metric representative of an amount of the cognitive state change for the respective two or more visual regions as induced by the system in the user (paragraph 219); The value-added subconscious engagement of cognitive processes can be factored into the cognitive benefits offered by the platform, eye-tracking to provide users with an adjusted baseline.
and determining, based on a relative amount of cognitive state change among the respective two or more visual regions, additional training representing feedback for one of the two or more visual regions for display to reduce the amount of the cognitive state change (paragraph 219 and 270); The perceptual switch, while occurring subconsciously, can also be linked to active engagement of cognitive processes where the user is guided and/or made aware of the switch and/or alternate percepts and/or other contiguities, and is directed to focus on particular areas of the image set. Therefore, at least two visual regions are disclosed to provide feedback to the system. the user may be prompted to try a higher skill level, or the system may, in a responsive adaptive manner automatically adjust the interactivity's skill level, via adjustment logic 236. Therefore, additional training via machine learning is disclosed.
and generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback to at least one region for display (paragraph 219). As the user is interacting with the image sets, a switch may occur in which an image appears as background, e.g., in the ground position but can switch to the figure position with a reciprocal exchange of the second image in a two-image or three-image composite based on the presence of contiguities in the component images. Therefore, the display regions may be adjusted based on machine learning feedback from logic 236.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the processing and display system of Bach with the machine learning and display processing steps from Ellison for the benefit of differentially evaluating multi-domain cognitive engagement especially in patients/users who have suffered traumatic brain injury, concussion, or following stroke where other areas of the brain may compensate for loss of function in one area.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 9, and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 9, and 17 recite a method, a system, and an apparatus comprising:
collecting data representative of a cognitive state of a user while the user performs one or more tasks while interacting with the system, wherein interacting with the system to perform the one or more tasks is configured to induce a cognitive state change in the user;
recording details of the one or more tasks and actions of the user while performing the one or more tasks, wherein the details of the one or more tasks comprise task results, wherein the task results are determined based on measurements of a plurality of attributes associated with the one or more tasks being performed wherein the data representative of the cognitive state comprises eye scan patterns over time of the user while performing the one or more tasks;
analyzing the data representative of the cognitive state and the recorded details of the one or more tasks and/or actions of the user, the analyzing comprising: correlating the data with the recorded details
determining, for each of the two or more regions, a cognitive workload of the user while the user is viewing the respective region;
determining, based at least on the correlating and the cognitive workload of the user, a metric representative of an amount of the cognitive state change for the respective two or more visual regions as induced by the system in the user, and determining, based on a relative amount of cognitive state change among the respective two or more visual regions, additional training representing feedback for one of the two or more visual regions for display to reduce the amount of the cognitive state change;
generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback, wherein the one or more eye tracker sensors comprises a calibration unit for calibrating the one or more eye tracker sensors to the user, and wherein calibration data captured by the calibration unit is associated with the user, capable of recalling the calibration data associated with the user when the user is performing the one or more tasks, wherein the calibrating the one or more eye tracker sensors including adjusting settings based on a baseline measurement associated with the user.
To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is evaluated according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.05. The instant claims are evaluated according to such analysis.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 is directed to a method, claim 9 is directed to a system to perform the steps of the
method and claim 17 is directed towards an apparatus, and thus meet the requirements for step 1.
Step 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
Claims 1, 9, and 17 recite a method, instructions to perform the method and a device
comprising:
collecting data representative of a cognitive state of a user while the user performs one or more tasks while interacting with the system, wherein interacting with the system to perform the one or more tasks is configured to induce a cognitive state change in the user;
recording details of the one or more tasks and actions of the user while performing the one or more tasks, wherein the details of the one or more tasks comprise task results, wherein the task results are determined based on measurements of a plurality of attributes associated with the one or more tasks being performed wherein the data representative of the cognitive state comprises eye scan patterns over time of the user while performing the one or more tasks;
analyzing the data representative of the cognitive state and the recorded details of the one or more tasks and/or actions of the user, the analyzing comprising: correlating the data with the recorded details
determining, for each of the two or more regions, a cognitive workload of the user while the user is viewing the respective region;
determining, based at least on the correlating and the cognitive workload of the user, a metric representative of an amount of the cognitive state change for the respective two or more visual regions as induced by the system in the user, and determining, based on a relative amount of cognitive state change among the respective two or more visual regions, additional training representing feedback for one of the two or more visual regions for display to reduce the amount of the cognitive state change;
generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback, wherein the one or more eye tracker sensors comprises a calibration unit for calibrating the one or more eye tracker sensors to the user, and wherein calibration data captured by the calibration unit is associated with the user, capable of recalling the calibration data associated with the user when the user is performing the one or more tasks, wherein the calibrating the one or more eye tracker sensors including adjusting settings based on a baseline measurement associated with the user.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the
limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Therefore, claims 1, 9, and 17 recite an abstract idea of a mental process.
Claims 1, 9, and 17 recite the abstract idea of a mental process. The limitations as drafted in the
claims, under its broadest reasonable interpretation, covers performance of the claimed steps in the mind, but for the recitation of a generic processor. Other than reciting one or more programmable processors configured to perform operations and memory, nothing in the elements of the claims precludes the step from practically being performed in the mind or manually by a clinician. For example:
“Collecting data representative of a cognitive state of a user while the user performs one or more tasks while interacting with the system, wherein interacting with the system to perform the one or more tasks is configured to induce a cognitive state change in the user.” A physician may observe and collect data while a patient is performing one or more task while interacting with the system.
“Recording details of the one or more tasks and actions of the user while performing the one or more tasks, wherein the details of the one or more tasks comprise task results, wherein the task results are determined based on measurements of a plurality of attributes associated with the one or more tasks being performed wherein the data representative of the cognitive state comprises eye scan patterns over time of the user while performing the one or more tasks.” A physician may record details of the user actions with performing one or more task with a pen and paper.
“Analyzing the data representative of the cognitive state and the recorded details of the one or more tasks and/or actions of the user, the analyzing comprising: correlating the data with the recorded details.” A physician is capable of comparing data to recorded data and making a correlation between the two for analysis.
“Determining, for each of the two or more regions, a cognitive workload of the user while the user is viewing the respective region;” A physician may determine a cognitive workload of the user based on eye tracking data obtained through observation.
“Determining, based at least on the correlating and the cognitive workload of the user, a metric representative of an amount of the cognitive state change for the respective two or more visual regions as induced by the system in the user, and determining, based on a relative amount of cognitive state change among the respective two or more visual regions, additional training representing feedback for one of the two or more visual regions for display to reduce the amount of the cognitive state change.” A physician may diagnosis a patient with additional training based on the analyzed data respective to two or more visual regions of the patient.
“Generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback, wherein the one or more eye tracker sensors comprises a calibration unit for calibrating the one or more eye tracker sensors to the user, and wherein calibration data captured by the calibration unit is associated with the user, capable of recalling the calibration data associated with the user when the user is performing the one or more tasks, wherein the calibrating the one or more eye tracker sensors including adjusting settings based on a baseline measurement associated with the user.” A physician may manually adjust or give a different training protocol based on the analyzed data. The method of adjust includes calibration based on the user profile that can be manually collected by the physician.
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial
exception into a practical application?
Claims 1, 9 and 17 recite the additional elements of a “one or more programmable processors”,
“one or more eye-tracker sensors”, “calibration unit”, and a “storage unit,” which are being interpreted as a processor configured to perform operations. However, these elements are recited at a high level of generality performing the function of generic data processing such that they amount to no more than mere instructions to simply implement the abstract idea using generic computer components. See MPEP 2106.05(b) and (f).
“One or more eye-tracker sensors.” Although the eye tracker sensors “used to collect such granular or precise tracking of a localized region of a user's head” are additional elements, they merely recite extra-solution activity to the step of data gathering.
“Processors with additional training and a storage unit.” These are recited as generic computer component used to perform the abstract idea of “determining to increase or decrease specific tasks (visual regions) based on cognitive change.”
“Generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback for a display.” The display is recited as extra-solution activity to the step of giving a diagnosis based on cognitive state of the patient.
Accordingly, the additional elements do not integrate the abstract idea into a practical
application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
The additional elements when considered individually and in combination are not enough to
qualify as significantly more than the abstract idea.
“One or more eye-tracker sensors.” Although the eye tracker sensors “used to collect such granular or precise tracking of a localized region of a user's head” are additional elements, they merely recite extra-solution activity to the step of data gathering.
“Processors with additional training and a storage unit.” These are recited as generic computer component used to perform the abstract idea of “determining to increase or decrease specific tasks (visual regions) based on cognitive change.”
“Generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback for a display.” The display is recited as extra-solution activity to the step of giving a diagnosis based on cognitive state of the patient.
As discussed above with respect to integration of the abstract idea into a practical application, “one or more programmable processors”, “one or more eye-tracker sensors”, “calibration unit”, and a “storage unit,” which are being interpreted as a processor of a system comprising:
collecting data representative of a cognitive state of a user while the user performs one or more tasks while interacting with the system, wherein interacting with the system to perform the one or more tasks is configured to induce a cognitive state change in the user;
recording details of the one or more tasks and actions of the user while performing the one or more tasks, wherein the details of the one or more tasks comprise task results, wherein the task results are determined based on measurements of a plurality of attributes associated with the one or more tasks being performed wherein the data representative of the cognitive state comprises eye scan patterns over time of the user while performing the one or more tasks;
analyzing the data representative of the cognitive state and the recorded details of the one or more tasks and/or actions of the user, the analyzing comprising: correlating the data with the recorded details
determining, for each of the two or more regions, a cognitive workload of the user while the user is viewing the respective region;
determining, based at least on the correlating and the cognitive workload of the user, a metric representative of an amount of the cognitive state change for the respective two or more visual regions as induced by the system in the user, and determining, based on a relative amount of cognitive state change among the respective two or more visual regions, additional training representing feedback for one of the two or more visual regions for display to reduce the amount of the cognitive state change;
generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback, wherein the one or more eye tracker sensors comprises a calibration unit for calibrating the one or more eye tracker sensors to the user, and wherein calibration data captured by the calibration unit is associated with the user, capable of recalling the calibration data associated with the user when the user is performing the one or more tasks, wherein the calibrating the one or more eye tracker sensors including adjusting settings based on a baseline measurement associated with the user.
amount to no more than mere instructions to apply the exception using generic computer
components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. These additional elements are well‐understood, routine (For example BACH et al. US Pub.: US 2020/0008725 A1, hereinafter Bach one or more sensor, a processor, and a storage unit) and conventional limitations that amount to mere instructions or elements to implement the abstract idea. In addition, the end result of the system/method, the essence of the whole, is a patent-ineligible concept. Therefore, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-17, 20, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over BACH et al. US Pub.: US 2020/0008725 A1, hereinafter Bach in view of Lemos et al. US Pub.: US 2007/0066916 A1, hereinafter Lemos in view of Ellison et al. US Pub.: US 20200253527 A1, hereinafter Ellison.
Regarding claim 1, Bach teaches a method of determining cognitive state of a user in association with use of a system, the method comprising: collecting, by one or more eye-tracker sensors, data representative of a cognitive state of a user while the user performs one or more tasks while interacting with the system, wherein interacting with the system to perform the one or more tasks is configured to induce a cognitive state change in the user (paragraph 188-198); Block 255 of [191] discloses collecting “neurometric measurements of each person both before and as he/she performs the tasks.” It also discloses “collecting performance data about each person while the person performs the tasks.”
concurrently recording, by a storage unit, details of the one or more tasks of the user while performing the one or more tasks, wherein the details of the one or more tasks comprise task results, wherein the task results are determined based on measurements of a plurality of attributes associated with the one or more tasks being performed wherein the data representative of the cognitive state comprises eye scan patterns over time of the user while performing the one or more tasks being performed, wherein the task results comprise assessments associated with two or more regions of one or more displays (fig. 1, 135; paragraph 117-119, 148, 188-198, 266 and 301); Block 255 of [191] discloses transmitting “the neurometric data to a record.” It also discloses “transmitting the performance data to the recorder.” It is further disclosed in [301] and table two that “To collect physiological and transactional data, the PMs were instrumented with eye tracking glasses.”
analyzing the data representative of the cognitive state and the recorded details of the one or more tasks and/or actions of the user, the analyzing comprising correlating the data with the recorded details (paragraph 188-198). Block 259 of [192] discloses “identify correlations between the performance data and the neurometric data to construct a functional assessment of neurophysiological functions of the brain's highways from the neurometric data. Block 263 of [195] also discloses “varying states of stress, exhaustion, emotional valence.” This equates to cognitive state change.
determining, based at least on the correlating, a metric representative of an amount of the cognitive state change induced by the system in the user, and determining, whether to assign the user to additional training based in part on the metric representative of the amount of the cognitive state change (paragraph 217 and 245); It is disclosed in [217] that “include in the assessment a comparison of task performance and corresponding brain activity metrics of the subject with normative metrics (e.g., a group performance metric and a corresponding group brain activity metric) that are representative of performance and corresponding brain activity metrics of a larger population of subjects.” It is disclosed [245] that “analyze the training subject's neurofeedback data to determine whether the training subject is performing at the targeted attentional state and to distinguish between at-par or above-par attentional states when the training subject is performing the training task.”
adjusting the additional training based in part on a set of eye-tracking data (paragraph 384 and 449). It is disclosed in [384] that “the method also comprises modifying the task for the person in real-time based on both the person's performance and physiological data/brain signatures.” It is disclosed in [449] that “analyzing data from the neurometric sensors to determine whether the subject is performing at the targeted attentional and/or neurocognitive state; and adapting the training task to steer the subject toward an enhanced attentional and/or neurocognitive state while performing the targeted task or skill.”
However, Bach does not explicitly teach determining, based at least on the correlating and the cognitive workload of the user, a metric representative of an amount of the cognitive state change for the respective two or more visual regions as induced by the system in the user; and determining, based on a relative amount of cognitive state change among the respective two or more visual regions, additional training representing feedback for one of the two or more visual regions for display to reduce the amount of the cognitive state change; and generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback to at least one region for display; wherein the one or more eye tracker sensors comprises a calibration unit for calibrating the one or more eye tracker sensors to the user, and wherein calibration data captured by the calibration unit is associated with the user, wherein the system is capable of recalling the calibration data associated with the user when the user is performing the one or more tasks, wherein the calibrating the one or more eye tracker sensors including adjusting settings based on a baseline measurement associated with the user.
Lemos, in the same field of endeavor, teaches wherein the one or more eye tracker sensors comprises a calibration unit for calibrating the one or more eye tracker sensors to the user, and wherein calibration data captured by the calibration unit is associated with the user, wherein the system is capable of recalling the calibration data associated with the user when the user is performing the one or more tasks, wherein the calibrating the one or more eye tracker sensors including adjusting settings based on a baseline measurement associated with the user (fig. 4; paragraphs 115-136). Calibration module 208 is used in combination with user profile module 204 to optimize collection and determination of human emotion. The calibration unit is further disclosed to be utilized to calibrate eye-tracking device 120. The calibration process is used to generate a baseline of the user for each sensor, including the eye-tracking sensor. It is lastly disclosed that the calibrated data may be stored in a collection database 292 for later recall.
Therefore, It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the eye-tracker sensor of Bach to add the calibration and user profile identifier from Lemos for the benefit of ensuring an accurate adjustment for the user to be as close to a desired cognitive state and make an optimized assessment of the user’s cognitive state.
Ellison, in the same field of endeavor, teaches determining, based at least on the correlating and the cognitive workload of the user, a metric representative of an amount of the cognitive state change for the respective two or more visual regions as induced by the system in the user (paragraph 219); The value-added subconscious engagement of cognitive processes can be factored into the cognitive benefits offered by the platform, eye-tracking to provide users with an adjusted baseline.
and determining, based on a relative amount of cognitive state change among the respective two or more visual regions, additional training representing feedback for one of the two or more visual regions for display to reduce the amount of the cognitive state change (paragraph 219 and 270); The perceptual switch, while occurring subconsciously, can also be linked to active engagement of cognitive processes where the user is guided and/or made aware of the switch and/or alternate percepts and/or other contiguities, and is directed to focus on particular areas of the image set. Therefore, at least two visual regions are disclosed to provide feedback to the system. the user may be prompted to try a higher skill level, or the system may, in a responsive adaptive manner automatically adjust the interactivity's skill level, via adjustment logic 236. Therefore, additional training via machine learning is disclosed.
and generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback to at least one region for display (paragraph 219). As the user is interacting with the image sets, a switch may occur in which an image appears as background, e.g., in the ground position but can switch to the figure position with a reciprocal exchange of the second image in a two-image or three-image composite based on the presence of contiguities in the component images. Therefore, the display regions may be adjusted based on machine learning feedback from logic 236.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the processing and display system of Bach with the machine learning and display processing steps from Ellison for the benefit of differentially evaluating multi-domain cognitive engagement especially in patients/users who have suffered traumatic brain injury, concussion, or following stroke where other areas of the brain may compensate for loss of function in one area.
Regarding claim 2, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches repeating the collecting data and the concurrently recording details for a plurality of users interacting with the system (paragraph 188-198); It is disclosed in [188] that the method can be used for a “population of persons.” This equates to repeatedly collecting data for a plurality of users.
and generating a statistical measure of cognitive state induced by the system on a representative user (paragraph 188-198). Block 261 of [193] discloses generating a “model or signature that can be a statistical one based on a PCA and/or ICA of the data.”
Regarding claim 3, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches comparing the statistical measure of cognitive state induced by the system with a second statistical measure of cognitive state induced by a second system (paragraph 188-198); [22] of the applicant’s specification discloses the second system to be “at some time before or after the interaction with the first system.” Block 255 of [191] of the prior art discloses taking “neurometric measurements of each person both before and as he/she performs the tasks.” This discloses two separate measurements at different times. Also Block 273 of [198] discloses comparing a “person's scores with that of a team or greater population.”
and ranking the first system as superior to the second system when the statistical measure of cognitive state induced by the system is lower than the second statistical measure of cognitive state induced by the second system (fig. 22-25; paragraph 198). Figures 22-25 shows graphs ranking the cognitive efficiency between individual, team, and greater population. All scores are recorded for each task.
Regarding claim 4, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches comparing the metric representative of the amount of cognitive state change induced by the system in the user with the statistical measure of cognitive state induced by the system on the representative user (paragraph 188-198). Block 273 of [198] discloses a “feedback that include comparisons of the person's scores with that of a team or greater population.” The feedback is neurometric data and this disclosure equates to comparing metric data of a user to a reference user.
and identifying the user as a candidate for additional training when the metric representative of the amount of cognitive state change induced by the system in the user is higher than the statistical measure of cognitive state induced by the system on the representative user by a statistically significant threshold (paragraph 188-198). Block 273 of [198] discloses providing a feedback to each person and ”includes suggestions to improve the person's cognitive state in order to improve the person's performance.” These suggestion include “how much longer the person will need to practice the training tasks.”
Regarding claim 5, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches wherein the concurrently recording details of the one or more tasks and actions further comprises temporally correlating the data representative of a cognitive state of the user with a specific task or action of the one or more tasks and actions (fig. 22-25; paragraph 188-198). Figures 22-25 shows graphs ranking the cognitive efficiency between individual, team, and greater population. All scores are recorded for each task. [192] discloses correlation “between the performance data and the neurometric data.”
Regarding claim 6, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches wherein the data representative of a cognitive state of a user comprises fatigue level, eye movement data, eyelid data, heart rate, respiration rate, electroencephalography (EEG) data, Galvanic Skin Response, functional near-infrared (fNIR) data, electromyography (EMG) data, head position data, head rotation data, electrocardiogram (ECG/EKG) data, emotion, excitement level, Facial Action Coding System (FACS) data, pupillometry, eye tracking data, or cognitive workload data (fig. 22-25; paragraph 198 and 301). Figures 22-25 shows graphs wherein the data represents visual processing, reaction time, and memory. [301] discloses EEG data, eye tracking, galvanic skin sensor, heart rate, and heart variability.
Regarding claim 7, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches wherein the one or more tasks comprise a memory training task, a flight training task, a flight simulation task, a virtual surgical task, a virtual driving task, a cognitive assessment task, a cognitive aptitude task, a command and control task, an air-traffic control task, a security monitoring task, a vigilance task, a skill aptitude task, or a data entry task (fig. 22-25; paragraph 198). Figures 22-25 shows graphs wherein the data represents visual processing, reaction time, and memory.
Regarding claim 8, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches wherein the cognitive state comprises one or more of fatigue level, level of distress, level of excitation, emotion, anxiety level, cognitive overload, cognitive underload, distraction, confusion, level of boredom, a level of tunnel vision, a level of attention, level of stress, level of dementia, level of aptitude, or level of relaxation (paragraph 188-198). Block 263 of [195] discloses “varying states of stress, exhaustion, emotional valence.”
Regarding claim 9, Bach teaches a system for determining cognitive state of a user in association with a task, the system comprising: one or more sensors (fig. 1, 120 or 130) configured to collect data representative of a cognitive state of a user while the user performs one or more tasks while interacting with the system, wherein interacting with the system to perform the one or more tasks is configured to induce a cognitive state change in the user (paragraph 117-119 and 188-198); Block 255 of [191] discloses collecting “neurometric measurements of each person both before and as he/she performs the tasks.” It also discloses “collecting performance data about each person while the person performs the tasks.”
and a storage unit (fig. 1, 140) configured to concurrently record details of the one or more tasks of the user while performing the one or more tasks, wherein the task results are determined based on measurements of a plurality of attributes associated with the one or more tasks being performed, wherein the data representative of the cognitive state comprises eye scan patterns over time of the user while performing the one or more tasks being performed, wherein the task results comprise assessments associated with two or more regions of one or more displays (fig. 1, 135; paragraph 117-119, 148, 188-198, 266 and 301); Block 255 of [191] discloses transmitting “the neurometric data to a record.” It also discloses “transmitting the performance data to the recorder.” It is further disclosed in [301] and table two that “To collect physiological and transactional data, the PMs were instrumented with eye tracking glasses.”
wherein analysis of the data representative of the cognitive state and the recorded details of the one or more tasks and actions of the user is performed, the analysis comprising correlating the data with the recorded details to determine a metric representative of an amount of cognitive state change induced by the system in the user (paragraph 117-119 and 188-198). Block 259 of [192] discloses “identify correlations between the performance data and the neurometric data to construct a functional assessment of neurophysiological functions of the brain's highways from the neurometric data. Block 263 of [195] also discloses “varying states of stress, exhaustion, emotional valence.” This equates to cognitive state change.
determining, based at least on the correlating, a metric representative of an amount of the cognitive state change induced by the system in the user, and determining, whether to assign the user to additional training based in part on the metric representative of the amount of the cognitive state change (paragraph 217 and 245); It is disclosed in [217] that “include in the assessment a comparison of task performance and corresponding brain activity metrics of the subject with normative metrics (e.g., a group performance metric and a corresponding group brain activity metric) that are representative of performance and corresponding brain activity metrics of a larger population of subjects.” It is disclosed [245] that “analyze the training subject's neurofeedback data to determine whether the training subject is performing at the targeted attentional state and to distinguish between at-par or above-par attentional states when the training subject is performing the training task.”
adjusting the additional training based in part on a set of eye-tracking data (paragraph 384 and 449). It is disclosed in [384] that “the method also comprises modifying the task for the person in real-time based on both the person's performance and physiological data/brain signatures.” It is disclosed in [449] that “analyzing data from the neurometric sensors to determine whether the subject is performing at the targeted attentional and/or neurocognitive state; and adapting the training task to steer the subject toward an enhanced attentional and/or neurocognitive state while performing the targeted task or skill.”
However, Bach does not explicitly teach determining, based at least on the correlating and the cognitive workload of the user, a metric representative of an amount of the cognitive state change for the respective two or more visual regions as induced by the system in the user; and determining, based on a relative amount of cognitive state change among the respective two or more visual regions, additional training representing feedback for one of the two or more visual regions for display to reduce the amount of the cognitive state change; and generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback to at least one region for display; wherein the one or more eye tracker sensors comprises a calibration unit for calibrating the one or more eye tracker sensors to the user, and wherein calibration data captured by the calibration unit is associated with the user, wherein the system is capable of recalling the calibration data associated with the user when the user is performing the one or more tasks, wherein the calibrating the one or more eye tracker sensors including adjusting settings based on a baseline measurement associated with the user.
Lemos, in the same field of endeavor, teaches wherein the one or more eye tracker sensors comprises a calibration unit for calibrating the one or more eye tracker sensors to the user, and wherein calibration data captured by the calibration unit is associated with the user, wherein the system is capable of recalling the calibration data associated with the user when the user is performing the one or more tasks, wherein the calibrating the one or more eye tracker sensors including adjusting settings based on a baseline measurement associated with the user (fig. 4; paragraphs 115-136). Calibration module 208 is used in combination with user profile module 204 to optimize collection and determination of human emotion. The calibration unit is further disclosed to be utilized to calibrate eye-tracking device 120. The calibration process is used to generate a baseline of the user for each sensor, including the eye-tracking sensor. It is lastly disclosed that the calibrated data may be stored in a collection database 292 for later recall.
Therefore, It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the eye-tracker sensor of Bach to add the calibration and user profile identifier from Lemos for the benefit of ensuring an accurate adjustment for the user to be as close to a desired cognitive state and make an optimized assessment of the user’s cognitive state.
Ellison, in the same field of endeavor, teaches determining, based at least on the correlating and the cognitive workload of the user, a metric representative of an amount of the cognitive state change for the respective two or more visual regions as induced by the system in the user (paragraph 219); The value-added subconscious engagement of cognitive processes can be factored into the cognitive benefits offered by the platform, eye-tracking to provide users with an adjusted baseline.
and determining, based on a relative amount of cognitive state change among the respective two or more visual regions, additional training representing feedback for one of the two or more visual regions for display to reduce the amount of the cognitive state change (paragraph 219 and 270); The perceptual switch, while occurring subconsciously, can also be linked to active engagement of cognitive processes where the user is guided and/or made aware of the switch and/or alternate percepts and/or other contiguities, and is directed to focus on particular areas of the image set. Therefore, at least two visual regions are disclosed to provide feedback to the system. the user may be prompted to try a higher skill level, or the system may, in a responsive adaptive manner automatically adjust the interactivity's skill level, via adjustment logic 236. Therefore, additional training via machine learning is disclosed.
and generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback to at least one region for display (paragraph 219). As the user is interacting with the image sets, a switch may occur in which an image appears as background, e.g., in the ground position but can switch to the figure position with a reciprocal exchange of the second image in a two-image or three-image composite based on the presence of contiguities in the component images. Therefore, the display regions may be adjusted based on machine learning feedback from logic 236.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the processing and display system of Bach with the machine learning and display processing steps from Ellison for the benefit of differentially evaluating multi-domain cognitive engagement especially in patients/users who have suffered traumatic brain injury, concussion, or following stroke where other areas of the brain may compensate for loss of function in one area.
Regarding claim 10, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches wherein the collecting data and the concurrently recording details for a plurality of users interacting with the system is repeated, and a statistical measure of cognitive state induced by the system on a representative user is generated (paragraph 188-198). It is disclosed in [188] that the method can be used for a “population of persons.” This equates to repeatedly collecting data for a plurality of users. Block 261 of [193] discloses generating a “model or signature that can be a statistical one based on a PCA and/or ICA of the data.”
Regarding claim 11, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches wherein the statistical measure of cognitive state induced by the system with a second statistical measure of cognitive state induced by a second system is compared, and the first system is ranked as superior to the second system when the statistical measure of cognitive state induced by the system is lower than the second statistical measure of cognitive state induced by the second system (fig. 22-25; paragraph 188-198). [22] of the applicant’s specification discloses the second system to be “at some time before or after the interaction with the first system.” Block 255 of [191] of the prior art discloses taking “neurometric measurements of each person both before and as he/she performs the tasks.” This discloses two separate measurements at different times. Also Block 273 of [198] discloses comparing a “person's scores with that of a team or greater population.” Figures 22-25 shows graphs ranking the cognitive efficiency between individual, team, and greater population. All scores are recorded for each task.
Regarding claim 12, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches wherein the metric representative of the amount of cognitive state change induced by the system in the user is compared with the statistical measure of cognitive state induced by the system on the representative user, and the user is identified as a candidate for additional training when the metric representative of the amount of cognitive state change induced by the system in the user is higher than the statistical measure of cognitive state induced by the system on the representative user by a statistical significant threshold (paragraph 188-198). Block 273 of [198] discloses a “feedback that include comparisons of the person's scores with that of a team or greater population.” The feedback is neurometric data and this disclosure equates to comparing metric data of a user to a reference user. Block 273 of [198] discloses providing a feedback to each person and ”includes suggestions to improve the person's cognitive state in order to improve the person's performance.” These suggestion include “how much longer the person will need to practice the training tasks.”
Regarding claim 13, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches wherein the concurrently recording details of the one or more tasks and actions further comprises temporally correlating the data representative of a cognitive state of the user with a specific task or action of the one or more tasks and actions (fig. 22-25; paragraph 188-198). Figures 22-25 shows graphs ranking the cognitive efficiency between individual, team, and greater population. All scores are recorded for each task. [192] discloses correlation “between the performance data and the neurometric data.”
Regarding claim 14, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches wherein the data representative of a cognitive state of a user comprises fatigue level, eye movement data, eyelid data, heart rate, respiration rate, electroencephalography (EEG) data, Galvanic Skin Response, functional near-infrared (fNIR) data, electromyography (EMG) data, head position data, head rotation data, emotion, excitement level, Facial Action Coding System (FACS) data, pupillometry, eye tracking data, or cognitive workload data (fig. 22-25; paragraph 198 and 301). Figures 22-25 shows graphs wherein the data represents visual processing, reaction time, and memory. [301] discloses EEG data, eye tracking, galvanic skin sensor, heart rate, and heart variability.
Regarding claim 15, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches wherein the task comprises a memory training task, a flight training task, a flight simulation task, a virtual surgical task, a virtual driving task, a cognitive assessment task, a cognitive aptitude task, command and control task, air-traffic control task, security monitoring task, vigilance task, a skill aptitude task, or a data entry task (fig. 22-25; paragraph 198). Figures 22-25 shows graphs wherein the data represents visual processing, reaction time, and memory.
Regarding claim 16, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches wherein the cognitive state comprises fatigue level, level of distress, level of excitation, emotion, anxiety level, cognitive overload, cognitive underload, distraction, confusion, level of boredom, a level of tunnel vision, a level of attention, level of stress, level of dementia, level of aptitude, or level of relaxation (paragraph 188-198). Block 263 of [195] discloses “varying states of stress, exhaustion, emotional valence.”
Regarding claim 17, Bach teaches an apparatus for determining cognitive state of a user in association with a task, the apparatus comprising: one or more programmable processors (fig. 1, 111) configured to perform operations comprising: receiving, from one or more sensors (fig. 1, 120 or 130) data representative of a cognitive state of a user while the user performs one or more tasks while interacting with the system, wherein interacting with the system to perform the one or more tasks is configured to induce a cognitive state change in the user (paragraph 117-119 and 188-198); Block 255 of [191] discloses collecting “neurometric measurements of each person both before and as he/she performs the tasks.” It also discloses “collecting performance data about each person while the person performs the tasks.”
concurrently recording, by a storage unit, details of the one or more tasks and actions of the user while performing the one or more tasks, wherein the details of the one or more tasks comprise task results, wherein the task results are determined based on measurements of a plurality of attributes associated with the one or more tasks being performed, wherein the data representative of the cognitive state comprises eye scan patterns over time of the user while performing the one or more tasks being performed, wherein the task results comprise assessments associated with two or more regions of one or more displays (fig. 1, 135; paragraph 117-119, 148, 188-198, 266 and 301); Block 255 of [191] discloses transmitting “the neurometric data to a record.” It also discloses “transmitting the performance data to the recorder.” It is further disclosed in [301] and table two that “To collect physiological and transactional data, the PMs were instrumented with eye tracking glasses.”
and analyzing the data representative of the cognitive state and the recorded details of the one or more tasks and/or actions of the user, the analyzing comprising correlating the data with the recorded details to determine a metric representative of an amount of cognitive state change induced by the system in the user (paragraph 117-119 and 188-198). Block 259 of [192] discloses “identify correlations between the performance data and the neurometric data to construct a functional assessment of neurophysiological functions of the brain's highways from the neurometric data. Block 263 of [195] also discloses “varying states of stress, exhaustion, emotional valence.” This equates to cognitive state change.
determining, based at least on the correlating, a metric representative of an amount of the cognitive state change induced by the system in the user, and determining, whether to assign the user to additional training based in part on the metric representative of the amount of the cognitive state change (paragraph 217 and 245); It is disclosed in [217] that “include in the assessment a comparison of task performance and corresponding brain activity metrics of the subject with normative metrics (e.g., a group performance metric and a corresponding group brain activity metric) that are representative of performance and corresponding brain activity metrics of a larger population of subjects.” It is disclosed [245] that “analyze the training subject's neurofeedback data to determine whether the training subject is performing at the targeted attentional state and to distinguish between at-par or above-par attentional states when the training subject is performing the training task.”
adjusting the additional training based in part on a set of eye-tracking data (paragraph 384 and 449). It is disclosed in [384] that “the method also comprises modifying the task for the person in real-time based on both the person's performance and physiological data/brain signatures.” It is disclosed in [449] that “analyzing data from the neurometric sensors to determine whether the subject is performing at the targeted attentional and/or neurocognitive state; and adapting the training task to steer the subject toward an enhanced attentional and/or neurocognitive state while performing the targeted task or skill.”
However, Bach does not explicitly teach determining, based at least on the correlating and the cognitive workload of the user, a metric representative of an amount of the cognitive state change for the respective two or more visual regions as induced by the system in the user; and determining, based on a relative amount of cognitive state change among the respective two or more visual regions, additional training representing feedback for one of the two or more visual regions for display to reduce the amount of the cognitive state change; and generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback to at least one region for display; wherein the one or more eye tracker sensors comprises a calibration unit for calibrating the one or more eye tracker sensors to the user, and wherein calibration data captured by the calibration unit is associated with the user, wherein the system is capable of recalling the calibration data associated with the user when the user is performing the one or more tasks, wherein the calibrating the one or more eye tracker sensors including adjusting settings based on a baseline measurement associated with the user.
Lemos, in the same field of endeavor, teaches wherein the one or more eye tracker sensors comprises a calibration unit for calibrating the one or more eye tracker sensors to the user, and wherein calibration data captured by the calibration unit is associated with the user, wherein the system is capable of recalling the calibration data associated with the user when the user is performing the one or more tasks, wherein the calibrating the one or more eye tracker sensors including adjusting settings based on a baseline measurement associated with the user (fig. 4; paragraphs 115-136). Calibration module 208 is used in combination with user profile module 204 to optimize collection and determination of human emotion. The calibration unit is further disclosed to be utilized to calibrate eye-tracking device 120. The calibration process is used to generate a baseline of the user for each sensor, including the eye-tracking sensor. It is lastly disclosed that the calibrated data may be stored in a collection database 292 for later recall.
Therefore, It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the eye-tracker sensor of Bach to add the calibration and user profile identifier from Lemos for the benefit of ensuring an accurate adjustment for the user to be as close to a desired cognitive state and make an optimized assessment of the user’s cognitive state.
Ellison, in the same field of endeavor, teaches determining, based at least on the correlating and the cognitive workload of the user, a metric representative of an amount of the cognitive state change for the respective two or more visual regions as induced by the system in the user (paragraph 219); The value-added subconscious engagement of cognitive processes can be factored into the cognitive benefits offered by the platform, eye-tracking to provide users with an adjusted baseline.
and determining, based on a relative amount of cognitive state change among the respective two or more visual regions, additional training representing feedback for one of the two or more visual regions for display to reduce the amount of the cognitive state change (paragraph 219 and 270); The perceptual switch, while occurring subconsciously, can also be linked to active engagement of cognitive processes where the user is guided and/or made aware of the switch and/or alternate percepts and/or other contiguities, and is directed to focus on particular areas of the image set. Therefore, at least two visual regions are disclosed to provide feedback to the system. the user may be prompted to try a higher skill level, or the system may, in a responsive adaptive manner automatically adjust the interactivity's skill level, via adjustment logic 236. Therefore, additional training via machine learning is disclosed.
and generating the additional training for interaction by the user or subsequent users of the system, the additional training including the feedback to at least one region for display (paragraph 219). As the user is interacting with the image sets, a switch may occur in which an image appears as background, e.g., in the ground position but can switch to the figure position with a reciprocal exchange of the second image in a two-image or three-image composite based on the presence of contiguities in the component images. Therefore, the display regions may be adjusted based on machine learning feedback from logic 236.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the processing and display system of Bach with the machine learning and display processing steps from Ellison for the benefit of differentially evaluating multi-domain cognitive engagement especially in patients/users who have suffered traumatic brain injury, concussion, or following stroke where other areas of the brain may compensate for loss of function in one area.
Regarding claim 20, Bach in view of Lemos in view of Ellison teaches the claimed invention and Bach further teaches wherein the data representative of a cognitive state comprises one or more of fatigue level, eye movement data, eyelid data, heart rate, respiration rate, electroencephalography (EEG) data, Galvanic Skin Response, functional near-infrared (fNIR) data, electromyography (EMG) data, head position data, head rotation data, emotion, excitement level, Facial Action Coding System (FACS) data, pupillometry, eye tracking data, or cognitive workload data (fig. 22-25; paragraph 198 and 301). Figures 22-25 shows graphs wherein the data represents visual processing, reaction time, and memory. [301] discloses EEG data, eye tracking, galvanic skin sensor, heart rate, and heart variability.
Regarding claims 26-28, Bach in view of Lemos in view of Ellison teaches the claimed invention and Ellison further teaches wherein the correlating comprising temporally correlating the data representative of the cognitive state with the recorded details of a specific task of the one or more tasks and/or actions of the user (paragraph 219 and 270). The value-added subconscious engagement of cognitive processes can be factored into the cognitive benefits offered by the platform, eye-tracking to provide users with an adjusted baseline. The perceptual switch, while occurring subconsciously, can also be linked to active engagement of cognitive processes where the user is guided and/or made aware of the switch and/or alternate percepts and/or other contiguities, and is directed to focus on particular areas of the image set. Therefore, at least two visual regions are disclosed to provide feedback to the system. the user may be prompted to try a higher skill level, or the system may, in a responsive adaptive manner automatically adjust the interactivity's skill level, via adjustment logic 236. Therefore, additional training via machine learning is disclosed.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THIEN J TRAN whose telephone number is (571)272-0486. The examiner can normally be reached M-F. 8:30 am - 5:30 pm.
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/T.J.T./Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792
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