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 .
Status of Claims
Claims 1, 9, 14, and 17 are currently amended.
Claims 18-19 and 21-25 are cancelled.
Claims 26-28 are added as new claims.
Response to Arguments
Applicant’s arguments, see pages 8-12, filed 12/30/2024, with respect to 35 U.S.C. 101 and
112(a) rejections have been fully considered, but are persuasive.
35 U.S.C. 101: Step 2A Prong 1
Regarding claim 1, applicant argues that the subject matter is performed by a computer system
and not by the human mind. The examiner respectfully disagrees and argues that the steps of:
analyzing the data representative of the cognitive state and the recorded details of the one or more tasks and/or actions of the user;
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 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;
adjusting the additional training based on data, which can be calibrated by a physician over time;
recalling the calibration data associated with the user when the user is performing the one or more tasks.
Recite an abstract for analyzing and adjusting eye tracking data to determine a cognitive state
change of the user.
35 U.S.C. 101: Step 2A Prong 2
Regarding claim 1, applicant argues that the additional elements, as a whole, integrates the
claimed subject matter into a practical application. The examiner respectfully disagrees and argues that the additional elements does not add significantly more to integrate the abstract idea into a practical application:
The one of more eye tracker sensors are recited to perform pre-solution activity to the step of data gathering.
The one or more programmable processors are recited as computer implementation to perform the abstract ideas listed in Prong 1.
The calibration unit is recited to as computer implementation to perform an adjustment step for the eye tracking data.
As a whole, the examiner argues that the combined additional elements recite a processor of a
data gathering device to perform cognitive state analysis.
35 U.S.C. 101: Step 2B
Regarding claim 1, applicant argues that the subject matter recite significantly more than the
judicial exception and the invention transforms the alleged abstract idea of cognitive state analysis into a practical application by improving the accuracy and adaptability of the process. The examiner respectfully disagrees and argues that the improvement is towards the abstract idea and not the computer system itself. 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 claim 1 are merely generically recited computer elements used as tools for executing the abstract ideas or insignificant extra-solution activity recited in Prongs 1 and 2.
Applicant’s arguments, see pages 13-15, filed 8/25/2025, with respect to the rejection(s) of
claim(s) 1-17, 20, and 26-28 under 35 U.S.C. 102 and 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 Marshall.
Regarding claim 1, applicant argues that Bach, alone or in combination with the prior art, does
not teach “determining, for each of the two or more regions, a cognitive workload of the user while the user is viewing the respective region.” After further search and consideration, the examiner will refer to Marshall to teach this limitation (paragraph 25-26). It is disclosed that “a value is assigned that corresponds to the number of instances of high pupil activity, or abrupt dilation, per second for each eye, i.e. the "dilation index". It should be understood that this dilation index is a measure of the change in pupil diameter, which may be expansion or contraction.” Furthermore, a cognitive workload may be determined from this index. “If the visual stimulus requires significant cognitive effort, the right eye index will be substantially larger than the left eye index, typically by a factor of 2 or 3 but occasionally up to 10 times greater. If the visual stimulus elicits an affective, emotional response, the left eye index is as great or greater than the right eye index, with the difference being small for positive affective response and considerably larger for negative affective response.”
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 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;
adjusting the additional training based in part on a set of eye-tracking data, 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 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;
adjusting the additional training based in part on a set of eye-tracking data, 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 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 despite of the task results.” A physician may diagnosis a patient with additional training based on the analyzed data.
“Adjusting the additional training based in part on a set of eye-tracking data, 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).
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. 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 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;
adjusting the additional training based in part on a set of eye-tracking data, 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 Marshall et al. US Pub.: US 20030078513 A1, hereinafter Marshall.
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, 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 induced by the system in the user; 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.
Marshall, in the same field of endeavor, teaches 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 induced by the system in the user (paragraph 25-26). It is disclosed that “a value is assigned that corresponds to the number of instances of high pupil activity, or abrupt dilation, per second for each eye, i.e. the "dilation index". It should be understood that this dilation index is a measure of the change in pupil diameter, which may be expansion or contraction.” Furthermore, a cognitive workload may be determined from this index. “If the visual stimulus requires significant cognitive effort, the right eye index will be substantially larger than the left eye index, typically by a factor of 2 or 3 but occasionally up to 10 times greater. If the visual stimulus elicits an affective, emotional response, the left eye index is as great or greater than the right eye index, with the difference being small for positive affective response and considerably larger for negative affective response.”
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 processor and eye tracker of Bach to correlate a dilation index and a cognitive workload of a user as taught by Marshall for the benefit of increasing cognitive state determination accuracy when evaluation involves a number of subjects whose left-brain vs. right-brain abnormalities can be normalized.
Regarding claim 2, Bach in view of Lemos in view of Marshall 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 Marshall 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 Marshall 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 Marshall 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 Marshall 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 Marshall 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 Marshall 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, 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 induced by the system in the user; 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.
Marshall, in the same field of endeavor, teaches 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 induced by the system in the user (paragraph 25-26). It is disclosed that “a value is assigned that corresponds to the number of instances of high pupil activity, or abrupt dilation, per second for each eye, i.e. the "dilation index". It should be understood that this dilation index is a measure of the change in pupil diameter, which may be expansion or contraction.” Furthermore, a cognitive workload may be determined from this index. “If the visual stimulus requires significant cognitive effort, the right eye index will be substantially larger than the left eye index, typically by a factor of 2 or 3 but occasionally up to 10 times greater. If the visual stimulus elicits an affective, emotional response, the left eye index is as great or greater than the right eye index, with the difference being small for positive affective response and considerably larger for negative affective response.”
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 processor and eye tracker of Bach to correlate a dilation index and a cognitive workload of a user as taught by Marshall for the benefit of increasing cognitive state determination accuracy when evaluation involves a number of subjects whose left-brain vs. right-brain abnormalities can be normalized.
Regarding claim 10, Bach in view of Lemos in view of Marshall 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 Marshall 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 Marshall 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 Marshall 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 Marshall 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 Marshall 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 Marshall 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