# US 2020/0401222 A1 · Gaming Cognitive Performance > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401222 A1 - Document ID: US20200401222A1 - Status: Published application - Kind: A1 - Application number: 16/869,187 - Filed: 2020-05-07 - Published: 2020-12-24 - Earliest claimed: 2019-06-19 - Assignees: MaddCog Limited - Applicants: MaddCog Limited - Inventors: Ben Wisbey; Guillaume Mathias - Web page: https://patentgrep.com/patent/US20200401222A1 ## CPC - G06F 3/015: {Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection} - A61B 5/02405: {Determining heart rate variability} - A61B 5/165: {Evaluating the state of mind, e.g. depression, anxiety} - A61B 5/374: Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves - A61B 5/6803: {Head-worn items, e.g. helmets, masks, headphones or goggles} - A61B 5/7267: {involving training the classification device} - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - A61B 5/746: {Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms} - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/20: Ensemble learning - H04R 1/1041: {Mechanical or electronic switches, or control elements (switches in general H01H)} - H04R 5/033: Headphones for stereophonic communication {(details thereof, e.g. relating to batteries, cables or control elements H04R1/10)} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04R: LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; ELECTRIC HEARING AIDS; PUBLIC ADDRESS SYSTEMS (producing sounds with frequency not determined by supply frequency G10K) ## Abstract Apparatus and associated methods relate to capturing physiological data from a sensor configured in a user's wearable device while the user performs a task, individualizing the physiological data to the user based on comparison with historical user physiological data, measuring the user's cognitive function determined in relationship to the individual's physiological data, and automatically notifying the user of cognitive fatigue and performance detected based on evaluating the measured cognitive function over time. In an illustrative example, the wearable device maybe a gaming headset. The measured cognitive function may be, for example, determined as a function of electroencephalograph or heart rate variability data captured from a user while the user performs a task. Some examples may provide recovery recommendations based on the detected cognitive fatigue. Various embodiments may advantageously recommend a recovery schedule determined as a function of a user's historical physiological data, to optimize cognitive performance restoration. ## Claims ### Claim 1 A computer-implemented process to assess task performance, the process comprising: capturing physiological data from a sensor configured in a user's wearable device while the user performs a task; individualizing the physiological data to the user based on comparison with historical user physiological data; measuring the user's cognitive function determined as a function of the individualized physiological data; and, automatically notifying the user of cognitive fatigue detected based on evaluating the measured cognitive load as a function of time. ### Claim 2 (depends on 1) The process of claim 1, wherein the wearable device further comprises a gaming headset. ### Claim 3 (depends on 1) The process of claim 1, wherein the task further comprises playing a game. ### Claim 4 (depends on 1) The process of claim 1, wherein the sensor further comprises an EEG sensor and the physiological data further comprises a signal encoding the user's brain activity. ### Claim 5 (depends on 1) The process of claim 1, wherein the physiological data further comprises HRV data encoding the user's cardiovascular activity. ### Claim 6 (depends on 1) The process of claim 1, wherein measuring the user's cognitive function further comprises evaluating the user's performance based on a mental function metric. ### Claim 7 (depends on 6) The process of claim 6, wherein the mental function metric further comprises power. ### Claim 8 (depends on 6) The process of claim 6, wherein the mental function metric further comprises pressure. ### Claim 9 (depends on 1) The process of claim 1, wherein notifying the user further comprises sending a notification from the user's wearable device to a mobile app configured in another device. ### Claim 10 A computer-implemented process to assess gaming performance, the process comprising: capturing live physiological data from a sensor configured in a user's gaming headset while the user plays a game, wherein the live physiological data comprises EEG and HRV data; individualizing the live physiological data to the user based on comparison with historical user physiological data; measuring the user's cognitive function determined as a function of: the individualized physiological data; a plurality of mental function metrics; and, a predictive analytic model trained with reference physiological data representative of a population of users playing a similar game; and, automatically notifying the user of cognitive fatigue detected based on evaluating the measured cognitive function as a function of time. ### Claim 11 (depends on 10) The process of claim 10, wherein the live physiological data further comprises PPG data. ### Claim 12 (depends on 10) The process of claim 10, wherein the historical user physiological data further comprises data selected from the group consisting of EEG, HRV, and PPG. ### Claim 13 (depends on 10) The process of claim 10, wherein the plurality of mental function metrics further comprise power, and pressure. ### Claim 14 (depends on 10) The process of claim 10, wherein the predictive analytic model further comprises an RDF. ### Claim 15 (depends on 10) The process of claim 10, wherein measuring the user's cognitive function further comprises training an individualized predictive analytic model based on the individualized physiological data and the reference physiological data. ### Claim 16 (depends on 10) The process of claim 10, wherein notifying the user of cognitive fatigue further comprises triggering an indication visible to the user in the user's in-game field of view. ### Claim 17 A computer-implemented process to assess gaming performance, the process comprising: capturing live physiological data from a sensor configured in a user's gaming headset while the user plays a game, wherein the live physiological data comprises EEG, HRV, and PPG data; individualizing the live physiological data to the user based on comparison with historical user physiological data, wherein the historical physiological data comprises EEG, HRV, and PPG data; training an individualized predictive analytic model based on a baseline predictive analytic model, the individualized physiological data, and reference physiological data representative of a population of users playing a similar game; measuring the user's cognitive load determined as a function of: the individualized physiological data; a plurality of mental function metrics; and, the individualized predictive analytic model; and, automatically notifying the user of cognitive fatigue detected based on evaluating the measured cognitive load as a function of time. ### Claim 18 (depends on 17) The process of claim 17, wherein training the individualized predictive analytic model further comprises a controlled training technique. ### Claim 19 (depends on 17) The process of claim 17, wherein capturing live physiological data from the sensor further comprises artifact correction. ### Claim 20 (depends on 17) The process of claim 17, wherein the process further comprises a sensor location in accordance with the International 10-20 system. ### Claim 21 A computer-implemented process to assess mental performance, the process comprising: storing physiological data captured from a sensor configured in a user's wearable device during a task performance by the user; determining if the task performance is complete; in response to determining the task performance is complete: individualizing the physiological data stored during the completed task performance to the user based on comparison with historical user physiological data; measuring the user's cognitive function based on the individualized stored physiological data; and, reporting the user's cognitive fatigue determined based on evaluating the measured cognitive load as a function of time. ### Claim 22 (depends on 21) The process of claim 21, wherein the task performance further comprises the user playing a game. ### Claim 23 (depends on 21) The process of claim 21, wherein reporting the user's cognitive fatigue further comprises providing the user feedback concerning the user's mental performance while the user performed the completed task. ### Claim 24 (depends on 23) The process of claim 23, wherein reporting the user's cognitive fatigue further comprises the user's mental performance evaluated as a function of the user's mental performance measured based on user performance of at least one task previous to the completed task. ### Claim 25 (depends on 21) The process of claim 21, wherein reporting the user's cognitive fatigue further comprises providing the user a prediction of the user's mental performance during a future task performance.