# US 2025/0147584 A1 · Methods, systems, and related aspects for determining a cognitive load of a sensorized device user > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0147584 A1 - Document ID: US20250147584A1 - Status: Published application - Kind: A1 - Application number: 19/008,989 - Filed: 2025-01-03 - Published: 2025-05-08 - Earliest claimed: 2021-04-28 - Assignees: The Johns Hopkins University - Applicants: The Johns Hopkins University - Inventors: Mathias UNBERATH; Kinjal SHAH; Wenhao GU - Web page: https://patentgrep.com/patent/US20250147584A1 ## CPC - A61B 3/11: for measuring interpupillary distance or diameter of pupils - G06F 3/013: {Eye tracking input arrangements (G06F3/015 takes precedence)} - A61B 3/145: {by video means} - A61B 5/16: Devices for psychotechnics (using teaching or educational appliances G09B1/00 - G09B7/00); Testing reaction times {; Devices for evaluating the psychological state} - A61B 5/163: {by tracking eye movement, gaze, or pupil change} - G06V 40/18: Eye characteristics, e.g. of the iris - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Provided herein are methods of determining a cognitive load of a sensorized device user in certain aspects. Provided herein are also methods of adjusting an interface between a user and a sensorized device in some aspects. Related devices, systems, and computer program products are also provided. ## Claims ### Claim 1 A method of determining a cognitive load change of a sensorized device user at least partially using a computer, the method comprising: receiving, by the computer, at least a first substantially real-time pupillometric data value from the user from at least a first time point and at least a first substantially real-time unconstrained environmental data value from at least one location proximal to the user from the first time point; receiving, by the computer, at least a second substantially real-time pupillometric data value from the user from at least a second time point and at least a second substantially real-time unconstrained environmental data value from at least one location proximal to the user from the second time point, wherein the user is using a sensorized device at least proximal to the first time point and/or the second time point; and, isolating, by the computer, one or more cognitive load factors from one or more non-cognitive load factors influencing the user at the first and second time points based at least upon the first substantially real-time pupillometric data value, the first substantially real-time unconstrained environmental data value, the second substantially real-time pupillometric data value, and/or the second substantially real-time unconstrained environmental data value to determine the cognitive load change in the user between the first and second time points, wherein determining the cognitive load change in the user between the first and second time points comprises determining an inverse probability of the cognitive load change given the first substantially real-time pupillometric data value, the first substantially real-time unconstrained environmental data value, the second substantially real-time pupillometric data value, and/or the second substantially real-time unconstrained environmental data value, thereby determining the cognitive load change of the sensorized device user. ### Claim 2 (depends on 1) The method of claim 1, wherein the method further comprises adjusting at least one function of the sensorized device at least proximal to the first time point based at least in part on the determined cognitive load of the user. ### Claim 3 (depends on 1) The method of claim 1, wherein the sensorized device comprises the computer. ### Claim 4 (depends on 1) The method of claim 1, wherein the sensorized device and the computer are operably connected to one another. ### Claim 5 (depends on 1) The method of claim 1, wherein the first and/or second substantially real-time pupillometric data value comprises a pupil diameter, a pupil radius, a pupil size, a blink rate, a saccade rate, or other measure of at least one eye of the user. ### Claim 6 (depends on 1) The method of claim 1, wherein the first and/or second substantially real-time unconstrained environmental data value comprises a measure of at least one visual property. ### Claim 7 (depends on 6) The method of claim 6, wherein the visual property comprises an ambient light measure and/or a focal brightness measure. ### Claim 8 (depends on 1) The method of claim 1, wherein the first and/or second substantially real-time unconstrained environmental data value comprises a measure of at least one visual property, at least one auditory property, at least one dictation property, at least one physiological property or state of the user, and/or at least one physical property of an environment external to the user. ### Claim 9 (depends on 1) The method of claim 1, comprising receiving the first and/or second substantially real-time pupillometric data value and/or the first and/or second substantially real-time unconstrained environmental data value from at least one detection device. ### Claim 10 (depends on 9) The method of claim 9, wherein the detection device comprises at least one video camera. ### Claim 11 (depends on 9) The method of claim 9, wherein a wearable device comprises the detection device, which wearable device is worn by the user. ### Claim 12 (depends on 11) The method of claim 11, wherein the wearable device comprises a head-mounted display, a headphone, a spectacle frame, and/or an article of clothing. ### Claim 13 (depends on 1) The method of claim 1, wherein the sensorized device comprises an intelligent personal assistant (IPA) device. ### Claim 14 (depends on 1) The method of claim 1, wherein the sensorized device comprises a virtual reality (VR) device. ### Claim 15 A system, comprising at least one controller that comprises, or is capable of accessing, computer readable media comprising non-transitory computer executable instructions which, when executed by at least one electronic processor, perform at least: receiving at least a first substantially real-time pupillometric data value from the user from at least a first time point and at least a first substantially real-time unconstrained environmental data value from at least one location proximal to the user from the first time point; receiving at least a second substantially real-time pupillometric data value from the user from at least a second time point and at least a second substantially real-time unconstrained environmental data value from at least one location proximal to the user from the second time point, wherein the user is using a sensorized device at least proximal to the first time point and/or the second time point; and, isolating one or more cognitive load factors from one or more non-cognitive load factors influencing the user at the first and second time points based at least upon the first substantially real-time pupillometric data value, the first substantially real-time unconstrained environmental data value, the second substantially real-time pupillometric data value, and/or the second substantially real-time unconstrained environmental data value to determine the cognitive load change in the user between the first and second time points, wherein determining the cognitive load change in the user between the first and second time points comprises determining an inverse probability of the cognitive load change given the first substantially real-time pupillometric data value, the first substantially real-time unconstrained environmental data value, the second substantially real-time pupillometric data value, and/or the second substantially real-time unconstrained environmental data value. ### Claim 16 (depends on 15) The system of claim 15, wherein the instructions further perform at least: adjusting at least one function of the sensorized device at least proximal to the first time point based at least in part on the determined cognitive load of the user. ### Claim 17 (depends on 15) The system of claim 15, wherein the detection device comprises at least one video camera. ### Claim 18 (depends on 15) The system of claim 15, wherein the sensorized device comprises an intelligent personal assistant (IPA) device or a virtual reality (VR) device. ### Claim 19 (depends on 15) A detection device operably connected, or connectable to, the system of claim 15. ### Claim 20 A computer readable medium comprising non-transitory computer executable instructions which, when executed by at least electronic processor, perform at least: receiving at least a first substantially real-time pupillometric data value from the user from at least a first time point and at least a first substantially real-time unconstrained environmental data value from at least one location proximal to the user from the first time point; receiving at least a second substantially real-time pupillometric data value from the user from at least a second time point and at least a second substantially real-time unconstrained environmental data value from at least one location proximal to the user from the second time point, wherein the user is using a sensorized device at least proximal to the first time point and/or the second time point; and, isolating one or more cognitive load factors from one or more non-cognitive load factors influencing the user at the first and second time points based at least upon the first substantially real-time pupillometric data value, the first substantially real-time unconstrained environmental data value, the second substantially real-time pupillometric data value, and/or the second substantially real-time unconstrained environmental data value to determine the cognitive load change in the user between the first and second time points, wherein determining the cognitive load change in the user between the first and second time points comprises determining an inverse probability of the cognitive load change given the first substantially real-time pupillometric data value, the first substantially real-time unconstrained environmental data value, the second substantially real-time pupillometric data value, and/or the second substantially real-time unconstrained environmental data value.