# US 10,368,741 B2 · Method of and system for processing signals sensed from a user > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,368,741 B2 - Document ID: US10368741B2 - Status: Granted - Kind: B2 - Application number: 15/552,788 - Filed: 2016-02-25 - Published: 2018-02-08 - Granted: 2019-08-06 - Earliest claimed: 2015-02-27 - Assignees: Valorisation-Recherche, Limited Partnership; Valorisation Gestion, Limited Partnership - Applicants: Valorisation-Recherche, Limited Partnership; Valorisation Gestion, Limited Partnership - Inventors: Francois Courtemanche; Marc Fredette; Sylvain Senecal; Pierre-Majorique Leger; Aude Dufresne; Vanessa Georges; Elise Labonte-Lemoyne - Web page: https://patentgrep.com/patent/US10368741B2 ## CPC - A61B 3/113: for determining or recording eye movement - A61B 3/0025: {characterised by electronic signal processing, e.g. eye models} - A61B 5/0205: Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition - 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 5/162: {Testing reaction times} - A61B 5/163: {by tracking eye movement, gaze, or pupil change} - A61B 5/7267: {involving training the classification device} - G06F 3/013: {Eye tracking input arrangements (G06F3/015 takes precedence)} - G06F 3/015: {Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection} - G06N 20/00: Machine learning - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract A system for and a method of processing signals sensed from a user. The method comprises accessing positions of a line of sight of the user over a time frame, a first set of data associated with a first physiological signal and a second physiological signal. The method further comprises executing, by a processor, for at least one position of the positions of the line of sight of the user, identifying a first subset of data from the first set of data, identifying a second subset of data from the second set of data, associating the at least one position with the first subset of data and the second subset of data and causing to generate, by a machine-learning algorithm, a predicted value reflective of a pattern associated with the user. The method also comprises storing the predicted value associated with the at least one position. ## Claims ### Claim 1 A computer-implemented method of processing signals sensed from a user, the method comprising: accessing, from a non-transitory computer readable medium, positions of a line of sight of the user over a time frame; accessing, from the non-transitory computer readable medium, a first set of data associated with a first physiological signal sensed from the user over the time frame; accessing, from the non-transitory computer readable medium, a second set of data associated with a second physiological signal sensed from the user over the time frame; executing, by a processor, for at least one position of the positions of the line of sight of the user: identifying a first subset of data from the first set of data based on a first latency and a first duration, the first latency and the first duration being associated with the first physiological signal, the first latency and the first duration being dynamically determined based on a pattern category; identifying a second subset of data from the second set of data based on a second latency and a second duration, the second latency and the second duration being associated with the second physiological signal, the second latency and the second duration being dynamically determined based on the pattern category; associating the at least one position with the first subset of data and the second subset of data; causing to generate, by a machine-learning algorithm, a predicted value reflective of a pattern associated with the user, the predicted value being generated by the machine-learning algorithm based on the first subset of data and the second subset of data, the predicted value being associated with the at least one position; and storing, in the non-transitory computer readable medium, the predicted value associated with the at least one position. ### Claim 2 (depends on 1) The method of claim 1, wherein prior to identifying a first subset of data from the first set of data based on a first latency and a first duration, the method comprises determining the pattern category. ### Claim 3 (depends on 1) The method of claim 1, wherein causing to generate, by the machine-learning algorithm, the predicted value further comprises accessing a database comprising a set of training data having been, at least partially, previously generated by the machine-learning algorithm. ### Claim 4 (depends on 3) The method of claim 3, wherein at least one of the first subset of data and the second subset of data is compared, by the machine-learning algorithm, with the set of training data to generate the predicted value. ### Claim 5 (depends on 1) The method of claim 1, wherein the predicted value is reflective of at least one of an intensity of the pattern and amplitude of the pattern. ### Claim 6 (depends on 1) The method of claim 1, wherein the at least one position is associated with a pixel of a screen. ### Claim 7 (depends on 1) The method of claim 1, wherein the method further comprises, generating, by the processor, a set of surrounding predicted values based on the predicted value, each one of the surrounding value of the set of surrounding values being associated with a corresponding pixel surrounding the pixel associated with the at least one position. ### Claim 8 (depends on 1) The method of claim 1, wherein executing, by the processor, the steps of identifying the first subset of data and identifying the second subset of data is carried out for each one of the positions of the line of sight of the user. ### Claim 9 (depends on 8) The method of claim 8, wherein causing to generate, by the machine-learning algorithm, the predicted value reflective of the pattern associated with the user is carried out for each one of the positions of the line of sight of the user. ### Claim 10 (depends on 1) The method of claim 1, wherein the method further comprises generating, by the processor, a heat map representing the predicted values, each one of the predicted values being positioned on the heat map based on its corresponding position. ### Claim 11 (depends on 1) The method of claim 1, wherein, prior to executing, by the processor, the steps of identifying the first subset of data and identifying the second subset of data, the method comprises synchronizing the first physiological signal, the second physiological signal and the at least one position. ### Claim 12 (depends on 1) The method of claim 1, wherein prior to accessing, from the non-transitory computer readable medium, the positions of the line of sight of the user over the time frame, the method comprises (1) receiving, from a sensor, an eye tracking signal; and (2) generating, by the processor, the positions based on the eye tracking signal. ### Claim 13 (depends on 1) The method of claim 1, wherein prior to accessing, from the non-transitory computer readable medium, the positions of the line of sight of the user over a time frame, the method comprises (1) receiving, from a first sensor, the first physiological signal; and (2) receiving, from a second sensor, the second physiological signal. ### Claim 14 A computer-implemented system for processing signals sensed from a user, the system comprising: a non-transitory computer-readable medium; a processor configured to perform: accessing, from the non-transitory computer readable medium, positions of a line of sight of the user over a time frame; accessing, from the non-transitory computer readable medium, a first set of data associated with a first physiological signal sensed from the user over the time frame; accessing, from the non-transitory computer readable medium, a second set of data associated with a second physiological signal sensed from the user over the time frame; executing, by the processor, for at least one position of the positions of the line of sight of the user: identifying a first subset of data from the first set of data based on a first latency and a first duration, the first latency and the first duration being associated with the first physiological signal, the first latency and the first duration being dynamically determined based on a pattern category; identifying a second subset of data from the second set of data based on a second latency and a second duration, the second latency and the second duration being associated with the second physiological signal, the second latency and the second duration being dynamically determined based on the pattern category; associating the at least one position with the first subset of data and the second subset of data; causing to generate, by a machine-learning algorithm, a predicted value reflective of a pattern associated with the user, the predicted value being generated by the machine-learning algorithm based on the first subset of data and the second subset of data, the predicted value being associated with the at least one position; and storing, in the non-transitory computer readable medium, the predicted value associated with the at least one position. ### Claim 15 (depends on 14) The system of claim 14, wherein the predicted value is reflective of at least one of an intensity of the pattern and amplitude of the pattern. ### Claim 16 (depends on 14) The system of claim 14, wherein the processor is further configured to cause: generating a set of surrounding predicted values based on the predicted value, each one of the surrounding value of the set of surrounding values being associated with a corresponding pixel surrounding the pixel associated with the at least one position. ### Claim 17 (depends on 14) The system of claim 14, wherein executing, by the processor, the steps of identifying the first subset of data and identifying the second subset of data is carried out for each one of the positions of the line of sight of the user. ### Claim 18 (depends on 14) The system of claim 14, wherein the processor is further configured to cause, prior to executing, by the processor, the steps of identifying the first subset of data and identifying the second subset of data, synchronizing the first physiological signal, the second physiological signal and the at least one position. ### Claim 19 (depends on 14) The system of claim 14, wherein the processor is further configured to cause, prior to accessing, from the non-transitory computer readable medium, the positions of the line of sight of the user over the time frame, (1) receiving, from a sensor, an eye tracking signal; and (2) generating, by the processor, the positions based on the eye tracking signal. ### Claim 20 (depends on 14) The system of claim 14, wherein the processor is further configured to cause, prior to accessing, from the non-transitory computer readable medium, the positions of the line of sight of the user over a time frame, (1) receiving, from a first sensor, the first physiological signal; and (2) receiving, from a second sensor, the second physiological signal.