Personalized lecture generator
AssigneeBIC Violex Single Member S.A.
InventorMichail Kokozidis
Life of this patent
Estimating the term…
Abstract
In a first aspect, the present disclosure relates to a computer-implemented method for generating a personalized lecture for a user. The method comprises obtaining at least one time series of electrodermal activity data of a user from a sensor and identifying a mental state of the user based on the at least one time series of electrodermal activity data. The method further comprises obtaining teaching content and generating a personalized lecture based on the mental state and the teaching content.
Claims20
A computer-implemented method for generating a personalized lecture video sequence for a user, comprising:
- obtaining at least one time series of electrodermal activity data of the user from a sensor;
- identifying a mental state of the user based on the at least one time series of the electrodermal activity data;
- obtaining a teaching content;
- and
- generating the personalized lecture video sequence based on the mental state and the teaching content, wherein the personalized lecture video sequence includes a person having generated mouth movements based on a related audio sequence, and further wherein the generated mouth movements are generated by a neural network trained on at least one prior series of real mouth movements by the person and a corresponding predetermined audio sequence.
A computer system for generating a personalized lecture video sequence for a user, comprising:
- one or more processors;
- and
- at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
- obtaining at least one time series of electrodermal activity data of the user from a sensor;
- identifying a mental state of the user based on the at least one time series of the electrodermal activity data;
- obtaining a teaching content;
- and
- generating the personalized lecture video sequence based on the mental state and the teaching content, wherein the personalized lecture video sequence includes a person having generated mouth movements based on a related audio sequence, and further wherein the generated mouth movements are generated by a neural network trained on at least one prior series of real mouth movements by the person and a corresponding predetermined audio sequence.
A writing instrument comprising:
- a sensor configured to measure electric conductivity of skin;
- a first interface configured to communicate and exchange data with a second interface of a processing unit, wherein the processing unit is configured to perform operations comprising:
- obtaining at least one time series of electrodermal activity data of a user of the writing instrument from the sensor;
- identifying a mental state of the user based on the at least one time series of the electrodermal activity data;
- obtaining a teaching content;
- and
- generating a personalized lecture video sequence based on the mental state and the teaching content, wherein the personalized lecture video sequence includes a person having generated mouth movements based on a related audio sequence, and further wherein the generated mouth movements are generated by a neural network trained on at least one prior series of real mouth movements by the person and a corresponding predetermined audio sequence.
A non-transitory computer-readable medium for generating a personalized lecture video sequence for a user, the non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:
- obtaining at least one time series of electrodermal activity data of the user from a sensor;
- identifying a mental state of the user based on the at least one time series of the electrodermal activity data;
- obtaining a teaching content;
- and
- generating the personalized lecture based on the mental state and the teaching content, wherein the personalized lecture video sequence includes a person having generated mouth movements based on a related audio sequence, and further wherein the generated mouth movements are generated by a neural network trained on at least one prior series of real mouth movements by the person and a corresponding predetermined audio sequence.
Dependencies are inferred from the claim text.