Intelligent mitigation of concentration conflicts
AssigneeInternational Business Machines Corporation
InventorsHernan A. Cunico, Sarbajit K. Rakshit, Jonathan Dunne, Martin G. Keen
Life of this patent
Estimating the term…
Abstract
Embodiments for intelligent mitigation of concentration conflicts by a processor. A concentration level of a user required for one or more primary activities may be determined from one or more data sources. A distraction level indicating a possible impact to the concentration level caused by one or more secondary objects may be determined. One or more actions may be provided to mitigate the possible impact to the concentration level.
Claims20
A method for intelligent mitigation of concentration conflicts by a processor, comprising:
- identifying, by a machine learning analysis using a plurality of Internet of Things (IOT) computing devices, one or more primary activities currently being performed by a user according to detection of a physical level of activity of the user in conjunction with detection of audiovisual and spatial characteristics of a focus area of the user, wherein the one or more primary activities are unknown prior to the identifying and are categorized based on detected behavior of the user, and a level of certainty in identifying the one or more primary activities is associated with a number of the plurality of IoT devices performing the identifying;
- determining, from one or more data sources, a concentration level of the user required for the one or more primary activities;
- determining a distraction level indicating a possible impact to the concentration level caused by one or more secondary objects identified by the machine learning analysis;
- and
- performing one or more actions to mitigate the possible impact to the concentration level.
A system for intelligent mitigation of concentration conflicts, comprising:
- one or more computers with executable instructions that when executed cause the system to:
- identify, by a machine learning analysis using a plurality of Internet of Things (IoT) computing devices, one or more primary activities currently being performed by a user according to detection of a physical level of activity of the user in conjunction with detection of audiovisual and spatial characteristics of a focus area of the user, wherein the one or more primary activities are unknown prior to the identifying and are categorized based on detected behavior of the user, and a level of certainty in identifying the one or more primary activities is associated with a number of the plurality of IoT devices performing the identifying;
- determine, from one or more data sources, a concentration level of the user required for the one or more primary activities;
- determine a distraction level indicating a possible impact to the concentration level caused by one or more secondary objects identified by the machine learning analysis;
- and
- perform one or more actions to mitigate the possible impact to the concentration level.
A computer program product for intelligent mitigation of concentration conflicts by a processor, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:
- an executable portion that identifies, by a machine learning analysis using a plurality of Internet of Things (IOT) computing devices, one or more primary activities currently being performed by a user according to detection of a physical level of activity of the user in conjunction with detection of audiovisual and spatial characteristics of a focus area of the user, wherein the one or more primary activities are unknown prior to the identifying and are categorized based on detected behavior of the user, and a level of certainty in identifying the one or more primary activities is associated with a number of the plurality of IoT devices performing the identifying;
- an executable portion that determines, from one or more data sources, a concentration level of the user required for the one or more primary activities;
- an executable portion that determines a distraction level indicating a possible impact to the concentration level caused by one or more secondary objects identified by the machine learning analysis;
- and
- an executable portion that performs one or more actions to mitigate the possible impact to the concentration level.
Dependencies are inferred from the claim text.