Abstract: A reward learning system includes a user interface configured to receive modes of user information related to a state of the user. A cognitive computing system includes a reward system. The reward system includes a dynamically upgraded profile model of the user which is updated in accordance with the user information related to the state. The reward system is updated by machine learning employing feedback from user responses measured by the user interface and searched information by the cognitive computing system. The reward system includes an increasing reward protocol based on learned user preferences and responses and rewarded in accordance with user achievements.

Abstract: A computer-implemented method includes detecting a first set and a second set of citations to a legal case in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining tones corresponding to each citation in the first and second sets of citations. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones. The computer-implemented method further includes aggregating a first and subset and a second of the first and second sets of citations, respectively. The computer-implemented method further includes generating an average score for the first and second subsets. The computer-implemented method further includes determining a degree of similarity between the first and second subsets based, in part, on a comparison of average scores. A corresponding computer program product and computer system are also disclosed.

Abstract: A reward learning system includes a user interface configured to receive modes of user information related to a state of the user. A cognitive computing system includes a reward system. The reward system includes a dynamically upgraded profile model of the user which is updated in accordance with the user information related to the state. The reward system is updated by machine learning employing feedback from user responses measured by the user interface and searched information by the cognitive computing system. The reward system includes an increasing reward protocol based on learned user preferences and responses and rewarded in accordance with user achievements.

Abstract: In an approach to predicting user touch events, one or more computer processors receive a detected electromagnetic noise signal of an object. The one or more computer processors compare the detected electromagnetic noise signal of the object to one or more stored electromagnetic noise signals associated with one or more objects. Based, at least in part, on the comparison, the one or more computer processors determine the identity of the object. Responsive to determining the identity of the object, the one or more computer processors store metadata associated with at least one of the objects and an electromagnetic noise signal detection event. The one or more computer processors determine whether an amount of the metadata associated with the object meets a learning threshold. If the amount of metadata meets the learning threshold, the one or more computer processors predict a subsequent electromagnetic noise signal detection event associated with the object.

Abstract: An apparatus includes a touchscreen having an input area and a processor. The apparatus also includes a memory that stores program code. The program code is executable by the processor to determine a location within the input area. The location corresponds to a position of at least a portion of an object relative to the input area. Further, the program code is executable by the processor to generate an output associated with an indication to move the object toward a region of the input area. The output is based on a comparison of the position of the object to the region. The region corresponds to a key of a keyboard.

Abstract: A reward learning system includes a user interface configured to receive modes of user information related to a state of the user. A cognitive computing system includes a reward system. The reward system includes a dynamically upgraded profile model of the user which is updated in accordance with the user information related to the state. The reward system is updated by machine learning employing feedback from user responses measured by the user interface and searched information by the cognitive computing system. The reward system includes an increasing reward protocol based on learned user preferences and responses and rewarded in accordance with user achievements.

Abstract: A reward learning system includes a user interface configured to receive modes of user information related to a state of the user. A cognitive computing system includes a reward system. The reward system includes a dynamically upgraded profile model of the user which is updated in accordance with the user information related to the state. The reward system is updated by machine learning employing feedback from user responses measured by the user interface and searched information by the cognitive computing system. The reward system includes an increasing reward protocol based on learned user preferences and responses and rewarded in accordance with user achievements.

Abstract: In an approach to predicting user touch events, one or more computer processors receive a detected electromagnetic noise signal of an object. The one or more computer processors compare the detected electromagnetic noise signal of the object to one or more stored electromagnetic noise signals associated with one or more objects. Based, at least in part, on the comparison, the one or more computer processors determine the identity of the object. Responsive to determining the identity of the object, the one or more computer processors store metadata associated with at least one of the objects and an electromagnetic noise signal detection event. The one or more computer processors determine whether an amount of the metadata associated with the object meets a learning threshold. If the amount of metadata meets the learning threshold, the one or more computer processors predict a subsequent electromagnetic noise signal detection event associated with the object.

Abstract: In an approach for controlling a computing device without mechanical buttons the computer detects an interaction with a touch capacitive sensing surface on a computing device. The computer determines that the detected interaction includes at least three interactions including at least two different touch capacitive sensing surfaces. The computer determines a virtual control button to display based on the detected interaction. The computer displays the determined virtual control button to the user. The computer receives an interaction with the displayed virtual control button. The computer implements an action associated with the determined virtual control button based on the received interaction.

Abstract: In an approach for controlling a computing device without mechanical buttons the computer detects an interaction with a touch capacitive sensing surface on a computing device. The computer determines that the detected interaction includes at least three interactions including at least two different touch capacitive sensing surfaces. The computer determines a virtual control button to display based on the detected interaction. The computer displays the determined virtual control button to the user. The computer receives an interaction with the displayed virtual control button. The computer implements an action associated with the determined virtual control button based on the received interaction.

Abstract: A computer-implemented method includes detecting a first set and a second set of citations to a legal case in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining tones corresponding to each citation in the first and second sets of citations. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones. The computer-implemented method further includes aggregating a first and subset and a second of the first and second sets of citations, respectively. The computer-implemented method further includes generating an average score for the first and second subsets. The computer-implemented method further includes determining a degree of similarity between the first and second subsets based, in part, on a comparison of average scores. A corresponding computer program product and computer system are also disclosed.

Abstract: A computer-implemented method includes detecting a first set and a second set of citations to a legal case in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining tones corresponding to each citation in the first and second sets of citations. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones. The computer-implemented method further includes aggregating a first and subset and a second of the first and second sets of citations, respectively. The computer-implemented method further includes generating an average score for the first and second subsets. The computer-implemented method further includes determining a degree of similarity between the first and second subsets based, in part, on a comparison of average scores. A corresponding computer program product and computer system are also disclosed.

Abstract: In an approach for controlling a computing device without mechanical buttons the computer detects an interaction with a touch capacitive sensing surface on a computing device. The computer determines that the detected interaction includes at least three interactions including at least two different touch capacitive sensing surfaces. The computer determines a virtual control button to display based on the detected interaction. The computer displays the determined virtual control button to the user. The computer receives an interaction with the displayed virtual control button. The computer implements an action associated with the determined virtual control button based on the received interaction.

Abstract: In an approach for controlling a computing device without mechanical buttons the computer detects an interaction with a touch capacitive sensing surface on a computing device. The computer determines that the detected interaction includes at least three interactions including at least two different touch capacitive sensing surfaces. The computer determines a virtual control button to display based on the detected interaction. The computer displays the determined virtual control button to the user. The computer receives an interaction with the displayed virtual control button. The computer implements an action associated with the determined virtual control button based on the received interaction.

Abstract: In an approach to predicting user touch events, one or more computer processors receive a detected electromagnetic noise signal of an object. The one or more computer processors compare the detected electromagnetic noise signal of the object to one or more stored electromagnetic noise signals associated with one or more objects. Based, at least in part, on the comparison, the one or more computer processors determine the identity of the object. Responsive to determining the identity of the object, the one or more computer processors store metadata associated with at least one of the objects and an electromagnetic noise signal detection event. The one or more computer processors determine whether an amount of the metadata associated with the object meets a learning threshold. If the amount of metadata meets the learning threshold, the one or more computer processors predict a subsequent electromagnetic noise signal detection event associated with the object.

Abstract: A computer-implemented method includes detecting a first and second set of citations to a plurality of legal cases cited in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining a first and second set of tones corresponding to each citation in the first and second sets of citations, respectively. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones, respectively. The computer-implemented method further includes detecting a first set of annotations corresponding to the first set of citations. The computer-implemented method further includes building a training model, based, at least in part, from at least a subset of the first set of annotations. The computer-implemented method further includes determining a second set of annotations corresponding to the second set of citations.

Abstract: A first touch gesture is sensed at a subset of a set of six Braille dot touch points at a virtual Braille keyboard. The first touch gesture corresponds to a Braille character. A second touch gesture is sensed that concurrently touches each of the six Braille dot touch points in the set of Braille dot touch points. A contact with a layout of the virtual Braille keyboard with at least one Braille dot touch point is continuously maintained while transitioning from the first touch gesture to the second touch gesture. In response to the second touch gesture, the Braille character is accepted.

Abstract: In an approach for controlling a computing device without mechanical buttons the computer detects an interaction with a touch capacitive sensing surface on a computing device. The computer determines that the detected interaction includes at least three interactions including at least two different touch capacitive sensing surfaces. The computer determines a virtual control button to display based on the detected interaction. The computer displays the determined virtual control button to the user. The computer receives an interaction with the displayed virtual control button. The computer implements an action associated with the determined virtual control button based on the received interaction.

Abstract: A method and associated computer program product for providing targeted digital advertisements to a user. The method includes receiving a detected electromagnetic noise signal of one or more objects, comparing the detected electromagnetic noise signal to one or more stored electromagnetic noise signals associated with one or more objects, and determining an identity of the one or more objects based on the comparison between the detected electromagnetic noise signal the stored electromagnetic noise signals. The method further includes providing targeted digital advertisement(s) to the user based on the determined identity of the one or more objects.

Abstract: A reward learning system includes a user interface configured to receive modes of user information related to a state of the user. A cognitive computing system includes a reward system. The reward system includes a dynamically upgraded profile model of the user which is updated in accordance with the user information related to the state. The reward system is updated by machine learning employing feedback from user responses measured by the user interface and searched information by the cognitive computing system. The reward system includes an increasing reward protocol based on learned user preferences and responses and rewarded in accordance with user achievements.