Abstract: A method for automatically generating scheduling algorithms, including determining a scheduling policy for a plurality of jobs to be executed on a computer system, where the scheduling policy specifies an execution order of a plurality of jobs; using the scheduling policy in a production environment for a period of time, and collecting data indicative of a business impact of each job executed during the period of time; generating a list of all pairwise comparisons of business impact between the plurality of jobs, together with outcomes of the comparisons; marking each pair for which the comparison outcome is inconsistent with the relative execution order of the pair of jobs according to a predefined criteria to create a reinforcement learning batch; and using the reinforcement learning batch to adjust a decision criteria used to determine the scheduling policy.

Abstract: A method for automatically generating scheduling algorithms, including determining a scheduling policy for a plurality of jobs to be executed on a computer system, where the scheduling policy specifies an execution order of a plurality of jobs; using the scheduling policy in a production environment for a period of time, and collecting data indicative of a business impact of each job executed during the period of time; generating a list of all pairwise comparisons of business impact between the plurality of jobs, together with outcomes of the comparisons; marking each pair for which the comparison outcome is inconsistent with the relative execution order of the pair of jobs according to a predefined criteria to create a reinforcement learning batch; and using the reinforcement learning batch to adjust a decision criteria used to determine the scheduling policy.

Abstract: A method for automatically generating scheduling algorithms, including determining a scheduling policy for a plurality of jobs to be executed on a computer system, where the scheduling policy specifies an execution order of a plurality of jobs; using the scheduling policy in a production environment for a period of time, and collecting data indicative of a business impact of each job executed during the period of time; generating a list of all pairwise comparisons of business impact between the plurality of jobs, together with outcomes of the comparisons; marking each pair for which the comparison outcome is inconsistent with the relative execution order of the pair of jobs according to a predefined criteria to create a reinforcement learning batch; and using the reinforcement learning batch to adjust a decision criteria used to determine the scheduling policy.

Abstract: A method of performing a notification event at a headphone device includes receiving, at the headphone device, a wireless notification signal. The wireless notification signal is received from a transmitting device including a camera sensor, a display and a touchscreen interface. The headphone device is selected to receive the wireless notification signal in response to a touchscreen event occurring at the transmitting device. An image including a representation of the headphone device is composed by the camera sensor and is presented on the display. The touchscreen event includes receiving a touch input via the touchscreen interface. The method further includes determining the notification event to be performed at the headphone device by decoding the wireless notification signal. The method further includes performing the notification event indicated by the wireless notification signal at the headphone device in response to receiving the wireless notification signal.

Abstract: A method of performing a notification event at a headphone device includes receiving, at the headphone device, a wireless notification signal. The wireless notification signal is received from a transmitting device including a camera sensor, a display and a touchscreen interface. The headphone device is selected to receive the wireless notification signal in response to a touchscreen event occurring at the transmitting device. An image including a representation of the headphone device is composed by the camera sensor and is presented on the display. The touchscreen event includes receiving a touch input via the touchscreen interface. The method further includes determining the notification event to be performed at the headphone device by decoding the wireless notification signal. The method further includes performing the notification event indicated by the wireless notification signal at the headphone device in response to receiving the wireless notification signal.

Abstract: A method for automatically generating scheduling algorithms, including determining a scheduling policy for a plurality of jobs to be executed on a computer system, where the scheduling policy specifies an execution order of a plurality of jobs; using the scheduling policy in a production environment for a period of time, and collecting data indicative of a business impact of each job executed during the period of time; generating a list of all pairwise comparisons of business impact between the plurality of jobs, together with outcomes of the comparisons; marking each pair for which the comparison outcome is inconsistent with the relative execution order of the pair of jobs according to a predefined criteria to create a reinforcement learning batch; and using the reinforcement learning batch to adjust a decision criteria used to determine the scheduling policy.