Abstract: In some embodiments, an electronic device displays a user interface associated with a first record label. In some embodiments, the user interface includes one or more representations of artists associated with the first record label and one or more representations of content items, and does not include representations of content or artists not associated with the first record label. In some embodiments, in response to a selection of a representation of a first artist, the electronic device displays a user interface of the content playback application associated with the first artist, which includes one or more representations of content associated with the first artist, but not one or more representations of content associated with the second artist. In some embodiments, in response to a selection of a representation of a first content item, the electronic device initiates a process to access the first content item via the content playback application.

Abstract: In some embodiments, an electronic device receives, via one or more input devices, an input corresponding to a request to display a user interface associated with a first respective content mix, wherein the first respective content mix is associated with a first respective time period. In some embodiments in response to receiving the input corresponding to the request to display the user interface associated with the first respective content mix, the electronic device displays, via a display, the user interface. In some embodiments, the user interface includes a plurality of representations of content items including visual indications of amounts of consumption, of a first consumption metric type, by a user during the first respective time period, and a plurality of representations of content creators including visual indications of consumption, of a second consumption metric type, by the user during the first respective time period.

Abstract: In some embodiments, an electronic device receives, via one or more input devices, an input corresponding to a request to display a user interface associated with a first respective content mix, wherein the first respective content mix is associated with a first respective time period. In some embodiments in response to receiving the input corresponding to the request to display the user interface associated with the first respective content mix, the electronic device displays, via a display, the user interface. In some embodiments, the user interface includes a plurality of representations of content items including visual indications of amounts of consumption, of a first consumption metric type, by a user during the first respective time period, and a plurality of representations of content creators including visual indications of consumption, of a second consumption metric type, by the user during the first respective time period.

Abstract: In some embodiments, an electronic device facilitates processes for subscribing to a content delivery service. For example, the electronic device subscribes to a voice-only subscription to a content delivery service or a voice and non-voice subscription to the content delivery service. In some embodiments, the electronic device accesses content items via a content delivery service.

Abstract: The embodiments disclosed herein relate to intelligent configuration of a cloud-service gateway based on a pattern recognition algorithm. A machine-learning model is trained to learn the patterns of correlation among many configuration parameters affecting the performance of the system when processing an observed or estimated workload. Training the model may be performed off-line with performance data observed during experiments performed with a variety of configurations and workloads. Once trained, the model may be used to recommend: (a) new configuration parameter values based on constraints of the system being configured, (b) an amount of work that can be performed at a certain performance level when the system is configured with certain parameter values, or (c) the expected performance level when running a certain workload on the system configured with certain configuration parameter values.

Abstract: The embodiments disclosed herein relate to intelligent configuration of a cloud-service gateway based on a pattern recognition algorithm. A machine-learning model is trained to learn the patterns of correlation among many configuration parameters affecting the performance of the system when processing an observed or estimated workload. Training the model may be performed off-line with performance data observed during experiments performed with a variety of configurations and workloads. Once trained, the model may be used to recommend: (a) new configuration parameter values based on constraints of the system being configured, (b) an amount of work that can be performed at a certain performance level when the system is configured with certain parameter values, or (c) the expected performance level when running a certain workload on the system configured with certain configuration parameter values.

Abstract: In some embodiments, an electronic device receives, via one or more input devices, an input corresponding to a request to display a user interface associated with a first respective content mix, wherein the first respective content mix is associated with a first respective time period. In some embodiments in response to receiving the input corresponding to the request to display the user interface associated with the first respective content mix, the electronic device displays, via a display, the user interface. In some embodiments, the user interface includes a plurality of representations of content items including visual indications of amounts of consumption, of a first consumption metric type, by a user during the first respective time period, and a plurality of representations of content creators including visual indications of consumption, of a second consumption metric type, by the user during the first respective time period.

Abstract: The embodiments disclosed herein relate to intelligent configuration of a cloud-service gateway based on a pattern recognition algorithm. A machine-learning model is trained to learn the patterns of correlation among many configuration parameters affecting the performance of the system when processing an observed or estimated workload. Training the model may be performed off-line with performance data observed during experiments performed with a variety of configurations and workloads. Once trained, the model may be used to recommend: (a) new configuration parameter values based on constraints of the system being configured, (b) an amount of work that can be performed at a certain performance level when the system is configured with certain parameter values, or (c) the expected performance level when running a certain workload on the system configured with certain configuration parameter values.

Abstract: The embodiments disclosed herein relate to intelligent configuration of a cloud-service gateway based on a pattern recognition algorithm. A machine-learning model is trained to learn the patterns of correlation among many configuration parameters affecting the performance of the system when processing an observed or estimated workload. Training the model may be performed off-line with performance data observed during experiments performed with a variety of configurations and workloads. Once trained, the model may be used to recommend: (a) new configuration parameter values based on constraints of the system being configured, (b) an amount of work that can be performed at a certain performance level when the system is configured with certain parameter values, or (c) the expected performance level when running a certain workload on the system configured with certain configuration parameter values.