Abstract: Technologies for content delivery and presentation tracking are disclosed. An example method includes tracking tokens generated during playback of a content stream, associating each of the tracked viewer measurement tokens with metadata that indicates timing, and merging the tracked viewer measurement tokens with baseline format tokens associated with the playback of the content stream, to generate merged token data.

Abstract: Systems, devices, and process described facilitate use of a first user device communicatively coupled to a local area network (LAN) The first user device may include a first hardware processor configured to execute first, non-transient, computer instructions for facilitating a first seamless DVR engine configured to identify a first content. A second user device is also coupled to the LAN. The second user device may include a second hardware processor configured to execute second, non-transient, computer instructions for facilitating a second seamless DVR configured to identify a second content. Each of the seamless DVR engines may be configured to generate a common universal directory identifying the first content and the second content as being available for use by each of the first user device and the second user device. User devices may independently populate the common universal directory. The universal directory may identify a distinct status for the content.

Abstract: Systems and methods for detecting and remediating roadway hazards are disclosed. A machine learning model is trained on a dataset related to roadway items. Input data is collected by a data collection engine and provided to a pattern recognizer. The pattern recognizer extracts roadway features and recognized patterns from the input data and provide the extracted features to a trained machine learning model. The trained model compares the extracted features to the model, and a risk value is generated. The risk value is compared to a risk value threshold. If the risk value is equal to or exceeds the risk threshold, then the input data may be classified as a roadway hazard. Remedial action is subsequently be triggered.

Abstract: Examples of the present disclosure describe systems and methods for detecting and remediating roadway hazards. In example aspects, a machine learning model is trained on a dataset related to roadway items. Input data may then be collected by a data collection engine and provided to a pattern recognizer. The pattern recognizer may extract roadway features (physical and non-physical) and recognized patterns from the input data and provide the extracted features to a trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a risk value may be generated. The risk value may be compared to a risk value threshold. If the risk value is equal to or exceeds the risk threshold, then the input data may be classified as a roadway hazard. Remedial action may subsequently be triggered, e.g., notifying other vehicles on the roadway, notifying other devices of drivers on the roadway, and/or notifying interested third parties (e.g.

Abstract: Devices, systems, and processes for facilitating seamless use of tuners across multiple devices within a LAN are described and may include use of a first user device, communicatively coupled to the LAN, that includes a first hardware processor configured to execute first, non-transient, computer instructions for facilitating a first seamless tuner engine configured to identify at least one first tuner. A second user device, communicatively coupled to the LAN, may include a second hardware processor configured to execute second, non-transient, computer instructions for facilitating a second seamless tuner configured to identify at least one second tuner. At least one of, if not each, of seamless tuner engines may be configured to generate a universal tuner directory identifying availability of each the tuners. The universal tuner directory may be populated and tuners allocated based upon an emergency broadcast, auto-tune, PPV, original content, re-run, or Joey request type.

Abstract: Devices, systems, and processes are described for seamless use of timers across multiple user devices on a LAN. A system may include a first user device, communicatively coupled to the LAN, having at least one first tuner and a first hardware processor configured for facilitating a first timer manager. A second user device communicatively coupled to the LAN may include at least one second tuner and a second hardware processor configured for facilitating a second timer manager. Each of the first timer manager and the second timer manager may be configured to populate a universal timer schedule that identifies at least one of a future availability and a future use for each of the at least one first tuner and the at least one second tuner. The universal timer schedule is populated based upon a request type, such as an auto-tune, PPV, original content, re-run, and a Joey request type.

Abstract: Systems, devices, and process described facilitate use of a first user device communicatively coupled to a local area network (LAN) The first user device may include a first hardware processor configured to execute first, non-transient, computer instructions for facilitating a first seamless DVR engine configured to identify a first content. A second user device is also coupled to the LAN. The second user device may include a second hardware processor configured to execute second, non-transient, computer instructions for facilitating a second seamless DVR configured to identify a second content. Each of the seamless DVR engines may be configured to generate a common universal directory identifying the first content and the second content as being available for use by each of the first user device and the second user device. User devices may independently populate the common universal directory. The universal directory may identify a distinct status for the content.

Abstract: Devices, systems, and processes are described for seamless use of timers across multiple user devices on a LAN. A system may include a first user device, communicatively coupled to the LAN, having at least one first tuner and a first hardware processor configured for facilitating a first timer manager. A second user device communicatively coupled to the LAN may include at least one second tuner and a second hardware processor configured for facilitating a second timer manager. Each of the first timer manager and the second timer manager may be configured to populate a universal timer schedule that identifies at least one of a future availability and a future use for each of the at least one first tuner and the at least one second tuner. The universal timer schedule is populated based upon a request type, such as an auto-tune, PPV, original content, rerun, and a Joey request type.

Abstract: Devices, systems, and processes for facilitating seamless use of tuners across multiple devices within a LAN are described and may include use of a first user device, communicatively coupled to the LAN, that includes a first hardware processor configured to execute first, non-transient, computer instructions for facilitating a first seamless tuner engine configured to identify at least one first tuner. A second user device, communicatively coupled to the LAN, may include a second hardware processor configured to execute second, non-transient, computer instructions for facilitating a second seamless tuner configured to identify at least one second tuner. At least one of, if not each, of seamless tuner engines may be configured to generate a universal tuner directory identifying availability of each the tuners. The universal tuner directory may be populated and tuners allocated based upon an emergency broadcast, auto-tune, PPV, original content, re-run, or Joey request type.

Abstract: Systems, devices, and process described facilitate use of a first user device communicatively coupled to a local area network (LAN) The first user device may include a first hardware processor configured to execute first, non-transient, computer instructions for facilitating a first seamless DVR engine configured to identify a first content. A second user device is also coupled to the LAN. The second user device may include a second hardware processor configured to execute second, non-transient, computer instructions for facilitating a second seamless DVR configured to identify a second content. Each of the seamless DVR engines may be configured to generate a common universal directory identifying the first content and the second content as being available for use by each of the first user device and the second user device. User devices may independently populate the common universal directory. The universal directory may identify a distinct status for the content.

Abstract: Devices, systems, and processes are described for seamless use of timers across multiple user devices on a LAN. A system may include a first user device, communicatively coupled to the LAN, having at least one first tuner and a first hardware processor configured for facilitating a first timer manager. A second user device communicatively coupled to the LAN may include at least one second tuner and a second hardware processor configured for facilitating a second timer manager. Each of the first timer manager and the second timer manager may be configured to populate a universal timer schedule that identifies at least one of a future availability and a future use for each of the at least one first tuner and the at least one second tuner. The universal timer schedule is populated based upon a request type, such as an auto-tune, PPV, original content, re-run, and a Joey request type.

Abstract: Systems, devices, and process described facilitate use of a first user device communicatively coupled to a local area network (LAN) The first user device may include a first hardware processor configured to execute first, non-transient, computer instructions for facilitating a first seamless DVR engine configured to identify a first content. A second user device is also coupled to the LAN. The second user device may include a second hardware processor configured to execute second, non-transient, computer instructions for facilitating a second seamless DVR configured to identify a second content. Each of the seamless DVR engines may be configured to generate a common universal directory identifying the first content and the second content as being available for use by each of the first user device and the second user device. User devices may independently populate the common universal directory. The universal directory may identify a distinct status for the content.

Abstract: Devices, systems, and processes for facilitating seamless use of tuners across multiple devices within a LAN are described and may include use of a first user device, communicatively coupled to the LAN, that includes a first hardware processor configured to execute first, non-transient, computer instructions for facilitating a first seamless tuner engine configured to identify at least one first tuner. A second user device, communicatively coupled to the LAN, may include a second hardware processor configured to execute second, non-transient, computer instructions for facilitating a second seamless tuner configured to identify at least one second tuner. At least one of, if not each, of seamless tuner engines may be configured to generate a universal tuner directory identifying availability of each the tuners. The universal tuner directory may be populated and tuners allocated based upon an emergency broadcast, auto-tune, PPV, original content, re-run, or Joey request type.

Abstract: Examples of the present disclosure describe systems and methods for detecting and remediating roadway hazards. In example aspects, a machine learning model is trained on a dataset related to roadway items. Input data may then be collected by a data collection engine and provided to a pattern recognizer. The pattern recognizer may extract roadway features (physical and non-physical) and recognized patterns from the input data and provide the extracted features to a trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a risk value may be generated. The risk value may be compared to a risk value threshold. If the risk value is equal to or exceeds the risk threshold, then the input data may be classified as a roadway hazard. Remedial action may subsequently be triggered, e.g., notifying other vehicles on the roadway, notifying other devices of drivers on the roadway, and/or notifying interested third parties (e.g.