Abstract: A multi-radius spherical microphone that includes an inner body defining an inner sphere having an inner radius from a center; a plurality of inner microphones coupled to the inner spherical body and defining an array of inner microphones; an outer body defining an dodecahedron, wherein the inner body and the outer body are concentric about the center; and a plurality of outer microphones coupled to the outer body at respective vertices of the dodecahedron and defining an array of outer microphones, wherein each of the plurality of outer microphones is positioned radially equidistant from the center.

Abstract: An apparatus, method, and computer program product are provided to adjust and modify input signals used in connection with predictive models by detecting events, such as changes in operating parameters of data objects and/or related systems and calculating adjusted decay rates to be applied to time-series data associated with times prior to an occurrence of an event. In some example implementations, an indication of an event associated with a given datastream is received, in a manner which indicates the change in an operating parameter and the time at which the change occurred. Based at least in part on the indication of the event associated with the datastream, a second decay rate associated with the set of time-series data is determined and applied to the set of time-series data, such that an updated future performance level can be calculated by a predictive model.

Abstract: In general, embodiments of the present invention provide for ranking data objects using machine learning and scoring to configure a graphical interface. In this regard, a machine learning model is applied to data object attributes to generate data object relevance scores for the candidate data objects. Additionally, a respective personalization score for the candidate data objects is generated based at least in part on a compounded ratio and a geometric mean of the compounded ratio. The data object relevance scores for the candidate data objects are modified based at least in part on the respective personalization score for the candidate data objects to generate modified object relevance scores for the candidate data objects. The candidate data objects are then ranked based at least in part on the modified object relevance scores to generate a ranked data object set and to facilitate configuration of a graphical interface.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for dynamically augmenting relevance rankings using data from external ratings sources.

Abstract: The present disclosure relates to inferring device rendered object interaction behavior. An apparatus is configured to receive historical device rendered object interaction data associated with a client device. The historical device rendered object interaction data includes a plurality of records. Each record is associated with a device rendered object category. For each record, the device rendered object category is extracted. For each device rendered object category, a penalty value is generated. The penalty value is a programmatically generated predicted network time delay during which device rendered objects associated with the device rendered object category are not to be transmitted to the client device. A penalty decision, used to determine whether to apply the penalty value to future transmissions of device rendered objects to the client device, is generated.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for dynamically augmenting relevance rankings using data from external ratings sources.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a universal relevance service framework for ranking and personalizing items.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a universal relevance service framework for ranking and personalizing items.

Abstract: An apparatus, method, and computer program product are provided to adjust and modify input signals used in connection with predictive models by detecting events, such as changes in operating parameters of data objects and/or related systems and calculating adjusted decay rates to be applied to time-series data associated with times prior to an occurrence of an event. In some example implementations, an indication of an event associated with a given datastream is received, in a manner which indicates the change in an operating parameter and the time at which the change occurred. Based at least in part on the indication of the event associated with the datastream, a second decay rate associated with the set of time-series data is determined and applied to the set of time-series data, such that an updated future performance level can be calculated by a predictive model.

Abstract: The present disclosure relates to inferring device rendered object interaction behavior. An apparatus is configured to receive historical device rendered object interaction data associated with a client device. The historical device rendered object interaction data includes a plurality of records. Each record is associated with a device rendered object category. For each record, the device rendered object category is extracted. For each device rendered object category, a penalty value is generated. The penalty value is a programmatically generated predicted network time delay during which device rendered objects associated with the device rendered object category are not to be transmitted to the client device. A penalty decision, used to determine whether to apply the penalty value to future transmissions of device rendered objects to the client device, is generated.

Abstract: An apparatus, method, and computer program product are provided to adjust and modify input signals used in connection with predictive models by detecting events, such as changes in operating parameters of data objects and/or related systems and calculating adjusted decay rates to be applied to time-series data associated with times prior to an occurrence of an event. In some example implementations, an indication of an event associated with a given datastream is received, in a manner which indicates the change in an operating parameter and the time at which the change occurred. Based at least in part on the indication of the event associated with the datastream, a second decay rate associated with the set of time-series data is determined and applied to the set of time-series data, such that an updated future performance level can be calculated by a predictive model.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a universal relevance service framework for ranking and personalizing items.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a universal relevance service framework for ranking and personalizing items.

Abstract: An apparatus, method, and computer program product are provided to predict and model the performance of option data objects prior to the introduction of such option data objects into a network environment through the application of specific rule sets that are applied in a multi-stage arrangement on sets of extracted option data object parameters. In some example implementations, option data object parameters are extracted and parsed into sets of categorical and non-categorical parameters, which may be both quantitative and qualitative, and transmitted to one or more predictive models. Through the operation of the predictive models, the option data object parameters are transformed into a weighted value associated with the option data object that reflects a predicted performance level of the option data object and can be used when comparing multiple option data objects against each other.

Abstract: An apparatus, method, and computer program product are provided to predict and model the performance of option data objects prior to the introduction of such option data objects into a network environment through the application of specific rule sets that are applied in a multi-stage arrangement on sets of extracted option data object parameters. In some example implementations, option data object parameters are extracted and parsed into sets of categorical and non-categorical parameters, which may be both quantitative and qualitative, and transmitted to one or more predictive models. Through the operation of the predictive models, the option data object parameters are transformed into a weighted value associated with the option data object that reflects a predicted performance level of the option data object and can be used when comparing multiple option data objects against each other.

Abstract: The present disclosure relates to inferring device rendered object interaction behavior. An apparatus is configured to receive historical device rendered object interaction data associated with a client device. The historical device rendered object interaction data includes a plurality of records. Each record is associated with a device rendered object category. For each record, the device rendered object category is extracted. For each device rendered object category, a penalty value is generated. The penalty value is a programmatically generated predicted network time delay during which device rendered objects associated with the device rendered object category are not to be transmitted to the client device. A penalty decision, used to determine whether to apply the penalty value to future transmissions of device rendered objects to the client device, is generated.

Abstract: The present disclosure relates to inferring device rendered object interaction behavior. An apparatus is configured to receive historical device rendered object interaction data associated with a client device. The historical device rendered object interaction data includes a plurality of records. Each record is associated with an device rendered object category. For each record, the device rendered object category is extracted. For each device rendered object category, a penalty value is generated. The penalty value is a programmatically generated predicted network time delay during which device rendered objects associated with the device rendered object category are not to be transmitted to the client device. A penalty decision, used to determine whether to apply the penalty value to future transmissions of device rendered objects to the client device, is generated.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a universal relevance service framework for ranking and personalizing items.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a universal relevance service framework for ranking and personalizing items.