Abstract: A machine learning system is provided that receives location information from one or more aircraft, determines a deviation of the actual aircraft flightpath from an expected flight path. The flight path may be determined based on a filed flight plan, one or more historical flight paths. The actual aircraft flightpath is scored with respect to the expected flight path and the score may be used to determine an anomalous condition. In some cases, an aircraft crossing a boundary creates the anomalous condition. The anomalous conditions may be used to create a flag or an alert to indicate further analysis may be required. Through machine learning algorithms, hundreds, thousands, or tens of thousands or more flights can be simultaneously tracked and analyzed for anomalous conditions.

Abstract: A shot fired detector can receive an audio signal or acoustic stream and determine that a firearm has been discharged. One or more detectors can be used to continuously capture acoustic streams and process the acoustic streams for anomaly detection. A detected anomaly can be classified by a machine learning model to detect that a shot has been fired. The detector can send acoustic data and meta data associated with the shot fired to a server for further storage and/or processing. An alert can be automatically generated that is associated with the shot fired.

Abstract: Disclosed are various embodiments of systems and methods for generating composite mediasets from mediasets, each comprising media items, associated with a plurality of users. In some embodiments, individual and/or group recommendations are provided for creating a group playlist by aggregating user taste data for a plurality of users in a group. In other embodiments, systems and methods are provided which allow for sharing and playing of a group playlist by users in a group, each of which has a media playback device. In still other embodiments, systems and methods are provided for dynamically building a composite playlist that may be updated as users enter and/or leave the group. One example of an interactive entertainment application also is described for use at a venue to entertain live guests with music selections that are dynamically influenced by the guests' individual tastes.

Abstract: Disclosed are various embodiments of systems and methods for generating composite mediasets from mediasets, each comprising media items, associated with a plurality of users. In some embodiments, individual and/or group recommendations are provided for creating a group playlist by aggregating user taste data for a plurality of users in a group. In other embodiments, systems and methods are provided which allow for sharing and playing of a group playlist by users in a group, each of which has a media playback device. In still other embodiments, systems and methods are provided for dynamically building a composite playlist that may be updated as users enter and/or leave the group. One example of an interactive entertainment application also is described for use at a venue to entertain live guests with music selections that are dynamically influenced by the guests' individual tastes.

Abstract: A computer implemented process for generating customized, user-specific programming for delivery over a network, each programming unit comprising one or more media items, such as song tracks, and at least one promotional media item, such as a commercial advertisement. The media items are selected in response to implicit user taste data, and promotional media items or ads are selected where a media item associated with the promotional media item matches at least one media data item identified as responsive to the user taste data. The media items and the promotional media items are selected so as to constrain the promotional program unit to incur a net cost of no more that a selected maximum cost, wherein the net cost is determined as a sum of the licensing costs of the selected media items, reduced by a sum of the expected revenues generated by the selected promotional media items.

Abstract: Disclosed are various embodiments of systems and methods for generating composite mediasets from mediasets, each comprising media items, associated with a plurality of users. In some embodiments, individual and/or group recommendations are provided for creating a group playlist by aggregating user taste data for a plurality of users in a group. In other embodiments, systems and methods are provided which allow for sharing and playing of a group playlist by users in a group, each of which has a media playback device. Each media item, such as a song, is played from one of the individual user devices for the benefit of all users in the vicinity at the time. Music thus can be “shared” without transferring files potentially in violation of copyrights.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: The application discloses a systems and methods for automatically managing and allocating funds deposited in one or more financial accounts owned by the user. The system allows the user to specify and dynamically modify multiple savings goals and to associate each goal with a savings account. As the user makes deposits or withdrawals from an account, the amount of funds allocated to each of the goals associated with the account are automatically adjusted based on the user's relative preferences for the goals as automatically inferred from the user's interactions with the system. The system may also provide incentive rewards to users as they meet interim subgoals or complete a goal to induce users to successfully achieve their savings goals.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: A computer implemented process for generating customized, user-specific programming for delivery over a network, each programming unit comprising one or more media items, such as song tracks, and at least one promotional media item, such as a commercial advertisement. The media items are selected in response to implicit user taste data, and promotional media items or ads are selected where a media item associated with the promotional media item matches at least one media data item identified as responsive to the user taste data. The media items and the promotional media items are selected so as to constrain the promotional program unit to incur a net cost of no more that a selected maximum cost, wherein the net cost is determined as a sum of the licensing costs of the selected media items, reduced by a sum of the expected revenues generated by the selected promotional media items.

Abstract: Disclosed are embodiments of systems and methods for selection of promotional media items and/or generation of promotional or advertising units. In one embodiment, a set of promotional media items may be dynamically identified responsive to an input set of media items. In another embodiment, a promotional program unit is generated in response to receipt of user taste data for a user or group of users. The promotional program unit is an integral unit made up of one or more media items and one or more promotional media items.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: Massively scalable, memory and model-based techniques are an important approach for practical large-scale collaborative filtering. We describe a massively scalable, model-based recommender system and method that extends the collaborative filtering techniques by explicitly incorporating these types of user and item knowledge. In addition, we extend the Expectation-Maximization algorithm for learning the conditional probabilities in the model to coherently accommodate time-varying training data.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: A computer-implemented method (FIG. 4), systems (FIG. 6) and data structures (420, 466) are disclosed for creating and exchanging a compact, machine-usable user taste profile (140,416,608). The method may include accessing an associational knowledge base “AKB” (124,406) that stores relationships among a catalog of items in computer-usable form. The AKB includes identification of a plurality of “categories” (304,306,310) wherein each category is a subset of the catalog of items (300), and the categories are defined based on similarity among the items within a category. User interactions (126,410) with an application (404) driven by an AKB (406) are analyzed relative to the categorization (412,414,416) by application of profile factors (450) to estimate a user profile (416). The user profile can be exported to other applications that are driven by a compatible AKB in order to provide an experience tailored to the user's individual taste preferences.

Abstract: A computer implemented method for incorporating media item data for use in a media item recommender system comprising: accessing a first database comprising a plurality of media item identifiers and associated metadata corresponding to each of a plurality of media items identified by the media item identifiers; generating first correlation data based on a comparison of the metadata corresponding to pairs of the media item identifiers to detect similarities between the media items identified; accessing a second database comprising a plurality of media item identifier sets for identifying sets of media items; generating second correlation data based on an analysis of the media item identifier sets to determine incidence of selected subsets of media item identifiers occurring together in a same media item identifier set; accessing a third database comprising a plurality of consumed media item identifier sets, wherein the consumed media item identifier sets associate one or more media item identifiers in a partic

Abstract: Disclosed are various embodiments of systems and methods for generating composite mediasets from mediasets, each comprising media items, associated with a plurality of users. In some embodiments, individual and/or group recommendations are provided for creating a group playlist by aggregating user taste data for a plurality of users in a group. In other embodiments, systems and methods are provided which allow for sharing and playing of a group playlist by users in a group, each of which has a media playback device. In still other embodiments, systems and methods are provided for dynamically building a composite playlist that may be updated as users enter and/or leave the group. One example of an interactive entertainment application also is described for use at a venue to entertain live guests with music selections that are dynamically influenced by the guests' individual tastes.

Abstract: Disclosed are various embodiments of systems and methods for generating composite mediasets from mediasets, each comprising media items, associated with a plurality of users. In some embodiments, individual and/or group recommendations are provided for creating a group playlist by aggregating user taste data for a plurality of users in a group. In other embodiments, systems and methods are provided which allow for sharing and playing of a group playlist by users in a group, each of which has a media playback device. Each media item, such as a song, is played from one of the individual user devices for the benefit of all users in the vicinity at the time. Music thus can be “shared” without transferring files potentially in violation of copyrights.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.