Abstract: Methods and systems receive machine-readable input data describing a job listing or employment opportunity. The system scans the input data and software algorithms process it to predict specific information required by a particular html schema. To predict the information required by the schema, in one aspect, the system applies machine learning tools, for example, prediction models, which may be generated from training data selected from large job listing data collections, and specifically adapted and tuned for each field of interest, for example, job description, job location, and salary. This information is used to generate structured data, for example, html code, consistent with the schema for automatic insertion into a web page to comply with the schema and make the listing widely available to automated search processes.

Abstract: Systems and methods for predicting the outcome of a business entity are presented. In embodiments, a system may receive explicit data reporting or indicating activities of a business entity, and other data from which information regarding the activities or level of operations of the entity may be inferred. Using one or more data processors, the system may generate inferred data regarding the business entity from a selected portion of the other data, and use at least some of the explicit data and the inferred data to determine which one of a series of defined sequential active states of development the entity currently is in. The system may further, using the result of the determination as the current state of the business, predict a final stage of the business entity, and a probability of evolving to that final stage from the current state. Other embodiments may be disclosed or claimed.

Abstract: Systems and methods for predicting the outcome of a business entity are presented. In embodiments, a system may receive explicit data reporting or indicating activities of a business entity, and other data from which information regarding the activities or level of operations of the entity may be inferred. Using one or more data processors, the system may generate inferred data regarding the business entity from a selected portion of the other data, and use at least some of the explicit data and the inferred data to determine which one of a series of defined sequential active states of development the entity currently is in. The system may further, using the result of the determination as the current state of the business, predict a final stage of the business entity, and a probability of evolving to that final stage from the current state. Other embodiments may be disclosed or claimed.

Abstract: Methods and systems receive machine-readable input data describing a job listing or employment opportunity. The system scans the input data and software algorithms process it to predict specific information required by a particular html schema. To predict the information required by the schema, in one aspect, the system applies machine learning tools, for example, prediction models, which may be generated from training data selected from large job listing data collections, and specifically adapted and tuned for each field of interest, for example, job description, job location, and salary. This information is used to generate structured data, for example, html code, consistent with the schema for automatic insertion into a web page to comply with the schema and make the listing widely available to automated search processes.

Abstract: Systems and methods that include characterizing individual team members in terms of specified “character elements” or attributes, and combining the individual characterizations into an aggregate characterization of the team. The team evaluation does not merely sum individual attributes; rather, it analyzes the composition of the team relative to predetermined metrics, taking into account what combinations of individual team member attributes are more likely to lead to success of the team.

Abstract: Systems and methods for predicting the outcome of a business entity are presented. In embodiments, a system may receive explicit data reporting or indicating activities of a business entity, and other data from which information regarding the activities or level of operations of the entity may be inferred. Using one or more data processors, the system may generate inferred data regarding the business entity from a selected portion of the other data, and use at least some of the explicit data and the inferred data to determine which one of a series of defined sequential active states of development the entity currently is in. The system may further, using the result of the determination as the current state of the business, predict a final stage of the business entity, and a probability of evolving to that final stage from the current state. Other embodiments may be disclosed or claimed.

Abstract: Systems and methods are disclosed for identifying a new set of media items in response to an input set (or “query set”) of media items and knowledge base metrics. The system uses a knowledge base consisting of a collection of mediasets. Various metrics among media items are considered by analyzing how the media items are grouped to form the mediasets in the knowledge base. Such association or “similarity” metrics are preferably stored in a matrix form that allows the system to efficiently identify a new set of media items that complements the input set of 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. 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: Systems and methods are disclosed for identifying a new set of media items in response to an input set (or “query set”) of media items and knowledge base metrics. The system uses a knowledge base consisting of a collection of mediasets. Various metrics among media items are considered by analyzing how the media items are grouped to form the mediasets in the knowledge base. Such association or “similarity” metrics are preferably stored in a matrix form that allows the system to efficiently identify a new set of media items that complements the input set of media items.

Abstract: Computer-implemented methods leverage internal data accumulated by a banking institution including merchant sales data and customer purchasing data, in order to best implement merchant offer campaigns by computing a set of “good campaigns” for a given user in real time, while maximizing the success of all active campaigns. (FIG. 1) Multiple factors (208, 210, 212, 214, 216) may be statistically evaluated and combined (218, 220) to determine the best campaigns for a given user. Other considerations preferably relate directly to a level of accomplishment of the active campaigns and their time remaining. Machine learning may be applied to assess a predicted level of interest of each user for the active campaigns (FIG. 3). In some embodiments, the respective weights of various factors can be changed in order to adapt the algorithm to specific business goals.

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: The present describes systems and methods to support financial management by determining the impact of a proposed purchase on a financial situation. For example, a device is configured to determine the impact of a proposed purchase on a user's financial situation based on a purchase amount associated with the proposed purchase.

Abstract: A system and method to navigate through a media item catalog and generate recommendations using behavioral metrics such as correlation metrics (FIGS. 1,2) from a knowledge base (400) of mediasets (FIG. 4, 1-7). One implementation uses a knowledge base comprising a collection of mediasets such as groupings of selected video items. Various metrics (Metric 1-Metric m) among media items (m1 . . . ) are considered by analyzing how the media items are grouped to form the sets in the knowledge base (400). Such metrics preferably are stored in a matrix (100) that allows the system to dynamically build appropriate navigation lists (FIG. 3) from media items that a user selects (FIG. 5).

Abstract: Systems and methods are disclosed for identifying a new set of media items in response to an input set (or “query set”) of media items and knowledge base metrics. The system uses a knowledge base consisting of a collection of mediasets. Various metrics among media items are considered by analyzing how the media items are grouped to form the mediasets in the knowledge base. Such association or “similarity” metrics are preferably stored in a matrix form that allows the system to efficiently identify a new set of media items that complements the input set of media items.

Abstract: A system and method to navigate through a media item catalog and generate recommendations using behavioral metrics such as correlation metrics (FIGS. 1,2) from a knowledge base (400) of mediasets (FIG. 4, 1-7). One implementation uses a knowledge base comprising a collection of mediasets such as groupings of selected video items. Various metrics (Metric 1-Metric m) among media items (m1 . . . ) are considered by analyzing how the media items are grouped to form the sets in the knowledge base (400). Such metrics preferably are stored in a matrix (100) that allows the system to dynamically build appropriate navigation lists (FIG. 3) from media items that a user selects (FIG. 5).

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 system and method to navigate through a media item catalog and generate recommendations using behavioral metrics such as correlation metrics (FIGS. 1,2) from a knowledge base (400) of mediasets (FIG. 4, 1-7). One implementation uses a knowledge base comprising a collection of mediasets such as groupings of selected video items. Various metrics (Metric 1-Metric m) among media items (m1 . . . ) are considered by analyzing how the media items are grouped to form the sets in the knowledge base (400). Such metrics preferably are stored in a matrix (100) that allows the system to dynamically build appropriate navigation lists (FIG. 3) from media items that a user selects (FIG. 5).

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 system and method to navigate through a media item catalog and generate recommendations using behavioral metrics such as correlation metrics (FIGS. 1,2) from a knowledge base (400) of mediasets (FIG. 4, 1-7). One implementation uses a knowledge base comprising a collection of mediasets such as groupings of selected video items. Various metrics (Metric 1-Metric m) among media items (m1 . . . ) are considered by analyzing how the media items are grouped to form the sets in the knowledge base (400). Such metrics preferably are stored in a matrix (100) that allows the system to dynamically build appropriate navigation lists (FIG. 3) from media items that a user selects (FIG. 5).

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.