Abstract: Dynamically modelling geospatial words in social media, in one aspect, generates a word set based on frequencies of words occurring in GPS annotated text data generated by a GPS-enabled device containing latitude and longitude coordinates. Locations are partitioned by mapping GPS coordinates in the GPS annotated text data to a set of discrete non-overlapped locations. A text stream contained in the GPS annotated text data is segmented into time windows. Footprints of locations in time windows are generated. Geospatial weights associated with words in the word set may be generated based on localness of words determined based on the footprints. Words in a text message are extracted and scores are determined for the set of discrete non-overlapped locations associated with the words.

Abstract: Dynamically modelling geospatial words in social media, in one aspect, generates a word set based on frequencies of words occurring in GPS annotated text data generated by a GPS-enabled device containing latitude and longitude coordinates. Locations are partitioned by mapping GPS coordinates in the GPS annotated text data to a set of discrete non-overlapped locations. A text stream contained in the GPS annotated text data is segmented into time windows. Footprints of locations in time windows are generated. Geospatial weights associated with words in the word set are generated based on localness of words determined based on the footprints. Words in a text message are extracted and scores are determined for the set of discrete non-overlapped locations associated with the words.

Abstract: Dynamically modelling geospatial words in social media, in one aspect, generates a word set based on frequencies of words occurring in GPS annotated text data generated by a GPS-enabled device containing latitude and longitude coordinates. Locations are partitioned by mapping GPS coordinates in the GPS annotated text data to a set of discrete non-overlapped locations. A text stream contained in the GPS annotated text data is segmented into time windows. Footprints of locations in time windows are generated. Geospatial weights associated with words in the word set may be generated based on localness of words determined based on the footprints. Words in a text message are extracted and scores are determined for the set of discrete non-overlapped locations associated with the words.

Abstract: Dynamically modelling geospatial words in social media, in one aspect, generates a word set based on frequencies of words occurring in GPS annotated text data generated by a GPS-enabled device containing latitude and longitude coordinates. Locations are partitioned by mapping GPS coordinates in the GPS annotated text data to a set of discrete non-overlapped locations. A text stream contained in the GPS annotated text data is segmented into time windows. Footprints of locations in time windows are generated. Geospatial weights associated with words in the word set are generated based on localness of words determined based on the footprints. Words in a text message are extracted and scores are determined for the set of discrete non-overlapped locations associated with the words.

Abstract: Dynamically modelling geospatial words in social media, in one aspect, generates a word set based on frequencies of words occurring in GPS annotated text data generated by a GPS-enabled device containing latitude and longitude coordinates. Locations are partitioned by mapping GPS coordinates in the GPS annotated text data to a set of discrete non-overlapped locations. A text stream contained in the GPS annotated text data is segmented into time windows. Footprints of locations in time windows are generated. Geospatial weights associated with words in the word set are generated based on localness of words determined based on the footprints. Words in a text message are extracted and scores are determined for the set of discrete non-overlapped locations associated with the words.

Abstract: A method of automating incoming message prioritization. The method including training a global classifier of a computer system using training data. Dynamically training a user-specific classifier of the computer system based on a plurality of feedback instances. Inferring a topic of the incoming message received by the computer system based on a topic-based user model. Computing a plurality of contextual features of the incoming message. Determining a priority classification strategy for assigning a priority level to the incoming message based on the computed contextual features of the incoming message and a weighted combination of the global classifier and the user specific classifier. Classifying the incoming message based on the priority classification strategy.

Abstract: An apparatus for automating a prioritization of an incoming message, including a batch learning module that generates a global classifier based on training data that is input to the batch learning module. A feedback learning module that generates a user-specific classifier based on a plurality of feedback instances. A feature extraction module that receives the incoming message and a topic-based user model, infers a topic of the incoming message based on the topic-based user model, and computes a plurality of contextual features of the incoming message. A classification module that dynamically determines a priority classification strategy for assigning a priority level to the incoming message based on the plurality of contextual features of the incoming message and a weighted combination of the global classifier and the user-specific classifier, and classifies the incoming message based on the priority classification strategy.

Abstract: Automatically generating a meeting digest of a set of meetings is provided. A set of topics of interest to the parties to the set of meetings is detected utilizing a user model associated with a user that is based on at least one of communications, relationships, and roles of the parties to the set of meetings. Topic-related content associated with the set of topics of interest to the parties is extracted from meeting data corresponding to the set of meetings. Then, the meeting digest of the set of meetings is generated using the topic-related content associated with the set of topics of interest to the parties extracted from the meeting data corresponding to the set of meetings.

Abstract: A method of automating incoming message prioritization. The method including training a global classifier of a computer system using training data. Dynamically training a user-specific classifier of the computer system based on a plurality of feedback instances. Inferring a topic of the incoming message received by the computer system based on a topic-based user model. Computing a plurality of contextual features of the incoming message. Determining a priority classification strategy for assigning a priority level to the incoming message based on the computed contextual features of the incoming message and a weighted combination of the global classifier and the user specific classifier. Classifying the incoming message based on the priority classification strategy.

Abstract: Automatically generating a meeting digest of a set of meetings is provided. A set of topics of interest to the parties to the set of meetings is detected utilizing a user model associated with a user that is based on at least one of communications, relationships, and roles of the parties to the set of meetings. Topic-related content associated with the set of topics of interest to the parties is extracted from meeting data corresponding to the set of meetings. Then, the meeting digest of the set of meetings is generated using the topic-related content associated with the set of topics of interest to the parties extracted from the meeting data corresponding to the set of meetings.

Abstract: An apparatus for automating a prioritization of an incoming message, including a batch learning module that generates a global classifier based on training data that is input to the batch learning module. A feedback learning module that generates a user-specific classifier based on a plurality of feedback instances. A feature extraction module that receives the incoming message and a topic-based user model, infers a topic of the incoming message based on the topic-based user model, and computes a plurality of contextual features of the incoming message. A classification module that dynamically determines a priority classification strategy for assigning a priority level to the incoming message based on the plurality of contextual features of the incoming message and a weighted combination of the global classifier and the user-specific classifier, and classifies the incoming message based on the priority classification strategy.

Abstract: Methods and apparatus are provided for presenting personalized information to consumers in a retail environment. A personalized price is determined for at least one product that is in physical proximity to the customer. The physical proximity may be determined, for example, by evaluating identifiers, such as RFID tags, associated with each customer and product. A personalized message can be also presented to a customer regarding at least one product that is in physical proximity to the customer. The personalized message can provide, for example, dietary or safety information relevant to the customer and at least one product in the proximity of the customer. The personalized information may be determined based on one or more personalized message rules.

Abstract: A method and structure for a tiled interface system provides a Tiled User Interface (TUI) in which a tile manager manages at least one tile cluster on a display device and translates an input event into a tile cluster event and at least one tile cluster controlled by the tile manager to be displayed on the display device. Each tile cluster includes at least one tile, each tile cluster corresponds to one or more predefined functions for a specific application, each tile cluster provides a complete interaction of all the predefined functions for the specific application respectively corresponding to that tile cluster, and each tile cluster can be presented in its entirety on a single frame of the display device using at most one input event.

Abstract: Methods and apparatus are provided for presenting personalized information to consumers in a retail environment. A personalized price is determined for at least one product that is in physical proximity to the customer. The physical proximity may be determined, for example, by evaluating identifiers, such as RHD tags, associated with each customer and product. A personalized message can be also presented to a customer regarding at least one product that is in physical proximity to the customer. The personalized message can provide, for example, dietary or safety information relevant to the customer and at least one product in the proximity of the customer. The personalized information may be determined based on one or more personalized message rules.

Abstract: A system and method to facilitate communications among team members or subscribers using a virtual assistant and a derived points of contact (POC) list to initiate contacts. The virtual assistant automates the task of reaching a party to be contacted through trying a variety of POC options until a connection is established or the options are exhausted. The system takes advantage of the POC provided by a person to be contacted, reducing the burden of maintaining a complete personal contact list by each team member or subscriber. The virtual assistant uses the derived POC list to establish a preferred method for contacting a team member or subscriber.

Abstract: A system (and method) includes a sound monitoring system, a detector for identifying a source of the sound, a computing system for processing the sound to trigger a notification event, and a user interface system for notifying at least one individual responsible for the sound.

Abstract: A method and structure for a tiled interface system provides a Tiled User Interface (TUI) in which a tile manager manages at least one tile cluster on a display device and translates an input event into a tile cluster event and at least one tile cluster controlled by the tile manager to be displayed on the display device. Each tile cluster includes at least one tile, each tile cluster corresponds to one or more predefined functions for a specific application, each tile cluster provides a complete interaction of all the predefined functions for the specific application respectively corresponding to that tile cluster, and each tile cluster can be presented in its entirety on a single frame of the display device using at most one input event.

Abstract: A system (and method) includes a sound monitoring system, a detector for identifying a source of the sound, a computing system for processing the sound to trigger a notification event, and a user interface system for notifying at least one individual responsible for the sound.

Abstract: The present invention is a system for translating text from a first source language into a second target language. The system assigns probabilities or scores to various target-language translations and then displays or makes otherwise available the highest scoring translations. The source text is first transduced into one or more intermediate structural representations. From these intermediate source structures a set of intermediate target-structure hypotheses is generated. These hypotheses are scored by two different models: a language model which assigns a probability or score to an intermediate target structure, and a translation model which assigns a probability or score to the event that an intermediate target structure is translated into an intermediate source structure. Scores from the translation model and language model are combined into a combined score for each intermediate target-structure hypothesis.