Abstract: A framework is provided for obtaining window information. The window information can be applied to different assignment models to assign windows to different groups. A group may correspond to a task being performed by a user. The window information can be semantic or temporal information captured as window events and properties of windows whose events are captured. Temporal information can be information about switches between windows. Semantic information can be window titles. Temporal information, semantic information, or both, can be used to assign windows to groups.

Abstract: A framework is provided for obtaining window information. The window information can be applied to different assignment models to assign windows to different groups. A group may correspond to a task being performed by a user. The window information can be semantic or temporal information captured as window events and properties of windows whose events are captured. Temporal information can be information about switches between windows. Semantic information can be window titles. Temporal information, semantic information, or both, can be used to assign windows to groups.

Abstract: A framework is provided for obtaining window information. The window information can be applied to different assignment models to assign windows to different groups. A group may correspond to a task being performed by a user. The window information can be semantic or temporal information captured as window events and properties of windows whose events are captured. Temporal information can be information about switches between windows. Semantic information can be window titles. Temporal information, semantic information, or both, can be used to assign windows to groups.

Abstract: A framework is provided for obtaining window information. The window information can be applied to different assignment models to assign windows to different groups. A group may correspond to a task being performed by a user. The window information can be semantic or temporal information captured as window events and properties of windows whose events are captured. Temporal information can be information about switches between windows. Semantic information can be window titles. Temporal information, semantic information, or both, can be used to assign windows to groups.

Abstract: A framework is provided for obtaining window information. The window information can be applied to different assignment models to assign windows to different groups. A group may correspond to a task being performed by a user. The window information can be semantic or temporal information captured as window events and properties of windows whose events are captured. Temporal information can be information about switches between windows. Semantic information can be window titles. Temporal information, semantic information, or both, can be used to assign windows to groups.

Abstract: A procedural inference system is described herein that infers procedural knowledge from various data sources to help a user complete one or more tasks for which the data sources provide information. The system understands users' queries, identifies a task at hand, provides recommendations on the steps to take and the agents to use based on a knowledge base of tasks and agents, and provides the fabric to determine which different agents can work together to help the user accomplish a task. Tasks can be started on one device and completed on another seamlessly. Users are able to finish complex, multi-step tasks efficiently, without trial and error or data reentry. Thus, the procedural inference system provides a generalized framework that helps users to complete tasks using already available data and does not ask each data provider to invest in infrastructure to build dedicated task information systems.

Abstract: A procedural inference system is described herein that infers procedural knowledge from various data sources to help a user complete one or more tasks for which the data sources provide information. The system understands users' queries, identifies a task at hand, provides recommendations on the steps to take and the agents to use based on a knowledge base of tasks and agents, and provides the fabric to determine which different agents can work together to help the user accomplish a task. Tasks can be started on one device and completed on another seamlessly. Users are able to finish complex, multi-step tasks efficiently, without trial and error or data reentry. Thus, the procedural inference system provides a generalized framework that helps users to complete tasks using already available data and does not ask each data provider to invest in infrastructure to build dedicated task information systems.

Abstract: A regression-based residual echo suppression (RES) system and process for suppressing the portion of the microphone signal corresponding to a playback of a speaker audio signal that was not suppressed by an acoustic echo canceller (AEC). In general, a prescribed regression technique is used between a prescribed spectral attribute of multiple past and present, fixed-length, periods (e.g., frames) of the speaker signal and the same spectral attribute of a current period (e.g., frame) of the echo residual in the output of the AEC. This automatically takes into consideration the correlation between the time periods of the speaker signal. The parameters of the regression can be easily tracked using adaptive methods. Multiple applications of RES can be used to produce better results and this system and process can be applied to stereo-RES as well.

Abstract: A regression-based residual echo suppression (RES) system and process for suppressing the portion of the microphone signal corresponding to a playback of a speaker audio signal that was not suppressed by an acoustic echo canceller (AEC). In general, a prescribed regression technique is used between a prescribed spectral attribute of multiple past and present, fixed-length, periods (e.g., frames) of the speaker signal and the same spectral attribute of a current period (e.g., frame) of the echo residual in the output of the AEC. This automatically takes into consideration the correlation between the time periods of the speaker signal. The parameters of the regression can be easily tracked using adaptive methods. Multiple applications of RES can be used to produce better results and this system and process can be applied to stereo-RES as well.

Abstract: Acoustic echo cancellation, residual echo suppression and sound-source localization/microphone array processes are combined in a two-way voice communication system that uses a microphone array to capture local speech. The processes can be configured according to various alternative architectures and enhancements made to the processes to avoid the adverse effects of non-linear operations in the residual echo suppression on the sound-source localization/microphone array process.

Abstract: A system that facilitates organization of emails comprises a clustering component that clusters a plurality of emails and creates topics for emails by assigning key phrases extracted from emails within one or more clusters. An organization component then utilizes the key phrases to organize documents. Furthermore, the organization component can comprise a probability component that determines a probability that a document belongs to a certain topic.

Abstract: A system for browsing items related to an email comprises a grouping component that groups items into a plurality of sets of related items. A tagging component associates one or more tags with each of the sets of related items, and an email display component displays an email and one or more tags associated with the displayed email. A related item display component receives a user selection of at least one of the one or more tags and displays one or more items related to the displayed email based at least in part upon the user selection.

Abstract: A keyphrase extraction system and method are provided. The system and method can be employed to create an automatic summary of a subset of document(s). The system can automatically extract a list of keyword(s) that can operate on multiple documents, and across many different domains. The system is unsupervised and requires no prior learning. A term identifier identifies candidate terms (e.g., words and/or phrases) in the document subset which are used to form a document-term matrix. A probability computation component calculates probability values of: (1) the joint probability of a word (e.g., term) and a document, (2) the marginal probability of the word (e.g., term), and (3) the marginal probability of the document. Based on the probability values, a partial mutual information metric can be calculated for each candidate term. Based on the partial mutual information metric, one or more of the terms can be identified as summary keyphrases.

Abstract: A regression-based residual echo suppression (RES) system and process for suppressing the portion of the microphone signal corresponding to a playback of a speaker audio signal that was not suppressed by an acoustic echo canceller (AEC). In general, a prescribed regression technique is used between a prescribed spectral attribute of multiple past and present, fixed-length, periods (e.g., frames) of the speaker signal and the same spectral attribute of a current period (e.g., frame) of the echo residual in the output of the AEC. This automatically takes into consideration the correlation between the time periods of the speaker signal. The parameters of the regression can be easily tracked using adaptive methods. Multiple applications of RES can be used to produce better results and this system and process can be applied to stereo-RES as well.

Abstract: The present invention provides systems and methods for signal detection and enhancement. The systems and methods utilize one or more discriminative classifiers (e.g., a logistic regression model and a convolutional neural network) to estimate a posterior probability that indicates whether a desired signal is present in a received signal. The discriminative estimators generate the estimated probability based on one or more signal-to-noise ratio (SNRs) (e.g., a normalized logarithmic posterior SNR (nlpSNR) and a mel-transformed nlpSNR (mel-nlpSNR)) and an estimated noise model. Depending on the resolution desired, the estimated SNR can be generated at a frame level or at an atom level, wherein the atom level estimates are utilized to generate the frame level estimate. The novel systems and methods can be utilized to facilitate speech detection, speech recognition, speech coding, noise adaptation, speech enhancement, microphone arrays and echo-cancellation.