Abstract: A system and method for distinguishing the sentiment of utterances in a dialog is disclosed. The system utilizes a lexicon that is expanded from a seed using unsupervised machine learning. What results is a sentiment classifier that may be optimized for a variety of environments (e.g., conversation, chat, email, etc.), each of which may communicate sentiment differently.

Abstract: An apparatus and techniques for constructing and utilizing a “dynamic dictionary” that is not a compiled dictionary, and therefore does not need to be recompiled in order to be updated. The dynamic dictionary includes respective data structures that represent (i) a management automaton that includes a plurality of management nodes, and (ii) a runtime automaton that is derived from the management automaton and includes a plurality of runtime nodes. The runtime automaton may be used to search input data, such as communication traffic over a network, for keywords of interest, while the management automaton manages the addition of keywords to the dynamic dictionary. Typically, at least two (e.g., exactly two) such dynamic dictionaries are used in combination with a static dictionary.

Abstract: Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.

Abstract: Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. At least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcribed customer service interaction.

Abstract: Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.

Abstract: Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.

Abstract: Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.

Abstract: Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.

Abstract: Systems and methods diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.

Abstract: A packet broker that performs a health-status check of a proxy server while the proxy server processes one or more proxy connections. The packet broker may attempt to exchange a heartbeat signal with the proxy server, and if unsuccessful, the proxy server is assumed to be failing. In such cases, a failover is desirable. Rather than implementing a “hard” failover, in which no further communication packets are directed to the proxy server, a “soft” failover is performed where the packet broker prevents new proxy connections from being processed by the proxy server, but maintains at least one (e.g., all) of the current proxy connections that are being processed by the proxy server.

Abstract: A subject can be tracking using a plurality of physical video monitoring or image acquisition devices deployed in a delimited area. A map represents the delimited area. Icons representing the physical monitoring devices can be placed and configured on the map representing the delimited area. Some or all of the placed and configured video monitoring devices can be logically linked together to form scenes. The video feed from the physical video monitoring devices may be recorded in stable storage. A layout for display of the video feeds may be selected. Upon playing the video feed, whether live or recorded, a subject can be tracked through the delimited area by selecting one of the video monitoring devices in one of the available scenes.

Abstract: Methods for obtain identifiers, such as International Mobile Subscriber Identities (IMSIs) and International Mobile Station Equipment Identities (IMEIs), of mobile communication terminals, and associate these identifiers with other items of identifying information provided by users of the terminals. A local interrogation device may be installed that imitates a legitimate base station belonging to a cellular network, at a control checkpoint. Local interrogation devices are connected to a global interrogation device in a hierarchical network, whereby the local interrogation devices are assigned a priority that is higher than that of the global interrogation device. The global interrogation device provides cellular coverage to a larger area that contains the control checkpoints, while the local interrogation devices provide more localized cellular coverage to the control checkpoints.

Abstract: Systems and methods of script identification in audio data obtained from audio data. The audio data is segmented into a plurality of utterances. A script model representative of a script text is obtained. The plurality of utterances are decoded with the script model. A determination is made if the script text occurred in the audio data.

Abstract: A method includes moving an interrogation device through multiple geographical positions. At each geographical position of the interrogation device, a wireless communication terminal is solicited to establish temporary communication with the interrogation device, and a distance value indicative of a distance between the wireless communication terminal and the interrogation device as well as a velocity of the interrogation device is estimated using the temporary communication. A geographical location of the wireless communication terminal is calculated based on multiple estimated distance values and velocities of the interrogation device corresponding to the respective multiple geographical positions of the interrogation device.

Abstract: A monitoring system monitors authentication sessions both on the air interface between the terminals and the network, and on at least one wired network-side interface between network-side elements of the network. The monitoring system constructs a database of sets of network-side authentication parameters using network-side monitoring. Each set of network-side authentication parameters originates from a respective authentication session and is associated with the International Mobile Station Identity (IMSI) of the terminal involved in the session. In order to start decrypting the traffic of a given terminal, the system obtains the off-air authentication parameters of that terminal using off-air monitoring, and finds an entry in the database that matches the air-interface authentication parameters. From the combination of correlated network-side and off-air authentication parameters, the processor is able to extract the parameters needed for decryption.

Abstract: Systems and method of diarization of audio files use an acoustic voiceprint model. A plurality of audio files are analyzed to arrive at an acoustic voiceprint model associated to an identified speaker. Metadata associate with an audio file is used to select an acoustic voiceprint model. The selected acoustic voiceprint model is applied in a diarization to identify audio data of the identified speaker.

Abstract: A video system for video surveillance or video monitoring includes a video source, a video control system, and a video management system. The video management system is configured to receive configuration data and generate maps based on the configuration data, wherein at least one of the maps is associated with the video source. The video management system displays the map associated with the video source and displays a graphical symbol on the map which indicates the location of the video source. The video management system then receives a selection of the video source, receives information associated with the selected video source, and displays the received information.

Abstract: Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.

Abstract: Systems and method of diarization of audio files use an acoustic voiceprint model. A plurality of audio files are analyzed to arrive at an acoustic voiceprint model associated to an identified speaker. Metadata associate with an audio file is used to select an acoustic voiceprint model. The selected acoustic voiceprint model is applied in a diarization to identify audio data of the identified speaker.

Abstract: Web pages may be rendered from a main page data element and a plurality of embedded data elements, which are separately fetched by a browser. Herein is provided a web decoder which includes a learning engine adapted to receive human indications of data elements which are unimportant and accordingly to adjust the web decoder's procedures for determining which data elements are displayed to the user. The learning engine may receive human indications of important data elements and uses both types of indications in its further determinations. Optionally, rule generalizations are performed in a manner which searches for parameters which differentiate between important and unimportant data elements. The rule generalizations optionally concentrate on groups of data elements having at least a predetermined number of parameters having the same values for both important and unimportant data elements, reducing the chances that a generalization rule will find important data elements as unimportant.