Abstract: Systems and methods for spotting keywords in data packets are provided. In particular, input data is received to be searched for occurrences of a set of patterns, the input data being divided into multiple segments. Then the input data and the patterns are assigned to first and second pattern matching algorithms, the first pattern matching algorithm is configured to search only within each of the segments, and the second pattern matching algorithm is configured to search across boundaries between adjacent segments. Then the input data is searched using the first and second pattern matching algorithms.

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: A plurality of pairs of video cameras and interrogation devices may be placed in a public place along various paths that a person-of-interest might be expected to move. The person-of-interest is then located in multiple images acquired, collectively, by multiple video cameras. From each of the interrogation devices that are paired with these video cameras, a subset of the captured identifiers is obtained. Candidate identifiers are then restricted to those identifiers that are included in each of the subsets. A given identifier may be rejected as a candidate identifier. To automatically locate the person-of-interest in the images acquired by the “paired” video cameras, a processor may utilize video-tracking techniques to automatically track the person-of-interest, such that the person-of-interest is not “lost.” By virtue of utilizing such tracking techniques, the person-of-interest may be repeatedly located automatically, and with minimal chance of a false detection.

Abstract: Methods and systems for monitoring activity on a local area networks (LAN). In particular, embodiments described herein provide systems and methods for associating packets with the devices from which they were communicated, despite the obfuscatory behavior of any network address translators (NAT). A processor first receives packets that were collectively communicated, by a plurality of devices, via a NAT-serviced LAN. The processor aggregates the packets into multiple packet aggregations on a per device basis. Fields that are contained in the respective packet headers of the packets are used. The packet aggregations may be grouped. The embodiments use unencrypted lower-level information (including, for example, IPIDs and domain names), such that aggregation and grouping may be successfully performed even if information in the application layer is encrypted.

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: Systems and methods of automated ontology development include a corpus of communication data. The corpus of communication data includes communication data from a plurality of interactions and is processed. A plurality of terms are extracted from the corpus. Each term of the plurality is a plurality of words that identify a single concept within the corpus. An ontology is automatedly generated from the extracted terms.

Abstract: Methods and systems for decoding monitored communication signals using previously identified side information. Information, which is used for decoding a given frame and is provided to the decoder not via the main communication channel between a base station and a mobile station, is referred to herein as “side information.” The side information can also be viewed as extrinsic information that was derived during previous decoding operations. The monitoring system holds, for certain frames, a-priori information of one or more data values that are expected in these frames. Decoding using this a-priori information enables an Error Correcting Code decoder to successfully decode such frames, which would otherwise fail to decode.

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: 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: 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: 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: 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: 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 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: Voice activity detection (VAD) is an enabling technology for a variety of speech based applications. Herein disclosed is a robust VAD algorithm that is also language independent. Rather than classifying short segments of the audio as either “speech” or “silence”, the VAD as disclosed herein employees a soft-decision mechanism. The VAD outputs a speech-presence probability, which is based on a variety of characteristics.

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: Methods and systems for range matching. The system holds a definition of one or more ranges of Internet Protocol (IP) addresses. The definition may specify any desired number of ranges of any suitable size, and some ranges may overlap one another or be contained in one another. The definition may also specify certain returned values and/or relative priorities for the various ranges. In a pre-processing phase, a hash table that is subsequently queried with addresses to be range-matched. The hash table may be updated at run-time. During operation, the system receives addresses (e.g., extracts addresses from monitored communication traffic) and identifies by querying the hash table, for each address, whether the address falls within any of the ranges.

Abstract: A monitoring system monitors traffic flows that are exchanged over a communication network. The system characterizes the flows in terms of their temporal traffic features, and uses this characterization to identify communication devices that participate in the same communication session. By identifying the communication devices that serve as endpoints in the same session, the system establishes correlations between the users of these communication devices. The monitoring system characterizes the flows using traffic features such as flow start time, flow end time, inter-burst time and burst size, and/or statistical properties of such features. The system typically generates compressed-form representations (“signatures”) for the traffic flows based on the temporal traffic features, and finds matching flows by finding similarities between signatures.

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.