Abstract: By formulizing a specific company's internal knowledge and terminology, the ontology programming accounts for linguistic meaning to surface relevant and important content for analysis. The ontology is built on the premise that meaningful terms are detected in the corpus and then classified according to specific semantic concepts, or entities. Once the main terms are defined, direct relations or linkages can be formed between these terms and their associated entities. Then, the relations are grouped into themes, which are groups or abstracts that contain synonymous relations. The disclosed ontology programming adapts to the language used in a specific domain, including linguistic patterns and properties, such as word order, relationships between terms, and syntactical variations. The ontology programming automatically trains itself to understand the domain or environment of the communication data by processing and analyzing a defined corpus of communication data.

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 faster and more streamlined system for providing summary and analysis of large amounts of communication data is described. System and methods are disclosed that employ an ontology to automatically summarize communication data and present the summary to the user in a form that does not require the user to listen to the communication data. In one embodiment, the summary is presented as written snippets, or short fragments, of relevant communication data that capture the meaning of the data relating to a search performed by the user. Such snippets may be based on theme and meaning unit identification.

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 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: Systems, methods, and media for the automated removal of private information are provided herein. In an example implementation, a method for automatic removal of private information may include: receiving a transcript of communication data; applying a private information rule to the transcript in order to identify private information in the transcript; tagging the identified private information with a tag comprising an identification of the private information; applying a complicate rule to the tagged transcript in order to evaluate a compliance of the transcript with privacy standards; removing the identified private information from the transcript to produce a redacted transaction; and storing the redacted transcript.

Abstract: Methods, systems, and computer readable media for automated transcription model adaptation includes obtaining audio data from a plurality of audio files. The audio data is transcribed to produce at least one audio file transcription which represents a plurality of transcription alternatives for each audio file. Speech analytics are applied to each audio file transcription. A best transcription is selected from the plurality of transcription alternatives for each audio file. Statistics from the selected best transcription are calculated. An adapted model is created from the calculated statistics.

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: 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. 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: Tracking of targets in video captured by a multi-camera surveillance system is often difficult due to incomplete camera coverage and due to the complexities associated with automated recognition in a dense and highly variable environment. The present disclosure embraces a system and method for target tracking in a multi-camera surveillance system that reduces the time required for a user to track a target by offering computer generated search results that include suggestions of candidates found in the video that match (to some degree) the target. When one of the candidates is selected by a user, the search is shifted to other cameras and continued. In this way, the target may be tracked quickly. Further, a playlist of video snippets of the target is accumulated during the search and can be played to show all captured video of the target moving about the facility.

Abstract: A method of zoning a transcription of audio data includes separating the transcription of audio data into a plurality of utterances. A that each word in an utterances is a meaning unit boundary is calculated. The utterance is split into two new utterances at a work with a maximum calculated probability. At least one of the two new utterances that is shorter than a maximum utterance threshold is identified as a meaning unit.

Abstract: Software, systems, and methods for recording a scene encompassing a transaction, such as a point-of-sale transaction, are presented. In one implementation of a method, the scene is recorded to a data storage system using a first video camera capable of capturing protected data associated with the transaction. While recording the scene using the first camera, and in response to receiving a first trigger, the recording of the scene is continued using a second video camera not capable of capturing the protected data. While then recording the scene using the second camera, and in response to receiving a second trigger, the recording of the scene is continued using the first video camera. The first and second timing triggers are initiated based on at least one event associated with the transaction.

Abstract: Embodiments disclosed herein provide systems and methods for performing video recorder failover. In a particular embodiment, a system for handing a failover of a first Network Video Recorder (NVR) is provided. The system includes a second NVR that receives a video stream and temporarily stores an amount of the video stream to the temporary storage, wherein the amount of the video stream stored in the temporary storage at any given time corresponds to a duration of time sufficient to accommodate a failover of the first NVR to the second NVR. In response to a detection of a failure of the first NVR, the second NVR records the video stream to the second long-term storage and transfers at least a portion of the video stream stored in the temporary storage to the second long-term storage.

Abstract: A method for developing an ontology for practicing communication data, wherein the ontology is a structural representation of language elements and the relationship between those language elements within the domain, includes providing a training set of communication data and processing the training set of communication data to identify terms within the training set of communication data, wherein a term is a word or short phrase. The method further includes utilizing the terms to identify relations within the training set of communication data, wherein a relation is a pair of terms that appear in proximity to one another. Finally, the terms in the relations are stored in a database.

Abstract: Video data from sources, such as cameras, is analyzed to create metadata descriptive of the events and objects occurring in the video. This metadata, which consists of data about the video, is then analyzed on a transaction basis to determine if a suspicious activity, such as a fraudulent Point of Sale (POS) return event, has occurred in relation to a transaction.

Abstract: Models are generated from objects identified in video. Each model is evaluated based on knowledge of the objects determined from video analysis, and preferred models are identified based on the evaluations. In some examples, each model could be evaluated by tracking a movement of each object in the video by using each model to track the object from which it was generated, evaluating an ability of each model to identify the objects in the video that are similar to the object from which it was generated, and determining an amount of false identifications made by each model of different objects in different video that does not include the object from which it was generated.

Abstract: The disclosed solution uses machine learning-based methods to improve the knowledge extraction process in a specific domain or business environment. By formulizing a specific company's internal knowledge and terminology, the ontology programming accounts for linguistic meaning to surface relevant and important content for analysis. Based on the self-training mechanism developed by the inventors, the ontology programming automatically trains itself to understand the business environment by processing and analyzing a defined corpus of communication data. For example, the disclosed ontology programming adapts to the language used in a specific domain, including linguistic patterns and properties, such as word order, relationships between terms, and syntactical variations. The disclosed system and method further relates to leveraging the ontology to assess a dataset and conduct a funnel analysis to identify patterns, or sequences of events, in the dataset.

Abstract: Embodiments disclosed herein provide systems and methods for performing video recorder failover. In a particular embodiment, a system includes a first Network Video Recorder (NVR) configured to receive a video stream and record the video stream to a first long-term storage. The system further includes a second NVR configured to receive the video stream and temporarily stores an amount of the video stream in temporary storage. In response to a detection of a failure of the first NVR, the second NVR is configured to record the video stream to the second long-term storage and transfer at least a portion of the video stream stored in the temporary storage corresponding to the amount of time between detection of the failure and when the second NVR began recording the video stream to the second long-term storage.