Verint Systems, Inc.

Roni Romano

Rahm Fehr

Yair Horesh

Omer Ziv

Jeremie Dreyfuss

Galia Zacay

Ido Shapira

Ran Achituv

Daniel Mark Baum

Uri Segal

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.

Labeling/names of themes

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.

System and method of text zoning

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.

Call summary

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.

System and method of automated model adaptation

An embodiment of the method of processing communication data to identify one or more themes within the communication data includes identifying terms in a set of communication data, wherein a term is a word or short phrase, and defining relations in the set of communication data based on the terms, wherein the relation is a pair of terms that appear in proximity to one another. The method further includes identifying themes in the set of communication data based on the relations, wherein a theme is a group of one or more relations that have similar meanings, and storing the terms, the relations, and the themes in the database.

Themes surfacing for communication data analysis

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.

Automated ontology development

Systems, methods, and media for developing ontologies and analyzing communication data are provided herein. In an example implementation, the method includes: identifying terms in in a set of communication data; identifying a list of possible relations of the identified terms; scoring the possible relations according to a set of predefined merits; ranking the possible relations into a list of possible relations in descending order according to their score; and tagging relations in the set of communication data. The relations may be tagged by identifying the possible relations in the communication data in order corresponding with the list of possible relations. The possible relations that have lower rankings that conflict with higher ranking relations are not tagged. The conflicts may be determined by a predefined set of conflict criteria.

Tagging relations with N-best

Labeling/naming of themes

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. 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. Based on the self-training mechanism developed by the inventors, 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.

Growing community of inventors

Even Yehuda, Israel

Roni Romano