Gong I.o Ltd.

Inbal Horev

Omri Allouche

Raquel Sitman

Eilon Reshef

Hanan Shteingart

Shlomi Medalion

Eyal Ben David

Adi Kopilov

Igal Grinis

Ortal Ashkenazi

Yoav Madorsky

Raz Nussbaum

A system and method for a multi-stage approach for creating topic models is presented. The method includes applying a first stage topic model to textual data, wherein the first stage topic model is trained to discover a first plurality of topics and distributions of words in each topic of the first plurality of topics from the textual data; generating at least one seeded word for a subset of topics of the first plurality of topics, wherein the at least one seeded word is determined based on a plurality of selection rules and the distributions of words in the subset of topics discovered in the first stage topic model; and creating a second stage topic model by feeding the generated at least one seeded word to direct identification of the subset of topics of the first plurality of topics.

System and method for rapid initialization and transfer of topic models by a multi-stage approach

A system and method for generating a multi-label classifier of textual data is presented. The method includes training a plurality of single-label classifiers, wherein each of the plurality of single-label classifiers is trained to classify textual data to a single predefined revenue-based label; and training the multi-label classifier using labeled data output by the plurality of single-label classifiers, wherein the multi-label classifier is trained to classify textual data to a vector including revenue-based labels and their respective classification scores.

System and method for generating a multi-label classifier for textual communications

A method for information processing includes computing, over a corpus of conversations, a conversation structure model including (i) a sequence of conversation parts having a defined order, and (ii) a probabilistic model defining each of the conversation parts. For a given conversation, a segmentation of the conversation is computed based on the computed conversation structure model. Action is taken on the given conversation according to the segmentation.

Unsupervised automated extraction of conversation structure from recorded conversations

Methods for generating a categorized, ranked, condensed summary of a transcript of a conversation, involving obtaining a diarized version of the transcript of the conversation, storing textual monologues from the transcript, determining classifications as to the textual monologues based on a classifier algorithm, associating the classifications with the textual monologues, creating textually-modified rephrasings of the textual monologues based on text and classification thereof, storing the textually-modified rephrasings, aggregating the textually-modified rephrasings based on associated clustering and scoring, and transmitting summary information pertaining to the aggregated textually-modified rephrasings to a user device.

Method for summarization and ranking of text of diarized conversations

Growing community of inventors

Tel Aviv, Israel

Inbal Horev