Intel Corporation

Thomson Reuters (markets) LLC

Zoominfo Converse LLC

Orgad Keller

Yael Karov

Reshef Shilon

Micha Breakstone

Eric Shellef

Russell Levy

Micha Yochanan Breakstone

Yehonatan Aumann

Erez Volk

Raphael Cohen

Ilana Tuil

Amit Ashkenazi

Ran Shlivinski

A speaker identification system ('system') automatically assigns a speaker to voiced segments in a call. The system identifies one or more speakers in a call using one or more speaker-identification parameters. The system processes the call to determine one or more speaker-identification parameters, such as a transcript of the call, a facial image of the speaker, a scene image, which is an image of a scene in which the speaker is located during the call, or textual data associated with the call such as names of the speaker or an organization that are retrieved from the scene images or video data of the call. The system analyzes one or more of the speaker-identification parameters and determines the identity of the speaker. The system then identifies the voice segments associated with the identified speaker and marks the voice segments with the identity of the speaker.

Automatic speaker identification in calls using multiple speaker-identification parameters

Technologies for natural language request processing include a computing device having a semantic compiler to generate a semantic model based on a corpus of sample requests. The semantic compiler may generate the semantic model by extracting contextual semantic features or processing ontologies. The computing device generates a semantic representation of a natural language request by generating a lattice of candidate alternative representations, assigning a composite weight to each candidate, and finding the best route through the lattice. The composite weight may include semantic weights, phonetic weights, and/or linguistic weights. The semantic representation identifies a user intent and slots associated with the natural language request. The computing device may perform one or more dialog interactions based on the semantic request, including generating a request for additional information or suggesting additional user intents. The computing device may support automated analysis and tuning to improve request processing. Other embodiments are described and claimed.

Self-learning statistical natural language processing for automatic production of virtual personal assistants

The present invention provides a corpus-independent method for determining relevancy of terms to content of text appearing in a document by analyzing the document itself. Conventional information extraction, or other methods, may be applied to a document to generate a list of terms. The invention analyzes the document using relevancy scoring algorithms to determine a term relevancy score representing the term's relevance to the text contained in the document. The scores, including an aggregate score, may be normalized in the process. Based on relevancy scoring, terms are then ranked and further processed. In this manner relevancy is determined based on the subject document itself and by analyzing the occurrences and locations of the terms within the document. Additional techniques may be applied to relate the relevancy scores generated by the present invention to a corpus or collection of documents.

Method and system for determining relevance of terms in text documents

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Tel Aviv, Israel

Orgad Keller