Intel Corporation

Elliot Smith

Victor Szilagyi

Stanley T Mo

Claudio J Ochoa

Gustavo D Domingo Yaguez

Max Waterman

Alok Barsode

Plamena Manolova

Karolina Kret

Mikael Metthey

Technologies for dynamic automated content discovery include a computing device that determines a contextual part of a document selected by a user and extracts one or more key terms from the contextual part of the document using an automated key phrase extraction algorithm. The computing device may perform a syntactic algorithm, named entity recognition, or the TextRank algorithm. The computing device may calculate a vagueness score for terms of the document by querying a semantic database and select the key terms based on the corresponding vagueness scores. The computing device performs a content search based on the key terms to generate one or more search results and presents the search results to the user. The computing device may associate each of the search results with the corresponding key term of the contextual part of the document, for example by visually highlighting the key term. Other embodiments are described and claimed.

Technologies for dynamic automated content discovery

Systems and methods for identifying and locating related content using natural language processing are generally disclosed herein. One embodiment includes an HTML5/JavaScript user interface configured to execute scripting commands to perform natural language processing and related content searches, and to provide a dynamic interface that enables both user-interactive and automatic methods of obtaining and displaying related content. The natural language processing may extract one or more context-sensitive key terms of text associated with a set of content. Related content may be located and identified using keyword searches that include the context-sensitive key terms. For example, text associated with video of a first content, such as text originating from subtitles or closed captioning, may be used to perform searches and locate related content such as a video of a second content, or text of a third content.

Real-time natural language processing of datastreams

Methods and systems may involve storing device-specific user preference data to a local device and receiving a real-time request from a remote device. One or more user coefficients may be used to filter the device-specific user preference data in response to the request. In one example, the user preference data includes keyword data and the filtered keyword data is used to discover and present information to the user via the remote device.

Collection and management of precision user preference data

In one example embodiment, a program navigation system involves a television (TV) set (or other audio/visual presentation device) and a mobile device. While the TV set is playing an audio/visual program, a keyword recognition module may extract keywords from textual data associated with the program. The textual data may include textual words that match words spoken in the program. Time markers for the keywords may also be saved. A time marker may indicate a play time within the program for the corresponding keyword. The TV set may automatically transmit the keywords to the mobile device, and the mobile device may automatically display the keywords in a user interface. A user may then select one of the keywords. In response, the mobile device may automatically cause the TV set to jump to a portion of the program containing the selected keyword. Other embodiments are described and claimed.

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Northfield, United Kingdom

Elliot Smith