Adobe, Inc.

Microsoft Technology Licensing, LLC

Shivani Gupta

Gaurush Hiranandani

Michael Jay Gilmore

Natwar Modani

Stephanie Horn

Pranav Ravindra Maneriker

Atanu R Sinha

Chakkaradeep Chinnakonda Chandran

Utpal

Ridhima Gupta

Sarah Zaki

Tarun Malik

Tiffany Michelle Smith

Vaishnavi Subramanian

Pranjal Saxena

Anshul Agrawal

Charanjit Singh Ghai

Techniques performed by a data processing system for a machine learning driven teleprompter include displaying a teleprompter transcript associated with a presentation on a display of a computing device associated with a presenter; receiving audio content of the presentation including speech of the presenter in which the presenter is reading the teleprompter transcript; analyzing the audio content of the presentation using a first machine learning model to obtain a real-time textual translation of the audio content, the first machine learning model being a natural language processing model trained to receive audio content including speech and to translate the audio content into a textual representation of the speech; analyzing the real-time textual representation and the teleprompter transcript with a second machine learning model to obtain transcript position information; and automatically scrolling the teleprompter transcript on the display of the computing device based on the transcript position information.

Machine learning driven teleprompter

Multimedia document summarization techniques are described. That is, given a document that includes text and a set of images, various implementations generate a summary by extracting relevant text segments in the document and relevant segments of images with constraints on the amount of text and number/size of images in the summary.

Multimedia document summarization

Target audience content interaction techniques are described. In one or more implementations, a plurality of content is quantified by one or more computing devices as a content feature representation for each of the plurality of content. A plurality of content feature clusters are generated by the one or more computing devices based at least in part on similarity of the content feature representations, one to another. Content cluster interaction data is also created by the one or more computing devices that describes interaction of each of a plurality of users with the plurality of content. A plurality of user interaction clusters are calculated from the content cluster interaction data by the one or more computing devices based at least in part on similarity of the content cluster interaction data of the plurality of users, one to another. The calculated plurality of user interaction clusters are employed by the one or more computing devices to determine content preferences of a target audience.

Target audience content interaction quantification

A quality metric of a multimedia summary of a multimedia content item is determined based, in part, on semantic similarities of the summary and content item, rather than just on word frequencies. This is accomplished in some embodiments by identifying a semantic meaning of the summary and multimedia content item using vector analysis. The vectors of the summary and the vectors of the multimedia content item are compared to determine semantic similarity. In other examples, the quality metric of the multimedia summary is determined based on, in part, a coherence between an image portion of a summary and a text portion of the summary for determining a quality metric of a multimedia summary.

Determining quality of a summary of multimedia content

A method and apparatus for performing object modifications by using change-specific executable files comprises accessing at least one modification stub associated with an object file, wherein the modification stub comprises an instruction for modifying the object file, wherein the instruction identifies an element in the object file and defines instructions for modifying the identified element; applying a selected modification stub to the object file; and modifying the object file per the instruction contained in the modification stub.

Method and system for performing object file modifications

Growing community of inventors

Greater Noida, India

Shivani Gupta