Evernote Corporation

Bending Spoons S.p.a.

Eugene Livshitz

Alexander Pashintsev

Boris Gorbatov

Vitaly Glazkov

Natalia Galaktionova

Ilya Buryak

Ilia Buriak

P J Tanzillo

Andrew Henderson Malcolm

Daniel Nicolae

Ruben Bakker

Dylan Marriott

Zdzislaw Pawel Losvik

Chris O'Neill

Phil Libin

Alex Pachikov

Jeffrey Zwerner

Keith Lang

Juan Carlos Jimenez

Alexandre Pashintsev

Vitaly Glaskov

Selecting objects in a video stream of a smart phone includes detecting quiescence of frame content in the video stream, detecting objects in a scene corresponding to the frame content, presenting at least one of the objects to a user of the smart phone, and selecting at least one of the objects in a group of objects in response to input by the user. Detecting quiescence of frame content in the video stream may include using motion sensors in the smart phone to determine an amount of movement of the smart phone. Detecting quiescence of frame content in the video stream may include detecting changes in view angles and distances of the smart phone with respect to the scene. Detecting objects in a scene may use heuristics, custom user preferences, and/or specifics of scene layout. At least one of the objects may be a person or a document.

Dynamic targeting of preferred objects in video stream of smartphone camera

A method and system for selecting data from a source text corpus for training a semantic data analysis system. The method includes selecting an item of the text corpus, wherein the item includes at least one section. The method includes extracting a section of the at least one section of the item. The method also includes determining a length of the section of the at least one section of the item. Based on the length of the section being greater than a predetermined amount, the method includes subdividing the section into a plurality of fragments. Each fragment of the plurality of fragments is deemed to be similar to each other. Further, the method includes building a training set based on the plurality of fragments. The training set is used to train the semantic data analysis system.

Building training data and similarity relations for semantic space

This application is directed to a method for automatically identifying and transferring relevant image data. The method includes obtaining a plurality of content items from a personal content collection and determining attributes based on the plurality of content items. The method includes generating a plurality of relevance rules. The method further includes obtaining unclassified content items and determining for a first unclassified content item a plurality of aggregate relevance scores using the plurality of relevance rules. The method include determining whether a first aggregate relevance score and/or a second aggregate relevance score satisfy threshold score. The method includes, in accordance with a determination that the first aggregate relevance score and the second aggregate relevance score do not satisfy the threshold score, forgoing determining the attributes corresponding to the first unclassified content item, and storing the first unclassified content item in a candidate list of the personal content collection.

Automatic detection and transfer of relevant image data to content collections

Searching for documents includes retrieving objects from a physical media image using a camera from a smartphone, a user selecting a subset of the objects, forming a search query based on the subset of objects, and applying the search query to a search engine to search for the documents. Retrieving objects from a media image may include waiting for a view of the camera to stabilize. Waiting for the view of the camera to stabilize may include detecting changing content of a video flow provided to the camera and/or using motion sensors of the camera to detect movement. Retrieving objects may include the smartphone identifying possible subsets of objects in the media image. The user selecting a subset of the objects may include the smartphone presenting at least some of the possible subsets to the user and the user selecting one of the possible subsets.

Restoring full online documents from scanned paper fragments

Methods and systems for training a neural network to distinguish between text documents and image documents are described. A corpus of text and image documents is obtained. A page of a text document is scanned by shifting a text window to a plurality of locations. In accordance with a determination that the text in the window at a respective location meets text line criteria, the text in the window is stored as a respective text snippet. A plurality of image windows are superimposed over at least one page of an image document. In accordance with a determination that the content of a respective image window meets image criteria, content of the image window is stored as a respective image snippet. The respective text snippet and the respective image snippet are provided to a classifier.

Fast identification of text intensive pages from photographs

This application is directed to recognizing unstructured information based on hints provided by structured information. A computer system obtains unstructured information collected from a handwritten or audio source, and identifies one or more terms from the unstructured information. The one or more terms includes a first term that is ambiguous. The computer system performs a recognition operation on the first term to derive a first plurality of candidate terms for the first term, and obtains first contextual information from an information template associated with the unstructured information. In accordance with the first contextual information, the computer system selects a first answer term from the first plurality of candidate terms, such that the first term is recognized as the first answer term.

Extracting structured data from handwritten and audio notes

This application is directed to vectoring a raster image in which an electronic device detects a contour of a component in the raster image, builds tangent vectors for each point of the contour and identifies a plurality of segmentation points on the contour. One or more points of sharp angle are identified on the contour in accordance with a determination that each point of sharp angle corresponds to two distinct tangent vectors and that an angle between the two distinct tangent vectors falls below a predefined threshold. A respective one of the segmentation points is positioned at each identified point of shape angle. The electronic device approximates a piecewise smooth fitting curve (e.g., a piecewise Bezier curve) having two or more fitting segments to connect the plurality of segmentation points on the contour. The piecewise smooth fitting curve is thereby provided to vectorize the raster image.

Coordinated piecewise Bezier vectorization

This application is directed to a method for automatically identifying and transferring relevant image data implemented at a server with a processor and memory including one or programs executable by the processor. The server obtains content items from a personal content collection of a user, analyzes the content items to identify attributes associated with the images, generates rule sets based on the identified attributes, applies the generated rule sets to a plurality of unclassified images, identifies a first set of one or more of the unclassified images, associates each image of the first set with the content items and stores the first set in the personal content collection. A second set of unclassified images is placed in a candidate list to be periodically, without user intervention, monitored and updated for storing in the personal content collection. The generated rule sets are updated after images are stored in the content collection.

Selecting data from a source text corpus for training a semantic data analysis system includes selecting an item of the text corpus, validating the item, extracting at least one section of the item, determining a length of each of the at least one section of the item, and subdividing each of the sections having a length greater than a predetermined amount into a plurality of fragments that are deemed to be similar. The predetermined amount may be approximately twice a size of a fragment. A fragment may have approximately 100 words or between 40 and 60 words. Fragments from different items may be deemed to be dissimilar. Sections having a length less than the predetermined amount may be ignored. Validating the item may include parsing editorial notes and other accompanying data. The source text corpus may be Wikipedia. The item may be an article.

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San Mateo, CA, United States of America

Eugene Livshitz