Abstract: Sharing content includes verifying occurrence of an event, determining participants of the event, gathering related content generated by the participants, where the related content corresponds to the event, and disseminating the related content to the participants. The event may be a business meeting. Verifying occurrence of an event may include determining scheduled participants, scheduled location, and scheduled time of the event using an electronic calendar of one of the scheduled participants. Participants may include the scheduled participants and other participants that are not listed in the electronic calendar where the other participants attend the event. Verifying occurrence of an event may also include confirming that at least an event organizer is at the scheduled location at the scheduled time and there is at least one other one of the participants. The content may include documents, photos, scanned images, audio clips, video clips, typed notes, and/or handwritten notes.

Abstract: Integrating an application and a chat session includes a first user identifying content from the application, wherein the application is independent of any chat session, the first user initiating the chat session with at least a second user that is remote from the first user by transmitting the content from the first user to the second user, where the first and second user establish the chat session in response to the content being transmitted, and the first user and the second user sharing the content in connection with the chat session. The content may be a portion of a photograph, a portion of an image, a portion of text from a document, and/or a portion of a screen. The portion may be automatically identified following the user pointing and marking up the content. The first user may mark up the content before transmitting it to the second user.

Abstract: Displaying relevant data subsets of at least one data collection includes providing an input data subset, determining a plurality of partial relevance values for different characteristics of each of the plurality of data subsets of the at least one data collection with respect to the input data subset according to a term frequency of terms in the input data subset and term in each of the plurality of data subsets of the at least one data collection and according to an inverse document frequency of the terms, determining a relevance of each of the plurality of data subsets of the at least one data collection based on the partial relevance values of the different characteristics, and displaying at least a portion of the data subsets of the at least one data collection in order of relevance.

Abstract: 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.

Abstract: Protecting a fragment of a document includes automatically detecting the fragment without user intervention based on the content of the fragment and/or the context of the fragment within a set of documents, selectively encrypting the fragment to prevent unauthorized access, and providing an alternative view of the fragment that prevents viewing and access of content corresponding to the fragment unless a decryption password is provided. Automatically detecting the fragment may include detecting numbers and alphanumeric sequences of sufficient length that do not represent commonly known abbreviations, detecting generic terms, detecting proper names, detecting terms signifying a type of content, detecting mutual location of terms and sensitive content, and/or detecting user defined terms. The generic terms may correspond to password, passcode, credentials, user name, account, ID, login, confidential, and/or sensitive. The proper names may be names of financial organizations and security organizations.

Abstract: While receiving a user input of content of a first document, a computer system, dynamically and in real time, identifies relevant information items from a plurality of information feeds. Specifically, one or more other documents is obtained in a collection of documents that are relevant to the first document being entered by a user. When it is determined that changes in member documents of the set of one or other documents satisfies a stabilization criterion while receiving the user input, related terms are extracted from the content of the first document and the one or more other documents and used to identify the relevant information items including the related terms from the plurality of information feeds that is distinct from the collection of documents. The relevant information items are then presented on a display for the user.

Abstract: 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.

Abstract: 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.

Abstract: A computer system obtains access to a content collection that includes a plurality of content items. For each of the content items, the computer system determines user access history and context associated with the access history. The system builds a classifier characterizing a user access pattern of the content items. The system constantly monitors in real-time environmental parameters of the user. It infers a current context of the user based on the environmental parameters. In accordance with the current context, for each of the content items, the system extracts numeric feature values, evaluates the item using the classifier, and determines a score for the item. The system identifies in real-time a subset of the content items that is most relevant to the current context. The system further generates a preferred view of the subset and causes the preferred view to be delivered to a mobile device.

Abstract: A digital content clipping system receives an indication that a first web page has been accessed by a first user, and determines that the first web page has a first content type that is selected from a plurality of content types. While the first web page is being accessed by the first user, the digital content clipping system provides the user with a plurality of clipping options based on the first content type.

Abstract: A method of providing audio information from a meeting includes receiving a first audio stream from a first input audio device and a second audio stream from a second input audio device during the meeting, identifying a first audio fragment from the first audio stream, and identifying a second audio fragment from the second audio stream. The method also includes compiling the audio fragments from the first and second audio streams into an audio file that includes at least the first audio fragment and the second audio fragment. The method further includes providing the audio file to one or more recipients. The audio file identifies the first audio fragment as corresponding to a first participant of the meeting and the second audio fragment as corresponding to a second participant of the meeting.

Abstract: The various implementations described herein include methods, devices, and systems for obtaining and presenting personal and public search results. In one aspect, a method includes: (1) receiving a search query from a user; (2) extracting terms from the search query; (3) determining whether relevant notes exist within a personal database of the user by comparing the extracted terms to stored notes of the user within the personal database; (4) obtaining public search results for the search query; (5) obtaining search results information by analyzing the public search results; (6) determining whether additional relevant notes exist by comparing the search results information to the stored notes of the user; (7) preparing note information for the relevant notes; and (8) concurrently presenting at least a subset of the prepared note information and at least a subset of the public search results to the user.

Abstract: 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.

Abstract: 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.

Abstract: A computer system receives data that includes indications of a plurality of agenda items that correspond to the group event. During the group event, the system captures, by a media recording device, media data, converts the captured media data to a searchable version of the media data and searches the searchable version of the media data to determine a first time frame of the media data that corresponds to a first agenda item of the plurality of agenda items. The system displays, in a user interface that is accessible to at least one user associated with the group event, the representation of the media data. A first portion of the representation of the media data that corresponds to the first time frame is visually distinguished from at least a portion of the representation of the media data that does not correspond to the first time frame.

Abstract: 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.

Abstract: Integrating an application and a chat session includes a first user identifying content from the application, wherein the application is independent of any chat session, the first user initiating the chat session with at least a second user that is remote from the first user by transmitting the content from the first user to the second user, where the first and second user establish the chat session in response to the content being transmitted, and the first user and the second user sharing the content in connection with the chat session. The content may be a portion of a photograph, a portion of an image, a portion of text from a document, and/or a portion of a screen. The portion may be automatically identified following the user pointing and marking up the content. The first user may mark up the content before transmitting it to the second user.

Abstract: 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.

Abstract: Displaying relevant data subsets of at least one data collection includes providing an input data subset, determining a plurality of partial relevance values for different characteristics of each of the plurality of data subsets of the at least one data collection with respect to the input data subset according to a term frequency of terms in the input data subset and term in each of the plurality of data subsets of the at least one data collection and according to an inverse document frequency of the terms, determining a relevance of each of the plurality of data subsets of the at least one data collection based on the partial relevance values of the different characteristics, and displaying at least a portion of the data subsets of the at least one data collection in order of relevance.

Abstract: 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.