Abstract:
Gathering information related to a meeting includes confirming that the meeting has occurred, analyzing an item submitted to a content collection to determine if the item relates to the meeting, and, if the item relates to the meeting, adding the item to a cluster of materials associated with the meeting, the cluster of materials being part of the content collection. The item may be a document created during a meeting at a meeting location using at least one of the following: a traditional whiteboard, an electronic whiteboard, an Easel Pad, an IdeaPaint wall, a dry erase surface, a presentation, and materials posted online by meeting participants. The item may include as least one photograph of handwritten materials created during the meeting and added to the content collection. Analyzing an item may include determining similarities between the item and the meeting.
Abstract:
Presenting items in a two-dimensional access pane includes presenting a first set of icons arranged contiguously and in two dimensions of the access pane, where each of the icons corresponds to an item and at least some of the items are container icons that represent containers that contain other items and expanding at least one of the container icons to show a first container pane having icons corresponding to items contained in a corresponding container, where icons in the first container pane are arranged contiguously and in two dimensions of the first container pane. At least some of the icons in the first container pane may be container icons that represent containers that contain other items. The items may be files in a file system provided in connection with a personal computer that runs Macintosh OS, MS Windows, and Linux. The items may be data in a database system.
Abstract:
Determining experts based on a search query of a user includes identifying items in a content collection that correspond to the search query, determining authors of the items, and ranking the authors according to relevance to the search query for each of the items for each of the authors. Determining experts based on a search query of a user may also include complementing the query with additional public search results prior to identifying the items. Complementing the query may include using an external data source to search based on the query. The external data source may be selected from the group consisting of Google Search, Yahoo Search, and Microsoft Bing. Determining experts based on a search query of a user may also include presenting the authors to the user in order of ranking The query may be a natural language query.
Abstract:
Handling data for a photographic image, includes detecting a reference pattern in the data, detecting at least one sticker in the data, determining an action associated with the at least one sticker, and performing the action on the data. The reference pattern may be a uniform grid pattern of dots preprinted on paper. A paper type may be determined according to the reference image. Determining an action associated with the at least one sticker may depend, at least in part, on the paper type. Detecting a particular one of the stickers may cause data for the photographic image to be stored in a particular location. Detecting a particular one of the stickers may cause data for the photographic images to be assigned particular tags used for organizing and searching.
Abstract:
Presenting database items includes providing a plurality of clusters, where each of the clusters is formed by grouping database items according to location information associated therewith, creating a plurality of geographic elements based on the clusters, and presenting the geographic elements to a user using a note atlas that represents all of the geographic elements corresponding to a set of the database items, where indicators of corresponding clusters are provided with each of the geographic elements. A quantity of database items may be provided with each of the corresponding clusters. The note atlas may show at least two levels of detail corresponding to a world level of detail, a points of interest level of detail and a city level of detail. Points of interest may be determined by having a user provide points of interest on a map.
Abstract:
Managing electronic notes includes storing data for at least one of the electronic notes, determining at least one particular action to be performed based on the content of the at least one of the notes, and automatically performing the at least one particular action. The at least one particular action may be determined automatically based on data stored in the at least one of the notes or may be determined by a user providing input to select an action. Actions may be recommended to a user based on at least one of: the extracted terms and the additional online information. Managing electronic notes may also include storing additional data and instructions to perform the at least one particular action.
Abstract:
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.
Abstract:
Methods and systems for detecting images in documents are described. A method implemented by an electronic device having one or more processors for determining whether a document is an image includes partitioning a document into a plurality of cells. The method includes scaling each of the cells to a standardized number of pixels to provide a corresponding snippet for each of the cells, classifying the snippets, using a neural network, to determine a set of cells classified as text, and determining a volume of text for the document based on a sum of an amount of text in each cell of the set of cells. The method further includes in response to a determination that the volume of text for the document is below a predetermined threshold, determining that the document is an image.
Abstract:
A digital content clipping system receives an indication that a first web page has been accessed by a first user, and searches a repository of digital content clips associated with the first web page. In accordance with a determination that the repository does not include any user-generated digital content clips associated with the first web page, the system offers one or more first clipping options based on predefined clip templates, receives selection, by the first user, of one of the first clipping options, extracts a digital content clip of the first web page in accordance with the selected one of the first clipping options, and stores the digital content clip of the first web page in a first clip collection associated with the first web page.