Abstract: Particular embodiments may retrieve information associated with one or more nodes of a social graph from one or more data stores. A node may comprise a user node or a concept node. Each node may be connected by edges to other nodes of a social graph. A first user may be associated with a first user node of the social graph. Particular embodiments may detect that the first user is entering an input term. Predictive typeahead results may be provided as the first user enters the input term. The predictive typeahead results may be based on the input term. Each predictive typeahead result may include at least one image. Each predictive typeahead result may correspond to at least one node of the social graph.

Abstract: In one embodiment, a computing device receives input from a user participating in a message session. The computing device detects an emoticon in the received input and identifies an image corresponding to the emoticon. The computing device accesses the image corresponding to the emoticon and replaces the emoticon with the image in the message session.

Abstract: Particular embodiments determine that a textual term is not associated with a known meaning. The textual term may be related to one or more users of the social-networking system. A determination is made as to whether the textual term should be added to a glossary. If so, then the textual term is added to the glossary. Information related to one or more textual terms in the glossary is provided to enhance auto-correction, provide predictive text input suggestions, or augment social graph data. Particular embodiments discover new textual terms by mining information, wherein the information was received from one or more users of the social-networking system, was generated for one or more users of the social-networking system, is marked as being associated with one or more users of the social-networking system, or includes an identifier for each of one or more users of the social-networking system.

Abstract: In one embodiment, a computing device receives input from a user participating in a message session. The computing device detects an emoticon in the received input and identifies an image corresponding to the emoticon. The computing device accesses the image corresponding to the emoticon and replaces the emoticon with the image in the message session.

Abstract: Particular embodiments determine that a textual term is not associated with a known meaning. The textual term may be related to one or more users of the social-networking system. A determination is made as to whether the textual term should be added to a glossary. If so, then the textual term is added to the glossary. Information related to one or more textual terms in the glossary is provided to enhance auto-correction, provide predictive text input suggestions, or augment social graph data. Particular embodiments discover new textual terms by mining information, wherein the information was received from one or more users of the social-networking system, was generated for one or more users of the social-networking system, is marked as being associated with one or more users of the social-networking system, or includes an identifier for each of one or more users of the social-networking system.

Abstract: In particular embodiments, one or more images associated with a primary user are received. The image(s) may comprise single images, a series of related images, or video frames. In each image, one or more faces are detected and/or tracked. For each face, a set of one or more candidates are selected who may be identified with the face. The primary user has a computed measure of affinity for candidates in the set through a social network, or the candidate in the set is otherwise known to the primary user. A facial recognition score is calculated for each candidate. A subset of candidates is selected, wherein each candidate in the subset has a facial recognition score above a predetermined threshold. A candidate score is calculated for each candidate based on the facial recognition score and the computed measure of affinity. A winning candidate is selected based on the candidate scores.

Abstract: Particular embodiments may retrieve information associated with one or more nodes of a social graph from one or more data stores. A node may comprise a user node or a concept node. Each node may be connected by edges to other nodes of a social graph. A first user may be associated with a first user node of the social graph. Particular embodiments may detect that the first user is entering an input term. Predictive typeahead results may be provided as the first user enters the input term. The predictive typeahead results may be based on the input term. Each predictive typeahead result may include at least one image. Each predictive typeahead result may correspond to at least one node of the social graph.

Abstract: Particular embodiments may retrieve information associated with one or more nodes of a social graph from one or more data stores. A node may comprise a user node or a concept node. Each node may be connected by edges to other nodes of a social graph. A first user may be associated with a first user node of the social graph. Particular embodiments may detect that the first user is entering an input term. Predictive typeahead results may be provided as the first user enters the input term. The predictive typeahead results may be based on the input term. Each predictive typeahead result may include at least one image. Each predictive typeahead result may correspond to at least one node of the social graph.

Abstract: In one embodiment, a method includes accessing a social graph that includes a plurality of nodes and edges, receiving from a first user a voice message comprising one or more commands, receiving location information associated with the first user, identifying edges and nodes in the social graph based on the location information, where each of the identified edges and nodes corresponds to at least one of the commands of the voice message, and generating new nodes or edges in the social graph based on the identified nodes or identified edges.

Abstract: In one embodiment, the method includes a computing device receiving one or more characters as a user enters the characters into a graphical user interface (GUI) of the computing device. The method also includes the computing device determining one or more auto-suggestions, where each of the auto-suggestions presents a character string determined based at least in part on the entered characters. The method also includes the computing device determining a degree of difficulty of the user completing the respective character string for each of the auto-suggestions. The method further includes, for each of the auto-suggestions, if the degree of difficulty is at least approximately equal to or exceeds a pre-determined threshold, the computing device providing to the user the auto-suggestion for completing the character string.

Abstract: In particular embodiments, one or more images associated with a primary user are received. The image(s) may comprise single images, a series of related images, or video frames. In each image, one or more faces may be detected and/or tracked. For each face, one or more candidates are selected who may be identified with the face. Each candidate may be connected to the primary user within a social network. A candidate score for each candidate associated with a detected face. Finally, the winning candidate is determined, and a suggestion to identify the detected face as being the winning candidate is presented. Some embodiments may operate upon video clips as the video is captured by a mobile device. Some embodiments may operate upon series of images as they are uploaded to or viewed on a website.

Abstract: In one embodiment, a method includes accessing a social graph that includes a plurality of nodes and edges, receiving from a first user a voice message comprising one or more commands, receiving location information associated with the first user, identifying edges and nodes in the social graph based on the location information, where each of the identified edges and nodes corresponds to at least one of the commands of the voice message, and generating new nodes or edges in the social graph based on the identified nodes or identified edges.

Abstract: In one embodiment, a method includes receiving, by a client computing device, an input associated with a user interface of an application. The user interface may include multiple elements. The client computing device may transition from a first state of the application to a second state of the application based on the input. The client computing device may then determine one or more listeners associated with the application that are registered to receive information when the second state of the application is entered. The client computing device may provide information associated with the second state of the application to one or more of the listeners, and the listeners may implement one or more changes to one or more elements of the user interface in response to the transition to the second state of the application.

Abstract: In one embodiment, a method includes receiving one or more characters of a character string as a user enters the character string into a graphical user interface (GUI) of a computing device. The method also includes determining a degree of difficulty of the user entering the character string into the GUI of the computing device. The method further includes, if the degree of difficulty is at least approximately equal to or exceeds a pre-determined threshold, providing for display to the user an auto-suggestion for completing the character string for the user.

Abstract: In one embodiment, a method includes, in connection with a user entering a string of symbols into a graphical user interface (GUI) of a mobile computing device, capturing an image of an object with a sensor on the mobile computing device, automatically identifying the object from the image, automatically determining one or more symbols associated with the object as identified from the image, and automatically entering the symbols into the string of symbols.

Abstract: In one embodiment, a method includes detecting that a first user is entering a text input at an input region of a computing device, wherein the input region includes multiple subregions and each subregion is associated with at least one character of a plurality of characters. The method also includes determining, for each character as the first user enters the text input, a probability that the character is next in the text input. The method further includes determining a size of each subregion based on the determined probability of the character associated with the subregion.

Abstract: In one embodiment, a method includes providing for display to a user a set of keys within a region of a touch-screen user interface, each key being responsive to a keystroke touch-gesture within an area of the key. The method also includes receiving a pre-defined user input other than a keystroke touch-gesture within an area of a key. The method further includes, in response to the pre-defined user input, providing within the region of the touch-screen user interface a trackpad in place of at least a portion of the set of keys.

Abstract: In one embodiment, a computing device receives input from a user participating in a message session. The computing device detects an emoticon in the received input and identifies an image corresponding to the emoticon. The computing device accesses the image corresponding to the emoticon and replaces the emoticon with the image in the message session.

Abstract: In one embodiment, a method includes providing for display a first set of touch-screen keys corresponding to a first set of symbols. The method also includes providing for display at least partially underneath the first set of touch-screen keys a second set of touch-screen keys corresponding to a second set of symbols. At least a portion of the second set of touch-screen keys are visible through the first set of keys. The method further includes detecting a touch gesture by the user over the first and second sets of keys intending to input a symbol. The method further includes determining a context of the input by the user. The method further includes selecting based at least in part on the context a symbol in the first set of symbols or a symbol in the second set of symbols as the symbol that the user intended to input.

Abstract: In one embodiment, a method includes receiving one or more characters of a character string as a user enters the character string into a graphical user interface (GUI) of a computing device. The method also includes determining a degree of difficulty of the user entering the character string into the GUI of the computing device. The method further includes, if the degree of difficulty is at least approximately equal to or exceeds a pre-determined threshold, providing for display to the user an auto-suggestion for completing the character string for the user.