Facebook, Inc.

Jenny Yuen

Luke St Clair

David Harry Garcia

Jasper Reid Hauser

Shaheen Ashok Gandhi

In one embodiment, a method includes receiving, from a client system of a first user of an online social network, voice input of the first user; receiving, from the client system, information indicating a current geo-location of the first user; parsing the voice input to identify one or more commands in the voice input; determining a data object of the online social network corresponding to the current geo-location of the first user and a command of the one or more commands associated with the current geo-location of the first user; and executing the command associated with the current geo-location of the first user on the online social network, wherein the executed command operates on the data object corresponding to the current geo-location of the first user.

Voice commands for online social networking systems

Particular embodiments may locally store, at a client device of a first user, information associated with a plurality of nodes and a plurality of edges of a social graph received from a social-networking system. The first user may be associated with a first node of the social graph. The client device may detect that the first user is entering an input term. The client device may provide predictive typeahead results as the first user enters the input term. The predictive typeahead results may be based on the input term. Each predictive typeahead result may correspond to one of the nodes of the social graph stored locally on the client device. Each predictive typeahead result may include at least one image associated with the corresponding node.

Enhanced predictive input utilizing a typeahead process

In one embodiment, a method includes providing for display a first set of touch-screen keys corresponding to a first set of symbols; providing for display, at least partially underneath the first set of touch-screen keys the first set of touch-screen keys, a second set of touch-screen keys corresponding to a second set of symbols; detecting a touch gesture by a user over the first and second sets of keys intending to input a first symbol; determining, based on an amount of lapsed time between the detected touch gesture and a previous touch gesture, a context associated with the detected touch gesture; and 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 first symbol that the user intended to input.

Systems and methods for selecting a symbol input by a user

In one embodiment, the method includes receiving one or more characters inputted by a user; calculating a degree of difficulty of a character string, wherein the degree of difficulty is based on the characters inputted by the user, and wherein the characters inputted by the user comprise a portion of the character string; and if the degree of difficulty is equal to or exceeds a pre-determined threshold, then presenting, on a display of the computing device, an auto-suggestion for completing the character string; else, not presenting, on the display of the computing device, the auto-suggestion for completing the character string.

Systems and methods for character string auto-suggestion based on degree of difficulty

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. A candidate score is calculated for each candidate based on a computed measure of affinity of the primary user for a particular candidate, a facial recognition score comparing the candidate to the face, and a geographic proximity of the candidate to the primary user at a time when the one or more images were created. A winning candidate is selected based on the candidate scores.

Facial recognition using social networking information

In one embodiment, a method includes detecting a communication session between a first user and one or more second users. The method also includes determining a social context of the communication session, and determining based at least in part on the social context a set of symbols for communication by the first user in the communication session with the second users. The method further includes providing for display to the first user a set of keys corresponding to the set of symbols. The keys indicate symbols for input by the first user in the communication session.

Systems and methods for a symbol-adaptable keyboard

A method may include providing for display a first set of touch-screen keys corresponding to a first set of symbols. The method may also include 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 may also include detecting a touch gesture by the user over the first and second sets of keys intending to input a symbol. The method may also include determining a context of the input by the user. The method may also include 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.

Systems and methods for determining a symbol input by a user from two sets of symbols on a multi-layer keyboard

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.

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.

Particular embodiments may store, at a client device, information associated with nodes and edges of a social graph. A node may comprise a user node or a concept node. Each node may be connected by edges to other nodes of the social graph. A first user may be associated with a first user node of the social graph. The client device may receive a character string from the first user, and identify, as the first user inputs the character string, an edge-type based on the character string and one or more edges of the identified edge-type, wherein the edges are locally stored on the client device. The client device may display one or more predictive typeahead results based on the identified edge-type and the identified edges. The predictive typeahead results may correspond to nodes stored locally on the client device.

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.

In one embodiment, a method includes detecting one or more user interactions, associated with a user of a computing device, each interaction occurring at a region associated with an input value, and determining, for at least one user interaction, that the at least one user intended to provide a different input value. Adaptation information is generated for the at least one user based on the at least one user interaction. The adaptation information is stored for the at least one user. A user interaction is detected at a region. The user's intended input value is determined based on the user interaction and the adaptation information.

Systems and methods for a user-adaptive keyboard

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.

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.

Growing community of inventors

Cambridge, MA, United States of America

Jenny Yuen