Abstract:
As is disclosed herein, user behavior in connection with a number of electronic messages, such as electronic mail (email) messages, can be used to automatically learn from, and predict, whether a message is wanted or unwanted by the user, where an unwanted message is referred to herein as gray spam. A gray spam predictor is personalized for a given user in vertical learning that uses the user's electronic message behavior and horizontal learning that uses other users' message behavior. The gray spam predictor can be used to predict whether a new message for the user is, or is not, gray spam. A confidence in a prediction may be used in determining the disposition of the message, such as and without limitation placing the message in a spam folder, a gray spam folder and/or requesting input from the user regarding the disposition of the message, for example.
Abstract:
Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods automatically generate and provide an interactive rich set of personalized query suggestions within a unified framework. The disclosed systems and methods are able to integrate attributes associated with message data and metadata by transforming such attributes into facets that are combined with term suggestions and presented to the user in a unified manner. The instant disclosure provides an interactive search suggestion mechanism that narrows the search as the user interacts with the dynamically generated and provided suggestions.
Abstract:
Methods, systems, and computer-readable media for anonymizing electronic documents. In accordance with one or more embodiments, structurally-similar electronic documents can be identified among a group of electronic documents (e.g., e-mail messages, documents containing HTML formatting, etc.). A hash function can be specifically tailored to identify the similarly structured documents. The structurally-similar electronic documents can be grouped into a same equivalence class. Masked anonymized document samples can be generated from the structurally-similar electronic documents utilizing the same equivalence class, thereby ensuring that the anonymized document samples when viewed as a part of an audit remain anonymous. An online process is provided to guarantee k-anonymity of the users over the entire lifetime of the auditing process. An auditor's productivity can be measured based on the amount of content revealed to the auditor within the samples he is assigned. The auditor's productivity is maximized while ensuring anonymization over the lifetime of the audit.
Abstract:
Users may engage with content that may invoke various emotions. For example, a user may find an image as inspirational, a social network post as funny, etc. Accordingly, content may be labeled with user emotion labels, specified by users that engaged with the content, to create labeled content (e.g., the image may be labeled as inspirational). Emotional transition triggers may be defined for users (e.g., 30 minutes of a user writing a school report using a word processing application). Responsive to a triggering of an emotional transition trigger, labeled content may be provided to a user (e.g., the image may be provided to the user as an inspirational break from writing the school report). In this way, content may be labeled based upon emotions that the content invokes in users, and such labeled content may be provided to users in a contextually relevant manner (e.g., a study break).
Abstract:
Users may engage with content that may invoke various emotions. For example, a user may find an image as inspirational, a social network post as funny, etc. Accordingly, content may be labeled with user emotion labels, specified by users that engaged with the content, to create labeled content (e.g., the image may be labeled as inspirational). Emotional transition triggers may be defined for users (e.g., 30 minutes of a user writing a school report using a word processing application). Responsive to a triggering of an emotional transition trigger, labeled content may be provided to a user (e.g., the image may be provided to the user as an inspirational break from writing the school report). In this way, content may be labeled based upon emotions that the content invokes in users, and such labeled content may be provided to users in a contextually relevant manner (e.g., a study break).
Abstract:
Disclosed are systems and methods for improving interactions with and between computers in content providing, generating and/or hosting systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the security and quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide added security features and functionality to messaging platforms. Message content within communicated or to be communicated messages can be subject to such security functionality through the identification of selected message portions having an identifier applied therewith that not only hides the selected message portions from being viewed within a message interface, but also modifies the message thereby rendering the selected message portion as unreadable and/or inaccessible by a user or computing device without the required security credentials.
Abstract:
Briefly, embodiments disclosed herein may relate to formulating synthetic questions, such as in response to a search query, for example. Candidate synthetic questions may be presented to a user who may initiate a search at least in part by selecting one or more candidate synthetic questions, for example, in an embodiment.
Abstract:
Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosure provides a novel, computerized framework for automatically identifying and recommending socially-engaging photos to their creators for sharing. Execution of the disclosed systems and methods turns a tedious manual chore into an automated, software-driven process. The disclosed systems and methods utilizes a novel, computerized learn-to-rank (LTR) algorithm for identifying the most engaging, socially driven photos by: (a) grouping near-duplicate photos; (b) selecting a representative photo for sharing per group; and (c) ranking of the groups by their likelihood to contain a “shareable” photo.
Abstract:
Relevant messages, or “hero results”, which are not ranked at the uppermost part of a time-based listing of search results are identified and such hero results can be displayed apart from the time-based listing of search results. A user can be provided with messages in a time-based presentation as well as messages in a relevance-based presentation. The user can be presented with the most relevant messages from a set of message generated from a search query, even where the most relevant messages are not the most recent ones.
Abstract:
Disclosed is a system and method for email management that leverages information derived from automatically generated messages in order to identify types of messages and message content. The disclosed systems and methods apply the information learned from decoding previously received messages to other messages in a user's inbox to fully, or at least partially decode the information included within such messages. The disclosed systems and methods analyze messages received in a user's inbox to detect message specific information corresponding to types of content in the message and the location of such content in the messages. The message specific information is then applied to other newly received or identified messages to learn message specific information about those messages. Based on such learning, information can be extracted from such messages in order to increase a user's experience and increase monetization.