Abstract: Methods and systems disclosed herein describe customizing searching. Search queries may be customized according to a user's preferences. A user may emphasize or indicate that additional weight should be given to one or more terms in a search query. Terms that are weighted higher may have a larger impact on the results that are returned in response to the search query. In addition to changing the terms in a search query, a user may provide a weight for each term. Each term in a search query may be weighted to varying degrees, giving a user more control over the results that are returned. The weights may be used with machine learning techniques to generate a vector representation of a search query. The vector representation of the search query may be compared with vector representations of search objects to determine results that match the search query.

Abstract: A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

Abstract: A method and system include receiving, by a processor of a server, from a computing device associated with a user, real-time user activity data identifying at least one activity performed on the computing device. User-inputted data elements from a plurality of elements of a graphical user interface displayed on the computing device are received, which identify user-specific data attributes. Potential user-specific knowledge information is identified from databases based on at least one user-specific data attribute. User-specific challenge questions based on the potential user-specific knowledge information are generated and displayed on the user's computing device. Answers to the user-specific challenge questions by the user are received. An answer score based on correct answers and a behavioral score based the real-time user activity data of the user are determined. The processor determines whether the user is or is not a fraudster based on the answer score and the behavioral score.

Abstract: A system may receive a request to verify an identity of a user. The system may transmit information that identifies a task to be performed via user interaction with a client device and information that identifies behavioral data to be collected by the client device in connection with performance of the task, wherein the task requires at least one of: movement of the client device, interaction with a user interface of the client device using a gesture, or interaction with a user interface of the client device using a cursor. The system may receive the behavioral data and may perform identity verification associated with the user based on the behavioral data and the task. The system may transmit an indication of a recommended action to be performed with respect to the user and the client device based on performing the identity verification.

Abstract: A system may receive, from a server device that provides an application form to a client device, device information associated with the client device, wherein the device information indicates a geolocation associated with the client device. The system may receive, from the server device, behavior information that indicates user behavior associated with inputting data into the application form using the client device, wherein the behavior information indicates a manner in which the data is input into one or more fields of the application form. The system may determine a fraud score based on the device information and the behavior information. The system may transmit an indication of a recommended action to be performed by the server device with respect to the application form and the client device based on the fraud score.