Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for network monitoring, user account compromise determination, and user behavior database system. The system monitors network actions of user accounts including user account access across multitudes of network accessible systems, determines user account transitions, and determines different types of high-risk user behavior indicative of compromise. Network actions can be obtained from generated information by the network accessible systems, and correlated across additional data sets including contextual ones. User interfaces are generated describing network actions of user accounts, and are configured for user interaction, which cause generation of updated user interfaces and access to electronic data sources to determine information relevant to the user interaction.

Abstract: A security system detects anomalous activity in a network. The system logs user activity, which can include ports used, compares users to find similar users, sorts similar users into cohorts, and compares new user activity to logged behavior of the cohort. The comparison can include a divergence calculation. Origins of user activity can also be used to determine anomalous network activity. The hostname, username, IP address, and timestamp can be used to calculate aggregate scores and convoluted scores.

Abstract: A security system detects anomalous activity in a network. The system logs user activity, which can include ports used, compares users to find similar users, sorts similar users into cohorts, and compares new user activity to logged behavior of the cohort. The comparison can include a divergence calculation. Origins of user activity can also be used to determine anomalous network activity. The hostname, username, IP address, and timestamp can be used to calculate aggregate scores and convoluted scores.

Abstract: A security system detects anomalous activity in a network. The system logs user activity, which can include ports used, compares users to find similar users, sorts similar users into cohorts, and compares new user activity to logged behavior of the cohort. The comparison can include a divergence calculation. Origins of user activity can also be used to determine anomalous network activity. The hostname, username, IP address, and timestamp can be used to calculate aggregate scores and convoluted scores.

Abstract: In various example embodiments, a textual identification system is configured to receive a set of search terms and identify a set of textual data based on the search terms. The textual identification system retrieves a data structure including textual identifications for the set of textual data and processes the data structure to generate a modified data structure. The textual identification system sums rows within the modified data structure and identifies one or more elements of interest. The textual identification system then causes presentation of the elements of interest in a first portion of a graphical user interface and the textual identifications for the set of textual data in a second portion of the graphical user interface.

Abstract: In various example embodiments, a textual identification system is configured to receive a set of search terms and identify a set of textual data based on the search terms. The textual identification system retrieves a data structure including textual identifications for the set of textual data and processes the data structure to generate a modified data structure. The textual identification system sums rows within the modified data structure and identifies one or more elements of interest. The textual identification system then causes presentation of the elements of interest in a first portion of a graphical user interface and the textual identifications for the set of textual data in a second portion of the graphical user interface.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for network monitoring, user account compromise determination, and user behavior database system. The system monitors network actions of user accounts including user account access across multitudes of network accessible systems, determines user account transitions, and determines different types of high-risk user behavior indicative of compromise. Network actions can be obtained from generated information by the network accessible systems, and correlated across additional data sets including contextual ones. User interfaces are generated describing network actions of user accounts, and are configured for user interaction, which cause generation of updated user interfaces and access to electronic data sources to determine information relevant to the user interaction.

Abstract: A security system detects anomalous activity in a network. The system logs user activity, which can include ports used, compares users to find similar users, sorts similar users into cohorts, and compares new user activity to logged behavior of the cohort. The comparison can include a divergence calculation. Origins of user activity can also be used to determine anomalous network activity. The hostname, username, IP address, and timestamp can be used to calculate aggregate scores and convoluted scores.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for network monitoring, user account compromise determination, and user behavior database system. The system monitors network actions of user accounts including user account access across multitudes of network accessible systems, determines user account transitions, and determines different types of high-risk user behavior indicative of compromise. Network actions can be obtained from generated information by the network accessible systems, and correlated across additional data sets including contextual ones. User interfaces are generated describing network actions of user accounts, and are configured for user interaction, which cause generation of updated user interfaces and access to electronic data sources to determine information relevant to the user interaction.

Abstract: A security system detects anomalous activity in a network. The system logs user activity, which can include ports used, compares users to find similar users, sorts similar users into cohorts, and compares new user activity to logged behavior of the cohort. The comparison can include a divergence calculation. Origins of user activity can also be used to determine anomalous network activity. The hostname, username, IP address, and timestamp can be used to calculate aggregate scores and convoluted scores.

Abstract: Various systems and methods are provided that identify prior art patent references for a subject patent application. For example, the system preprocesses a corpus of patent references to identify keywords that are present in each of the patent references, n-grams present in the corpus, and a weighting associated with the identified n-grams. To identify prior art patent references, the system requests a user to provide a patent application. The system extracts n-grams found in the provided patent application and orders the n-grams based on the assigned n-gram weights. The system compares the top Y-rated n-grams with the identified keywords and retrieves patent references that include a keyword that matches one of the top Y-rated n-grams. The system re-ranks the retrieved patent references using, for example, artificial intelligence. The top Z-ranked retrieved patent references are transmitted to a user device for display in a user interface.

Abstract: Aspects of the present disclosure relate to a machine determining a quality ranking of a subject. The machine accesses a current input representing answers to a plurality of questions about the subject, each answer representing a ranking of a quality of the subject at a specified time, where the subject has a geographic location. The machine determines, based on the current input, an overall ranking of the quality of the subject. The machine determines, based on the current input and based on one or more of input received about the subject at a time different from the specified time and input about other subjects proximate to the geographic location, a future ranking of the quality of the subject. The machine transmits a graphical output corresponding to the overall ranking and the determined future ranking of the quality of the subject.

Abstract: A security system detects anomalous activity in a network. The system logs user activity, which can include ports used, compares users to find similar users, sorts similar users into cohorts, and compares new user activity to logged behavior of the cohort. The comparison can include a divergence calculation. Origins of user activity can also be used to determine anomalous network activity. The hostname, username, IP address, and timestamp can be used to calculate aggregate scores and convoluted scores.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for network monitoring, user account compromise determination, and user behavior database system. The system monitors network actions of user accounts including user account access across multitudes of network accessible systems, determines user account transitions, and determines different types of high-risk user behavior indicative of compromise. Network actions can be obtained from generated information by the network accessible systems, and correlated across additional data sets including contextual ones. User interfaces are generated describing network actions of user accounts, and are configured for user interaction, which cause generation of updated user interfaces and access to electronic data sources to determine information relevant to the user interaction.

Abstract: A security system detects anomalous activity in a network. The system logs user activity, which can include ports used, compares users to find similar users, sorts similar users into cohorts, and compares new user activity to logged behavior of the cohort. The comparison can include a divergence calculation. Origins of user activity can also be used to determine anomalous network activity. The hostname, username, IP address, and timestamp can be used to calculate aggregate scores and convoluted scores.

Abstract: A security system detects anomalous activity in a network. The system logs user activity, which can include ports used, compares users to find similar users, sorts similar users into cohorts, and compares new user activity to logged behavior of the cohort. The comparison can include a divergence calculation. Origins of user activity can also be used to determine anomalous network activity. The hostname, username, IP address, and timestamp can be used to calculate aggregate scores and convoluted scores.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for network monitoring, user account compromise determination, and user behavior database system. The system monitors network actions of user accounts including user account access across multitudes of network accessible systems, determines user account transitions, and determines different types of high-risk user behavior indicative of compromise. Network actions can be obtained from generated information by the network accessible systems, and correlated across additional data sets including contextual ones. User interfaces are generated describing network actions of user accounts, and are configured for user interaction, which cause generation of updated user interfaces and access to electronic data sources to determine information relevant to the user interaction.

Abstract: A security system detects anomalous activity in a network. The system logs user activity, which can include ports used, compares users to find similar users, sorts similar users into cohorts, and compares new user activity to logged behavior of the cohort. The comparison can include a divergence calculation. Origins of user activity can also be used to determine anomalous network activity. The hostname, username, IP address, and timestamp can be used to calculate aggregate scores and convoluted scores.