Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that, upon request for a trait-intersection count of users (or other digital entities) corresponding to traits for a target time period, use a machine-learning model to analyze a semantic-trait embedding of the traits and to generate an estimated trait-intersection count of such entities sharing the traits for the target time period. By applying a machine-learning model trained to estimate trait-intersection counts, the disclosed methods, non-transitory computer readable media, and systems can analyze both a semantic-trait embedding of traits and an initial trait-intersection count of trait-sharing entities for an initial time period to estimate the trait-intersection count for the target time period.

Abstract: Systems and methods for generating an enterprise process graph are provided. Sets of process data relating to an implementation of RPA (robotic process automation) acquired using a plurality of discovery techniques is received. An enterprise process graph representing the implementation of RPA is generated based on the received sets of process data.

Abstract: Systems and methods for generating an enterprise process graph are provided. Sets of process data relating to an implementation of RPA (robotic process automation) acquired using a plurality of discovery techniques is received. An enterprise process graph representing the implementation of RPA is generated based on the received sets of process data.

Abstract: Various embodiments describe user segmentation. In an example, potential rules are generated by applying a frequency-based analysis to user interaction data points. Each of the potential rules includes a set of attributes of the user interaction data points and indicates that these data points belong to a segment of interest. An objective function is used to select an optimal set of rules from the potential rules for the segment of interest. The potential rules are used as variable inputs to the objective function and this function is optimized based on interpretability and accuracy parameters. Each rule from the optimal set is associated with a group of the segment of interest. The user interaction data points are segments into the groups by matching attributes of these data points with the rules.

Abstract: Various embodiments describe user segmentation. In an example, potential rules are generated by applying a frequency-based analysis to user interaction data points. Each of the potential rules includes a set of attributes of the user interaction data points and indicates that these data points belong to a segment of interest. An objective function is used to select an optimal set of rules from the potential rules for the segment of interest. The potential rules are used as variable inputs to the objective function and this function is optimized based on interpretability and accuracy parameters. Each rule from the optimal set is associated with a group of the segment of interest. The user interaction data points are segments into the groups by matching attributes of these data points with the rules.

Abstract: Techniques are disclosed that provide more accurate clustering of devices by forming clusters of devices and merging or changing clusters based on predetermined criteria. The technique starts with a large number of clusters (e.g., one for each account) and refines the clusters, for example, by merging clusters or determining which cluster a given device should be in when the device is associated with multiple clusters. One technique iteratively adjusts clusters of devices by merging clusters determined to be associated with a single user until a cluster contains all of the devices and accounts expected to be associated with a single user.

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that, upon request for a trait-intersection count of users (or other digital entities) corresponding to traits for a target time period, use a machine-learning model to analyze a semantic-trait embedding of the traits and to generate an estimated trait-intersection count of such entities sharing the traits for the target time period. By applying a machine-learning model trained to estimate trait-intersection counts, the disclosed methods, non-transitory computer readable media, and systems can analyze both a semantic-trait embedding of traits and an initial trait-intersection count of trait-sharing entities for an initial time period to estimate the trait-intersection count for the target time period.

Abstract: Various embodiments describe clustering of nodes of a directed graph based on the oriented edges of the directed graph and on a set of rules. In an example, each node represents a device identifier associated with a computing device. The device identifier facilitates an online activity provided by a computing service. A computing system accesses the directed graph and generates clusters that contain subsets of the nodes by at least iteratively updating the directed graph based on the set of rules. The set of rules specifies (i) removal of leaf nodes from the directed graph, (ii) reconnection of nodes that form a chain in the directed graph, and (iii) reconnection of nodes that form a split in the directed graph. The computing system also associates a client profile with a subset of the nodes contained in a cluster from the clusters.

Abstract: Persistent profile identifiers can be produced to identify clusters of devices accessing a network in different time periods. In one embodiment, an apparatus uses a first identifier from a first group of identifiers to identify a first cluster of devices and uses a second identifier from a second group of identifiers to identify a second cluster of devices. Further, the apparatus determines that the first cluster of devices identified by the first identifier and the second cluster of devices identified by the second identifier form an edge in a maximum cluster matching. The apparatus provides the first identifier as a persistent identifier for the first cluster of devices and the second cluster of devices.

Abstract: Various embodiments describe user segmentation. In an example, potential rules are generated by applying a frequency-based analysis to user interaction data points. Each of the potential rules includes a set of attributes of the user interaction data points and indicates that these data points belong to a segment of interest. An objective function is used to select an optimal set of rules from the potential rules for the segment of interest. The potential rules are used as variable inputs to the objective function and this function is optimized based on interpretability and accuracy parameters. Each rule from the optimal set is associated with a group of the segment of interest. The user interaction data points are segments into the groups by matching attributes of these data points with the rules.

Abstract: Various embodiments describe clustering of nodes of a directed graph based on the oriented edges of the directed graph and on a set of rules. In an example, each node represents a device identifier associated with a computing device. The device identifier facilitates an online activity provided by a computing service. A computing system accesses the directed graph and generates clusters that contain subsets of the nodes by at least iteratively updating the directed graph based on the set of rules. The set of rules specifies (i) removal of leaf nodes from the directed graph, (ii) reconnection of nodes that form a chain in the directed graph, and (iii) reconnection of nodes that form a split in the directed graph. The computing system also associates a client profile with a subset of the nodes contained in a cluster from the clusters.

Abstract: Techniques are disclosed that provide more accurate clustering of devices by forming clusters of devices and merging or changing clusters based on predetermined criteria. The technique starts with a large number of clusters (e.g., one for each account) and refines the clusters, for example, by merging clusters or determining which cluster a given device should be in when the device is associated with multiple clusters. One technique iteratively adjusts clusters of devices by merging clusters determined to be associated with a single user until a cluster contains all of the devices and accounts expected to be associated with a single user.

Abstract: Techniques are disclosed that provide more accurate clustering of devices by forming clusters of devices and merging or changing clusters based on predetermined criteria. The technique starts with a large number of clusters (e.g., one for each account) and refines the clusters, for example, by merging clusters or determining which cluster a given device should be in when the device is associated with multiple clusters. One technique iteratively adjusts clusters of devices by merging clusters determined to be associated with a single user until a cluster contains all of the devices and accounts expected to be associated with a single user.

Abstract: Systems and methods are provided for identifying stable attributes in clusters of devices and naming the clusters of devices using the stable attributes. The stable attribute is selected from data associated with user/household behavior and data associated with each device. Because the stable attribute is associated with the device in the cluster determined to be the most stable, it is a persistent identifier that can be used to identify the user/household over time. Because the stable attribute is determined each time the clusters are determined, resources that would be required to track the user/household over time are saved and the system of tracking users/households is more efficient. Attributes evaluated to determine the stable attribute include IP address, the location of the device, hardware characteristics of the device, the date a CRM profile was created, timestamp of the first or last user authentication, and the number of authentications on that device.

Abstract: Techniques are disclosed that provide more accurate clustering of devices by forming clusters of devices and merging or changing clusters based on predetermined criteria. The technique starts with a large number of clusters (e.g., one for each account) and refines the clusters, for example, by merging clusters or determining which cluster a given device should be in when the device is associated with multiple clusters. One technique iteratively adjusts clusters of devices by merging clusters determined to be associated with a single user until a cluster contains all of the devices and accounts expected to be associated with a single user.

Abstract: Systems and methods are provided for identifying stable attributes in clusters of devices and naming the clusters of devices using the stable attributes. The stable attribute is selected from data associated with user/household behavior and data associated with each device. Because the stable attribute is associated with the device in the cluster determined to be the most stable, it is a persistent identifier that can be used to identify the user/household over time. Because the stable attribute is determined each time the clusters are determined, resources that would be required to track the user/household over time are saved and the system of tracking users/households is more efficient. Attributes evaluated to determine the stable attribute include IP address, the location of the device, hardware characteristics of the device, the date a CRM profile was created, timestamp of the first or last user authentication, and the number of authentications on that device.

Abstract: Persistent profile identifiers can be produced to identify clusters of devices accessing a network in different time periods. In one embodiment, an apparatus uses a first identifier from a first group of identifiers to identify a first cluster of devices and uses a second identifier from a second group of identifiers to identify a second cluster of devices. Further, the apparatus determines that the first cluster of devices identified by the first identifier and the second cluster of devices identified by the second identifier form an edge in a maximum cluster matching. The apparatus provides the first identifier as a persistent identifier for the first cluster of devices and the second cluster of devices.

Abstract: Certain embodiments involve matching devices that access online services with users or households using a combination of real-time data and batch-processed data about the devices. For example, a data management system generates a device cluster that identifies devices associated with a user or household. The device cluster is generated by batch-processing data received from devices accessing the online services and data received from third parties that describes devices that have accessed other online services. The data management system subsequently identifies a device that is accessing a first online service. The data management system matches the identified device to the device cluster based on a combination of the batch-processed data and data about the identified device received in real-time from a second online service. The identified device is matched to the device cluster while the identified device is accessing the online service.

Abstract: Systems and methods for multi-pressure interaction on touch-sensitive surfaces are disclosed. One disclosed embodiment of a system includes a display; a memory configured to store images; a camera in communication with the memory, the camera configured to capture images; and a processor in communication with the memory and the display. In this embodiment, the processor is configured to receive a first image; receive a selection of a portion of the first image, wherein the portion is less than all of the first image; receive a second image, the second image comprising a different image than the first image; determine a portion of the second image corresponding to the portion of the first image; and replace the portion of the first image with the portion of the second image.