Abstract: An artificial intelligence system for generating zoological instruction sets from biological extractions. The system includes a computing device, configured to retrieve a biological extraction pertaining to an animal. The computing device is further configured to generate a zoological classifier wherein the zoological classifier utilizes a biological extraction as an input and outputs a zoological profile. The computing device is further configured to receive a zoological input from a remote device, wherein the zoological input identifies a zoological habit. The computing device is further configured to select a zoological machine-learning model utilizing a zoological input, wherein the zoological machine-learning model utilizes a zoological profile as an input and outputs a zoological instruction set. The computing device is further configured to calculate a zoological instruction set utilizing a zoological machine-learning model.

Abstract: A device may receive schedule data identifying schedules of appointments for drivers of vehicles and may receive location data identifying geographical locations of the vehicles. The device may receive traffic data identifying traffic conditions, and may process the received data, with a first model, to determine status data identifying estimated statuses of the appointments. The device may process the received data, with a second model, to generate sets of isochrones for destinations of the appointments, and may calculate, based on the sets of isochrones and the location data, estimated travel time data identifying estimated travel times for the appointments. The device may process the status data, the estimated travel time data, and the schedule data, with a third model, to calculate estimated time of arrival data identifying estimated times of arrival for the appointments, and may perform actions based on the estimated time of arrival data.

Abstract: A computer-implemented method for analyzing refuse includes operations of receiving sensor data indicating an operational state of a vehicle body component of a refuse collection vehicle (RCV); analyzing the sensor data to detect a presence of a triggering condition based at least partly on a particular operational state of the vehicle body component, as indicated by the sensor data; in response to detecting the triggering condition, accessing image data indicating a physical state of refuse collected by the RCV; providing the image data as input to at least one contaminant detection model trained, using at least one machine learning (ML) algorithm, to output a classification of the image data, the classification indicating a degree of contamination of the refuse; and storing, in a machine-readable medium, the classification of the image data.

Abstract: A computer-implemented method for generation of dashboard templates for operations management is disclosed. The computer-implemented method includes monitoring a user interactions with an editable dashboard user interface when handling issue instances to gather chart selections, wherein a chart is a graphical visualization of one or more parameters of a data source. The computer-implemented method further includes identifying an issue type to chart pairs from the gathered chart selections and, for each pair, generalizing the one or more data source parameters. The computer-implemented method further includes identifying from the gathered chart selections common associations between the issue type and a chart visualization type.

Abstract: In an approach to improve multi-data center environments by minimizing the environmental impact of workloads in multi-data center environments embodiments migrate at least a portion of one or more workloads between one or more data centers automatically to maximize a usage of renewable energy based on a predetermined threshold score of input power and a combination of renewal energy sources. Further, embodiments dictate, by a policy engine, where at least a portion of the one or more workloads can be hosted. Additionally, embodiments control, by a scheduling engine, how, when, and where at least a portion of the one or more workloads will migrate, and perform data replication to migrate data between a plurality of data center locations.

Abstract: A computer system includes a memory with instructions to receive telematics data regarding a vehicle, analyze the telematics data to determine a low fuel condition based on the fuel of the vehicle violating a predetermined threshold, and determine a location of the vehicle based on at least one of the telematics data. The instructions, in response to determining the low fuel condition, further identify one or more fuel dispensing stations in proximity to the location of the vehicle, provide information related to the one or more fuel dispensing stations to a mobile application displayed to a driver of the vehicle, and determine a scope of fueling permissions associated with the vehicle at the one of the one or more fuel dispensing stations. The instructions also automatically initiate a fueling session at the one of the one or more fuel dispensing stations contingent upon the scope of fueling permission.

Abstract: In some embodiments, image input data is received from multiple sources. The received image input data may then be aggregated and mapped to create a set of image input data. An event in the set of image input data may be automatically detected, such as by being triggered by a rule and an associated tag. An image mining result database may be updated by adding an entry to the database identifying each detected event and the triggering rule. An indication associated with the image mining result database may then be transmitted to a plurality of risk applications.

Abstract: Systems, methods, and other embodiments are disclosed that are configured to segment customers using mixed attribute types. In one embodiment, a computerized data structure is read. The computerized data structure has numerical demographic attribute data, categorical demographic attribute data, and target attribute data that is associated with customers and is stored in a computerized memory. The numerical demographic attribute data and the categorical demographic attribute data are converted to a same numerical scale, based at least in part on the target attribute data, to form congruent attribute data in a format that is compatible with performing a cluster analysis on the congruent attribute data. The cluster analysis is performed on the congruent attribute data to generate segmented customer data representing a segmentation of the customers. The segmented customer data may be used to control at least one enterprise function performed by a computerized management system.

Abstract: The present invention discloses a method, a system and a computer program product for risk management in supply chain. The invention includes structuring of risk data fields on an application user interface for creating risk data objects based on a SCM task. The invention includes predicting inherent risk associated with execution of the SCM task and generating control data fields on the application user interface for mitigating the inherent risk.

Abstract: Aspects of the present disclosure relate to identifying friction points in customer data. In some embodiments, identifying friction points can include receiving a set of input sequence data and predicted class labels for the set of input sequence data; selecting input sequences, from the set of input sequence data, that have class labels matching a ground truth class label; reducing the selected sequences to anchor points; and grouping the reduced selected sequences into critical data set signatures using discriminatory subsequence mining.

Abstract: A method, apparatus, and non-transitory computer readable medium for data analytics are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include monitoring online activity corresponding to a plurality of users; receiving aggregate marketing data for a marketing activity; identifying online activity data for a time period corresponding to the marketing activity based on the monitoring; generating a regression model based on the aggregate marketing data and the online activity data using Bayesian regression, wherein the regression model represents a relationship between the marketing activity and the online activity, comprises a time effect coefficient, and is based on a prior distribution of the time effect coefficient that decays to zero as time increases; and estimating a treatment effect for the marketing activity on the online activity based on the regression model, wherein the treatment effect comprises a rate of effect decay.

Abstract: Systems and methods for data analytics are described. One or more embodiments of the present disclosure receive target time series data and candidate time series data, where the candidate time series data includes data corresponding to each of a plurality of candidate metrics, train a prediction network to predict the target time series data based on the candidate time series data by setting temporal attention weights corresponding to a plurality of rolling time windows and by setting candidate attention weights corresponding to the plurality of candidate metrics, identify a leading indicator metric for the target time series data from the plurality of candidate metrics based on the temporal attention weights and the candidate attention weights, and signal the leading indicator metric for the target time series data.

Abstract: In an example embodiment, a specialized machine learned model, called a look-alike model, is trained using a machine learning algorithm to predict future job engagement for a user. This look-alike model is then used to create new segments on top of the segments provided by a rules-based approach. Specifically, the look-alike model is designed to take users who have been segmented by a rule-based approach into an “inactive job seeker” categorization (such as those assigned to the resting users and dormant users segments) and calculate a predicted job engagement score for these users. Based on the predicted job engagement score, a user may then be reassigned from one of the inactive job seeker categorizations to one of one or more new job seeker categorizations (such as predicted open job seekers or predicted opportunistic job seekers).

Abstract: Automatic evaluation of recorded interactions is disclosed, including: detecting a plurality of signals in an interaction; combining at least a subset of the plurality of signals using a prescribed set of operators into a combined signal; and determining whether an event criterion has been met with respect to the interaction based at least in part on the combined signal.

Abstract: A method of smart urban public transport management, an Internet of Things system, and a storage medium are provided. The method of smart urban public transport management is implemented by the public transport management platform. The method of smart urban public transport management includes: obtaining the predicted pedestrian flow at a plurality of places through the place management platform; determining the target places where the predicted pedestrian flow is greater than the pedestrian flow threshold based on the predicted pedestrian flow at the plurality of places; obtaining the bus lines passing by the target places and adjusting the departure frequency of the bus lines. The Internet of Things system includes a user platform, a service platform, a public transport management platform, a sensor network platform, and an object platform. The method can be executed after the computer instructions stored in the computer-readable storage medium are read.

Abstract: The present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to disclosed embodiments, a transportation matching system receives session information from a requestor computing device in response to one or more session events. The system utilizes the received session information to determine a likelihood that the associated session on the requestor computing device will result in a transportation request. If the determined likelihood is above a threshold amount, the system generates an upfront dispatch that matches the associated session to a provider computing device. The system then reserves the provider computing device and generates a predicted dispatch for the reserved provider computing device.

Abstract: The novel invention provides methods and systems for automated production of merchandising displays. The displays can be assembled in whole, shipped in whole and filled with customer-selected products in customer-selected amounts, filled with criteria-selected products in criteria-selected amounts or both.

Abstract: A security threat candidate (STC) detector examines a set of activity log records representing access requests directed to data artifacts by various entities of an organization. In a first threat analysis, the STC detector determines that an activity log record indicates a use of an access path which is not present in a database of legitimate access paths for an entity. In a second threat analysis, the STC detector determines whether the behavior of the entity matches a stored suspect behavior pattern template. Results of the first and/or second analysis are transmitted to a selected security management component of the organization.

Abstract: An improved analytics system generates actionable KPI-based customer segments. The analytics system determines predicted outcomes for a key performance indicator (KPI) of interest and a contribution value for each variable indicating an extent to which each variable contributes to predicted outcomes. Topics are generated by applying a topic model to the contribution values for the variables. Each topic comprises a group of variables with a contribution level for each variable that indicates the importance of each variable to the topic. User segments are generated by assigning each user to a topic based on attribution levels output by the topic model.

Abstract: A System, Method, and Apparatus for Predicting Medical No-Shows and for Scheduling is provided. The system is based on a patient appointment schedule and indicators including office type, practice area, appointment type, engagement with the office leading up to appointment, and a variety of other characteristics of each patient and appointment. The indicators can be selected, weighted proportionately, and input into an algorithm for analysis and processing. The algorithm can then predict the likelihood of a no-show for the respective patient and appointment. The likelihood can then be used for scheduling to minimize the negative impact on the medical professional of the no-show. The algorithm can be used in conjunction with machine learning to improve its accuracy as it informs itself with experience and additional data points. The likelihood of a no-show can be incorporated into scheduling programs and made accessible on mobile devices, computers, and other monitoring devices.