Abstract: Determination of high value customer journey sequences is performed by determining customer interactions that are most frequent as length N=1 sub-sequences, recursively determining most frequent length N+1 sub-sequences that start with the length N sub-sequences, determining a first count indicating how often one of the sub-sequences appears in the sequences, determining a second count indicating how often the one sub-sequence resulted in the goal, and using the counts to determine the most or least effective sub-sequences for achieving the goal.

Abstract: Embodiments of the invention are directed to a system, method, or computer program product for an integrated entity resource distribution device set-up and deliver platform for distribution of devices to an entity for resource acceptance. An entity must be activated and on-boarded to accept resource acceptance and processing. The invention creates a platform for identification of entity needs with respect to resource acceptance devices. With this information the platform generates configured resource acceptance devices for the entity with correct components and sub-components. Furthermore, the system integrate software and firmware in an appropriate sequence onto resource acceptance devices components and sub-components.

Abstract: A computing system can maximize throughput for a common rendezvous location by determining estimated times of arrival (ETAs) to the common rendezvous location for matched users and/or transport providers. Based on the ETAs of each of the transport providers, the computing system can generate a dynamic queue comprising the transport providers for the common rendezvous location and manage the dynamic queue by routing the transport providers through the common rendezvous location. The computing system can further dynamically adjust the queue based on changes to the ETAs by transmitting updated navigation-related data to one or more of the matched transport providers.

Abstract: Various embodiments provide a method of connecting an employer with a candidate, comprising: receiving criteria data from the employer regarding a job opening; receiving background data from the candidate; recording data of the candidate in a video interview; analyzing the data of the candidate with speech-to-text analysis to identify candidate data, comparing real-time connection attributes to the candidate data; if a threshold amount of real-time connection attributes are satisfied by the candidate data, then sending, a first offer to the employer for a real-time connection with the candidate; receiving an employer acceptance of the first offer for a real-time connection with the candidate; sending a second offer to the candidate for a real-time connection with the employer; receiving a candidate acceptance; and connecting the candidate and the employer in real time by establishing a live audio connection or a live audio and video connection.

Abstract: A method performed by a data analysis apparatus according to an embodiment of the present disclosure includes generating a plurality of module combination processes using a plurality of data analysis modules defined by a user, calculating a score for each of the data analysis modules based on an execution result of the plurality of module combination processes and generating a recommendation module candidate group including a combination of data analysis modules selected based on the score.

Abstract: A device is fitted with a camera and an extended reality (XR) software application program executing on a processor. Via the XR software application program, a technique is performed for automating adaptive workflows in the XR environment. In the technique, the XR software application program determines an identifier of an asset in the XR environment. The XR software application program sends to a data intake and query system a request associated with a playbook having one or more execution tasks associated with the asset. The XR software application program receives the playbook and generates an XR object associated with an execution task in the playbook. The XR software application program causes the XR object to be displayed at a location in the XR environment corresponding to a determined location, relative to the asset, of a portion of the asset with which the execution task is associated.

Abstract: Disclosed herein are system, method, and computer program product embodiments for identifying and managing emerging risks. Businesses, such as those participating in regulated industries, may need to regularly monitor enforcement actions that are cited in a variety of sources. Embodiments disclosed herein provide for scraping information from those sources and comparing it to existing risk events in order to determine possible discrepancies that should be assessed.

Abstract: The present invention provides a system, method, software and data structure for independently predicting attitudinal and message responsiveness, using a plurality of attitudinal or other identification classifications and a plurality of message content or version classifications, for a selected population of a plurality of entities, such as individuals or households, represented in a data repository. The plurality of predictive attitudinal (or identification) classifications and plurality of predictive message content (ore version) classifications have been determined using a plurality of predictive models developed from a sample population and applied to a reference population represented in the data repository, such as attitudinal, behavioral, or demographic models. For each predictive attitudinal (or identification) classification, at least one predominant predictive message content or version classification is independently determined.

Abstract: A machine-learning tool evaluates an acquirer's current portfolio and then develops a model portfolio that mathematically redistributes the effect of the current portfolio by suggesting business categories that would better serve the acquirer from a risk/reward perspective. The machine-learning tool is trained with model portfolios and then generates a suggested portfolio that incorporates the acquirer's current partners and supplements them with additional business categories that would improve the risk/reward metric. The machine-learning tool may also select specific businesses from within the suggested business categories for the acquirer to use in achieving the suggested improvement.

Abstract: A system is provided for determining customer attrition risk. Data can be aggregated, and risk determined by machine learning methods such as random forest. Alternate scenarios can be simulated. Relative importance of customer attrition risk factors can be determined and ranked. Individualized recommendations can be issued to a customer based on the results of determining that customer's attrition risk.

Abstract: A computerized process useful for automating Risk Identification, Quantification, Benchmarking and Mitigation in an enterprise computer system, includes the step of integrating an enterprise security, privacy and compliance system in an enterprise computer system. The enterprise security, privacy and compliance system monitors a set of risk sources. The method includes the step of implementing an identification and a weighted scoring of a set of risks associated with each risk source. The method includes the step of, with a specified machine learning technique, matching a set of similar risk inputs with an associated weight. The set of similar risk inputs are similar to the risk sources in the RPPBM practice. The method includes the step of monitoring the relevant enterprise systems for changes in risk levels of each risk source. The method includes the step of generating a risk-score number for each risk source. The risk-score number is used to avoid a subjective assessment of the risk source.

Abstract: Systems and methods presented herein provide project efficiency tracking based on comparing estimated material items and quantities from a design in a computer-aided design (“CAD”) application to the actual purchased material items and quantities. A beacon symbol can be placed on a project drawing and include a progress ring, a materials status, work package number, and a percentage complete. The materials status can illustrate the percentage of budgeted materials that have been delivered. The progress ring can illustrate the job completion percentage. In one example, the progress ring can illustrate the percentage of budgeted labor hours that have been performed.

Abstract: A system, method, and computer readable device that detects hidden correlation relationships among entities, such as companies and/or individuals is presented. A dataset that corresponds to a predefined set of correlation relationships of these companies and/or individuals may be collected. The dataset may be stored in a graph database and a machine learning system may be built using features computed from the graph database. At least a new pair of companies or a new pair of an individual and a company may be evaluated. The system, method, and/or computer readable device may determine whether a hidden correlation relationship exists between them.

Abstract: Methods and systems are disclosed for generating a summary view of raw data to emphasize similar orders or patterns. Raw data is received comprising a set of orders in a non-standard format, each order including a quantity and a value. The raw data is disaggregated, which includes converting the raw data to a standard format and identifying similar orders. The similar orders are identified using a data disaggregation model. A graphical object is generated to represent some orders in the set of orders based on the disaggregated data. Each graphical object is defined based on a quantity at a particular value and sized in proportion to a total number of orders at a particular price or differentiated from a quantity of orders associated with other groups of orders. A summary view comprising the graphical objects is displayed in a graphical user interface.

Abstract: An online shopping concierge system identifies a set of delivery orders and a set of delivery agents associated with a location. The system allocates the orders among the agents, each agent being allocated at least one order. The system obtains agent progress data describing travel progress of the agents to the location, and order preparation progress data describing progress of preparing the orders for delivery. The system periodically updates the allocation of the orders among the agents based on the agent progress data and the order preparation progress data. This involves re-allocating at least one order to a different delivery agent. When a first agent arrives at the location, the system assigns to the first agent the orders allocated to the first agent. The system then removes the first agent from the set of available delivery agents, and removes the assigned delivery orders from the set of delivery orders.

Abstract: Methods and systems for analyzing and evaluating item recommendations presented on a web site are disclosed. In one aspect, a user interface is generated for display on a website. Item recommendations personalized for an individual user are displayed within in item recommendation region of the website. Impression, clickstream, and sales data are received from user activity on the website. The impression, clickstream, and sales data are displayed on a dashboard of an administrator user interface. An administrator user can select visualizations to display on the dashboard using selectors for impressions, clicks, sales, users, and time. Two or more sets of data can be overlaid to illustrate relationships between user interactions with recommended items and actual sales of those items. The dashboard is dynamically updated in response to new data received from the website in real-time.

Abstract: The present disclosure relates to a safety management system. The safety management system calculates a real-time data risk score and an incident data risk score based on real-time data received from a wearable device and incident data selected from big data, calculates a total risk score by summing all values obtained by multiplying calculated risk for respective data by weights for respective data, compares the total risk store with a preset threshold score, and transmits a dangerous situation message to a risk recognition subject when it is determined that a user is at risk. The safety management system of the present disclosure may transmit the real-time data, the incident data, and the dangerous situation message using a 5G communication system, and a safety management server for determining whether or not the user is at risk may be implemented using an artificial neural network.

Abstract: A computer-implemented method for evaluating team effectiveness and acting on one or more outliers based on the evaluated team effectiveness. The method retrieves electronic data of one or more users, wherein the electronic data comprises a plurality of electronic communications. The method further extracts one or more concepts and metadata from the retrieved electronic data. The method further determines one or more outliers based on the extracted one or more concepts and metadata, wherein the determined one or more outliers are based on one or more poorly connected concepts and one or more poorly connected users. The method further acts on the determined one or more outliers and displays a report based on the determined one or more outliers.

Abstract: An intelligent task assistant program can identify tasks associated with a user over a reporting period based on an analysis of, and inferences drawn from, data associated with one or more personal information programs, such as an electronic calendar application, an electronic reminder application, an electronic collaborative application, and/or an electronic communication application. The task assistant may also prioritize the identified tasks based on content associated with the tasks. Some or all of the prioritized tasks can be provided to an output device for perception by the user.

Abstract: Certain aspects involve providing automated performance monitoring of statistical models. For example, a processing device is used for performing a statistical analysis on information in an archive to extract historical data, scores, and attributes. The processing device calculates performance metrics based at least in part on the historical data, scores, and attributes. The processing device pre-calculates summary performance data based at least in part on the performance metrics. The summary performance data is stored in files with predefined layouts, which are stored in a non-transitory, computer-readable medium. Segmented data is presented from a file to a user through a graphical user interface (GUI). In some aspects, various reports of the segmented data are presented interactively by detecting a selection by the user of a segmentation and displaying the corresponding segmented data.