Abstract: The present application provides a method, system, and storage medium of personnel situational awareness evaluation and training, the method comprises: obtaining situational awareness scenes and tasks; performing the personnel situational awareness testing or training through the situational awareness task in the situational awareness scene, obtaining an objective data for each dimension of a plurality of situational awareness tasks collected by personnel based on a plurality of sensing devices during an implementation of the situational awareness tasks, obtaining subjective evaluation results of the personnel after the situational awareness testing or training; a multiple regression analysis is performed based on the objective data and the subjective evaluation results of each dimension of each situational awareness task to obtain a single task and single dimension evaluation result, a single task and total task evaluation results of the situational awareness task; after each personnel situational awareness

Abstract: The present disclosure provides a method for a safety inspection of an LNG distributed energy smart terminal, an Internet of Things (IoT) system, and a storage medium.

Abstract: A computerized-system for converting a time-off request of an agent to a shift-trade with another agent or to a self-swap schedule, in a contact center is provided here in. Upon receiving a time-off request from a source-agent, operating a Time-off Convertor (TC) module. The TC module includes (i) searching open trade requests in dates where the source-agent doesn't have a scheduled-shift, to yield a list-of-trading-agents; (ii) retrieving from the yielded list-of-trading-agents, compatible-agents who do not have a scheduled-shift in the specified date; (iii) presenting the compatible-agents, via a display unit; (iv) enabling the source-agent a selection of an agent from the presented compatible-agents and upon selection of the agent by the source-agent, sending an approval request to the agent; and (v) upon acceptance of the agent, sending to the source-agent and to the agent a trade-success-notification, and operating change of scheduled shifts in one or more WFM MSs.

Abstract: An initial sequence representing a partially configured engineering project is processed by a recurrent neural network to generate recommendations being a sequence of complementary items that completes an engineering project. A feature predictor component computes a set of features for each recommendation. A bisection component selects a feature from the sets of features that distinguishes some of the recommendations and forms pruned recommendations by choosing all instances from the recommendations that have the selected feature. A user interface displays the selected feature, detects a user interaction indicating that the selected feature is required, outputs the pruned recommendations. The engineering project is completed by combining the initial sequence with the chosen pruned recommendation. As a result, a user is supported in choosing optimal modules, as the selected feature can distinguish the recommendations that have the desired technical properties or target system KPI.

Abstract: A method includes receiving historical data housed in the one or more computer systems, the historical data including structured data and unstructured data, storing the historical data in a central database, aggregating the historical data in the central database according to subject matter, validating the aggregated historical data, analyzing the validated aggregated historical data using a series of tools, generating normal profiles in the first computer system from the analyzed validated aggregated historical data, storing the generated normal profiles as horizon data sets, receiving real time data housed in the one or more computer systems, comparing the real time data to the horizon data sets to identify normal and out of normal profiles, and generating rules-based, predictive and interventive outcomes for out of normal profiles based on parameters for investigation.

Abstract: Systems and methods are provided for generating surprise gift objects presentable to intended recipients based on real-time location data associated with recipients. Users are provided with recommendations for surprise gift objects that can be provided to recipients based on information associated with recipients and inventory data associated with locations through which options associated with these surprise gift objects are made available. Through these systems and methods, recipients are notified of the availability of surprise gift objects based on their real-time location data. As recipients select different options associated with these surprise gift objects or otherwise redeem these surprise gift objects at the one or more locations, data corresponding to these redemptions is used to further enhance the systems and methods in order to provide more accurate tailored recommendations to users wishing to send surprise gift objects to recipients and to the recipients when redeeming surprise gift objects.

Abstract: Examples are described that enable a sales notification application executing on the mobile device to provide an indication of a better price for a product. In the examples, product information may be obtained from product purchase receipts uploaded by mobile devices executing an instance of a sales notification application. Similarities may be determined between the product information received from each product purchase of the plurality of product purchases. The product information from each of the uploaded receipts may be aggregated based on the determined similarity between the product information. A selected product and a location at which the selected product has been purchased previously may be received for comparison to the aggregated information in a product purchase database. A comparison result may be output to the sales notification application. A payment card may be enabled to purchase the selected product.

Abstract: Techniques for evaluating a user experience experiment designed to use one user experience variant selected from a number of user experience variants as a global-best user experience variant to be used across users relative to a machine model trained to use user data to identify a user-preferred user experience variant. Disclosed systems and methods provide techniques for optimizing user response. In one embodiment, a global-best user experience variant is evaluated by comparing an aggregate user response determined for the global-best user experience variant to an aggregate user response determined using user response predictions determined using the trained machine model, and using the outcome of the comparison to make a recommendation as to which one of the global-best user experience variant and the trained machine model to adopt for providing a user experience to users.

Abstract: A work management system includes a calibration instruction recorder and a work instruction recorder, wherein the calibration instruction recorder receives and stores a calibration work record of the calibration work from a first terminal, stores the calibration work record, stores device identification information included in the calibration work record as the device identification information, and stores “usable” or “not usable” as use propriety of the device, depending on whether the calibration determination result included in the calibration work record is “appropriate” or “inappropriate”. The work instruction recorder receives a work request from a second terminal and transmits the work contents to the second terminal when the use propriety of the device is “usable” and transmits an error notification to the second terminal when the use propriety of the device is “not usable”. The device is used for the work performed by the worker as a user of the second terminal.

Abstract: The present invention is directed to a software-based technology system for managing and executing high volumes of work on complex projects across multiple organizations involving multiple users that results in superior outcomes at significantly reduced time and cost. Litigation is the initial application of this invention; each case is handled as a project. As applied to litigation, the system of the invention empowers a novel method of end-to-end case assessment, planning, execution and management by integrating tools, processes and procedures allowing for more informed decision-making and deliberate execution of all case-related actions as well as enhanced coordination and communication among all constituents, resulting in efficient achievement of targeted outcomes.

Abstract: Online retailers and advertisers typically attempt to glean information about a potential customer from his or her web browser click history. However, relying on only a potential customer's click history is not an effective means for tailoring and personalizing web content for the user in the moment of customer interaction with the web content. Obtaining information about the user's engagement with web content is critical to successful personalization of web content in real-time. The present invention provides solutions for monitoring, tracking and calculating user engagement with web content that enables successful personalization of web content in the moment of interaction.

Abstract: The online concierge system generates task units based on orders and assigns batches of task units to pickers. The online concierge system generates task units based on received orders. The online concierge system generates permutations of these task units to generate candidate sets of task batches. The online concierge system scores each of these candidate sets, and selects a set of task batches to assign to pickers based on the scores. Additionally, to determine which task UI to display to the picker, the picker client device uses a UI state machine. The UI state machine is a state machine where each state corresponds to a task UI to display on the picker client device. The state transitions between the UI states of the UI state machine indicate which UI state to transition to from a current UI state based on the next task unit in the received task batch.

Abstract: A method and apparatus for managing events. A stream of event data for a plurality of events corresponding to an entity is received. A timeline for the plurality of events is generated based on the stream of the event data. A set of event chains is identified based on the timeline, the event data, and a set of policies. A trigger related to an event belonging to an event chain of the set of event chains is detected. A number of effects of the trigger is identified in which the number of effects includes at least one of a number of insights, a number of actions, or a number of opportunities.

Abstract: A data prediction subsystem stores hourly event potential data indicating an expected amount of removal events as a function of time of day. Based at least in part on event data, it is determined that a first item associated with a first location has an empty status at a time after a start of a day. For the day, an anticipated event value is determined for the first item at the first location. Using the anticipated event value and the hourly event potential data, a time-adjusted event value is determined. Based at least in part on the time-adjusted event value, a prediction value is determined that corresponds to a recommended amount of the first item to request for a future time.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an analytic server for data rationalization and migration. The server receives data from a plurality of electronic data sources and stores the received data into a database within a central hub. The server demobilizes the data and performs enhancement on the data to prepare the data for complex calculation. The server copies the enhanced data to an operational data store, performs various calculation functions that filer, rationalize the data, and create data linkage. The server copies back the calculation result to the database for further processing and downstream consumption. The calculation results provide a consolidated view of data from different source systems and satisfy the reporting needs for different consumers. The server uses common data model to generate reports for different consumers. After the data migration and consolidation into a central hub, the server decommissions legacy components.

Abstract: In an aspect, input data can be received, including at least time series data associated with purchases of at least one product and causal influencer data associated with the purchases. The causal influencer data can include at least non-stationary data, where lost shares associated with said at least one product are unobserved. An artificial neural network can be trained based on the received input data to predict a future global demand associated with at least one product and individual market shares associated with at least one product. The artificial neural network can include at least a first temporal network to predict the global demand and a second temporal network to predict each of the individual market shares. The first temporal network and the second temporal network can be trained simultaneously.

Abstract: A system, and method for controlling a computing system, comprising: reading a stored energy-performance characteristic of a plurality of different phases of execution of software, an execution of each phase being associated with a consumption of a variable amount of energy in dependence on at least a processing system performance state, the performance state being defined by a selectable performance-energy consumption optimization for at least two processing system components; scheduling a plurality of phases of execution of the software, in dependence on the stored energy-performance characteristics, for each of the respective phases of execution of the software and at least one system-level energy criterion; and executing the phases of execution of the software in accordance with the scheduling.

Abstract: Systems and methods for monitoring a quality of interactions include: receiving one or more quality metrics relating to one or more quality targets; determining, on a periodic basis, a current quality value for each of the one or more quality targets; calculating a deviation of the current quality value from a desired value of the quality target; weighting the one or more quality targets according to the calculated deviation; and determining a set of one or more interactions for review based on one or more quality metric deviations and the weighted quality targets.

Abstract: A method for providing context-aware cueing for daily interactions, navigation, and accessibility is disclosed. In one embodiment, such a method includes receiving routine data describing routine-based tasks associated with a user. The method further receives interaction data describing current interactions of the user in a particular environment. The method processes, using a neural network, the routine data and interaction data to determine a next step to be performed by the user that is consistent with the routine-based tasks and the current interactions. The method provides a cue to the user to perform the next step in the particular environment. This cue may include one or more of a visual, auditory, and haptic cue. The method may continuously refine the neural network to increase the accuracy of the cue. A corresponding system and computer program product are also disclosed.

Abstract: An automated checkout system applies environmental effects to physical regions within a store. The automated checkout system logs the environmental effect, a time the environmental effect was applied, and the physical region to which the environmental effect was applied. The automated checkout system detects an interaction event and logs a time associated with the interaction event. The automated checkout system identifies a location of the automated shopping cart and identifies a physical region within the store that contains the automated shopping cart's location. The automated checkout system identifies the environmental effect that was applied to the physical region at the time of the interaction event and generates a data point. For each environmental effect, the automated checkout system computes a success metric based on the generated data points. The automated checkout system applies environmental effects to physical regions based on the success metrics.