Abstract: Examples for a prospect assessment system are provided. The system may receive a query from a user and obtain prospect data from a plurality of data sources. The system may create a plurality of prospect clusters. The system may determine a cluster significance value. The system may determine a prospect churn value. The system may determine a set of significant prospect clusters from among the plurality of prospect clusters having the cluster significance value and the prospect churn value above a threshold value. The system may determine a prospect pattern matrix including a plurality of patterns from the set of significant prospect clusters. The system may identify a set of products for each of the plurality of patterns. The system may determine a prospect profile for each of the plurality of patterns. The system may generate a prospect assessment result based on the prospect profile for the resolution of the query.

Abstract: There is provided a system for in-store consumer behaviour event metadata aggregation, data verification and the artificial intelligence analysis thereof for data interpretation and associated action triggering. The system may collect in-store consumer behaviour event metadata from a plurality of consumer electronic devices and then uses a trained artificial intelligence analytics engine to provide various artificial intelligence insights useful to such consumers which may further modify consumer behaviour. The trained artificial intelligence analytics engine may have a data interpretation controller configured for intelligently interpreting such aggregated in-store consumer behaviour event metadata and triggering actions accordingly which are then sent electronically to the consumer electronic devices.

Abstract: Offline consumer behavior and interactions are observed using beacons positioned at venues where consumers spend time and interact with each other. The beacons communicate with mobile computing devices that are carried by the observed consumers. The observed consumer behavior provides actionable insights into how consumers influence each other. For example, the people that a particular consumer spends time with form a “circle of influence” associated with that consumer. If the consumer makes a purchase, members of the circle of influence are observed to understand the degree to which they were influenced by the purchase, if at all. Metrics that quantify a consumer's influence over other consumers allow marketers to more effectively target both the influencing and influenced consumers. Also, if relatively little information is known about a particular consumer, that consumer's digital marketing profile can be supplemented using information known about the consumers with whom he/she often spends time.

Abstract: The present disclosure relates to a customer flow line and customer flow hot zone determining method and apparatus. The customer flow line determining method comprises: receiving coordinate data sent from multiple mobile devices, wherein an environment in which the multiple mobile devices are located is divided into a plurality of grids; on the basis of the coordinate data of each mobile device, determining the grid to which each mobile device belongs; determining the number of the mobile devices in each grid; and on the basis of the number of the mobile devices in each grid, outputting a customer flow line distribution map.

Abstract: A graphical user interface (GUI) for a sponsor portal system includes a first display area that persistently displays a list of a plurality of tabs and a second display area. When the GUI is executed by a computing device of a sponsor of the plurality of sponsors, the second display area is dynamically updatable to display a scrollable list of customers of the sponsor, a list of vendors approved by the sponsor, and a list of active offers. In response to selection of an active offer, the GUI is configured to display a draft offer message and an approval control. In response to selection of the approval control, the sponsor engine is configured to transmit an interactive graphic to mobile computing devices, the interactive graphic configured to communicate information to the mobile computing devices for display on a screen of limited size.

Abstract: An activity recording device includes: a work specifying unit for specifying a work of a worker; a position specifying unit for specifying a position of the worker from a position coordinate of the worker; a state specifying unit for specifying a state of the work of the worker from the position specified by the position specifying unit; and a recording unit for recording, as activity data, the work, the position, and the state in association with an activity time, wherein the position specifying unit includes a position estimation unit and a position correction unit.

Abstract: A system may include persistent storage containing representations of requests associated with a managed network. The persistent storage may include lists of capabilities associated with agents, and each request may include a textual description of a situation experienced by a user and a resolution of the situation by a particular agent. A computing device may obtain a set of requests from the persistent storage, apply an unsupervised machine learning clustering technique to textual descriptions included in the set of requests, and arrange the requests into groups such that each group contains requests including textual descriptions with at least a threshold degree of similarity to one another. The computing device may perform, for the requests in a particular group, a textual analysis on associated resolutions to identify capabilities used by agents to resolve the requests, and update the lists of capabilities to associate the capabilities with agents that used them.

Abstract: Example methods, apparatus/systems and articles of manufacture for determining intensity of a biological response to a presentation are disclosed. An example method includes accessing galvanic skin response (GSR) data obtained from a subject while exposed to a presentation. The GSR data includes a plurality of trough-to-peak instances. The example method includes generating a GSR intensity profile by assigning trough-to-peak scores to corresponding ones of the trough-to-peak instances, defining a plurality of time windows, and assigning window scores to corresponding ones of the time windows based on the trough-to-peaks scores of the trough-to-peak instances occurring within the corresponding time windows. The example method also includes determining an effectiveness of the presentation based on the window scores of the GSR intensity profile.

Abstract: A system, method, and computer program product are provided for automatically mapping business models to technical models. In use, business entity information and corresponding business entity fields are received for defining a business model. The business entity information is associated with one or more business entities selected by a user in accordance with a business request. Additionally, each business entity of the one or more business entities and the corresponding business entity fields are mapped to one or more technical entities and corresponding technical entity fields of a technical model. Further, an online translation of the business model to the technical model is performed such that changes or updates to the business model will cause changes or updates to the technical model, based on the mapping.

Abstract: A system and method for presenting information for evaluating asset allocation. In example embodiments, the method includes determining a readiness score associated with a request location and in response to determining the readiness score associated with the request location transgresses a threshold readiness score, causing display of an alert notification within a user interface. The method further includes receiving a request submitted via the user that specifies a requested quantity of the asset type to be allocated to the request location. Based on the request, the user interface displays a fulfillment indicator to indicate a portion of the requested quantity of the asset type that is fulfilled based on combination of reallocation quantities of the asset type to be allocated to the request location from each destination location in a set of destination locations.

Abstract: A device trains a machine learning model with historical productivity data and skills data to generate a trained machine learning model that determines allocations of tasks to workers. The device receives new task data identifying new tasks to allocate to the workers and performs natural language processing on the new task data to convert the new task data to processed new task data. The device receives, from sensors associated with the workers, real-time productivity data identifying productivity of the workers in completing current tasks assigned to the workers. The device processes the processed new task data and the real-time productivity data, with the trained machine learning model, to determine allocations of the new tasks to the workers, and causes the new tasks to be allocated to the workers by one or more devices and based on the determined allocations of the new tasks.

Abstract: System, methods, and devices for measuring the performance of a component of a communications network recently installed by a technician using proactive network maintenance parameters, and further assessing the installation performed by the technician.

Abstract: A method receiving image information with one or more processor(s) and from a sensor disposed at a worksite and determining an identity of a work tool disposed at the worksite based at least partly on the image information. The method further includes receiving location information with the one or more processor(s), the location information indicating a first location of the sensor at the worksite. Additionally, the method includes determining a second location of the work tool at the worksite based at least partly on the location information. In some instances, the method includes generating a worksite map with the one or more processor(s), the worksite map identifying the work tool and indicating the second location of the work tool at the worksite, and at least one of providing the worksite map to an additional processor and causing the worksite map to be rendered via a display.

Abstract: A method for controlling a data center, comprising a plurality of server systems, each associated with a cooling system and a thermal constraint, comprising: a concurrent physical condition of a first server system; predicting a future physical condition based on a set of future states of the first server system; dynamically controlling the cooling system in response to at least the input and the predicted future physical condition, to selectively cool the first server system sufficient to meet the predetermined thermal constraint; and controlling an allocation of tasks between the plurality of server systems to selectively load the first server system within the predetermined thermal constraint and selectively idle a second server system, wherein the idle second server system can be recruited to accept tasks when allocated to it, and wherein the cooling system associated with the idle second server system is selectively operated in a low power consumption state.

Abstract: Techniques are described for automatically generating meta-alerts based on operational and security risk alert volumes for an entity. In particular, a central device receives entity alert information from other devices in a computer system of an enterprise business, which may have office branches that are each staffed by employees. The central device compares alert volumes of a given entity for a given period of time (e.g., a month) to one or more baseline thresholds determined based on the average alert volume of peer entities during the same period of time and the entity's own historical alert volumes to identify spikes or increases in the volume of alerts for the given entity. If the entity is trending high for the period of time, the central device generates a meta-alert to bring awareness to the relatively high volume of alerts identified for that entity.

Abstract: Methods, apparatus, and processor-readable storage media for dynamically determining customer intent and related recommendations using deep learning techniques are provided herein.

Abstract: An approach is provided in which an information handling system trains on a set of historical data that includes a set of first infractions caused by a set of first businesses and a set of fines imposed on the set of first businesses based on the set of first infractions. The trained information handling system then performs a risk assessment of a second business that includes predicting a set of possible infractions of the second business based on a set of characteristics of the second business. Then, the information handling system predicts a set of possible fines corresponding to the set of possible infractions based on the historical data. In turn, the information handling system generates a risk report that includes the set of possible infractions and the corresponding set of possible fines.

Abstract: A method for assessing an asset status is provided. The method may include identifying, by a processor, an asset within a plurality of tangible, deployed assets. The method may also include retrieving a plurality of images from at least one data repository, whereby the plurality of images are captured within a preconfigured distance of the identified asset. The method may further include determining a portion of the retrieved plurality of images depict the identified asset. The method may also include performing a plurality of image processing techniques on the determined portion. The method may further include creating an assessment of the asset status of the identified asset based on the performed plurality of image processing techniques, whereby the created assessment details whether the identified asset needs a repair or a replacement.

Abstract: A data analysis apparatus generates M (M is an integer of 3 or greater) groups each including data regarding a plurality of units from data where first KPIs and K (K is an integer of 2 or greater) explanatory variables are given by 1:1, generates a second KPI indicating the state of the group based on the values of a plurality of first KPIs included in the group, and selects a feature for the first KPIs based on a correlation analysis between the second KPI of each group and the feature of each group calculated based on the explanatory variables.

Abstract: A method for emulating a fuel pump interface on a mobile device and for marketing on a mobile device. The user of a mobile device is presented with a user interface such that the user receives information from and interacts with the mobile device to carry out certain functions as if the user is at the fuel pump and is interacting with the fuel pump directly. The mobile device may be used to present marketing information on the mobile device.