Abstract: A method for predicting changes in customer demand is provided. The method comprises collecting subscription data for a number of customers at specified time intervals, wherein each customer is subscribed to one of a number of defined bundles of services. Any changes in customer bundle subscriptions during a given time interval are determined along with metrics for defined customer tasks for subscribed services during the given time interval. Multimodal multi-task learning is used to simultaneously model both bundle subscription change events and time-to-event for each bundle subscription change. Using the modeling, types and timing of changes in customer bundle subscriptions are predicted based on customer service activities.

Abstract: Methods and systems for determining a set of lowest corresponding price data related to a salable unit are disclosed herein. An example method includes receiving an input indicative of the salable unit, the input including a current date. The example method further includes generating a set of comparable salable unit data based on the input. Each respective comparable salable unit data in the set of comparable salable unit data includes a respective prior date within a date threshold from the current date. The example method further includes determining the set of lowest corresponding price data by applying an exclusion model to the set of comparable salable unit data, and transmitting a notification of the set of lowest corresponding price data for display to a user. The example method further includes storing the set of lowest corresponding price data into an historical transaction log.

Abstract: There is thus provided a computerized-method for calculating a Repeated-Information-Request (RIR) score of an interaction in a contact center, by which a related interaction-recording is filtered for evaluation. The computerized-method is operating in a computerized-system which includes a processor, a data-storage and a memory to store the data-storage. The processor is operating a RIR score calculation module. The operating of the RIR score calculation module includes: (i) retrieving from the data-storage an interaction-recording and an interaction-recording-length thereof; (ii) operating a module on the retrieved interaction-recording to count a number of requests to repeat information; (iii) calculating a RIR score according to the number of requests to repeat information and the interaction-recording-length; and (iv) storing the RIR score of the retrieved interaction-recording in the data-storage.

Abstract: A method including operations of displaying a scheduling interface including a first work item user interface element corresponding to a first work item, the first work item user interface element displayed to visually indicate a scheduled start date of the first work item and a scheduled end date for the first work item; receiving forecast data generated in respect of the first work item; determining, based on the forecast data, a first forecast range in respect of the first work item; and displaying a first forecast user interface element on the scheduling interface, the first forecast user interface element corresponding to the first work item and having an appearance based on the first forecast range.

Abstract: A method may include applying, to a content associated with a first supplier, a machine learning model to determine one or more objectives of an enterprise affected by an incident associated with the content. A change in a first risk associated with the first supplier may be detected based on the objectives affected by the incident. In response to detecting the change in the first risk of the first supplier, a cost associated with replacing the first supplier with the second supplier may be determined by applying the machine learning model to analyze a first electronic document associated with the first supplier. If the cost of replacing the first supplier with the second supplier and/or a second risk of the second supplier satisfy one or more thresholds, a second electronic document associated with the second supplier may be generated to address the second risk of the second supplier.

Abstract: A method includes receiving input identifying a dimensional framework and forecasting time period. The method also includes generating a workforce plan based on the dimensional framework and the forecasting time period. The method further includes receiving first data defining a first scenario of the workforce plan, where the first scenario is associated with at least a first turnover assumption, and determining a first set of forecasted headcount values corresponding to the first scenario. The method includes receiving second data defining a second scenario of the workforce plan, where the second scenario is associated with at least a second turnover assumption that differs from the first turnover assumption, and determining a second set of forecasted headcount values corresponding to the second scenario. The method includes generating graphical user interface (GUI) data that includes a comparison of the first set of forecasted headcount values and the second set of forecasted headcount values.

Abstract: A method, computer system, and computer program product are provided for managing time record events. Time record events are collected for a number of users. Each time record event includes a geolocation of one of a number of users. The time record events and geolocations for each of the number of users are models via machine learning. A current geolocation for a given user is identified. A suggested event is predicted based on the current geolocation and a current time. The suggested event is pushed to the user.

Abstract: An abstraction system for generating a standard customer profile in a data processing system has a processing device and a memory. The abstraction system may receive customer data from a computing device over a network, perform unsupervised learning on the customer data to produce a plurality of clusters of customers with a plurality of features in common, and determine that a cluster represents a standard customer, and store a plurality of standard customer profiles based on the determined standard customers, wherein the standard customer profiles comprise a plurality of data distributions for the plurality of features in common. The abstraction system also derives additional standard customer profiles by applying a boundary limiter to the customer data. The abstraction system additionally provides the standard customer profiles and the additional standard customer profiles to a cognitive system for generating synthetic transaction data.

Abstract: A method for identifying prospective purchasers of battery electric vehicles (BEVs) may include defining a predetermined time frame associated with vehicle ownership, receiving a first dataset including a first subset of drivers are drivers who have owned or leased any first internal combustion engine (ICE) vehicle followed by any second ICE vehicle within the predetermined time frame, a second subset of drivers are drivers who have owned or leased any first ICE vehicle followed by any second BEV within the predetermined time frame, and a third subset of drivers are drivers who have not changed vehicles within the predetermined time frame, receiving a second dataset including a set of drivers who own or lease any first ICE vehicle which is in a same vehicle class as a target BEV, and identifying a target set of prospective purchasers of BEVs.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system determines features related to an application for an opportunity by a member of an online network, wherein the features include a historical response rate for a poster of the opportunity and a submission number of the application for the opportunity. Next, the system applies a machine learning model to the features to produce a score representing a likelihood of the member receiving a response to the application from the poster. The system then compares the score to a threshold to determine a recommendation related to applying to the opportunity by the member. Finally, the system outputs the recommendation to improve an experience of applying to the opportunity.

Abstract: Devices, systems and processes are described, for managing technician logistics. A process may include executing computer instruction for determining an estimated time of completion (ETC) for an assigned technician for a current task at a current service site. The ETC is determined based upon an average statistical time for task completion (ASTTC) for the current task. An ETC is based upon a stress differential (S) and a task complexity coefficient (TCC). The stress differential (S) is a difference between an expected stress level (SE) for the current task and a present stress level (SP) for the assigned technician. The SP is based upon data provided by a technician monitor, such as a biometric monitor. The stress differential (S) is weighted by a technician specific weighting factor (Tn), which is based on a technician rating. The TCC is based upon a task categorization (TC) specified by a regulatory body.

Abstract: A work machine control device including a memory and a processor is disclosed. The processor may be configured to receive work order data associated with a haul machine. The work order data may identify the haul machine, a material requested for the haul machine, an amount of the material requested for the haul machine, and/or a work machine associated with the material. The processor may be configured to set an operating parameter of the work machine to perform an operation according to the work order data, and receive payload data associated with a payload of the work machine. The payload data may relate to a weight of the payload, a volume of the payload, and/or an operating state of the work machine. The processor may be configured to determine productivity data associated with the operation, and cause an action to be performed in connection with the productivity data.

Abstract: A computer-implemented method may include obtaining article information associated with the one or more articles; obtaining historical transactional data associated with purchasing the one or more articles; obtaining, via the one or more processors, article preference data associated with purchasing the one or more articles; determining, via the one or more processors, one or more assumptions based on the article preference data; determining, via the one or more processors, the quantity associated with purchasing the one or more articles based on the article information and the one or more assumptions; determining, via the one or more processors, the size distribution associated with purchasing the one or more articles based on the determined quantity, the historical transactional data, and the one or more assumptions; and transmitting, to a purchaser, a notification indicating the quantity and the size distribution associated with purchasing the one or more articles.

Abstract: Technologies for using an inferred relationship between data sets to computationally align the data sets and generate actionable output include obtaining, from an online system, a set of member records; inferring a job category based at least partly on job function data extracted from the set of member records; obtaining, from the management system, a set of job records that correspond to the inferred job category; calculating an aggregate alignment score by comparing member skills data extracted from the set of member records to job skills data extracted from the set of job records, where the aggregate alignment score is a measure of a mismatch between the member skills data for the set of member records and the job skills data for the set of job records; based on the aggregate alignment score, identifying a skill that is actionable to address the mismatch between the member skills data and the job skills data; generating actionable output that replaces, modifies, or supplements first course recommendation d

Abstract: Systems and methods for managing resources in an IT infrastructure are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for managing resources in an IT infrastructure may include: (1) retrieving, from a database, an identification of a plurality of IT infrastructure resources for an organization; (2) retrieving at least one attribute for each IT infrastructure resource; (3) graphing an IT infrastructure for the organization by illustrating a relation of each IT infrastructure resource to at least one other IT infrastructure resource and the attributes for each IT infrastructure resource; (4) analyzing the graph in order to identify a potential IT infrastructure resource for removal from the IT infrastructure for the organization; and (5) implementing a decision engine to recommend removal or maintenance of the identified IT infrastructure resource.

Abstract: A method including operations of displaying a scheduling interface including a first work item user interface element corresponding to a first work item, the first work item user interface element displayed to visually indicate a scheduled start date of the first work item and a scheduled end date for the first work item; receiving forecast data generated in respect of the first work item; determining, based on the forecast data, a first forecast range in respect of the first work item; and displaying a first forecast user interface element on the scheduling interface, the first forecast user interface element corresponding to the first work item and having an appearance based on the first forecast range.

Abstract: In an information processor, an acquirer acquires a departure point and transportation of each of multiple users and a destination in common. A provisional setting unit provisionally sets a meeting place multiple times. For the meeting place provisionally set each time, a calculation unit derives a travel cost of each user required for traveling from the departure point of the user through the meeting place to the destination, and a join cost of each user required for joining at the meeting place based on the transportation of the user, and calculates the sum of the travel costs and the join costs of the respective users. Based on the multiple calculated sums, a formal setting unit formally sets a meeting place.

Abstract: Systems and methods are provided for using information obtained from a various databases to efficiently identify new or additional geographic locations in which a user, such as a seller, a manufacturer, a distributor, etc. can offer its goods and/or services for acquisition. A user interface is provided that provides information on such locations and value details for a user to utilize in determining where to offer or list an item for acquisition.

Abstract: A method, computer system, and a computer program product for a cognitive-based meeting format recommendation is provided. The present invention may include receiving an input. The present invention may also include determining a preferred mode of user communication based on analyzing the received input. The present invention may then include analyzing an upcoming meeting based on the determined preferred mode of user communication. The present invention may further include analyzing a plurality of technical equipment available based on the analyzed upcoming meeting. The present invention may also include providing a recommendation to a user based on the group of analyses consisting of the preferred mode of user communication, the upcoming meeting and the plurality of technical equipment available. The present invention may then include analyzing the provided recommendation. The present invention may further include adjusting a recommendation model based on the analyzed recommendation.

Abstract: Examples of job allocation are described hereon. In an example, a job for allocation may be received. The job may be analyzed to obtain information pertaining to the job. The information may comprise at least one of a domain of the job and a priority level of the job. Further, performance of resources may be determined to provide resource information. The resource information may be determined using a supervised learning model comprising a job vector for each job type and a resource vector corresponding to each resource. The resource information may include a list of resources with at least one of a corresponding probability of each resource completing the job and a performance score of each resource. Based on the job information and the resource information, the resource may be recommended for the job using an expertise-estimation modeling technique and the job may be assigned to the recommended resource, accordingly.