Abstract: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises predicting details associated with collected data of an activity by generating a simulated activity based on a result associated with a plurality of attributes within the collected data associated with the activity; simulating the activity within a virtual reality environment by generating virtual items for assistance with a performance of the simulated activity within the virtual reality environment; in response to receiving user feedback for each generated virtual item based on the simulated activity, generating a need-based inventory from user input based on an analysis of the received user feedback for each respective item associated with the simulated activity within the virtual environment; and automatically obtaining each respective item within the generated need-based inventory.

Abstract: An information processing apparatus comprising a traffic line information generation unit configured to generate, based on information provided from an image capturing apparatus arranged in a store, traffic line information in the store of a customer who visits the store, an acquisition unit configured to acquire purchased product information associated with a product purchased by the customer in the store, a purchase information generation unit configured to generate purchase information based on the purchased product information by associating the traffic line information and the purchased product with the customer, and a display information generation unit configured to generate, based on the purchase information, display information for displaying information about one of a customer and a product in association with a time taken to select the purchased product and a selection order of the purchased product.

Abstract: An unstructured data input is accessed that includes an electronic communication. Content of the unstructured data is parsed to determine one or more terms in the unstructured data input. It is determined that one or more particular elements defined in a structured business data model correspond to the terms. Tags are assigned to the unstructured data based on the terms corresponding to the one or more particular elements, where the tags define an association between the unstructured data and the structured data model.

Abstract: A communication by a particular user within a digital collaboration is determined as related to a particular business entity. A relationship between one or more additional business entities and the particular business entity is identified from one or more business models. The communication is associated with the additional business entities based on the relationship.

Abstract: Described herein are various technologies pertaining to generating and/or updating a schedule such that the schedule includes a recurring appointment. The schedule is generated and/or updated based upon inputs received at a GUI that includes a matrix of checkboxes, such that the schedule is able to be constructed to include a recurring appointment for an Nth occurrence of a selected day in each month.

Abstract: Methods, apparatus, and processor-readable storage media for determining user retention values using machine learning and heuristic techniques are provided herein. An example computer-implemented method includes processing multiple forms of input data pertaining to interactions between a user and an enterprise; generating one or more user sentiment values from the processed input data by applying machine learning techniques to the processed input data; determining a user-specific estimate for the enterprise retaining the user, wherein determining the user-specific estimate comprises combining the one or more sentiment values with one or more storage system heuristics-based values derived from enterprise-related data; and outputting the user-specific estimate to at least one entity within the enterprise for use in connection with user-support actions.

Abstract: A method may include receiving a request to save the exception to the first rule; in response to the request, updating the user interface, wherein: the first portion is updated to include a parameter of a second rule to exclude a subset of meetings from the ECDS; the second portion is updated to include an impact metric that indicates a percentage of meetings excluded from the ECDS based on an application of the second rule to the ECDS; and the third portion is updated to include selectable elements for creating an exception to the second rule; and a fourth portion of the user interface includes an indication of a number of meetings excluded from the ECDS based on application of the first rule with any exceptions to the first rule.

Abstract: Embodiments for implementing intelligent customer journey prediction and customer segmentation of a processor in a computing environment. A response outcome of a customer journey for a user may be predicted according to an assigned score based on one or more discriminatory sequence patterns identified between one or more groups of customers using one or more machine learning operations.

Abstract: A computer implemented method and system of setting values of parameters of nodes in an omnichannel distribution system, the method comprising is provided. Input parameters are received from a computing device. Historical data related to the network of nodes is received from a data repository. A synthetic scenario is determined based on the received input parameters and the historical data. Each node is clustered into a corresponding category. For each category of nodes, key parameters are identified. A range of each key parameter is determined based on the synthetic scenario. A number of simulations N to perform with data sampled from the synthetic scenario within the determined range of each key parameter is determined. For each of the N simulations, a multi-objective optimization is performed to determine a cost factor of the parameter settings. The parameter settings with a lowest cost factor are selected.

Abstract: Embodiments of this application provide a seed group spreading method performed at a server. The method includes the following steps: obtaining a positive sample set; obtaining a negative sample set; concatenating each positive sample in the positive sample set with a corresponding positive sample feature to form a positive sample feature vector, and concatenating each negative sample in the negative sample set with a corresponding negative sample feature to form a negative sample feature vector; obtaining a target sample feature set from the positive sample feature vector and the negative sample feature vector according to at least two of a target group index, an information gain, and a logistic regression model; and sending the target sample feature set to a decision end, receiving feedback information of the decision end, and determining, according to the feedback information, whether to spread the seed group.

Abstract: A computer implemented method and system of calculating labor resources for a network of nodes in an omnichannel distribution system. Input parameters are received from a computing device of a user. Historical data related to a network of nodes is received, from a data repository. A synthetic scenario is determined based on the received input parameters and the historical data. For each node, key parameters are identified and set based on a multi-objective optimization, wherein the multi-objective optimization includes a synthetic inventory allocation to the node based on the synthetic scenario. A synthetic labor efficiency is determined for the node from the synthetic scenario. Labor resources are calculated based on the synthetic inventory allocation for the synthetic scenario. The labor resources of at least one node are displayed on a user interface of a user device.

Abstract: Methods and systems for managing inventory movements are disclosed. Transfer orders are issued to move inventory from location to location in response to real-time demand events. One method includes receiving an inventory request in response to demand signals. The inventory request is analyzed to determine if inventory needs to be moved within the supply chain to satisfy the request. Origin locations are identified that have available stock of the requested items. Transfer orders are issued for each movement on an individual item basis regardless of the type of event that prompted the transfer. The transfer management system responds in real-time to inventory demands from both online and in-store sales.

Abstract: Embodiments provide a computer-executable method for programmatically generating one or more survey queries. The method includes accessing information on a commercial entity or object. The method also includes, based on the accessed information, determining an attribute descriptor associated with the attribute. The method further includes programmatically generating a survey query indicating the attribute descriptor. The method further includes transmitting one or more computer-executable instructions to a computing device to cause a visual display device to render a user interface, the user interface displaying the survey query and enabling user input in response to the survey query.

Abstract: A plan model system provides interactive graphical user interfaces allowing users to view and navigate to multiple alternative views of measures modeled in one or more planning models. A modeled event is provided by a user relating to a measure of a particular plan model modeling outcomes of a particular business domain of a business organization. An effect of the event on values of one or more measures of the plan model is determined. A graphical representation is presented in the graphical user interface illustrating the effect.

Abstract: Disclosed are blockchain-based systems and methods for analyzing and tracking work performance. An example method may commence with detecting a plurality of actions performed by one or more users with regard to one or more digital files. The method may continue with creating a timestamp for each of the plurality of actions. The method may further include generating a hash value for each of the plurality of actions. The method may continue with storing the hash value and the timestamp associated with each of the plurality of actions to a record on a blockchain. The method may further include analyzing the hash value and the timestamp associated with the plurality of actions of the one or more users to evaluate the work performance of the one or more users.

Abstract: A system for providing a visual display of a visitor's journey pathways through various digital touchpoints and quantitative measurements of the visitor's interaction with each digital touchpoint includes an online visitor meta-data database, an online images database, a visitor journey analytics code, a visitor journey analytics engine, a computing system, and a user interface. The visitor journey analytics code is embedded in each digital touchpoint and the visitor journey analytics code extracts visitor meta-data associated with each digital touchpoint that a visitor visits and uploads them to the online visitor meta-data database. The visitor journey analytics engine includes a customer journey analytics processing software product that receives visitor meta-data and specific online images for each digital touchpoint a visitor visits, calculates a visitor experience performance index for each digital touchpoint, and records visitor's journey pathways through various digital touchpoints.

Abstract: Methods, apparatuses, and computer program products are provided for automatically identifying a shortest duration of time for an aspect of an operation. Electronic data related to instances of past operations of at least one vehicle is received. The data comprises respective available durations of time and actual durations of time for an aspect of the past operations. A candidate data set is generated by removing data related to past operations identified as isolated instances of available durations of time and actual durations of time. A mathematical function is generated that fits the candidate data set. An instance from the candidate data set with the shortest actual duration of time that is less than a threshold distance from the mathematical function is identified. The actual duration of time for the identified instance is assigned as a future duration of time for future operations of the at least one vehicle.

Abstract: Systems and methods relate to predicting improved operating hours for a merchant. For example, a method may include determining operating hours of a target merchant; identifying one or more merchants other than the target merchant having one or more common characteristics with the target merchant; obtaining transactional data indicating customer transactions at the one or more merchants other than the target merchant; computing an estimate of potential customer transactions at the target merchant during a the period of time not within the operating hours by processing input data including the obtained transactional data using a trained machine learning model to produce the estimate, the estimate being a number or value of consumer transactions missed as a result of the target merchant being closed during the period of time; and transmitting, to the target merchant, information indicating the computed estimate.

Abstract: Crowdsourcing using active learning is described, for example, to select pairs of tasks and groups of workers so that information gained about answers to the tasks in the pool is optimized. In various examples a machine learning system learns variables describing characteristics of communities of workers, characteristics of workers, task variables and uncertainty of these variables. In various examples, the machine learning system predicts task variables and uncertainty of the predicted task variables for possible combinations of communities of workers and tasks. In examples the predicted variables and uncertainty are used to calculate expected information gain of the possible combinations and to rank the possible combinations. In examples, the crowdsourcing system uses the expected information gain to allocate tasks to worker communities and observe the results; the results may then be used to update the machine learning system.

Abstract: A method for determining a risk of an emotional response of an audience to at least one publication is described. Additionally, a computing device for determining a risk of an emotional response of an audience to at least one publication is described. Moreover, a computer-readable storage device having processor-executable instructions embodied thereon, for determining a risk of an emotional response of an audience to at least one publication, is described.