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: A device may receive network data, business data, and user configuration data associated with an entity that is a candidate for a private network and may process the business data and the user configuration data, with a classification machine learning model, to determine a network hardware equipment prediction. The device may process the network data and the business data, with a first linear regression machine learning model, to determine a business output prediction and may utilize a second linear regression machine learning model to determine a data consumption prediction based on the network hardware equipment prediction. The device may process the network hardware equipment prediction, the business output prediction, and the data consumption prediction, with a machine learning model, to determine a financial profitability prediction for the private network and may perform one or more actions based on the financial profitability prediction.

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: The disclosed embodiments relate to systems and methods calculation of an estimation solution for a constraint defined within a computation space. The apparatus may include a constraint computation controller. The constraint computation controller analyzes the plurality of data points and performs a projection of the plurality of data points to the constraint within the computation space and generates a dynamic adjustment for reducing a margin requirement consistent with the constraint.

Abstract: A collaborative development project having a plurality of contributors is provided. A first contributor of the plurality of contributors as an influencer of the collaborative development project. Contributions of the influencer to a sample project are analyzed to determine a development characteristic of the influencer. One or more development rules are then generated based on the determined development characteristic of the influencer. Further, a second contributor of the plurality of contributors may be identified as a follower of the development project. Identified contributions of the follower may then be modified based on the obtained one or more development rules.

Abstract: The present disclosure relates to system(s) and method(s) for building an ARIMA based Time Series prediction/forecast model for Key Performance Indicators (KPIs) and Performance Management (PM) counters in a telecommunication network. The system receives historical data, for a predefined period, associated with a prediction/forecast model. The system further pre-processes the historical data in order to evaluate statistical characteristics of stationarity of the historical data. Based on the evaluation, the system stationarizes the data first by backfilling anomalies and missing data and then applying techniques associated with differencing, moving averages and auto-correlation. The system further builds the Time Series based prediction/forecast model using the data using ACF and PACF correlation functions.

Abstract: A method of enabling expedited manufacture of one or more instantiations of a structure modeled as a 3D computer model, the method implemented on one or more computer devices including displaying one or more user selectable expedited manufacturing reservation options for a plurality of requests for manufacture, generating a prompt to select a user selectable expedited manufacturing reservation option, receiving an expedited manufacturing, storing the expedited manufacturing reservation, receiving a request for manufacture of one or more instantiations of a structure modeled in an associated 3D computer model from a user, compare one or more of the requests for manufacture and one or more aspects of the associated 3D computer model with the expedited manufacturing reservation, determining an applicability of the expedited manufacturing reservation to the request for manufacture, calculating a total time to complete the request for manufacture, and presenting the total time to complete the request for manufactu

Abstract: A task management platform generates an interactive display tasks based on multi-team activity data of different geographic locations across a plurality of distributed guided user interfaces (GUIs). Additionally the task management platform uses a distributed machine-learning based system to determine a suggested task item for a remote team based on multi-team activity data of different geographic locations.

Abstract: Provided is an information processing system including an accumulating unit that accumulates an emotion history in which a level of a specific emotion calculated on a basis of a behavior history of a user, a date and time and a position at which the specific emotion occurs, and an associated keyword are associated, and a control unit that predicts a level of the specific emotion in a specific place at a specific date and time from a relation between at least any one of the specific date and time, the specific place, and a keyword associated with the date and time or the place and the emotion history accumulated in the accumulating unit.

Abstract: Most current systems decompose sales volume of a product into base volume and additional volumes associated to each sales driver. Here base volume refers to sales quantity that is derived from non-promoted sales period. It leads base volume of a product with partial price effect. In addition, base volume includes volume contributed by store location, space allocated to product category and assortment etc. In the present disclosure a system and method for sales decomposition is described by computing reference base volume and sales contribution by each sales driver. Here reference base volume is the volume derived by excluding the effect of sales drivers namely, retailer price, competitor price, demographics, weather conditions, space occupied by the category to which the product belongs, number of available substitute products in assortment. Retail strategies are recommended in real time based on sales contribution of the sales driver for the product.

Abstract: Disclosed is a method and system for modifying data cleansing techniques for training and validating an Artificial Neural Network (ANN) model. The method comprises normalizing and validating data of different formats, obtained from different sources. The ANN model is trained using the normalized and validated data. Alternatively, the ANN model could be trained using data of a common format obtained from a deterministic data model. The trained ANN model is used to generate ANN stochastic data. Data validation component from the deterministic data model is reused for the normalizing and the validating of the data, for validating the ANN stochastic data.

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: Embodiments can provide a computer implemented method for simulating customer data using a reinforcement learning model, including: generating an artificial customer profile by combining randomly selected information from a set of real customer profile data; providing standard customer transaction data representing a group of real customers having similar transaction characteristics as a goal; performing a plurality of iterations to simulate the standard customer transaction data; and combining the artificial customer profile with the simulated customer transaction data to form simulated customer data. In each iteration, the method includes conducting an action including a plurality of simulated transactions; comparing the action with the goal; providing a feedback associated with the action based on a degree of similarity relative to the goal; adjusting a policy based on the feedback.

Abstract: A system, method, and computer-readable medium for determining customer adoption based on monitored data, including receiving product usage parameters from a product data store on the computer network, each product usage parameter being determined based on tracking usage of the product by the customer over a predetermined time period, storing a customer profile for the customer comprising customer parameters, the customer parameters being determined based on customer information stored in a customer database on the computer network, receiving service parameters from a customer support data store on the computer network, each service parameter being determined based on tracking support services provided to the customer for the product over the predetermined time period, and generating a product adoption score by applying a machine learning model to the product usage parameters and the customer profile to generate a usage-based adoption score and adjusting the usage-based adoption score based on the service par

Abstract: Systems and methods are provided for recommending price of benefit items offered through a membership platform. Exemplary implementations may: obtain benefit information for content creators of a membership platform; obtain consumption information, the consumption information describing acceptance of offers for the benefit items at the requested amounts by the subscribers of the content creators; train a machine learning model on input/output pairs to generate a trained machine learning model, the individual input/output pairs including training input information and training output information; store the trained machine learning model; determine, using the trained machine learning model, recommended amounts of consideration for the benefit items that correspond to greater acceptance; generate recommendations for individual content creators conveying the recommended amounts for the benefit items offered by the individual content creators; and/or perform other operations.

Abstract: A prediction of the performance of a movie is generated in response to receiving an identifier of the movie. Features of the movie are identified and a set of similar movies is identified based on the features. Performance statistics are calculated for the set of similar movies that indicate the average financial performance of those movies and a prediction of performance for the movie is generated based on the performance statistics. The prediction is provided for presentation to a user.

Abstract: Systems and techniques are generally described for detecting evasive terms in item listings. In some examples, a first list of terms is determined. Terms of the first list of terms may be associated with evasive listing practices. In various examples, a first embedding representing a first term in an embedding space may be determined. In some examples, the top k closest embeddings in the embedding space to the first embedding may be determined. A term associated with one of the top k embeddings may be determined. A first Levenshtein distance between the first term and the second term may be determined. A list of items associated with the second term may be determined based on the Levenshtein distance being less than a threshold distance. An alert related to the list of items may be generated.

Abstract: A method includes executing operations to generate a first enhancement function based on a parent-child link in a content hierarchy including a link between a parent node in a first level of the content hierarchy to a child node in a second level of the content hierarchy below the first level. A second enhancement function is generated based on a sibling link in the content hierarchy including a link between a sibling node in a third level of the content hierarchy and a sibling node in the third level of the content hierarchy sharing a common parent node with the first sibling node in a fourth level of the content hierarchy above the third level. A user content consumption metric is generated based on the first and second enhancement functions. A content list including a set of candidate content items ranked based on the user content consumption metric is generated.

Abstract: Product demand forecasting accuracy utilizes partitional clustering of time series data with dynamic time warping. The product demand forecasting disclosed herein is particularly suited to forecasting product demand for products with limited sales data. Time-series sales data of a producs (or group of products) with limited sales data (e.g. a sparse or no time series of sales data) are dynamically time warped with sales data of products, or groups of products, having extensive sales data (e.g., an extensive time series of sales data) to determine a clustering model with an optimal number of clusters and a prototype time series for each cluster in the model. The prototype time series for the cluster in which the product (or group of products) with limited sales data lies is utilized as its product demand forecast.

Abstract: As described herein, a machine learning system, method, and computer program provide marketing segmentation of residential spaces. In use, network usage data is collected from each residential network router of a plurality of residential network routers operating in a different residential space of a plurality of residential spaces. Additionally, the network usage data is processed by a machine learning algorithm to segment the plurality of residential spaces into a plurality of segments. Further, the plurality of segments are output.