Abstract: A system and a method of managing a manufacturing process includes receiving production data relating to the manufacturing process and determining an operational mode associated with the manufacturing process using historical, multivariate senor data. The method may further determine a recommended action to affect production based on the determined operational mode. The operational mode may be based on at least one of: a level of operation in a continuous flow process relating to a joint set of process variables, a representation of a joint dynamic of the set of process variables over a predefined length, and a joint configuration of an uptime/downtime of a plurality of units comprising a process flow.

Abstract: Methods provide cognitive management of multiple subaccounts wherein processors are configured to determine an expense type for an input of an expense event to a linked account structure that links subaccounts that are each associated to different respective ones of a plurality of group members. In response to determining that the input expense event expense type most-strongly matches an expense type of a nearby in time calendar expense, methods allocate expense costs of the input expense to member subaccounts pursuant to a first expense allocation rule utilized in the nearby calendar event for allocating expenses. In response to determining that the input expense event expense type does not match the expense types of a nearby calendar events, methods further allocate the input expense event costs pursuant to a second expense allocation rule selected as a function matching the input event expense type to behavior data of a member.

Abstract: The disclosed embodiments relate to systems and methods calculation of an estimation solution for a constraint polytope. The apparatus may include a database, a communication interface, and a polytope controller. The database stores data indicative of a constraint polytope. The communication interface is configured to receive a first point batch from an external computer system, and the first point batch includes a plurality of data points representing transactions. The polytope controller analyzes the plurality of data points and performs a projection of the plurality of data points to the constraint polytope and generates a dynamic adjustment for a second point batch for the constraint polytope.

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: 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: This disclosure relates to a system and method to estimate demand transfer of a product while considering performance of all the products of a category simultaneously. It would be appreciated that the demand of a removed product transfers to other products of same category in a store. In addition the demand transfer is influenced by sales drivers such as product level promotion and competitor prices, store location, weather and seasonality. By considering these factors the proposed approach provides a method to estimate demand transfer of a product. It is addressed by creating multivariate multi structure machine learning models and estimating demand transfer values by using suitable scenario generator for product availability. It enables to estimate more holistic demand transfer values by simultaneous consideration of individual product behaviours with respect to other products availability and other sales drivers.

Abstract: This disclosure relates generally to methods and systems for predicting wait time of queues at service area such as market places including retail stores and super markets. The present methods and systems accurately predicts the wait times of the plurality of queues by utilizing various visual cues of the customers, along with the number of service items and the efficiency of the service operator. The visual cues including a demographic factor such as age, gender, ethnicity of the customer and a senti-motional factor such as sentiments including positive attitude or negative attitude and emotions of the customers including happy state, sad state, and irritation state of the customer. The customers may choose the queue having least predicted wait time and may take informed decision hoping for faster check-out, based on the predicted wait times of the queues. Hence, customer experience and customer satisfaction may be achieved.

Abstract: An insight engine that takes into account a wide variety of information from a wide variety of sources for predicting a sales insight is generated. The insight engine is generated using machine learning. Historical customer-specific information, product-specific information, and environmental information are aggregated, based on customer, product, and/or time period, into historical customer profiles. The historical customer profiles are labeled with historical sales insights to form a training set. A machine learning algorithm is applied to the training set to generate an insight engine. The insight engine is applied to a target customer profile to determine a predicted sales insight for a target entity.

Abstract: Predictive modeling within a special purpose hardware platform to determine scenarios that are most likely to increase conversion potential for each trial user and retention potential for each active subscriber of a service, collectively referred to as a propensity score. The predictive models are integrated with a contextual marketing system that uses a loss risk assessment to learn user behavior and optimize content messaging designed to improve actual conversion or retention behavior for the user.

Abstract: An online connection server is configured to more accurately predict connections for a viewing member of an online connection network. The online connection server may implement a machine-learning model that uses prior interactions by the viewing member to determine those connections that are likely to lead to more substantial interactions with the viewing member. The machine-learning model may be implemented using a reinforcement learning technique, such as a Deep Q network. The online connection server may further implement a state representation module that generates a state from a graph-based embedding of the viewing member profile, where the state is used to train the machine-learning model and determine an optimal candidate to recommend as a connection for the viewing member.

Abstract: A device may receive data that identifies applications utilized by users, databases utilized by the applications, and the users, and may process the received data, with first models, to determine context data that matches the users and events associated with the applications, and task data that identifies tasks to be performed by the users in response to the events. The device may process the received data and the context data, with a second model, to generate role data that identifies user interfaces utilized by the users to access the applications, and credentials of the users, and may process the context data, the task data, and the role data, with a third model, to generate persona data that identifies personas, and assignment data that assigns each of the users to one of the personas. The device may perform actions based on the persona data and the assignment data.

Abstract: Systems and methods are provided for evaluating workplace risk by associating sensors with individual members of a workforce to measure conditions affecting the individual. Data is obtained from the sensors for reoccurring work periods for each individual so that a risk analysis may be performed to derive a risk score. Probability of injury is calculated aggregating risk scores for multiple reoccurring work periods. The calculated probability of injury is used to quantify workplace risk for the at least one individual and an enterprise risk assessment is provided by combining the quantified risks for each individual.

Abstract: Machine learning object-searching methods and apparatuses are disclosed. The method comprises: selecting a state from a set of states of a target object-searching scene as a first state; obtaining a target optimal object-searching strategy whose initial state is the first state for searching for a target object; performing strategy learning by taking the target optimal object-searching strategy as a learning target to obtain an object-searching strategy by which a robot searches for the target object, and adding the obtained object-searching strategy into an object-searching strategy pool; determining whether the obtained object-searching strategy is consistent with the target optimal object-searching strategy; if yes, determining that the strategy learning in which the first state is taken as the initial state of the object-searching strategy is completed; and if not, returning to the step of selecting a state from a set of states of a target object-searching scene.

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 maintenance range optimization apparatus 10 optimizes a range of maintenance on an object that requires maintenance at a plurality of places. The maintenance range optimization apparatus 10 includes a learning processing unit 20 that executes machine learning, using, as learning data, information from when maintenance was previously executed, including a pre-maintenance state, a maintenance cost and a movement cost of a place subjected to maintenance, and constructs a model indicating a relationship between the range of maintenance and an overall cost incurred in maintenance, and a maintenance range setting unit 30 that sets the range of maintenance using the model.

Abstract: In one embodiment, a method carried out on a computer system for parsing a software model to identify manufacturing requirements for a designed product and comparing those requirements, along with received user preferences. to corresponding attribute information from potential suppliers that is stored on a database, so as to automatically select a preferred supplier.

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: The purpose of the present invention is to effectively deploy limited human resources. Provided is an information processing device (210) including: a specifying unit (211) that specifies positions of customers in a certain space; a determination unit (212) that determines the deployment of store clerks on the basis of the number of store clerks and the specified positions; and an output unit (213) that outputs deployment information indicating the determined deployment.

Abstract: The present teaching relates to managing computing resources. In one example, a search query is received from a user. A plurality of performance scores associated with a plurality of providers is obtained from a provider score database. One of the plurality of providers is selected based on the plurality of performance scores. The search query is transmitted to be performed by the selected one of the plurality of providers. One or more search results are received in response to the search query from the selected one of the plurality of providers. The one or more search results are presented to the user to be displayed on a user device.

Abstract: A processing device is configured to detect arrival of at least a particular user in a group of a plurality of users competing for designated resources of a system, and responsive to the detected arrival, to solve a resource allocation optimization problem that considers individualized characteristics of user resource allocation requests of respective ones of the users to obtain potential assignments of respective ones of the users to respective ones of the resources. The processing device is further configured to update one or more game model parameters based at least in part on the solution to the resource allocation optimization problem, to update a specified look-ahead function based at least in part on the one or more updated game model parameters, and to assign the particular user to a particular one of the resources based at least in part on a difference between an updated value of the look-ahead function and a previous value of the look-ahead function.