Embodiments of the present invention provide systems and methods for automated and intelligent analysis of information. The system receives interaction data, interaction metadata, and external information in order to identify parties of interactions, subjects of interactions, and infer relationships between parties and subjects based on the content, context, frequency, and amount of available interaction data. Weighted score scores are generated and used to rank the inferred relationships and determined relevance between parties and subjects. This data may be stored in a graphical database and later used to response to user data queries to facilitate collaboration.

A method includes receiving target data for a paving project corresponding to a work period. The method also includes receiving sensor data from one or more components associated with the paving project, determining a progress value representing progress of the one or more components of the paving project, and determining a threshold progress value for a time period, the time period having a duration that is less than a duration of a work period. The method further includes determining that the progress value is less than the threshold progress value, generating at least one of an alarm or a recommendation, and sending the alarm or the recommendation to an electronic device.

This disclosure relates generally to a method and system for dynamically predicting vehicle arrival time using a temporal difference learning technique. Due to varying uncertainties predicting vehicle arrival time and travel time are crucial elements to make the public transport travel more attractive and reliable with increased traffic volumes. The method includes receiving a plurality of inputs in real time and then extracting a plurality of temporal events from a closest candidate trip pattern using a historical database. Further, a trained temporal difference predictor model (TTDPM) is utilized for dynamically predicting the arrival time from the current location of the vehicle to the target destination based on the plurality of nonlinear features. The non-linear features and linear approximator formulation of TTDPM provides fast gradient computation improves training time. Additionally, updating the revised state information at every iteration provides better accuracy of arrival time prediction in real time.

An enterprise resource planning system. The system comprises an enterprise resource planning (ERP) server operable to implement an enterprise resource planning application configured to be able to process planned data that is input by one or more users via a respective user device of a plurality of user devices. The planned data relates to a plurality of events associated with delivery of a plurality of items within an item distribution network. The system comprises a plurality of marker generation devices. Each marker generation device is operable to generate a plurality of markers that are each associated with a respective item for tracking delivery of that item within the item distribution network. Each marker comprises respective identification data relating to one or more attributes of the item with which is it associated. The system comprises a data collection server operable to communicate with the ERP server via a network, and a plurality of marker readers located at respective item reading locations. The plurality of marker readers are each operable to read the identification data from the markers and send the identification data together with respective measured attribute data to the data collection server via the network. The ERP server is operable to receive actual event data. The actual event data comprises the identification data and respective measured attribute data generated by the plurality of marker readers. The ERP server is operable to generate analytic data for analysis based on the planned data and the actual event data.

The invention relates to a method, a system, a computer program product and a service for providing up-to-date and verified information of an intermediate product-specific sustainability indicator. The method comprises receiving a first input data (102, 202, 302) including internal information on the intermediate product, receiving a second input data (104, 204, 304) including external information on raw material(s) used for the intermediate product, processing at least the first input data (102, 202, 302) according to a life cycle assessment method in order to produce an intermediate product-specific life cycle parameter, processing at least the second input data (104, 204, 304) in order to produce an intermediate product-specific sustainability parameter, providing an intermediate product-specific sustainability indicator (108) comprising the intermediate product-specific life cycle parameter and the intermediate product-specific sustainability parameter, and associating the intermediate product-specific sustainability indicator (108) for the intermediate product.

Provided is a method for classifying information technology (IT) service request messages. The method may include receiving data associated with an IT service request message, determining a plurality of number values associated with a plurality of characters included in the IT service request message, generating a vector that includes index values, generating a first bitmap based on generating the vector, generating a second bitmap based on the first bitmap, where the second bitmap has a first dimension and a second dimension, and where the first dimension and the second dimension are equal, and determining a classification of the IT service request message using a neural network algorithm. A system and computer program product are also disclosed.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

A method includes generating an intervention model for a population of users based on contact data, demographic data, and engagement data indicating successfulness of prior interventions for each of the population of users. The method includes, obtaining first data related to a first user, including engagement data indicating successfulness of prior interventions with the first user. The method includes supplying the obtained data as input to the intervention model to determine an intervention expectation, which indicates a likelihood that the first user will take action in response to an intervention. The method includes determining a likelihood of a gap in care. The method includes, in response to the care gap likelihood exceeding a minimum threshold, selecting and scheduling execution of a first intervention. The first intervention is one of a real-time communication with the first user by a specialist and an automated transmission of a message to the first user.

The present invention relates to an artificial intelligence method and system for event predication, comprising: receiving, user messages, user activity data, event data, user identification information and transaction data; scraping webpages for additional event data; applying a natural language processing module to process the event data; constructing a training data set using the processed event data; constructing user preferences from the user messages, the user activity data, the user identification information and the transaction data; training a predictive model using the training data set to determine at least one upcoming event predictions determining to display the at least one event predictions based on the user profile; if it is determined to display one of the at least one event predictions, generating a graphical user interface display with a calendar depicting the at least one event prediction; and presenting the graphical user interface display to the user.

A computer-based method is provided for managing a transaction including provision of a process intelligence engine comprising a workflow aligner and process tool box, receiving deal parameters at the process intelligence engine, defining transaction subjects, each requiring the participation of at least one network partner, where each transaction subject is a requirement for achieving the objective of the transaction, defining, for each transaction subject, a plurality of subject goals to be addressed by a network partner, and defining, for each subject goal at least one action item required for satisfying the subject goal. The subject goals are then sequenced by the workflow aligner by defining prerequisites for at least one subject goal and transaction modules are defined based on the sequencing. During execution of a deal using the method, subject goals are not made available until prerequisite subject goals have been completed.