Abstract: A device may receive information associated with an entity. The information may include a first resource and a second resource. The first resource may be associated with a first file type, and the second resource may be associated with a second file type that is different than the first file type. The first resource may be associated with a first source, and the second resource may be associated with a second source that is different than the first source. The device may extract a plurality of attributes associated with the entity based on the information. The device may implement a natural language processing technique to extract the plurality of attributes. The device may associate the plurality of attributes with a plurality of elements based on extracting the plurality of attributes. The device may provide information that identifies the plurality of elements and the plurality of attributes to permit and/or cause an action to be performed.

Abstract: A system may receive video data generated by a camera device positioned in a physical environment. The system may receive electromagnetic (EM) response signals generated by an array of 5G antenna devices. The system may refine the EM response signals. The system may determine location information for a metallic object. The system may generate a movement trajectory of the metallic object. The system may generate a feature vector comprising an aggregation of the refined EM response signals and the movement trajectory. The system may submit the feature vector to a machine learning framework. The system may classify the metallic object as a weapon. The system may generate a virtual render comprising a graphical indication of the metallic object. The system may combine the virtual render with the video input data and display the combined virtual render and video data.

Abstract: An asset tracking system helps an enterprise correctly and efficiently determine when assets are created and when they are re-used. Re-use of assets often creates a positive impact, e.g., on other projects ongoing within the enterprise. The system detects the asset re-use, determines the asset impact, and automatically updates a dynamic profile of the asset creator to register the value of their work. In some implementations, the system implements a virtual currency system that also rewards the supervisors of the asset creator, and allows the virtual currency to be exchanged for project resources needed by the supervisors.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for accessing a database comprising multiple datasets. Each dataset includes data derived from a respective application. A machine-learning engine determines an analytical rule using at least one dataset of the multiple datasets. The analytical rule is determined by processing input data obtained from the at least one dataset derived from the respective application. A structured dataset is generated based on the determined analytical rule. The structured dataset is generated in response to using the determined analytical rule to analyze data from each dataset of the multiple datasets derived from the respective application. One or more data sequences that represent information flow of a transaction process are determined based on the structured dataset.

Abstract: A power usage prediction system implements a long short term memory (LSTM) neural network to receive power usage inputs and generate predicted values of power consumption for a plurality of devices. A user provides configuration input regarding the time steps at which the predicted values are to be generated and the various devices for which the predicted values of power consumption are desired. Whenever a power usage input is received, the LSTM neural network outputs the corresponding hidden state values for a plurality of time steps as the predicted values. The hidden state values are each compared to a final cell state value corresponding to a power consumption threshold of the time interval which includes the time steps. Based on the comparison, a power usage condition is recorded. Various actions to mitigate the high power consumption can be implemented in response to recording the power usage condition.

Abstract: An information request processor analyzes an information request and automatically selects search queries and information sources that are responsive to the information request. Prior reports and portions of browsing history that were generated during the creation of the prior reports are selected based at least on a primary entity included in the information request. The entities extracted from the prior reports using trained Information Extraction (IE) models are mapped to the search terms extracted from the portions of the browsing history in order to identify the successful search queries that provided the information for the prior reports. A report responsive to the information request can be generated either automatically or by receiving user input that validates and rephrases the successful search queries.

Abstract: A device may extract content, path data, and query data from API requests, and may identify events based on the extracted data. The device may combine a sequence of the events with the events to generate sequence-embedded events, and may train a neural network model with the sequence-embedded events to generate a trained neural network model. The device may receive an API request, may extract new content, new path data, and new query data from the API request, and may identify new events based on the newly extracted data. The device may process the new events, with the trained neural network model, to generate predicted events, and may calculate an anomaly score based on the predicted events. The device may compare the anomaly score with a threshold to determine a risk for the API request, and may cause the API request to be blocked or permitted based on the risk.

Abstract: A system for generating an architecture diagram includes an input processor, a machine learning processor, and an advice generator. The input processor is configured to receive, from a terminal, entity data associated with a plurality of entities of an architecture and path data associated with a plurality of paths that correspond to interconnections between the plurality of entities. The machine learning processor utilizes a training dataset to assess whether the entities defined by the entity data are correctly interconnected as defined by the path data. The advice generator receives the assessment from the machine learning processor, prepares a recommendation based on the assessment, and communicates the recommendation to the terminal. User feedback is represented in the training data to improve the relevancy of the recommendation.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to analyze and categorize events associated with an equipment asset, such as industrial machinery, to determine a status (e.g., insight) associated with the equipment asset, and to determine maintenance actions to be performed with respect to the equipment asset to prevent, or reduce the likelihood or severity of, occurrence of a fault at the equipment asset. Machine learning (ML) models may be trained to categorize events that are detected based on operating characteristics data associated with the equipment asset, to determine a status of the equipment asset, and to recommend one or more maintenance actions (or other actions). Output that indicates the maintenance actions may be displayed to a user or used to automatically initiate performance of one or more of the maintenance actions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for service oriented software-defined security framework are disclosed. In one aspect, a system includes a security control device, one or more assets, and a security controller that communicates with the security control device and the one or more assets. The security controller includes a processing engine configured to register the security control device by creating a physical-logical attribute mapping for the security control device, and generating a security service description associated with the security control device. The processing engine is further configured to register the one or more assets by creating a physical-logical attribute mapping for each of the one or more assets, and generating security service requirements for each of the one or more assets. The processing engine is further configured to generate a security service binding based on a request for service.

Abstract: A system for detecting and preventing an imminent failure in a target system includes an interface, a processor in communication with the interface, and non-transitory computer readable media in communication with the processor. The interface receives training data items. Each item corresponds to either a service ticket or a machine-generated log that specifies an issue and a resolution to the issue. The instruction code is executed by the processor and causes the processor to group the training data items according to different categories. For each group, the processor trains a model to match the issue of each item of the group with the corresponding resolution and associates a model configuration of the trained model with the category of the group. The processor receives a sequence of new data items that include service tickets or machine generated logs for which a resolution is unknown. The processor groups the new data items according to one or more categories.

Abstract: A device may determine a behavioral pattern of an account over a past time period based on data relating to one or more transactions associated with the account. The device may identify one or more quantitative features of the behavioral pattern and one or more spatial features of the behavioral pattern. The device may determine an account type cluster to which the account belongs, based on the one or more quantitative features and the one or more spatial features identified. The device may determine, based on the account type cluster that is determined, a model for processing the behavioral pattern. The device may predict, using the model that is determined, an amount of funds that is likely to remain in the account during a future time period. The device may perform one or more actions based on the amount of funds that is predicted.

Abstract: The vulnerability of network devices may be predicted by performing a survival analysis on big data. A prediction algorithm may be built by considering historical data from heterogeneous data sources. The operating state of the network devices on a network may be predicted. The services potentially affected by a predicted outage may be determined and displayed. Alternatively or in addition, the number of clients potentially affected by a predicted outage may be determined and displayed.

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.

Abstract: A productivity measuring, modeling and illustrating system receives input data pertaining to the productivity of an organization to generate various productivity views using a productivity maturity model. The productivity maturity model is based on suggested actions for increasing the productivity which are received in the input data. The productivity maturity model computes various productivity gains and provides productivity levels for the productivity levers associated with the organization. Various productivity views generated to display the productivity information include a summary view that displays the productivity gains and productivity levels using different filters.

Abstract: A machine-to-machine communication platform provides a flexible system for device control and solution hosting. In particular, the communication platform hosts and manages third party vertical solutions that interact with external devices. A third party gateway provides the third parties with access to the communication platform so that the third parties may define, configure, and monitor custom vertical solutions that are locally hosted in the communication platform. The communication platform provides a communication manager that implements a device independent communication facility for communicating with the external devices. As a result, the communication platform connects to, communicates with, and controls virtually any external device.

Abstract: Examples of systems and methods for automatic population of electronic documents are described. In an example, a digital base document having the information to be populated in a data field of the electronic document may be obtained. From the digital base document a data item to provide the information may be extracted. Further, for the digital base document, a similarity score may be computed with respect to each document type defined in predefined mapping data, the predefined mapping data including, for each document type, a weight associated with data items occurring in the document type, the weight being associated based on the importance of the data item to the document. Based on the similarity score, a document type of the digital base document may be identified. Further, based on a position of the data item in the digital base document and the identified document type, the data field may be populated.

Abstract: Examples of a prediction explanation system are be provided. The system may obtain a first data record comprising multiple case instances and a first data record neural network providing multiple predictions, each prediction being associated with a case instance. Each case instance may be analyzed to determine a corresponding hidden neuron contribution score, which may be used for clustering the cases instances into multiple instance clusters. For each instance cluster, a decision tree model may be generated, where a decision tree model comprises an explanation for a prediction associated with the case instance in the instance cluster. For a second data record obtained by the system, a second data index comprising a cluster mapping score assigned to each decision tree model may be determined. Based on the decision trees model with a highest cluster mapping score, a second data prediction output providing an explanation for a prediction may be generated.

Abstract: Examples of a content alignment system are provided. The system may receive a content record and a content creation requirement. The system may implement an artificial intelligence component to sort the content record into a plurality of objects and for identifying an object boundary for each of the plurality of objects. The system may identify a plurality of images and implement a first cognitive learning operation to identify an image boundary for each of the plurality of images. The system may identify a plurality of exhibits and implement a second cognitive learning operation to identify a data pattern associated with each of the plurality of exhibits. The system may implement a third cognitive learning operation for determining a content creation model by evaluating the plurality of objects, the plurality of images, and the plurality of exhibits. The system may generate a content creation output to resolve the content creation requirement.

Abstract: Examples of prospect recommendation including a prospect recommendation system are provided. The system may receive a prospect assessment query. The system may receive and sort prospect data, sales attribute data, and product data by applying an artificial intelligence component. The system may determine a prospect matrix by correlating the prospect data with the product data. The system may determine a prospect profile by collating historical prospect data and the product data. The system may determine a prospect assessment matrix by correlating the prospect matrix and the prospect profile. The system may determine a decisional pathway based on a comparison between the prospect profile and the prospect assessment matrix. The decisional pathway may include a plurality of assessment interpretations. The system may determine a prospect recommendation index based on the decisional pathway.