Abstract: A method for controlling access to one or more of a plurality of target systems includes receiving profile data that defines one or more features associated with a plurality of individuals with one or more entitlements of those individuals. Each entitlement is indicative of target system access. The method further includes generating a model that relates the one or more features and the one or more entitlements of the plurality of individuals. Profile data that defines one or more features associated with a target individual is received from a first user management system. A listing that includes one or more entitlements associated with the target individual, and confidence values associated with the one or more entitlements is generated based on the profile data and the model. Each confidence value is indicative of whether the target individual should be granted a corresponding entitlement.

Abstract: In some examples, artificial intelligence based plantable blank spot detection may include generating a plurality of clusters of input images of areas that are to be analyzed for plantable blank spot detection. For each cluster of the plurality of clusters, a model may be identified to analyze corresponding images of a cluster. A model may be selected, from the models identified for the plurality of clusters, to analyze the input images. Canal lines may be identified in the analyzed images. Plantable blank spots may be determined in the analyzed images. An operation of a drone may be controlled to validate the determination of the plantable blank spots.

Abstract: This disclosure relates to a system for managing personal data over a blockchain network. In an implementation, the system includes a user access provider (UAP) node in communication with a service provider (SP) node. The SP node may include an SP blockchain storing personal data on a SP transaction chain. The UAP node may store meta data of the user on a UAP transaction chain. The meta data may include access control information for the service provider to access the personal data. The UAP node may receive, from the SP node, an access request for the service provider to access a portion of the personal data of the user. The UAP node may access control information for the service provider based on the access request, update the meta data with the configured access control information, and replicate the meta data to the SP node via a blockchain transaction.

Abstract: A device receives first network information associated with a first portion of a network, and second network information associated with a second portion of the network, and determines, based on the first network information and the second network information, an alarm condition for the network. The device determines, based on correlation rules, whether the alarm condition relates to a currently handled alarm condition or a previously handled alarm condition. The device correlates the alarm condition with network inventory information, network topology information, and network service information, when the alarm condition does not relate to the currently handled alarm condition or the previously handled alarm condition, to generate a correlated alarm condition. The device automatically generates a resolution for the correlated alarm condition based on the correlated alarm condition, and automatically performs one or more actions based on the resolution for the correlated alarm condition.

Abstract: An Artificial Intelligence (AI) based document processing system receives a request including one or more documents related to a process to be automatically executed. The information including the fields and an intent required for the process are extracted from one or more of the request and the documents. The required documents and fields are selected based on the intent and a domain model. The required fields are validated using external knowledge and the discrepancies identified therein are resolved. An internal master document is built based on the required fields. The internal master document is employed for the automatic execution of the process.

Abstract: In an example, data, such as, a journal entry in a ledger, to be validated and associated supporting documents may be extracted. Further, an entity, indicative of a feature of the data may be extracted. Based on the extracted entity, one or more probable values for a field of the data may be determined. A probability score may be associated each of the probable values of the field. At least one of the probable values of the field may be compared with an actual value of the field of the data. Based on comparison, a notification indicative of a potential error in the data may generated. The data and historical data associated with the data may be processed, based on at least one of predefined rules and a machine learning technique, to detect an anomaly in the data, the anomaly being related to a contextual information associated with the data.

Abstract: An optimized test data selection strategy references a sampling file that identifies data attributes that serve as the basis of the test data selection strategy. By analyzing fields and the corresponding field values of the sample imprint, a total number of test data selected for inclusion into a sample dataset is reduced. The test data selection strategy provides an efficient methodology for implementing a data comparison testing process.

Abstract: The disclosure relates to methods, systems, and apparatuses, including computer programs on a computer storage medium, for generating a dynamic user interface and playing a video within the dynamic user interface. A dynamic computer user guidance system includes a user interface generation module that causes a display of a dynamic user interface. The dynamic user interface provides a plurality of instructions via one or more dynamic elements. The user interface generation module generates a user interface status indicator. The system includes a video control module that an integrated video to play in the dynamic user interface in response to receipt of the user interface status indicator. The video control module generates a video status indicator. A synchronization module communicably coupled to the user interface generation module and the video control module synchronize a state change in one or more dynamic elements in response to receipt of the video status indicator.

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: Examples of a digital orchestration system are provided. The system may obtain orchestration data on a real-time basis. The system may identify a plurality of events for offering a plurality of user services across a plurality of user interaction stages. The system may identify a plurality of actions associated with each of the plurality of events. The system may create a recipe associated with each of the plurality of actions. The system may identify and implement the associated recipe. The system may create an event sequence for each of the plurality of user interaction stages. The system may create a user service sequence comprising the plurality of user services associated with the event sequence. The system may generate a user experience result based on the event sequence and the user service sequence.

Abstract: A system for improving network capabilities is provided for enabling diagnostics and deployment of hybrid cloud technology. The system may obtain a plurality of display prompts. The display prompts may be associated with a stack of respective software criteria indicative of software capabilities and network functions. The system generates a prompt interface including a plurality of display prompts. The system may include an analytics engine to analyze the inputs responsive to the stack interface. The output of the analytics engine may include a capability score representing a measurement of satisfaction of a software criterion by a network node. The system may identify, based on a capability metric, a recommendation text associated with the software criterion. The system may generate a report interface comprising the recommendation text. The recommendation text may include a suggested modification to the network node including hybrid cloud architecture transformation and virtualization management deployment.

Abstract: A system for migrating data from a legacy system to a target system includes an input/output (IO) processor configured to receive legacy data from a plurality of different types of legacy systems and to communicate target data to a plurality of different types of target systems; a staging area database configured to store legacy data according to a common database schema; a localized database configured to store target data according to a target schema that is associated with a target system type; a processor in communication with the interface, the staging area database, and the localized database; and non-transitory computer readable media in communication with the processor that stores instruction code.

Abstract: Implementations are directed to methods for providing an enhanced encounter via a holographic virtual assistant, including detecting, by one or more processors, an encounter request from a user, selecting a first encounter including a first holographic representation and a first dialog output, providing the first encounter for presentation to the user on the holographic virtual assistant, receiving, from the user, a first user reaction, the first user reaction including a first user dialog input and a first user engagement input, and training, using the first user reaction, a conversational goal model.

Abstract: A machine learning system includes a data set processing subsystem to store data objects from multiple sources. An ensemble machine learning subsystem and a data search function may execute queries and identify related structured and unstructured data from the stored data objects. The structured and unstructured data may be used to create new data objects.

Abstract: A mechanism automatically reconfigures and later restores the network configuration and settings for network devices installed at one location according to configuration and settings of corresponding network devices installed at another location. This enables a user to access these network devices the same way in both locations. This mechanism further avoids having to perform any network reconfiguration of mobile devices that may be carried between the two locations.

Abstract: A heat map creation engine receives a series of image frames from amongst a plurality of image frames of a media, where the series of image frames includes a target frame and at least one neighboring frame. The heat map creation engine also extracts spatial-temporal features of the image frames, rescales the spatial-temporal features to obtain heat distribution over the target frame, and creates a heat map for the target frame based on the heat distribution. A semantic segmentation engine segments the image frames into multiple media objects based on pre-defined classes, and selects one or more media objects from amongst the media objects based on pre-defined conditions. A heat ranking engine ranks the selected one or more media objects in the media based on the heat scores and the created heat map.

Abstract: A system for categorizing words into clusters includes a receiver to receive a set of sentences formed by a plurality of words. The set of sentences is indicative of interaction of a user with a virtual assistant. A categorizer categorizes the plurality of words into a first set of clusters by using a first clustering technique, and categorizes the plurality of words into a second set of clusters by using a second clustering technique. A detector detects words that appear in similar clusters after categorization by the first clustering technique and the second clustering technique. Similarity of clusters is based on a nature of words forming the clusters. A generator generates a confidence score for each of the plurality of words based on the detection. The confidence score of a word is indicative of accuracy of the categorization of the word.

Abstract: In some examples, machine learning based incident classification and resolution may include analyzing an issue associated with performance of a task or operation of an application or a device, and determining, based on the analysis of the issue and based on a machine learning based automated incident resolution model, whether the issue is appropriate for automated resolution. If so, automated resolution of the issue may be implemented to resolve the issue. Alternatively, a machine learning based incident classification model may be used to determine whether an incident associated with the issue is actionable or non-actionable. If the incident is actionable, a machine learning based incident ticket creation and routing model may be used to generate an incident ticket associated with the incident, and determine support personnel selected from a plurality of support personnel to resolve the incident ticket.

Abstract: Techniques are described for routing service requests in a computer-implemented service environment. A received service request may be initially analyzed to determine a priority of the request. In some implementations, one or more actions may be automatically performed to provide an initial response to the requester. The text of the request may be analyzed to automatically determine a category of the request. In some implementations, a classification engine may determine the category of the request through use of a classification model that has been trained using one or more machine learning (ML) techniques and/or that employs Natural Language Processing (NLP). Based on the category, the request may be routed to agent(s) for handling. Routing may include generating a ticket that includes the request, the category, the priority, and/or other information, and the ticket may be provided to the appropriate agent(s) through a ticketing service.

Abstract: A digital manufacturing system includes a web portal to receive product configuration information. An enterprise resource planning subsystem generates a production order based on the product configuration information, and determines a manufacturing resource plan and production steps for manufacturing the product. A manufacturing execution subsystem generates an identifier for the manufacturing resource plan, and communicate the manufacturing resource plan and the production steps to a production control subsystem controlling machines performing the manufacturing of the product according to the manufacturing resource plan and the production steps. A production control subsystem stores the identifier in a near field communication chip attachable to the product during the manufacturing to provide real-time tracking of the manufacturing throughout the steps. An empirical analysis subsystem connects to different social media applications to determine sentiment of the manufactured product.