Abstract: Systems and methods of communicating in a network use a physical device. The physical device includes hardware including a management data input/output interface and firmware configured to cause the hardware to provide a logical message interface using the management data input/output interface. The logical message interface is used to receive messages for configuring and/or managing the physical device.

Abstract: In some examples, a server may determine that a case, created to address an issue of a computing device, is closed and perform an analysis of steps in a process used to close the case. The analysis may determine a length of time of each step and determine that a time to close the case or complete a particular step was at least a predetermined amount faster than average. The server may use machine learning to create a survey question to determine a technique used to close the case or complete the particular step faster than average and to determine one or more incentives to provide a technician that closed the case. An answer from the technician to the survey question may include the technique used to close the case or complete the particular step faster than average. The technique may be shared with other technicians.

Abstract: Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given duration of time in the future; and storing the trained predictive machine learning model in memory.

Abstract: The disclosed systems and methods are directed to a computer-implemented method for use in designing a service. In at least one embodiment, a method includes designing a service with one or more generic virtual network functions (VNFs), where the generic VNFs are defined independent of vendor sourcing information. One or more trained machine learning (ML) models are used to identify VNFs available from VNF vendors that may source one or more VNFs similar to the generic VNFs. The service is implemented using VNFs provided by one or more VNF vendors, where the VNFs provided by the one or more VNF vendors have network functionality generating similar to the one or generic VNFs.

Abstract: Methods, systems, apparatus, and program products that can generate scaled-down load test models for testing real-world loads are disclosed herein. One method includes providing a test environment of a system including multiple nodes. The test environment includes virtual nodes corresponding to the system nodes and each virtual node functions under a virtual load similar to each corresponding node functioning under a real-world load. The method further includes utilizing a machine learning algorithm to repeatedly apply at least one virtual load to the virtual node(s) in the test environment until a scaled-down load test model mimicking the system under a pre-defined real-world load is generated. Here, the virtual load(s) applied to the virtual node(s) is/are comparatively smaller relative to each of corresponding real-world loads for the node(s) defining the pre-defined real-world load. Systems, apparatus, and program products that include and/or perform the methods are also disclosed herein.

Abstract: Techniques described herein relate to a method for managing nodes. The method may include sending, by a first node of nodes, a node information request to a social manager, where the node information request specifies a portion of a service to be provided to the first node; obtaining node information associated with a portion of the nodes from the social manager, where the portion of the plurality of nodes previously expressed node capability information and node configuration information associated with the portion of the service; identifying a second node of the portion of the nodes based on the node information to perform the portion of the service; and performing the service using the second node, where the second node performs the portion of the service.

Abstract: Methods, apparatus, and processor-readable storage media for automated issue detection and remediation across multiple user systems using healing-as-a-service techniques are provided herein. An example computer-implemented method includes obtaining system configuration data from at least a portion of multiple user systems within a network; obtaining an alert pertaining to an issue attributed to a first of the user systems; training a machine learning model related to user system issue detection using at least a portion of the system configuration data and data related to the alert; determining user system configuration adjustments related to remedying at least a portion of the issue, by processing the data related to the alert using the trained machine learning model; automatically performing the user system configuration adjustments in connection with the first user system; and sharing, using at least one healing-as-a-service component, the trained machine learning model with the user systems in the network.

Abstract: Provided are methods and systems for combining business process management (BPM) and robotic process automation (RPA) into a unified BPM and RPA process. An example method commences with creating a BPM process flow. The BPM process flow can include one or more BPM models for executing BPM tasks. The method then proceeds with creating an RPA process flow. The RPA process flow can include one or more RPA models for executing the one or more RPA tasks. The method can further include linking the one or more RPA models to the BPM process flow. Thereafter, the method proceeds with creating an application package associated with the unified BPM and RPA process. The application package can include the one or more BPM models and the one or more RPA models. The method can further include exporting the application package for deployment.

Abstract: Systems and methods for assessing the skills of a customer support agent using one or more Artificial Intelligence/Machine Learning (AI/ML) models are disclosed. In at least one embodiment, one or more benchmarks against which the performance of the customer support agent is to be measured are established. The one or more benchmarks may be derived through direct and/or indirect analysis of historical customer service data by an AI/ML benchmark model. In at least one embodiment, data relating to performance of the customer support agent during a customer call is monitored. In at least one embodiment, the AI/ML benchmark model is used to determine one or more benchmark scores identifying whether the customer support agent is meeting the one or more benchmarks.

Abstract: Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given duration of time in the future; and storing the trained predictive machine learning model in memory.

Abstract: In some examples, a server may receive a first order to acquire a product. The server may receive a request to cancel the first order and determine that the product has been shipped. The server may determine, based on sensor data received from a sensor in the container, that the container is unopened. After receiving a second order to ship the product to a second location, the server may instruct a courier to transport the product to the second location. For example, if the container is located at the first location, the server may instruct the courier to transport the container from the first location to the second location. If the server determines that the courier is currently transporting the container to the first location, the server may instruct the courier to transport the container to the second location instead of to the first location.

Abstract: A carbon nanotube (CNT) hybrid material that includes a blend comprising a catalyst supported on at least one of a metal, metalloid, metal oxide or carbon support, and at least one material selected from the group of materials consisting of: cementitious materials, materials used in the production of cementitious materials, and materials used to enhance cementitious materials, and CNT on the blend.

Abstract: In some examples, before saving a file, a device analyzes contents (e.g., text and images) of the file to create a bag of words. The device performs Latent Dirichlet Allocation of the bag of words to determine topics included in the file and orders the topics in descending order of frequency. The device generates a particular name of the file based on the ordered topics and automatically save (or suggest saving) the file with the particular name. The device determines, based on the ordered topics, a folder name of a folder in which to store the file. If an existing folder has the folder name, then the device saves the file in the existing folder. If not, then the device creates a new folder with the folder name and saves the file in the new folder.

Abstract: In some examples, a server may receive a first order to acquire a product. The server may receive a request to cancel the first order and determine that the product has shipped. The server may determine, based on sensor data received from a sensor of a circuit board in the container, that the container is unopened. After receiving a second order to ship the product to a second location, the server may instruct a courier to transport the product to the second location. For example, if the container is located at the first location, the server may instruct the courier to transport the container from the first location to the second location. If the server determines that the courier is currently transporting the container to the first location, the server may instruct the courier to transport the container to the second location instead of to the first location.

Abstract: A method for establishing message priority in network slices based on quality of service (QoS) parameters includes receiving, by a network slice configuration service provider entity and from a network slice management service provider entity, a network slice creation notification message containing at least a QoS profile corresponding to a created network slice, utilizing the QoS profile to derive a service based interface (SBI) message priority (SMP) associated with the network slice, and providing the SMP to a requesting access and mobility management function (AMF) entity in response to receiving a network slice selection request message from the AMF entity, wherein the AMF entity assigns the SMP level value to network traffic directed to the network slice.

Abstract: In some examples, a server may receive an order from a customer to acquire a computing device. The server may determine a customer and a customer location associated with the order and determine a documentation specification based at least in part on the customer and the customer location. The server may determine content identifiers corresponding to content (e.g., documentation and software) to be provided with the order. The server may associate a device identifier that uniquely identifies the computing device with the content identifiers. The computing device may be shipped to the customer location without the plurality of content. When the computing device is booted at the customer location, the computing device may send the device identifier to the server, the server may determine the plurality of content identifiers associated with the device identifier, and send the plurality of content in a digital form for installation on the computing device.

Abstract: A drone-based product delivery mechanism in which power is transferred between the drone's battery and a product's battery while the product is in transit to its destination. For a short distance delivery, the drone battery may be used to charge the product battery so that the product is delivered with a fully-charged battery. On the other hand, for long distance deliveries, the power from a fully-charged product battery may be used to charge the drone's battery to extend the flight time/radius of the drone or to supplement the drone battery to conserve its power. The power transfer may be carried out using a wireless connection or a wired connection. The wireless connection may be a Qi interface, whereas the wired connection may be a Universal Serial Bus (USB) connection. The product packaging may be re-designed to allow the desired power transfer between the drone and the product inside the packaging.

Abstract: Methods and systems for managing facility access credentials for two or more facilities are disclosed. The method may include electronically receiving a user request to gain access to a designated facility of the two or more facilities and electronically receiving user information related to a user that is making the user request. A facility access credential from a group of facility access credentials that are assigned by a third-party credential issuer may be obtained and linked to the user information and the designated facility. The obtained facility access credential for use in gaining access to the designated facility may be activated resulting in an activated facility access credential and a notification transmitted to the user notifying the user of the activated facility access credential.

Abstract: A system adapted to detect road condition in a vehicle and a method thereof uses geometrical laser projections and an image processing system. The system includes a laser source, an imaging unit and at least a processing unit. The laser source is adapted to project geometrical laser projections on the road. The imaging unit is adapted to capture images of the geometrical projections. The processing unit is configured to calculate a surface reflectance for the projected geometrical projection. Further it is configured to compute geometrical parameters of the projections at regular time intervals based on the captured images. It determines a road condition based on the surface reflectance and the geometrical parameters.

Abstract: Methods, information handling systems and computer readable media are disclosed for determining a priority score for a pending support request document. According to one embodiment, a method includes receiving current support request information from within a pending support request document and accessing current additional information associated with the pending support request document. The method further includes associating a set of parameter values with the pending support request document, wherein the values within the set of parameter values are based on information within one or both of the current support request information or the current additional information.