Abstract: A method of managing user experience with a virtual reality (VR) headset includes: with a server, receiving data from a number of sensors that are detecting parameters associated with a user during use of the VR headset; with the server, analyzing the data from the sensors to predict a negative effect on the user from the use of the VR headset; and with the server, taking action to minimize the negative effect on the user that is predicted.

Abstract: A method of collecting and reporting inventory of resources deployed in a data center that includes hardware resources, a virtualization management software executed to provision virtual resources from the hardware resources, and a cloud management server executed to provision the virtual resources for tenants of the data center, includes the steps of: executing a first API call to the virtualization management software to collect first inventory of virtual resources deployed in the data center and a second API call to the cloud computing management software to collect second inventory of virtual resources deployed in the data center; storing the first and second inventory; and in response to an inventory request from a central orchestrator, initially sending a subset of the stored first and second inventory to the central orchestrator in accordance with parameters included in the inventory request, and thereafter sending updates to the subset to the central orchestrator periodically.

Abstract: A server may include a receiving unit to obtain a set of battery attributes associated with a battery of a client device and a prediction unit to predict a battery condition by applying at least one first machine learning model to the set of battery attributes. The battery condition may include battery swelling, battery memory effect, battery performance degradation, or any combination thereof. Further, the server may include a recommendation unit to apply a second machine learning model to the predicted battery condition to predict a remaining life of the battery and recommend an action to be performed based on the predicted remaining life of the battery.

Abstract: A method of collecting and reporting inventory of resources deployed in a data center that includes hardware resources, a virtualization management software executed to provision virtual resources from the hardware resources, and a cloud management server executed to provision the virtual resources for tenants of the data center, includes the steps of: executing a first API call to the virtualization management software to collect first inventory of virtual resources deployed in the data center and a second API call to the cloud computing management software to collect second inventory of virtual resources deployed in the data center; storing the first and second inventory; and in response to an inventory request from a central orchestrator, initially sending a subset of the stored first and second inventory to the central orchestrator in accordance with parameters included in the inventory request, and thereafter sending updates to the subset to the central orchestrator periodically.

Abstract: A computer-implemented method, medium, and system for upgrade of telco node cluster running cloud-native network functions are disclosed. In one computer-implemented method, a worker node group that includes a plurality of worker nodes is determined in a container orchestration platform. A first node to upgrade is determined within the worker node group. All pods in the first node are deactivated by a high availability as a service (HAaaS) module. Standby pods in a second node are activated by the HAaaS module and as active pods. All network traffic associated with all the pods in the first node is migrated to the active pods. The first node is deleted from the worker node group. Hardware resources associated with running the first node are released. A third node is generated as a new worker node in the worker node group and uses the released hardware resources.

Abstract: In some examples, a non-transitory machine-readable medium stores machine-readable instructions, which, when executed by a processor, cause the processor to receive operational data of a videoconferencing solution, to predict, utilizing a machine learning process on the operational data, an incident of the videoconferencing solution, and to generate, based on the prediction, an incident alert.

Abstract: Multiple regions of a scene are identified, for example through user inputs to a touchscreen while the touchscreen displays preview frames of the scene. Multiple exposure settings are determined based on the identified regions. Each exposure setting is determined based on one of the identified regions, for instance to optimally expose that region. Multiple image frames are captured of the scene, with each image frame captured at a different one of the determined exposure settings. A high dynamic range (HDR) image of the scene is generated by merging the multiple image frames of the scene.

Abstract: The present invention provides oral film of HIV/Anti-retroviral drugs and its pharmaceutically acceptable salt thereof, which is useful for the treatment of HIV infections. Further the present invention provides composition and process for preparing oral film of HIV/Anti-retroviral drugs and its pharmaceutically acceptable salt thereof.

Abstract: Some embodiments provide a method that generates (i) a node profile of a worker node in a workload cluster for deploying a first network function and (ii) a host profile of a virtual machine that implements the workload cluster and a management cluster that manages the workload cluster. The method updates a configuration map of the worker node based on the node profile. The method uses a node configuration operator in a remote data center to configure the worker node based on the configuration map. The method uses a virtual machine configuration operator in the remote data center to configure one or more nodes of the management cluster based on the host profile.

Abstract: Some embodiments provide a method that generating a host profile for deploying a first network function. the method uses a virtual machine configuration operator in a remote data center to configure one or more virtual machines implementing a workload cluster to perform the first network function based on the host profile. The method uses the virtual machine configuration operator to configure one or more virtual machines implementing a management cluster based on the host profile. The workload cluster is managed by the management cluster.

Abstract: An air cleaner assembly comprises a filter element and a housing assembly. The filter element comprises a filter media for filtering a fluid and a first endcap positioned along a first end of the filter media and through which a first filtered air flow portion flows. The housing assembly at least partially contains the filter element and comprises a cover that defines a first outlet through which the first filtered air flow portion flows to a downstream device. The cover comprises a noise reduction device that receives the first filtered air flow portion and dissipates sound waves traveling upstream from the downstream device.

Abstract: A device with automated device temperature control is described. In one example, the device includes a processor and a weather forecast engine coupled to the processor. The weather forecast engine obtains weather data of a geographical location in which the device is located. The weather data includes values of environmental parameters. The weather data is then shared with a prediction engine. The device further includes a control engine coupled to the processor. The control engine receives a first threshold operating temperature determined for the device based on the weather data, a device model, and a mapping table from the prediction engine. The control engine then initiates a temperature control device, connected to the device, to cool the device if a current device temperature of the device is greater than the first threshold operating temperature.

Abstract: In one example in accordance with the present disclosure, an electronic device is described. An example electronic device includes a battery to provide power to the electronic device, a keyboard, and a keyboard-based charger to charge the battery by actuation of the keyboard. The example electronic device also includes a processor and memory storing executable instructions that when executed cause the processor to determine that a battery power is less than a threshold battery power and a power source is unavailable. The instructions also cause the processor to activate keyboard-based charging to charge the battery in response to determining that the battery power is less than the threshold battery power and a power source is unavailable. The instructions further cause the processor to generate a user notification to request keystrokes on the keyboard to charge the battery with the keyboard-based charger.

Abstract: In one example in accordance with the present disclosure, an electronic device is described. An example electronic device includes a processor and memory storing executable instructions that when executed cause the processor to import user feedback for a product from multiple websites. The instructions also cause the processor to combine the user feedback with product health data for the product. The instructions further cause the processor to run a machine-learning (ML) model to determine a rating for the product based on the combined user feedback and product health data. In some examples, the instructions also cause the processor to implement a chatbot to receive a user query and provide a product recommendation based on the user feedback and product rating.

Abstract: In one example in accordance with the present disclosure, an electronic device is described. An example electronic device includes a processor and memory storing executable instructions that when executed cause the processor to determine availability of memory resources and processing resources of multiple computing devices. The instructions also cause the processor to form a computing cluster based on the availability of the memory resources and the processing resources. The instructions further cause the processor to assign a computing task to the computing cluster to replace a cloud service.

Abstract: Various example embodiments relate to a filtration system. The filtration system includes a filter housing having a first housing end and a second housing end. The filter housing includes a housing cover defining an outlet. The housing cover includes a first guidance element extending from an internal portion of the housing cover axially away from the outlet. A second guidance element extends from the internal portion of the housing cover axially away from the outlet. A filter element has a first filter end and a second filter end. The filter element includes an end cap adjacent the first filter end and defining an end cap opening therethrough. The end cap includes a channel around the end cap opening. Filter media has a first media end and a second media end. The filter media extends axially from the first media end toward the second media end.

Abstract: Examples are described herein for selecting remediation facilities. In various examples, data associated with a data processing device may be processed using a trained machine learning model. The data may be collected from multiple sources associated with the data processing device. Based on the processing, a deficiency may be inferred that is, or is likely to be, exhibited by a component of the data processing device. Based on the inferred deficiency, a location of the data processing device, and locations of a plurality of candidate remediation facilities, a given remediation facility of the plurality of candidate remediation facilities may be selected to remediate the deficiency.

Abstract: In some examples, resource availability prediction can include a non-transitory machine-readable medium having instructions thereon executable to receive at the first processing resource, the memory resource, or both, a plurality of input data from a plurality of sources comprising a first plurality of sensors to monitor a plurality of workstations and write from the first processing resource to the memory resource the received input data. The instructions can be executable to identify at the first processing resource or a second processing resource, output data representative of a resource availability prediction based on input data representative of the written data, receive from a mobile device accessible by a user, a request for the resource availability prediction, and transmit the output data to the mobile device via signaling sent via a radio in communication with a third processing resource of the mobile device.

Abstract: Some examples described herein relate to multi-chip devices. In an example, a multi-chip device includes first and second chips. The first chip includes a power supply circuit and a logic circuit. The first and second chips are coupled together. The second chip is configured to receive power from the power supply circuit. The second chip includes a programmable circuit, a pull-up circuit, and a detector circuit. The detector circuit is configured to detect a presence of a power voltage on the second chip and responsively output a presence signal. The power voltage on the second chip is based on the power from the power supply circuit. The logic circuit is configured to generate a pull-up signal based on the presence signal. The pull-up circuit is configured to receive the pull-up signal and configured to pull up a voltage of a node of the programmable circuit responsive to the pull-up signal.

Abstract: An example of an electronic device includes a processor to collect data of an input/output (I/O) interface of a docking station to which the electronic device is to couple, use at least one artificial intelligence (AI) processing model to process the collected data of the I/O interface to provide an AI processing model result that calculates past usage and predicts future usage of the I/O interface, and determine an estimated health of the docking station based on the AI processing model result.