Patent classifications
H04L67/34
UPGRADE METHOD, COMPUTER SYSTEM, AND REMOTE UPGRADE DEVICE IMPLEMENTING EFFICIENT REMOTE UPGRADE
An upgrade method, a remote upgrade device and a computer system implementing the upgrade method. The remote upgrade device transmits an upgrade notification to the computer system, comprising a list of block numbers involved for upgrading a new file. The new file comprises a plurality of file. The storage device comprises a plurality of storage blocks. Check codes of data in the storage block are transmitted to the remote upgrade device, each corresponding to one of the block numbers transmitted to the computer system. The remote upgrade device compares the received check codes with check codes of the file blocks corresponding to same block numbers in the new file. The upgrade method can skip the steps to transmit, erase and write existed file blocks based on the comparison result, and thus significantly improves the efficiency of remote upgrade.
DYNAMIC MULTI-STREAM DEPLOYMENT PLANNER
A computer deploys one or more stream instances, where each one of the one or more stream instances having an instance deployment manager to control one or more operators of the each of the one or more stream instances. The computer causes the instance deployment manager to collect information of one or more operators in the one or more stream instances The computer may determine a deployment plan for the one or more stream instances based on the information and send one or more instructions to the instance deployment manager to optimize performance of at least one of the one or more stream instances, based on the deployment plan.
DIGITAL MEDIA MOCKING TOOL
Described herein are methods, systems, and media for mocking a digital channel page for testing an advertising creative on the digital channel page. An exemplary method includes receiving, at a mock application running on a server and from a client device, an address of the digital channel page, an advertising creative, and desired dimensions of the advertising creative in the digital channel page; and retrieving source code of the digital channel page based on the address, the digital channel page including an advertising space. The method further includes locating the advertising space in the digital channel page; injecting the advertising creative into the advertising space in the source code of the digital channel page with the desired dimensions; and sending the source code of the digital channel page to the client device for display.
SYSTEM AND METHOD FOR APP DISCOVERY, INSTALLATION, AND USAGE
A system and method functions to make an app as installed on a smart device usable to access a service, such as a media streaming service. A presence of a counterpart to the app as installed on an appliance within a home network which includes the smart device is detected. A user credential, such as a username and password combination, that is associated with the counterpart to the app is then identified. The user credential is caused to be automatically associated with the app as installed on the smart device.
NETWORK PROTOCOL FOR COMMUINCATION AMONG LIGHTING AND OTHER DEVICES
A protocol for controlling lighting devices within a network enables bidirectional communication between different connected devices, any of which may function as a server, a device, a broker, or multiple of these roles. Upon initialization of the network, a server device requests a basic configuration data file from each device on the network and, thereafter, requests a more extensive configuration data file identifying capabilities and functionalities of the device which the manufacturer has made discoverable. Each device sends its basic configuration file to the server device upon any of power on, reboot, reset, hardware configuration change, or software change to the respective device. A device on the network can also be a collection of sub-devices each of which may be separately identified, and their respective capabilities evaluated, so that the server can separately control either the device or any of the individual sub-devices.
ROADSIDE EDGE NODE CENTRAL MANAGEMENT
A system and method for managing third-party software packages at a roadside edge node. The method includes: receiving a third-party software package at an edge node management controller that is located at a roadside edge node, wherein the edge node management controller is used to execute a software manager application; using the software manager application, installing the third-party software package at the roadside edge node; executing the third-party software package at the roadside edge node; and providing sensor information to the third-party software package indicating information concerning one or more sensors that are present at the roadside edge node.
ASSEMBLY TYPE EDGE SYSTEM
This application relates to an assembly type edge system. In one aspect, the edge system includes M protocol modules connected to and interworked with the at least one interworking target device according to a specified interworking protocol, and a collection module configured to collect a collection data set of a specified collection data structure through at least one protocol module. The system may also include P processing modules configured to generate n (1≤n≤N) pieces of data to be transmitted to a specified higher-level system, and S structuring modules configured to generate a transmission data set by structuring a data group including the n pieces of data. The system may also include T communication modules configured to apply a specified communication protocol to the transmission data set, and transmit the transmission data set to a specified higher-level system, and a control module configured to control one or more of the modules.
Methods and systems for content processing
Mobile phones and other portable devices are equipped with a variety of technologies by which existing functionality can be improved, and new functionality can be provided. Some aspects relate to visual search capabilities, and determining appropriate actions responsive to different image inputs. Others relate to processing of image data. Still others concern metadata generation, processing, and representation. Yet others concern user interface improvements. Other aspects relate to imaging architectures, in which a mobile phone's image sensor is one in a chain of stages that successively act on packetized instructions/data, to capture and later process imagery. Still other aspects relate to distribution of processing tasks between the mobile device and remote resources (“the cloud”). Elemental image processing (e.g., simple filtering and edge detection) can be performed on the mobile phone, while other operations can be referred out to remote service providers. The remote service providers can be selected using techniques such as reverse auctions, through which they compete for processing tasks. A great number of other features and arrangements are also detailed.
Updating electronic devices using a push model
Automatically updating electronic devices using a push model. A set of electronic devices may be selected for an update. A first plurality of devices of the set of devices for which the update is valid may be determined in accordance with a set of rules, e.g., safety check rules or business rules, where the determining is based on stored configuration information for each of the devices. Current configuration information and accessibility information may be received for each of the first plurality of devices, and based on the received current configuration information and accessibility information and the stored configuration information, a second plurality of devices of the set of devices for which the update is valid may be determined, where the second plurality is a subset of the first plurality. The update may be applied automatically to at least some of the second plurality of devices.
DYNAMIC WEIGHT UPDATES FOR NEURAL NETWORKS
Apparatuses, systems, and techniques to improve federated learning for neural networks. In at least one embodiment, a federated server dynamically selects neural network weights according to one or more learnable aggregation weights indicating a contribution from each of one or more edge devices or clients during federated training according to various characteristics of each edge device or client model and training data.