Hewlett Packard Enterprise Development LP

Lenovo (beijing) Limited

Other

Hewlett-packard Development Company, L.p.

Beijing Didi Infinity Technology and Development Co., Ltd.

Jun Qing Xie

Shuai Wang

Xunteng Xu

Qun Yang Lin

Xiaofeng Yu

Yongqiang Liu

Shijian Li

Kyu-Han Kim

Lina Xu

Xiaoyu Zhou

Chengxiang Zhuo

Xiaohui Yan

Zhi-Yong Shen

Portable case for battery charger or power adaptor

A video annotation system for deep learning based video analytics and corresponding methods of use and operation are described that significantly improve the efficiency of video data frame labeling and the user experience. The video annotation system described herein may be deployed at a network edge and may support various intelligent annotation functionality including annotation tracking, adaptive video segmentation, and execution of predictive annotation algorithms. In addition, the video annotation system described herein supports team collaboration functionality in connection with large-scale labeling tasks.

Video annotation system for deep learning based video analytics

In some examples, a user equipment may perform a method that includes identifying that a first wireless network operated by an enterprise is accessible to the user equipment as well as identifying that a second wireless network different from the first wireless network and also operated by the enterprise is accessible to the user equipment. The method performed by the user equipment may further include selecting the first wireless network to connect to instead of the second wireless network based on an enterprise employee characteristic associated with the user equipment.

Enterprise-based network selection

An information processing method includes determining an image display orientation of a first image collected by a first electronic apparatus, determining an image display orientation of a second image received by the first electronic apparatus, and adjusting the image display orientation of the second image to be consistent with the image display orientation of the first image displayed on the first electronic apparatus. The second image is collected by a second electronic apparatus.

Information processing method and electronic apparatus

The present disclosure relates to systems and methods for providing online to offline services. The systems and methods may obtain a plurality of historical positions of a user terminal within a first predetermined time period in a plurality of days. The systems and methods may identify at least one candidate area based on the plurality of historical positions of the user terminal. The systems and methods may identify one or more of the at least one candidate areas as one or more active areas of the user terminal. The systems and methods may further provide data associated with one or more service requests relating to the one or more active areas to a customer application executed by the user terminal.

Systems and methods for on-demand services

Examples relate to predicting available access points. In one example, a computing device may: obtain a set of training fingerprints, each training fingerprint specifying, for a client device, i) a set of access points, and ii) cellular signal strength measurements for each cellular tower in a set of cellular towers; generate a plurality of classes based on the set of training fingerprints, each class specifying at least one access point, the access points of each class corresponding to the set of access points specified by at least one training fingerprint, and each combination being different from combinations specified by each other class in the plurality of classes; and train a predictive model to receive, as input, an input fingerprint specifying a cellular signal strength measurement for each cellular tower in a set of input cellular towers and produce, as output, at least one of the plurality of classes.

Predicting available access points

Certain implementations of the present disclosure relates to a network device that receives an Internet Protocol (IP) flow and determines a protocol type of wireless communication networks associated with a particular network on which the IP flow is transmitted to the network device. Then, the network device generates, using a reflection mode, a set of rules corresponding to the IP flow based on the protocol type of wireless communication networks. Next, the network device transmits the IP flow and the set of rules to another network node.

Generating a set of rules corresponding to an internet protocol flow using a reflection mode

Example embodiments relate to preventing MAC spoofing. In this manner, the embodiments disclosed herein enable sending, from a requesting mobile device, a generic advertisement service (GAS) message on a channel. Responsive to sending the GAS message, a first GAS response message and a second GAS response message may be received on the channel within a predetermined time period after sending the GAS message on the channel. A first CSI fingerprint from the first GAS response message may be compared to a stored CSI fingerprint. A second CSI fingerprint from the second GAS response message may also be compared to the stored CSI fingerprint. Responsive to the first CSI fingerprint being a closer match to the stored CSI fingerprint than the second CSI fingerprint, the requesting mobile device may be connected to an access point associated with first GAS response message.

Preventing MAC spoofing

In an example, a set of training fingerprints may be accessed, in which each of the training fingerprints specifies an access point of a plurality of access points to which a mobile device made a successful connection, a relative signal strength of the connection to the access point, and a cellular signal strength of a cellular tower near the mobile device when the successful connection to the access point was made. Level counts of the relative signal strengths of the connections to the access points corresponding to the cellular signal strengths of the cellular tower may be cumulated from the set of training fingerprints. The cumulated level counts of the relative signal strengths of the access points may be ranked.

Access point availability prediction

An apparatus includes a display, a wireless communication device, an input device, and a processor. The processor to provide a first connection plan and a second connection plan via the display to a user. The first connection plan identifies a first set of rules of wireless access point selection and the second connection plan identifies a second set of rules for wireless access point selection. The processor also to receive a selection corresponding one of the first connection plan and the second connection plan from the user via the input device and to select one of a first wireless access point and a second wireless access point based on first network performance data of the first wireless access point, second network performance data of the second wireless access point, and the selection. The processor further to establish a network connection with the selected wireless access point via the wireless communication device.

Wireless access point selection based on a connection plan

An example system can include a communication device to communicate with a network. The system can include a long-term evolution (LTE) interface to communicatively couple the communication device to the network. The system can include a wireless local area network (WLAN) interface to communicatively couple the communication device to the network. A session associated with each of the LTE interface and the WLAN interface can be maintained together and an identical IP address can be associated with the LTE interface and the WLAN interface.

Network interface communication

A SCI (Split, Compression and Intersection) packet classifier, wherein the SCI packet classifier comprises a construction module to generate rule ID array, h pairs of ISG and three binary search trees, and a lookup engine to look up the binary search trees and ISGs to find the highest priority rule matching the incoming packet and to output a corresponding rule ID.

Memory efficient packet classification method

Examples relate to predicting wireless access point availability. In one example, a computing device may: generate, for a wireless access point, a mapping for predicting availability of the wireless access point, the mapping specifying: one or more in-range cellular towers to which at least one client device has been connected while the at least one client device was also connected to the wireless access point; one or more border cellular towers to which at least one client device has been connected to i) subsequent to being connected to one of the one or more in-range cellular towers, and ii) while not connected to the wireless access point; and one or more out-of-range cellular towers to which at least one client device has been connected to i) subsequent to being connected to one of the one or more border cellular lowers, and ii) while not connected to the wireless access point.

Predicting wireless access point availability

In one implementation, instructions stored on a non-transitory computer-readable medium are executable by a processing resource to cause a computing device to fetch a token code from a home subscriber server (HSS) for a user and log in to a Virtual Private Network (VPN) server using the token code.

User authentication

In an example, a set of training fingerprints may be access, in which each of the training fingerprints may specify an access point of a plurality of access points to which a mobile device made a successful connection and a cellular signal strength of a cellular tower near the client device when the successful connection was made. An interim model may be generated from the accessed set of training fingerprints, in which the interim model may contain a subset of the information in the set of training fingerprints to enable a destination device to generate a prediction model to predict an availability of an access point. The generated interim model may be transferred to the destination device.

Growing community of inventors

Beijing, China

Jun Qing Xie