Apple Inc.

Pure Storage, Inc.

Gaurav Jain

Ivan Jibaja

Stefan Dorsett

Prashant Jaikumar

Srinivas Chellappa

Yong Liu

Jinjing Jiang

Su Khiong Yong

Guoqing Li

Tianyu Wu

Jarkko L Kneckt

Neil A Vachharajani

Charles Francis Dominguez

Curtis Pullen

Ronald Keryuan Huang

Jason D Gosnell

Daryl Mun-Kid Low

Puneet Mishra

Jeffrey Alan Bush

Neil Buda Vachharajani

Jeff Bush

Some embodiments include utilizing a multilink media access control (MAC) address structure to support multilink devices (MLDs) that can operate concurrently in more than one link such as extremely high throughput (EHT) access points (APs) and EHT stations (STA), where the multilink MAC address structure is compatible with legacy devices. An EHT AP can utilize a multilink basic service set (BSS) identification (BSSID) MAC address to communicate with an EHT STA identified by a multilink MAC address. Values of the multilink BSSID and the multilink MAC address of the EHT STA are independent of which of the multiple links are used in the communication. In addition, to utilizing a multilink BSSID, the EHT AP can also support unique link-specific MAC addresses to concurrently support legacy and MLD stations. The EHT STA can also utilize unique link-specific MAC addresses that can be different than the EHT AP's link-specific MAC addresses.

System and method for multilink devices

Creating a containerized data analytics pipeline, including: creating a data analytics pipeline having a component that is deployed within a container that executes in a container cluster that is external to shared storage accessible by components of the data analytics pipeline; and responsive to detecting that a component of the data analytics pipeline has failed, deploying another instance of the component in a failover container that is configured to support one or more components of the data analytics pipeline.

Creating a containerized data analytics pipeline

System and method for multilink devices (MLDs)

Providing for high availability in a data analytics pipeline without replicas, including: creating a data analytics pipeline, wherein each component of the data analytics pipeline is deployed within a container; creating a failover container; detecting that a component within the data analytics pipeline has failed; and responsive to detecting that the component within the data analytics pipeline has failed, deploying the component within the data analytics pipeline that has failed in the failover container.

Creating a highly available data analytics pipeline without replicas

Managing a data analytics pipeline

System and method for enhanced high throughput (EHT) stations

Providing for high availability in a data analytics pipeline without replicas

A number of devices co-located at a geographic location can broadcast and receive tokens. Tokens can be exchanged using a communication link having limited communication range. Tokens that are received by a device can be stored locally on the device and/or transmitted to a trusted service operating remotely on a network. In some implementations, the tokens can be stored with corresponding timestamps to assist a trusted service in matching or otherwise correlating the tokens with other tokens provided by other devices. The trusted service can perform an analysis on the tokens and timestamps to identify devices that were co-located at the geographic location at or around a contact time which can be defined by the timestamps. A group can be created based on results of the analysis. Users can be identified as members of the group and invited to join the group.

Group formation using anonymous broadcast information

Growing community of inventors

Sunnyvale, CA, United States of America

Gaurav Jain