Google Inc.

Zynga Inc.

Townsend Street Labs, Inc.

Okta, Inc.

Appurify, Inc.

Pratyus Patnaik

Jay Srinivasan

Manish Lachwani

Rajhans Samdani

Nathaniel Ackerman Rook

Suchit Agarwal

Nathan Arthur Etter

David Colby Kaneda

Rahul Jain

Marissa Mary Montgomery

Ankit Goyal

Mark Larson

Timothy D Wang

Pierre Rappolt

Jong Hyeop Kim

William Stone Potter

Chase Phillips

Abhinav Singh

Keun Soo Yim

Fergus Gerard Hurley

Emily Wang

Richard Michael Gaywood

Emma E Marsh

Eric Richard Shieh

Andrea Cawili Gumacal

Sam Huynh

Roopak Venkatakrishnan

Adam Hicklin

George Patrick Siu

Terence Kwan

Min Sung

Niranjan S Tulpule

Boldbaatar Tsend-Ayush

Jordan Carlson

Frank Farzan

Andy Chang

Systems for processing queries may first determine correspondence between the parameters of the query and a set of existing data entries, a set of previous queries that have been received, or both the existing data entries and the previous queries. If the query parameters do not correspond to the data entries or pervious queries, correspondence is determined between the query parameters and group data that associates at least a subset the query parameters with a particular group that may generate a response to the query. The same group or the generated response may be used when similar queries are received. If the group transmits the query to a different group or if negative user feedback is received, the group data may be modified to indicate the different group or to remove the association with the initial group that received the query.

System for routing of requests

A system creates and searches knowledge base (KB) articles inside an organization while supporting operation as a SaaS (Software as-a-Service) across a plurality of organizations. The system implements a real-time online learning-to-rank (L2R) algorithm for learning relevance scoring that is customized to each organization. This algorithm incorporates rich lexical features using a query similarity kernel. A scoring function includes a pairwise static module, which may be trained off-line using training data, and a lexical adaptive module, which is trained on-line based on user feedback. The scoring function makes the system easy to deploy, modify, and suitable for handling events that naturally happen over the lifecycle of any KB deployment, without manual training.

System for handling workplace queries using online learning to rank

A system stores resources such as text articles, videos, and so forth for an organization. During operation, the system receives a query and provides a response. During initial use of the system, there is little or no historical data available to help determine which resource is most relevant to a particular query. In this 'cold-start' situation, the system determines attributes associated with a user account of the user making the query. The query is used to search a data store and retrieve a set of resources based on a term match with the query and to find the resources which correspond to the attributes of the user account. This allows the system to provide simplified output that is more likely to be relevant to that particular user in the 'cold-start' situation.

Personalized search based on account attributes

Systems and methods for determining whether a data entry has deprecated may receive a query from a user device and provide a data entry responsive to the query. If user feedback associated with the response indicates that the response resolved the query, confirmation data that associates the data entry with the query parameters is generated. If the user feedback indicates that the response did not resolve the query, existing confirmation data is accessed to determine whether a previous association between the data entry and the parameters of the query was generated. If correspondence between the confirmation data and the query and response is determined, a notification indicating the deprecation of the data entry is generated and the data entry may be suppressed from future output.

System for determining and modifying deprecated data entries

A system dynamically routes queries from an organization for processing. An incoming query is classified using a base model to determine a canonical group. The query is routed based on the canonical group. Responsive to the query, response data is then provided to the user device. Event data about the query, the routing, and the response data is used to train a first model that is used to determine subsequent routing. When a threshold is reached with respect to the available event data, a second model is trained using the event data. Processing of subsequent queries using the second model results in reduced demand for computer resources and reduces response times. The second model may be retrained on a schedule, or upon occurrence of a trigger event, such as an addition or removal of a canonical group.

Dynamic query routing system

A system for processing queries from a user device may first generate an augmented query by determining weight values and synonyms for at least a portion of the parameters in the query, and adding or removing one or more query parameters. Correspondence between the augmented query and an existing set of data entries may be used to determine a subset of data entries that may be responsive to the query. Correspondence may then be determined between the augmented query and previous queries that were addressed by the subset of data entries, to determine a particular previous query having the greatest correspondence with the augmented query. The data entry associated with the particular previous query may be used to generate a response to the query received from the user device.

Request processing system

An example system may include a first device connected over respective communications interfaces to a plurality of second devices. The first device may establish respective remote terminals between the first device and one or more second devices. Establishing the respective remote terminals may indicate that the devices are in an online state. The first device may periodically update respective heartbeat files on the second devices. A respective second device may detect that a pre-determined time period has elapsed since the heartbeat file was updated. The respective second device may determine that a respective remote terminal between the first device and the respective second device has been terminated. Determining that the remote terminal has been terminated may indicate that the device is in an offline state. The respective second device may reset itself so as to transition the respective second device from the offline state to the online state.

Device cloud monitoring and stability

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing tests on mobile devices. In one aspect, a power distribution system includes a set of mobile devices and a computer that manages the execution of a test using the set of mobile devices. The computer can include one or more data communication ports. The system can also include one or more Universal Serial Bus (USB) hubs connected to the mobile devices using USB cables. Each mobile device can be connected to one of the one or more USB hubs. The system can also include one or more USB charging hubs connected between each data communication port of the computer and at least one of the one or more USB hubs. The system can also include a power supply connected between a power source and each of the one or more USB hubs.

Power distribution for mobile devices

Various embodiments provide an automated testing analysis tool, termed a 'pre-release analysis tool', that tests applications for functional and nonfunctional requirements. In at least some embodiments, the pre-release analysis tool can perform both static analysis and dynamic analysis on an application. A report module can provide rich feedback, including actionable feedback, to an application developer in order to enable the developer to make their applications more secure, reliable, efficient, and performant. Actionable feedback can include feedback that suggests various remedial measures that an application developer may put in place in order to improve their applications. Application analysis can be performed in various different categories with actionable feedback provided in each. These different categories can include, by way of example and not limitation, application crashes, performance, security, usability, application statistics, code warnings, localization issues, and network issues.

Application pre-release report

Methods and apparatus are provided for providing testing as a service. A loading computing device can receive a request to restore a testing computing device to a known software configuration. The loading computing device, after receiving the request to restore the testing computing device, can restore the testing computing device by at least: obtaining a software image storing the known software configuration, where the software image includes security-context data having security contexts associated with the plurality of software components, and where a particular security context can have an identity, a role, and a domain or type; storing at least part of the obtained software image onto the testing computing device, and after storing at least part of the obtained software image onto the testing computing device, setting security contexts for the obtained software image stored on the testing computing device using the security-context data.

Methods and apparatus for mobile computing device security in testing facilities

Application validation is performed based on object level hierarchy data associated with the application. An application is executed on a physical or emulated host device, and assembly code is generated for the executing application. The assembly code is analyzed to identify objects associated with the application, and to identify relationships between the objects. Based on the object and relationship data, an object level hierarchy is generated for the application. Validation of the application may be performed by comparing an object level hierarchy for a current version of the application to a previously generated hierarchy for a previous version of the application to identify differences between the two hierarchies.

Validating applications using object level hierarchy analysis

A method includes receiving an application package for a software application and determining a test compatibility of the software application on virtual devices and on physical devices based on the application package. The method further includes selecting a test device based on the test compatibility of the software application. The test device includes one of a test virtual device or a test physical device. The method routes the software application to the test device and executes the software application on the test device.

Testing application software using virtual or physical devices

A system is described for testing markup language-based applications, such as a web page, executing on an external computing device. A development device executes development tool modules which may include a proxy module and a driver module. The proxy module enables communication between one or more modules which execute on the development device and one or more embedded test tools on the computing device. The driver module provides a command line interface to facilitate automated testing, and generates commands appropriate to the use of the embedded test tools and may use the proxy module to communicate with the computing device. Using the proxy module, the driver module, or both, a developer is able to test and interact with a markup language application executing on the computing device with a variety of development modules.

System for testing markup language applications on a device

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San Francisco, CA, United States of America

Pratyus Patnaik