Thoughtspot, Inc.

Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Shashank Gupta

Amit Prakash

Ajeet Singh

Satyam Shekhar

Shikhar Agarwal

Shailesh Chauhan

Michael Phillip Czamara

Srikanth Thirumalai

Sanjay Agrawal

Osvaldo Patricio Morales

Adam Callahan Sanders

Judith Ying Priest

Abhishek Rai

Scot Edward Heath

Adolfo Bravo Ferreira

Uday Kumar Bandaru

Priyendra Singh Deshwal

Ravindra Nath Bhartiya

Kevin Carter Keaney

Yong Gang Cao

Stephane Antonin Kiss

Vibhor Nanavati

Vijay Krishnan Ganesan

Joy Dutta

Shikhar Agrawal

Semantic analysis to resolve ambiguous user input data with respect to a request for data includes identifying tokens based on the text string, identifying a tables in a database, wherein a token indicates a column from a table and a token indicates a column from another table, identifying candidate join paths for joining tables, obtaining respective candidate results using the candidate join paths, outputting combined candidate results including values from the respective candidate results, obtaining second user input data indicating a selected value, identifying the request for data as unambiguous and identifying a selected join path based on the selected value, and, in response to identifying the request for data as unambiguous, outputting data responsive to the request for data using the selected join path.

Token based dynamic data indexing with integrated security

Cloud services intelligently provision new VMs for a VM scale set when the original label that included the OS or other software images used to provision existing VMs of the VM scale set is no longer available or has been changed. Metadata of the existing VMs are analyzed to identify an OS image or other software image used to provision the existing VMs. The metadata also reveals updates that are running on the new VMs. These updates include software that was not part of the original label used to provision the existing VMs and are used to find the second label in a label database. The second labels include the OS or software of the existing VMs—or a later version thereof—and some portion of the updates. A VM-provisioning service uses the second label to provision the new VM.

Automatic update of VM sets

Examples are disclosed that relate to computing systems having a common conductive pathway. One example provides a computing system comprising a power supply configured to output electrical power for delivery to one or more power nodes, and one or more power monitors configured to identify a power overload condition based on the power output by the power supply. The computing system further comprises a parent controller configured to, based at least on receiving an indication of the power overload condition, transmit an instruction to one or more child controllers that causes each child controller to effect a change in an operational state of a corresponding power node. The computing system also comprises a conductive pathway along which electrical power output from the power supply is transmitted for delivery to the one or more power nodes, and along which the instruction is transmitted to the one or more child controllers.

Computing system including power nodes

Token based dynamic data indexing with integrated security includes generating attribute tokens based on a database, each attribute token including an ordered sequence of symbols, generating an index indexing the attribute tokens, such that the index includes a root node, a first branch depending from the root node representing a first symbol from a first attribute token, and a security bitmask for the first attribute token, such that a security bitmask for the first branch at the root node is a hierarchical logical disjunction based on the security bitmask for the first attribute token, such that generating a response receiving an information retrieval request indicating a search symbol and a search context, is based on a determination that a horizontal logical disjunction of a vertical logical conjunction of the security bitmask for the first branch and a security bitmask for the information retrieval request indicates authorization.

Various embodiments of a system and method for highly scalable data clustering are described. Embodiments may include generating contexts of information from item-level data for multiple items; each context may include tokens that each represents an aggregate characteristic of items associated with that context. Embodiments may also include comparing the tokens of different contexts to determine measures of similarity between the contexts. Embodiments may also include grouping at least some of the contexts into clusters with other contexts based on the determined measures of similarity. Embodiments may include, subsequent to detecting a first context and a second context as being members of a common cluster, correcting item level-data of an item associated with the second context based on item-level data of an item associated with the first context.

System and method for highly scalable data clustering

An information retrieval system converts unstructured ad-hoc search queries into structured search instructions that retrieve data in a structured relational database or an unstructured database. Data from the database is uploaded into a distributed in-memory database system. Tokens are automatically generated based on attributes, measures, and other metadata extracted from the relational database. The tokens are then compared with the non-structured ad-hoc user search queries. The information retrieval system uses the tokens to identify or predict what structured data is associated with user search queries. The tokens guide the user through a set of search terms that the system then uses to generate the structured query instructions. The structured query instructions retrieve specific data and answers from in the database system.

Information retrieval system

Systems and methods are provided for presenting item information to a user based on entity relationships. The items may be presented, for example, in response to a search request or as a recommendation of an item of potential interest to a user. In some embodiments, item information associated with a first item of interest to a user may be retrieved from a data store. A first entity related to the first item may be determined based on information retrieved from the data store. A second entity related to the first entity may then be determined, and a second item related to the second entity may be identified. Information identifying the second item may then be presented as a recommended item to the user.

Presenting item information based on associated entity relationships

Growing community of inventors

Sammamish, WA, United States of America

Shashank Gupta