Google Inc.

Synopsys, Inc.

Stratify, Inc.

Shashidhar Anil Thakur

Kavi J Goel

Christina R Dhanaraj

Ashutosh Shukla

Jared L Levy

Sushrut Suresh Karanjkar

Paul Bunn

John Ogden Lamping

Pravir Kumar Gupta

Emily Kay Moxley

Peter Jin Hong

Jack Wright Menzel

Omer Bar-or

John R Provine

Subramaniam Ganapathy

Viswanath Subramanian

Ramana Venkata

Grace Chung

Nathaniel J Gaylinn

Ramakrishnan Kazhiyur-Mannar

Eyal Carmi

Benson Tsai

Rahul Hari Dani

Thorsten Brants

Neil C Fernandes

Pavel Levin

Pavel Nalivayko

Ramnath N Rao

Samdeer Siruguri

Sameer Siruguri

A server system associates one or more locations with a query by identifying the query, selecting a set of documents responsive to the query, and assigning weights to respective documents in the set of documents based, at least in part, on historical data of user clicks selecting search result links in search results produced for historical queries substantially the same as the identified query. Websites hosting the selected documents are identified, and, for each website, location-specific information for one or more locations is retrieved, including a location-specific score that corresponds to the likelihood that the respective location corresponds to a respective website. For each respective location for which location-specific information was retrieved, aggregating the location-specific scores, as weighted by the document weights, to compute an aggregated likelihood that the respective location is associated with the query. A specific location is assigned to the query when predefined criteria are satisfied.

Inferring geographic locations for entities appearing in search queries

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing knowledge panels. In one aspect, a method includes determining an entity that is referenced by a received search query. Content items relevant to the entity are identified. A set of knowledge modules is selected for presentation in a knowledge panel for the entity. The knowledge panel can be a portion of a search results page in which knowledge modules are presented. Data that causes the knowledge modules to be presented in the knowledge panel is provided to a user device.

Knowledge panel

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying entities that are related to an entity to which a search query is directed. One of the methods includes receiving a search query, wherein the search query has been determined to relate to a first entity of a first entity type, and wherein one or more entities of a second entity type have a relationship with the first entity; receiving search results for the search query; determining that a count of search results identifying a resource containing a reference to the first entity satisfies a first threshold value; determining that a count of search results identifying a resource having the second entity type as a relevant entity type satisfies a second threshold value; and transmitting information identifying the one or more entities of the second entity type as part of the response to the search query.

Related entities

A computer device obtains a search query; obtains product search results based on the search query and a products search index that includes information regarding documents associated with products; determines a category associated with a set of result of the product search results; identifies candidate queries for the search query; determines whether the category matches one of the candidate queries; identifies that the search query is associated with a type of media identified by the category when the category matches one of the candidate queries; and provides, in response to identifying that the search query is associated with the type of media, a result document based on the type of media.

Identifying media queries

A product search engine uses web-crawling software to index textual webpage content from multiple internet sources. The product data obtained from the web-crawling process is then granulized by parsing methods into key words, such as, words and/or phrases. These components are compared with existing key words from search queries or webpage content. Each key word includes component tags, where the component tags map each component to product classification information in the form of structural parameters; and other product information such as, URLs, product images and product descriptions. When at least one matching key word is found, structural parameters are extracted from the matching components and assigned to the received key words. A search results webpage including product information grouped by associated structural parameter may be provided.

Mining for product classification structures for internet-based product searching

Methods, systems and apparatus, including computer program products, for ranking expertise. In some implementations a method is provided that includes identifying a plurality of identities, and identifying a plurality of topics using one or more documents in a repository. For a document in a corpus of documents identifying one or more occurrences of any identity in the plurality of identities and one or more occurrences of any topic in the plurality of topics, determining an association between the identities occurring in the document and the document including deriving an identity score for each unique identity occurring in the document, determining an association between the topics occurring in the document and the document including deriving a topic score for each unique topic occurring in the document, and using the determined associations to derive a score of the document with respect to identities and topics occurring in the document.

Ranking expertise

A ranked list of first search results based on a search query may be associated with a first search category, and each first search result may be associated with a first score. A ranked list of second search results based on the search query may be associated with a second, different search category, and a second search result may be associated with a second score. A final second score may be determined based on the second score and a function that maps scores associated with the second search category to scores associated with the first search category, information may be inserted relating to the ranked list of second search results at a position within the ranked list of first search results to form a set of search results, and the set of search results may be provided.

Floating ranking of product results

This specification describes technologies relating to providing search results. One aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving a network resource, the network resource including text content; generating a language model score for the resource including applying a language model to the text content of the resource; generating a query stuffing score for the reference, the query stuffing score being a function of term frequency in the resource content and a query index; calculating a gibberish score for the resource using the language model score and the query stuffing score; and using the calculated gibberish score to determine whether to modify a ranking score of the resource.

Identifying gibberish content in resources

A server is configured to determine translations of queries from a first language into a second language, associated with a first specialized search engine, to obtain translated queries. The server is also configured to use a first model, associated with the first specialized search engine, to determine values for the translated queries. A value of the values, corresponding to a translated query of the translated queries, reflects a probability that the translated query is a type of query for which first specialized search results are responsive. The server is configured to create training data based on the queries and the values, and to create a second model based on the training data. The second model may be used to predict whether a particular query, received by the web search engine, is the type of query for which second specialized search results, from a second specialized search engine, are responsive.

Creating model or list to identify queries

A computer is programmed to automatically generate in memory, goals for functional verification of a design of a circuit by use of constraints that are specified in the normal manner. Specifically, a predetermined set of rules are automatically applied to the constraints, on random values for signals to be input to the circuit during simulation of the design. Application of the rules identifies one or more templates of goal(s) to be met. The computer is programmed to automatically use constraint(s) and template(s) to instantiate goal(s) in memory. Each goal identifies a signal to be input to the circuit, and defines a counter for a value of the signal. The goals are used in the normal manner, i.e. used to measure coverage of functional verification during simulation of the design of the circuit.

Method for automatically extracting a functional coverage model from a constraint specification

A method (and system) for clustering a plurality of items. Each of the items includes information. The method includes inputting a plurality of items. Each of the items includes information. The items are provided into a clustering process. The method also inputs an initial organization structure into the clustering process. The initial organization structure includes one or more categories, at least one of the categories being associated with one of the items. The method processes the plurality of items based upon at least the initial organization structure and the information in each of the items; and determines a resulting organization structure based upon the processing. The resulting organization structure relates to the initial organization structure.

Method and system for guided cluster based processing on prototypes

Growing community of inventors

Saratoga, CA, United States of America

Shashidhar Anil Thakur