Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computer increases coverage in simulation of a design of a circuit by processing goals for coverage differently depending on whether or not the goals are on input signals of the circuit. Specifically, goals on input signals are used to automatically formulate constraints (“directly-derived constraints”) on values of input signals in test vectors. Goals on non-input signals (e.g. internal/output signals) are used with correlations to automatically formulate more additional constraints (“correlation-derived constraints”), by use of goals on non-input signals. The correlations indicate which non-input signals are associated with which input signals. The correlations are received from, for example, a human designer of the circuit. Depending on the embodiment, one or more of the automatically derived constraints are used with human-supplied constraints, to generate test vectors e.g. using a constraints solver, such as a satisfiability (SAT) engine.

Abstract: A computer increases coverage in simulation of a design of a circuit by processing goals for coverage differently depending on whether or not the goals are on input signals of the circuit. Specifically, goals on input signals are used to automatically formulate constraints (“directly-derived constraints”) on values of input signals in test vectors. Goals on non-input signals (e.g. internal/output signals) are used with correlations to automatically formulate more additional constraints (“correlation-derived constraints”), by use of goals on non-input signals. The correlations indicate which non-input signals are associated with which input signals. The correlations are received from, for example, a human designer of the circuit. Depending on the embodiment, one or more of the automatically derived constraints are used with human-supplied constraints, to generate test vectors e.g. using a constraints solver, such as a satisfiability (SAT) engine.