Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to providing query suggestions based on a number of data sources that include person's personal data and non-personal data. The disclosed techniques may include receiving an input from a person, obtaining a first set of suggestions based on a person corpus derived from at least one data source private to the person, obtaining a second set of suggestions based on information from an additional data source, ranking the first and second sets of suggestions to generate a ranked list of suggestions, and presenting at least some of the ranked suggestions.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to providing query suggestions based on a number of data sources including person's personal data and non-personal data. The disclosed techniques may include receiving an input from a person, obtaining a first set of suggestions based on information from a first data source, presenting the first set of suggestions to the person, and presenting, to the person, a second set of suggestions obtained from a person corpus when a portion of the person corpus relevant to the input is accessible. The person corpus may be from a second data source that is private to the person.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to providing query suggestions based on multiple data sources including at least person's personal data. The disclosed techniques may include receiving an input from a person, obtaining one or more suggestions based on a person corpus derived from at least one data source private to the person, and presenting at least the one or more suggestions.

Abstract: Techniques are provided for automatically determining when to trigger instant search. In one technique, while a user is entering text to formulate a search query, and prior to receiving an indication that the user has completed formulation of the search query, a search assistant system determines the most popular queries that include the text the user has already entered. The search assistant system then determines whether to proactively provide, to the user, search results for the top-candidate of those completed queries. That determination may take into account factors such as whether the top-candidate qualifies as a dominant completed query, a richness metric determined for the search results for the particular completed query, and/or a satisfaction metric determined for the search results for the particular completed query.

Abstract: Techniques are provided for automatically determining when to trigger instant search. In one technique, while a user is entering text to formulate a search query, and prior to receiving an indication that the user has completed formulation of the search query, a search assistant system determines the most popular queries that include the text the user has already entered. The search assistant system then determines whether to proactively provide, to the user, search results for the top-candidate of those completed queries. That determination may take into account factors such as whether the top-candidate qualifies as a dominant completed query, a richness metric determined for the search results for the particular completed query, and/or a satisfaction metric determined for the search results for the particular completed query.

Abstract: Techniques are provided for automatically determining when to trigger instant search. In one technique, while a user is entering text to formulate a search query, and prior to receiving an indication that the user has completed formulation of the search query, a search assistant system determines the most popular queries that include the text the user has already entered. The search assistant system then determines whether to proactively provide, to the user, search results for the top-candidate of those completed queries. That determination may take into account factors such as whether the top-candidate qualifies as a dominant completed query, a richness metric determined for the search results for the particular completed query, and/or a satisfaction metric determined for the search results for the particular completed query.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to providing query suggestions based on a number of data sources including person's personal data and non-personal data. The disclosed techniques may include receiving an input from a person, obtaining a first set of suggestions based on information from a first data source, presenting the first set of suggestions to the person, and presenting, to the person, a second set of suggestions obtained from a person corpus when a portion of the person corpus relevant to the input is accessible. The person corpus may be from a second data source that is private to the person.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to providing query suggestions based on a number of data sources that include person's personal data and non-personal data. The disclosed techniques may include receiving an input from a person, obtaining a first set of suggestions based on a person corpus derived from at least one data source private to the person, obtaining a second set of suggestions based on information from an additional data source, ranking the first and second sets of suggestions to generate a ranked list of suggestions, and presenting at least some of the ranked suggestions.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to providing query suggestions based on multiple data sources including at least person's personal data. The disclosed techniques may include receiving an input from a person, obtaining one or more suggestions based on a person corpus derived from at least one data source private to the person, and presenting at least the one or more suggestions.

Abstract: Techniques are provided for automatically determining when to trigger instant search. In one technique, while a user is entering text to formulate a search query, and prior to receiving an indication that the user has completed formulation of the search query, a search assistant system determines the most popular queries that include the text the user has already entered. The search assistant system then determines whether to proactively provide, to the user, search results for the top-candidate of those completed queries. That determination may take into account factors such as whether the top-candidate qualifies as a dominant completed query, a richness metric determined for the search results for the particular completed query, and/or a satisfaction metric determined for the search results for the particular completed query.

Abstract: The present teaching relates to methods, systems, and programming for indexing and providing suggestions. In one example, a method, implemented on at least one machine each of which has at least one processor, storage, and a communication platform connected to a network for providing a suggestion is presented. An input from a user is first received. At least a part of the input is processed to generate a plurality of tokens. At least one multi-layered key is generated based on one or more of the plurality of tokens. One or more suggestions are retrieved based on the at least one multi-layered key. At least one of the one or more suggestions is provided to be presented to the user.