Evernote Corporation

Mark Ayzenshtat

Phil Libin

Phil Constantinou

Zeesha Currimbhoy

Jakob Bignert

Connie Chan

Clinton Burford

Adam Walz

Displaying relevant data subsets of at least one data collection includes providing an input data subset, determining a plurality of partial relevance values for different characteristics of each of the plurality of data subsets of the at least one data collection with respect to the input data subset according to a term frequency of terms in the input data subset and term in each of the plurality of data subsets of the at least one data collection and according to an inverse document frequency of the terms, determining a relevance of each of the plurality of data subsets of the at least one data collection based on the partial relevance values of the different characteristics, and displaying at least a portion of the data subsets of the at least one data collection in order of relevance.

Related notes and multi-layer search in personal and shared content

A computer system obtains access to a content collection that includes a plurality of content items. For each of the content items, the computer system determines user access history and context associated with the access history. The system builds a classifier characterizing a user access pattern of the content items. The system constantly monitors in real-time environmental parameters of the user. It infers a current context of the user based on the environmental parameters. In accordance with the current context, for each of the content items, the system extracts numeric feature values, evaluates the item using the classifier, and determines a score for the item. The system identifies in real-time a subset of the content items that is most relevant to the current context. The system further generates a preferred view of the subset and causes the preferred view to be delivered to a mobile device.

Automatic generation of preferred views for personal content collections

Providing a view of relevant items of a content collection includes identifying a current context based temporal parameters, spatial parameters, navigational parameters, lexical parameters, organizational parameters, and/or events, evaluating each of the items of the content collection according to the current context to provide a value for each of the items, and displaying a subset of the items corresponding to highest determined values. The temporal parameters may include a time of recent access of an item, frequency of access of an item, frequency of location related access of an item, and frequency of event related access of an item. Temporal patterns of accessing items may be numerically assessed based on time of day, time of week, and/or time of month. Evaluating each item may include determining a distance from a separating hyperplane using a support vector machine classification method.

Providing incremental search suggestions as a user enters terms in a search query includes determining if a recently entered term is in a context-free dictionary of natural language phrases, generating natural language query search completion candidates corresponding to terms provided by the user in the search query if the recently entered term is in the context free dictionary, determining if the recently entered term contains a keyword hint if the recently entered term is not in the context free dictionary, generating keyword search completion candidates if the recently entered term is at least part of a keyword hint, scoring the candidates from a current iteration and any previous iterations from previously entered terms to provide a set of possible candidates, and ordering the possible candidates based on at least the scoring. Providing incremental search suggestions may also include building compound search queries that include natural language phrases and keywords.

Combining natural language and keyword search queries for personal content collections

Providing incremental search suggestions from a content database include accessing the content database to determine possible candidates for the search suggestions, scoring each of the candidates based at least partially on a non-monotonic document frequency function, where candidates that appear a first amount corresponding to a relatively frequent occurrence in the content database and candidates that appear a second amount corresponding to a relatively infrequent occurrence in the content database both score lower than candidates that appear in the content database with a frequency that is between the first amount and the second amount, and ordering the possible candidates based on at least the scoring. Possible candidates may include named entities and n-grams. The n-grams may only be unigrams and bigrams. Stop words may be filtered out of the n-grams. Scoring may include taking into account term frequency.

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San Mateo, CA, United States of America

Mark Ayzenshtat