Samsung Electronics Co., Ltd.

Xueying Dai

Eric J Glover

Eric Chen

Zhaohui Chen

Yifu Wang

Chen Zhang

Justin Feinman

A method for recommending content based on user profiles clustered by subscription data includes receiving usage data of users. The usage data for each user includes device usage data and subscription data associated with the one or more applications installed on the user device. The method further includes receiving a recommendation request from a first user device of a first user for new subscription data and determining first usage data of the first user. The method also includes identifying a second user having second usage data that has a threshold similarity to the first usage data amongst the users. The method includes determining the new subscription data as being at least some of the subscription data of the second usage data that is different than the subscription data of the first usage data and outputting the new subscription data to the user device of the first user.

Recommending content based on user profiles clustered by subscription data

A method for personalizing deep search results using subscription data includes receiving usage data of one or more applications installed on a user device, and receiving a search query. The method further includes identifying one or more application states of the one or more installed applications based on the search query and the usage data, and generating personalized search results including one or more application access mechanisms of the identified one or more application states. Each application access mechanism has a reference to a corresponding installed application on the user device and indicates a performable operation for the corresponding installed application.

Personalizing deep search results using subscription data

A method includes receiving a request from a user device executing a native application. The request indicates an accessed state of the native application to which the native application is set. The method includes identifying one or more categories of the accessed state of the native application and recommended application resource identifiers based on features of the accessed state. Each of the recommended application resource identifiers references a different state of a native application. When a native application is selected by the user, the method includes setting the native application to the different state. For each of the application resource identifiers, the method includes identifying one or more recommended categories of the state referenced by the recommended application resource identifier and determining whether to recommend the different state referenced by the recommended application resource identifier based on features of the accessed state and features of the state of the application.

In-application recommendation of deep states of native applications

A computerized system for transforming an input string includes a dictionary with tokens and associated scores. A chart parser generates a chart parse of the input string by, for each position within the input string, (i) identifying a string of at least one consecutive character in the input string that begins at that position and matches one of the tokens and (ii) unless the identified string is a single character matching the start character for another entry in the chart parse, creating an entry corresponding to the identified string. A partition selection module determines a selected partition of the input string. The selected partition includes an array of tokens selected from the chart parse such that their concatenation matches the input string. The selected partition is a minimum score partition, where the score is based on a sum of the tokens' associated scores from the dictionary.

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Xueying Dai