A method, computer system, and a computer program product for modifying a user interface. Attributes of a source object identified by a user in connection with a user input for storing the source object are determined. Attributes of one or more target storage locations are determined. A target storage location for storing the source object is predicted, along with a confidence value associated with the prediction. The prediction is made using a machine learning model that predicts the predicted target storage location and associated confidence value based on the determined attributes of the source object. A plurality of target storage location usage patterns are determined. The user interface is modified based on the predicted target storage location.

A computer system including a processor in communication with a memory and a database may be provided. The processor may be programmed to: (i) execute a query on the database including a list of user identifiers associated with a plurality of users, (ii) receive license data associated with the user identifiers including a list of licenses and respective license renewal data associated with each user, (iii) determine, from the license data, that one or more licenses of a group of users is in a renewal period, (iv) notify each user of the group of users of the one or more licenses in the renewal period, (v) pre-populate a license renewal application for the one or more licenses in the renewal period the group of users, (vi) transmit the pre-populated application to be approved by the group of users, and (vii) receive the approved pre-populated application from the group of users.

The present application describes a system and method for searching for content items in an application executing on a computing device. In order to increase the efficiency of the search, the present disclosure provides a refiner that is used to filter or otherwise refine search results. The refiner is user-specific and/or tenant/entity-specific. The refiner may be based on long-term aggregated data and/or contextual information associated with the user.

The present application describes a system and method for searching for content items in an application executing on a computing device. In order to increase the efficiency of the search, the present disclosure provides a refiner that is used to filter or otherwise refine search results. The refiner is user-specific and/or tenant/entity-specific. The refiner may be based on long-term aggregated data and/or contextual information associated with the user.

According to an embodiment of a recommending educational content method includes: acquiring search information of target user; acquiring learning set information based on the search information; acquiring a search database of a plurality of users based on the leaning set information, the search database including user identification information and a reference value allocated according to whether the user searches for a question included in the learning set information; allocating a feature value according to whether to search for at least one question included in the learning set information based on the search information; generating a first matrix based on the reference value of the search database and the feature value related to the target user; transforming the first matrix into a second matrix based on similarity of the reference value and the feature value; and calculating a learning ability score of the target user based on the second matrix.

According to an embodiment of a recommending educational content method includes: acquiring search information of target user; acquiring learning set information based on the search information; acquiring a search database of a plurality of users based on the leaning set information, the search database including user identification information and a reference value allocated according to whether the user searches for a question included in the learning set information; allocating a feature value according to whether to search for at least one question included in the learning set information based on the search information; generating a first matrix based on the reference value of the search database and the feature value related to the target user; transforming the first matrix into a second matrix based on similarity of the reference value and the feature value; and calculating a learning ability score of the target user based on the second matrix.

A method is provided for automatic purchase of digital media and digital assets. The method may include asking at least one user to provide a preselected list of products. The method may also include generating a recommended list of additional products using a machine-learning algorithm, where the machine-learning algorithm includes one of collaborative filtering algorithm, content-based filtering, desirable content model, personalized video game ranker, or knowledge based recommendation systems. The method may also include adding the recommended list of additional products to the preselected list of the at least one user to provide an augmented interest list to the at least one user. The method may also include asking the at least one user to establish parameters for automatic purchases based upon the augmented interest list. The method may further include automatically purchasing one or more products based upon the prices of the one or more products meeting the parameters.

A method is provided for automatic purchase of digital media and digital assets. The method may include asking at least one user to provide a preselected list of products. The method may also include generating a recommended list of additional products using a machine-learning algorithm, where the machine-learning algorithm includes one of collaborative filtering algorithm, content-based filtering, desirable content model, personalized video game ranker, or knowledge based recommendation systems. The method may also include adding the recommended list of additional products to the preselected list of the at least one user to provide an augmented interest list to the at least one user. The method may also include asking the at least one user to establish parameters for automatic purchases based upon the augmented interest list. The method may further include automatically purchasing one or more products based upon the prices of the one or more products meeting the parameters.

Systems and methods for determining identifying information associated with a customer of a service business are disclosed. Data indicative of assets associated with a service business may be received from at least one database. Data indicative of a plurality of interactions with at least one device of the service business may be determined based on the data indicative of assets associated with the service business. An identity associated with at least one customer of the service business may be determined based on the data indicative of the plurality of interactions with the at least one device of the service business. The identity may indicate, for example, a identify of an unknown customer of the service business. As another example, the identify may indicate a relationship between two known customers of the service business.

Systems and methods for determining identifying information associated with a customer of a service business are disclosed. Data indicative of assets associated with a service business may be received from at least one database. Data indicative of a plurality of interactions with at least one device of the service business may be determined based on the data indicative of assets associated with the service business. An identity associated with at least one customer of the service business may be determined based on the data indicative of the plurality of interactions with the at least one device of the service business. The identity may indicate, for example, a identify of an unknown customer of the service business. As another example, the identify may indicate a relationship between two known customers of the service business.