A digital brand asset system is provided enabling a brand owner to create, distribute, maintain, manage, merchandise and analyze smart brand assets. The system enables distribution and sharing of smart brand assets across the websites. The websites can host webpages containing codes representing the smart brand assets. When a user device retrieves a webpage from one of the websites and renders the webpage, it executes the codes and requests the content of the smart brand assets from a brand asset server. Through the brand asset server, a brand owner can control the content and the presentation of the smart brand asset hosted by the websites. The system further enables the brand partners to adjust the content of the smart brand assets based on their needs.

An online system monitors actions performed by users of the online system in association with being presented with content items that are associated with various categories. A histogram may be generated to describe a pattern of an action previously performed by a user in response to being presented with content items associated with a category. The online system trains a model to predict a likelihood that the user will perform the action in response to being presented with a content item associated with the category based on the pattern of the action previously performed by the user and information describing one or more recent performances of the action by the user with a content item associated with the category. The predicted likelihood may be included in a content selection process that selects one or more content items for presentation to the user.

Systems and methods for controlling ad delivery to mobile clients while maintaining user privacy are herein provided. One exemplary method involves a location broker service. The method includes receiving, at an ad delivery service, a location use token from a mobile client, which may be a single use token. The method includes sending, from the ad delivery service, the location use token to the location broker service for verification at the location broker service. The method includes receiving, at the ad delivery service, the mobile client location from the location broker service based on the verification. The method includes delivering, from the ad delivery service, a location-targeted ad to the mobile client at the mobile client location, where the delivering is further based on a geographic density of a plurality of mobile clients. An advertiser using the ad delivery service may be billed based on location use token history.

A system described herein may use automated techniques, such as machine learning techniques, to identify products that a user may be interested in purchasing. For example, a model may be created for a user, and attributes of products available for sale may be compared to the model. When determining that a user may be interested in a particular product, a graphical user interface (GUI) may be pre-populated and presented to a device of the user, to facilitate the user purchasing the product with minimal interaction.

A system and method allow a user to define a search request by selecting and weighting graphical icons provided on a search creation interface. The graphical icons may be selected by using an icon selection feature provided on the search creation interface. The graphical icons may further be assigned relative search weights using an icon weighting feature provided on the search creation interface. The system and method generate search results based on metadata associated with the selected ones of the graphical icons and the search weights assigned to each of the selected ones of the graphical icons. The search results may comprise a ranked list of items, such as products listed for sale on an e-commerce server.

Systems and methods for determining advertisement content based on cluster density analysis of advertisement targets within a dynamic visibility fence are disclosed. A computer-implemented method includes determining, by an advertisement determination device, a dynamic visibility fence for a time, the dynamic visibility fence defining a first set of advertisement targets, determining, by the advertisement determination device, an advertisement to display based on cluster analysis of the first set of advertisement targets, and displaying, by the advertisement determination device, the determined advertisement.

A computer system may be configured to analyze a set of data and detect divergent trends of one or more subsets of the set of data. The set of data may include reactions of a plurality of customers in response to an action of an organization. For example, the action may relate to goods or services offered by the organization, and the reactions may include responses of the customers to the goods or services. The customer reactions may include divergent trends that indicate a source of risk to the organization. Once detected, the computer system may send an alert to the organization related to the divergent trends, such that the organization may address a source of risk as indicated by the divergent trends. In some examples, the computer system may further offer or autonomously execute a mitigating action to address the source of risk.

A method for enabling a user to view information filtered to align with the user's life state includes enabling the user to register and become a member of an information delivery system. The user provides the information about the user's life state. This information may include at least demographic information, ethnic information, social information and psychological information. The user may grant partners permission to access the information about the user's life. The granting of the permissions is performed and controlled by the user and may be limited to some information about the user's life. The partners are also members of the information delivery system. The partners are evaluated using a set of criteria before they become members of the information delivery system. The partners may use the information about the user's life to filter its information according to the information about the user's life. The method also enables the partner to participate in a marketplace to procure the information about the user's life, to rate the user according to the user's behavior, and to enable search engines to filter search results according to the information about the user's life.

Provided are various mechanisms and processes for generating dynamic merchant scoring predictions. A system is configured to receive datasets comprising pairings between training customer profiles and training merchant profiles. For each pairing, a set of feature values corresponding to features specified by the customer and merchant profiles are extracted and converted into a training vector to train a machine learning model to determine a weighted coefficient for each feature. Once sufficiently trained, the system determines a set of available merchant profiles for a customer profile in response to receiving a search request from a customer associated with the customer profile. For each pairing between the customer profile and an available merchant profile, the system determines an order score for the available merchant based on the weighted coefficients and an input set of feature values specified by the customer profile and the available merchant profile.

A transaction-enabling system is provided. The system includes a controller configured to access a distributed ledger comprising a firmware data value and tokenize the firmware data value. The controller is further configured to interpret an access request for the firmware data value and, in response to the access request, provide a provable access to a firmware corresponding to the firmware data value. The controller is further configured to interpret one of a download operation or an install operation of a firmware asset corresponding to the firmware data value, and record a transaction on the distributed ledger in response to the one of the download operation or the install operation.