Machine readable instructions for providing targeted advertisements for a piece of digital content receive a set of user metadata for a group of users. The machine readable instructions select at least one user from the group of users. In addition, the machine readable instructions select a term from a category of terms related to the piece of digital content. Moreover, the machine readable instructions determine a first value corresponding to the at least one user. Also, the machine readable instructions determine a second for the group of users. In addition, the machine readable instructions determine a user score based at least in part on the first value and the second value. When the user score is within a particular range, the machine readable instructions provide an advertisement for the piece of digital content to an electronic device associated with the at least one user.

A computer-implemented method can facilitate delivery of targeted content to user devices in situations in which historic tracking data (e.g., cookie data) is generally unavailable and/or unreliable. A p-dimensional embedding of websites can be generated based on a group of user devices for whom tracking data is available. Conversion event data that indicates indicating whether that audience member performed a conversion action can be received. A machine learning model can be trained using the conversion event data and the positions of websites appearing in the conversion event data within the p-dimensional embedding to predict a likelihood of conversion and/or a type of content to provide given a position in the p-dimensional embedding. When an indication that a user device is accessing a website is received, a position of that website in the p-dimensional embedding can be determined and targeted content can be delivered to the user device.

A computer-implemented method can facilitate delivery of targeted content to user devices in situations in which historic tracking data (e.g., cookie data) is generally unavailable and/or unreliable. A p-dimensional embedding of websites can be generated based on a group of user devices for whom tracking data is available. Conversion event data that indicates indicating whether that audience member performed a conversion action can be received. A machine learning model can be trained using the conversion event data and the positions of websites appearing in the conversion event data within the p-dimensional embedding to predict a likelihood of conversion and/or a type of content to provide given a position in the p-dimensional embedding. When an indication that a user device is accessing a website is received, a position of that website in the p-dimensional embedding can be determined and targeted content can be delivered to the user device.

A system and method reduces or minimizes the number of characteristic values required to evaluate a rule by selecting elements of the rule in an order most likely to allow evaluation of the rule without requiring evaluation of other elements of the rule. The selection may be a function of one or both of the structure of the rule and the probability that an element will resolve to a particular value of true or false.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adjusting physical location usage for a plurality of particular locations. Methods can include obtaining a three-dimensional (3D) representation of the given geographic area, wherein the 3D representation depicts a view of the given geographic area from a specified viewing perspective. For the given geographic area, traffic data is obtained indicating different traffic volumes during different time periods and one or more traffic characteristics. The 3D representation is segmented into a plurality of particular locations. For each particular location among the plurality of particular locations and based on the traffic data, a viewability score is determined that indicates an aggregate amount of time that the particular location is viewable by traffic passing the different locations. Physical location usage is then adjusted based on the viewability scores for the plurality of particular locations.

The exemplary embodiments include processes that integrate computer-implemented digital platforms into programmatically generated virtual environments. For example, based on a first signal received from a computing device operable by a user, an apparatus may determine that an element of digital content is consistent with a prior interaction between the user and a digital platform. The element of digital content may be associated with data exchange of data involving the digital platform, and the apparatus may transmit a second signal that includes the element of first digital content and information characterizing the data exchange to the computing device. The computing device may present the element of digital content within a digital interface associated with an executed application program, and based on the presentation, the computing device request an initiation of the data exchange at the apparatus without exiting a virtual environment of the executed application program.

The system provides a digital solution for access to service providers, with a personalized user experience, reduced fees, and better choice. The system builds customer loyalty, optimizes time, and uses predictive analytics to improve performance. The system uses a combination of personalization, artificial intelligence, reduced fees, and advanced analytics to provide personalized services. The system provides better services for consumers and better experiences for providers.

A selected comparative statistical result of an event is the deciding factor in the mechanics of the function. Result grid data of the event determine specific grid location and variable (symbol, value, character). As the stats change, so does the active square location on the grid. At the end of the event or select period, a grid location becomes the selected square and the variable (symbol, value, character) is determined as the result for that iteration or grid set. (each participant can have a different variable at that same square location). Back-end management process controls and creates weighted frequency of variables.

Used for sport organizations, competitive organizations, corporate programs, gaming events. A scalable program and digital platform for spectator events, philanthropic campaigns, corporate programs and gaming that can be administered as traditional printed items or in digital applications (mobile and otherwise).

Provided are methods and systems for, in one aspect, managing content delivered to one or more devices. Methods may comprise receiving a first identifier associated with a user of content. The content may comprise one or more advertisement or placement opportunities. A second identifier may be determined based at least on the first identifier. The second identifier may be a perishable identifier configured to expire based on an event. The second identifier may be transmitted to an entity associated with an advertisement inventory, wherein the second identifier facilitates the targeted placement of one or more advertisements from the advertisement inventory without exposing the first identifier to the entity.

Systems and methods related to resource acquisition on a resource market are disclosed. A system may include a machine having a resource requirement for a task. A system controller may include a resource requirement circuit to determine an amount of a resource for the machine to service the task requirement, a resource market circuit to access a resource market, and a market testing circuit to execute a first transaction of the resource on the resource market. The controller may further include an arbitrage execution circuit to execute a second transaction of the resource on the resource market in response to an outcome of the first transaction, wherein the second transaction comprises a larger transaction than the first transaction.