Abstract: Systems and methods are provided to create training data, validate, deploy and test artificial intelligence (AI) systems in a virtual development environment, incorporating virtual spaces, objects, machinery, devices, subsystems, and actual human action and behavior.

Abstract: Systems and methods provide for user segmentation based on viewability of content displayed on user devices during content impressions. Pixel tags are launched when content is displayed on user devices to capture viewability data for a number of different viewability ranges. The viewability data for each content impression is associated with a unique user identifier (UUID). A segment definition sets forth criteria for a user segment based on viewability, and the user segment is generated by including UUIDs associated with viewability data satisfying the segment definition.

Abstract: Systems and methods provide for determining optimal viewability ranges for content impressions. During content impressions, a content server receives content requests via an ad tag that is enhanced with a content impression identifier or cost data from a bidding platform. The content server serves a pixel tag with content in order to collect viewability data for each content impression. The content server stores the viewability data with the content impression identifier or cost data from the bidding platform for each content impression. This allows for the correlation of cost data and viewability data on a per-content impression basis. The cost data and viewability data are used to compute cost-per-conversion information for each of a number of different viewability ranges.

Abstract: Systems and methods provide for delivering a marketer's attribution data to media partners on a per-content impression basis to allow the media partners to make actionable decisions based on how valuable the marketer views the media partners' content impressions. During content impressions, a pixel tag is fired that captures and sends media partner metadata and content impression data for each content impression to an attribution engine. The attribution engine scores each content impression using an attribution model and associates an attribution score with the media partner metadata for each content impression. An attribution file is generated and sent to each media partner. The attribution file for a given media partner includes an attribution score associated with the media partner metadata for each content impression served by that media partner. The attribution file can also include additional data related to each content impression otherwise only available to the marketer.

Abstract: Systems and methods provide for determining optimal viewability ranges for content impressions. During content impressions, a content server receives content requests via an ad tag that is enhanced with a content impression identifier or cost data from a bidding platform. The content server serves a pixel tag with content in order to collect viewability data for each content impression. The content server stores the viewability data with the content impression identifier or cost data from the bidding platform for each content impression. This allows for the correlation of cost data and viewability data on a per-content impression basis. The cost data and viewability data are used to compute cost-per-conversion information for each of a number of different viewability ranges.

Abstract: Systems and methods provide for user segmentation based on viewability of content displayed on user devices during content impressions. Pixel tags are launched when content is displayed on user devices to capture viewability data for a number of different viewability ranges. The viewability data for each content impression is associated with a unique user identifier (UUID). A segment definition sets forth criteria for a user segment based on viewability, and the user segment is generated by including UUIDs associated with viewability data satisfying the segment definition.