Aspects relate to delivering directed content using frequency capping concepts without third-party cookie technology or other user-identifiable information. Systems and methods are described for determining an estimated number of impressions a user may view or experience based on historical, anonymized data. A covisitation graph is used to estimate the number of times an unidentified user may see an item of directed content, within in a predetermined period of time, based on a single visit to a particular website. The covisitation graph accounts for and also provides for estimated views of the item of directed content on other websites deemed related to the particular website. Therefore the estimated delivery rate for that website can be calculated and used to enforce frequency capping principals.

The Bidding on an Asset in Progress (BAIP) system allows live, real-time bidding on a work (song, artwork, invention, story, concept, etc.) during its inception and evolution. The system captures video and/or audio of the work and/or artist during inception and creation and/or production of the work. The live performance in the auction can take place in a metaverse. Depending on how the artist/musician/creator sets up an auction, fans can bid on digital representations of versions and/or parts of the work, the recorded video and/or audio of the work being created, the physical work created, specific copyright rights, and supplemental assets associated with the work or the artist/musician/creator. Such a system enables bidding on any number of combinations of assets captured, created, or modified during one or more auctions. Fans can band together to bid on any asset and establish fractionalized ownership that is optionally governed through a DAO. After completion of the work and auction, ownership of the asset(s) is preferably transferred through NFTs and the winning bidder(s) receives the finished asset(s) (digital and/or physical).

A method including obtaining historical revenue per click (RPC) data. The method also can include generating hourly RPC prediction data for a predetermined time period based on the historical RPC data. The method additionally can include determining (i) time intervals from within the predetermined time period and (ii) a respective modifier for each of the time intervals, based on the hourly RPC prediction data. The acts method can include uploading the time intervals and the respective modifiers for the time intervals to a dayparting system of a search engine. Other embodiments are described.

A system, method and computer program product for interfacing a decision engine and a marketing engine in order to provide vendor-related data in response to decision-related data is disclosed. In at least one embodiment, the system and method may include providing a decision engine on a user-accessible network; interfacing a marketing engine with the decision engine on the network; receiving a plurality of user inputs with the decision engine; processing decision-related data with the decision engine in accordance with the plurality of user inputs; sharing the decision-related data with the marketing engine; processing the decision-related data with the marketing engine; and transmitting vendor-related data via the network.

Provided is a system that identifies a tag in a media content of a media stream based on a user-attribute of a client device. A candidate time-period is identified in a playback duration of the media content based on the identified tag in the media content. Based on a degree of correlation between the identified tag in the media content and a corresponding context for the media content at the candidate time-period, an overlay-graphic item corresponding to the identified tag is presented at the candidate time-period in the media content.

A media presentation and distribution system (MPDS) communicatively coupled to a client device, which handles media content distribution via a content delivery network, to a client device associated with a user: identifies candidate time intervals in programming media content played at the client device based on at least a request received from the client device. The MPDS retrieves media content from a media store in the MPDS. The media content is retrieved based on at least one of the user intent information and a plurality of targeting parameters associated with the user. The MPDS dynamically generates personalized media content that corresponds to the candidate time intervals in the programming media content and further instructs playback of the dynamically generated personalized media content at the identified candidate time intervals based on the specified version of the programming media content played at the client device.

A media presentation and distribution system includes a verification server that handles dynamic verification of playback of media assets on a client device. The client device receives an asset stream of media assets that comprises one or more tags embedded in the media assets. The client device detects an asset identifier associated with each of the media assets during playback of each media asset on the client device, based on identification of a tag of the one or more tags. The verification server verifies the playback of the media assets on the client device based on received support information from the client device. The playback of the media assets are verified to satisfy defined asset delivery criteria and to identify and debug a deviation or one or more errors with the playback of the media assets.

Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.

Systems, methods, and computer-readable media are disclosed for filtering data with probabilistic filters for content selection. In one embodiment, an example method may include determining a user interaction history with a first product identifier for a user account, determining a first parent product identifier of the first product identifier, and generating a database with the first parent product identifier and a user account identifier for the user account. Example methods may include determining a set of candidate content with first content and second content for the user account, determining a second product identifier associated with the first content, and determining a second parent product identifier of the second product identifier. Example methods may include determining that the second parent product identifier is not present in the database using a probabilistic filter, and determining that the first content is eligible for presentation.

Embodiments of the present disclosure provide methods, systems, and apparatuses for programmatically determining and adjusting electronic bid values for a digital content object using a machine learning model.