According to some embodiments, a method performed by an extended reality (XR) system comprises: obtaining an XR spatial mapping of a physical location; identifying a space within the XR spatial mapping to which virtual content can be overlayed; associating the identified space with a unit identifier; and transmitting the unit identifier to an inventory system so that the space associated with the unit identifier is available to providers of virtual content.

To provide a platform, a system, a method, and a program each adapted to predict an information distribution medium which attracts interest of a plurality of users. A platform according to the present disclosure includes: a gathering unit configured to gathering user information items that are used to predict an information distribution medium which attracts interest of a user out of a plurality of the information distribution media; and a prediction unit for predicting the information distribution medium which attracts interest of a plurality of the users at a prescribed time, which is a predetermined point in time ahead of the current time by a predetermined time length, out of the plurality of the information distribution media based on the user information items of the plurality of the users and information about the plurality of the information distribution media from the past up to the current time.

A computer-implemented method for optimizing electronic content delivery for non-measurable users includes receiving a feature vector for each electronic content impression opportunity, receiving a feature vector for each delivered item of electronic content for measurable users, receiving an in-target indication for each delivered item of electronic content for measurable users, estimating a probability that an electronic content impression opportunity with a specified feature vector will meet targeting requirements based on the received feature vectors and the received in-target indications, receiving an in-target threshold value, generating an in-target rate control signal based on a number of total delivered items of electronic content for measurable users and a number of in-target delivered items of electronic content for measurable users, determining whether the estimated probability is greater than the in-target rate control signal, and generating conditions for delivering a new item of electronic content for an electronic content impression opportunity.

Systems and methods for improving the liquidity and distribution network for luxury and other illiquid items are provided. These systems and methods preferably include the trading of futures and options contracts, which will provide the liquidity and distribution network for luxury items. Possible embodiments of these systems and methods include the trading of futures and options contracts for diamonds and wine. Another embodiment of this invention preferably includes generating indexes for diamond prices, wine prices, luxury item prices, housing values, mortgage prepayments, privately-held companies or for anything with from sufficiently liquid points of value. Another embodiment of this invention preferably includes a centralized data base for retrieving closing and current auction prices for determining the value of, and best method for the auctioning of various items. The data provided by this data base would improve liquidity by creating greater price transparency.

A system, method, and computer program product for implementing electronically generated promotion deployment is provided. The method includes receiving electronic data comprising shipment identification for a shipment of an item from a shipping client to a recipient. Specialized programable software code is generated and a resulting profile is generated for the recipient. A visible promotional structure associated with a package comprising the shipment is generated and presented during delivery of the package. Generating the visible promotional structure may include generating a physical structure or a digital structure.

A system for providing advertisements with search results in response to a search query comprises a front end and an advertisement server. The front end is configured: to receive a search query; to send a first search request to a search server and sending a first advertisement request to an advertisement server, wherein the first search request includes the search query or information based on the search query, and wherein the first advertisement request includes the search query or information based on the search query and an indication that an advertisement response is not to be provided; to receive search results from the search server; and to send at least some of the search results to the advertisement server in a second advertisement request, wherein the second advertisement request includes an indication that an advertisement response is to be provided. The advertisement server is configured: in response to receiving the first advertisement request, to search for advertisements related to the search query to produce plural advertisement results each with an associated score; in response to receiving the second advertisement request, to modify the score of at least one of the plural advertisement results; to rank the plural advertisement results according to their scores; to select one or more of the highest ranked plural advertisement results; and to send an advertisement response to the front end, the advertisement response including the selected one or more highest ranked plural advertisement results. The front end is configured to provide the search results with the selected one or more highest ranked plural advertisement results.

The subject matter of this specification can be embodied in, among other things, a method that receives at a server a first request to rebroadcast a first sponsored content item that was displayed on the social network to the first user to a second user. A second request to display at least one sponsored content item to the second user when the second user accesses an electronic document associated with the social network is received at the server. The first sponsored content item is retrieved for rebroadcast from one or more sponsored content items indicated for rebroadcast to the second user. The first sponsored content item is output to the second user of the social network in response to the second request to display and without requiring the second user to select the first sponsored content item for display.

The present teaching relates to generating combination distributions for ads. A prediction model is obtained via machine learning with respect to a criterion. Training data are associated with multiple ads each having multiple attributes, and include combinations with recorded performance for each ad. Each combination has multiple assets representing respective attributes of an ad. Using the prediction model, performance of each combination of each ad can be predicted and used for generating combination distributions for the ads. Such generated combination distributions are then sent to an explore/exploit layer (EEL) at a frontend ad serving engine so that it can draw a combination associated with an auction winning ad for rendering on a webpage viewed by a user on a user device.

The present teaching relates to displaying ads. An explore/exploit layer (EEL) is provided at frontend ad serving engine for storing combination distributions with respect to multiple ads. Each ad has multiple attributes. Each attribute can be instantiated using one of multiple assets. The frontend ad serving engine requests a recommended ad for bidding an ad display opportunity in a slot of a webpage viewed by a user on a user device. The recommended ad is one of the multiple ads. When the auction is successful, a combination of assets for the ad is drawn from the combination distributions in EEL and each of the assets instantiates a corresponding attribute of the ad. The combination is transmitted to the user device to render the ad.

The present teaching relates to generating combination distributions for ads. Features are computed based on training data associated with ads, each of which has a plurality of attributes. The training data include asset combinations with past performance thereof for each of the ads. Each combination includes multiple assets representing respective attributes of an ad. The features are used in machine learning to obtain an auxiliary model, which is used to generate combination distributions for each ad based on predicted performance for each combination associated with the ad. Such generated combination distributions are sent to an explore/exploit layer (EEL) for a frontend ad serving engine to draw a combination therefrom for an auction winning ad for rendering on a webpage viewed by a user on a user device.