- Number
- 20260203791
- Published
- 2026-07-16
- Filed
- 2025-01-15
- Assignee
- Disney Enterprises, Inc.
- Inventors
- GAO; Pengfei, GAO; Yupeng, ZHANG; Yan, WANG; Zhe
- CPC
- G06Q30/0255; G06Q30/0246
- Verdict
- Set aside ad-tech
- In edition
- 2026-W29
- Source
- Google Patents · FreePatentsOnline
The keeper's note
Embodiments provide for improved resource distribution.
Abstract
Embodiments provide for improved resource distribution. A first set of pacing results for a content distribution plan is accessed, and a target pacing for the content distribution plan is determined. A first coverage threshold is generated based on the first set of pacing results and the target pacing. A first ranking of a plurality of users is generated using a machine learning model and based on the content distribution plan, and distribution of content associated with the content distribution plan is facilitated based on the first ranking and the first coverage threshold.
Background
BACKGROUND
A wide variety of content (e.g., multimedia content such as video, audio, music, and the like) can be distributed according to an equally wide variety of distribution schemes and plans. In many cases, it is desirable to distribute or provide content in a targeted manner, improving the probability that the receiving user(s) will be interested in or otherwise engage with the delivered content. In some cases, how the user interacts or engages with the delivered content can be monitored to learn to predict future user engagement (whether for the same user or for other users) with the same content, similar content, and/or dissimilar content. For example, efforts have been made to predict whether a user will enjoy specific content, whether the user will engage more deeply with specific content (e.g., clicking the content, following a provided link, or otherwise requesting or seeking additional information about the content), and the like.
In some applications, distribution plans for media content are generated with goals related to such engagement. For example, a content provider may wish to distribute a given content asset in such a way a specified number of users will engage with or seek further information about the content. As one example, supplemental content providers (e.g., advertisers) who provide content that is distributed as supplemental media along with primary content (e.g., movies and television shows) often specify the desired number of users
Claims
1. A method, comprising: accessing a first set of pacing results for a content distribution plan for a prior window of time; determining a target pacing for the content distribution plan; generating, during an online phase, a first coverage threshold using a coverage threshold algorithm and based on the first set of pacing results and the target pacing, the first coverage threshold indicating a number of users, from a plurality of users, to receive content associated with the content distribution plan; generating, during an offline phase prior to the online phase, a first ranking of the plurality of users using a machine learning model and based on the content distribution plan, wherein: the machine learning model was trained to predict a single outcome for each respective user of the plurality of users, and the coverage threshold algorithm incurs less computational expense, as compared to the machine learning model; facilitating distribution of content associated with the content distribution plan based on the first ranking and the first coverage threshold during a second window of time, the second window of time occurring during the online phase; accessing a second set of pacing results for the content distribution plan for the second window of time during the online phase; generating, during the online phase, a second coverage threshold using the coverage threshold algorithm and based on the second set of pacing results; and facilitating distribution of content associated with the content distribution plan based on the first ranking and the second coverage threshold during a third window of time, the third window of time occurring during the online phase. ||
9. One or more non-transitory computer readable media containing, in any combination, computer program code that, when executed by operation of any combination of one or more processors, performs an operation comprising: accessing a first set of pacing results for a content distribution plan for a prior window of time; determining a target pacing for the content distribution plan; generating, during an online phase, a first coverage threshold using a coverage threshold algorithm and based on the first set of pacing results and the target pacing, the first coverage threshold indicating a number of users, from a plurality of users, to receive content associated with the content distribution plan; generating, during an offline phase prior to the online phase, a first ranking of the plurality of users using a machine learning model and based on the content distribution plan, wherein: the machine learning model was trained to predict a single outcome for each respective user of the plurality of users, and the coverage threshold algorithm incurs less computational expense, as compared to the machine learning model; facilitating distribution of content associated with the content distribution plan based on the first ranking and the first coverage threshold during a second window of time, the second window of time occurring during the online phase; accessing a second set of pacing results for the content distribution plan for the second window of time during the online phase; generating, during the online phase, a second coverage threshold using the coverage threshold algorithm and based on the second set of pacing results; and facilitating distribution of content associated with the content distribution plan based on the first ranking and the second coverage threshold during a third window of time, the third window of time occurring during the online phase. ||
15. A system, comprising: one or more processors; and one or more memories storing a program, which, when executed on any combination of the one or more processors, performs operations, the operations comprising: accessing a first set of pacing results for a content distribution plan for a prior window of time; determining a target pacing for the content distribution plan; generating, during an online phase, a first coverage threshold using a coverage threshold algorithm and based on the first set of pacing results and the target pacing, the first coverage threshold indicating a number of users, from a plurality of users, to receive content associated with the content distribution plan; generating, during an offline phase prior to the online phase, a first ranking of the plurality of users using a machine learning model and based on the content distribution plan, wherein: the machine learning model was trained to predict a single outcome for each respective user of the plurality of users, and the coverage threshold algorithm incurs less computational expense, as compared to the machine learning model; facilitating distribution of content associated with the content distribution plan based on the first ranking and the first coverage threshold during a second window of time, the second window of time occurring during the online phase; accessing a second set of pacing results for the content distribution plan for the second window of time during the online phase; generating, during the online phase, a second coverage threshold using the coverage threshold algorithm and based on the second set of pacing results; and facilitating distribution of content associated with the content distribution plan based on the first ranking and the second coverage threshold during a third window of time, the third window of time occurring during the online phase.