ONLINE SERVING THRESHOLD AND DELIVERY POLICY ADJUSTMENT
First Claim
1. A method comprising:
- using one or more computers, during an offline period, initially determining a set of serving thresholds to be utilized in online advertisement serving, wherein a serving threshold is associated with a minimum anticipated click through rate;
using one or more computer computers, during an offline period, initially determining a set of delivery policies, wherein a delivery policy is associated with one or more rules relating to serving of advertisements in accordance with required or optimal distribution of advertising inventory across serving opportunities;
using one or more computers, during, and based at least in part on information obtained during, an online period, adjusting at least one of the set of serving thresholds to determine at least one adjusted serving threshold, and adjusting at least one of the set of delivery policies to determine at least one adjusted delivery policy; and
using one or more computers, during an online period, utilizing a machine learning-based model in decision-making with regard to serving of online advertisements in connection with serving opportunities based at least in part on the at least one adjusted serving threshold and the at least one adjusted delivery policy, wherein the machine learning-based model is trained during an offline period, wherein an online period is a period of active advertisement serving in which the model is utilized.
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Accused Products
Abstract
The present invention provides techniques for use in association with online advertising, relating to use of serving thresholds, associated with predicted click through rates, and delivery policies, associated with advertising inventory serving and distribution. An offline-trained machine learning-based model may be utilized in advertising serving decision-making in connection with serving opportunities. However, serving thresholds and delivery policies, for use in association with the model in serving decision-making, may be adjusted online, such as in real-time or near real-time, based on information obtained online affecting factors such as predicted click through rates and advertising inventory distribution.
45 Citations
20 Claims
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1. A method comprising:
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using one or more computers, during an offline period, initially determining a set of serving thresholds to be utilized in online advertisement serving, wherein a serving threshold is associated with a minimum anticipated click through rate; using one or more computer computers, during an offline period, initially determining a set of delivery policies, wherein a delivery policy is associated with one or more rules relating to serving of advertisements in accordance with required or optimal distribution of advertising inventory across serving opportunities; using one or more computers, during, and based at least in part on information obtained during, an online period, adjusting at least one of the set of serving thresholds to determine at least one adjusted serving threshold, and adjusting at least one of the set of delivery policies to determine at least one adjusted delivery policy; and using one or more computers, during an online period, utilizing a machine learning-based model in decision-making with regard to serving of online advertisements in connection with serving opportunities based at least in part on the at least one adjusted serving threshold and the at least one adjusted delivery policy, wherein the machine learning-based model is trained during an offline period, wherein an online period is a period of active advertisement serving in which the model is utilized. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A system comprising:
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one or more server computers coupled to a network; and one or more databases coupled to the one or more server computers; wherein the one or more server computers are for; during an offline period, initially determining a set of serving thresholds to be utilized in online advertisement serving, wherein a serving threshold is associated with a minimum anticipated click through rate; during an offline period, initially determining a se of delivery policies, wherein a delivery policy is associated with one or more rules relating to serving of advertisements in accordance with required or optimal distribution of advertising inventory across serving opportunities; during, and based at least in part on information obtained during, an online period, adjusting at least one of the set of serving thresholds to determine at least one adjusted serving threshold, and adjusting at least one of the set of delivery policies to determine at least one adjusted delivery policy; and during an online period, utilizing a machine learning-based model in decision-making with regard to serving of online advertisements in connection with serving opportunities based at least in part on the at least one adjusted serving threshold and the at least one adjusted delivery policy, wherein the machine learning-based model is trained during an offline period, wherein an online period is a period of active advertisement serving in which the model is utilized. - View Dependent Claims (17, 18, 19)
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20. A computer readable medium or media containing instructions for executing a method for use in association with an online advertising auction relating to an online advertising marketplace, the method comprising:
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using one or more computers, during an offline period, initially determining a set of serving thresholds to be utilized in online advertisement serving, wherein a serving threshold is associated with a minimum anticipated click through rate; using one or more computer computers, during an offline period, initially determining a set of delivery policies, wherein a delivery policy is associated with one or more rules relating to serving of advertisements in accordance with required or optimal distribution of advertising inventory across serving opportunities; wherein the distribution of advertising inventory across serving opportunities relates to distribution of advertising inventory across serving opportunities associated with different nodes of a hierarchical taxonomy of behavioral targeting categorical nodes; using one or more computers, during, and based at least in part on information obtained during, an online period, adjusting at least one of the set of serving thresholds to determine at least one adjusted serving threshold, and adjusting at least one of the set of delivery policies to determine at least one adjusted delivery policy; wherein the at least one adjusted serving threshold and the at least one adjusted delivery policy are adjusted at least in part to better optimize user targeting or advertising inventory distribution, based at least in part on the information obtained during the period; and using one or more computers, during an online period, utilizing a machine learning-based model in decision-making with regard to serving of online advertisements in connection with serving opportunities based at least in part on the at least one adjusted serving threshold and the at least one adjusted delivery policy, wherein the machine learning-based model is trained during an offline period, wherein an online period is a period of active advertisement serving in which the model is utilized; wherein the machine-learning based model is trained using information including historical user behavior information.
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Specification