Method and system for scheduling online targeted content delivery

• US 7,174,305 B2
• Filed: 01/23/2001
• Issued: 02/06/2007
• Est. Priority Date: 01/23/2001
• Status: Active Grant

• Current Assignee: OpenTV, Inc. (Kudelski SA)
• Original Assignee: OpenTV, Inc. (Kudelski SA)
• Inventors: Carruthers, Tim, Hyland, Robert, Larson, Richard, McCready, Robert, Moran, John, Thurston, Nathaniel
• Primary Examiner(s): CHAMPAGNE, DONALD
• Application Number:
  US09/767,793
• Publication Number:
  US 20020128904A1
• Time in Patent Office: 2,205 Days
• Field of Search: 705/14, 434/307
• US Class Current: 705/14.52
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0244 Optimization
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0264 based upon schedule
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0272 Period of advertisement exposure
• CPC Class Codes: G06Q 30/0277 Online advertisement

Abstract

A method and system are provided for scheduling the delivery of multiple items of content selectively to online users. Expected values relating to each user being online during a given time period are determined. An ordered list of the items of content to be selectively delivered to the users is generated based on the expected values. The list is prioritized to meet delivery requirements associated with the items of content.

Claims

1. A method of scheduling delivery of multiple items of content selectively to a plurality of online users, comprise:

determining expected values relating to each user being online during a given time period;

generating an ordered list of the items of content to be selectively delivered to the users based on the expected value, said ordered list being prioritized to meet delivery requirements associated with said items of content; and

generating an individual list of items of content to be delivered to each user based on the ordered list,

wherein generating said individual list comprises determining the position of each of said items of content in said individual list in accordance with the following equation;

E[Q_j]≦

−

τ

(lnM_j−

lnP_j−

lnp)/Δ

where, E[Qj] is the position of a given item of content, τ

is the mean expected time period of a user online session, M_j is the desired number of impressions of the item of content to be delivered per day, P_j is the number of online users who are eligible to receive the item of content,p is the probability that a given eligible user is online on a given day, and Δ

is time period between deliveries of items of content to a user.

2. A method of determining the viability of a proposed plan to deliver an item of content to online users having specified characteristics a specified number of times during a specified time period, said item of content to be delivered to said online users as one of a series of items of content delivered sequentially to the users, the method comprising:

determining an expected position of the item of content in the series based on the number of online users having the specified characteristics, a probability that a random user having the specified characteristics will be online during the specified time period, a predicted session length for the random user, and the time period between deliveries of said items of content;

determining an expected number of deliveries of said items of content based on the expected position; and

comparing the expected number of deliveries to the specified number of deliveries.

3. The method of claim 2 further comprising suggesting an alternative constraint if the plan is determined not to be viable.

4. The method of claim 3 wherein said constraint comprises the specified number of times said item of content is to be delivered.

5. The method of claim 3 wherein said constraint comprises the specified time period.

6. The method of claim 3 wherein said constraint comprises the specified characteristics.

7. The method of claim 2 wherein the probability that a random user having the specified characteristics will be online during the specified time period is based on observed behavior of individual users.

8. The method of claim 7 wherein a predicted session length for the random user is based on observed behavior of individual users.

9. The method of claim 2 wherein said specified characteristics comprise user profile or preference data.

10. The method of claim 2 wherein said item of content comprises an advertisement.