Public Display Network For Online Advertising

• US 20080004953A1
• Filed: 06/30/2006
• Published: 01/03/2008
• Est. Priority Date: 06/30/2006
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Li, Minjing, Ma, Wei-Ying, Zhang, Lei
• Application Number:
  US11/428,204
• Publication Number:
  US 20080004953A1
• Time in Patent Office: Days
• US Class Current: 705/14
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

A public advertising system uses sensors at each public display to gather characteristics of audiences at each of multiple locations. In one implementation, the system compiles the audience characteristics into an audience distribution model. The audience distribution model matches advertising content to audiences and also selects locations, times, and durations for creating distribution strategies. Such distribution strategies provide advertisers with a cost-effective business tool. The system can also match advertising to target features of an audience in real time. In one implementation, computer vision and speech recognition provide rigorous analysis of audience characteristics.

Claims

1. A method, comprising:

associating one or more sensors with each of multiple public displays;

detecting characteristics of an audience in proximity to each display via the one or more sensors;

selecting advertisements for each display based on the characteristics detected at each display; and

distributing the selected advertisements to the audience at each display.

2. The method as recited in claim 1, wherein the sensors are selected from the group of sensors consisting of a camera, a microphone, a motion detector, a heat detector, a radio frequency identification (RFID) sensor, a pressure pad, a computer input device, and a cell phone input device.

3. The method as recited in claim 1, wherein detecting characteristics includes one of detecting a number of people in the audience, detecting a presence of a person, detecting an image of the person, detecting a voice of the person, detecting a movement of the person, detecting a facial feature of the person, detecting a clothing type of the person, and detecting a personal effect in the vicinity of the person.

4. The method as recited in claim 1, further comprising:

analyzing the detected characteristics to obtain, for at least one member of the audience, a gender of the member, an approximate age of the member, an estimated attention focus of the member, or a recognition of a facial feature of the member; and

selecting an advertisement to display to the member based on the obtained gender, age, estimated attention focus, or recognized facial feature.

5. The method as recited in claim 1, further comprising detecting the characteristics and distributing the advertising in substantially real time.

6. The method as recited in claim 1, further comprising:

combining the detected characteristics from all of the multiple public displays;

analyzing the combined characteristics; and

selecting advertisements for each display based on the analysis of the combined characteristics.

7. The method as recited in claim 1, further comprising:

deriving audience demographics from the characteristics detected at the multiple public displays; and

selecting the advertisements based on the demographics.

8. The method as recited in claim 1, further comprising:

deriving an audience distribution model based on the characteristics of changing audiences detected over time at the multiple public displays;

selecting the advertisements based on the audience distribution model;

distributing the advertisements across at least some of the multiple public displays based on the audience distribution model; and

wherein the audience distribution model statistically correlates the characteristics with times and locations.

9. The method as recited in claim 1, further comprising distributing the advertisements according to schemata that designate times, locations, and durations to display the advertisements, wherein each schema targets a type of audience or a type of location based on an analysis of the combined detected characteristics from the multiple public displays.

10. The method as recited in claim 1, further comprising modifying a part of an advertisement to be shown on substantially all of the public displays, wherein the part to be modified is customized for each public display according to an audience detected at each public display.

11. The method as recited in claim 1, wherein the advertisement to be distributed to one of the multiple public displays is based on at least one characteristic of an audience detected at a different one of the multiple public displays.

12. The method as recited in claim 1, further comprising:

distributing a test advertisement to create reactions in audiences at the multiple public displays;

detecting the characteristics of the audiences to measure the reactions;

selecting advertisements for each display based on the measured reactions.

13. The method as recited in claim 1, further comprising:

compiling the detected characteristics of the audiences of the multiple public displays; and

selecting an advertisement based on the compiled characteristics to lure future audiences to the multiple public displays.

14. A system, comprising:

multiple public displays communicatively coupled into a network;

a server in the network to administer the multiple public displays;

sensors associated with each of the multiple public displays to detect characteristics of an audience at each of the multiple public displays;

an audience analyzer in the network to compile the characteristics; and

an advertisement correlator in the network to select advertisements for display at each of the multiple public displays based on the characteristics detected at each public display and the compiled characteristics.

15. The system as recited in claim 14, further comprising an audience distribution modeler to produce geographic profiles and audience profiles from the compiled characteristics.

16. The system as recited in claim 14, further comprising a computer vision engine to receive input from the sensors and detect the characteristics.

17. The system as recited in claim 14, further comprising a speech recognition engine to receive input from the sensors and detect the characteristics.

18. The system as recited in claim 14, wherein the characteristics analyzer includes one of a group size estimator, a facial feature recognizer, a gender analyzer, an age estimator, or an attention estimator.

19. The system as recited in claim 14, further comprising an advertisement distributor for learning correlations between audience characteristics, locations, and times, wherein the advertisement distributor selects a coverage for displaying an advertisement based on the learned correlations.

20. A computerized public advertising display system, comprising:

means for gathering characteristics of audiences at each of multiple public advertising displays via sensors;

means for compiling the characteristics into an audience distribution model;

means for selecting at least part of an advertising content based on the audience distribution model; and

means for selecting locations, times, and durations for distributing the advertising content based on the audience distribution model.