# US 8,751,418 B1 · Using proxy behaviors for audience selection

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- Number: US 8,751,418 B1
- Document ID: US8751418B1
- Status: Granted
- Kind: B1
- Application number: 13/275,098
- Filed: 2011-10-17
- Granted: 2014-06-10
- Earliest claimed: 2011-10-17
- Assignees: Quantcast Corporation
- Applicants: Paul G. Sutter; Konrad S. Feldman
- Inventors: Paul G. Sutter; Konrad S. Feldman
- Web page: https://patentgrep.com/patent/US8751418B1

## CPC

- G06N 20/00: Machine learning
- G06F 16/951: Indexing; Web crawling techniques
- G06F 16/9535: Search customisation based on user profiles and personalisation
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06Q 30/0255: {based on user history}
- G06Q 30/0277: {Online advertisement}
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Method and system for assessing the suitability of an entity using a proxy. A description of a behavior associated with a desirable audience is received. A proxy behavior estimated to be characteristic of the desirable audience is selected. The proxy behavior comprises the performance of proxy events related to the consumption of media received by an entity over a network, which can be found in an entity's consumption history. An entity can be assessed for inclusion in a proxy audience, by examining the entity's consumption history for proxy behaviors. A behavioral model is built using a training set comprising the proxy audience. By applying the behavioral model to the consumption history of a specified entity, the specified entity's suitability for selection can be determined. Advantageously, in an embodiment, the invention enables the use of behavioral modeling techniques even when the complete behavior of the desirable audience is not available.

## Claims

### Claim 1

A computer-implemented method comprising:
receiving focus information descriptive of a search engine advertising campaign from an advertising campaign manager, the focus information comprising two or more keyword search strings, each keyword search string paired with at least one bid parameter, a relative importance of each keyword search string characterized by the at least one bid parameter;
accessing a respective consumption history of each of a plurality of entities in a storage;
identifying, at an audience selection system, a training set of entities from the storage by examining each of the respective consumption histories for one or more proxy events, each proxy event comprising a keyword search matching at least one of the two or more keyword search strings described in the focus information;
weighting each proxy event according to the at least one bid parameter paired with each proxy event's keyword search string;
creating a weighted training set by weighting each entity in the training set according to the proxy event weights of the one or more proxy events found in each entity's respective consumption history;
building a behavioral model based on the weighted training set;
receiving a specified entity's consumption history; and
assessing the suitability of the specific entity for selection by applying the behavioral model to the specified entity's consumption history.

### Claim 2 (depends on 1)

The method of claim 1 wherein:
the bid parameter increases the weight of the proxy event.

### Claim 3 (depends on 1)

The method of claim 1 wherein:
the bid parameter decreases the weight of the proxy event.

### Claim 4 (depends on 1)

The method of claim 1 wherein:
the bid parameter comprises a maximum bid.

### Claim 5 (depends on 1)

The method of claim 1 wherein creating the weighted training set further comprises:
weighting each entity in the training set according to a frequency of the one or more matching keyword search strings in the entity's consumption history.

### Claim 6 (depends on 1)

The method of claim 1 wherein creating the weighted training set further comprises:
weighting each entity in the training set according to a recency of the one or more matching keyword search strings in the entity's consumption history.

### Claim 7 (depends on 1)

The method of claim 1 wherein assessing comprises receiving the consumption history of the specified entity wherein the consumption history of the specified entity is not stored in the storage.

### Claim 8

A non-transitory computer readable storage medium including computer program instructions that when executed, cause a computer processor to perform operations comprising:
receiving focus information descriptive of a search engine advertising campaign from an advertising campaign manager, the focus information comprising two or more keyword search strings, each keyword search string paired with at least one bid parameter, a relative importance of each keyword search string characterized by the at least one bid parameter;
accessing a respective consumption history of each of a plurality of entities in a storage;
identifying, at an audience selection system, a training set of entities from the storage by examining each of the respective consumption histories for one or more proxy events, each proxy event comprising a keyword search matching at least one of the two or more keyword search strings described in the focus information;
weighting each proxy event according to the at least one bid parameter paired with each proxy event's keyword search string;
creating a weighted training set by weighting each entity in the training set according to the proxy event weights of the one or more proxy events found in each entity's respective consumption history;
building a behavioral model based on the weighted training set;
receiving a specified entity's consumption history; and
assessing the suitability of the specific entity for selection by applying the behavioral model to the specified entity's consumption history.

### Claim 9 (depends on 8)

The medium of claim 8 wherein:
the bid parameter increases the weight of the proxy event.

### Claim 10 (depends on 8)

The medium of claim 8 wherein:
the bid parameter decreases the weight of the proxy event.

### Claim 11 (depends on 8)

The medium of claim 8 wherein:
the bid parameter comprises a maximum bid.

### Claim 12 (depends on 8)

The medium of claim 8 wherein the operation of creating the weighted training set further comprises:
weighting each entity in the training set according to a recency of the one or more matching keyword search strings in the entity's consumption history.

### Claim 13 (depends on 8)

The medium of claim 8 wherein the operation of creating the weighted training set further comprises:
weighting each entity in the training set according to a frequency of the one or more matching keyword search strings in the entity's consumption history.

### Claim 14 (depends on 8)

The medium of claim 8 wherein the operation of assessing comprises:
receiving the consumption history of the specified entity wherein the consumption history of the specified entity is not stored in the storage.

### Claim 15

A system comprising:
a processor; and
a non-transitory computer readable storage medium storing processor-executable computer program instructions that when executed, cause a computer processor to perform operations comprising:
receiving focus information descriptive of a search engine advertising campaign from an advertising campaign manager, the focus information comprising two or more keyword search strings, each keyword search string paired with at least one bid parameter, a relative importance of each keyword search string characterized by the at least one bid parameter;
accessing a respective consumption history of each of a plurality of entities in a storage;
identifying, at an audience selection system, a training set of entities from the storage by examining each of the respective consumption histories for one or more proxy events, each proxy event comprising a keyword search matching at least one of the two or more keyword search strings described in the focus information;
weighting each proxy event according to the at least one bid parameter paired with each proxy event's keyword search string;
creating a weighted training set by weighting each entity in the training set according to the proxy event weights of the one or more proxy events found in each entity's respective consumption history;
building a behavioral model based on the weighted training set;
receiving a specified entity's consumption history; and
assessing the suitability of the specific entity for selection by applying the behavioral model to the specified entity's consumption history.

### Claim 16 (depends on 15)

The system of claim 15 wherein:
the bid parameter increases the weight of the proxy event.

### Claim 17 (depends on 15)

The system of claim 15 wherein:
the bid parameter decreases the weight of the proxy event.

### Claim 18 (depends on 15)

The system of claim 15 wherein:
the bid parameter comprises a maximum bid.

### Claim 19 (depends on 15)

The system of claim 15 wherein the operation of creating the weighted training set further comprises:
weighting each entity in the training set according to a frequency of the one or more matching keyword search strings in the entity's consumption history.

### Claim 20 (depends on 15)

The system of claim 15 wherein the operation of creating the weighted training set further comprises:
weighting each entity in the training set according to a recency of the one or more matching keyword search strings in the entity's consumption history.

### Claim 21 (depends on 15)

The system of claim 15 wherein the operation of assessing comprises:
receiving the consumption history of the specified entity wherein the consumption history of the specified entity is not stored in the storage.