Information-processing apparatus, information-processing method and storage medium

• US 7,062,477 B2
• Filed: 10/11/2001
• Issued: 06/13/2006
• Est. Priority Date: 10/12/2000
• Status: Expired due to Term

• Current Assignee: Sony Corporation (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.)
• Inventors: Nishioka, Hisao, Fujiwara, Yoshihito, Kurami, Naoya, Kaneko, Yasuyoshi, Matsubayashi, Masako
• Primary Examiner(s): DAVIS, GEORGE B
• Application Number:
  US09/975,799
• Publication Number:
  US 20020091875A1
• Time in Patent Office: 1,706 Days
• Field of Search: 706/21, 725/46
• US Class Current: 706/21
• CPC Class Codes: G06Q 30/0251 Targeted advertisements

Abstract

In order to increase a response rate, a data mining server selects an advertisement mail among a plurality of advertisement mails to be sent in a test transmission to personal computers selected at random among those owned by registered customers. The data mining server then computes learning parameters for each of sent advertisement mails from learning data created from response results of the test transmissions. Then, the data mining server applies learning parameters of each advertisement mail to original assessment data of other personal computers each serving as an object of an actual transmission to find predicted values. Subsequently, the data mining server extracts assessment data with largest advertisement-mail predicted values from the original assessment data. The data mining server then synthesizes the extracted pieces of assessment data. Finally, the data mining server sorts the synthesized pieces of assessment data in an order of decreasing customer predicted values to create an assessment chart.

Claims

1. An information-processing apparatus comprising:

computation means for computing an expected value of a response transmitted by a plurality of information-processing terminals in response to each of a plurality of contents transmitted to said information-processing terminals; and

select means for selecting some of the plurality of contents to be transmitted to each of said information-processing terminals based on said expected value computed by said computation means for each of said contents,

wherein said information-processing terminals comprise at least a pair of terminals used by independent users, each user having independent preferences,

wherein said plurality of contents includes user specific information relating to each of said information-processing terminals,

wherein said computation means computes said expected value by regular extraction based on a formula, wherein said formula is one of a linear association expression, a nueral network, a signoid function, a rule form of a conditional, a decision tree model, or a statistical technique based on a linear model, a discriminative analysis, a logistic recurssion/regression, or a cluster analysis,

wherein said linear association expression is a linear expression of a sum of terms which are each a product of a numerical data denoting each user'"'"'s independent preferences and a coefficient denoting the plurality of transmitted contents, and

wherein said expected value can be expressed as a ratio of a maximum response rate and a minimum response rate.

2. The information-processing apparatus according to claim 1, wherein said information-processing apparatus further comprises transmission means for transmitting contents selected by said select means to any of said information-processing terminals.

3. The information-processing apparatus according to claim 1, wherein said computation means computes an expected value of any one of said information-processing terminals from results of a test transmission carried out for said information-processing terminal.

4. The information-processing apparatus according to claim 1, wherein, for any specific one of said information-processing terminals, said select means selects a content whose expected value computed by said computation means.

5. The information-processing apparatus according to claim 1, wherein said expected value is a probability of a response expected to be received from any one of said information-processing terminals or an expected response rate of responses received from said information-processing terminals.

6. The information-processing apparatus according to claim 1, wherein said expected value is a predicted probability of a response.

7. The information-processing apparatus according to claim 1, wherein said contents are different from each other because some text parts are modified.

8. The information-processing apparatus according to claim 1, wherein said contents are each an electronic mail or a web banner advertisement.

9. The information-processing apparatus according to claim 1, wherein said contents each include hyperlink information.

10. The information-processing apparatus according to claim 9, wherein said computation means computes said expected value on the basis of click information of said hyperlink information.

11. An information-processing method comprising:

computing an expected value of a response transmitted by a plurality of information-processing terminals in response to each of a plurality of contents transmitted to said information-processing terminals; and

selecting some of the plurality of contents to be transmitted to each of said information-processing terminals based on said expected value computed for each of said contents,

wherein said information-processing terminals comprise at least a pair of terminals used by independent users, each user having independent preferences,

wherein said plurality of contents includes user specific information relating to each of said information-processing terminals,

wherein the computing step computes said expected value by regular extraction based on a formula, wherein said formula is one of a linear association expression, a nueral network, a signoid function, a rule form of a conditional, a decision tree model, or a statistical techniciue based on a linear model, a discriminative analysis, a logistic recurssion/regression, or a cluster analysis,

wherein said linear association expression is a linear expression of a sum of terms which are each a product of a numerical data denoting each user'"'"'s independent preferences and a coefficient denoting the plurality of transmitted contents, and

wherein said expected value can be expressed as a ratio of a maximum response rate and a minimum response rate.

12. A method, stored on a computer-readable medium, comprising:

computing an expected value of a response transmitted by a plurality of information-processing terminals in response to each of a plurality of contents transmitted to said information-processing terminals; and

selecting some of the plurality of contents to be transmitted to each of said information-processing terminals based on said expected value computed for each of said contents,

wherein said information-processing terminals comprise at least a pair of terminals used by independent users, each user having independent preferences,

wherein said plurality of contents includes user specific information relating to each of said information-processing terminals,

wherein the computing steps computes said expected value by regular extraction based on a formula, wherein said formula is one of a linear association expression, a nueral network, a sigmoid function, a rule form of a conditional, a decision tree model, or a statistical technique based on a linear model, a discriminative analysis, a logistic recurssion/regression, or a cluster analysis,

wherein said linear association expression is a linear expression of a sum of terms which are each a product of a numerical data denoting each user'"'"'s independent preferences and a coefficient denoting the plurality of transmitted contents, and

wherein said expected value can be expressed as a ratio of a maximum response rate and a minimum response rate.

13. An information-processing apparatus comprising:

computation means for computing an expected value of a response transmitted by a plurality of information-processing terminals in response to each of a plurality of contents transmitted to said information-processing terminals;

first producing means for producing a first assessment information on a set of largest expected values computed by said computation means for said responses transmitted by said information-processing terminals in response to said plurality of contents based on said set of largest expected values which are each computed by said computation means for one of said contents; and

second producing means for producing a second assessment function of said set of largest expected values computed for all said contents including user specific information relating to each of said information-processing terminals by synthesizing pieces of said assessment information which are each produced by said first producing means for one of said contents,

wherein said information-processing terminals comprise at least a pair of terminals used by independent users, each user having independent preferences,

wherein said plurality of contents includes user specific information relating to each of said information-processing terminals,

wherein said computation means computes said expected value by regular extraction based on a formula, wherein said formula is one of a linear association expression, a nueral network, a sigmoid function, a rule form of a conditional, a decision tree model, or a statistical technique based on a linear model, a discriminative analysis, a logistic recurssion/regression, or a cluster analysis,

wherein said linear association expression is a linear expression of a sum of terms which are each a product of a numerical data denotina each user'"'"'s independent preferences and a coefficient denoting the plurality of transmitted contents,

wherein said expected value can be expressed as a ratio of a maximum response rare and a minimum response rate, and

wherein said response transmitted in response to each of the plurality of contents may be a selective transmission or a random transmission.

14. An information-processing method comprising:

computing an expected value of a response transmitted by a plurality of information-processing terminals in response to each of a plurality of contents transmitted to said information-processing terminals;

producing assessment information on a set of largest expected values for said responses transmitted by said information-processing terminals in response to said contents based on said set of largest expected values each computed for one of said contents; and

producing an assessment function of said set of largest expected values for all said contents by synthesizing pieces of said assessment information each produced for one of said contents,

wherein said information-processing terminals comprise at least a pair of terminals used by independent users, each user having independent preferences,

wherein said plurality of contents includes user specific information relating to each of said information-processing terminals,

wherein the computing step computes said expected value by regular extraction based on a formula, wherein said formula is one of a linear association expression, a nueral network, a sigmoid function, a rule form of a conditional, a decision tree model, or a statistical techniciue based on a linear model, a discriminative analysis, a logistic recurssion/regression, or a cluster analysis,

wherein said linear association expression is a linear expression of a sum of terms which are each a product of a numerical data denoting each user'"'"'s indenendent preferences and a coefficient denoting the plurality of transmitted contents.

wherein said expected value can be expressed as a ratio of a maximum response rate and a minimum response rate, and

wherein said response transmitted in response to each of the plurality of contents may be a selective transmission or a random transmission.

15. A method, stored on a computer-readable medium, comprising:

computing an expected value of a response transmitted by a plurality of information-processing terminals in response to each of a plurality of contents transmitted to said information-processing terminals;

producing assessment information on a set of largest expected values for said responses transmitted by said information-processing terminals in response to said contents based on said set of largest expected values each computed for one of said contents; and

producing an assessment function of said set of largest expected values for all said plurality of contents by synthesizing pieces of said assessment information produced for each one of said plurality of contents,

wherein said information-processing terminals comprise at least a pair of terminals used by independent users, each user having independent preferences,

wherein said plurality of contents includes user specific information relating to each of said information-processing terminals,

wherein the computing step computes said expected value by regular extraction based on a formula, wherein said formula is one of a linear association expression, a nueral network, a sigmoid function, a rule form of a conditional, a decision tree model, or a statistical technique based on a linear model, a discriminative analysis, a logistic recurssion/regression, or a cluster analysis,

wherein said linear association expression is a linear expression of a sum of terms which are each a product of a numerical data denotina each user'"'"'s independent preferences and a coefficient denoting the plurality of transmitted contents,

wherein said expected value can be expressed as a ratio of a maximum response rate and a minimum response rate, and

wherein said response transmitted in response to each of the plurality of contents may be a selective transmission or a random transmission.