초록 ▼

A method and apparatus for adaptively targeting advertisements to a specific client computer from a server within a distributed data processing system is provided. As a user of the client browses the World Wide Web, the material that is downloaded to the client constitutes a datastream. At some loca

A method and apparatus for adaptively targeting advertisements to a specific client computer from a server within a distributed data processing system is provided. As a user of the client browses the World Wide Web, the material that is downloaded to the client constitutes a datastream. At some location during the routing of the datastream, either on the server or at the client, the datastream is scanned to generate a list of keywords that are present within the datastream. The datastream may be analyzed in real-time or cached and analyzed on a delayed basis. The generated list of keywords represents a summary of the content that appears to be the focus of interest of the user. The keywords are compared against a database of advertisements, and the server selects an advertisement that matches the user's area of interest in comparison to the analysis of the user's browsing history. The selected advertisement is then inserted into the datastream to be routed to the client. In consideration for viewing targeted advertisements and to entice a Web viewer to allow the monitoring of a datastream so that targeted advertisements may be placed into the datastream, a Web viewer may receive online connection service for free, for a reduced cost, at a premium level of service, or for other some other value, such as frequent viewer credits that may be exchanged for goods and services.

대표청구항 ▼

What is claimed is: 1. A method for sending advertisements from a server to a client in a distributed data processing system, the method comprising the computer-implemented steps of: scanning, on the server, a datastream electronically for a session connecting the server with the client, wherein th

What is claimed is: 1. A method for sending advertisements from a server to a client in a distributed data processing system, the method comprising the computer-implemented steps of: scanning, on the server, a datastream electronically for a session connecting the server with the client, wherein the datastream comprises HTML tags and ASCII text; generating a list of keywords from content within the datastream; selecting advertisements based on the generated keyword list; and inserting the selected advertisements into the datastream, wherein the step of generating a list of keywords comprises deriving a list of keywords from a moving window of content within the datastream. 2. The method of claim 1 wherein the step of scanning the datastream further comprises: storing the datastream as a cached datastream; and reading the cached datastream as input for scanning the datastream for the session. 3. The method of claim 1 wherein the content of the datastream comprises Uniform Resource Identifiers (URIs) and content within Hypertext Transport Control Protocol (HTTP) response messages received in response to HTTP requests directed to the URIs. 4. The method of claim 1 wherein the moving window of content is a variable number of downloaded Web pages. 5. The method of claim 1 wherein a size of the moving window of content is a variable number of bytes in the datastream. 6. The method of claim 1 wherein a size of the moving window is a function of a size of the keyword list. 7. The method of claim 1 wherein the step of generating a list of keywords further comprises compiling a list of keywords that most frequently occur within a variable amount of time within the session. 8. The method of claim 1 wherein the step of selecting advertisements further comprises: applying a first level of importance to HTML tags within the datastream and a second level of importance to ASCII text within the datastream, wherein said first level of importance is greater than said second level of importance; generating a keyword list comprising a first and second quantity of keywords, wherein a first quantity of keywords is selected from the datastream based on said first level of importance and a second quantity of keywords is selected from the datastream based on said second level of importance, and wherein said first quantity of keywords is greater than said second quantity of keywords; comparing the generated keyword list against a set of predetermined keyword lists, wherein each predetermined keyword list represents a category for a set of advertisements; determining a relative match as a result of the comparison; and selecting an advertisement from the set of advertisements in the matched category. 9. The method of claim 8 wherein the step of selecting advertisements further comprises: determining information that characterizes the session; and selecting an advertisement from the set of advertisements in a subcategory of the matched category based on the session characterization information. 10. The method of claim 9 wherein the session characterization information is selected from one or more types of information in a group comprising: a type of computer platform for the client; a current time of day for the session; a current season of the year for the session; an amount of connection bandwidth for the session; and a geographic location for the client. 11. The method of claim 1 wherein the step of selecting advertisements further comprises basing the selection on session characterization information selected from one or more types of information in a group comprising: a type of computer platform for the client; a current time of day for the session; a current season of the year for the session; an amount of connection bandwidth for the session; and a geographic location for the client. 12. The method of claim 1 wherein the step of generating a list of keywords further comprises: analyzing the datastream to identify a set of terms; and applying an adaptive weighting scheme in which a subset of terms in the set of terms are given greater emphasis as a keyword in the list of keywords. 13. The method of claim 12 wherein the content of the datastream comprises Uniform Resource Identifiers (URIs) and terms within the URIs are weighted with greater value than other terms not within the URIs. 14. The method of claim 9 wherein the content of the datastream comprises metadata, and wherein the metadata is weighted with greater value than other content in the datastream. 15. The method of claim 9 wherein the adaptive weighting of terms is a function of a time at which the terms appear in the datastream such that more recent terms are weighted more heavily that more distant terms. 16. A method for receiving advertisements at a client from a server in a distributed data processing system, the method comprising the computer-implemented steps of: scanning, on the client, a datastream electronically for a session connecting the client with the server; generating a list of keywords from content within the datastream; sending the generated list of keywords to the server; receiving advertisements within the datastream, wherein the advertisements comprise content semantically related to the generated list of keyword; and caching advertisements placed as hidden advertisement content within markup language tags in the datastream, wherein the hidden advertisement content is not displayed by the browser when a markup language document containing the hidden advertisement content is displayed. 17. The method of claim 16 further comprising: retrieving the hidden advertisement content during periods of user inactivity; and displaying advertisements derived from the hidden advertisement content on a display device of the client. 18. A distributed data processing system for sending advertisements from a server to a client, the distributed data processing system comprising: scanning means for scanning, on the server, a datastream electronically for a session connecting the server with the client, wherein the datastream comprises HTML tags and ASCII text; generating means for generating a list of keywords from content within the datastream; selecting means for selecting advertisements based on the generated keyword list; and inserting means for inserting the selected advertisements into the datastream, wherein the generating means for generating a list of keywords comprises deriving means for deriving a list of keywords from a moving window of content within the datastream. 19. The data processing system of claim 18 wherein the scanning means for scanning the datastream further comprises: storing means for storing the datastream as a cached datastream; and reading means for reading the cached datastream as input for scanning the datastream for the session. 20. The data processing system of claim 18 wherein the content of the datastream comprises Uniform Resource Identifiers (URIs) and content within Hypertext Transport Control Protocol (HTTP) response messages received in response to HTTP requests directed to the URIs. 21. The data processing system of claim 18 wherein the moving window of content is a variable number of downloaded Web pages. 22. The data processing system of claim 18 wherein a size of the moving window of content is a variable number of bytes in the datastream. 23. The data processing system of claim 18 wherein a size of the moving window is a function of a size of the keyword list. 24. The data processing system of claim 18 wherein the generating means for generating a list of keywords further comprises compiling means for compiling a list of keywords that most frequently occur within a variable amount of time within the session. 25. The data processing system of claim 18 wherein the selecting means for selecting advertisements further comprises: applying means for applying a first level of importance to HTML tags within the datastream and a second level of importance to ASCII text within the datastream, wherein said first level of importance is greater than said second level of importance; generating means for generating a keyword list comprising a first and second quantity of keywords, wherein a first quantity of keywords is selected from the datastream based on said first level of importance and a second quantity of keywords is selected from the datastream based on said second level of importance, and wherein said first quantity of keywords is greater than said second quantity of keywords; comparing means for comparing the generated keyword list against a set of predetermined keyword lists, wherein each predetermined keyword list represents a category for a set of advertisements; determining means for determining a relative match as a result of the comparison; and selecting means for selecting an advertisement from the set of advertisements in the matched category. 26. The data processing system of claim 25 wherein the selecting means for selecting advertisements further comprises: determining means for determining means for determining information that characterizes the session; and selecting means for selecting an advertisement from the set of advertisements in a subcategory of the matched category based on the session characterization information. 27. The data processing system of claim 26 wherein the session characterization information is selected from one or more types of information in a group comprising: a type of computer platform for the client; a current time of day for the session; a current season of the year for the session; an amount of connection bandwidth for the session; and a geographic location for the client. 28. The data processing system of claim 18 wherein the selecting means for selecting advertisements further comprises selecting means for basing the selection on session characterization information selected from one or more types of information in a group comprising: a type of computer platform for the client; a current time of day for the session; a current season of the year for the session; an amount of connection bandwidth for the session; and a geographic location for the client. 29. The data processing system of claim 18 wherein the generating means for generating a list of keywords further comprises: analyzing means for analyzing the datastream to identify a set of terms; and applying means for applying an adaptive weighting scheme in which a subset of terms in the set of terms are given greater emphasis as a keyword in the list of keywords. 30. The data processing system for claim 29 wherein the content of the datastream comprises Uniform Resource Identifiers (URIs) and terms within the URIs are weighted with greater value than other terms not within the URIs. 31. The data processing system of claim 29 wherein the content of the datastream comprises metadata, and wherein the metadata is weighted with greater value than other content in the datastream. 32. The data processing system of claim 29 wherein the adaptive weighting of terms is a function of a time at which the terms appear in the datastream such that more recent terms are weighted more heavily than more distant terms. 33. A distributed data processing system for receiving advertisements at a client from a server, the distributed data processing system comprising: scanning means for scanning, on the client, a datastream electronically for a session connecting the client with the server; generating means for generating a list of keywords from content within the datastream; sending means for sending the generated list of keywords to the server; receiving means for receiving advertisements within the datastream, wherein the advertisements comprise content semantically related to the generated list of keywords; and caching means for caching advertisements placed as hidden advertisement content within markup language tags in the datastream, wherein the hidden advertisement content is not displayed by the browser when a markup language document containing the hidden advertisement content is displayed. 34. The data processing system of claim 33 further comprising: retrieving means for retrieving the hidden advertisement content during periods of user inactivity; and displaying means for displaying advertisements derived from the hidden advertisement content on a display device of the client. 35. A computer program product in a computer-readable medium for use in a distributed data processing system for sending advertisements from a server to a client, the computer program product comprising: first instructions for scanning, on the server, a datastream electronically for a session connecting the server with the client, wherein the datastream comprises HTML tags and ASCII text; second instructions for generating a list of keywords from content within the datastream; third instructions for selecting advertisements based on the generated keyword list; and fourth instructions for inserting the selected advertisements into the datastream, wherein the second instructions for generating a list of keywords comprises instructions for deriving a list of keywords from a moving window of content within the datastream. 36. The computer program product of claim 35 wherein the first instructions for scanning the datastream further comprises: instructions for storing the datastream as a cached datastream; and instructions for reading the cached datastream as input for scanning the datastream for the session. 37. The computer program product of claim 35 wherein the content of the datastream comprises Uniform Resource Identifiers (URIs) and content within Hypertext Transport Control Protocol (HTTP) response messages received in response to HTTP requests directed to the URIs. 38. The computer program product of claim 35 wherein the moving window of content is a variable number of downloaded Web pages. 39. The computer program product of claim 35 wherein a size of the moving window of content is a variable number of bytes in the datastream. 40. The computer program product of claim 35 wherein a size of the moving window is a function of a size of the keyword list. 41. The computer program product of claim 35 wherein the second instructions for generating a list of keywords further comprises instructions for compiling a list of keywords that most frequently occur within a variable amount of time within the session. 42. The computer program product of claim 35 wherein the third instructions for selecting advertisements further comprises: instructions for applying a first level of importance to HTML tags within the datastream and a second level of importance to ASCII text within the datastream, wherein said first level of importance is greater than said second level of importance; instructions for generating a keyword list comprising a first and second quantity of keywords, wherein a first quantity of keywords is selected from the datastream based on said first level of importance and a second quantity of keywords is selected from the datastream based on said second level of importance, and wherein said first quantity of keywords is greater than said second quantity of keywords; instructions for comparing the generated keyword list against a set of predetermined keyword lists, wherein each predetermined keyword list represents a category for a set of advertisements; instructions for determining a relative match as a result of the comparison; and instructions for selecting an advertisement from the set of advertisements in the matched category. 43. The computer program product of claim 42 wherein the instructions for selecting an advertisement from the set of advertisements in the matched category further comprises: instructions for determining information that characterizes the session; and instructions for selecting an advertisement from the set of advertisements in a subcategory of the matched category based on the session characterization information. 44. The computer program product of claim 43 wherein the session characterization information is selected from one or more types of information in a group comprising: a type of computer platform for the client; a current time of day for the session; a current season of the year for the session; an amount of connection bandwidth for the session; and a geographic location for the client. 45. The computer program product of claim 35 wherein the third instructions for selecting advertisements further comprises instructions for basing the selection on session characterization information selected from one or more types of information in a group comprising: a type of computer platform for the client; a current time of day for the session; a current season of the year for the session; an amount of connection bandwidth for the session; and a geographic location for the client. 46. The computer program product of claim 35 wherein the second instructions for generating a list of keywords further comprises: instructions for analyzing the datastream to identify a set of terms; and instructions for applying an adaptive weighting scheme in which a subset of terms in the set of terms are given greater emphasis as a keyword in the list of keywords. 47. The computer program product of claim 46 wherein the content of the datastream comprises Uniform Resource Identifiers (URIs) and terms within the URIs are weighted with greater value than other terms not within the URIs. 48. The computer program product of claim 46 wherein the content of the datastream comprises metadata, and wherein the metadata is weighted with greater value than other content in the datastream. 49. The computer program product of claim 46 wherein the adaptive weighting of terms is a function of a time at which the terms appear in the datastream such that more recent terms are weighted more heavily than more distant terms. 50. A computer program product for receiving advertisements at a client from a server in a distributed data processing system, the computer program product comprising: first instructions for scanning, on the client, a datastream electronically for a session connecting the client with the server; second instructions for generating a list of keywords from content within the datastream; third instructions for sending the generated list of keywords to the server; fourth instructions for receiving advertisements within the datastream, wherein the advertisements comprise content semantically related to the generated list of keywords; and fifth instructions for caching advertisements placed as hidden advertisement content within markup language tags in the datastream, wherein the hidden advertisement content is not displayed by the browser when a markup language document containing the hidden advertisement content is displayed. 51. The computer program product of claim 50 further comprising: instructions for retrieving the hidden advertisement content during periods of user inactivity; and instructions for displaying advertisements derived from the hidden advertisement content on a display device of the client.