CA2353303A1 - Secure web user profiling system and method - Google Patents
Secure web user profiling system and method Download PDFInfo
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- CA2353303A1 CA2353303A1 CA002353303A CA2353303A CA2353303A1 CA 2353303 A1 CA2353303 A1 CA 2353303A1 CA 002353303 A CA002353303 A CA 002353303A CA 2353303 A CA2353303 A CA 2353303A CA 2353303 A1 CA2353303 A1 CA 2353303A1
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- 238000000034 method Methods 0.000 title claims abstract description 8
- 230000008901 benefit Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 1
- 230000003466 anti-cipated effect Effects 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- PUIYMUZLKQOUOZ-UHFFFAOYSA-N isoproturon Chemical compound CC(C)C1=CC=C(NC(=O)N(C)C)C=C1 PUIYMUZLKQOUOZ-UHFFFAOYSA-N 0.000 description 1
- JTJMJGYZQZDUJJ-UHFFFAOYSA-N phencyclidine Chemical compound C1CCCCN1C1(C=2C=CC=CC=2)CCCCC1 JTJMJGYZQZDUJJ-UHFFFAOYSA-N 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/10—Network architectures or network communication protocols for network security for controlling access to devices or network resources
- H04L63/102—Entity profiles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/53—Network services using third party service providers
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L2463/00—Additional details relating to network architectures or network communication protocols for network security covered by H04L63/00
- H04L2463/102—Additional details relating to network architectures or network communication protocols for network security covered by H04L63/00 applying security measure for e-commerce
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
- H04L69/30—Definitions, standards or architectural aspects of layered protocol stacks
- H04L69/32—Architecture of open systems interconnection [OSI] 7-layer type protocol stacks, e.g. the interfaces between the data link level and the physical level
- H04L69/322—Intralayer communication protocols among peer entities or protocol data unit [PDU] definitions
- H04L69/329—Intralayer communication protocols among peer entities or protocol data unit [PDU] definitions in the application layer [OSI layer 7]
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- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Signal Processing (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Computing Systems (AREA)
- Computer Security & Cryptography (AREA)
- Computer Hardware Design (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The present invention is directed to a secure web user profiling system and method. The system includes a browser helper object for gathering user information, an analysis engine for analysing the information gathered by the helper object and a secure user profile for maintaining information derived from the analysis.
Description
Secure Web User Profiling System and Method Field of the Invention s The present invention relates to Internet e-commerce, and more particularly to a secure web user profiling system and method.
Brief Description of the Drawings to These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 shows an overview of a secure web user profiling system is according to the present invention.
As shown in Figure 1, the present invention is directed to a secure web user profiling system and method. The system includes a browser helper object for gathering user information, an analysis engine for analysing the 2o information gathered by the helper object and a secure user profile for maintaining information derived from the analysis.
In an embodiment of the present invention, the system exists as a clientlserver application residing on a user's computer. In an embodiment of 2s the present invention, the BHO would typically exist as a COM DLL
implementing the necessary interfaces to load with every instance of a web browser. Information is gathered through the E3H0 regarding web documents the user views. This information consists of actual text and metadata of the web page, as well as the behavioral aspects. of the page, such as mouse 3o movement and button clicks. This information is sent from each BHO using the Hypertext Transport Protocol (HTTP) protocol to the analysis engine.
The analysis engine incorporates techniques capable of determining °, , 2 several defining characteristics of a user. These characteristics are represented in extensible markup language (XML) as an anonymous secure profile containing no identifying information. p~ sample of this XML profile is shown in Table 1.
s Table 1 - Sample Secure Profile <Profile TimeStamp="Mon Jul 16 14:17:27 2001" StartTime=="Fri Jul 13 17:00:02 2001" EndTime="Mon Jul 16 10:37:48 2001" TimeStampInMillisecs="99530i'447"
StartTimeInMillisecs="995058002"
EndTimeInMillisecs="995294268">
<Interests>
<Learned TotalPages="56">
<Interest CIassID="1049" CIassName="windows" Strength="3" Pages="13" I>
<Interest CIassID="1328" CIassName="programming" Strength="3" Pages--"10" />
<Interest CIassID="627" CIassName="travel" Strength="3" Pages="8" I>
<Interest CIassID="1513" CIassName="anonymity_on the. intemet" Strength='3"
Pages="4" I>
<Interest CIassID="1313" CIassName="software development" Strength="2"
Pages="3" I>
<Interest CIassID="137" CIassName="consulting" Strength="2" Pages="3" I>
<ILearned>
<Stated />
</lnterests>
<WebStats>
<DownIoadSize TimeFrame="AvgByDayOtWeek" NumUnits="7" Sun="0" Sat "376956"
Fri="0" Thu="0"
Wed="0" Tue="366784" Mon='0" I>
<DownIoadTime TimeFrame="AvgByDayOfWeek" NumU,nits="7" Sun="0" Sat "2574"
Fri="0" Thu="0"
Wed="0" Tue="1024" Mon="0" I>
<ViewDuration TimeFrame="AvgByDayOtWeek" NumUnits="7" Sun='0" Sat="58935"
Fri="0" Thu="0"
Wed="0" Tue="38356" Mon="0" I>
<WebPagesViewed TimeFrame="AvgByDayOfWeek" NumUnits="7" Sun="0" Sat="1"
Fri="0" Thu="0"
Wed_'0" Tue="55" Mon="0" I>
<ActiveSurtTime TimeFrame="AvgByDayOtWeek" NumlJnits="7" Sun="0" Sat "70"
Fri="0" Thu="0"
Wed="0" Tue="4712" Mon="0" />
<WebSitesVisited TimeFrame="AvgByDayOtWeek" NumUnits="7" Sun--"0' Sat="1"
Fri="0" Thu='0"
Wed="0" Tue="9" Mon='0" I>
</WebStats>
<Harciware Resolution="3200 x 1200" ProcessorType--'Intel Pentium"
ScreenColorDepth="32"
Processorlnfo="AuthenticAMD 1099 MHz" ProcessorLevel=:"Pentium (IIIPro), MMX, 3D Now"
IEBrowser="6.00.2462.0000" WindowsVersion="Windows 2000 Service Pack 2 Build 2195" I>
<WebUsages>
<pseudonym I>
<HomePage URL="http:Ilmsdn.microsoft.comldefault.asp" I>
<SearchDomains URl 0--"www.google.com" URL_1='groups.google.com" I>
<SearchWords Phrase_0--"%22Shared+Development+Process%22" I>
<IVllebUsages>
<IProfile>
The analysis engine makes use of the topics tree, which is a subset of the well-known Open Directory Project (ODP). The ODP is a hierarchical s catalog of the web that powers the directory services for web search engines and portals.
The topics tree is comprised of a structure that leverages the ODP.
The ODP's thousands of nodes provide rapid and accurate web page 1o analysis. The system applies associated keyword logic to user profiling, providing keyword and phrase grouping extensions associated with each node.
Currently, the topics tree includes over 3000 topics arranged is hierarchically. The topics have been selected from the ODP catalog according to the anticipated needs of users. Each topic in the tree is assigned a unique Class ID that identifies this topic within the tree hierarchy. These Class IDs are a language agnostic representation of topics.
2o The profile is made up of several elements, such as interests, web statistics, hardware, and web usage. There cain be several types of interests present in a profile. Interests can be stated by the user or learned from a variety of sources such as web pages viewed, searches performed, email and documents.
The user has the ability to search the topics tree to manually add interests to their profile. For example, a user could search for 'hockey'. The top search results show topics such as sports.winter.ice hockey, sports.winter.ice skating, products and services.sporting goods, 3o sports.winter.ice hockey.women's hockey. The user may add any or all of these topics to their list of stated interests.
As discovered during experimentation, the interests indicated in the sample secure profile shown in Table 1 are learned web interests determined by the system. These interests were built from two days of surfing the web.
No special action or form fills were required to build this section of the profile;
only regular surfing. The system was running in the background analyzing the s pages that were visited and the behaviour of the user. The topics represented in this profile will decay over time unless the user repeatedly visits sites with the same topics. In this way, the learned iinterests in the profile always represent the current interests of the user as determined by their recent web surfing.
io The web statistics section of the profile contains statistics on the number of web pages visited, active surf time and length of time that pages were viewed. This information may be requested in several different formats including average by time of day, average by day of week, last 21 days and is last 20 weeks.
The hardware section of the profile contains a simple view of the environment in which the user is working. The web usages section contains information regarding the searches that the uaer has performed, the search 20 engines used and the user's homepage. In an embodiment of the present invention, this section can be expanded to represent information such as online transactions completed and abandoned shopping carts.
The data in the secure profile can be highly sensitive in nature. It is as therefore necessary to ensure that this information exists as an anonymous profile. A subset of the secure profile is part of the system. An example of a subset is shown in Table 2.
Table 2 - Sample Secure Profile Subset <invention>
<Interests Type="Search">
<Interest CIassID--"3" CIa~Name--"golf">
<Preference Term="Callaway Wedges"/>
<Preference Term="Irish Golf Courses"I>
</lnterest>
<Interest CIassID="2" CIassName="nba">
<Preference Term="Vince Carter"I>
5 <Preference Term='Toronto Ftaptors"/>
</lnterest>
<Ilnterests>
<Interests Type="Web">
<Interest CIassID="1049" CIassName="windows" Strength="3" Pages="13"I>
10 <Interest CIassID="1328" CIassName="programming" Strength="3" Pages="10"/>
<Interest CIassID="627" CIassName="travel" Strength="3" Pages="8"I>
<llnterests>
<Interests Type="Stated">
<Interest CIassID="932" CIassName="Programming Languages">
<preference Term--"C++"I>
<Preference Term="C#"I>
<Itnterest>
<llnterests>
<lnvention>
This subset can include several elements including search interests, web interests and stated interests The search interests are learned from the searches a user performs.
2s For example, the search result page from a search for 'Callaway Wedges' would be classified as "sports.ball sports.golf' and as such golf could be added to their profile with a specific interestlpreference in Callaway Wedges.
This allows preferences to automatically be associated with Class IDs in the topics tree. The web interests are learned from the web pages a user views.
3o Stated interests are interests that the user wishes to explicitly make known.
Adding personal preferences can refine stated interests.
Benefits of invention include tailored content, targeted advertising, intelligent searches, eCommerce and online auctions.
Content providers want to know what a surfer is interested in as soon as they arrive at their site. The invention allows content providers to serve up the most appropriate articles and news with fewer clicks. To date, the Internet advertising model has not been as successful as hoped.
' , 6 The current model relies on maximizing banner click-throughs. By confidently knowing the interests of a web user, the most appropriate banners can be selected for each individual rather than grouping individuals into a s demographic segment. Not only can the invention improve the response rate of banners but it will also allow more specialized advertising of niche products.
By knowing more about users, vendors can display banners only to interested consumers. This could completely change the advertising revenue model from a predetermined exposure rate to a rate based on actual exposure to io interested consumers, creating a more efficient royalty revenue model and enabling the user to benefit from seeing fewer irrelevant ads.
The invention can be used to enhances the relevance of web search results. Typically, web searches yield many irrelevant results mixed in with the is ideal results. By sorting and filtering search results according to interests a user can get the sites they are looking for with less effort.
The first thing consumers see on an eCommerce site does not need to be a general selection of products with wide appeal but rather an offering built 2o specifically for them. By receiving invention and product preferences, an eCommerce site can make tailored product offerings. For example, if a web shopper is currently in the market for a particular brand of portable stereo then Invention can capture that. An electronics e-commerce retailer will then receive those product preferences and immediately present the appropriate Zs products.
An important aspect of the success of the invention is the selection of a subset of topics from ODP. This subset should have universal appeal to web sites in all industries and geographic locations. Representatives from search 3o engines and portals will be beneficial in providing input about their needs.
Search engines typically have taxonomies of i:heir own that can offer greater insight into requirements of invention.
" . 7 It is important to meet the needs of the large eCommerce vendors since they can reap the greatest benefits from invention. The hierarchy of topics must address the top online eCommerce products and services. As well, online advertisers are making great efforts to determine product interests s of web users. These companies will have their own way of describing a user's interests.
Although the present invention has bE;en described in considerable detail with reference to certain preferred embodiments thereof, other versions io are possible. Therefore, the spirit and scope of the appended claims should not be limited to the description of the preferred embodiments contained herein.
Brief Description of the Drawings to These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 shows an overview of a secure web user profiling system is according to the present invention.
As shown in Figure 1, the present invention is directed to a secure web user profiling system and method. The system includes a browser helper object for gathering user information, an analysis engine for analysing the 2o information gathered by the helper object and a secure user profile for maintaining information derived from the analysis.
In an embodiment of the present invention, the system exists as a clientlserver application residing on a user's computer. In an embodiment of 2s the present invention, the BHO would typically exist as a COM DLL
implementing the necessary interfaces to load with every instance of a web browser. Information is gathered through the E3H0 regarding web documents the user views. This information consists of actual text and metadata of the web page, as well as the behavioral aspects. of the page, such as mouse 3o movement and button clicks. This information is sent from each BHO using the Hypertext Transport Protocol (HTTP) protocol to the analysis engine.
The analysis engine incorporates techniques capable of determining °, , 2 several defining characteristics of a user. These characteristics are represented in extensible markup language (XML) as an anonymous secure profile containing no identifying information. p~ sample of this XML profile is shown in Table 1.
s Table 1 - Sample Secure Profile <Profile TimeStamp="Mon Jul 16 14:17:27 2001" StartTime=="Fri Jul 13 17:00:02 2001" EndTime="Mon Jul 16 10:37:48 2001" TimeStampInMillisecs="99530i'447"
StartTimeInMillisecs="995058002"
EndTimeInMillisecs="995294268">
<Interests>
<Learned TotalPages="56">
<Interest CIassID="1049" CIassName="windows" Strength="3" Pages="13" I>
<Interest CIassID="1328" CIassName="programming" Strength="3" Pages--"10" />
<Interest CIassID="627" CIassName="travel" Strength="3" Pages="8" I>
<Interest CIassID="1513" CIassName="anonymity_on the. intemet" Strength='3"
Pages="4" I>
<Interest CIassID="1313" CIassName="software development" Strength="2"
Pages="3" I>
<Interest CIassID="137" CIassName="consulting" Strength="2" Pages="3" I>
<ILearned>
<Stated />
</lnterests>
<WebStats>
<DownIoadSize TimeFrame="AvgByDayOtWeek" NumUnits="7" Sun="0" Sat "376956"
Fri="0" Thu="0"
Wed="0" Tue="366784" Mon='0" I>
<DownIoadTime TimeFrame="AvgByDayOfWeek" NumU,nits="7" Sun="0" Sat "2574"
Fri="0" Thu="0"
Wed="0" Tue="1024" Mon="0" I>
<ViewDuration TimeFrame="AvgByDayOtWeek" NumUnits="7" Sun='0" Sat="58935"
Fri="0" Thu="0"
Wed="0" Tue="38356" Mon="0" I>
<WebPagesViewed TimeFrame="AvgByDayOfWeek" NumUnits="7" Sun="0" Sat="1"
Fri="0" Thu="0"
Wed_'0" Tue="55" Mon="0" I>
<ActiveSurtTime TimeFrame="AvgByDayOtWeek" NumlJnits="7" Sun="0" Sat "70"
Fri="0" Thu="0"
Wed="0" Tue="4712" Mon="0" />
<WebSitesVisited TimeFrame="AvgByDayOtWeek" NumUnits="7" Sun--"0' Sat="1"
Fri="0" Thu='0"
Wed="0" Tue="9" Mon='0" I>
</WebStats>
<Harciware Resolution="3200 x 1200" ProcessorType--'Intel Pentium"
ScreenColorDepth="32"
Processorlnfo="AuthenticAMD 1099 MHz" ProcessorLevel=:"Pentium (IIIPro), MMX, 3D Now"
IEBrowser="6.00.2462.0000" WindowsVersion="Windows 2000 Service Pack 2 Build 2195" I>
<WebUsages>
<pseudonym I>
<HomePage URL="http:Ilmsdn.microsoft.comldefault.asp" I>
<SearchDomains URl 0--"www.google.com" URL_1='groups.google.com" I>
<SearchWords Phrase_0--"%22Shared+Development+Process%22" I>
<IVllebUsages>
<IProfile>
The analysis engine makes use of the topics tree, which is a subset of the well-known Open Directory Project (ODP). The ODP is a hierarchical s catalog of the web that powers the directory services for web search engines and portals.
The topics tree is comprised of a structure that leverages the ODP.
The ODP's thousands of nodes provide rapid and accurate web page 1o analysis. The system applies associated keyword logic to user profiling, providing keyword and phrase grouping extensions associated with each node.
Currently, the topics tree includes over 3000 topics arranged is hierarchically. The topics have been selected from the ODP catalog according to the anticipated needs of users. Each topic in the tree is assigned a unique Class ID that identifies this topic within the tree hierarchy. These Class IDs are a language agnostic representation of topics.
2o The profile is made up of several elements, such as interests, web statistics, hardware, and web usage. There cain be several types of interests present in a profile. Interests can be stated by the user or learned from a variety of sources such as web pages viewed, searches performed, email and documents.
The user has the ability to search the topics tree to manually add interests to their profile. For example, a user could search for 'hockey'. The top search results show topics such as sports.winter.ice hockey, sports.winter.ice skating, products and services.sporting goods, 3o sports.winter.ice hockey.women's hockey. The user may add any or all of these topics to their list of stated interests.
As discovered during experimentation, the interests indicated in the sample secure profile shown in Table 1 are learned web interests determined by the system. These interests were built from two days of surfing the web.
No special action or form fills were required to build this section of the profile;
only regular surfing. The system was running in the background analyzing the s pages that were visited and the behaviour of the user. The topics represented in this profile will decay over time unless the user repeatedly visits sites with the same topics. In this way, the learned iinterests in the profile always represent the current interests of the user as determined by their recent web surfing.
io The web statistics section of the profile contains statistics on the number of web pages visited, active surf time and length of time that pages were viewed. This information may be requested in several different formats including average by time of day, average by day of week, last 21 days and is last 20 weeks.
The hardware section of the profile contains a simple view of the environment in which the user is working. The web usages section contains information regarding the searches that the uaer has performed, the search 20 engines used and the user's homepage. In an embodiment of the present invention, this section can be expanded to represent information such as online transactions completed and abandoned shopping carts.
The data in the secure profile can be highly sensitive in nature. It is as therefore necessary to ensure that this information exists as an anonymous profile. A subset of the secure profile is part of the system. An example of a subset is shown in Table 2.
Table 2 - Sample Secure Profile Subset <invention>
<Interests Type="Search">
<Interest CIassID--"3" CIa~Name--"golf">
<Preference Term="Callaway Wedges"/>
<Preference Term="Irish Golf Courses"I>
</lnterest>
<Interest CIassID="2" CIassName="nba">
<Preference Term="Vince Carter"I>
5 <Preference Term='Toronto Ftaptors"/>
</lnterest>
<Ilnterests>
<Interests Type="Web">
<Interest CIassID="1049" CIassName="windows" Strength="3" Pages="13"I>
10 <Interest CIassID="1328" CIassName="programming" Strength="3" Pages="10"/>
<Interest CIassID="627" CIassName="travel" Strength="3" Pages="8"I>
<llnterests>
<Interests Type="Stated">
<Interest CIassID="932" CIassName="Programming Languages">
<preference Term--"C++"I>
<Preference Term="C#"I>
<Itnterest>
<llnterests>
<lnvention>
This subset can include several elements including search interests, web interests and stated interests The search interests are learned from the searches a user performs.
2s For example, the search result page from a search for 'Callaway Wedges' would be classified as "sports.ball sports.golf' and as such golf could be added to their profile with a specific interestlpreference in Callaway Wedges.
This allows preferences to automatically be associated with Class IDs in the topics tree. The web interests are learned from the web pages a user views.
3o Stated interests are interests that the user wishes to explicitly make known.
Adding personal preferences can refine stated interests.
Benefits of invention include tailored content, targeted advertising, intelligent searches, eCommerce and online auctions.
Content providers want to know what a surfer is interested in as soon as they arrive at their site. The invention allows content providers to serve up the most appropriate articles and news with fewer clicks. To date, the Internet advertising model has not been as successful as hoped.
' , 6 The current model relies on maximizing banner click-throughs. By confidently knowing the interests of a web user, the most appropriate banners can be selected for each individual rather than grouping individuals into a s demographic segment. Not only can the invention improve the response rate of banners but it will also allow more specialized advertising of niche products.
By knowing more about users, vendors can display banners only to interested consumers. This could completely change the advertising revenue model from a predetermined exposure rate to a rate based on actual exposure to io interested consumers, creating a more efficient royalty revenue model and enabling the user to benefit from seeing fewer irrelevant ads.
The invention can be used to enhances the relevance of web search results. Typically, web searches yield many irrelevant results mixed in with the is ideal results. By sorting and filtering search results according to interests a user can get the sites they are looking for with less effort.
The first thing consumers see on an eCommerce site does not need to be a general selection of products with wide appeal but rather an offering built 2o specifically for them. By receiving invention and product preferences, an eCommerce site can make tailored product offerings. For example, if a web shopper is currently in the market for a particular brand of portable stereo then Invention can capture that. An electronics e-commerce retailer will then receive those product preferences and immediately present the appropriate Zs products.
An important aspect of the success of the invention is the selection of a subset of topics from ODP. This subset should have universal appeal to web sites in all industries and geographic locations. Representatives from search 3o engines and portals will be beneficial in providing input about their needs.
Search engines typically have taxonomies of i:heir own that can offer greater insight into requirements of invention.
" . 7 It is important to meet the needs of the large eCommerce vendors since they can reap the greatest benefits from invention. The hierarchy of topics must address the top online eCommerce products and services. As well, online advertisers are making great efforts to determine product interests s of web users. These companies will have their own way of describing a user's interests.
Although the present invention has bE;en described in considerable detail with reference to certain preferred embodiments thereof, other versions io are possible. Therefore, the spirit and scope of the appended claims should not be limited to the description of the preferred embodiments contained herein.
Claims (2)
1. A secure web user profiling system comprising:
a browser helper object for gathering user information;
an analysis engine for analysing the information gathered by the helper object; and a secure user profile for maintaining information derived from the analysis.
a browser helper object for gathering user information;
an analysis engine for analysing the information gathered by the helper object; and a secure user profile for maintaining information derived from the analysis.
2. A secure web user profiling method comprised substantially as described and illustrated herein.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA002353303A CA2353303A1 (en) | 2001-07-18 | 2001-07-18 | Secure web user profiling system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA002353303A CA2353303A1 (en) | 2001-07-18 | 2001-07-18 | Secure web user profiling system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2353303A1 true CA2353303A1 (en) | 2003-01-18 |
Family
ID=4169519
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA002353303A Abandoned CA2353303A1 (en) | 2001-07-18 | 2001-07-18 | Secure web user profiling system and method |
Country Status (1)
| Country | Link |
|---|---|
| CA (1) | CA2353303A1 (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8776103B2 (en) | 1996-12-11 | 2014-07-08 | The Nielsen Company (Us), Llc | Interactive service device metering systems |
| US9100132B2 (en) | 2002-07-26 | 2015-08-04 | The Nielsen Company (Us), Llc | Systems and methods for gathering audience measurement data |
| US9209917B2 (en) | 2005-09-26 | 2015-12-08 | The Nielsen Company (Us), Llc | Methods and apparatus for metering computer-based media presentation |
-
2001
- 2001-07-18 CA CA002353303A patent/CA2353303A1/en not_active Abandoned
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8776103B2 (en) | 1996-12-11 | 2014-07-08 | The Nielsen Company (Us), Llc | Interactive service device metering systems |
| US9100132B2 (en) | 2002-07-26 | 2015-08-04 | The Nielsen Company (Us), Llc | Systems and methods for gathering audience measurement data |
| US9209917B2 (en) | 2005-09-26 | 2015-12-08 | The Nielsen Company (Us), Llc | Methods and apparatus for metering computer-based media presentation |
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