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Pre-Grant Publication Number: 20070282832

Automatic tracking of user data and reputation checking

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Prior Art Detail

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#173An efficient Web traversal pattern mining algorithm based on suffix array

Applies to Claims 1,10,11,12,13,14,15,16,17,18,19,2,20,3,4,5,6,7,8,9

Submitted by: Chad ZhongLast updated: almost 4 years ago

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Summary / Description

Summary / Description	Traversal patterns reflect regularities of Web users browsing and selecting Web pages along URL hyper-links. The discovery of traversal patterns is quite useful for improving the Web site design, offering clients personalized service, carrying on e-commercial activities, constructing intelligent Web sites and so on.

Basic Information

Type of Prior Art	Print Publication

Publication Title *	An efficient Web traversal pattern mining algorithm based on suffix array

Author	Tao Jing, Wan-Li Zuo, Bang-Zuo Zhang

ISBN	0-7803-8403-2

Page Range	1535- 1539

Medium	Journal article

Publication Date *	August 26, 2004

URL	http://ieeexplore.ieee.org/xpl/...

Notes / To Do

Notes	Need IEEE account to access this document

Excerpt

Excerpt

Traversal patterns reflect regularities of Web users browsing and selecting Web pages along URL hyper-links. The discovery of traversal patterns is quite useful for improving the Web site design, offering clients personalized service, carrying on e-commercial activities, constructing intelligent Web sites and so on. The discovery of user traversal patterns comprises the following three steps: 1) extracting maximal forward reference paths from server log; 2) discovering frequent reference paths based on the result of the first step; and 3) filtering to get maximal frequent reference paths from the output of the second step. The second step constitutes the core of the whole mining process. Essentially, a Web access pattern is a sequential pattern in a large set of pieces of Web logs, which is pursued frequently by users. Although some attempts have been made to mine traversal patterns from Web logs, most of the research efforts try to employ techniques of sequential pattern mining, which is based on a generate-and-test paradigm, involving multi-scan of the entire dataset. This paper presents a novel approach based on suffix array for frequent reference path generation. Experimental results on both synthetic and real-life data sets show the effectiveness of the novel algorithm.

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Claims

A system that facilitates automatic tracking of user data comprising:a keystroke capture component that captures keystrokes in the form of a character string;

an extraction component that extracts one or more character substrings having at least a minimum length of characters from the character string;

a string analysis component that analyzes the character substrings extracted from the character string to determine a frequency of occurrence for each extracted character substring; and

an order component that orders the extracted character substrings according to at least one parameter comprising the frequency of occurrence.

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This IEEE reference provides a strong fundamental background prior art. It tracks where the users have went to on the world wide web, through URL logs. Presumably, the URLs have been typed into the computer to access webpages. The system keeps track of URL and frequency of each URL, which may read onto a string or a substring. Significantly, this improves the user experience, personalized service, and operates in an e-commerce architecture. A detailed review by the Examiner is highly recommended.

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The system of Claim 1, wherein the keystroke capture component captures the keystrokes for a designated period of time.

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The server log is logging data for a specific amount of time.

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The system of Claim 1, wherein the character string is a long, continuous string.

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URL itself is a continous string.

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The system of Claim 1, wherein the extraction component locates and extracts one or more repeating character substrings with at least a minimum character length that repeat at least one time.

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URLs occurs more than 1 time, it will be extracted

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The system of Claim 1, wherein the at least one parameter further comprises date of entry, alphabetical order, numerical order, and location of entry.

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sort algorithm, here it sorts by frequency, so that is considered numerical order.

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The system of Claim 1 is embedded in a browser.

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It operates in a browser

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The system of Claim 1 further comprises an input monitoring component that monitors input entered into an active browser.

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this is using a browser

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The system of Claim 7 wherein the input is entered into the active browser in at least one of the following manners: typed and manual selection.

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please check the body of the journal for details.

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The system of Claim 7 further comprises an input tracking component that tracks substrings which repeat at least q times over a period of time.

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URLs repeat certain times, the system sorts by frequency.

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The system of Claim 7 further comprises a field extraction component that extracts field character string data from one or more web form fields and associates the character string data with respective metatag labels on the web form fields.

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system extracts URL strings and sort the URL strings in a frequency order.

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The system of Claim 10 further comprises one or more data stores that collect extracted field character string data.

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Server logs.

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The system of Claim 11 further comprises a count component that incrementally increases a count of the relevant extracted field character string data as each new occurrence is observed.

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see body of the journal, almost inherent this is in there because it is keeping track of frequency of visit, there is a counter in the system.

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The system of Claim 10 further comprises a reputation service that is triggered to retrieve on-demand site or merchant reputation data when input is entered into at least one sensitive form field.

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Check the journal itself, it may be in there. Check amazon or ebay.

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A method that facilitates automatic tracking of user data comprising:capturing keystrokes entered into an active web browser as a continuous string of characters;

analyzing the string of characters in order to find one or more substrings that repeat; and

recording one or more repeating substrings along with their associated entry locations and/or times of entry.

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The method of Claim 14 further comprises arranging the one or more repeating substrings by frequency of occurrence.

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see abstract, it is arranging by frequency of occurrences

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The method of Claim 14 further comprises determining a context of the one or more repeating substrings.

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filtering to get the maximum frequency string URLs.

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The method of Claim 14 further comprises monitoring and tracking user input as it entered, the user input comprising repeating and non-repeating substrings.

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repeat or non repeat URLs, revisted URLs

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The method of Claim 17 further comprises triggering a reputation service to obtain site or merchant reputation data when at least one of the following occurs: at least a portion of the user input indicates a financial transaction or the user input is entered into at least one sensitive field.

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Doesnt specifically mention in abstract, but check ebay/amazon user feedback systems.

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The method of Claim 14 further comprises extracting field character strings from their respective web form fields; and increasing their counts respectively as additional occurrences of the field character strings are observed.

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URL field is a form field, it is keeping track of the URL frequency occurrences

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A system that facilitates automatic tracking of user data comprising:means for capturing keystrokes in the form of a character string;

means for extracting one or more character substrings having at least a minimum length of characters from the character string;

means for analyzing the character substrings extracted from the character string to determine a frequency of occurrence for each extracted character substring; and

means for ordering the extracted character substrings according to at least one parameter comprising the frequency of occurrence.

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