Method and device for publishing cross-network user behavioral data

The present invention relates to summarizing cross-network user behavioral data. The summarizing cross-network user behavioral data may particularly include publishing the data to one or more data structures that become accessible to a server hosting an authorized domain when a user accesses the authorized domain.

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Description
CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and seeks priority to U.S. nonprovisional application Ser. No. 11/226,066, entitled “Method and Device for Publishing Cross-Network User Behavioral Data,” filed Sep. 14, 2005, published Jun. 22, 2006 as U.S. Publication No. 2006-0136528 A1, issued Apr. 6, 2010 as U.S. Pat. No. 7,693,863, which claims benefit of U.S. provisional patent application No. 60/637,684, filed Dec. 20, 2004, both of which are hereby incorporated by reference herein.

This application is related to commonly owned U.S. application Ser. No. 10/289,123, now U.S. Pat. No. 7,512,603, entitled “Responding to End-User Request for Information in a Computer Network” by inventors Eugene A. Veteska, David L. Goulden, and Anthony G. Martin, filed on Nov. 5, 2002, and issued on Mar. 31, 2009. The related application is also incorporated by reference.

BACKGROUND

The present invention relates to summarizing cross-network user behavioral data and further relates to restoring damaged data structures, either data structures summarizing cross-network user behavioral data or data structures remotely identifying to logic running on a user computer. The summarizing cross-network user behavioral data may particularly include publishing the data to one or more data structures that become accessible to a server hosting an authorized domain when a user accesses the authorized domain. The restoring of damaged data structures may particularly include automatically detecting deletion of or damaged to data structures and restoring the damaged data structures.

Advertising is most successful when it matches the viewer's desires. When the viewer uses a client computer to surf the web and visit websites hosted by servers, advertising can be targeted. One way to target users is to provide sponsored links in response to a search, using a search engine such as the engines provided by Google and Yahoo. Another way is to trigger advertising when a user visits a website, for instance using a so-called web beacon. Cookies can be used to identify a user that is searching or visiting a website and match the user to a server-side database that selects advertising to deliver. Server-side databases currently reflect information collected by servers connected to websites that users visit. Websites that are aggregated into a particular server-side database can be described as belonging to a user behavior collection network.

An opportunity arises to better target users, taking advantage of client software running on the client computer. Client-side software can monitor a user's web browsing activity, regardless of whether websites visited belong to a first or second user behavior collection network or no network at all. New technology can publish user behavior data during browsing and reduce or eliminate reliance on matching the user to the server-side database.

SUMMARY OF THE INVENTION

The present invention relates to summarizing cross-network user behavioral data. The summarizing cross-network user behavioral data may particularly include publishing the data to one or more data structures that become accessible to a server hosting an authorized domain when a user accesses the authorized domain. Particular aspects of the present invention are described in the claims, specification and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a client computer interacting with a server and a behavior responsive server.

FIG. 2 illustrates a schematic diagram of a computer network.

FIG. 3 illustrates browsing to a search engine “FindersKeepers”.

FIG. 4 shows a browser displaying search results.

FIG. 5 depicts a page in a familiar browser for a “Search+” web site.

FIG. 6 is an example of a cookie that stores the most recent commercial keyword of interest.

FIG. 7 is an example of summary data maintained by the behavior watcher that may be written to persistent memory.

FIG. 8 illustrates a bit string further summarized by category.

FIG. 9 illustrates buckets that may be applied to recency of using search words, clicking through, entering URL destinations or other indications of user interest.

FIG. 10 illustrates buckets that can be applied to user category involvement.

FIG. 11 illustrates how a category ID can be associated with both the category name and a revenue priority.

FIG. 12 illustrates interaction among the client computer, a server and a reported-behavior responsive server.

FIG. 13 depicts one embodiment of information flow among the behavior watcher, the browser, one or more servers, one or more reported-behavior responsive servers, persistent memory, and cookie memory.

FIG. 14 depicts republication and restoration of cookies.

 DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The following detailed description is made with reference to the figures. Preferred embodiments are described to illustrate the present invention, not to limit its scope, which is defined by the claims. Those of ordinary skill in the art will recognize a variety of equivalent variations on the description that follows.

To illustrate an environment in which the present invention may be useful, we point to and incorporate by reference U.S. application Ser. No. 10/289,123, now U.S. Pat. No. 7,512,603, entitled “Responding to End-User Request for Information in a Computer Network” by inventors Eugene A. Veteska, David L. Goulden, and Anthony G. Martin, filed on Nov. 5, 2002, and issued on Mar. 11, 2009. Some of the discussion that immediately follows is borrowed from the prior application, which is commonly owned, but much is recast to a new paradigm of assembling user profile information client-side and publishing it using cookies.

Although embodiments of the present invention are described herein in the context of the Internet, the present invention is not so limited and may be used in other data processing applications.

FIG. 1 is a schematic illustration of a client computer 100 interacting with a server 130 and a behavior responsive server 132. In FIG. 1, an end-user on a client computer indicates their interest in a subject by submitting a search request to a search engine, typing a URL or clicking on a link or banner ad. Their interest becomes a request across on a network such as the Internet. A search engine may perform an Internet-wide search, or a search limited to a particular web site or domain name, or a search limited in some other way. The search request may be a query comprising one or more keywords, for example. The original search engine server 130 responds to the search request by sending a search result to the client computer 100. The search result may include a list of documents (e.g., web pages), links to documents, or other information relating to the keywords. A specific example of the just described scenario is an end-user typing the keyword “car” in a search engine and receiving back links to web sites that talk about car clubs, exotic cars, car racing, and like information.

A user submits a search request because of the user's interest in a topic. To make good use of the information about a user's interest, a behavior watching and/or behavior summarizing program 120 resident in the client computer listens for requests, for instance made using a browser 110 or other hyperlink enabled programs such as word processors or presentation programs. It listens for requests for information such as a search request, a click-through on a banner ad, or entry into the browser or click through of a destination URL. When the behavior watching program 120 detects that an end-user is performing a search or otherwise indicating their interest in a web site or subject, the behavior watching program records the interest. The interest may be recorded to program memory or to a persistent memory 122. When a user clicks on an advertisement related to the end-user's search request, the probability that the end-user will be interested in one or more related advertisements increases. In the car example, just above, the behavior watching program may note that the search was followed by certain click-throughs.

The behavior watcher 120 is resident in the client computer 100 so that it can respond to an end-user's search request even when the end-user uses different search engines. That is, unlike other targeting techniques that only work when an end-user is on a particular web site, the behavior watching program observes user interest across different search engines, web sites and even browsers. An end-user may obtain the behavior watching program by itself or along with free or reduced-cost computer programs, services, and other products.

The behavior responsive server 132 presents a new approach to providing content responsive to recent user behavior. User behavior has in the past been observed using a cookie with a unique user ID that allows server-side tracking. Server aggregated user behavior data is compiled among an organization of cooperating server owners. Cooperation may relate to ad serving (e.g., Double-Click), portals (e.g., Yahoo), search engines (e.g., Google) or other organizational basis. Cooperation in this sense is contractual more than technical. Server aggregated user behavior data is used, for instance, by an ad server when a web beacon (a small, null image with a URL pointing to an ad server) contacts the ad server and causes a unique user ID cookie to be transmitted. The ad server accesses a database, to update user behavior data with the new contact. The ad server could also access data from the server aggregated database, based on the unique user ID. A serious limitation of server aggregated user behavior data is that it is limited to contacts detected by cooperating servers when the user accesses cooperating web sites and the user's unique user ID cookie is transmitted. Mismatches of unique user IDs or multiple unique user IDs for the same user diminish the effectiveness of server-side aggregation. The combination of client-side user behavior tracking 120 and a reported-behavior responsive server 132 presents a new approach.

A reported-behavior responsive server 132 accepts one or more messages (e.g., cookies) that report recent user behavior as a basis for deciding among potential messages (e.g., banner ads) which message to give the user. The reported-behavior responsive server 132 need not access a server-side user behavior database to decide which message to send. Those of skill in the art will recognize that the new paradigm of reported behavior could be combined with server-side aggregation and that that combination also would be novel.

FIG. 2 is shown a schematic diagram of a computer network. Network 200 may include one or more client computers 100, one or more web server computers 130, one or more behavior responsive server computers 132, and other computers not shown. Intermediate nodes such as gateways, routers, bridges, Internet service provider networks, public-switched telephone networks, proxy servers, firewalls, and other network components are not shown for clarity. In the example of FIG. 2, network 200 includes the Internet; however, other types of computer networks may also be used. Computers may be coupled to network 200 using any type of connection 115 without detracting from the merits of the present invention.

A client computer 100 is typically, but not necessarily, a personal computer such as those running the Microsoft Windows™, Apple Macintosh™, Linux, or UNIX operating systems. An end-user may employ a suitably equipped client computer 100 to get on network 200 and access computers coupled thereto. For example, a client computer 100 may be used to access web pages from a web server computer 130. It is to be noted that as used in the present disclosure, the term “computer” includes any type of data processing device including personal digital assistants, digital telephones, wireless terminals, and the like.

A web server computer 130 may host a web site containing information designed to attract end-users surfing on the Internet. A web server computer 130 may host or cooperate with another server that hosts advertisements, downloadable computer programs, a search engine and products available for online purchase. A web server computer 130 may also host or cooperate with an ad server for that delivers advertisements to a client computer 100.

A reported-behavior responsive server computer 132 serves message units for delivery to a client computer 100. The message units may contain advertisements, for example. Message units are further described below. A reported-behavior responsive server computer 132 may also host downloadable computer, programs and files for supporting, updating, or maintaining components on a client computer 100.

Web server computers 130 and reported-behavior responsive server computer 132 are typically, but not necessarily, server computers such as those available from Sun Microsystems, Hewlett-Packard, Dell or International Business Machines. A client computer 100 may communicate with a web server computer 130 or a reported-behavior responsive server computer 132 using client-server protocol. It is to be noted that client-server computing will not be further described here.

The components of a client computer 100 are not illustrated in a separate figure, but are well-understood. In one embodiment, the components of client computer 100 shown in FIG. 1 are implemented in software. It should be understood, however, that components in, the present disclosure may be implemented in hardware, software, or a combination of hardware and software (e.g., firmware). Software components may be in the form of computer-readable program code stored in a computer-readable storage medium such as memory, mass storage device, or removable storage device. For example, a computer-readable storage medium may comprise computer-readable code for performing the function of a particular component. Likewise, computer memory may be configured to include one or more components, which may then be run by a microprocessor. Components may be implemented separately in multiple modules or together in a single module.

As shown in FIG. 1, client computer 100 may include a web browser 110 and a behavior watching program 120. Web browser 110 may be a commercially available web browser or web client running on a client computer 100. In one embodiment, web browser 201 is the Microsoft Internet Explorer™ web browser. In another, it is a Mozilla, Netscape or Apple web browser. Alternatively, a web enabled word processor or presentation program may be monitored by the behavior watcher 120.

The behavior watching program 120 may be downloadable from a message server computer 103. Behavior watching program 120 may be downloaded in conjunction with the downloading of another computer program. For example, behavior watching program 120 may be downloaded to a client computer 100 along with a utility program that is provided free of charge or at a reduced cost. The utility program may be provided to an end-user in exchange for the right to deliver messages to the end-user via behavior watching program 120. In essence, revenue (e.g., advertising fees) from messages delivered to the end-user helps defray the cost of creating and maintaining the utility program. Other components such as operating system components, utility programs, application programs, and the like are not shown for clarity of illustration.

The behavior watcher 120 includes computer-readable program code that compiles a user profile from behavior watching. It monitors web browser 110 for search requests, uniform resource locators (URLs) of web sites visited by an end-user surfing on the Internet and ad click-throughs. FIG. 3 illustrates browsing to a search engine “FindersKeepers”. A user invokes a browser 310 and enters the address of the search engine in an address window 315. Of course, links, buttons, a search bar component or a dedicated searching program (for instance, from the systray) can connect a user with a search engine, through a browser or other program. The address remains visible when the page for the web site is displayed. The user enters search terms “hotrod car” in a window 320. A request is sent or “posted” to the search engine.

FIG. 4 shows a browser 310 displaying search results. Part of the behavior watcher 120 may parse a search request, as part of the process of compiling a user profile. In one embodiment the behavior watcher 120 parses a search request URL displayed in the address window of a web browser in response to a search request, to extract one or more keywords. Popular search engines respond to a search request by pointing the web browser employed by the end-user to a web page containing the search result. The URL of that web page may be parsed to extract the keywords chosen by the end-user for his search. For example, performing a search for “hotrod” and “car” in a fictitious search engine referred to herein as “finderskeepers.com” will result in a web browser being pointed to a web page with the URL (440) http://search.finderskeepers.com/bin/search?p=hotrod+car.

The web page with the URL search.finderskeepers.com/bin/search?p=hotrod+car” contains the search result for the keywords “hotrod” and “car”. Knowing the URL format employed by finderskeepers.com allows extraction of the words following “ . . . /search?p=” and separated by “+”. String manipulation functions may be used to extract keywords from the URL. It is to be noted that different search engines may employ different URL formats. Accordingly, a keyword extractor component of the behavior watcher may first parse the URL to determine the name of the search engine, and then employ parsing rules for that search engine. In the present example, keyword extractor may first parse the URL “http://search.finderskeepers.com/bin/search?p=hotrod+car” to get the name of the search engine, which is “ . . . finderskeepers.com/ . . . ”, and then apply parsing rules for finderskeepers.com. Keyword extractor may take advantage of delimiters and end of string indicators employed by a particular search engine to parse a URL. In one embodiment, keyword extractor includes parsing rules for popular search engines. The keyword extractor also may act on entry or click-throughs of URLs and click-throughs of banner ads, to cross-reference those interest indications to key words. A keyword extractor is further described in the application incorporated by reference.

FIG. 5 depicts a page in a familiar browser 310 for a “Search+” web site. The familiar address window appears just below the button bar and above a tool bar. A search window 520 accepts keyword searches. Search results 521A, 521B are displayed in the body of the browser. Banner ads, which a user is welcome to click through, are positioned at the top 531A and right side 531B of the display.

Referring back to FIG. 4, a keyword extractor also may include a keyword file. In one embodiment, keyword file is a text file containing a list of relevant keywords referred to as “an include list” and a list of non-relevant keywords referred to as an “exclude list”. Note that keyword file may have also have an include list but not an exclude list or vice versa. Keyword file may be downloadable from a server computer 130 to ensure that client computer 100 always has the most current keyword file. Keywords in the include list can be assigned to categories of interest, whereas keywords in the exclude list have no corresponding categories of interest. Keywords in the include list are processed by a keyword processor, whereas keywords in the exclude list are not processed. In one embodiment, keyword processor processes a keyword by sending the keyword to message server computer 130 to obtain further information about the keyword. By first examining the exclude list before sending keywords to server computer 132, keywords that have no corresponding message units are identified at client computer 100, thereby minimizing traffic to server computer 132. Likewise, traffic to server computer 132 may be minimized by first determining if a keyword is in the include list before sending the keyword to message server computer 103.

TABLE 1 Include List Exclude List hotrod paper car the books for computer Britney cellular . . . sports . . . wine . . . . . . . . .

In the example of Table 1, the exclude list includes non-relevant words such as commonly used words that are too general to be useful. The exclude list may also include words that have no commercial value. For example, “Britney” may have no commercial value to most advertisers except those affiliated with or licensed by Britney the pop-star.

In the example of Table 1, the include list includes relevant words. In one embodiment, a word is considered relevant if there is a corresponding message unit for that word. For example, the word “computer” may be relevant if there is a message unit containing an advertisement for a personal computer. Similarly, the word “book” may be relevant if there is a message unit relating to books.

Data collected by the behavior watcher 120 are illustrated by FIGS. 6-11. Data are collected to generate a client-side profile. These profiles allow ad servers, which receive reported user behavior data, to improve ad performance. Advertisers will pay more for ads that are directed to interested customers than for ads that are distributed randomly. Moreover, ad impressions that generate click-throughs generate more revenue than ads that are displayed but evoke no response. Ad performance can be improved either by better targeting or greater success in generating click-throughs.

A client-side profile includes things like category visits, searches, user identification, language and time zone. Indications of user interest are assigned to categories. In one embodiment, 200-300 categories are recognized. Of course, fewer or more categories might be recognized. The more categories recognized, the more data bits required to store category IDs. Potentially, categories could be organized hierarchically. Recency may be recorded by time bins, such as within the last hour, 1-2 hours, 2-4 hours, 4-24 hours, 1-7 days, 7-30 days, 31-60 days. Alternatively, bins or categories for 7-14 days, 0-5 minutes, 5-15 minutes, 15-30 minutes and 30-60 minutes may be of interest. Searches for key words of commercial interest may be more important than some other indications of user interest. The behavior watcher 120 may keep track of the last commercial keyword entered in a search and a time bucket for the last commercial keyword entered. Over a certain outer history limit, the time bucket may be empty. Machine-related information may include a unique machine ID, language settings, time zone of the user, and installation date of the relevant software. Data stored either to persistent memory 122 or cookie memory 124 may be compressed and encrypted to save space and prevent hacking. Compression, beyond the categorization described, and encryption schemes are well known and not described here. Cookies, at least, should include version number so that the server receiving the cookie will understand how to interpret the cookie. Other header information may be supplied as desired. Cookies tracking user behavior are written by the behavior watcher 120, as opposed to the server 130.

Machine information is one type of data that may be stored in persistent memory 122 and cookies 124. The information associated with the machine may include version of the cookie, the time written, a unique ID, a locale (such as a ZIP code, area code or something similar), user's time zone, and an application name, version and installation time. Machine information may be rewritten to memory every 24 hours, every new day, or on some other periodic basis. It may be stored as columns separated by delimiters, then encrypted and compressed.

The behavior watcher may track and summarize visits. It may record, such as to persistent memory 122, each indication of user interest, including use of key words in searches, entry of URLs and click-throughs of URLs and banner ads. Banner ads may be segregated into regular banner ads, site exit banner ads that are triggered when leaving a particular URL, brander banner ads that are triggered when visiting a first URL, then displayed in visiting a second URL, and run of network banner ads that are triggered based on a count of navigations from one URL to the next.

FIG. 6 is an example of a cookie that stores the most recent commercial keyword of interest. This may be the most recent commercial keyword of interest from a search or a keyword associated with a URL or click-through. More than one recent commercial keyword of interest may be stored in one or more cookies, following the example of FIG. 6. One attribute or field of this cookie is the version of the crumb 602. Another is the time written 604, which may be expressed in seconds from 1970 to the time the cookie is written. A commercial flag 606 may be used to categorize the keyword type, for instance between key words that are of current commercial interest and key words that are for sale. The keyword may be included 608, either in text or as a shorthand code. A character set 610 such as UFT-8 may be included to assist with decoding of the keyword 608. This may be important in international applications.

FIG. 7 is an example of summary data maintained by the behavior watcher 120 that may be written to persistent memory 122. In this example, a category ID 802 is a shorthand for a particular category. Bins are allocated for a current period 702, such as a day. The periods may be of equal length for different lengths. In one embodiment, a bit string implements the bins. A history of 60 days may be implemented by 60 bits plus storage for a category ID. Each bin corresponds to a particular time segment. A bit flag indicates whether there was activity related to the category during the particular time segment. At a regular time corresponding to the shortest period represented by any bin, the bit string is updated. Data are shifted into older bins, making room in the most recent bin. Data regarding recent activity are summarized into the most recent bin, labeled zero in the figure. If bins represent unequal amounts of time, special provisions are made at the boundaries between shorter and longer time bins, either algorithmically or by allocating additional storage. For instance, activity during any one-hour segment during a preceding day may result in setting the bit flag in a day-long time segment. The day-long time segment is flag “0” only if the hour segment flag at midnight is “0” when the midnight update takes place. This is an example of an algorithmic provision at the boundary between a one-hour segment and a day-long segment. Alternatively, storage may be allocated for bins to record the number of visits in a particular time segment. This may significantly increase the amount of storage required, without necessarily changing how a reported-behavior aware server 132 responds. Compact storage is preferred when there are hundreds of categories and the practical limit for the size of a cookie is 2K to 4K bytes. Cookies can be sent to a server that archives user activity data. The user activity data can be used for analysis or targeting of ads. With the right user ID information, the user activity data could be matched to data from other sources, such as shopper loyalty programs or market research programs. A bit string can be further summarized by category, shown in FIGS. 8-10.

FIG. 9 illustrates buckets that may be applied to recency of using search words, clicking through, entering URL destinations or other indications of user interest. The most recent visit is recorded by membership in a particular bucket. Fifteen or 16 buckets can be indicated with four bits. In the figure, the buckets are of unequal length. A single, most recent visit record such as illustrated by FIG. 6 or an entry kept in memory can be used to assign a category to a particular bucket.

FIG. 10 illustrates buckets that can be applied to frequency of visits. When the bit string of FIG. 7 is organized by days, activity in a category can be assigned to buckets of different visit frequencies, such as 0, 1, 2, 3, 4 or 5, 6 to 10, 11 to 30, or 31 to 60 days out of the last 60 days in which the user indicated interest. The number of bits set in the bit string is counted and summarized using this categorization, as a measure of user category involvement.

With the categorical coding of recency and user category involvement in mind, we turn to FIG. 8. A single cookie may record data for many category IDs 802. For each category ID, three or more metrics of user interest may be recorded. One or more recency 804 categories can be recorded. The recording of recency can be applied to key words or other kinds of user interest indications, described above. One or more frequency 806 categories can be recorded, applied to key words or other kinds of user interest indications. Banner clicks 808 can be separately categorized by recency, frequency or both. The comments in column 810 are editorial comments to explain the data in the table. Category ID 98409 experienced heavy and recent user interest. Category ID 65625 experienced heavy user interest last month. Category ID 68530 experienced light, recent user interest. Category 147374 experienced light activity last month, which probably also should be reflected by a nonzero value under frequency 806.

The behavior watcher 120 preferably sorts the category history information in FIG. 8 so that the most important categories are listed first. Alternatively, the system could leave the category sorting to the reported-behavior responsive server 132. As illustrated in FIG. 11, a category ID 1102 can be associated with both the category name 1104 and a revenue priority 1106. It may be useful to sort category history information by revenue priority 1106, especially if only a portion of the client-sided user behavior data is transmitted to a reported-behavior responsive server 132.

FIG. 12 illustrates interaction among the client computer 100, a server 130 and a reported-behavior responsive server 132. A browser 110 or similar software on the client computer 100 accesses a server 130. Server 130 sends back a web page 1240, which includes embedded references to addresses 1230 and 1232. When the browser 110 accesses web page components identified by the addresses, it may send cookies identified with those addresses 1250, 1252 to a server 130 and a reported-behavior responsive server 132. The server 130 may be different server than one that initially sent the web page 1240. During the exchange of data responsive to the request from the browser 110, the cookie 1252 that is transmitted to the behavior-responsive server 132 conveys data such as depicted in FIG. 8. It also may convey other data, such as the time of the last user interest indications in the category, similar to the information depicted in FIG. 6.

FIG. 13 depicts one embodiment of information flow among the behavior watcher 120, the browser 110, one or more servers 130, one or more reported-behavior responsive servers 132, persistent memory 122 and cookie memory 124. In this figure, the user indicates their interest in a subject to a browser 110 or similar software. This indication of interest typically results in the browser 110 transmitting a search request, URL or similar message 1302 to a server 130. The behavior watcher 120 receives the indication of interest 1304 from its monitoring of the browser 110. Optionally, it records the indication of interest 1306 to persistent memory 122. Recording to persistent memory may take place immediately, periodically or both. For instance, the most recent keyword, search term, banner click or other indication of interest may be recorded to memory with a date and time stamp. As described above, a periodic history of interests may be updated periodically. The behavior watcher 120 publishes information about user behavior to cookies 124 or similar memory structures that are readily accessible to reported-behavior responsive servers 132 when a browser 110 accesses a server 130 that triggers a message to a reported-behavior responsive server 132 or contacts the server 132 directly. This publishing of behavior may include both publishing information about the most recent indication of user interest and publishing categorical information summarizing recent user interest.

Use of information published by the behavior watcher 120 to accessible memory structures is also illustrated in FIG. 13. Typically, a server 130 returns a web page 1310 to the browser 110. Depending on the addresses embedded in the web page 1310, various cookies 1312 are automatically retrieved by the browser 110. When the web page 1310 includes an address of a reported-behavior responsive server 132, one or more cookies that summarize user behavior and interests observed by the client-side behavior watcher are transmitted 1314 to the server 132. In addition, one or more cookies 1316 may be transmitted to server 130. The reported-behavior responsive server 132 decodes the cookie 1314, determines what message or ad to return to the browser 110 and sends a customized message 1320 that is responsive to the reported behavior 1314 of the user.

One issue with publishing behavioral data to cookies is deletion of cookies. Cookies are automatically deleted by a browser 110 when they reach an expiration date or when there are too many cookies. Users manually delete cookies using browser controls. Many software applications have been developed to analyze and delete certain cookies. Therefore, publication of behavioral data to cookies is sometimes not enough to assure that the resulting memory structures will be accessible to reported-behavior responsive servers 132. It is useful to have a mechanism to republish or restore cookies that have been deleted.

FIG. 14 depicts republication and restoration of cookies. Republication of cookies that summarize observed user behavior is the simpler case. As described above, the behavior watcher 120 may record observed behavior and interests 1306 to persistent memory 122 and publish similar information 1308 to cookies 124. For republication, the behavior watcher 120 periodically inspects cookie memory 124 to determine whether the cookies have been deleted or damage 1438. Alternatively, the behavior watcher 120 might detect cookie deletion events or possible cookie deletion events and schedule republication following cookie deletion. When it detects that the cookies have been deleted or damaged 1438, it republishes the cookies 1440 to the cookie memory 124. The cookies are again in place as memory structures readily accessible to reported-behavior responsive servers 132.

Backup and restoration of cookies is also illustrated by FIG. 14. A control server 1434 instructs the behavior watcher 120 or a backup and restore component of the behavior watcher regarding cookies to be backed up and restored 1435. The behavior watcher 120 retrieves from cookie memory 124 cookies for which a backup and restore service is to be provided 1436. The behavior watcher 120 records or backs up 1437 the cookie information to persistent memory 122. The behavior watcher 120 need not be aware of the content of the cookies in order to provide this backup service. The cookies need not include observed behavior data and may be directed to ordinary servers 130. The behavior watcher 120 periodically inspects cookie memory to determine whether the cookies have been deleted or damage 1438. Alternatively, the behavior watcher can detect cookie deletion events and schedule restoration of cookies following cookie deletion. When it detects that the cookies have been deleted or damaged 1438, it restores the cookies 1440 to the cookie memory 124. The cookies are again in place as memory structures readily accessible to servers 130.

SOME PARTICULAR EMBODIMENTS

The present invention may be practiced as a method or device adapted to practice the method. The same method can be viewed from the perspective of a behavior summarizing module optionally including a behavior observing module and a summary publishing module. The invention may be an article of manufacture such as media impressed with logic to carry out computer-assisted behavior summarizing optionally including behavior observing module and a summary publishing module.

One embodiment is a method of publishing behavioral data from a client computer via network to an authorized domain. This method includes summarizing observed cross-network user behavior data using a behavior summarizing module operating on a user's computer, wherein the observed cross-network user behavior includes accessing web sites that are not all associated with a particular user behavior data collection network. Alternatively, cross-network may refer to web sites or other content delivery vehicles that are not all associated with a particular portal or a particular virtual storefront or a particular content provider. The behavior further includes at least one of a mouse click-through, enter keystroke or other selection action by the user and at least one keyword derived from context of the selection action. The summarizing includes deriving a categorical metric of the user's degree of interest in subject categories, from the observed cross-network user behavior data, for a multiplicity of subject categories. The method includes publishing the summarized cross-network user behavior data from the behavior summarizing module to a memory structure, wherein the memory structure becomes accessible via a network to a server at an authorized domain when the user uses the network to access the authorized domain. This method may be extended in several ways.

A further aspect of this method includes prioritizing a multiplicity of subject categories, selecting a plurality of the multiplicity of subject categories, and publishing the summarized cross-network behavior data for the plurality of subject categories to a single memory structure. This memory structure, optionally, may be a so-called cookie. Another aspect of this method may include observing the cross-network user behavior data using a behavior watching module operating on the user's computer. The behavior watching module may be integrated with the behavior summarizing module. Alternatively, the two behavior-related modules may be separate modules, even using separate hardware.

The cross-network behavioral data may include user selection of one or more of the following: submission of a search term to a search engine, either using a browser interface or a search engine-specific interface, visiting a portal, visiting a virtual storefront, visiting a content provider, entering a URL in a browser address window, selecting a URL hyperlink, or clicking on a banner ad associated with a link. More generally, the user selection may be any navigation among documents that are linked semantically or by addresses. The meaning given to “cross-network” above also applies here.

Publishing to the memory structure may take place on a periodic basis and/or on an update basis. By an update basis, it is meant that the memory structure update follows detection of the user selection.

Yet another aspect of this method may include receiving at the user's computer advertising targeted using the summarized cross-network user behavior data published to the memory structure that became accessible when the user used the network to access the authorized domain.

The categorizing may include rolling up indicators of visits into non-overlapping categorical time segments of differing lengths. This may be accomplished by representing a plurality of granular time segments with flags to indicate user category involvement and summarizing a portion of the granular time segments by aggregation to the categorical time segments.

Another embodiment is a method of publishing behavioral data collected by a behavior watching module operating on the user's computer, including summarizing cross-network user behavior data recorded to memory. The summarizing includes, for a multiplicity of subject categories, deriving one, two, three or four of the following items: (1) categorizing recency of visiting a web site in the subject category by evaluating a most recent visit time-date indicator; (2) categorizing frequency of user visits to web sites in the subject category by rolling up indicators of visits during seven, ten or more time segments or any number of categorical time segments, which time segments were recorded on a rolling basis; (3) categorizing recency of selection of a banner ad to obtain additional information in the subject category by evaluating a most recent visit time-date indicator; or (4) categorizing frequency of user selections of the banner ad to obtain additional information in the subject category by rolling up indicators of visits during seven, ten or more time segments or any number of categorical time segments, which time segments are tracked on a rolling basis. The method further includes publishing the summarized cross-network behavior data from a behavior summarizing module to memory structure that becomes accessible via a network to a server at an authorized domain when the user uses the network to access the authorized domain.

Any and all aspects of the first embodiment may be applied to the second embodiment.

A related embodiment is a method of republishing a memory structure on a user's computer, wherein the memory structure summarizes cross-network user behavior data and becomes accessible via a network to a server at an authorized domain when the user uses the network to access the authorized domain. This method includes providing a logic to be installed on a user's computer, the logic having access to a storage area for the memory structure, having access to persistent memory distinct from the memory structure storage area, and the logic operating on the user's computer without requiring the user to specifically invoke the logic. This method further includes the logic automatically detecting when the memory structure that summarizes cross-network user behavior data has been deleted or damaged and responding by restoring at least part of the data structure to the data structure storage area from the distinct persistent memory.

One aspect of this embodiment is that the data structure may be a so-called cookie used for HTTP state management. The names of cookies may be used to associate the cookies with authorized domains. The distinct persistent memory may be a registry including name-value pairs or a separate storage area.

A variation on the immediately preceding embodiment is a method of backing up and restoring a memory structure on a user's computer. This method includes providing a logic to be installed on a user's computer, the logic having access to a storage area for the memory structure, having access to persistent memory distinct from the memory structure storage area, and the logic operating on the user's computer without requiring the user to specifically invoke the logic. This method further includes remotely identifying the logic one or more favored memory structures to be backed up and automatically backing up the favored memory structures to the distinct persistent memory. The method further includes automatically detecting that at least one particular favored memory structure has been deleted or damaged and automatically restoring at least part of that particular memory structure to the memory structure storage area using data from the distinct persistent memory. The aspects of the immediately preceding embodiment apply to this embodiment as well.

Device embodiments of these methods may include a memory structure area of memory, a persistent memory distinct from the memory structure storage area and logic operating as part of the user's computer, without requiring the user to specifically invoke the logic, the logic having access to the memory structure storage area and the distinct persistent memory. The device further includes the communications channel connected to the network. The logic is adapted to carry out any of the method embodiments described above using the communications channel.

Claims of Provisional Application No. 60/637,684 1 [observing keyword for selection, summarizing and publishing to memory structure] A method of publishing behavioral data from a client computer via a network to an authorized domain using a cookie, including: observing cross-network user behavior data using a behavior watching module operating on a user's computer, wherein the cross-network user behavior includes accessing web sites that are not all associated with a particular user behavior data collection network, and further includes at least one of a mouse click-through, enter keystroke or other selection action and at least one keyword derived from context of the selection action; summarizing by subject category for a multiplicity of subject categories the observed cross-network user behavior data using a behavior summarizing module, including deriving a categorical metric of the user's degree of interest in the subject category from the observed cross- network user behavior data; and publishing the summarized cross-network behavior data from the behavior summarizing module to a memory structure, wherein the memory structure is accessible via a network to a server at an authorized domain when the user uses the network to access the authorized domain. 2 [keyword for selection previously observed, summarizing and publishing to memory structure] A method of publishing behavioral data from a client computer via a network to an authorized domain, including: summarizing observed cross-network user behavior data using a behavior summarizing module operating on a user's computer, wherein the observed cross-network user behavior includes accessing web sites that are not all associated with a particular user behavior data collection network, and further includes at least one of a mouse click-through, enter keystroke or other selection action by the user and at least one keyword derived from context of the selection action; wherein the summarizing includes deriving a categorical metric of the user's degree of interest in subject categories, from the observed cross- network user behavior data, for a multiplicity of subject categories; and publishing the summarized cross-network user behavior data from the behavior summarizing module to a memory structure, wherein the memory structure becomes accessible via a network to a server at an authorized domain when the user uses the network to access the authorized domain. 3 The method of claim 2, further including prioritizing a multiplicity of subject categories, selecting a plurality of the multiplicity of subject categories, and publishing the summarized cross-network behavior data for the plurality of subject categories to a single memory structure. 4 The method of claim 2, further including observing the cross-network user behavior data using a behavior watching module operating on the user's computer. 5 The method of claim 2, wherein the cross-network behavioral data include visits to a plurality of web sites or selections of banner ads that are not all associated with a particular behavioral data collection network. [portal, virtual storefront, content provider]. 6 The method of claim 3, wherein the cross-network behavioral data include behavioral data corresponding to a plurality of visits to web sites or selections of banner ads that are not all associated with a particular behavioral data collection network. 7 The method of claim 2, wherein the cross-network behavioral data include behavioral data corresponding to a plurality of visits to web sites or selections of banner ads that are accessed using a plurality of browsers. 8 The method of claim 2, wherein publishing takes place on a periodic basis. 9 The method of claim 2, wherein publishing takes place on an update basis, after one or more web sites have been visited or one or more banner ads have been selected. 10 The method of claim 2, further including receiving at the user's computer advertising targeted using the summarized cross-network user behavior data published to the memory structure that became accessible when the user used the network to access the authorized domain. 11 [summarizing and publishing any of four categories of data] A method of publishing behavioral data collected by a behavior watching module operating on a user's computer, including: summarizing cross-network user behavior data recorded to [persistent] memory, the summarizing including, for a multiplicity of subject categories, deriving any one or more of categorizing recency of visiting a web site in the subject category by evaluating a most recent visit time-date indicator, categorizing frequency of user visits to web sites in the subject category by rolling up indicators of visits during ten or more discrete time segments [non-overlapping; daily], which discrete time segments were recorded on a rolling basis [bit string; rolled daily], categorizing recency of selections of a banner ad to obtain additional information in the subject category by evaluating a most recent visit time-date indicator, categorizing frequency of user selections of a banner ad to obtain additional information in the subject category by rolling up indicators of visits during ten or more discrete time segments, which discrete time segments are tracked on a rolling basis; publishing the summarized cross-network behavior data from a behavior summarizing module to a memory structure that becomes accessible via a network to a server at an authorized domain when the user uses the network to access the authorized domain. 12 The method of claim 11, further including prioritizing a multiplicity of subject categories, selecting a plurality of the multiplicity of subject categories, and publishing the summarized cross-network behavior data for the plurality of subject categories to a single memory structure. 13 The method of claim 11, further including observing the cross-network user behavior data using a behavior watching module operating on a user's computer. 14 The method of claim 11, wherein the cross-network behavioral data include visits to a plurality of web sites or selections of banner ads that are not all associated with a particular behavioral data collection network. [portal, virtual storefront, content provider]. 15 The method of claim 12, wherein the cross-network behavioral data include behavioral data corresponding to a plurality of visits to web sites or selections of banner ads that are not all associated with a particular behavioral data collection network. 16 The method of claim 11, wherein the cross-network behavioral data include behavioral data corresponding to a plurality of visits to web sites or selections of banner ads that are accessed using a plurality of browsers. 17 The method of claim 11, wherein publishing takes place on a periodic basis. 18 The method of claim 11, wherein publishing takes place on an update basis, after one or more web sites have been visited or one or more banner ads have been selected. 19 The method of claim 11, further including receiving at the user's computer advertising targeted using the summarized cross-network user behavior data published to the memory structure that became accessible when the user used the network to access the authorized domain. 20 A method of backing up and recreating cookies on a user's computer, including: providing logic to be installed on the user's computer, the logic having access to a storage area for cookies, having access to persistent memory distinct from the cookies storage area, and operating without requiring the user to specifically invoke the logic; remotely identifying to the logic one or more favored cookies to be backed up; the logic automatically backing up the favored cookies to the distinct persistent memory; the logic automatically detecting that at least one particular favored cookie has been deleted or damaged; and the logic automatically restoring at least part of the particular favored cookie to the cookies storage area using data from the distinct persistent memory. 21 The method of claim 20, wherein the cookies are used for HTTP state management. 22 The method of claim 20, wherein names of the cookies associate the cookies with web sites. 23 The method of claim 20, wherein the distinct persistent memory is a registry including name-value pairs. 24 The method of claim 20, wherein the cookies are encoded and the backing up preserves the encoding without understanding how to decode the cookies. 25 The method of claim 20, further including identifying to the logic one or more identifiers for the persistent memory to be used for backing up the cookies. 26 The method of claim 20, further including a web server receiving data from the restored particular favored cookie. 27 The method of claim 20, wherein the logic is invoked when a browser is started. 28 A device operating as part of a user's computer, the device including: a cookies storage area of memory; a persistent memory distinct from the cookies storage area; logic operating as part of the user's computer, without requiring the user to specifically invoke the logic, the logic having access to the cookies storage area and to the distinct persistent memory; a communications channel connected to a network; the logic adapted to receive through the communications channel identifications of favored cookies that should be backed up, automatically back up the favored cookies from the cookies storage area to the distinct persistent memory; automatically detect that at least one particular favored cookie has been deleted or damaged; and automatically restore at least part of the particular favored cookie to the cookies storage area using the data from the distinct persistent memory.

While the present invention is disclosed by reference to the preferred embodiments and examples detailed above, it is understood that these examples are intended in an illustrative rather than in a limiting sense. Computer-assisted processing is implicated in the described embodiments. It is contemplated that modifications and combinations will readily occur to those skilled in the art, which modifications and combinations will be within the spirit of the invention and the scope of the following claims.

Claims

1. A computer-implemented method, implemented, at least in part, by hardware in combination with software, the method comprising:

collecting, using hardware in combination with software on a client computer, cross-network user behavior data related to a user's interactions on the client computer with a plurality of web sites, wherein not all of the plurality of web sites are associated with a content provider, and not all of the plurality of web sites are associated with a portal;
summarizing the cross-network user behavior data on the client computer, the summarizing including, for a plurality of subject categories, one or more of: categorizing recency of the user visiting a web site on the client computer in at least some of the plurality of subject categories, categorizing user category involvement from the user visiting the website on the client computer in at least some of the subject categories by rolling up indicators of visits into categorical time segments, categorizing recency of selections of at least one banner advertisement on the client computer, and categorizing user category involvement from user selections of the at least one banner advertisement on the client computer; and
publishing the summarized cross-network user behavior data on the client computer to one or more memory structures on the client computer; and
in response to the user accessing an authorized domain via the client computer via a network, providing at least some of the summarized cross-network user behavior data in the one or more memory structures on the client computer to a server at the authorized domain.

2. The method of claim 1, wherein the categorizing the user category involvement includes rolling up the indicators of visits into non-overlapping categorical time segments of differing lengths.

3. The method of claim 1, wherein the categorizing user category involvement includes representing granular time segments with flags to indicate user category involvement during the granular time segment and summarizing a portion of the granular time segments by aggregation into categorical time segments.

4. The method of claim 1, further comprising:

prioritizing a plurality of subject categories,
selecting at least one subject category of the plurality of subject categories, and
publishing the summarized cross-network behavior data for the plurality of subject categories to a single memory structure.

5. The method of claim 1, wherein the cross-network behavioral data include data relating to visits to the plurality of web sites or selections of banner advertisements that are not all associated with a behavioral data collection network.

6. The method of claim 1, wherein the cross-network behavioral data is further related to selections of banner advertisements that are not all associated with the portal.

7. The method of claim 1, wherein the cross-network behavioral data include data relating to visits to the plurality of web sites or selections of banner advertisements that are not all associated with a virtual storefront.

8. The method of claim 1, wherein the cross-network behavioral data is further related to selections of banner advertisements that are not all associated with the content provider.

9. The method of claim 1, wherein the cross-network behavioral data include data relating to behavioral data corresponding to a plurality of visits to the plurality of web sites or selections of banner advertisements that are not all associated with a behavioral data collection network.

10. The method of claim 1, wherein the publishing takes place on a periodic basis.

11. The method of claim 1, wherein the publishing takes place in response to one or more web site visit or one or more banner advertisements selection.

12. The method of claim 1, further including receiving at the client computer advertising targeted using the summarized cross-network user behavior data published to the one or more memory structures on the client computer.

13. The method of claim 1 wherein the summarizing the cross-network user behavior data on the client computer comprises two or more of:

the categorizing recency of the user visiting the web site on the client computer in the at least some of the plurality of subject categories,
the categorizing user category involvement from the user visiting the website on the client computer in the at least some of the subject categories by the rolling up the indicators of the visits into the categorical time segments,
the categorizing recency of the selections of the at least one banner advertisement on the client computer, and
the categorizing user category involvement from the user selections of the at least one banner advertisement on the client computer.

14. A computer-implemented method, implemented, at least in part, by hardware in combination with software, the method comprising:

observing, by hardware in combination with software on a client computer, cross-network user behavior of a user of the client computer, wherein the cross-network user behavior relates, at least in part, to the client computer accessing a plurality of web sites that are not all associated with a user behavior data collection network, are not all associated with a content provider, and are not all associated with a portal, and at least one keyword derived from context of a selection action by the user on the client computer;
selecting a subject category from a plurality of subject categories and deriving a summary metric of the user's degree of involvement in the selected subject category for a particular cross-network user behavior of the user of the client computer;
publishing the summary metric to one or more memory structures on the client computer; and
in response to the user accessing an authorized domain via the client computer via a network, providing at least some of the information in the summary metric in the one or more memory structures from the client computer to a server at the authorized domain.

15. The method of claim 14, wherein data in the memory structure are periodically reported via a network to a server.

16. A computer-implemented method, implemented, at least in part, by hardware in combination with software, the method comprising:

summarizing observed cross-network user behavior data using a behavior summarizing module operating on a client computer, wherein the observed cross-network user behavior includes: access to web sites that are not all associated with a content provider, a user behavior collection network, and a portal; and at least one keyword derived from context of the access to the web sites; and wherein the summarizing for the observed cross-network user behavior includes deriving a summary metric of the user's degree of interest in subject categories from a plurality of subject categories; and
publishing the summary metric from the behavior summarizing module to one or more memory structures; and
providing information in the summary metric in the one or more memory structures from the client computer to a server at an authorized domain in response to the user accessing the authorized domain via the client computer via a network.

17. The method of claim 16, further including:

prioritizing the plurality of subject categories;
selecting at least one subject category of the plurality of subject categories; and
publishing the summarized cross-network behavior data for the plurality of subject categories to a single memory structure.

18. The method of claim 16, wherein the one or more memory structures are accessible via the network to the server at the authorized domain in response to the user accessing the authorized domain.

19. The method of claim 16, wherein the cross-network behavioral data include data relating to behavioral data corresponding to a plurality of visits to the web sites or selections of banner advertisements that are accessed using more than one browser on the client computer.

20. The method of claim 16, further including receiving at the client computer advertising targeted using the summarized cross-network user behavior data published to the one or more memory structures that became accessible in response to the user accessing the authorized domain.

Referenced Cited
U.S. Patent Documents
4455083 June 19, 1984 Elmes
4575579 March 11, 1986 Simon et al.
4719567 January 12, 1988 Whittington et al.
4775935 October 4, 1988 Yourick
4782449 November 1, 1988 Brinker et al.
4799146 January 17, 1989 Chauvel
4850007 July 18, 1989 Marino et al.
4977594 December 11, 1990 Shear
5027400 June 25, 1991 Baji et al.
5029104 July 2, 1991 Dodson et al.
5093718 March 3, 1992 Hoarty et al.
5099420 March 24, 1992 Barlow et al.
5105184 April 14, 1992 Pirani et al.
5159669 October 27, 1992 Trigg et al.
5165012 November 17, 1992 Crandall et al.
5196838 March 23, 1993 Meier et al.
5202961 April 13, 1993 Mills et al.
5220420 June 15, 1993 Hoarty et al.
5220516 June 15, 1993 Dodson et al.
5220564 June 15, 1993 Tuch et al.
5231499 July 27, 1993 Trytko
5247517 September 21, 1993 Ross et al.
5253341 October 12, 1993 Rozmanith et al.
5268963 December 7, 1993 Monroe et al.
5283639 February 1, 1994 Esch et al.
5283731 February 1, 1994 Lalonde et al.
5285442 February 8, 1994 Iwamura et al.
5297249 March 22, 1994 Bernstein et al.
5305195 April 19, 1994 Murphy
5307456 April 26, 1994 MacKay
5313455 May 17, 1994 van der Wal et al.
5315580 May 24, 1994 Phaal
5319455 June 7, 1994 Hoarty et al.
5321740 June 14, 1994 Gregorek et al.
5325423 June 28, 1994 Lewis
5325483 June 28, 1994 Ise et al.
5327554 July 5, 1994 Palazzi et al.
5333237 July 26, 1994 Stefanopoulos et al.
5347632 September 13, 1994 Filepp et al.
5355472 October 11, 1994 Lewis
5355501 October 11, 1994 Gross et al.
5361091 November 1, 1994 Hoarty et al.
5361199 November 1, 1994 Shoquist et al.
5361393 November 1, 1994 Rossillo
5367621 November 22, 1994 Cohen et al.
5373375 December 13, 1994 Weldy
5392447 February 21, 1995 Schlack et al.
5412720 May 2, 1995 Hoarty
5418549 May 23, 1995 Anderson et al.
5438518 August 1, 1995 Bianco et al.
5442771 August 15, 1995 Filepp et al.
5446919 August 29, 1995 Wilkins
5469553 November 21, 1995 Patrick
5481741 January 2, 1996 McKaskle et al.
5483466 January 9, 1996 Kawahara et al.
5491785 February 13, 1996 Robson et al.
5499340 March 12, 1996 Barritz
5500890 March 19, 1996 Rogge et al.
5515098 May 7, 1996 Carles
5515270 May 7, 1996 Weinblatt
5515490 May 7, 1996 Buchanan et al.
5517612 May 14, 1996 Dwin et al.
5524195 June 4, 1996 Clanton, III et al.
5524197 June 4, 1996 Uya et al.
5530472 June 25, 1996 Bregman et al.
5530759 June 25, 1996 Braudaway et al.
5530852 June 25, 1996 Meske et al.
5532735 July 2, 1996 Blahut et al.
5541986 July 30, 1996 Hou
5544302 August 6, 1996 Nguyen
5544320 August 6, 1996 Konrad
5548745 August 20, 1996 Egan et al.
5563804 October 8, 1996 Mortensen et al.
5564043 October 8, 1996 Siefert
5572643 November 5, 1996 Judson
5579381 November 26, 1996 Courville et al.
5583560 December 10, 1996 Florin et al.
5583763 December 10, 1996 Atcheson et al.
5584025 December 10, 1996 Keithley et al.
5590046 December 31, 1996 Anderson et al.
5594509 January 14, 1997 Florin et al.
5594779 January 14, 1997 Goodman
5596718 January 21, 1997 Boebert et al.
5602905 February 11, 1997 Mettke
5604542 February 18, 1997 Dedrick
5606359 February 25, 1997 Youden et al.
5608850 March 4, 1997 Robertson
5615131 March 25, 1997 Mortensen et al.
5615325 March 25, 1997 Peden
5617526 April 1, 1997 Oran et al.
5621456 April 15, 1997 Florin et al.
5627886 May 6, 1997 Bowman
5629978 May 13, 1997 Blumhardt et al.
5630081 May 13, 1997 Rybicki et al.
5635979 June 3, 1997 Kostreski et al.
5638443 June 10, 1997 Stefik et al.
5642484 June 24, 1997 Harrison, III et al.
5649186 July 15, 1997 Ferguson
5657450 August 12, 1997 Rao et al.
5664948 September 9, 1997 Dimitriadis et al.
5673322 September 30, 1997 Pepe et al.
5675510 October 7, 1997 Coffey et al.
5680562 October 21, 1997 Conrad et al.
5682525 October 28, 1997 Bouve et al.
5684715 November 4, 1997 Palmer
5684969 November 4, 1997 Ishida
5701451 December 23, 1997 Rogers et al.
5704017 December 30, 1997 Heckerman et al.
5706434 January 6, 1998 Kremen et al.
5706502 January 6, 1998 Foley et al.
5708709 January 13, 1998 Rose
5708780 January 13, 1998 Levergood et al.
5710884 January 20, 1998 Dedrick
5710918 January 20, 1998 Lagarde et al.
5712979 January 27, 1998 Graber et al.
5715453 February 3, 1998 Stewart
5717860 February 10, 1998 Graber et al.
5717923 February 10, 1998 Dedrick
5721827 February 24, 1998 Logan et al.
5721908 February 24, 1998 Lagarde et al.
5724521 March 3, 1998 Dedrick
5724556 March 3, 1998 Souder et al.
5724567 March 3, 1998 Rose et al.
5727129 March 10, 1998 Barrett et al.
5727159 March 10, 1998 Kikinis
5734863 March 31, 1998 Kodosky et al.
5737619 April 7, 1998 Judson
5737739 April 7, 1998 Shirley et al.
5740252 April 14, 1998 Minor et al.
5740549 April 1998 Reilly et al.
5742768 April 21, 1998 Gennaro et al.
5742905 April 21, 1998 Pepe et al.
5745681 April 28, 1998 Levine et al.
5751956 May 12, 1998 Kirsch
5754830 May 19, 1998 Butts et al.
5754938 May 19, 1998 Herz et al.
5754939 May 19, 1998 Herz et al.
5758111 May 26, 1998 Shiratori et al.
5758257 May 26, 1998 Herz et al.
5760767 June 2, 1998 Shore et al.
5761601 June 2, 1998 Nemirofsky et al.
5761662 June 2, 1998 Dasan
5768508 June 16, 1998 Eikeland
5768510 June 16, 1998 Gish
5774670 June 30, 1998 Montulli
5781894 July 14, 1998 Petrecca et al.
5787253 July 28, 1998 McCreery et al.
5787254 July 28, 1998 Maddalozzo, Jr. et al.
5793972 August 11, 1998 Shane
5794210 August 11, 1998 Goldhaber et al.
5794259 August 11, 1998 Kikinis
5796952 August 18, 1998 Davis et al.
5801685 September 1, 1998 Miller et al.
5802320 September 1, 1998 Baehr et al.
5805735 September 8, 1998 Chen et al.
5805815 September 8, 1998 Hill
5809242 September 15, 1998 Shaw et al.
5809481 September 15, 1998 Baron et al.
5809512 September 15, 1998 Kato
5812642 September 22, 1998 Leroy
5812769 September 22, 1998 Graber et al.
5819047 October 6, 1998 Bauer et al.
5819092 October 6, 1998 Ferguson et al.
5822526 October 13, 1998 Waskiewicz
5832502 November 3, 1998 Durham et al.
5835087 November 10, 1998 Herz et al.
5835092 November 10, 1998 Boudreau et al.
5835722 November 10, 1998 Bradshaw et al.
5838458 November 17, 1998 Tsai
5848246 December 8, 1998 Gish
5848396 December 8, 1998 Gerace
5848397 December 8, 1998 Marsh et al.
5850352 December 15, 1998 Moezzi et al.
5850433 December 15, 1998 Rondeua
5854897 December 29, 1998 Radziewicz et al.
5855008 December 29, 1998 Goldhaber et al.
5861880 January 19, 1999 Shimizu et al.
5861883 January 19, 1999 Cuomo et al.
5862260 January 19, 1999 Rhoads
5862325 January 19, 1999 Reed et al.
5864823 January 26, 1999 Levitan
5870769 February 9, 1999 Freund
5872850 February 16, 1999 Klein et al.
5875296 February 23, 1999 Shi et al.
5877766 March 2, 1999 Bates et al.
5878231 March 2, 1999 Baehr et al.
5883955 March 16, 1999 Ronning
5884025 March 16, 1999 Baehr et al.
5886683 March 23, 1999 Tognazzini et al.
5887133 March 23, 1999 Brown et al.
5890152 March 30, 1999 Rapaport et al.
5892917 April 6, 1999 Myerson
5893053 April 6, 1999 Trueblood
5893118 April 6, 1999 Sonderegger
5894554 April 13, 1999 Lowery et al.
5897622 April 27, 1999 Blinn et al.
5898434 April 27, 1999 Small et al.
5901287 May 4, 1999 Bull et al.
5903892 May 11, 1999 Hoffert et al.
5905492 May 18, 1999 Straub et al.
5905800 May 18, 1999 Moskowitz et al.
5907838 May 25, 1999 Miyasaka et al.
5913040 June 15, 1999 Rakavy et al.
5918012 June 29, 1999 Astiz et al.
5918013 June 29, 1999 Mighdoll et al.
5918014 June 29, 1999 Robinson
5918214 June 29, 1999 Perkowski
5920697 July 6, 1999 Masters et al.
5923845 July 13, 1999 Kamiya et al.
5923853 July 13, 1999 Danneels
5929850 July 27, 1999 Broadwin et al.
5930446 July 27, 1999 Kanda
5930700 July 27, 1999 Pepper et al.
5930801 July 27, 1999 Falkenhainer et al.
5931901 August 3, 1999 Wolfe et al.
5931907 August 3, 1999 Davies et al.
5933811 August 3, 1999 Angles et al.
5933832 August 3, 1999 Suzuoka et al.
5936679 August 10, 1999 Kasahara et al.
5937037 August 10, 1999 Kamel et al.
5937390 August 10, 1999 Hyodo
5937392 August 10, 1999 Alberts
5937411 August 10, 1999 Becker
5943478 August 24, 1999 Aggarwal et al.
5944791 August 31, 1999 Scherpbier
5946646 August 31, 1999 Schena et al.
5946664 August 31, 1999 Ebisawa
5946697 August 31, 1999 Shen
5948061 September 7, 1999 Merriman et al.
5951300 September 14, 1999 Brown
5956693 September 21, 1999 Geerlings
5956716 September 21, 1999 Kenner et al.
5958008 September 28, 1999 Pogrebisky et al.
5958015 September 28, 1999 Dascalu
5959621 September 28, 1999 Nawaz et al.
5959623 September 28, 1999 Van Hoff et al.
5960409 September 28, 1999 Wexler
5961593 October 5, 1999 Gabber et al.
5961602 October 5, 1999 Thompson et al.
5961603 October 5, 1999 Kunkel et al.
5963909 October 5, 1999 Warren et al.
5963915 October 5, 1999 Kirsch
5966121 October 12, 1999 Hubbell et al.
5970473 October 19, 1999 Gerszber et al.
5974219 October 26, 1999 Fujita et al.
5974451 October 26, 1999 Simmons
5978807 November 2, 1999 Mano et al.
5978833 November 2, 1999 Pashley et al.
5978836 November 2, 1999 Ouchi
5978841 November 2, 1999 Berger
5978842 November 2, 1999 Noble et al.
5983227 November 9, 1999 Nazem et al.
5983244 November 9, 1999 Nation
5983268 November 9, 1999 Freivald et al.
5987466 November 16, 1999 Greer et al.
5987606 November 16, 1999 Cirasole et al.
5991735 November 23, 1999 Gerace
5991799 November 23, 1999 Yen et al.
5995597 November 30, 1999 Woltz et al.
5995943 November 30, 1999 Bull et al.
5996007 November 30, 1999 Klug et al.
5996011 November 30, 1999 Humes
5999526 December 7, 1999 Garland et al.
5999731 December 7, 1999 Yellin et al.
5999740 December 7, 1999 Rowley
5999912 December 7, 1999 Wodarz et al.
6002401 December 14, 1999 Baker
6006241 December 21, 1999 Purnaveja et al.
6006252 December 21, 1999 Wolfe
6006265 December 21, 1999 Rangan et al.
6009236 December 28, 1999 Mishima et al.
6009409 December 28, 1999 Adler et al.
6009410 December 28, 1999 LeMole et al.
6011537 January 4, 2000 Slotznick
6012083 January 4, 2000 Savitzky et al.
6014502 January 11, 2000 Moraes
6014638 January 11, 2000 Burge et al.
6014698 January 11, 2000 Griffiths
6014711 January 11, 2000 Brown
6016509 January 18, 2000 Dedrick
6020884 February 1, 2000 MacNaughton et al.
6023726 February 8, 2000 Saksena
6025837 February 15, 2000 Matthew, III et al.
6025886 February 15, 2000 Koda
6026368 February 15, 2000 Brown et al.
6026413 February 15, 2000 Challenger et al.
6026433 February 15, 2000 D'arlach et al.
6026933 February 22, 2000 King et al.
6029045 February 22, 2000 Picco et al.
6029141 February 22, 2000 Bezos et al.
6029145 February 22, 2000 Barritz
6029182 February 22, 2000 Nehab et al.
6029195 February 22, 2000 Herz
6035332 March 7, 2000 Ingrassia, Jr. et al.
6047318 April 4, 2000 Becker et al.
6047327 April 4, 2000 Tso et al.
6049821 April 11, 2000 Theriault et al.
6052709 April 18, 2000 Paul
6052717 April 18, 2000 Reynolds et al.
6052730 April 18, 2000 Felciano et al.
6055572 April 25, 2000 Saksena
6058141 May 2, 2000 Barger et al.
6061054 May 9, 2000 Jolly
6061659 May 9, 2000 Murray
6061716 May 9, 2000 Moncreiff
6065024 May 16, 2000 Renshaw
6065056 May 16, 2000 Bradshaw et al.
6067559 May 23, 2000 Allard et al.
6067561 May 23, 2000 Dillon
6067565 May 23, 2000 Horvitz
6070140 May 30, 2000 Tran
6073105 June 6, 2000 Sutcliffe et al.
6073167 June 6, 2000 Poulton et al.
6073241 June 6, 2000 Rosenberg et al.
6076166 June 13, 2000 Moshfeghi et al.
6078916 June 20, 2000 Culliss
6081840 June 27, 2000 Zhao
6084581 July 4, 2000 Hunt
6085193 July 4, 2000 Malkin et al.
6085226 July 4, 2000 Horvitz
6085242 July 4, 2000 Chandra
6088731 July 11, 2000 Kiraly et al.
6091411 July 18, 2000 Straub et al.
6092196 July 18, 2000 Reiche
6094655 July 25, 2000 Rogers et al.
6094677 July 25, 2000 Capek et al.
6098064 August 1, 2000 Pirolli et al.
6098065 August 1, 2000 Skillen et al.
6101510 August 8, 2000 Stone et al.
6108637 August 22, 2000 Blumenau
6108691 August 22, 2000 Lee et al.
6108799 August 22, 2000 Boulay et al.
6112215 August 29, 2000 Kaply
6112246 August 29, 2000 Horbal et al.
6115680 September 5, 2000 Coffee et al.
6119098 September 12, 2000 Guyot et al.
6119165 September 12, 2000 Li et al.
6122632 September 19, 2000 Botts et al.
6125388 September 26, 2000 Reisman
6128655 October 3, 2000 Fields et al.
6128663 October 3, 2000 Thomas
6133912 October 17, 2000 Montero
6133918 October 17, 2000 Conrad et al.
6134380 October 17, 2000 Kushizaki
6134532 October 17, 2000 Lazarus et al.
6138146 October 24, 2000 Moon et al.
6138155 October 24, 2000 Davis et al.
6141010 October 31, 2000 Hoyle
6144944 November 7, 2000 Kurtzman, II et al.
6151596 November 21, 2000 Hosomi
6154738 November 28, 2000 Call
6154771 November 28, 2000 Rangan et al.
6157924 December 5, 2000 Austin
6157946 December 5, 2000 Itakura et al.
6161112 December 12, 2000 Cragun et al.
6163778 December 19, 2000 Fogg et al.
6167438 December 26, 2000 Yates et al.
6167453 December 26, 2000 Becker et al.
6178443 January 23, 2001 Lin
6178461 January 23, 2001 Chan et al.
6182066 January 30, 2001 Marques
6182097 January 30, 2001 Hansen et al.
6182122 January 30, 2001 Berstis
6182133 January 30, 2001 Horvitz
6183366 February 6, 2001 Goldberg et al.
6185558 February 6, 2001 Bowman
6185586 February 6, 2001 Judson
6185614 February 6, 2001 Cuomo et al.
6191782 February 20, 2001 Mori et al.
6192380 February 20, 2001 Light et al.
6195622 February 27, 2001 Altschuler et al.
6198906 March 6, 2001 Boetje et al.
6199079 March 6, 2001 Gupta et al.
6202093 March 13, 2001 Bolam et al.
6204840 March 20, 2001 Petelycky et al.
6208339 March 27, 2001 Atlas et al.
6216141 April 10, 2001 Straub et al.
6216212 April 10, 2001 Challenger et al.
6219676 April 17, 2001 Reiner
6222520 April 24, 2001 Gerszberg et al.
6223215 April 24, 2001 Hunt et al.
6233564 May 15, 2001 Schulze, Jr.
6237022 May 22, 2001 Bruck et al.
6249284 June 19, 2001 Bogdan
6253188 June 26, 2001 Witek et al.
6253208 June 26, 2001 Wittgreffe et al.
6266058 July 24, 2001 Meyer
6269361 July 31, 2001 Davis et al.
6275854 August 14, 2001 Himmel et al.
6279112 August 21, 2001 O'Toole et al.
6280043 August 28, 2001 Ohkawa
6285987 September 4, 2001 Roth et al.
6286043 September 4, 2001 Cuomo et al.
6286045 September 4, 2001 Griffiths et al.
6295061 September 25, 2001 Park et al.
6297819 October 2, 2001 Furst
6304844 October 16, 2001 Pan et al.
6308202 October 23, 2001 Cohn et al.
6311194 October 30, 2001 Sheth et al.
6314451 November 6, 2001 Landsman et al.
6314457 November 6, 2001 Schema et al.
6317761 November 13, 2001 Landsman et al.
6321209 November 20, 2001 Pasquali
6321256 November 20, 2001 Himmel et al.
6324553 November 27, 2001 Cragun et al.
6324569 November 27, 2001 Ogilvie et al.
6324583 November 27, 2001 Stevens
6327574 December 4, 2001 Kramer et al.
6327617 December 4, 2001 Fawcett
6332127 December 18, 2001 Bandera
6334111 December 25, 2001 Carrott
6335963 January 1, 2002 Bosco
6336131 January 1, 2002 Wolfe
6338059 January 8, 2002 Fields et al.
6338066 January 8, 2002 Martin et al.
6341305 January 22, 2002 Wolfe
6347398 February 12, 2002 Parthasarathy et al.
6351279 February 26, 2002 Sawyer
6351745 February 26, 2002 Itakura et al.
6353834 March 5, 2002 Wong et al.
6356898 March 12, 2002 Cohen et al.
6356908 March 12, 2002 Brown et al.
6360221 March 19, 2002 Gough et al.
6366298 April 2, 2002 Haitsuka
6370527 April 9, 2002 Singhal
6377983 April 23, 2002 Cohen et al.
6378075 April 23, 2002 Goldstein et al.
6381735 April 30, 2002 Hunt
6381742 April 30, 2002 Forbes et al.
6385592 May 7, 2002 Angles et al.
6392668 May 21, 2002 Murray
6393407 May 21, 2002 Middleton et al.
6393415 May 21, 2002 Getchius et al.
6397228 May 28, 2002 Lamburt et al.
6401075 June 4, 2002 Mason et al.
6415322 July 2, 2002 Jaye
6418440 July 9, 2002 Kuo et al.
6418471 July 9, 2002 Shelton et al.
6421675 July 16, 2002 Ryan et al.
6421724 July 16, 2002 Nickerson et al.
6421729 July 16, 2002 Paltenghe et al.
6434745 August 13, 2002 Conley, Jr. et al.
6437802 August 20, 2002 Kenny
6438215 August 20, 2002 Skladman et al.
6438578 August 20, 2002 Schmid et al.
6438579 August 20, 2002 Hosken
6441832 August 27, 2002 Tao et al.
6442529 August 27, 2002 Krishan et al.
6446128 September 3, 2002 Woods et al.
6449657 September 10, 2002 Stanbach, Jr. et al.
6452612 September 17, 2002 Holtz et al.
6457009 September 24, 2002 Bollay
6459440 October 1, 2002 Monnes et al.
6460036 October 1, 2002 Herz
6460042 October 1, 2002 Hitchcock et al.
6460060 October 1, 2002 Maddalozzo et al.
6466918 October 15, 2002 Spiegel et al.
6466970 October 15, 2002 Lee et al.
6477550 November 5, 2002 Balasubramaniam et al.
6477575 November 5, 2002 Koeppel et al.
6480837 November 12, 2002 Dutta
6484148 November 19, 2002 Boyd
6487538 November 26, 2002 Gupta et al.
6489968 December 3, 2002 Ortega et al.
6490722 December 3, 2002 Barton et al.
6493702 December 10, 2002 Adar et al.
6496931 December 17, 2002 Rajchel et al.
6499052 December 24, 2002 Hoang et al.
6502076 December 31, 2002 Smith
6505201 January 7, 2003 Haitsuka
6513052 January 28, 2003 Binder
6513060 January 28, 2003 Nixon et al.
6516312 February 4, 2003 Kraft et al.
6523021 February 18, 2003 Monberg et al.
6526411 February 25, 2003 Ward
6529903 March 4, 2003 Smith et al.
6539375 March 25, 2003 Kawasaki
6539424 March 25, 2003 Dutta
6564202 May 13, 2003 Schuetze et al.
6567850 May 20, 2003 Rajan et al.
6567854 May 20, 2003 Olshansky et al.
6570595 May 27, 2003 Porter
6572662 June 3, 2003 Manohar et al.
6581072 June 17, 2003 Mathur et al.
6584479 June 24, 2003 Chang et al.
6584492 June 24, 2003 Cezar et al.
6584505 June 24, 2003 Howard et al.
6594654 July 15, 2003 Salam et al.
6601041 July 29, 2003 Brown et al.
6601057 July 29, 2003 Underwood et al.
6601100 July 29, 2003 Lee et al.
6604103 August 5, 2003 Wolfe
6606652 August 12, 2003 Cohn et al.
6615247 September 2, 2003 Murphy
6622168 September 16, 2003 Datta
6631360 October 7, 2003 Cook
6642944 November 4, 2003 Conrad et al.
6643696 November 4, 2003 Davis et al.
6665656 December 16, 2003 Carter
6665838 December 16, 2003 Brown et al.
6678731 January 13, 2004 Howard et al.
6678866 January 13, 2004 Sugimoto et al.
6681223 January 20, 2004 Sundaresan
6681247 January 20, 2004 Payton
6686931 February 3, 2004 Bodnar
6687737 February 3, 2004 Landsman et al.
6691106 February 10, 2004 Sathyanarayan
6697825 February 24, 2004 Underwood et al.
6701362 March 2, 2004 Subramonian et al.
6701363 March 2, 2004 Chiu et al.
6714975 March 30, 2004 Aggarwal et al.
6718365 April 6, 2004 Dutta
6721741 April 13, 2004 Eyal et al.
6721795 April 13, 2004 Eldreth
6725269 April 20, 2004 Megiddo
6725303 April 20, 2004 Hoguta et al.
6741967 May 25, 2004 Wu et al.
6757661 June 29, 2004 Blaser et al.
6760746 July 6, 2004 Schneider
6760916 July 6, 2004 Holtz et al.
6763379 July 13, 2004 Shuster
6763386 July 13, 2004 Davis et al.
6771290 August 3, 2004 Hoyle
6772200 August 3, 2004 Bakshi et al.
6785659 August 31, 2004 Landsman et al.
6785723 August 31, 2004 Genty et al.
6801906 October 5, 2004 Bates et al.
6801909 October 5, 2004 Delgado et al.
6826534 November 30, 2004 Gupta et al.
6826546 November 30, 2004 Shuster
6827669 December 7, 2004 Cohen et al.
6847969 January 25, 2005 Mathai et al.
6848004 January 25, 2005 Chang et al.
6850967 February 1, 2005 Spencer et al.
6853982 February 8, 2005 Smith et al.
6857024 February 15, 2005 Chen et al.
6871196 March 22, 2005 Chan et al.
6874018 March 29, 2005 Wu
6877027 April 5, 2005 Spencer et al.
6880123 April 12, 2005 Landsman
6882981 April 19, 2005 Philippe et al.
6892181 May 10, 2005 Megiddo et al.
6892223 May 10, 2005 Kawabata et al.
6892226 May 10, 2005 Tso et al.
6892354 May 10, 2005 Servan-Schreiber et al.
6904408 June 7, 2005 McCarthy et al.
6910179 June 21, 2005 Pennell et al.
6934736 August 23, 2005 Sears et al.
6938027 August 30, 2005 Barritz
6957390 October 18, 2005 Tamir et al.
6958759 October 25, 2005 Safadi et al.
6968507 November 22, 2005 Pennell et al.
6973478 December 6, 2005 Ketonen et al.
6976053 December 13, 2005 Tripp et al.
6976090 December 13, 2005 Ben-Shaul et al.
6990633 January 24, 2006 Miyasaka
6993532 January 31, 2006 Platt et al.
7003734 February 21, 2006 Gardner et al.
7016887 March 21, 2006 Stockfisch
7039599 May 2, 2006 Merriman et al.
7043526 May 9, 2006 Wolfe
7051084 May 23, 2006 Hayton et al.
7054900 May 30, 2006 Goldston
7065550 June 20, 2006 Raghunandan
7069515 June 27, 2006 Eagle et al.
7076546 July 11, 2006 Bates et al.
7085682 August 1, 2006 Heller et al.
7100111 August 29, 2006 McElfresh et al.
7133924 November 7, 2006 Rosenberg et al.
7136875 November 14, 2006 Anderson et al.
7149791 December 12, 2006 Sears et al.
7155729 December 26, 2006 Andrew et al.
7162739 January 9, 2007 Cowden et al.
7181415 February 20, 2007 Blaser et al.
7181488 February 20, 2007 Martin et al.
7194425 March 20, 2007 Nyhan
7254547 August 7, 2007 Beck et al.
7283992 October 16, 2007 Liu et al.
7346606 March 18, 2008 Bharat
7349827 March 25, 2008 Heller et al.
7363291 April 22, 2008 Page
7421432 September 2, 2008 Hoelzle et al.
7424708 September 9, 2008 Andersson et al.
7451065 November 11, 2008 Pednault et al.
7454364 November 18, 2008 Shkedi
7464155 December 9, 2008 Mousavi et al.
7512603 March 31, 2009 Veteska et al.
7630986 December 8, 2009 Herz
7743340 June 22, 2010 Horvitz et al.
7844488 November 30, 2010 Merriman et al.
20010011226 August 2, 2001 Greer et al.
20010029527 October 11, 2001 Goshen
20010030970 October 18, 2001 Wiryaman et al.
20010032115 October 18, 2001 Goldstein
20010037240 November 1, 2001 Marks et al.
20010037325 November 1, 2001 Biderman et al.
20010037488 November 1, 2001 Lee
20010044795 November 22, 2001 Cohen et al.
20010047354 November 29, 2001 Davis et al.
20010049320 December 6, 2001 Cohen et al.
20010049321 December 6, 2001 Cohen et al.
20010049620 December 6, 2001 Blasko
20010049716 December 6, 2001 Wolfe
20010051559 December 13, 2001 Cohen et al.
20010053735 December 20, 2001 Cohen et al.
20010054020 December 20, 2001 Barth et al.
20020002483 January 3, 2002 Siegel et al.
20020002538 January 3, 2002 Ling
20020004754 January 10, 2002 Gardenswartz
20020007307 January 17, 2002 Miller
20020007309 January 17, 2002 Reynar
20020007317 January 17, 2002 Callaghan et al.
20020008703 January 24, 2002 Merrill et al.
20020010626 January 24, 2002 Agmoni
20020010757 January 24, 2002 Granik et al.
20020010776 January 24, 2002 Lerner
20020016736 February 7, 2002 Cannon et al.
20020019763 February 14, 2002 Linden et al.
20020019834 February 14, 2002 Vilcauskas, Jr. et al.
20020023159 February 21, 2002 Vange et al.
20020026390 February 28, 2002 Ulenas et al.
20020032592 March 14, 2002 Krasnick et al.
20020035568 March 21, 2002 Benthin
20020038363 March 28, 2002 MacLean
20020040374 April 4, 2002 Kent
20020042750 April 11, 2002 Morrison
20020046099 April 18, 2002 Frengut et al.
20020049633 April 25, 2002 Pasquali
20020052785 May 2, 2002 Tenenbaum
20020052925 May 2, 2002 Kim et al.
20020053078 May 2, 2002 Holtz et al.
20020054089 May 9, 2002 Nicholas
20020055912 May 9, 2002 Buck
20020057285 May 16, 2002 Nicholas, III
20020059094 May 16, 2002 Hosea et al.
20020059099 May 16, 2002 Coletta
20020065802 May 30, 2002 Uchiyama
20020068500 June 6, 2002 Gabai et al.
20020069105 June 6, 2002 do Rosario Botelho et al.
20020073079 June 13, 2002 Terheggen
20020077219 June 20, 2002 Cohen et al.
20020078076 June 20, 2002 Evans
20020078192 June 20, 2002 Kopsell et al.
20020087499 July 4, 2002 Stockfisch
20020087621 July 4, 2002 Hendriks
20020091700 July 11, 2002 Steele et al.
20020091875 July 11, 2002 Fujiwara et al.
20020094868 July 18, 2002 Tuck et al.
20020099605 July 25, 2002 Weitzman et al.
20020099767 July 25, 2002 Cohen et al.
20020099812 July 25, 2002 Davis et al.
20020099824 July 25, 2002 Bender et al.
20020103811 August 1, 2002 Fankhauser et al.
20020107847 August 8, 2002 Johnson
20020107858 August 8, 2002 Lundahl et al.
20020111910 August 15, 2002 Walsh
20020111994 August 15, 2002 Raghunandan
20020112035 August 15, 2002 Carey et al.
20020112048 August 15, 2002 Gruyer et al.
20020116494 August 22, 2002 Kocol
20020120648 August 29, 2002 Ball et al.
20020122065 September 5, 2002 Segal et al.
20020123912 September 5, 2002 Subramanian et al.
20020128904 September 12, 2002 Carruthers et al.
20020128908 September 12, 2002 Levin et al.
20020128925 September 12, 2002 Angeles
20020152121 October 17, 2002 Hiroshi
20020152126 October 17, 2002 Lieu et al.
20020152222 October 17, 2002 Holbrook
20020154163 October 24, 2002 Melchner
20020156781 October 24, 2002 Cordray et al.
20020156812 October 24, 2002 Krasnoiarov et al.
20020156881 October 24, 2002 Klopp et al.
20020161673 October 31, 2002 Lee et al.
20020169670 November 14, 2002 Barsade et al.
20020169762 November 14, 2002 Cardona
20020170068 November 14, 2002 Rafey et al.
20020171682 November 21, 2002 Frank et al.
20020175947 November 28, 2002 Conrad et al.
20020194151 December 19, 2002 Fenton et al.
20020198778 December 26, 2002 Landsman et al.
20020198939 December 26, 2002 Lee et al.
20030004804 January 2, 2003 Landsman et al.
20030005000 January 2, 2003 Landsman et al.
20030005067 January 2, 2003 Martin et al.
20030005134 January 2, 2003 Martin et al.
20030009497 January 9, 2003 Yu
20030011639 January 16, 2003 Webb
20030014304 January 16, 2003 Calvert et al.
20030014399 January 16, 2003 Hansen et al.
20030018778 January 23, 2003 Martin et al.
20030018885 January 23, 2003 Landsman et al.
20030023481 January 30, 2003 Calvert et al.
20030023488 January 30, 2003 Landsman et al.
20030023698 January 30, 2003 Dieberger et al.
20030023712 January 30, 2003 Zhao et al.
20030028529 February 6, 2003 Cheung et al.
20030028565 February 6, 2003 Landsman et al.
20030028870 February 6, 2003 Weisman et al.
20030032409 February 13, 2003 Hutcheson et al.
20030033155 February 13, 2003 Peerson et al.
20030040958 February 27, 2003 Fernandes
20030041050 February 27, 2003 Smith et al.
20030046150 March 6, 2003 Ader et al.
20030050863 March 13, 2003 Radwin
20030052913 March 20, 2003 Barile
20030074448 April 17, 2003 Kinebuchi
20030088554 May 8, 2003 Ryan et al.
20030105589 June 5, 2003 Liu et al.
20030110080 June 12, 2003 Tsutani et al.
20030115157 June 19, 2003 Circenis
20030120593 June 26, 2003 Bansal et al.
20030120654 June 26, 2003 Edlund et al.
20030130910 July 10, 2003 Pickover et al.
20030131100 July 10, 2003 Godon et al.
20030135490 July 17, 2003 Barrett et al.
20030135853 July 17, 2003 Goldman et al.
20030154168 August 14, 2003 Lautenbacher
20030171990 September 11, 2003 Rao et al.
20030172075 September 11, 2003 Reisman
20030176931 September 18, 2003 Pednault et al.
20030182184 September 25, 2003 Strasnick et al.
20030195837 October 16, 2003 Kostic et al.
20030195877 October 16, 2003 Ford et al.
20030206720 November 6, 2003 Abecassis
20030208472 November 6, 2003 Pham
20030220091 November 27, 2003 Farrand et al.
20030221167 November 27, 2003 Goldstein et al.
20030229542 December 11, 2003 Morrisroe
20040002896 January 1, 2004 Alanen et al.
20040024756 February 5, 2004 Rickard
20040030798 February 12, 2004 Andersson et al.
20040044677 March 4, 2004 Huper-Graff et al.
20040068486 April 8, 2004 Chidlovskii
20040073485 April 15, 2004 Liu et al.
20040078294 April 22, 2004 Rollins et al.
20040095376 May 20, 2004 Graham et al.
20040098229 May 20, 2004 Error et al.
20040098449 May 20, 2004 Bar-Lavi et al.
20040117353 June 17, 2004 Ishag
20040122943 June 24, 2004 Error et al.
20040133845 July 8, 2004 Forstall et al.
20040162738 August 19, 2004 Sanders et al.
20040162759 August 19, 2004 Willis
20040163101 August 19, 2004 Swix et al.
20040167926 August 26, 2004 Waxman et al.
20040167928 August 26, 2004 Anderson et al.
20040181525 September 16, 2004 Itzhak et al.
20040181604 September 16, 2004 Immonen
20040193488 September 30, 2004 Khoo et al.
20040210533 October 21, 2004 Picker et al.
20040225716 November 11, 2004 Shamir et al.
20040247748 December 9, 2004 Bronkema
20040249709 December 9, 2004 Donovan et al.
20040249938 December 9, 2004 Bunch
20040254810 December 16, 2004 Yamaga et al.
20040254844 December 16, 2004 Torres
20040267723 December 30, 2004 Bharat
20050015366 January 20, 2005 Carrasco et al.
20050021397 January 27, 2005 Cui et al.
20050027821 February 3, 2005 Alexander et al.
20050027822 February 3, 2005 Plaza
20050033657 February 10, 2005 Herrington et al.
20050038819 February 17, 2005 Hicken et al.
20050080772 April 14, 2005 Bem
20050086109 April 21, 2005 McFadden et al.
20050091106 April 28, 2005 Reller et al.
20050091111 April 28, 2005 Green et al.
20050097088 May 5, 2005 Bennett et al.
20050102202 May 12, 2005 Linden et al.
20050102282 May 12, 2005 Linden
20050125382 June 9, 2005 Karnawat et al.
20050131762 June 16, 2005 Bharat et al.
20050132267 June 16, 2005 Aviv
20050149404 July 7, 2005 Barnett et al.
20050155031 July 14, 2005 Wang et al.
20050182773 August 18, 2005 Feinsmith
20050187823 August 25, 2005 Howes
20050188423 August 25, 2005 Motsinger et al.
20050203796 September 15, 2005 Anand et al.
20050204148 September 15, 2005 Mayo
20050216572 September 29, 2005 Tso et al.
20050222901 October 6, 2005 Agarwal et al.
20050222982 October 6, 2005 Paczkowski et al.
20050240599 October 27, 2005 Sears
20050273463 December 8, 2005 Zohar et al.
20050289120 December 29, 2005 Soulanille et al.
20060015390 January 19, 2006 Rijsinghani et al.
20060026233 February 2, 2006 Tenembaum et al.
20060031253 February 9, 2006 Newbold et al.
20060053230 March 9, 2006 Montero
20060136524 June 22, 2006 Wohlers et al.
20060136528 June 22, 2006 Martin et al.
20060136728 June 22, 2006 Gentry et al.
20060235965 October 19, 2006 Bennett et al.
20060253432 November 9, 2006 Eagle et al.
20070016469 January 18, 2007 Bae et al.
20070038956 February 15, 2007 Morris
Foreign Patent Documents
0631231 December 1994 EP
0822535 February 1998 EP
1045547 October 2000 EP
1154611 November 2001 EP
1207468 May 2002 EP
343825 February 1991 JP
11066099 March 1999 JP
2001084256 March 2001 JP
2001147894 May 2001 JP
20010222535 August 2001 JP
2001312482 November 2001 JP
2002024221 January 2002 JP
2002032401 January 2002 JP
2002073545 March 2002 JP
2002259371 September 2002 JP
2002334104 November 2002 JP
2003058572 February 2003 JP
2003141155 May 2003 JP
2003178092 June 2003 JP
20030271647 September 2003 JP
2004-355376 December 2004 JP
WO 9847090 October 1998 WO
WO 99/38321 July 1999 WO
WO 99/44159 September 1999 WO
WO 99/46701 September 1999 WO
WO 99/55066 October 1999 WO
WO 99/59097 November 1999 WO
WO 00/04434 January 2000 WO
WO 00/54201 September 2000 WO
WO 01/03028 January 2001 WO
WO 01/15052 March 2001 WO
WO 01/39024 May 2001 WO
WO 01/44992 June 2001 WO
WO 01/63472 August 2001 WO
WO 01/69929 September 2001 WO
WO 01/90917 November 2001 WO
WO 02/44869 June 2002 WO
WO 03/010685 February 2003 WO
WO 03/010685 February 2003 WO
Other references
  • Claypool, Mark, et al., “Inferring User Interest”, IEEE Internet Computing, vol. 5, Issue 6, Nov./Dec. 2001, pp. 32-39.
  • Montgomery, Alan, et al., “Learning About Customers Without Asking”, Carnegie Mellon University, Tepper School of Business, Pittsburgh, PA, 35 pages.
  • “Shopping cart software”, Wikipedia, Downloaded from: en.wikipedia.org/wiki/Shopping—cart—software on Mar. 14, 2012, pp. 1-3.
  • “HTTP cookie”, Wikipedia, Downloaded from: en.wikipedia.org/wiki/Browser—cookie on Mar. 14, 2012, pp. 1-18.
  • Bucklin, Randolph E., et al., “Choice and the Internet: From Clickstream to Research Stream”, Marketing Letters, vol. 13, No. 3, Kluwer Academic Publishers, The Netherlands, © 2002, pp. 245-258.
  • Fenstermacher, Kurt D., et al., “Mining Client-Side Activity for Personalization”, WECWIS 2002, Newport Beach, CA, Jun. 26-28, 2002, pp. 205-212.
  • Cooley, R., et al., “Web Mining: Information and Pattern Discovery on the World Wide Web”, TAI 1997, Newport Beach, CA, Nov. 3-8, 1997, pp. 558-567.
  • Fenstermacher, Kurt D., et al., “Client-Side Monitoring for Web Mining”, Journal of the American Society for Information Science and Technology, vol. 54, No. 7, Wiley Periodicals, Inc., © 2003, pp. 625-637.
  • Nelte, Michael, et al., “Cookies: Weaving the Web into a State”, Crossroads —The ACM Student Magazine, vol. 7, Issue 1, Sep. 2000, pp. 1-8.
  • Montgomery, Alan, et al., “Learning About Customers Without Asking”, Carnegie Mellon University, Tepper School of Business, Pittsburgh, PA, 2002, pp. 1-33.
  • ACM Portal USPTO Search, “Communications of the ACM: vol. 52, Issue 1”, Association for Computing Machinery, dated Jan. 2009, 1 page.
  • Aggarwal, Charu C. et al. 2001. Intelligent Crawling on the World Wide Web with Arbitrary Predicates. ACM, pp. 96-105.
  • BackWeb Technologies Ltd., BackWEB User's Guide, 1997, [48 pgs.].
  • Bae, Sung Min, et al., “Fizzy Web Ad Selector”, IEEE Intelligent Systems, vol. 18 Issue 6, Nov./Dec. 2003, pp. 62-69.
  • Broder, Alan J., “Data Mining the Internet and Privacy”, WEBKDD '99, LNAI 1836, Springer-Verlag, Berlin, Germany, (c) 2000, pp. 56-73.
  • Bucklin, Randolph E., et al., “Choice and the Internet: From Clickstream to Research Stream”, Marketing Letters, vol. 13, No. 3, Aug. 2002, pp. 245-258.
  • Claypool, et al., Inferring User Interest, Worcester Polytechnic Inst., IEEE Internet Computing, Nov.-Dec. 2001, pp. 32-39 [8 pgs.].
  • comScore Marketing Solutions and Media Metrix (9 sheets), webpage) [online), 2003 comScore Networks, Inc. (retrieved on Oct. 8, 2003), retrieved from the intemet: <URL:http://vAvw.comscore.com>.
  • Copernic, Copernic: Software to Search, Find, and Manage Information, Copernic Technologies, Inc. 2004, 2 pgs. (retrieved on Apr. 6, 2004). Retrieved from the Internet:<URL:http://www.copernic.com/en/index.html>.
  • Diligenti , Michelangelo et al. 2004. A Unified Probalistic Framework for Web Page Scoring Systems. IEEE, vol. 16, No. 1. Jan. 2004, pp. 4-16.
  • Dogpile, Dogpile Web Search Home Page, InfoSpace, Inc. 2004, p. 1 of 1, (retrieved on Apr. 1, 2004). Retrieved from the Internet: <URL:http://www.dogpile.com.html>.
  • Eick, Stephen G., “Visual Analysis of Website Browsing Patterns”, Visual Interfaces to Digital Libraries, Springer-Verlag, Berlin, Germany, (c) 2002, pp. 65-77.
  • Eirinaki, Magdalini, et al., “Web Mining for Web Personalization”, ACM Transactions on Internet Technology (TOIT), vol. 3, Issue 1, Feb. 2003, pp. 1-27.
  • EPO Communication in European Appln. No. 02778589, completed Dec. 21, 2005 (2 Sheets).
  • EPO Communication in European Appln. No. 04795209, mailed Feb. 17, 2011 [160 pages].
  • EPO Communication in European Appln. No. 04795209, mailed. Feb. 17, 2011 [4 pages].
  • EPO File History of EP 2004795209, downloaded from European Patent Office on Apr. 13, 2011 [164 pgs.].
  • EPO, Communication for EP 03 75 5344, Feb. 14, 2011 [4 pgs.].
  • EPO, File history of EP—03755344.3, Method and Apparatus for Displaying Messages in Computer Systems, as of Feb. 21, 2011 [121 pgs.].
  • EPO, File history of EP 2004794999, downloaded from EPO Apr. 13, 2011 [204 pgs.].
  • EPO, File history of EP1714221 (downloaded from EPO on Apr. 13, 2011) [139 pages].
  • EPO, Supplementary European Search Report for EP 03 75 5344, Jun. 30, 2009, mailed Jul. 10, 2009 [2 pgs.].
  • Favela, Jesus et al. 1997. Image-Retrieval Agent: Integrating Image Content and Text. IEE, vol. 1.14, pp. 36-39.
  • Fenstermacher, Kurt D., et al., “Client-Side Monitoring for Web Mining”, Journal of the American Society for Information Science and Technology, vol. 54, Issue 7, May 2003, pp. 625-637.
  • Fenstermacher, Kurt D., et al., “Mining Client-Side Activity for Personalization”, WECWIS 2002, (c) 2002, pp. 205-212.
  • Friedman, Batya, et al., “Informed Consent in the Mozilla Browser: Implementing Value-Sensitive Design”, HICSS-35 '02, Jan. 2002, pp. 10-19.
  • Fu, Xiaobin, et al., “Mining Navigation History for Recommendation”, IUI 2000, New Orleans, LA, (c) 2000, pp. 106-112.
  • Google Search, “Result Search Query Build Search Engine Index Using Gather Consumer Navigate Search”, http://scholar.google.com/scholar, dated Apr. 7, 2010, 3 pages.
  • Gralla, Preston, How the Internet Works, Special Edition, Ziff-Davis Press, Emeryville, CA, .(c) 1997, pp. 254 and 266-271.
  • Greening, Dan R., “Tracking Users: What Marketers Really Want to Know”, Web Techniques, Jul. 1999, downloaded from: www.webtechniques.com/archives/1999/07/, pp. 1-9.
  • Hancock, Wayland, “A new way to get information from the Internet,” American Agent & Broker, Nov. 1997, 69, 11, pp. 65-66 [2 pgs.].
  • Heinle et al. 1997. Designing with JavaScript: Creating Dynamic Web Pages.Sep. 1997, pp. 1-33, 46, 83 [Cited by EPO as “A” reference for EP 02 77 8589 in Supplementary European Search Report dated Dec. 21, 2005].
  • Hun, Ke et al. 2003. A Probabilistic Model for Intelligent Web Crawlers. IEEE, pp. 278-282.
  • Japanese Patent Office, Official Action issued Dec. 17, 2010, mailed Dec. 27, 2010 in Japanese Patent Application No. 2007-528053 (non-official translation), 7 pgs.
  • Keys. 1998. Every Possible Internet Advertisement Drastic Increase of Click Through Rate by Interactivity and Multimedia. Nikkei Internet Technology. Dec. 22, 1998. Jan. Issue (1999), vol. 18, pp. 118-127.
  • Kiyomitsu, Hidenari, et al., “Web Reconfiguration by Spatio-Temporal Page Personalization Rules Based on Access Histories”, Applications and the Internet, San Diego, CA, Jan. 8-12, 2001, pp. 75-82.
  • Klemm, Reinhard P., “WebCompanion: A Friendly Client-Side Web Prefetching Agent”, IEEE Transactions on Knowledge and Data Engineering, vol. 11, No. 4, Jul./Aug. 1999, pp. 577-594.
  • Kurohashi, Sadao, et al. 1997. The Method for Detecting Important Descriptions of a Word Based on its Density Distribution in Text. Information Processing Society of Japan. vol. 38, Issue No. 4 (Apr. 15, 1997), pp. 845-854.
  • Lee, Ching-Cheng, et al., “Category-Based Web Personalization System”, COMPSAC 2001, Oct. 8-12, 2001, pp. 621-625.
  • Leuski, Anton et al. 2000. Lighthouse: Showing the Way to Relevant Information. IEEE, Oct. 2000, pp. 125-129.
  • Liu, Jian-Guo, et al., “Web Mining for Electronic Business Application”, PDCAT 2003, Aug. 27-29, 2003, pp. 872-876.
  • Liu, Jian-Guo, et al., “Web Usage Mining for Electronic Business Applications”, Machine Learning and Cybernetics, Shanghai, China, Aug. 2004, pp. 1314-1318.
  • Lu, Hongjun, et al., “Extending a Web Browser with Client-Side Mining”, APWeb 2003, LNCS 2642, Springer-Verlag, Berlin, Germany, (c) 2003, pp. 166-177.
  • Luxenburger, Julia, et al., “Query-Log Based Authority Analysis for Web Information Search”, Wise 2004, LNCS 3306, Springer-Verlag, Berlin, Germany, Nov. 1, 2004, pp. 90-101.
  • Metacrawler, Web Search Home Page—MetaCrawler, InfoSpace, Inc. 2004, p. 1 of 1 (retrieved on Apr. 6, 2004), Retrieved from the internet: <URL:http://www.metacrawler.com.html>.
  • Mitchell, T., “Decision Tree Learning Based on Machine Learning” (Apr. 5, 2003), lecture slides for textbook Machine Learning, McGraw Hill, 1997, pp. 46-74 [29 pgs.].
  • Mobasher, Bamshad, et al., “Automatic Personalization Based on Web Usage Mining”, Communications of the ACM, vol. 43, Issue 8, Aug. 2000, pp. 142-151.
  • Montgomery, Alan et al., Learning About Customers Without Asking, Carnegie Mellon University, Tepper School of Business, Jan. 2002 [35 pgs.].
  • NETSCAPE Communications Corp., Netcaster Developer's Guide, Netscape Communicator, Sep. 25, 1997 [112 pgs.].
  • NielsenllNetRatings (24 sheets), webpage [online), 2003 NetRatings, Inc. (retrieved on Oct. 8, 2003), retrieved from the intemet: <URL:http://www.nielsen-netratings.com>.
  • Nilsson, B. A., and Robb, J., “Invasion of the Webcasters,” PC World, vol. 15, No. 9 (Sep. 1997) p. 204-216 [9 pgs.].
  • Otsuka, Shingo, et al. 2004. The Analysis of Users Behavior Using Global Web Access Logs. IPSJ SIG Technical Report, vol. 2004 No. 71 (Jul. 13, 2004), pp. 17-24.
  • Paepcke, Andreas, et al., “Beyond Document Similarity: Understanding Value-Based Search and Browsing Technologies”, ACM SIGMOD Record, vol. 29, Issue 1, Mar. 2000, pp. 80-92.
  • Paganelli, Laila, et al., “Intelligent Analysis of User Interactions with Web Applications”, IUI '02, San Francisco, CA, Jan. 13-16, 2002, pp. 111-118.
  • Park, Joon S., et al., “Secure Cookies on the Web”, IEEE Internet Computing, vol. 4, Issue 4, Jul./Aug. 2000, pp. 36-44.
  • Payton, D., et al. 1999. Dynamic collaborator discovery in information intensive environments. ACM Comput. Surv. 31, 2es, Article 8 (Jun. 1999), pp. 1-8.
  • PCT International Search Report for PCT/US03/13985 mailed Aug. 8, 2003; total of 1 sheet.
  • Pierrakos, Dimitrios, et al., “Web Usage Mining as a Tool for Personalization: A Survey”, User Modeling and User-Adapted Interaction, vol. 13, No. 4, (c) 2003, pp. 311-372.
  • Schonberg, Edith, et al., “Measuring Success”, Communications of the ACM, vol. 43, Issue 8, Aug. 2000, pp. 53-57.
  • Shahabi, Cyrus, et al., “Efficient and Anonymous Web-Usage Mining for Web Personalization”, INFORMS Journal on Computing, vol. 15, No. 2, Spring 2003, pp. 123-148.
  • Soumen Chakrabarti et al., “Focused Crawling: A New Approach to Top-Specific Web Resource Discovery” Computer Networks 31 (1999), pp. 1623-1640.
  • Srivastava, Jaideep, et al., “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data”, SIGKDD Explorations, vol. 1. Issue 2, Jan. 2000, pp. 12-23.
  • Takahashi. 2001. JavaScript Lab: Safe Control of Pop-up Windows. Web Designing. Mainichi Communications, Inc. Jun. 10, 2001. No. 9, vol. 12, pp. 86-89 (separate volume of Mac Fan of Jun. 10 issue).
  • U.K. Intellectual Property Office, Examination Report in Great Britain application, GB0724938.6, dated Jan. 11, 2010, 2 pages.
  • U.K. Intellectual Property Office, Official Action mailed Jul. 16, 2010 for British Application No. GB0724938.6, filed Jun. 28, 2008 [1 pg.].
  • Uehara, Satoru, et al., “An Implementation of Electronic Shopping Cart on the Web System Using component-Object Technology”, Proc. of the 6th International Conf./ on Object-Oriented Real-Time Dependable Systems, Jan. 8-10, 2001, pp. 77-84.
  • Wenyin, Liu, et al., “Ubiquitous Media Agents: A Framework for Managing Personally Accumulated Multimedia Files”, Multimedia Systems, vol. 9, No. 2, Aug. 2003, pp. 144-156.
  • WIPO, International Preliminary Report on Patentability (Chap. I of PCT) for PCT/US04/33777, Jan. 23, 2006 [4 pgs].
  • WIPO, International Preliminary Report on Patentability (Chap. I of PCT) for PCT/US05/01022, Aug. 14, 2006 [6 pgs].
  • WIPO, International Search Report for PCT/US04/33777, Jan. 19, 2006 [3 pgs.].
  • WIPO, International Search Report for PCT/US05/01022, Apr. 24, 2006 [3 pgs.].
  • WIPO, Written Opinion of the International Searching Authority for PCT/US04/33777, Jan. 23, 2006 [3 pgs].
  • WIPO, Written Opinion of the International Searching Authority for PCT/US05/01022, Apr. 26, 2006 [5 pgs].
  • Xu, Cheng-Zhong, et al., “A Keyword-Based Semantic Prefetching Approach in Internet News Services”, IEEE Transactions on Knowledge and Data Engineering, vol. 16, Issue 5, May 2004, pp. 601-611.
  • Yuichi Yagawa et al. TV Program Planning Agent using Analysis Method of User's Taste; IEICE Technical Report vol. 98, No. 437: The Institute of Electronics, Information and Communication Engineers; Dec. 1, 1998, vol. 98, No. 437, pp. 9-16.
  • Zaiane, Osmar R., et al., “Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs”, IEEE International Forum on Research and Technology Advances in Digital Libraries, Santa Barbara; CA, Apr. 22-24, 1998, pp. 19-29.
  • Advisory Action dated Nov. 19, 2008 issued in U.S. Appl. No. 11/015,583, 3 pages.
  • Final Office Action dated Mar. 26, 2010 issued in U.S. Appl. No. 11/207,590, 8 pages.
  • Final Rejection dated May 21, 2008 issued in U.S. Appl. No. 11/015,583, 8 pages.
  • International Preliminary Report on Patentability in PCT Appln. No. PCT/US2004/07714, dated Oct. 1, 2005 [4 pages].
  • International Search Report in PCT Appln. No. PCT/US2004/07714, mailed Mar. 31, 2005 [1 page].
  • Langheinrich, M., et al. “Unintrusive Customization Techniques for Web Advertising,” NEC Corporation, C&C Media Research Laboratories, Kanagawa, Japan, Computer Networks, vol. 31, No. 11, pp. 1259-1272, May, 1999 (in Conf. Proc. 8th Int'l WWW Conf., Toronta, Canada, May 11-14, 1999).
  • Non-Final Rejection dated Dec. 20, 2009 issued in U.S. Appl. No. 11/015,583, 8 pages.
  • Non-Final Rejection dated Jul. 20, 2009 issued in U.S. Appl. No. 11/015,583, 7 pages.
  • Pierre Maret, et al.; Multimedia Information Interchange: Web Forms Meet Data Servers; Proceedings of the IEEE International Conference on Multimedia Computing and Systems; vol. 2, Jun. 7-11, 1999, pp. 499-505; XP000964627; Florence, Italy.
  • Ready, Kevin et al., “Plug-n-Play Java Script” (Indianapolis, IN: New Riders Publishing, 1996): 19-22, 39, 40 and 43-45.
  • Restriction Requirement dated Oct. 31, 2007 issued in U.S. Appl. No. 11/015,583, 6 pages.
  • Written Opinion in PCT Appln. No. PCT/US2004/07714, mailed Mar. 31, 2005 [3 pages].
Patent History
Patent number: 9372921
Type: Grant
Filed: Mar 1, 2010
Date of Patent: Jun 21, 2016
Patent Publication Number: 20100306053
Assignee: GULA CONSULTING LIMITED LIABILITY COMPANY (Dover, DE)
Inventors: Anthony Martin (Los Altos, CA), David L. Goulden (Redwood City, CA), Dominic V. Bennett (Los Altos, CA), Roger Petersen (Palo Alto, CA), Remigiusz K. Paczkowski (Belmont, CA)
Primary Examiner: Joshua Bullock
Application Number: 12/715,311
Classifications
Current U.S. Class: Computer-to-computer Session/connection Establishing (709/227)
International Classification: G06F 17/30 (20060101); G06Q 10/06 (20120101); G06Q 30/02 (20120101);