Abstract:
A network object predictive pre-download device operable on a network logically between a web client and a web server on a network is described. The pre-download device includes a prediction engine that intercepts a request from the web client to the web server for a first network object and determines a second network object most probable to be requested in the future from the web server. The pre-download device also includes a cache primer that intercepts a response to the request from the web server and sends a first message to the web client informing the web client of the second network object to be cached by the web client. The cache primer also sends the second network object to the web client such that the second network object is predicatively pre-downloaded to the web client whereby second network object latency is reduced when an actual user request occurs.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation of application Ser. No. 11/262,225, filed Oct. 28, 2005, entitled “Predictive Pre-Download of a Network Object,” and issued as U.S. Pat. No. 7,548,947 on Jun. 16, 2009 which is a continuation of application Ser. No. 09/734,910, filed Dec. 11, 2000, entitled “Predictive Pre-Download Using Normalized Network Object Identifiers,” and issued as U.S. Pat. No. 6,981,017 on Dec. 27, 2005, which is a continuation-in-part of application Ser. No. 09/436,136, filed Nov. 9, 1999, entitled “Predictive Pre-Download of Network Objects,” and issued as U.S. Pat. No. 6,721,780 on Apr. 13, 2004; and hereby incorporated by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     This invention relates to pre-download of network objects from a server and incorporates a process for normalizing a network object identifier in a request message. 
     2. Related Art 
     In computer networks, client devices often request and receive information from server devices. A common example of such client-server information occurs during use of the World Wide Web, in which a web client (sometimes called a web “browser”) requests web pages and embedded network objects from a web server (sometimes called a “web site”). 
     One problem in the known art is that web clients experience some delay between the time they request a web page from a web server and the time that that web page is presented by the web client software to the user. This delay can be due to the size of the web page, the limited connection bandwidth, the network distance to the web server, the load on the web server, or other factors. 
     A method exists that provides a technique for pre-download of network objects from a server, such as is used in conjunction with an internetworking environment, which reduces the time users wait for delivery and presentation of those network objects. A device which is coupled to the web-server maintains statistical information regarding which network objects are most likely to be requested by users and gives hints to a client-side component to pre-download those network objects to associated web clients in advance of actual requests being made by the user for those network objects. This is achieved by analyzing the request messages sent by the user to predict which subsequent network objects are likely to be requested by that user. 
     This method is less effective when the network object identifier, such as a URL (Uniform Resource Locator), contains a search string, a variable, or some other dynamic value. In such cases, two problems are likely to occur: (1) the number of possible URL&#39;s becomes arbitrarily large and unmanageable because each page has an infinite number of URL&#39;s that refer to it; and (2) the predictive value is decreased because the URL is specific to a single user, such as when the value is a user identifier, and there are too few links between similar URL&#39;s. The invention provides a method for increasing the effectiveness of the pre-download method and system. 
     SUMMARY OF THE INVENTION 
     The invention provides a method and system for pre-downloading network objects from a server when requests for those network objects contain dynamically changeable identifiers with variable data included. In a web server, there is a device which maintains information regarding which network objects are most likely to be requested by users, and which pre-downloads those network objects to associated web clients in advance of user prompted requests. The invention provides a method and system for (1) normalizing requests containing variables; (2) retrieving a normalized prediction; (3) de-normalizing the prediction; and (4) pre-downloading a network object in response to the de-normalized prediction. The invention incorporates a pre-download statistic server to assist in adjusting the prediction process. With this process, the invention is better able to predict which network objects will be requested by the user and reduce the time the user waits for delivery of those network objects. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a block diagram of a system for pre-downloading network objects from a Web server  130 . 
         FIG. 2  shows a block diagram of a preferred embodiment of a system for pre-downloading network objects from a server using a pre-download device coupled with a Web server  130 . 
         FIG. 3  shows a block diagram of an alternative embodiment of a system for pre-downloading network objects from a Web server using an applet  170  stored on a Web client  110 . 
         FIG. 4  shows a Web site graph  231 . 
         FIG. 5  shows a process flow diagram of a preferred method for predicting and pre-downloading network objects from a server. 
         FIG. 6  shows a block diagram of an alternative method for predicting and pre-downloading network objects from a web server. 
         FIG. 7  shows a block diagram of a pre-download device, as used in a system for pre-downloading network objects from a server. 
         FIG. 8  shows the use of a finite state machine to detect the presence of an incoming URL possibly managing each regular expression. 
         FIG. 9  shows a process flow diagram of a method for operating a system for pre-downloading network objects from a server. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     In the following description, preferred and alternative embodiments of the invention are described with regard to process steps and data structures. Embodiments of the invention can be implemented using general purpose processors or special purpose processors operating under program control, or other circuits, adapted to particular process steps and data structures described herein. Implementation of the process steps and data structures described herein would not require undue experimentation or further invention. 
     Lexicography 
     The following terms refer or relate to aspects of the invention as described below. The descriptions of general meanings of these terms are not intended to be limiting, only illustrative. 
     Network objects—In general, web pages and data elements embedded therein. 
     Web server—In general, a server for providing web pages and data elements therein to requesting clients. 
     Pre-download—In general, requesting web pages and data elements, by a web client from a web server, before a user at that web client specifically requests them. 
     Actual request—In general, an action taken by a user to specifically request any network object. 
     Delivery—In general, sending a web page from a web server to a web client. 
     Statistical information—In general, information regarding which network objects and links between network objects appear on a web page and which network objects are relatively more or less likely to be selected by a user viewing that web page. 
     Pre-download hint (Hint)—In general, information provided by a web server to web clients prompting the Web client to request pre-download of network objects before a user prompted request for those predicted network objects is made. 
     Pre-download rules—In general, information at a web server regarding which network objects are relatively more or less desirable for the user to receive without delivery latency. 
     Pre-download Applet—In general, a program that may be distributed as an attachment and transferred using a communications network, which causes at least some of the predictive elements of the system to be downloaded at the Web client. 
     Demographic information—In general, information about the user at a web client (such as their locale, time zone, or primary language.) 
     Past behavior—In general, information about a user responsive to whether that user is a frequent user of the web site, frequent purchaser of goods or services at that web site, or other relevant past behavior. 
     Web Client and Server—These terms refer to a relationship between two devices, particularly to their relationship as Web client and server, not necessarily to any particular physical devices. 
     For example, but without limitation, a particular Web client device in a first relationship with a first server device can serve as a server device in a second relationship with a second Web client device. 
     Client device and Server device—These terms refer to devices taking on the role of a Web client or a server in a Web client-server relationship (such as an HTTP Web client and web server.) There is no particular requirement that any Web client devices or server devices must be individual physical devices. They can each be a single device, a set of cooperating devices, a portion of a device, or some combination thereof. 
     Referrer URL—In general, the URL from which a request for download of a network object is made. 
     Target URL—in general, that portion of a Universal Resource Locator that refers to the location of the network object requested from the Web server. 
     Requestor Bit—In general, the binary digit contained in a request for a network object; such request usually originating from a user prompt at the Web client or from the Web client in response to direction by a pre-download device. 
     Requestor Bit Log—In general, a database recording whether requests for pre-download of network objects came from the user or the predictive download device. 
     Dynamic names—In general, a portion of a URL string that identifies the Web client or user and which is associated with a variable value. The string may contain state information of any kind and is dynamic if its value is not relevant for prediction purposes (i.e. if you can make better predictions by working on the quotient set rather than the set itself.) 
     Non-dynamic names—In general, a portion of a URL string that identifies a Web client and does not contain variable values. 
     Log File—In general, a file containing a list of the URL identifiers for Web clients. 
     Normalize—In general, the process of changing variable values to placeholder values. 
     Predict—In general, the process of determining which network objects are most likely to be requested by a user at the Web client. 
     As noted above, these descriptions of general meanings of these terms are not intended to be limiting, only illustrative. Other and further applications of the invention, including extensions of these terms and concepts, would be clear to those of ordinary skill in the art upon review of this application. These other and further applications are part of the scope and spirit of the invention, and would be clear to those of ordinary skill in the art, without further invention or undue experimentation. 
     System Elements 
       FIG. 1  shows a block diagram of a system for pre-downloading network objects from a server. 
     A system  100  includes a set of Web clients  110 , a communication network  120 , a Web server  130 , a pre-download device  140 , and a pre-download statistics server  150 . 
     Each Web client  110  includes a processor, program and data memory, mass storage, and a client communication link  111 . The processor, program and data memory and mass storage operate in conjunction to perform the functions of a Web client  110  (also known as a Web “browser”). The processor, program and data memory and mass storage may also function as a local Web client cache  160 . In an alternative embodiment, the Web client  110  includes an applet  170  supplied by the Web server  130 . The Web client  110 , using the HTTP (“hypertext transfer protocol”) requests network objects from the Web server  130  and, using HTTP responses, receives those network objects from the Web server  130 . Although, in a preferred embodiment, the Web client  110  uses the HTTP protocol or variant thereof, there is no particular requirement for use of that specific protocol or its variants. The requests and responses are routed using the communication network  120  and are received and processed by the Web server  130 . 
     The client communication link  111  operates to couple a Web client  110  to the communication network  120 . 
     In a preferred embodiment, the communication network  120  includes an Internet, intranet, extranet, virtual private network, enterprise network, or another form of communication network. In a preferred embodiment, the communication network  120  includes a network capable of routing messages between and among one or more Web clients  110  and Web servers  130 . However, there is no particular requirement that the communication network  120  must comprise an actual network, so long as the communication network  120  includes at least some technique for communication between the Web client  110  and Web servers  130 . 
     The Web server  130  includes a processor, program and data memory, mass storage, and a server communication link  131 . The processor, program and data memory, and mass storage operate in conjunction to perform the functions of a Web server  130  (also known as a web “site”). The Web server  130  responds to the Web client  110  using the HTTP protocol. Although, in a preferred embodiment, the Web server  130  uses the HTTP protocol or variant thereof, there is no particular requirement for use of that specific protocol or its variants. 
     The pre-download statistics server  150  includes a processor, program and data memory, mass storage, and a statistics server communication link  151 . The pre-download statistics server  150  operates in a similar manner to a Web server  130 , with a difference that the pre-download statistics server  150  receives and maintains pre-download statistics for the Web server  130 . Interested parties can later examine those pre-download statistics; these could include a user at the Web client  110 , an operator or web site administrator at the Web server  130 , or a user (or program) at another device coupled to the communication network  120 . 
     A prediction is made at the pre-download device  140  or at the Web client  110  to determine what web object the Web client  110  is likely to request from the Web server  130  based on the initial request. 
     Preferred Embodiment: Prediction Occurs at the Pre-Download Device 
     In the preferred embodiment, the pre-download device  140  intercepts requests for network objects from the Web client  110  as well as responses to the request from the Web server  130 . The pre-download device  140  determines which network objects are most likely to be requested by the Web client  110  from the Web server  130 . The pre-download device  140  then directs the Web client  110  to request those network objects from the Web server  130  prior to their actual request from a user at the Web client  110 . Downloading of the requested network object begins immediately after the pre-download request is received by the server. Thus, the Web client  110  will have those network objects available in its local Web client cache  160  prior to any user prompted request for the same. If and when a user makes a request for the predicted and pre-downloaded network objects, the Web client  110  will be able to present those network objects to the user with relatively little latency. 
       FIG. 2  shows a block diagram of a preferred embodiment of a system for pre-downloading network objects from a server using a pre-download device  140  coupled with a Web server  130 . 
     Each message  211  includes a first URL  212  (referred to herein as a “referrer URL”) indicating a referring network object, and a second URL  213  (referred to herein as a “target URL”) indicating a requested network object. Each message  211  also includes a requester-bit  214 , indicating whether the request for the network object comes from a user prompted request made at the Web client  110 , or from the Web client  110  in response to a hint provided by the pre-download device  140 . Each message  211  also includes additional relevant information  215  such as a time stamp, a source IP address and port, and a destination IP address and port. 
     The pre-download device includes a URL identification element  141 , a normalization element  142 , a prediction element  143 , a denormalization element  144 , a variable database  145 , a hint attachment element, and a Web site graph  231 . The Web site graph  231  may be stored either locally or externally. 
     The combination of the URL identification element  141 , the normalization element  142 , the prediction element  143 , a denormalization element  144 , the variable database  145 , the hint attachment element  146 , and the Web site graph  231 , also may be referred to as a request processing element. 
     The pre-download device  140  intercepts a message  211  (i.e. and request for a network object) from a Web client  110  to the Web server  130  and the response  148  (i.e. the network object) sent from the Web server  130  to the Web client  110 . 
     Upon receipt of the message by the pre-download device, the URL identification element  141  parses the message  211  and identifies the referrer URL  212 , the target URL  213 , the requester bit  214 , and any additional relevant information  215  contained in the message  211  about the user or the Web client  110 , such as a time stamp, the source IP address and port, and the destination IP address and port. 
     The normalization element  142  then accesses the variable database  145  and determines which values contained in the target URLs are dynamic (i.e. are variables). The normalization element  142  removes the bindings from the dynamic target URLs and replaces them with placeholder values. 
     The prediction element  143  accesses the Web site graph  231  and predicts which network objects the Web client  110  is likely to request from the Web server  130 . The prediction, otherwise known as a “hint,” is in a similar form as the normalized target URL and may contain placeholder values. 
     The denormalization element  144  then accesses the variable database  145  and substitutes in the hint URL the bindings previously removed from the target URL. The hint  147  is then sent to the Web client  110 . 
     The hint attachment element  146  attaches the hint  147  to the requested network object  148  received from the Web server  130 . As a result, the hint response and the network object  148  are sent together  149  through the communication network  120  to the Web client  120 . 
     The Web client  110  receives the hint  147  and responds by pre-downloading the network object that corresponds to the hint  147 . Thus, the Web client  110  pre-downloads the predicted network object prior to Web client  110  making a user prompted request for that network object. Thus, the latency period between an actual request for a network object and the download of the network object is decreased. 
     Alternative Embodiment: Prediction Occurs at the Web Client 
       FIG. 3  shows a block diagram of an alternative embodiment of a system for pre-downloading network objects from a server using an applet  170  stored on a Web client  110 . 
     In an alternative embodiment, at least part of the prediction process occurs at the Web client  110  and not at the pre-download device  140 . The Web client  110  contains an applet  170  including a URL identification element  141 , a normalization element  142 , a prediction element  143 , a denormalization element  144 , a variable database  145 , and a Web site graph  231 , similar to those elements shown in  FIG. 2 . In this alternative embodiment, inclusion of a hint attachment element  146  depends on whether the applet is written such that the functionality of the predownload process occurs in the same process as the Web client  110  (in which case it would not be needed) or whether the predownload process runs separate from the process of the Web client  110  (in which case it may be included to attach the hint  147 ). As in the preferred embodiment, the Web site graph  231  may also be stored either locally or externally. 
     The combination of the URL identification element  141 , the normalization element  142 , the prediction element  143 , the denormalization element  144 , the variable database  145 , and the Web site graph  231 , also may be referred to as a request processing element. 
     The Web client  110  sends a user prompted request (i.e. a message  211 ) to the Web server  130  requesting a specified network object. 
     As in the preferred embodiment of the system, each message  211  includes a first URL  212  (the “referrer URL”) indicating a referring network object, and a second URL  213  (the “target URL”) indicating a network object. Each message  211  also includes additional relevant information  215 , such as a time stamp, a source IP address and port, and a destination IP address and port. Each message  211  also includes a requester-bit  214 , indicating whether the request for the target network object comes from a user prompted request made at the Web client  110 , or from the Web client  110  in response to direction by the applet  170 . 
     The URL identification element  141  parses the request and identifies the referrer URL  212 , the target URL  213 , the requester bit  214 , and any additional relevant information  215  such as a time stamp, the source IP address and port, and the destination IP address and port. 
     The normalization element  142  then accesses the variable database  145  and determines which values contained in the referrer URL&#39;s are dynamic (i.e. are variables). The normalization element  142  removes the bindings from the dynamic referrer URLs and replaces them with placeholder values. 
     The prediction element  143  accesses the Web site graph  231  and predicts which network objects the Web client  110  is likely to request from the Web server  130 . The prediction  147 , otherwise known as a “hint,” is in a similar form as the normalized target URL and may contain placeholder values. Although the prediction  147  may be based on the likelihood that a user will request a certain network object, the prediction may be based on other factors as discussed in other parts of this application. 
     The denormalization element  144  then accesses the variable database  145  and replaces the bindings and dynamic values previously removed from the referrer URLs by the normalization element  142 . The hint  147  is then sent to the Web client  110 . 
     The Web client  110  receives the hint  147  and responds by pre-downloading the predicted network object before the Web client  110  makes a user prompted request for that network object. If and when a user prompted request is made at the Web client  110  for the predicted and pre-downloaded network object, that target object will already have been requested. Thus, the latency period between the time the user makes a request at the Web client  110  for a predicted network object and the download of that network object is decreased. 
     Maintenance of Statistical Information Using a Web-Site Graph 
       FIG. 4  shows an example of a Web-site graph  231 . 
     The nodes shown on  FIG. 4  represent network objects that exist on a web site (i.e. a web destination hosted by a web server  130 .) For example, node P 1  may represent a single network object (e.g. the text on a first page of a web hosted document.) Predictive statistics associated with node P 1  may correlate with a likelihood that a user will make a request at the Web client  110  for other network objects (or pages) located at the server hosted web site. In  FIG. 4 , for example, network object P 1  is associated with network objects P 2  and P 3 . 
     In addition to associations between network objects, the Web site graph  231  may reflect predictive statistics or weighted values that may control the order in which associated network objects are pre-downloaded to the Web client  110 .  FIG. 4 , the predictive statistics or weighted values associated with network objects P 2  and P 3  are 70% and 30% respectively. These statistics or values may reflect such factors as the likelihood that P 2  or P 3  will be requested by the user immediately subsequent to users request for P 1 . Considering these predictive statistics, the pre-download device may cause P 2  to be pre-downloaded before P 3 . Alternatively, pre-download priority may be set by weighted values assigned by an operator or web site administrator. 
     Predictive statistics may reflect probabilities, as stated above, based on actual results made by a single user or any number of users over time. A statistics server  150  may be employed to keep a tally of all requests made by a single web server or a number of web servers and adjust associations between network objects as well as statistics or values affecting pre-download order. 
     Predictive statistics or weighted values may be based on the content and architecture of the web site. For example, P 1  represent a first page of text of a web hosted document and P 2  represent the second page text. The pre-download statistics server or a web administrator may assign a value of 70% to P 2  because there is a 70% chance that users who view the first page will make a subsequent request for the second page. If, for example, network object P 3  represents the table of contents of the web site, the assigned value of 30% may reflect a 30% chance that users who view the first page will not be interested in viewing the second page and will subsequently request the table of contents. While the values associated with each network object may reflect statistics automatically calculated by a statistics server, they may also be assigned manually. 
     The values associated with each network object on the graph may reflect other factors such as the size of the network object. For example, the pre-download statistics server or administrator may assign values to network objects based on the time it takes a user to review an associated network object relative to the time is takes to pre-download an associated network object. For example, if P 1  is the title page of a web based document, one can expect that a user will take relatively little time to review it and request another page. If there will not be enough time to pre-download all associated file prior to the user making a subsequent request, the order in which associated network objects P 2  and P 3  are pre-downloaded may then depend on how long it takes each to be pre-downloaded (i.e. how large the files are.) For example, it may be preferable to pre-download the smaller file first so that user will better realize the benefit of the pre-download process. Alternatively, it may be preferable to pre-download larger files first because the time to download a smaller network object may be negligible. The pre-download device and pre-download statistics server may adjustable to incorporate any number of preferences. 
     The Web site graph  231  not only reflects predictive statistics or weighted values regarding such network objects as web-pages, but to network objects that are actually embedded elements within those web-pages. Thus, if a web-page contains embedded elements, these elements will be requested by the Web client  110  at the same time as their parent web-page. In terms of the example provided in  FIG. 4 , if P 2  denotes a network object that is a web page and that web page contains embedded element E 1 , then the likelihood that E 1  will be requested is the same as the likelihood P 2  will be requested, or 70%. Additionally, since elements may be shared by different web pages, they may be statistically more likely to be requested. For example, if both P 2  and P 3  have embedded element E 1 , then E 1  will be requested 100% of the time (according to  FIG. 4 ), and is, thus, statistically more likely to be requested that either P 1  or P 2 . 
     Alternatively, the predictive statistics or weighted values associated with network object on the web site graph  231  may reflect the objectives of the web site or the preferences of a web site administrator. For example, the pre-download device and pre-download statistics server may be programmed to pre-download certain network objects regardless of user preferences or other factors. Where the objective of the site is to produce income through online purchase, for example, the administrator may want to make sure that the user does not have to wait for download of the network objects associated with making a purchase (such as the check-out page.) The web site administrator may adjust the web site graph so that when a Web client  110  requests a network object showing pricing for certain product available for purchase, that the network object showing check out information is pre-downloaded immediately. 
     The web site graph may be modified automatically using pre-download statistics server  150  or modified manually by an operator or the web site administrator. The pre-download statistics server  150  may determine network object association and calculate probabilities based on averages calculated for all users accessing the site, averages based on any single users or group of users activity, or preferences based on demographic factors such as geographical location, age, or income. The web site graph may be modified according information provided by users in response to online questionnaires. Probabilities and/or profiles pertaining to particular users or user groups may be stored in the pre-download statistics server  150  database and accessed each time a user accesses the subject web site. 
     The embodiments described above are not mutually necessarily exclusive and may be used in combination. 
     Preferred Embodiment: Prediction Incorporating Use of a Requestor Bit Log 
     In a preferred embodiment, the invention incorporates a Requestor Bit Log which keeps track of whether requests for pre-download of network objects were made by the user or by the predictive pre-download device. The four possible states associated with each request for pre-download are: 1) a request made by the user which was not previously pre-downloaded; 2) a request made by the user which was previously pre-downloaded and recorded in the Requestor Bit Log, 3) a request made by the predictive pre-download device which was not previously recorded in the Requestor Bit Log, and 4) a request made by the predictive pre-download device which was previously recorded in the Requestor Bit Log. A request is not recorded in the Requestor Bit Log if the requested network object had already been pre-downloaded (i.e. when the prediction was successful) or when the user downloads a network object that the predictive pre-download device did not predict the user would request (which is the case, for example, when the user hits the “back” button to revisit a target object that that user has already reviewed and the predictive pre-download devise could not predict the user would be reviewing.) 
     The purpose of the Requestor Bit Log is to insure that the statistics which may be attached to each associated network object on the web site graph  231  reflect requests and preferences from the user and not requests for pre-download from the pre-download device  140 . By distinguishing user prompted requests from pre-download requests prompted by the pre-download device, the system may prevent improper associations between network objects or skewed statistics based on requests prompted by the pre-downloaded device which were not subsequently made by the user. 
     The Requestor Bit Log may exist as part of the part of the pre-download statistics server, the pre-download device, the pre-download applet, or some other device or program connected to the system through the communications network. 
     Preferred Method of Prediction and Pre-Download 
       FIG. 5  shows a process flow diagram of a preferred method for predicting and pre-downloading network objects from a server. 
     Method  500  is performed by the system  100 . The method is described and performed serially. 
     At a flow point  510 , the pre-download device  140  is coupled to the Web server  130 , and is ready to receive incoming requests from one or more Web clients. The pre-download device  140  includes a URL identifying element  141 , a normalization element  142 , a prediction element  143 , a denormalization element  144 , a variable database  145 , and a hint attachment element  146 . 
     At a step  511 , the pre-download device  140  receives an incoming message  211  from a Web client  110  asking for a network object from the Web server  130 . 
     At a step  512 , the URL identification element  141  parses the message  211  and identifies the referrer URL, the target URL, and any other relevant information contained in the request about the user or the Web client  110 , such as the source IP address and port 
     At a step  513 , the normalization element  142  uses the variable database to identify and remove the bindings from the dynamic referrer URLs and replace the variable values with static values. 
     At a step  514 , the prediction element  143  accesses the Web site model graph and predicts which network object pages the Web client  110  is likely to request from the Web server  130 . The prediction is known as a “hint”. 
     At a step  515 , the denormalization element  144  accesses the variable database  145  and replaces the placeholder values in the hint URL with the dynamic values that were removed from the target URL by the normalization element  142 . 
     At a step  516 , the hint attachment element  146  attaches the hint to the requested network object and both are sent to the Web client  110 . 
     At a step  517 , the Web client  110  receives the hint and requested network object. 
     At step  518 , the Web client  110  responds to the hint by requesting pre-downloads the predicted network object. 
     Alternative Method of Prediction and Pre-Download 
       FIG. 6  shows a block diagram of an alternative method for predicting and pre-downloading network objects from a web server. 
     A method  600  is performed by the system  100 . The method is described and performed serially. 
     At flow point  610 , a Web client  110  includes an applet  170  and local Web client cache  160 . 
     At a step  611 , the Web client sends a message  211  to the Web server  130  asking for a network object. 
     At a step  612 , the applet  170  intercepts the message  211  sent to the Web server  130  asking for a network object. 
     At a step  613 , the URL identification element  141  parses the message  211  and identifies the referrer URL, the target URL, and any other relevant information contained in the request about the user or the Web client  110 , such as the source IP address and port 
     At a step  614 , the normalization element  142  removes the bindings from the dynamic referrer URLs and replaces variable values with static values pertaining to certain network objects stored on the Web server  130 . 
     At a step  615 , the prediction element  143  uses the Web site graph  231  to predict which network object pages the Web client  110  will request from the Web server  130 . The prediction is otherwise known as a “hint”). 
     At a step  616 , the denormalization element  144  replaces the bindings and the dynamic values in the referrer URLs. 
     At a step  617 , the local Web client cache  160  receives and saves the hint. 
     At step  618 , the Web client  110  pre-downloads a request for a network object in response to the hint. 
     Pre-Download Device 
       FIGS. 7 ,  8  and  9  show an alternative preferred embodiment system to the system shown and described in  FIGS. 2 through 6 .  FIG. 7  shows a block diagram of a pre-download device, as used in a system for pre-downloading network objects from a server. The pre-download device  140  includes a prediction engine  1210 , and a cache primer  1220 . 
     Prediction Engine 
     The prediction engine  1210  intercepts and processes a sequence of messages  1211  including requests from the Web client  110  that are directed to the Web server  130 . 
     Each message  1211  includes a first URL  1212  indicating a referring network object, and a second URL  1213  indicating a target network object. Each message  1211  also includes additional information, including a time stamp, a source IP address and port, a destination IP address and port, and a file size for the target network object. Each message  1211  also includes a requester-bit  1214 , indicating whether the request for the target network object comes from an actual request by user at the Web client  110 , or from the Web client  110  in response to direction by the pre-download device  140 . 
     The prediction engine  1210  includes a request-processing element  1230 , a web-site model graph  1231 , a model-change queue  1232 , and a model-updating element  1240 . 
     In a preferred embodiment, the request-processing element  1230  includes a separate processing thread, so as to operate in conjunction or in parallel with other operations performed by the prediction engine  1210 . Separate processing threads are known in the art of computer systems architecture. 
     The request-processing element  1230  maintains the Web-site model graph  1231 , including a directed graph of nodes (each indicating a network object), and transitions between nodes (each indicating a possible transition selected by a user at the Web client  110 ). 
     The request-processing element  1230  receives each message  1211 , and parses the message  1211  for the first URL  1212 , the second URL  1213 , and the requester-bit  1214 . In response thereto, the request-processing element  1230  determines (in the Web-site model graph  1231 ) an initial node, a final node, a transition from the initial note to the final node, and a measure of a weighted probability of transition from to the final note from the initial node. 
     The request-processing element  1230  can determine from each message  1211  whether that message  1211  was responsive to an actual request by a user at the Web client  110 . If so, the request-processing element  1230  writes information from that message  1211  to a raw log file  1233 . 
     The raw log file  1233  comprises information regarding traffic at the Web  1111  server  130 , so the Web server  130  can run an independent process or thread for determining conclusions from that traffic information. 
     Since the Web site  130  is a collection of network objects that may possibly change over time, there are likely to be periodic changes to the Web-site model graph  1231 . These changes can include new nodes, new transitions, and new weighted probabilities assigned to transitions in response to selections made by the user at the Web client  110 . 
     Accordingly, the request-processing element  1230 , in response to changes to the Web-site model graph  1231 , maintains the model-change queue  1232 , indicating relatively recent changes to the web-site model graph  1231 . 
     In a preferred embodiment, the model-updating element  1240  includes a separate processing thread, so as to operate in conjunction or in parallel with other operations performed by the prediction engine  1210 . 
     The model-updating element  1240 , responsive to the model-change queue  1232 , determines whether and when there have been sufficient changes to the web-site model graph  1231  to rewrite a Web-site model database  1221 . For example, the model-updating element  1240  can determine there have been sufficient changes to the web-site model graph  1231  when the model-change queue  1232  is longer than a pre-selected threshold. When there have been sufficient changes to rewrite the web-site model database  1221 , the model-updating element  1240  performs the update. 
     Cache Primer 
     The cache primer  1220  responds to each message  1211 , to the web-site model database  1221 , and to a policy rules database  1222 , to generate a sequence of pre-download request messages  1223 . Each pre-download request message  1223  instructs the Web client  110  to request one or more target network objects without any actual request for those target network objects from a user at the web client  110 . 
     In a preferred embodiment, the cache primer  1220  acts as a reverse-proxy device, thus caching network objects from the Web server  130  for sending to Web clients  110 . In so doing, the cache primer  1220  preferably follows a set of administrative rules, including one or more of, or some combination of, the following:
         The cache primer  1220  is responsive to facts about the Web server  130 , such as a computation load on the Web server  130 , or a time of day or data of the week at the Web server  130 .   The cache primer  1220  is responsive to facts about the Web client  110 , such as a source IP address and port, a user ID for a user at the Web client  110 , or a cookie stored at the Web client  110 . In a preferred embodiment, a cookie stored at the Web client  110  can include a purchase history for the user, demographic information for the user, or other information about the user that might be a value (such as their ISP, country of origin, or time zone).   The cache primer  1220  is responsive to facts about the usage of the Web server  130  by the user at the Web client  110 , such as an amount of time spent by the user at the Web server  130 , a number of links followed by the user local to the Web server  130 , or a relative distance of the requested network object from a “purchase page” or other important network object at the Web server  130 .       

     The cache primer  1220  examines each intercepted message  1211 , and determines whether one or more policy rules in the policy rules database  1222  applies to that intercepted message  1211 . The policy rules database  1222  includes a set of pairs, each of which it tends to match against a requested URL, and an identifier for the Web client  110 , and each of which indicates a hint for a network object to be pre-downloaded, in response to a matched policy rule. For example, a policy rule can attempt to match against a particular user and a particular Web client  110 , so as to take that particular user directly to a “purchase page” or other important network object at the Web server  130 . 
     In a preferred embodiment, the policy rules database  1222  is constructed in the form of an expert pattern language, using a graphical user interface and developed by an operator (not shown) at the cache primer  1220 . 
     If no policy rules from the policy rules database  1222  are successfully matched against the intercepted message  1211 , the cache primer  1220  defaults at a lowest priority to statistical information in the web-side model database  1221 . 
     As noted above, the web-site model database  1221  is constructed in response to incoming messages  1211 . In a preferred embodiment, the policy rules database  1222  also includes a set of statistical matching rules, each of which it tends to match against an incoming URL having imaginable pattern. 
     Each statistical matching rule includes a matchable regular expression, and a rule for generating a node or transition in the web-site model graph  1231 , and the same note or transition in the web-site model database  1221 . Regular expressions are known in the art of compilers and pattern matching. 
     In a preferred embodiment, the cache primer  1220  uses a finite state machine to detect the presence of an incoming URL possibly matching each regular expression. The use of the finite state machine is further described with reference to  FIG. 8 . 
     Finite State Machine 
       FIG. 8  shows the use of a finite state machine to detect the presence of an incoming URL possibly managing each regular expression. 
     An incoming URL  1301  is input, one character at a time, in parallel, to a set of finite state machines  1310 . Each finite state machine  1310  includes a plurality of nodes  1311  and a plurality of transitions  1312 . Each transition  1312  is triggered explicitly by a single defined character, or by a single character of any type. Each node  1311  can also be labeled with a “begin variable” or an “end variable” marker, so as to begin or end a character-string variable. One selected node  1311  is labeled as a “match/no match” node  1311 , and associated with an action to be taken in response to matching the selected finite state machine  1310 . 
     For example, a regular expression can be such as that indicated in equation  1320 :
 
^1id=^2&amp;^3
 
In equation  1320 , the boldface value is preceded by a ^, such as ^ 1, indicate a character-string variable to be parsed and identified by at least one finite state machine  1310 . The non-boldface characters, such as “id=”, are recognized by at least one finite state machine  1310 , so as to parse and identify at least one associated character-string variable.
 
     As noted above, each incoming URL  1301  is input in parallel to a plurality of finite state machines  1310 . Thus, matching (or possible determination of non-match) of the each incoming URL  1301  can be performed in order of (constant time). 
     Method of Operation 
       FIG. 9  shows a process flow diagram of a method for operating a system for pre-downloading network objects from a server. 
     A method  1400  is performed by the system  100 . Although the method  1400  is described serially, the steps of the method  1400  can be performed by separate elements in conjunction or in parallel, whether a synchronously, in a pipelined manner, or otherwise. There is no particular requirement that the method  1400  be performed in the same order in which this description lists the steps, except where so indicated. 
     At a flow point  1410 , the pre-download device  140  is coupled to the Web server  130 , and is ready to receiving incoming requests from one or more Web clients  110 . 
     At a step  1411 , the pre-download device  140  receives an incoming request from a Web client  110 . 
     At a step  1412 , the pre-download device  140  intercepts an incoming message  1211  associated with the incoming request. As part of this step, the pre-download device  140  parses the incoming message  1211  for a reference URL and for a target URL. As part of this step, the pre-download device  140  determines both the reference URL and the target URL, and any other relevant information about the user or the Web client  110 , such as the source IP address and port. 
     At a step  1413 , the pre-download device  140  determines whether or not to alter the web-site model graph  1231  (such as by adding nodes, adding transitions, for altering weighted probabilities associated with internode transitions). As part of this step, the pre-download device  140  determines whether or not to rewrite the Web-site model database  1221 . 
     At a step  1414 , the pre-download device  140  determines whether or not to respond to the Web client  110  with a hint, indicating a network object to pre-download from the Web server  130 . If so, as part of this step, the pre-download device inserts the hint into the network object, before sending the network object from the Web server  130  to the Web client  110 . 
     At a step  1415 , the Web client  110  receives the hint from the pre-download device  140  (along with the network object it requested from the Web server  130 ). 
     At a step  1416 , the Web client  110  pre-downloads the network object indicated by the hint from the Web server  130 . As part of this step, the request for the network object to be pre downloaded includes a requester-bit indicating that the network object is being requested in response to a hint from the pre-download device  140 . 
     At a step  1416 , the pre-download device  140  determines a set of statistics associated with actual requests from one or more users at Web clients  110 , and outputs that set of statistics to the pre-download statistics server  150 . As part of this step, the pre-download statistics server  150  maintains those statistics associated with actual requests for use by other parties. 
     The method continues with the flow point  1410 . 
     Generality of the Invention 
     The invention has general applicability to various fields of use, not necessarily to the services described above. For example, these fields of use can include one or more of, or some combination of, the following:
         Queries to database servers, in which queries are correlated with regard to database records when relatively close in time.   Requests for information from information libraries, in which queries are correlated with regard to library documents when relatively close in time.   Other types of requests for information from server devices.       

     Other and further applications of the invention in its most general form, would be clear to those skilled in the art after review of this application, and are within the scope and spirit of the invention. 
     ALTERNATIVE EMBODIMENTS 
     Although preferred embodiments are disclosed herein, many variations are possible which remain within the concept, scope, and spirit of the invention, and these variations would become clear to those skilled in the art after review of this application.