Patent Document

CROSS-REFERENCES TO RELATED APPLICATIONS  
       [0001]     The present application is a continuation application of and claims priority to U.S. Non-Provisional patent application Ser. No. 10/402,844, filed on Mar. 28, 2003 (Atty. Docket No.: 015358-004810US), which is a continuation of U.S. Non-Provisional patent application Ser. No. 09/149,920, filed on Jun. 24, 2003 (Atty. Docket No.: 015358-004800US), the entire contents of which are herein incorporated by reference for all purposes.  
         [0002]     The following related commonly-owned copending application is hereby incorporated by reference in its entirety for all purposes: U.S. Non-Provisional patent application Ser. No. 09/149,921, filed on Sep. 9, 1998 (Atty. Docket No.: 015358-004900US/ID-CRC- 188 ) entitled, “AUTOMATIC ADAPTIVE DOCUMENT HELP FOR PAPER DOCUMENTS.” 
         [0003]     Further, this application incorporates by reference the following commonly owned copending U.S. patent application in its entirety for all purposes: U.S. Non-Provisional patent application Ser. No. 08/995,616, filed Dec. 22, 1997, entitled “AUTOMATIC ADAPTIVE DOCUMENT HELP SYSTEM.” 
     
    
     STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT  
       [0004]     NOT APPLICABLE  
       REFERENCE TO A “SEQUENCE LISTING,” A TABLE, OR A COMPUTER PROGRAM LISTING APPENDIX SUBMITTED ON A COMPACT DISK  
       [0005]     NOT APPLICABLE  
       BACKGROUND OF THE INVENTION  
       [0006]     The present invention relates to printing of electronic documents and more particularly to method and apparatus for augmenting the printing of electronic documents with features to enhance the experience of reading or using the hardcopy of an electronic document.  
         [0007]     Increasingly, readers of documents are being called upon to assimilate vast quantities of information in a short period of time. To meet the demands placed upon them, readers find they must read documents “horizontally,” rather than “vertically,” i.e., they must scan, skim, and browse sections of interest in multiple documents rather than read and analyze a single document from beginning to end.  
         [0008]     Documents are becoming more widely available in electronic form. Some documents are available electronically by virtue of their having been created using word processing software. Other electronic documents are accessible via the Internet. Yet others may become available in electronic form by virtue of being scanned in, copied, or faxed. Commonly assigned U.S. application Ser. No. 08/754,721, entitled AUTOMATIC AND TRANSPARENT DOCUMENT ARCHIVING, the contents of which are incorporated herein by reference for all purposes, details a system for generating electronic as well as hardcopy format of documents.  
         [0009]     However, the mere availability of documents in electronic form does not assist the reader in confronting the challenges of assimilating information quickly. Indeed, many time-challenged readers still prefer paper documents because of their portability and the ease of flipping through pages.  
         [0010]     Certain tools take advantage of the electronic form documents to assist harried readers. Tools exist to search for documents both on the Internet and locally. Once a document is identified and retrieved, automatic summarization techniques, such as the Reader&#39;s Helper™, described in a commonly owned copending U.S. patent application Ser. No. 08/995,616, entitled AUTOMATIC ADAPTIVE DOCUMENT HELP SYSTEM, the contents of which are incorporated herein by reference for all purposes, helps the reader to find as well as assimilate the information he or she wants more quickly. However, there is heretofore no automatic assistance available to the reader who desires to work with printed hardcopy of electronic documents.  
         [0011]     What is needed is a document printing system that helps the reader print the information he or she wants more quickly. The document printing system should be easily personalizable, flexible and adaptive as well.  
       BRIEF SUMMARY OF THE INVENTION  
       [0012]     An automatic printing assistant application for documents in electronic form is provided by virtue of the present invention. In certain embodiments, an elongated thumbnail image of all or part of an electronically stored document is displayed. A section of the document of interest to the reader is emphasized. Movement of the emphasized area in the elongated thumbnail image assists the user with the selection of sections or pages of the document for printing. The operation of the assistant is personalizable for a particular user by setting of a sensitivity level and selection of relevant topics of interest. Some embodiments of the assistant are also capable of improved performance over time by both automatic and manual feedback. The assistant is usable with many popular electronic document formats.  
         [0013]     In accordance with a first aspect of the present invention, a method for adaptively controlling printing of an electronically stored document includes a step of accepting user input indicating a user-specified concept of interest. A step of analyzing the electronically stored document to identify locations of discussion of the user-specified concept of interest may also be included. Embodiments can also include a step of displaying visual indications of the identified locations. In another step, user input indicating a print preference for certain locations is accepted. Finally, portions of the electronic document corresponding to the user&#39;s print preferences are printed.  
         [0014]     In accordance with a second aspect of the present invention, the method for assisting a reader in printing an electronically stored document also includes a step of accepting user input indicating a print preference by emphasizing an area of interest to the user in a thumbnail image corresponding to a section of interest to the user in the document. The user can control printing by sliding the emphasized area through thumbnail image for the purposes of indicating sections of the electronically stored document to print.  
         [0015]     In select embodiments in accordance with the present invention, the method includes a step of displaying an elongated thumbnail image of a portion of the electronically stored document in a viewing area of a display.  
         [0016]     In certain embodiments in accordance with the present invention, the step of analyzing the electronically stored document to identify locations of discussion of the user-specified concept of interest may be realized by exploiting a probabilistic inference method, such as a Bayesian belief network or its equivalent to identify such locations.  
         [0017]     Numerous benefits are achieved by way of the present invention over conventional techniques. In some embodiments, the present invention is more user friendly than conventional techniques. The present invention can provide a way for the user to obtain hardcopy of only those sections of a large document which contain concepts of interest. Some embodiments according to the invention are more robust than known techniques. These and other benefits are described throughout the present specification and more particularly below.  
         [0018]     A further understanding of the nature and advantages of the inventions herein may be realized by reference to the remaining portions of the specification and the attached drawings. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0019]      FIG. 1  depicts a representative computer system suitable for implementing the present invention.  
         [0020]      FIG. 2  depicts a representative document printing dialog in accordance with a particular embodiment of the present invention.  
         [0021]      FIG. 3  depicts a simplified flowchart of representative process steps in accordance with a specific embodiment of the invention.  
         [0022]      FIG. 4  depicts a simplified flowchart of representative process steps in accordance with an alternative embodiment of the invention.  
         [0023]      FIG. 5  depicts a top-level software architectural diagram for automatic annotation in accordance with one embodiment of the present invention.  
         [0024]      FIGS. 6A-6C  depict a detailed software architectural diagram for automatic annotation in accordance with one embodiment of the present invention.  
         [0025]      FIG. 7  depicts a representative Bayesian belief network useful in automatic annotation in accordance with one embodiment of the present invention.  
         [0026]      FIG. 8  depicts a user interface for defining a user profile in accordance with one embodiment of the present invention.  
         [0027]      FIGS. 9A-9B  depict an interface for providing user feedback in accordance with one embodiment of the present invention.  
         [0028]      FIG. 10  depicts a portion of an HTML document processed in accordance with one embodiment of the present invention. 
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0029]     The present invention provides a personalizable system for printing automatically annotated documents based upon concepts of interest to a particular user. An embodiment according to the present invention has been reduced to practice under the name Reader&#39;s Helper™.  
         [0000]     Computer System Usable for Implementing the Present Invention  
         [0030]      FIG. 1  depicts a representative computer system suitable for implementing the present invention.  FIG. 1  shows basic subsystems of a computer system  10  suitable for use with the present invention. In  FIG. 1 , computer system  10  includes a bus  12  which interconnects major subsystems such as a central processor  14 , a system memory  16 , an input/output controller  18 , an external device such as a printer  20  via a parallel port  22 , a display screen  24  via a display adapter  26 , a serial port  28 , a keyboard  30 , a fixed disk drive  32  and a floppy disk drive  33  operative to receive a floppy disk  33 A. Many other devices may be connected such as a scanner  34  via I/O controller  18 , a mouse  36  connected to serial port  28  or a network interface  40 . Many other devices or subsystems (not shown) may be connected in a similar manner. Also, it is not necessary for all of the devices shown in  FIG. 1  to be present to practice the present invention, as discussed below. The devices and subsystems may be interconnected in different ways from that shown in  FIG. 1 . The operation of a computer system such as that shown in  FIG. 1A  is readily known in the art and is not discussed in detail in the present application. Source code to implement the present invention may be operably disposed in system memory  16  or stored on storage media such as a fixed disk  32  or a floppy disk  33 A. Image information may be stored on fixed disk  32 .  
         [0000]     Annotated Document Printing User Interface  
         [0031]      FIG. 2  depicts a representative user interface  200  in accordance with a particular embodiment of the invention. The representative user interface of  FIG. 2  includes a print dialog  200  which provides the user with the capability to control the printing of an annotated document. As will be explained further below, an automatic annotation system according to the present invention adds annotations to any document available in electronic form. The document need not include any special information to assist in locating discussion of concepts of interest. These annotations denote text relevant to user-selected concepts. The print dialog  200  enables the user to obtain printed copy of sections of an electronically stored document having the greatest relevance to the concepts of interest to the user.  
         [0032]     An elongated thumbnail image  214  of many pages, or all of an electronically stored document  502  is presented in viewing area  215 . Elongated thumbnail image  214  provides a convenient view of the basic document structure. The annotations incorporated into the document are visible within elongated thumbnail image  214 . Within elongated thumbnail image  214 , an emphasized area  214 A shows a highlighted view of a portion of  502  the document. Thus, a user  504  may change the window size, causing emphasized area  214 A to change in size accordingly. The greater the viewing area allocated to elongated thumbnail image  214  and emphasized area  214 A, the more detail is visible. With very small allocated viewing areas, only sections of the document may be distinguishable. As the allocated area increases, individual lines and eventually individual words become distinguishable. Emphasized viewing area  214 A may be understood to be a lens or a viewing window over the part of elongated thumbnail image  214  corresponding to the document section of greatest interest to user  504 . User  504  may scroll through elongated thumbnail  214  by sliding emphasized area  214 A through elongated thumbnail  214  using mouse  36  or keys on keyboard  30 . As emphasized area  214 A shifts, the section of document  502  displayed in elongated thumbnail visible through emphasized area  214 A will also shift.  
         [0033]     The elongated thumbnail image  214  in  FIG. 2  displays each page of document  502  as being displayed at the same reduced scale. In  FIG. 2 , the user-configured ratio is approximately 6:1. The present invention also contemplates other modes of scaling elongated thumbnail image  214 . For example, one may display emphasized area  214 A at a scale similar to that shown in  FIG. 2  and use a variable scale for the rest of elongated thumbnail image  214 . Text from far away emphasized area  214 A would be displayed at a highly reduced scale and the degree of magnification would increase with nearness to emphasized area  214 A.  
         [0034]     The annotations in elongated thumbnail image  214  make it very easy to find relevant text anywhere in document  502 . Furthermore, elongated thumbnail image  214  provides a highly useful way of keeping track of a lengthy document. These features enable the user  504  to readily locate portions of the document for printing.  
         [0035]     A series of concept indicators  206  permit the user to view which concepts of interest are to be noted in the document. Concepts are defined by means of a defined concepts list  806 , which lists all of the concepts which have been added to a user profile. By selecting a concept add button  808 , the user may add a new concept. The user profile is discussed below in greater detail.  
         [0036]     A sensitivity control  208  permits the user to select the degree of sensitivity to apply in identifying potential locations of relevant discussion for printing. At low sensitivity, more locations will be denoted as being relevant, even though some may not be of any actual interest. At high sensitivity, most all denoted locations will in fact be relevant but some other relevant locations may be missed. After each concept name appearing by one of concept indicators  206  appears a percentage giving the relevance of the currently viewed document to the concept. These relevance levels offer a quick assessment of the relevance of the document to the selected concepts.  
         [0037]     Miscellaneous printing options are found on a printing options button bar  216 .  
         [0000]     Printing Assistance  
         [0038]      FIG. 3  depicts a representative flowchart  301  of simplified process steps in a particular embodiment of the computer-implemented method for controlling printing of an electronically stored document according to the present invention. In a step  302 , user input indicating user-specified concepts of interest is accepted. Then, in a step  304 , the electronically stored document  504  is analyzed in order to identify locations of discussion of the user-specified concepts of interest input in step  302 . The analysis step  304  is detailed in  FIG. 5  and the accompanying text below. Next, in a step  306 , visual indications of the locations identified in step  304  are displayed to the user. In a step  308 , user input indicating a print preference among the locations identified in step  304  is accepted. Finally, in a step  310 , portions of the electronic document corresponding to the user&#39;s print preference and the locations discussing the user&#39;s concepts of interest are printed.  
         [0039]      FIG. 4  depicts a representative flowchart  401  of simplified process steps in a particular embodiment of the computer-implemented method for controlling printing of an electronically stored document according to the present invention. In a step  402 , user input indicating user-specified concepts of interest is accepted. Then, in a step  404 , the electronically stored document  504  is analyzed in order to identify locations of discussion of the user-specified concepts of interest input in step  402 . Next, in a step  406 , visual indications of the locations identified in step  404  are displayed to the user  504  by means of an elongated thumbnail image of a portion of the electronically stored document  502  in a viewing area of a display. Then, in a step  408 , an area of a thumbnail image corresponding to a section of interest of electronically stored document  502  is emphasized in order to form an emphasized area. In a step  410 , input from user  504  controlling sliding of the emphasized area formed in step  408  through the thumbnail image is accepted for the purpose of indicating sections of electronically stored document  504  to print. Finally, in a step  412 , portions of the electronic document corresponding to the user&#39;s print preference and the locations discussing the user&#39;s concepts of interest are printed.  
         [0040]     In a particular embodiment, user  504  uses a sensitivity control  208  to select the degree of sensitivity to apply in identifying potential locations of relevant discussion. At low sensitivity, more locations will be denoted as being relevant, even though some may not be of any actual interest. At high sensitivity, most all denoted locations will in fact be relevant but some other relevant locations may be missed.  
         [0000]     Automatic Annotation Software  
         [0041]      FIG. 5  depicts a top-level software architectural diagram for analyzing electronically stored document  502  in accordance with step  304  of  FIG. 3  and step  404  of  FIG. 4 . The method for analyzing electronically stored document  502  is more fully detailed in a commonly owned copending U.S. patent application Ser. No. 08/995,616, entitled AUTOMATIC ADAPTIVE DOCUMENT HELP SYSTEM, which is incorporated herein by reference in its entirety for all purposes. Document  502  is stored in an electronic format. It may have been scanned in originally. It may be, e.g., in HTML, Postscript, LaTeX, other word processing or e-mail formats, etc. The description that follows assumes an HTML format. However, other formats may be used without departing from the scope of the present invention. A user  504  accesses document  502  through a document browser  506 , an annotation agent  508  or elongated thumbnail  214  of printing dialog  200 . Document browser  506  is preferably a hypertext browsing program such as Netscape Navigator or Microsoft Explorer but also may be, e.g., a conventional word processing program.  
         [0042]     Annotation agent  508  adds the annotations to document  502  to prepare it for viewing by document browser  506  and printing by printing dialog  200 . Processing by annotation agent  508  may be understood to be in three stages, a text processing stage  510 , a content recognition stage  512 , and a formatting stage  514 . The input to text processing stage  510  is raw text. The output from text processing stage  510  and input to content recognition stage  512  is a parsed text stream, a text stream with formatting information such as special tags around particular words or phrases removed. The output from content recognition stage  512  and input to formatting stage  514  is an annotated text stream. The output of formatting stage  514  is a formatted text file which may be printed using print dialog  200  or viewed with document browser  506 .  
         [0043]     The processing of annotation agent  508  is preferably a run-time process. The annotations are not preferably pre-inserted into the text but are rather generated when user  504  requests document  502  for browsing. Thus, this is preferably a dynamic process. Annotation agent  508  may also, however, operate in the background as a batch process.  
         [0044]     The annotation added by annotation agent  508  depends on concepts of interest selected by user  504 . User  504  also inputs information used by annotation agent  508  to identify locations of discussion of concepts of interest in document  502 . In a preferred embodiment, this information defines the structure of a Bayesian belief network. The concepts of interest and other user-specific information are maintained in a user profile file  516 . User  504  employs a profile editor  518  to modify the contents of user profile file  516 .  
         [0045]      FIG. 6A  depicts the automatic annotation software architecture of  FIG. 5  with text processing stage  510  shown in greater detail.  FIG. 6A  shows that the source of document  502  may be accessed via a network  602 . Possible sources include e.g., the Internet  604 , an intranet  606 , a digital copier  608  that captures document images, or other office equipment  610  such as a fax machine, scanner, printer, etc. Another alternative source is the user&#39;s own hard drive  32 .  
         [0046]     Text processing stage  510  includes a file I/O stage  612 , an updating stage  614 , and a language processing stage  616 . File I/O stage reads the document file from network  602 . Updating stage  614  maintains a history of recently visited documents in a history file  618 . Language processing stage  616  parses the text of document  502  to generate the parsed text output of text processing stage  510 .  
         [0047]      FIG. 6B  depicts the automatic annotation software architecture of  FIG. 5  with content recognition stage  512  shown in greater detail. A pattern identification stage  620  looks for particular patterns in the parsed text output of text processing stage  510 . The particular patterns searched for are determined by the contents of user profile file  516 . Once the patterns are found, annotation tags are added to the parsed text by an annotation tag addition stage  622  to indicate the pattern locations. In a preferred HTML embodiment, these annotation tags are compatible with the HTML format. However, the tagging process may be adapted to LaTeX, Postscript, etc. A profile updating stage  624  monitors the output of annotation tag addition stage  622  and analyzes text surrounding the locations of concepts of interest. As will be further discussed with reference to  FIG. 7  changes the contents of user profile file  516  based on the analysis of this surrounding text. The effect is to automatically refine the patterns searched for by pattern identification stage  620  to improve annotation performance.  
         [0048]      FIG. 6C  depicts the automatic annotation software architecture of  FIG. 5  with formatting stage  514  shown in greater detail. Formatting stage  514  includes a text rendering stage  626  that formats the annotated text provided by content recognition stage  512  to facilitate viewing by document browser  506  and printing by print dialog  200 .  
         [0049]     Pattern identification stage  620  looks for keywords and key phrases of interest and locates relevant discussion of concepts based on the located keywords. The identification of keywords and the application of the keywords to locating relevant discussion is preferably accomplished by reference to a belief system. The belief system is preferably a Bayesian belief network.  
         [0050]      FIG. 7  depicts a portion of a representative Bayesian belief network  700  implementing a belief system as used by pattern identification stage  622 . A first oval  702  represents a particular user-specified concept of interest. Other ovals  704  represent subconcepts related to the concept identified by oval  702 . Each line between one of subconcept ovals  704  and concept oval  702  indicates that discussion of the subconcept implies discussion of the concept. Each connection between one of subconcept ovals  704  and concept oval  702  has an associated probability value indicated in percent. These values in turn indicate the probability that the concept is discussed given the presence of evidence indicating the presence of the subconcept. Discussion of the subconcept is in turn indicated by one or more keywords or key phrases (not shown in  FIG. 7 ).  
         [0051]     The structure of Bayesian belief network  700  is only one possible structure applicable to the present invention. For example, one could employ a Bayesian belief network with more than two levels of hierarchy so that the presence of subconcepts is suggested by the presence of “subsubconcepts” and so on. In the preferred embodiment, presence of a keyword or key phrase always indicates presence of discussion of the subconcept but it is also possible to configure the belief network so that presence of a keyword or key phrase suggests discussion of the subconcept with a specified probability.  
         [0052]     The primary source for the structure of Bayesian belief network  700  including the selection of concepts, keywords and key phrases, interconnections, and probabilities is user profile file  516 . In a preferred embodiment, user profile file  516  is selectable for both editing and use from among profiles for many users.  
         [0053]     The structure of belief system  700  is modifiable during use of the annotation system. The modifications may occur automatically in the background or may involve explicit user feedback input. The locations of concepts of interest determined by pattern identification stage  620  are monitored by profile updating stage  624 . Profile updating stage  624  notes the proximity of other keywords and key phrases within each analyzed document to the locations of concepts of interest. If particular keywords and key phrases are always near a concept of interest, the structure and contents of belief system  700  are updated in the background without user input by profile updating stage  624 . This could mean changing probability values, introducing a new connection between a subconcept and concept, or introducing a new keyword or key phrase.  
         [0054]     User  504  may select a word or phrase in document  502  as being relevant to a particular concept even though the word or phrase has not yet defined to be a keyword or key phrase. Belief system  700  is then updated to include the new keyword or key phrase.  
         [0055]     User  504  may also give feedback for an existing key word or key phrase, indicating the perceived relevance of the keyword or key phrase to the concept of interest. If the selected keyword or key phrase is indicated to be of high relevance to the concept of interest, the probability values connecting the subconcept indicated by the selected keywords or key phrases to the concept of interest increases. If, on the other hand, user  504  indicates the selected keywords or key phrases to be of little interest, the probability values connecting these keywords or key phrases to the concept decrease.  
         [0000]     User Profile and Feedback Interfaces  
         [0056]      FIG. 8  depicts a user interface for defining a user profile in accordance with one embodiment of the present invention. User interface screen  800  is provided by profile editor  518 . A profile name box  802  permits the user to enter the name of the person or group to whom the profile to be edited is assigned. This permits the annotation system according to the present invention to be personalized to particular users or groups. A password box  804  provides security by requiring entry of a correct password prior to profile editing operations.  
         [0057]     A defined concepts list  806  lists all of the concepts which have already been added to the user profile. By selecting a concept add button  808 , the user may add a new concept. By selecting a concept edit button  810 , the user may modify the belief network as it pertains to the listed concept that is currently selected. By selecting a remove button  812 , the user may delete a concept.  
         [0058]     If a concept has been selected for editing, its name appears in a concept name box  813 . The portion of the belief network pertaining to the selected concept is shown in a belief network display window  814 . Belief network display window  814  shows the selected concept, the subconcepts which have been defined as relating to the selected concept and the percentage values associated with each relationship. The user may add a subconcept by selecting a subconcept add button  815 . The user may edit a subconcept by selecting the subconcept in belief network display window  814  and then selecting a subconcept edit button  816 . A subconcept remove button  818  permits the user to delete a subconcept from the belief network.  
         [0059]     Selecting subconcept add button  815  causes a subconcept add window  820  to appear. Subconcept add window  820  includes a subconcept name box  822  for entering the name of a new subconcept. A slider control  824  permits the user to select the percentage value that defines the probability of the selected concept appearing given that the newly selected subconcept appears. A keyword list  826  lists the keywords and key phrases which indicate discussion of the subconcept. The user adds to the list by selecting a keyword add button  828  which causes display of a dialog box (not shown) for entering the new keyword or key phrase. The user deletes a keyword or key phrase by selecting it and then selecting a keyword delete button  830 . Once the user has finished defining the new subconcept, he or she confirms the definition by selecting an OK button  832 . Selection of a cancel button  834  dismisses subconcept add window  820  without affecting the belief network contents or structure. Selection of subconcept edit button  816  causes display of a window similar to subconcept add window  820  permitting redefinition of the selected subconcept.  
         [0060]     By determining whether a background learning checkbox  836  has been selected, the user may enable or disable the operation of profile updating stage  624 . A web autofetch check box  838  permits the user to select whether or not to enable an automatic web search process. When this web search process is enabled, whenever a particular keyword or key phrase is found frequently near where a defined concept is determined to be discussed, a web search tool such as AltaVista′ is employed to look on the World Wide Web for documents containing the keyword or key phrase. A threshold slider control  840  is provided to enable the user to set a threshold relevance level for this autofetching process.  
         [0061]      FIGS. 9A-9B  depict a user interface for providing feedback in accordance with one embodiment of the present invention. User  502  may select any text and call up a first feedback window  902 . The text may or may not have been previously identified by the annotation system as relevant. In first feedback window  902  shown in  FIG. 9A , user  504  may indicate the concept to which the selected text is relevant. First feedback window  902  may not be necessary when adjusting the relevance level for a keyword or key phrase that is already a part of belief network  700 . After the user selects a concept in first feedback window  902 , a second feedback window  904  is displayed for selecting the degree of relevance. Second feedback window  904  in  FIG. 9B  provides three choices for level of relevance: good, medium (not sure), and bad. Alternatively, a slider control could be used to set the level of relevance. If the selected text is not already a keyword or key phrase in belief network  700 , a new subconcept is added along with the associated new keyword or key phrase. If the selected text is already a keyword or key phrase, above, probability values within belief system  622  are modified appropriately in response to this user feedback.  
         [0062]      FIG. 10  depicts a portion of an HTML document  1000  processed in accordance with one embodiment of the present invention. A sentence including relevant text is preceded by an a &lt;RH.ANOH.S . . . &gt; tag  1002  and followed by an &lt;/RH.ANON.S&gt; tag  1004 . The use of these tags facilitates the annotation mode where complete sentences are highlighted. The &lt;RH.ANOH.S . . . &gt; tag  1002  includes a number indicating which relevant sentence is tagged in order of appearance in the document. Relevant text within a so-tagged relevant sentence is preceded by an &lt;RH.ANOH . . . &gt; tag  1006  and followed by an &lt;/RH.ANOH&gt; tag  1008 . The &lt;RH.ANOH . . . &gt;  1006  tag include the names of the concept and subconcept to which the annotated text is relevant, an identifier indicating which relevant sentence the text is in and a number which identifies which annotation this is in sequence for a particular concept. An HTML browser that has not been modified to interpret the special annotation tags provided by the present invention will ignore them and display the document without annotations.  
         [0000]     Software Implementation  
         [0063]     In a preferred embodiment, software to implement the present invention is written in the Java language. Preferably, the software forms a part of a stand-alone browser program written in the Java language. Alternatively, the code may be in the form of a so-called “plug-in” operating with a Java-equipped web browser used to browse HTML documents including the special annotation tags explained above.  
         [0064]     In the foregoing specification, the invention has been described with reference to specific exemplary embodiments thereof. For example, any probabilistic inference method may be substituted for a Bayesian belief network. It will, however, be evident that various modifications and changes may be made thereunto without departing from the broader spirit and scope of the invention as set forth in the appended claims and their full scope of equivalents.

Technology Category: 4