Abstract:
A method and apparatus for processing user entered input and providing a response in a system for autonomously processing requests includes rules. For each rule, whether the input is recognized is determined. If it is, a response is sent to the user. To determine recognized input, the method attempts to match the rule to a pattern. If a match is not found, the input is not recognized. If a match is found, the input is recognized and the response is sent. Alternatively, the input is conditionally recognized and a statement validator is executed which queries structured data to determine if a logic statement evaluates to true. Depending on how the statement evaluates: i) the input is recognized and the response is sent, ii) the structured data is queried again for the next statement validator, or iii) the input is not recognized and the method continues to the next rule.

Description:
[0001]     This application is a continuation application claiming priority to U.S. patent application Ser. No. 10/705,679 entitled “Method and System for Responding to Requests Relating to Complex Data Maintained in a Structured Form,” filed Nov. 10, 2003, which is incorporated herein by reference in its entirety. 
     
    
     BACKGROUND OF THE INVENTION  
       [0002]     1. Field of the Invention  
         [0003]     The present invention is directed to a method and system for autonomously processing requests. More particularly, this invention is directed to a method and system for acting on requests and queries received from users regarding complex data maintained in a structured form.  
         [0004]     2. Description of Related Art  
         [0005]     For the purposes of the present invention, data maintained in a database, file, or other source of structured and/or tagged data is referred to as “structured data.” So called “virtual robots” (or “Bots”) are software programs that interact and/or communicate with users (human, machine, or otherwise) and take actions or make responses according to input from these users. “Bot” refers to any program which interacts with a user in some fashion and should not be assumed to refer only to physically embodied robots. “Input” refers to any description of a situation the Bot may encounter; although the most common inputs are textual inputs from users, inputs can be actions taken by users, external circumstances, or even events internal to the Bot such as an internal alarm clock.  
         [0006]     A common use of a Bot is as an interface to a web site where the administrator of that site (the “administrator”) has programmed the Bot to answer simple inquiries (the “input”) that are typically asked by visitors to the site. The Bot finds a pattern, consisting of text and/or code, that best matches the input, and then takes the action that it is programmed to take in connection with that pattern (the “response”). The response can take the form of a text string that contains the information sought by the user (which text string can be transmitted to the user in text form, “read” by a text-to-speech engine, played back to the user as a wave file, or otherwise transmitted to the user in a comprehensible form) or the response can be any other action of which a program is capable, for example, opening a web page, turning a circuit on or off, initiating or ending a program, and the like.  
         [0007]     It is desirable that the Bot be scripted to anticipate the inputs that it is likely to receive and the situations that it is likely to encounter. Because users may ask questions or otherwise create inputs in a wide variety of different ways, a large variety of patterns are required to comprehensively anticipate the variety of inputs that the Bot may receive. This complexity is greatly increased by the number of different ways a user may create any particular input. For example, if a user wants to know the name of the president of the Administrator&#39;s company, the user may input a text string reading “Who is your President?”, “What&#39;s the President&#39;s name?”, or even “Who&#39;s the top dog at AdminCo.?” 
         [0008]     Historically, Bots have been scripted manually, by having one or more human scripters write patterns for the Bot and tie those patterns to appropriate responses. Such human scripting, although usually necessary, has a number of drawbacks. First, scripting is time-consuming. A typical Bot may contain thousands of possible patterns and responses, all of which need to be scripted. Second, the list of patterns and responses is usually incomplete. It is almost impossible for the scripters to comprehensively cover all possible patterns for a large substantial body of information and desired responses. Furthermore, there is a compound increase in the number of patterns where large bodies of data are involved. For example, the complexity and difficulty of scripting, by hand, patterns that pertain to all the ways a user might input a query about a baseball player&#39;s team affiliation, hits, walks, runs, RBI&#39;s, batting average, and errors, together with appropriate responses, is very high. This task rapidly becomes insurmountable if, for example, one is trying to script this information by hand for all the baseball players in the major leagues for the past 20 years. The time, expense, and difficulty become very high, as does the opportunity for scripter error and omission. Moreover, as the information changes or is added to over time, the time, expense, and difficulty of maintaining the patterns and responses that refer to the information are very substantial as well. Similar problems are encountered where scripters are faced with a company having a large line of products or a government agency having a large number of employees, assets, or services.  
         [0009]     Scripters have tried to work around this problem in a limited way by referring to databases or external software programs (e.g., a search engine, time clock, or weather report) when scripting the responses. This has the advantage of allowing dynamic information to be included in an answer, such that it will change as necessary. However, even this method is of limited utility, because patterns must still be hard coded with all necessary variations to generate the appropriate response. Where users are likely to ask for the information that they want by reference to another piece of data that would itself typically be stored as structured data, for example, the name of a baseball player, a product, or a government employee, properly hard coding appropriate patterns is both daunting initially and expensive to maintain and update.  
         [0010]     This process has been eased somewhat by maintaining data about users, such as their address, phone number, stock portfolio, or the like. This is useful in that, when a user inputs a query such as “what is the weather like?”, the Bot can assume that the input means “what is the weather like at my address?” and can respond appropriately. Although useful, it will be appreciated that this technique does not obviate the need to disambiguate those items of information that refer not to the user, but to a large amount of data unrelated to the user. For example, if a user&#39;s favorite baseball player was Jose Canseco, and this information was maintained in a data field, the technique described in this paragraph could enable the Bot to look in response to the question “How did my favorite ballplayer do today?” for information regarding the baseball player Jose Canseco. However, if the same user input the question “How did Jose Canseco do today?”, the same Bot would not know who Jose Canseco was, or even that he was a baseball player, without this information being hard coded into a pattern containing the words “Jose Canseco.” 
         [0011]     Thus, there is a need in the art to provide a method enabling scripters to be able to create patterns that refer to information that is maintained in a database, file, spreadsheet, or otherwise as structured data, without manually hard coding the structured data itself into the patterns. Additionally, there is a need for a method that acts on requests and queries received from users regarding complex data maintained in a structured form.  
       BRIEF SUMMARY OF THE INVENTION  
       [0012]     It is an object of this invention to provide Bots that include patterns (or text strings) that are written in a very high level language that closely resembles a human natural language and that are intended to anticipate the inputs that may be received from users.  
         [0013]     The present invention meets these objectives by providing a variety of mechanisms for referring to structured data. The invention cooperates with an automated interface program designed to interact and communicate with users. The invention executes actions to enable an engine of such program to recognize inputs containing terms that are made available to the engine in the form of structured data.  
         [0014]     In various embodiments of the present invention, relevant portions of the input are used either to query the structured data or to test the validity of the logical statement.  
         [0015]     Generally, the method according to the present invention includes receiving input, matching the input to a pattern, querying structured data based on instructions contained in a rule containing the pattern, using the result of the structured data inquiry to determine the validity or invalidity of a logical statement, recognizing or not recognizing the input based upon the validity or invalidity of the logical statement, triggering the rule, and generating a response.  
         [0016]     More specifically, the present invention is a method for processing input entered by a user and providing at least one response in a system for autonomously processing requests. A set of rules is provided. A user enters an input or a request. For each rule in the set, it is determined whether the input is recognized. If the input is recognized, an appropriate response is sent to the user.  
         [0017]     To determine if the input is recognized, the invention attempts to match the input to a pattern contained in a set of patterns scripted to match potential inputs. If no match is found, the input is not recognized and the invention proceeds to the next rule. If a match is found, the input is recognized and the appropriate response is sent or at least one statement validator is executed to determine if a logic statement provided by the statement validator is a valid statement. One or more statement validators may be used.  
         [0018]     A statement validator queries structured data to determine if the logic statement is true. If the logic statement is true, the input is recognized and if another statement validator is present, then the structured data is queried again, otherwise the appropriate response is sent. If the logic statement is false, the input is not recognized and the process continues to the next rule. 
     
    
     BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS  
       [0019]      FIG. 1  is a schematic drawing of an operating environment of the present invention;  
         [0020]      FIG. 2 . 1  is a flow chart of the processes used by an engine of the present invention;  
         [0021]      FIG. 2 . 2  is a flow chart of the processes used by a preprocess input component of an engine of the present invention;  
         [0022]      FIG. 3 . 1  is a schematic drawing of a script and associated component parts of the present invention;  
         [0023]      FIG. 3 . 2  is a flow chart of an input recognizer component of a script of the present invention;  
         [0024]      FIG. 4 . 1  is a flow chart of a statement validator of the present invention;  
         [0025]      FIG. 4 . 2  is a flow chart of another statement validator of the present invention;  
         [0026]      FIG. 4 . 3  is a flow chart of yet another statement validator of the present invention;  
         [0027]      FIG. 4 . 4  is a flow chart of yet another statement validator of the present invention;  
         [0028]      FIG. 5 . 1  is a flow chart of a logic layer component of a script of the present invention; and  
         [0029]      FIG. 5 . 2  is a flow chart of a response layer component of a script of the present invention. 
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0030]     A complete understanding of the present invention will be obtained from the following description when taken in connection with the accompanying drawing figures wherein like reference characters identify like elements throughout.  
         [0031]     The general architecture of the present invention will now be described. Referring to  FIG. 1 , an operating environment of the present invention is depicted. The environment can be characterized generally into three sections: a front end section  120 , a Bot processor section  140 , and an administration section  160 .  
         [0032]     The front end section  120  is generally an environment in which a user  101  interacts with a Bot connection interface  104 , possibly via a user interface  102  that may be connected to the Bot connection interface  104  via a network  103 . The user interface  102  can be anything capable of receiving human or machine language input, including, without limitation, a computer, a Personal Digital Assistant (PDA), a telephone, or a pager. The user interface  102  will also typically have some form of client software  110  installed to provide a text box, buttons, or other method for the entry of user  101  inputs and some method for displaying intelligible responses from the Bot. The network  103  can be any system capable of transmitting such input over any distance, including, without limitation, a local area network (LAN), the Internet, a “wifi” (wireless fidelity), cellular or other wireless data connection, a virtual private network (VPN), or simply a hard wired telephone system. The user  101  can also simply act directly upon the Bot connection interface  104 . In such circumstances (as well as in circumstances such as telephony where the user input will not support client software  110 ), client software  110  will usually be resident in the Bot connection interface  104  to facilitate user  101  interaction. It will be appreciated that many other means of connection to the Bot processor section  140  are well known to those skilled in the art and that the present invention should not be limited to any particular aspects of the general operating environment as disclosed herein.  
         [0033]     In a common use of Bot technology, the user  101  connects to a site where the user interface  102  includes client software  110 . The advantage for the site developer is that the user  101  may have a help or information request that is easily handled via a Bot using the client software  110 . It is not uncommon to find sites having a list of FAQs (Frequently Asked Questions) which serve the purpose of handling very low level user concerns and questions. However, where there are a substantial number of FAQ&#39;s, pointing and clicking through web pages becomes an inefficient method of finding the required information, as does searching with a conventional search engine. Bots provide a more efficient method of obtaining information and of handling more advanced questions or interactions with the site.  
         [0034]     In the operating environment of this embodiment of the present invention, the Bot connection interface  104  consists of hardware, an operating system, and any application software necessary to support a Bot engine  210  and enable the Bot engine  210  to receive inputs and send responses in a chosen communications mode. Necessary application software in the Bot connection interface  104  may include an email application, an instant messaging application, an internet relay chat (IRC) application, voice recognition software, or other applications, as necessary, to support the chosen mode or modes of communication between the Bot engine  210  and the user  101 . The client software  110 , along with structured data  105  and script storage  106 , may be resident on the Bot connection interface  104 , although these may also be hosted on a remote computer and made available to the Bot engine  210  via a network  103  or other connection.  
         [0035]     As the user  101  sends inputs, the Bot engine  210  receives the inputs, processes the inputs, and generates responses. Typically, where the user  101  is human, a two way communications dialogue occurs between the user  101  and the Bot engine  210  in that the user  101  may ask questions, make declarative statements, and perform other normal communications patterns that typify modes of human communications. For the purposes of the present invention, “communications” is intended to be a broad concept. Indeed, suitable communications may be in the form of written or spoken language, graphics, URL&#39;s, or the like that may be passed to and from a user and an automatic interface program, such as the present invention.  
         [0036]     In turn, the Bot engine  210  accepts the inputs generated by the user  101  and generates responses by processing the inputs according to a script or scripts  310  that are stored in the script storage  106 . As will be discussed in greater detail in connection with  FIGS. 3 . 1  and  3 . 2 , the scripts  310  contain rules  311  and are typically created at the administration section  160  as necessary or appropriate for the specific use to which the Bot will be put. For example, if the site using the Bot engine  210  is a site for a reseller of personal computers, then the scripts  310  should be designed to handle questions and discussions concerning personal computers and their peripherals. Thus, the administration section  160  will generate the scripts  310  such that the scripts  310  will guide the discussion concerning many computer-related topics. The scripts  310  are then stored for use by the Bot engine  210 , or, alternatively, the scripts  310  may be compiled by a compiler and the compiled code incorporated into an engine (see, for example, U.S. Pat. No. 6,532,401).  
         [0037]     The administration section  160  consists of an administrator  108 , an administrator interface  109 , and an editor  111 . The administrator  108  is the human being who creates the scripts  310  that govern the behavior of the Bot engine  210 . Typically, this human being accomplishes this task through the use of the administrator interface  109  that has a text box or boxes or other entry points for the input of patterns, as well as a response or responses associated with that input. The administrator interface  109  may also provide various tools to facilitate the process of inputting the patterns in an organized and efficient way. The editor  111  takes the patterns provided by the administrator  108  and associates them with the appropriate response or responses. The administrator interface  109  and the editor  111  may be created as a single unit or may be designed to reside in separate computers. It will be appreciated by those skilled in the art that the scripts  310  can be written by human administrators or by automated or partially automated script creation tools and that the present invention should not be limited to scripts written by humans or otherwise.  
         [0038]     Although  FIG. 1  gives a general description of various operating environments in which Bots may exist, it will be appreciated that many other operating environments are obvious to those skilled in the art and that the scope of the present invention should not be so limited to the exemplary descriptions as given above.  
         [0039]     The Bot processor section  140  will now be described.  FIG. 2 . 1  provides a detailed depiction of the processes used by the Bot engine  210  according to the present invention. In step  211 , inputs are brought to the Bot engine  210  via the Bot connection interface  104 , as shown  FIG. 1 . The Bot engine  210  takes the input in step  211  and then, typically, but not necessarily, preprocesses the input to some degree to enable recognition and added functionality in step  220 . Examples of some typical functions that may be contained in the preprocessing of input in step  220  are detailed below. The input is then taken to an input recognizer component  320  of each rule  311  in the script  310 , where it is determined for each rule  311  whether the input is recognized, step  212 . Step  212  is repeated for each rule  311 , for so long as the input is not recognized. Once the input recognizer component  320  of a rule  311  recognizes an input in step  212 , the process continues at step  213  to the next layer of the rule  311 , which is either a response layer (or routine)  340  or a logic layer  330 . Details of the workings of the input recognizer  320 , the logic layer  330 , and the response layer  340  are provided below in connection with  FIGS. 3 . 2 ,  5 . 1 , and  5 . 2   
         [0040]     The preprocessing of input, step  220 , will now be described.  FIG. 2 . 2  provides a detailed depiction of the processes used by the preprocess input step  220 , if utilized, of the Bot engine  210  according to the present invention. The functions contained in the preprocess input step  220  can vary greatly among different Bot designs, depending upon the overall strategy employed by the designer. Typically the preprocess input step  220  is composed of processes that are intended to either: (i) standardize the inputs in some regard in order to reduce the complexity of the input faced by the engine or (ii) extract some level of structure or meaning from the input and embody this as code so that the Bot engine  210  can manipulate or manage it. Examples of the first purpose include a remove punctuation process  222 , a spell check process  223 , an expand contractions process  224 , and a standardize case  225  process. Examples of the second purpose include a lexical analysis process  226 , a semantic analysis process  227 , and other translation processes  228 .  
         [0041]     In the embodiment described herein, the preprocess input step  220  begins by taking the input in step  221  and then proceeding to remove punctuation in step  222 . Removing the punctuation from a text string removes the ambiguity created by the fact that people punctuate their sentences differently and that some people forget to punctuate at all.  
         [0042]     Next the input is spell checked at step  223  so that spelling errors can be removed, further minimizing text variation due to error or variant usage by the user  101 .  
         [0043]     By proceeding to expand contractions in step  224 , the input is further standardized so that the Bot engine  210  can recognize contracted words, for example, “what&#39;s” as being identical to its constituent parts “what is”, further reducing the complexity of the inputs that the Bot engine  210  must be able to recognize.  
         [0044]     The next step  225  standardizes case, allowing the Bot engine  210  to recognize, for example, “the”, “The”, and “THE” as being identical, and removing as a variable the scheme of capitalization that may have been employed by the user  101 .  
         [0045]     The input is then passed to lexical analysis in step  226 , where processes relating to the meaning of words are performed. As an example, lexical analysis might parse or partition the input to determine those text strings that are synonymous (at least for the administrator&#39;s purposes) with other text strings, for example, “I want”, “I need”, and “Give me”. Typically these text strings would be replaced with a text or code string that stands in for them in the input, allowing a single rule  311  to recognize an input phrased in any of these different ways.  
         [0046]     Next the input goes through semantic analysis in step  227 , which is useful in identifying parts of the sentence, for example, the subject of the sentence, the object of the verb, or the referent of a pronoun. Depending upon the methodologies used, this step can be useful for pattern recognition and/or for maintaining context in a “conversation” between the user  101  and the Bot.  
         [0047]     Finally, the input is passed through other translations in step  228 , where the other translations are any other processes whereby strings are added to or substituted for portions of the input in order to add functionality to the Bot. These processes may include language translation, substitutions of machine language for natural language, or other methodologies.  
         [0048]     Those skilled in the art will readily understand that some or all of the above exemplary processes might be included at this stage in various orders and configurations and that there are other processes of similar purpose that may be undertaken in a Bot suitable for the present invention. Similarly, some or all of these objectives may be achieved by incorporating the functionality into the rules used to recognize inputs.  
         [0049]     The recognition of input, step  212 , will now be described.  FIG. 3 . 1  depicts the structure of an embodiment of a script  310  and its component parts, suitable for the purposes of the present invention. The script  310  contains one or more rules  311  that are in turn composed of an input recognizer  320  and one or more response layers  340 . Some rules  311  may also contain a logic layer  330 , enabling them to fire one or more responses of those that are available. The detailed processes of each of these components are described in more detail below. As those skilled in the art will readily understand, there are many different strategies and methods by which the rules  311  can be ordered, grouped, or sorted in order to enhance the speed or accuracy of the Bot engine  210  and that the present invention should not be limited to any particular method or strategy of ordering, grouping, or sorting the rules  311 .  
         [0050]     The steps of the input recognizer  320  are depicted in more detail in  FIG. 3 . 2 . The first step  321  in input recognition is typically the matching of the preprocessed input to a pattern contained in a set of pattern matches of the input recognizer  320 . A pattern is a coded text string that represents a set of strings. A string matches a pattern if the string is in the set that the pattern represents. Pattern matching may be accomplished by, for example, regular expressions. As those skilled in the art will also be aware, there are many different languages and protocols in which such pattern matchings are commonly carried out, including, without limitation, Perl, Java, PHP, and others, and that the present invention should not be limited by the use of any particular query, language, or protocol. If there is no match found in the pattern matches, the input will not be recognized and the Bot engine  210  will continue to search for a match in other rules  311 . If a pattern match is found, for most Bot engines  210 , the rule  311  will then go into effect.  
         [0051]     The administrator  108  has the option of creating one or more statement (input) validators  41   a - d  involving the querying of the structured data  105  which, if true, will result in the successful recognition of the input in step  324  and the effectiveness of the rule  311 , and which if false, will provide for the non-recognition of the input in step  322  by the input recognizer  320 , with the result that the Bot engine  210  will continue to seek for a matching pattern in other rules  311 . Each of these statement validators  41   a - d  is tested in turn in step  323 , for so long as they continue to be valid. If any statement validator  410   a - d  is invalid, the input is not recognized in step  322 . If all are valid, the input is recognized in step  324 .  
         [0052]     For the purposes of the present invention, four different variations of statement validators  410   a - d  have been identified. The detailed processes of these four statement validators  410   a - d  are depicted in  FIGS. 4 . 1 ,  4 . 2 ,  4 . 3 , and  4 . 4 . Each of these four statement validators  410   a - d  deals with a different method of using the information contained in the input and the information contained in the structured data  105 . As those skilled in the art will also be aware, there are many different languages and protocols in which such queries are commonly carried out, including, without limitation, SQL, XQUERY, LDAP, SOAP, and many others, including many that are adapted to specific data sources and uses, and that the present invention should not be limited by the use of any particular query, language, or protocol. It is also important to emphasize that these statement validators  410   a - d  are not simply a method of querying the structured data  105  to provide elements of a response to an input. Rather, the statement validators  410   a - d  form an integral part of the input recognition process. If the statement validator  410   a - d  provides the anticipated result, the input will be considered recognized in step  324  and the rule  311  will be used. If the statement validator  410   a - d  provides a different result, the input will not-be considered to be recognized in step  322 , and the Bot engine  210  will continue on to other rules  311  in search for a match.  
         [0053]     The first type of statement validator  41   a  (type 1 statement validator) is depicted in detail in  FIG. 4 . 1 . This type of statement validator  410   a  uses a relevant string or strings of input to query the structured data  105  and then finds a logical statement to be true or false based upon the result. In this process the first step  411   a  is to obtain the user&#39;s input. Next, the statement validator  410   a  takes the relevant part of the input in step  412   a.  This will typically be a text string, the position and extent of which is determined by code that is written into the rule  311 . The text string is used to query the structured data  105  in step  413   a , using any of the many queries that those skilled in the art will understand to be available to query the structured data  105 . The result of the query is then used to determine whether a specific logic statement is true or false in step  414   a.  Depending upon the result, either the process will continue in stop  416   a  through the rest of the input recognition process  320  or the input will be considered not to be recognized at step  415   a,  and the Bot engine  210  will go on to the next rule  311  ( FIG. 2  at step  203 ).  
         [0054]     An example of an input recognizer  320  that uses the type 1 statement validator  410   a  ( FIG. 4 . 1  just described) is as follows:  
                                   input: “Is Barry Bonds a baseball player?”       input recognizer:       1) get pattern “*is (playername) a baseball player*” (where (playername)       is not required to match anything)       2) get input “Is Barry Bonds a baseball player?”       3) pattern matches       statement validator type 1:       4) statement validator: get input       5) extract relevant part of input (playername = “Barry Bonds”)       6) run query “select COUNT(playerid) from PLAYERS where       name=‘(playername)’“       7) run logic statement “resuit[0] == 1”       8) statement true       9) continue                  
 
         [0055]     If the logic statement is true, then there is one “Barry Bonds”, so the rule can be used. Had the statement not been true, the input would not be recognized at step  415   a,  and the Bot engine  210  would have continued to the next rule  311  at step  203 . The next rule  311  might be identical, but for the fact that the logic statement tests “result[0]=0”, with the result that it would successfully identify the input where “Barry Bonds” is not, in fact, the name of a baseball player contained in the structured data  105 .  
         [0056]     The second type of statement validator  410   b  (type 2 statement validator) is depicted in detail in  FIG. 4 . 2 . This type of statement validator  410   b  uses a relevant string or strings of input to query the structured data  105  and then finds a logical statement to be true or false based upon both the result of the query and the use of the relevant input string itself. In this process the first step is to obtain the user&#39;s input in step  411   b.  Next, the statement validator  410   b  takes the relevant part of the input in step  412   b.  This will typically be a text string, the position and extent of which is determined by code that is written into the rule  311 . The text string is used to query the structured data  105  in step  413   b,  using any of the many queries that those skilled in the art will understand to be commonly used to query the structured data  105 . The result of the query is then used, together with the relevant part of the input from step  412   b  to determine whether a specific logic statement is true or false in step  414   b.  Depending upon the result, either the process will continue at step  416   b  through the rest of the input recognition process  320  or the input will be considered not to be recognized at step  415   b,  and the Bot engine  210  will go on to the next rule  311  ( FIG. 2  at step  203 ).  
         [0057]     An example of an input recognizer  320  that uses the type 2 statement validator  410   b  ( FIG. 4 . 4  just described) is as follows. Please note that this example first uses two iterations of the type 1 statement validator  410   a  ( FIG. 4 . 1 ) as well.  
                                   input: “Is Barry Bonds a Dodger?”       input recognizer:       1) get pattern “*is (playername) a (teamname)*” (where (playername)         and (team name) are not required to match anything)       2) get input “Is Barry Bonds a Dodger?”       3) pattern matches,       statement validator type 1:       4) statement validator: get input       5) extract relevant part of input (playername = “Barry Bonds”)       6) run query “select COUNT(playerid) from PLAYERS where         name=‘(playername)’“       7) run logic statement “result[0] == 1”       8) statement true       9) continue       statement validator type 1:       10) statement validator: get input       11) extract relevant part of input (teamname = “Dodger”)       12) run query “select COUNT(teamid) from TEAMS where          name=‘(teamname)’“       13) run logic statement “result[0] == 1”       14) statement true       15) continue       statement validator type 2:       16) statement validator: get input       17) extract relevant part of input (playername = “Barry Bonds”)       18) extract relevant part of input (teamname = “Dodger”)       19) run query “select teams.name from TEAMS, PLAYERS where          players.name=‘(playername)’ and players.teamid=teams.teamid”       20) run logic statement “result[0] == (teamname)”       21) statement false       22) input not recognized                  
 
         [0058]     In this example, the input recognizer  320  first uses two type 1 statement validators  410   a  ( FIG. 4 . 3 ) to establish that the relevant parts of the input refer to a player and a team name, respectively. If, for example, the input had not read “Is Barry Bonds a Dodger?”, but “Is Barry Bonds a shortstop?”, the second of the two type 1 statement validators  410   a  (initiating at line  10  above) would have returned a negative result, the input would not be recognized at step  415   b,  and the engine would go on to the next rule  311  ( FIG. 2 . 1  step  213 ). In the present example, the input recognizer continued at step  416   b  to the type 2 statement validator  410   a.  Here the logic statement is false. The player name “Barry Bonds” is not associated with the team name “Dodgers” in our structured data  105 . Had the statement been true, the process would have continued at step  416   b  with the rule  311 . Because the statement is not true, the input is not recognized in step  415   b,  and the Bot engine  210  continues to the next rule  311  ( FIG. 2 . 1  step  213 ). The next rule  311  could be designed to be the same, but for the fact that the next rule  311  exhibits recognition where the logic statement is false, not true.  
         [0059]     The third type of statement validator  410   c  (type 3 statement validator) is depicted in detail in  FIG. 4 . 3 . This type of statement validator  410   c  queries the structured data  105  without using a relevant string or strings of input and then finds a logical statement to be true or false based upon both the result of the query and the use of a relevant input string. The statement validator  410   c  queries the structured data  105  in step  413   c,  using any of the many queries that those skilled in the art will understand to be commonly used to query the structured data  105 , but without using any part of the input. At the same time, the statement validator  410   c  obtains the user&#39;s input in step  411   c.  The statement validator  410   c  takes the relevant part of the input in step  412   c.  This will typically be a text string, the position and extent of which is determined by code that is written into the rule  311 . The result of the query is then used, together with the relevant part of the input in step  412   c  to determine whether a specific logic statement is true or false in step  414   c.  Depending upon the result, either the process will continue at step  416   c  through the rest of the input recognition process  320  or the input will be considered not to be recognized in step  415   c,  and the Bot engine  210  will go on to the next rule  311  ( FIG. 2  step  203 ).  
         [0060]     An example of an input recognizer  320  that uses the type 3 statement validator  410   c  ( FIG. 4 . 3 ) just described is as follows. Please note that the example used is the same as the type 1 statement validator  410   a  ( FIG. 4 . 1 ) above, providing a different method for accomplishing the same result.  
                                   input: “Is Barry Bonds a baseball player?”       input recognizer:       1) get pattern “*is (playername) a baseball player*” (where (playername)         is not required to match anything)       2) get input “Is Barry Bonds a baseball player?”       3) pattern matches,       statement validator type 3:       4) statement validator: get input       5) extract relevant part of input (playername = “Barry Bonds”)       6) run query “select name from PLAYERS”       7) run logic statement “result contains (playername)”       8) statement true       9) continue                  
 
         [0061]     If the logic statement is true, then there is at least one “Barry Bonds” who is a baseball player, so the input is recognized and the process continues at step  416   c  to execute the rule  311 . Had the statement not been true, the input would not be recognized in step  415   c,  and the Bot engine  210  would have continued to the next rule  311  ( FIG. 2 . 1  step  213 ). The next rule  311  might be identical, but for the fact that the logic statement tests “result does not contain [playername]” with the result that it would successfully identify the input where “Barry Bonds” is not, in fact, the name of a baseball player contained in the structured data  105 .  
         [0062]     A fourth type of statement validator  410   d  (type 4 statement validator) is depicted in detail in  FIG. 4 . 4 . This type of statement validator  410   d  queries the structured data  105  without using a relevant string or strings of input and then finds a logical statement to be true or false based upon the result of the query. The statement validator  410   d  queries the structured data  105  in step  413   d,  using any of the many queries that those skilled in the art will understand to be commonly used to query the structured data  105 , but without using any part of the input. Note that this may mean that the information that would be provided by obtaining part of the input is instead made part of the query string that is part of the coding of the input recognizer  320 . The result of the query is then used to determine whether a specific logic statement is true or false in step  414   d.  Depending upon the result, either the process will continue at step  416   d  through the rest of the input recognition process  320  or the input will be considered not to be recognized in step  415   d,  and the Bot engine  210  will go on to the next rule  311  ( FIG. 2  step  203 ).  
         [0063]     An example of an input recognizer  320  that uses the type 4 statement validator  410   d  ( FIG. 4 . 4  just described) is as follows.  
                                                   input: “Do you have stats for last year?”           input recognizer:           1) get pattern “*Do you have stats for last year*”           2) get input “Do you have stats for last year?”           3) pattern matches,           statement validator type 4:           4) run query “select * from PLAYERSTATS where year = 2002”           7) run logic statement “result != false” (result is not empty)           8) statement true           9) continue                      
 
         [0064]     If the logic statement is true, then there are statistics for 2002, so the input is recognized and the process continues at step  416   d  to execute the rule  311 . Had the statement not been true, the input would not be recognized in step  415   d,  and the Bot engine  210  would have continued to the next rule  311  ( FIG. 2 . 1  step  213 ). The next rule might be identical, but for the fact that the logic statement tests “result=false” with the result that it would successfully recognize the input and continue in step  416   d  where there are, in fact, no statistics for the year 2002 in the structured data  105 .  
         [0065]     As demonstrated, there can be any number of statement validators  41   a - d  that work with pattern matches  321  in the input recognizer  320  or none at all. Upon completion of pattern matches  321  and validation  323  of the statement validators  410   a - d,  if any, contained in the input recognizer, the input is ultimately recognized  324  or not recognized  322 . If recognized  324 , the process continues to the next layer of the rule  311 , whether that is a response layer  340  that generates a response to be transmitted to the user or a logic layer  330  that chooses between the various responses to be used in the response layer  340 .  
         [0066]     Those skilled in the art will readily understand that the steps of the input recognizer  320  might occur in various orders (or contemporaneously with each other) and configurations and that there are other processes of similar purpose that may be undertaken in a Bot suitable for the present invention.  
         [0067]     The generation of responses will now be described. The next step in the execution of a rule  311  following recognition of an input at step  324  by the input recognizer  320  is typically to go to a response layer  340  ( FIG. 5 . 2 ), the purpose of which is to obtain and prepare the appropriate response to the user&#39;s input. A typical data flow for a response layer  340  simply involves getting the response in step  521  and sending it to the connection interface in step  526 . A response can typically consist of (i) text, (ii) code to be run in the user interface  102 , and/or (iii) code to be extracted and run locally before sending the response to the Bot connection interface  104 . The response may consist entirely of text, where this is appropriate. However, more complexity and functionality can be provided by adding code to the response. The use of code allows for dynamic information to be added to the answer and is typically used for frequently changing information, such as the time, stock quotes, weather, or the like. Most typically, the code is non-extractable and is sent to the Bot connection interface  104  in step  526 , to be sent to and run in the user interface  102 , bringing a web page, running a java applet, or taking some other action that brings the required information to the user  101 . Where it is desirable to embed the information provided by running the code in the response, the response is determined to contain extractable code in step  522 , the code is extracted in step  523 , and the code is run locally in step  524 , so that the dynamic information required is embedded in the response in step  525  before transmission to the Bot connection interface  104  in step  526 .  
         [0068]     A rule  311  can also be designed to employ a logic layer  330  as shown in  FIG. 5 . 1 . The purpose of the logic layer  330  is neither input recognition  320 , nor response generation, but rather the choosing of an appropriate response upon recognition of an input. This is accomplished by the use of a logical function in step  511 . The logical function step  511  may result in a random choice of responses, choosing responses in rotation, or choosing the proper response after appeal to some outside piece of information  107  (for example, the time) or after querying the structured data  105  using simple queries and/or any of the statement validators  410   a - d  described herein. In this case, the truth or falsity of the logical statement in step  414   a - d  in the statement validator  410   a - d  would result in a choice in step  511  between two or more different results (responses) in step  520 . It is important to distinguish between such a choice between results in step  511 , and the above-described function of the statement validator  410   a - d,  so as to enable an input recognizer  320  to either recognize or not recognize an input.  
         [0069]     The present invention enables scripters to create patterns that refer to information that is maintained in a database, file, spreadsheet, or otherwise as structured data, without manually hard coding the structured data itself into the patterns. Additionally, the present invention efficiently acts on requests and queries received from users regarding complex data maintained in a structured form. This results in the ability to use complex data, in real time, using fewer rules, which, in turn, results in a dramatically faster and more powerful engine.  
         [0070]     It will be understood by those skilled in the art that while the foregoing description sets forth in detail preferred ordering of steps of the various processes, other ordering of the steps are contemplated by the present invention.  
         [0071]     It will be understood by those skilled in the art that while the foregoing description sets forth in detail preferred embodiments of the present invention, modifications, additions, and changes might be made thereto without departing from the spirit and scope of the invention.