the present invention improves upon the problems with the prior art by providing a more effective and efficient automated method and system for providing quick and simple legal analysis using probabilities of legal conclusions or results . the present invention improves over the prior art by increasing usability by a single user , even in complex legal cases that involve large amounts of facts and numerous laws or regulations . this feature saves time and expenses by providing an empirical data - based legal conclusion in a short period of time . the present invention also improves upon the prior art by providing higher accuracy in the legal evaluation . due to its automated nature , the present invention does not rely on a human to make legal analysis decisions , thereby reducing or eliminating the risk of making a mistake in the course of evaluating large numbers of facts , laws , regulations and court decisions . further , the automated nature of the present invention removes the natural bias of a legal professional in the legal analysis process , thereby providing for a more accurate legal conclusion . the present invention further improves upon the conventional approach and the inference engine approach by providing the ability to account for probabilities in legal evaluations . this is advantageous since probabilities are a more practical and understandable method for presenting a legal analysis . further , the present invention provides a mechanism for showing or explaining the relationships between the given facts of the case and the legal conclusions or analysis , thereby providing an educational benefit to the user . lastly , the present invention adequately accounts for the fast paced issuance of new laws , regulations and court decisions that could affect the result of a legal evaluation . the periodic update feature of the present invention provides an automated mechanism for updating legal conclusions based on new legal updates and even notifying the user if a legal conclusion has changed in light of the legal update . referring now to the drawing figures in which like reference designators refer to like elements , there is shown in fig1 a block diagram illustrating the network architecture of a system for aiding in the analysis of a legal matter using a bayesian network over a communications network , in accordance with one embodiment of the present invention . fig1 shows an embodiment of the present invention wherein users 110 - 112 , each comprising an individual and a computer , interact with server 102 over a network 106 , which can be a packet switched network such as the internet or the world wide web . the computer of users 110 - 112 can be a desktop , a laptop , handheld computer , a smart phone , a tablet computer or the like . server 102 , which may be a web server , is the main operative element of the present invention , executing the steps that comprise the method of the present invention . server 102 includes a software engine that delivers applications and data content ( including text files , html files , music files , video files , electronic book files , app files , information files , and any other media content ) to users 110 - 112 . server 102 may also deliver data content to users 110 - 112 based on search parameters or identifying information selected by a client . it should be noted that although fig1 shows only two users 110 - 112 and one server 102 , the system of the present invention supports any number of client users and web servers connected via network 106 . server 102 includes program logic 155 comprising computer source code , scripting language code or interpreted language code that is compiled to produce computer instructions that perform various functions of the present invention . in one embodiment of the present invention , the program logic is a scripting language . program logic 155 may reside on a client computer , the server 102 or any combination of the two . fig1 further shows that server 102 is connected to a user record database 122 and a legal content database 126 . database 122 is used to store user records , such as profiles and other user account data , which have been created for each user 110 - 112 . database 126 stores all legal data content of the present invention . databases 122 and 126 are collectively referred to as the “ data repository ” or the “ central repository ” for all resident data served by server 102 in the present invention . note that although fig1 shows only two databases 122 and 126 , the present invention supports any number of databases holding various types of data that is served by server 102 . fig1 also shows a payment authority 145 to effectuate payments by users 110 - 112 for legal data content . in one embodiment of the present invention , the payment authority 145 is a payment gateway that authorizes payments and transfers funds from one entity , the buyer , to another , the seller . payment gateways accept payment via the use of credit cards , charge cards , bank cards , gift cards , account cards , etc . fig1 also shows a third party legal data content provider 148 , which provides updates on legal data content . lastly , fig1 shows a backup server 140 which makes copies of data on server 102 and / or its associated databases 122 and 126 , so that these additional copies may be used to restore the original after a data loss event . the backup server 140 may be used to restore a state following a disaster or to restore small numbers of files after they have been accidentally deleted or corrupted . note that although server 102 is shown as a single and independent entity , in one embodiment of the present invention , the functions of server 102 may be integrated with the functions of another entity , such as entities 140 , 145 , and 148 of fig1 . further server 102 and its functionality , according to a preferred embodiment of the present invention , can be realized in a centralized fashion in one computer system , or in a distributed fashion where different elements are spread across several interconnected computer systems . fig2 is a flow chart describing the control flow of the process for setting up and updating the bayesian network of fig1 over a communications network ( i . e ., network 106 ), in accordance with one embodiment of the present invention . the flow chart of fig2 describes the process undertaken during the setup of the bayesian network by the administrator 112 , as well as the periodic updating of the network . the flow chart of fig2 is described in association with fig1 . for exemplary purposes , a running example shall be used involving a legal query into the constitutionality or propriety of a criminal drug - related arrest of a legal client . in a first step 202 , an administrator or legal professional 112 reads or otherwise consults with the latest state of the law on a particular subject — in this case , drug - related criminal arrests . the administrator 112 may consult , for example , federal and state statutes , regulations , rules , treaties , laws , court decisions , administrative decisions , legal opinions from non - governmental authorities and the like . this step may comprise downloading or accessing legal information from a legal content provider 148 in exchange for a fee using payment authority 145 . the legal content downloaded from 145 may be stored in database 126 . based on the current state of the law on this subject , in step 204 the administrator 112 generates and uploads a set of legal conclusions to server 102 . in this example , the legal conclusions may include : a ) the arrest violated the constitutional rights of the client and b ) the arrest did not violate the client &# 39 ; s constitutional rights , i . e ., it is constitutional . in one alternative to the upload of legal conclusions , the administrator may upload other outcomes , such as guidance , possible courses of action , possible legal consequences , relevant and applicable case law , relevant and applicable statutes , samples of correspondence that the user may need to complete in order to pursue a particular course of action , and electronic hyperlinks to other relevant or helpful websites of any kind . that is , the administrator may upload data and educational information that is displayed for the user after he has undergone the data input process of 302 - 306 below . in step 206 , the administrator 112 generates and uploads a set of variables representing facts relevant to the legal conclusions of step 204 . in this example , the variables representing facts may include : 1 ) whether the drug at issue was regulated by federal law , 2 ) whether the law enforcement officer had probable cause to arrest the client , and 3 ) whether the requisite amount of the drug at issue was found on the client &# 39 ; s person . in step 208 , the administrator 112 generates and uploads a description of dependencies between the facts of step 206 and between facts and the conclusions of step 204 . for example , the administrator may specify that the conclusion of whether the arrest violated the constitutional rights of the client is dependent on the facts 1 ), 2 ) and 3 ) above — i . e ., the question of whether the arrest violated the constitutional rights of the client is dependent on whether the drug was regulated by federal law , whether the law enforcement officer had probable cause to arrest the client and whether the requisite amount of the drug was found on the client &# 39 ; s person . in step 210 , the administrator 112 generates and uploads probability functions for the conclusions of step 204 . a probability function takes as input one or more values of variables from one or more facts on which the legal conclusion depends . based on the values of those variables , the probability function provides a probability that the legal conclusion is true . for example , the administrator may specify a probability function dictating that the probability the arrest did not violate the constitutional rights of the client is 70 % if the facts showed only that the drug was regulated by federal law and the law enforcement officer had probable cause to arrest the client . the same probability function , however , may dictate that the probability the arrest did not violate the constitutional rights of the client is only 20 % if the facts showed only that the requisite amount of the drug was found on the client &# 39 ; s person . the probability functions of step 210 may represent empirical data garnered from legal authorities , such as courts of law . in one embodiment of the present invention , the probability functions of step 210 may represent the probabilities of certain legal outcomes , as shown by empirical data pertaining to court judgments , jury decisions , judge decisions , board decisions , etc . that relate to the same legal issues and facts entered in steps 202 - 206 . further , the probability functions of step 210 may be specific to certain judges or judge panels , such that the probabilities of certain legal outcomes may be reviewed according to the identity of the judge or judges presiding over a case . the legal facts , conclusions , dependencies and probability functions of steps 204 - 210 may be stored in database 126 . in one alternative to a probability function , in step 210 , the administrator provides other program logic for generating a legal conclusion or decision based on one or more values of variables from one or more facts . for example , the administrator may input decision tree reasoning , simple predicate logic , or other suitable ai techniques . a decision tree is a decision support tool that uses a tree - like graph or model of decisions and their possible consequences , including chance event outcomes , resource costs , and utility . predicate logic is the generic term for symbolic formal systems like first - order logic , second - order logic , many - sorted logic or infinitary logic . in step 212 , the program logic 155 of server 102 generates a bayesian network representing probabilistic relationships between variables representing facts and a plurality of legal conclusions . the bayesian network of step 212 is based on the legal conclusions , variables , dependencies , and probability functions defined in steps 204 - 210 . step 212 includes generating a node for each legal conclusion of step 204 and a node for each set of variables representing facts ( of step 206 ) relevant to the legal conclusions . step 212 further includes inserting edges between nodes , wherein an edge represents a conditional dependency between variables of nodes , as those dependencies are defined in step 208 . lastly , the probability functions defined in step 210 are entered in each node representing a legal conclusion . a bayesian network may comprise , and the present invention may generate , via step 212 , instantiable nodes , fault nodes , intermediary nodes , a utility node and a decision node . instantiable nodes are nodes into which evidence is entered . usually , they will correspond to questions with discrete or continuous input that are instantiated by the user ; i . e ., evidence “ observed ” by the user will be entered to the network at these nodes . fault nodes are output nodes , the results of which are of interest to the user . decisions and the information sought to be provided by the network are modeled in these nodes . these nodes are not instantiated . they are monitored for answers that are needed . in a network , there can be more than one fault node , and as a result , fault nodes may be interpreted in conjunction or separately . for example , one fault node could provide the legal conclusion level , and another fault node could provide the overall legal status of the client . intermediary nodes are neither instantiated nor monitored or faulted . their purpose is grouping and at times simplifying the overall network design . utility nodes provide the quantitative background for a decision node to make a decision . the utility node comprises a table of values representing utilities for various decisions given a state in the chief complaint fault node . a decision node calculates a utility value for all states in the decision node . to do this , the decision node uses a table of corresponding utility values for all states in the chief complaint fault node . decision nodes are fault nodes . the decision node will have different decisions as its states . at any given time based on probabilities for different states in the diagnosis fault node and table of utilities in the utility node a utility value will be calculated for all states of the decision node . utilities are provided such that the state with greatest utility will be considered the best decision . decisions could be of any nature . examples include a legal situation where the goal would be to find out if the client &# 39 ; s legal rights have been violated . in step 213 , a period of time passes . in step 214 , a legal update is received by server 102 . a legal update may comprise new legislation , laws or regulations or a new court or administrative decision . in step 216 it is determined whether the bayesian network necessitates modification in light of the legal update . in one embodiment , step 216 may be performed automatically by program logic 155 and in another embodiment , step 216 may be performed with the assistance of an administrator 112 , wherein the administrator reviews the legal update and provides instructs the server 102 as to whether the bayesian network must be defined . if the network must be modified , control flows to step 218 . otherwise , control flows back to step 213 . in step 218 , it is determined how the bayesian network must be modified in light of the legal update . in one embodiment , step 218 may be performed automatically wherein the program logic 155 : a ) automatically identifies which of the nodes , edges , probability functions , facts or plurality of legal conclusions should be deleted , b ) automatically identifies how one or more nodes , edges , probability functions , facts or plurality of legal conclusions should be modified and c ) automatically identifies which nodes , edges , probability functions , facts or legal conclusions should be added to the bayesian network . in another embodiment , step 218 may be performed with the assistance of an administrator 112 , wherein the administrator reviews the legal update and provides a description to the server 102 of whether nodes , edges , probability functions , facts or legal conclusions should be deleted , modified or added . in step 220 , the bayesian network is modified as defined in step 218 . in one embodiment , the modification is performed automatically by program logic 155 . in another embodiment , the modification is performed with the assistance of administrator 112 . in this embodiment , the items identified in step 218 are presented to the administrator via a graphical user interface so that the administrator may review the items and decide how to modify the bayesian network . subsequently , the administrator may manually modify the nodes , edges , probability functions , facts or plurality of legal conclusions of the bayesian network . control then flows back to step 213 . fig3 is a flow chart describing the control flow of the process for executing the bayesian network of fig1 over a communications network , in accordance with one embodiment of the present invention . the flow chart of fig3 describes the process undertaken during the use of the bayesian network by a user 110 over the network 106 . the flow chart of fig3 is described in association with fig1 . the running example involving the legal query into the constitutionality of a drug - related arrest of a legal client will continue to be used herein . in step 302 , the user 110 accesses the web server 102 over the network 106 . in step 304 , the user 110 identifies the legal issue he would like to analyze . the user 110 may provide a search parameter to server 102 , which may respond with a list of legal issues from which user 110 may select . for example , the user 110 may specify “ drug - related arrests ” as his legal topic of choice . based on this selection , the program logic 155 accesses the bayesian network corresponding to the legal issue identified by the user 110 . in step 306 the user 110 is presented with a graphical user interface that displays a series of legal inquiries , each corresponding to a legal fact of step 206 . for example , the user 110 may be presented with questions such as : 1 ) was the drug at issue a schedule 1 drug such as heroin ? 2 ) was the drug at issue found by a police officer on your person ? 3 ) how much of the drug at issue was found on your person ? subsequently , the user 110 provides his answers to the legal inquiries via the graphical user interface . in step 308 , program logic 155 executes the probability functions of the bayesian network based on the answers provided by the user 110 . that is , the variable of each node corresponding to an answer provided by the user 110 is replaced with a value representing the answer . subsequently , the probability functions of each node are executed , thereby calculating the probability of each legal conclusion based on the answers provided by the user 110 in step 306 . in step 310 , the graphical user interface displays the probability of each legal conclusion to user 110 . in one alternative to the display of legal conclusions , in step 310 the interface may display guidance , possible courses of action , possible legal consequences , relevant and applicable case law , relevant and applicable statutes , samples of correspondence that the user may need to complete in order to pursue a particular course of action , and electronic hyperlinks to other relevant or helpful websites of any kind . also in step 310 , a record associated with the user 110 is stored in database 122 . the record may also be associated with the probability of each legal conclusion , as displayed for the user 110 , the plurality of legal inquiries , the answers provided by the user and the plurality of legal conclusions . in step 311 , a period of time passes . in step 312 , a legal update is received . in step 314 ( which may be executed in the same manner as step 216 ), it is determined whether the legal update comprises a change in law affecting how the legal conclusions are reached . if the legal update comprises a change in law , then control flows to step 316 . otherwise control flows back to step 311 . in step 316 , the bayesian network is modified in light of the legal update . see steps 218 - 220 above for a description of how the bayesian network can be modified . in step 318 , previous step 308 is re - executed . that is , the modified bayesian network is executed using the answers provided by the user 110 , thereby re - calculating the probability of each legal conclusion based on the answers provided by the user . in step 320 , it is determined whether the probability of each legal conclusion in the modified bayesian network matches the probability of each legal conclusion in the record that was stored in step 310 . if there is a match , then control flows back to step 311 . if there is no match , then a message , such as an email message , is sent to the user 110 in step 322 , notifying him of the discrepancy . in addition to the implementations described above with relation to automated legal analysis , the present invention can also be used in other decision - making capacities , such as medical diagnosis , evaluation of infrastructures via an engineering inspection , network health analysis and building code compliance . in other embodiments , the present invention may be used as an educational tool that highlights the most pertinent aspects of a decision - making process . the present invention can be realized in hardware , software , or a combination of hardware and software in the system described in the figures above . a system according to a preferred embodiment of the present invention can be realized in a centralized fashion in one computer system or in a distributed fashion where different elements are spread across several interconnected computer systems . any kind of computer system — or other apparatus adapted for carrying out the methods described herein — is suited . a typical combination of hardware and software could be a general - purpose computer system with a computer program that , when being loaded and executed , controls the computer system such that it carries out the methods described herein . an embodiment of the present invention can also be embedded in a computer program product , which comprises all the features enabling the implementation of the methods described herein , and which — when loaded in a computer system — is able to carry out these methods . computer program means or computer program as used in the present invention indicates any expression , in any language , code or notation , of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following : a ) conversion to another language , code or , notation ; and b ) reproduction in a different material form . a computer system may include , inter alia , one or more computers and at least a computer readable medium , allowing a computer system , to read data , instructions , messages or message packets , and other computer readable information from the computer readable medium . the computer readable medium may include non - volatile memory , such as rom , flash memory , disk drive memory , cd - rom , and other permanent storage . additionally , a computer readable medium may include , for example , volatile storage such as ram , buffers , cache memory , and network circuits . in this document , the terms “ computer program medium ,” “ computer usable medium ,” and “ computer readable medium ” are used to generally refer to media such as main memory removable storage drive , a hard disk installed in hard disk drive , and signals . these computer program products are means for providing software to the computer system . the computer readable medium allows the computer system to read data , instructions , messages or message packets , and other computer readable information from the computer readable medium . the computer readable medium , for example , may include non - volatile memory , such as floppy , rom , flash memory , disk drive memory , cd - rom , and other permanent storage . it is useful , for example , for transporting information , such as data and computer instructions , between computer systems . although specific embodiments of the invention have been disclosed , those having ordinary skill in the art will understand that changes can be made to the specific embodiments without departing from the spirit and scope of the invention . the scope of the invention is not to be restricted , therefore , to the specific embodiments . furthermore , it is intended that the appended claims cover any and all such applications , modifications , and embodiments within the scope of the present invention .