Patent Publication Number: US-2017364827-A1

Title: Scenario Analytics System

Description:
COPYRIGHT NOTICE 
     A portion of the disclosure of this patent document contains material, which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. The following notice applies to this document: Copyright © 2017 Thomson Reuters. 
     CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority to U.S. Provisional Application No. 62/350,754, filed Jun. 16, 2016, entitled “SCENARIO ANALYTICS” and is incorporated herein by reference in its entirety. 
     TECHNICAL FIELD 
     This disclosure relates to evaluation of scenarios, their merits and their consequences, and more particularly methods and systems for generating prospective legal strategies. 
     BACKGROUND 
     A significant proportion of what knowledge workers do, including attorneys, can be described as falling under one of three tasks: finding information, analyzing information and deciding information. Legal information providers like THOMSON REUTERS™, provide information to facilitate these tasks. For example, indices and citator databases are intended to help with the ‘finding’ task. Case summaries, the key number system and analytical material are designed to help legal practitioners with the ‘analyzing’ and ‘finding’ tasks, while practice guides are designed to help attorneys with the ‘deciding’ task. However, most legal information provider applications focus on the ‘finding’ task, while the ‘analyzing’ and ‘deciding’ tasks requires extensive attorney or user involvement. Generally, the analyzing and deciding tasks are currently performed manually or semi-manually in a laborious and inconsistent manner. Many attorneys rely on their trial-based experiences to formulate an assessment of the prospects of various litigation strategies, which can be biased and error prone. 
     Accordingly, there is a need for improved systems and techniques that enable an attorney to thoroughly examine the landscape of a case and formulate a legal strategy. 
     SUMMARY 
     Systems, technologies and techniques for generating prospective legal strategies are disclosed. The system and technologies, such as THOMSON REUTERS SCENARIO ANALYTICS™, employ data mining, natural language processing and machine learning approaches to generate prospective legal strategies. The system and technologies analyze given case facts (i.e., background facts, event types such as accident, injury, malpractice, discrimination) and provide a rich set of insights that assist in formulating effective legal arguments and strategies. 
     While there are attempts to utilize data-driven decision support processes, the present disclosure differs in that it does not focus on providing summary statistics, for example on how judges ruled on certain motions, but focuses on unearthing deeper patterns in the data. 
     The system identifies and harnesses the underlying fact patterns in a case and compares them with similar fact patterns in prior cases stored in a large legal database. The database can include hundreds of thousands of jury verdict cases. The system generates a set of potential outcomes based on pools of editorially annotated data about similar cases that utilize similar arguments and a set of related characteristics (trial strategies, trial arguments, trial lengths, award asks, award assignments, etc.). 
     Various aspects of the system relate to generating prospective legal strategies. For example, according to one aspect, the system includes a legal data store comprising a set of cases and corresponding case summaries, and a scenario analytics server including a processor and memory storing instructions that, in response to receiving an input from at least one data source, causes the processor to apply a set of process modules to the input to generate an input summary. The input summary includes a set of identified concepts and corresponding attributes of the input. The input includes background facts and causes the processor to select one or more cases from the legal data store based in part on the case summaries and input summary. The system analyzes the one or more selected cases from the legal data store, and generates one or more legal strategies using the analysis of the one or more selected cases. 
     In one implementation, the input can also include one or more plaintiff claim or defendant claim. In another implementation, one of the set of process modules parses out the unstructured text fields of the input. In yet another implementation, one of the set of process modules classifies the input into a predefined taxonomy. 
     In a further implementation, selection of one or more cases from a verdict data store comprises clustering cases to form one or more sets of clustered cases and a set of characteristic properties may be extracted. The set of characteristic properties may be but are not limited to noun phrases, mean length of time for each case to complete, the shortest or longest duration case, the mean award value for the clustered cases, and the lowest and highest awards. 
     The legal data store may comprise a Jury Verdict and Settlements data store, wherein the set of cases of the legal data store are segmented into at least one of background facts, plaintiff claims or defendant claims. 
     In one implementation, a negligence module is provided that applies a negligence analysis to the set of cases of the legal data store, wherein negligence analysis comprises in part of determining negligence by the plaintiff or defendant based on jurisdiction. 
     In another implementation, a time factorization module is provided that applies temporal analysis to the set of cases of the legal data store, wherein temporal analysis comprises at least one of identifying the jurisdiction of a case or mean duration of a case. 
     The resulting one or more legal strategies may be presented on a graphical user interface. 
     Advantageously, the present application allows a user to choose the most advantageous strategy for them and their clients by allowing the user to instantaneously examine the effect of different strategies have had on outcomes and the associated award. Users may also explore the impact that different claims or combinations claims may have on the expected outcome of the case dynamically. 
     The present disclosure is distinct from existing technologies as it integrates relevant, large scale content, dedicated technological capabilities of both staff and know-how, and in-house subject matter expertise (such as, THOMSON REUTERS® editors and their expert annotation and summarization skills) to provide insights, decision-making assistance capabilities and predictive capabilities that are generally unavailable in the domain. 
     Additional features and advantages will be readily apparent from the following detailed description, the accompanying drawings and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is an exemplary architectural diagram of the system; 
         FIG. 2  is an exemplary flow chart of one implementation of the disclosure; 
         FIG. 3  is an exemplary flow chart of another implementation of the disclosure; 
         FIGS. 4-6  illustrates the exemplary graphical user interface (GUI) of the disclosure; 
         FIGS. 7 a  and 7 b    illustrates exemplary unstructured textual descriptions; and 
         FIGS. 8 a  and 8 b    illustrates exemplary award distributions. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific implementations in which the disclosure may be practiced. It is to be understood that other implementations may be utilized and structural changes may be made without departing from the scope of the present disclosure. 
       FIG. 1  shows an exemplary system  100  for analyzing a scenario and generating prospective strategies. As shown in  FIG. 1 , in one implementation, the system  100  is configured to include an access device  170  that is in communication with a scenario analytics server  110  over a network  160 . Access device  170  can include a personal computer, laptop computer, or other type of electronic device, such as a mobile phone, smart phone, tablet, PDA or PDA phone. In one implementation, for example, the access device  170  is coupled to I/O devices (not shown) that include a keyboard in combination with a point device such as a mouse for sending a request to the scenario analytics server  110 . Preferably, memory (not shown) of the access device  170  is configured to include a user interface  174  that is used to request and receive information from scenario analytics server  110 . In one implementation, the user interface  174  may be part of a web browser (not shown), which allows a user to access and retrieve information from the scenario analytics server  110 . Communication between the user interface  174  of the access device  170  and scenario analytics server  110  may utilize one or more networking protocols, which may include HTTP, HTTPS, RTSP, or RTMP. Although one access device  170  is shown in  FIG. 1 , the system  100  can support one or multiple access devices. 
     The network  160  can include various devices such as routers, servers, and switching elements connected in an Intranet, Extranet or Internet configuration. In some implementations, the network  160  uses wired communications to transfer information between the access device  170  and scenario analytics server  110 . In another implementation, the network  160  employs wireless communication protocols. In yet other implementations, the network  160  employs a combination of wired and wireless technologies. 
     As shown in  FIG. 1 , in one implementation, the scenario analytics server  110 , may be a special purpose server, and preferably includes a processor  112 , such as a central processing unit (‘CPU’), random access memory (‘RAM’)  114 , input-output devices  116 , such as a display device (not shown), and non-volatile memory  120 , all of which are interconnect via a common bus  111  and controlled by the processor  112 . 
     In one implementation, the non-volatile memory  120  is configured to include an input module  122 , an analysis module  124 , a classification module  126 , a clustering module  128 , a segmentation module  130 , a negligence analysis module  132 , a time factorization module  134 , a graphical rendering module  136  and a prediction module  138 . 
     The input module  122  receives the user&#39;s data from the user interface  174  on the user&#39;s access device  170 . Received user data, from the user interface  174  may be but is not limited to data in one or more unstructured text fields, unstructured or structured textual description, a string of data, and may contain information such as facts of a case or other relevant information. As used herein, the phrase ‘received user data’ is used interchangeably with input data, user&#39;s data or user&#39;s input. In one implementation, the user&#39;s data is transferred through network  160  from the user interface  174  to the scenario analytics server  110 . In a further implementation, the user&#39;s data may be stored in user data store  142 . In another implementation, the user&#39;s input may also be stored locally (not shown) on the user&#39;s access device  170  or externally in a data store (not shown). 
     The analysis module  124  processes data received by input module  122 . In one implementation, the analysis module  124  may process the user&#39;s input by parsing out the one or more unstructured text fields. In a further implementation, analysis of the user&#39;s input may also comprise sentence analysis following the application of the segmentation module. In another implementation, the analysis module  124  retrieves cases from a legal data store  180  or a Jury Verdict and Settlements data store  182  for further processing by modules  126 ,  128 ,  130 ,  132 ,  134  and  136 . The analyzed data may be stored in analysis data store  144 . 
     The classification module  126  classifies the facts of the user&#39;s input in a similar manner as others in similar situations. In another implementation, the classification module  126  classifies facts of cases from the legal data store  180  (i.e., a repository of jury verdicts and settlements cases, Jury Verdict and Settlements data store  182 ). The information may be classified using a classification engine like CaRE™. CaRE™ may be trained on a legal corpus such as WEST CENTRAL HEADNOTE™ database, in order to categorize a case according to a highly granular legal taxonomy. An exemplary highly granular legal taxonomy is WEST KEY NUMBER SYSTEM®. Exemplary classification engine from U.S. Pat. No. 7,062,498 entitled “Systems, methods, and software for classifying text from judicial opinions and other documents” and U.S. Pat. No. 7,580,939, entitled “Systems, methods, and software for classifying text from judicial opinions and other documents” are incorporated herein by reference. The classification module  126  may “roll up” a few levels of classification in order to produce a less granular, slightly broader assignment category for the given textual description. The classification process permits the system to compare and examine the instant case with similar cases in the Jury Verdict and Settlements data store  182 . The classified data may then be stored in classification data store  146 . 
     The clustering module  128  recognizes common elements across cases, distinguishes and groups them into separate and distinct clusters. The clustering module  128  may store the resulting cluster in clustering data store  148 . 
     The segmentation module  130  parses sentences and/or the text in one or more cases and segment the information into, but not limited to, background facts, the plaintiff claims and defendant claims. 
     The negligence analysis module  132  generates a negligence model or weights negligence in a correlation analysis of awards (or verdict) in one or more cases. In another implementation, jurisdiction of the case and/or plaintiff&#39;s negligence contributes to the analysis. 
     The time factorization module  134  analyzes time and its influence to the overall assessment of positive versus negative outcomes in one or more cases. 
     The graphical rendering module  136  generates a graphical representation of award distributions based on the analysis generated by the scenario analytics server&#39;s  110  modules  124 ,  126 ,  128 ,  130 ,  132 ,  134  and  138 . 
     The prediction module  138  compiles the analysis generated by the one or more scenario analytics server&#39;s  110  modules  124 ,  126 ,  128 ,  130 ,  132 ,  134  and  136  and generates a prediction output or a forecast for a given scenario. In one implementation, the prediction output may comprise one or more prediction models generated by modeling of the underlying data and may be associated with prediction scores. In another implementation, the prediction output or forecast may comprise one or more strategies for a given situation or for a scenario that has been defined by the user&#39;s input. In a further implementation, the prediction output or forecast may be presented on a graphical user interface. 
     As shown in the exemplary  FIG. 1 , a data store  140  is provided that is utilized by one or more of the software modules  122 ,  124 ,  126 ,  128 ,  130 ,  132 ,  134 ,  136  and  138  to access and store information relating to the creation of a prediction output or processing of the cases in the legal data store  180 , such as the Jury Verdict and Settlements data store  182 . In one implementation, the data store  140  is a relational database. In another implementation, the data store  140  is a file server. In yet other implementations, the data store  140  is a configured area in the non-volatile memory  120  of the scenario analytics server  110 . Although the data store  140  shown in  FIG. 1  is part of the scenario analytics server  110 , it will be appreciated by one skilled in the art that the data store  140  can be distributed across various servers and be accessible to the scenario analytics server  110  over the network  160 . 
     As shown in  FIG. 1 , in one implementation, the data store  140  is configured to include a user data store  142 , an analysis data store  144 , a classification data store  146 , a clustering data store  148 , a segmentation data store  150 , a negligence data store  152 , a time factor data store  154 , a graphical data store  156  and a prediction data store  158 . 
     The user data store  142  includes data (i.e., user&#39;s input) received by input module  122 . 
     The analysis data store  144  includes the analysis of the user&#39;s input that has been processed by analysis module  124 . 
     The classification data store  146  includes determined facts of the user&#39;s input received by input module  122  that have been processed by the classification module  126 . The classification data store  146  may also include the determined facts of data from the legal data store  180  such as the Jury Verdict and Settlements data store  182 . In one implementation, the determined facts may include categorization or topic classification by classification module  126 . In another implementation, the determined facts of the data may be represented using the KEY NUMBER SYSTEM™. 
     The clustering data store  148  includes the determined facts of data that have been organized by the classification module  126  and processed by the clustering module  128  to differentiate one set from another based on the underlying claims categorization, classification, principles or strategies. 
     The segmentation data store  150  includes the parsing and segmentation of the data in the legal data store  180  (i.e., cases in the Jury Verdict and Settlements data store  182 ) as determined by segmentation module  130 . In one implementation, the determined segments may be related to the facts, the plaintiffs&#39; claims and defendants&#39; claims. In a further implementation, the determined segments may be recorded in the segmentation data store  150  as separate fields. 
     The negligence data store  152  includes the analysis of the data in the legal data store  180  (i.e., cases in the Jury Verdict and Settlements data store  182 ) by negligence analysis module  132 . In another implementation, the negligence data store  152  also includes the negligence model or the correlation analysis as determined by the negligence analysis module  132 . 
     The time factor data store  154  includes the assessments determined by the time factorization module  134 . Exemplary time assessments may include but are not limited to the time to process a case for a certain jurisdiction or mean duration for a case according to an identified legal strategy, or time in relation to the outcome or award rendered for a case. 
     The graphical data store  156  includes the generated graphical representation of award distributions as determined by the graphical rendering module  136 . An exemplary graphical representation may be in the form of a bar graph illustrating the award levels against proposed options or predictions. In another implementation, the graph may be dynamically calculated based on clustered data. 
     The prediction data store  158  includes on a diverse set of features and its related analysis as determined by prediction module  138 . In one implementation, the prediction data store  158  may include one or more features originating from the facts and claims sections of cases stored in the Jury Verdicts and Settlements data store  182 . 
     In a further implementation, as shown in  FIG. 1 , the legal data store  180  includes a processor (not shown), random access memory (not shown) and non-volatile memory (not shown) which are interconnected via a common bus and controlled by the processor. In one implementation, the data in legal data store  180  may contain a Jury Verdict and Settlements data store  182  which may be communicated through network  160  and utilized by the scenario analytics server  110 . In another implementation, the Jury Verdict and Settlements data store  182  contains a repository of cases or records with information about the duration of each case, concept lists representing events like accidents, resulting torts like injuries and other litigation or legal topics, the claims made by the plaintiff, and the claims made by the defendant. In a further implementation, the Jury Verdict and Settlements data store  182  may also contain a knowledge base of vocabularies and concept lists. 
     It should be noted that the system  100  shown in  FIG. 1  is one implementation of the disclosure. Other system implementations of the disclosure may include additional structures that are not shown, such as secondary storage and additional computational devices. In addition, various other implementations of the disclosure include fewer structures than those shown in  FIG. 1 . 
     Referring now to  FIG. 2 , an exemplary method  200  for processing of the cases in the Jury Verdict and Settlements data store  182  is disclosed. 
     Starting at step  202 , the analysis module  124  retrieves cases from the Jury Verdict and Settlements data store  182  for processing. In one implementation, the Jury Verdict and Settlements data store  182  includes a set of cases, records or case summaries, covering a diverse set of litigation categories and comprises of short paragraphs for free text, with some editorial guidelines. Such a repository allows for identification, organization and analysis of underlying fact patterns and legal strategies used for similar cases and to determine which strategies have been more effective and which were less effective. 
     In another implementation, an exemplary Jury Verdict and Settlements data store  182  may be THOMSON REUTERS LRP™, which consists of roughly 400,000 cases or records, covers 50 states and a wide variety of legal topics, such as but are not limited to premises liability, medical malpractice and employment discrimination, as well as award ranges. 
     There may be a predetermined number of fields, such as 25 fields, of case-related information in each Jury Verdict and Settlements data store  182  record. The fields describe the scenario or the case, and may include but are not limited to a section containing the seminal facts of the event, noun phrases, identified concepts, the entities involved, the plaintiffs&#39; claims and the defendants&#39; claims, which may be in the form of unstructured textual descriptions and are produced using a standard, semi-closed vocabulary in describing the facts and claims of a case to ensure consistency. These unstructured textual summaries may accompany a set of metadata comprising in part: State Jurisdiction; Court; General Description of Event/Accident; Specific Description of Event/Accident; Primary Injury; Secondary Injury; Case Type (Liability/Other (e.g., Discrim.)) Examples of the unstructured textual description are shown in  FIGS. 7 a    and  7   b.    
     In yet a further implementation, the records of the Jury Verdict and Settlements data store  182  may also contain a wide variety of informative fields such as but not limited to: date of activity (accident, filing, trial or settlement); event-type (rear-end collision, sexual harassment, . . . ); docket no.; jurisdiction (county, state, court); case-type (liability, discrimination, malpractice . . . ); description (general and specific); injury type (primary, secondary . . . ); award (award category, award range, exact award); damage summary (plaintiff profile); unstructured textual description, including fact paragraph, plaintiff claims, defendant claims, and/or other information. 
     Continuing onto step  204 , the cases are then analyzed by modules  126 ,  128 ,  130 ,  132 ,  134  and  136 . The types of analysis include but are not limited to topic classification analysis  204   a , case segmentation analysis  204   b , clustering analysis  204   c , award analysis  204   d , trial length analysis  204   e , and negligence evaluation analysis  204   f.    
     The classification module  126  performs topic classification analysis  204   a  which classifies the cases in the Jury Verdict and Settlements data store  182  along topical lines and may be but not limited to finer-grained topical lines than those covered by the associated metadata. In one implementation, the classification module  126  may utilize the WEST KEY NUMBER SYSTEM® to classify the descriptions of the facts, the plaintiff claims and/or defendant claims. The WEST KEY NUMBER SYSTEM® (or KEY NUMBER SYSTEM™) represents a legal taxonomy consisting of approximately 100,000 leaf nodes and 200,000 total nodes. The depth of the taxonomic tree may range from 3 to 11, with the average depth being about 6. In another implementation, classification module  126  may utilize a key number assigner classification tool that has been trained on the order of 10 million documents (e.g., O(10M)) editorially produced and WEST KEY NUMBER SYSTEM® classified points of law (a.k.a. headnotes). 
     The segmentation module  130  performs Case Segmentation Analysis  204   b  which parses or segments the record or cases in the Jury Verdict and Settlements data store  182 . In one implementation, the segmentation module  130  may segment the record based on but not limited to background facts, plaintiff claims, defendant claims and remaining case details as separate fields. In a further implementation, the segmentation module  130  may utilize python, java, scala programming language or the like for segmenting. 
     The clustering module  128  performs Clustering Analysis  204   c  which groups similar scenarios or cases in the Jury Verdict and Settlements data store  182 . In one implementation, the clustering module  128  may differentiate one set of cases from another based on categorization, classification, principles or strategies. 
     In another implementation, the clustering module  128  may apply a k-means clustering algorithm over the plaintiff claims for values of k in the low single digits. K-means clustering algorithm may partition n data objects or documents into k clusters in which each data object or document belongs to the cluster with the nearest mean or center point. For example, values of 3≦K≦6 however, values of k can vary depending on litigation type and underlying fact pattern. In a further implementation, the NLTK 3.0 toolkit was used to conduct the clustering. 
     In a further implementation, the clustering module  128  may compute a metric to distinguish one set of clustered plaintiff claims from another in terms of utility (i.e., differentiating those that have been more effective from those that have been less effective). The metric is computed based on the award behavior for a given cluster and may be referred to as the ‘award_quotient’, which is the ratio of a cluster&#39;s (or cases) with non-zero awards to its zero awards: 
     
       
         
           
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     This metric allows the determination of a cluster that has a high degree of awards. In one implementation, an award_quotient of ≧2.5 is used as a threshold for clustering. As award quotient of ≧2.5 may be a noteworthy threshold for a cluster of distinction and worthy of added scrutiny. 
     In yet another implementation, the clustering module  128  uses the average length in tokens (i.e., the count of words) in the plaintiff&#39;s claim(s) to differentiate one cluster from another. In a further implementation, the clustering module  128  filters out short plaintiff claims or claims not meeting a predetermined threshold, such as but not limited to, 55 or more tokens are used. 
     Once the cases have been clustered, the graphical rendering module  136  performs an Award Analysis  204   d , which compiles the award distributions associated with each of cases and/or each of the cluster sets for presentation. In one implementation, an exemplary graphical display of the award distribution is illustrated in  FIG. 8  wherein topically clustered cases are assigned the same key number are shown with the cluster&#39;s award distribution. 
     The time factorization module  134  performs a Trial Length Analysis  204   e  which determines and analyzes trial length of the cases in the Jury Verdict and Settlements data store  182 . In one implementation, the time factorization module  134  may determine time related information such as length of the trial in a case, mean length of the trial for an identified strategy or cluster, or length of the trial for certain jurisdictions. In another implementation, the time factorization module  134  may also identify relations between trial length and outcome or trial length and award level, or relations between trial length and outcome accounting for court cost or other legal fees incurred. These evaluations may be incorporated into the generation of proposed legal strategies as discussed in Step  312  of  FIG. 3 . In an exemplary implementation, the information regarding trial duration may be presented to the user as illustrated by  508  in  FIG. 5 . 
     In a further implementation, the cases in the Jury Verdict and Settlements data store  182  may contain up to three court dates: (1) incident date; (2) filing date; (3) trial or settlement date. The time factorization module  134  may calculate the difference between filing date and trial or settlement date and use the difference for further analysis. In another implementation, the time factorization module  134  examines the difference value in relation to the state of the case. In yet another implementation, the time factorization module  134  examines the statistics for trial length and award level are evaluated, for example, trial length vs. award level for non-zero award levels, or trial length vs. award level for zero award levels. 
     The Negligence Analysis Module  132  performs Negligence Evaluation  204   f  which analyzes negligence in the cases in the Jury Verdict and Settlements data store  182 . The Negligence Analysis Module  132  may evaluate the relationship of negligence, the jurisdiction or state underlying the cases and the award level. In one implementation, negligence by plaintiff or defendant may be evaluated as a factor in the verdict and/or award determination. In another implementation, the negligence treatment as dictated by a statute for a jurisdiction or state may be used in the negligence analysis. In another implementation, negligence models may be generated by the negligence analysis module  132 . Exemplary models may be but are not limited to: 
     Contributory Negligence System—where plaintiff receives no award if found negligent even in part; 
     Comparative Fault System—where the award to the plaintiff is reduced by percentage the plaintiff is found negligent in the case, e.g., the plaintiff was found to be 25% negligent in the accident, so the award is reduced by this amount; and 
     Modified Comparative Fault System—where the plaintiff receives no award if plaintiff negligence is usually found to be 50% or greater. 
     The results of the negligence evaluation may also be incorporated into the generation of proposed legal strategies as discussed in Step  312  of  FIG. 3 . 
     Continuing onto step  206 , the resulting analysis performed in relation to steps  204   a - 204   f  are then saved into their corresponding data store. In one implementation, the analysis results may become part of the case summaries for their corresponding cases in the Jury Verdict and Settlements data store  182 , or contribute to as an enriched case summary for an existing case summary. In a further implementation, the corresponding data stores may be part of data store  140  of the scenario analytics server  110 , in particular the classification data store  146 , segmentation data store  150 , clustering data store  148 , graphical data store  156 , time factor data store  154 , negligence data store  152 , as illustrated in  FIG. 1 . In another implementation, the corresponding data stores (not shown) may be part of the Legal Data Store  180  or the Jury Verdict and Settlements data store  182 . In yet another implementation, the corresponding data stores (not shown) may be external to the scenario analytics server  110  and the legal data store  180 . 
     Referring now to  FIG. 3 , an exemplary method  300  of generating prospective legal strategies is disclosed. 
     As shown in the  FIG. 3 , at step  302 , data is provided by the user through user interface  174  of access device  170  and is received by the input module  122  and may be stored into user data store  142 . 
     In one implementation, the data may be textual description of a case inputted by a user and may comprise but is not limited to a description of the facts of a case, plaintiff claims or defendant claims. The data provided by the user maybe a case record, comprising at least of the party of the plaintiff or a set of facts surrounding some form of legal topic, such as injury or tort action, or identified concepts and corresponding attributes, all of which is provided by the user (i.e., an attorney, paralegal or the like) through a user interface tool (i.e., litigation interface tool). In a further implementation, the user may also provide plaintiff claims or defendant claims that they want to explore using the litigation tool. An exemplary user interface is illustrated in  FIG. 4 , with separate fields for Case Facts Description  402 , Plaintiff claims  404  and/or Defendant claims  406  for user input. 
     In a further implementation, the user may narrow the scope of the cases to be explored or used in a search by selecting from a series of options, which may in the form of, but not limited to, drop down menus as depicted by reference numbers  408 - 420 . The series of options comprises at least the Plaintiff/Defendant, jurisdiction (State or Country), Event-type, Tort, Case Duration (i.e., focus on shorter vs. longer cases) or Classification Taxonomy (i.e., a different type of legal classification system besides the default Key Number System). 
     Referring back to  FIG. 3 , next at step  304 , the analysis module  124  retrieves the user&#39;s data (i.e., the received case record) from the user data store  142  and processes the data. In one implementation, the received case record is analyzed in terms of its unstructured text representation or through sentence analysis and compared against cases in the jury verdicts and settlement data store  182  enriched by the analysis described in relation of steps  204 - 206  of  FIG. 2  to determine similar cases. In another implementation, the received case record and its relation to the cases in the jury verdicts and settlement data store  182  may be identified through the entities present in the record, entities associated with a closed vocabulary, legal topics or identified concepts (i.e., events like accidents, resulting torts like injuries, or other litigation topics). The resulting analysis may be stored in analysis data store  144  by analysis module  124 . 
     Continuing onto step  306 , in one implementation, the classification module  126  may retrieve the user&#39;s data from the user data store  142  and classify the data using a classification engine such as like CARE™, in order to categorize the data according to a legal taxonomy such as the WEST KEY NUMBER SYSTEM®. The resulting classification may be compared with cases in the jury verdicts and settlement data store  182  that were analyzed or enriched by steps  204 - 206  of  FIG. 2 , to identify similar cases based on classification. The resulting classification and/or the identified similar cases may be stored in classification data store  146 . 
     In another implementation, the resulting aforementioned analysis and classification may form part of a generated input summary. The input summary may comprise but are not limited to, a set of identified concepts and/or corresponding attributes of the input (i.e., categorized taxonomy). 
     Next at step  308 , clustering module  128  may retrieve the analysis results from analysis data store  144  and/or the similar cases identified by classification from classification data store  146  and apply a clustering algorithm. The resulting clusters may be stored in clustering data store  148 . 
     The clustering module  128  may utilize an algorithm or technology that recognizes common elements across the cases (i.e., the facts) but distinguish and/or group them into separate and distinct clusters based on the different variables. For example, the plaintiff&#39;s claims or defendant&#39;s claims or nature of the claims may be used as a variable for clustering. In another implementation, language patterns may be used as a variable for clustering. In yet another implementation, taxonomies, such as THE WEST KEY NUMBER SYSTEM™ may be used. Examplary clustering technology includes but is not limited to CLUTO, the NLTK toolkit, or even customized coding of these algorithms. Exemplary clustering algorithm includes but is not limited to k-means clustering, or partitional, agglomerative or graph-based clustering. 
     Continuing onto step  310 , the clustering module  128  may utilize the resulting clusters from clustering data store  148  and determine its characteristic properties for each of the cases, clustered plaintiff claims and/or the overall cluster for extraction and analysis. Exemplary characteristic properties may be but are not limited to, language patterns including representative noun phrases, emphasized verbal expressions, repeated use of strong or severe language and other linguistic patterns that may be identified, measured and quantified by various forms of sentiment analysis, and also properties such as the mean length of time for each case to complete, the shortest and longest duration cases in the cluster or the graphically represented distribution of the awards for the cases, all of which have been determined in steps  204 - 206  of  FIG. 2 . 
     Finally, at step  312 , in one implementation, the clustering module  128  compiles the resulting clusters from clustering data store  148  and the related characteristic properties to generate one or more strategies. The one or more strategies may be stored in the clustering module  128 . In one exemplary implementation, a proposed set of strategies may be identified based on a litigation case type such as “Slip and Fall” and further divided by the type of injuries, such as “Multiple Injuries” “Defendant&#39;s Repeated Negligence” or “Permanence of Injuries”. In another implementation, clustering module  128  may present the proposed strategies along with a listing of the cases that were part of the cluster that formed the strategy, a graphical rendering of the award distribution and/or relevant information regarding the distributional statistics for the cases (i.e., mean duration, shortest, or longest). An exemplary user interface with one or more proposed legal strategies presented is illustrated in  FIG. 5 . 
     In a further implementation, it may be defendant claims that are used to form the clusters, though the same types of language analyses described above may be harnessed. It is these essential characteristic properties that serve the user by illustrating the different outcomes associated with each cluster and the strategies that they represent. As such, they permit users to make more informed decisions about the legal strategy options they have at their disposal when formulating their arguments. 
     In yet a further implementation, the user may also revise the resulting legal strategies by adjusting case parameters by selecting from the series of options as previously described in step  302 . 
     In another distinct implementation the scenario analytics server  110  may utilize the prediction module  138  in a manner that is separate from the steps described above. The prediction module  138  may rely on a diverse set of features derived from the facts, the anticipated claims and the metadata associated with the candidate case. These features may include, for example, the type of litigation, the injury categories, the properties of the plaintiff or the defendant, the patterns of past settlements for the state county involved and similar. These feature sets may also include elements mined from the unstructured text of the fact and claims representations of a newly entered case. Given such a varied set of features, the system as illustrated in  FIG. 1  or similar, can harness machine learning predictive analytic techniques such as naïve Bayes, logistic regression, sequence modeling, support vector machines, random forests, boosting trees, neural networks, or other types of majority classifier or prediction modeling approaches to train computational models based on the particular features associated with the input variables. The prediction module  138  may output its assessment of a given case, in terms of predicted trial length (or range of trial length, with boundaries associated with a certain confidence interval), predicted award level (or range of award level, with boundaries associated with a certain confidence interval), or other form of trial outcome (or range of trial outcome, with boundaries associated with a certain confidence internal). In order to produce such predictive assessments, the system may internally evaluate its prospective performance, that is, its predictive capabilities, by utilizing many known outcomes for related cases present in the jury verdicts and settlements data store  182 . Such a repository of known outcomes may be viewed as a gold data store. In one implementation, models developed for the system have relied upon tractable quantities of features, namely, on the order of O(100). It is this combination of state-of-the-art machine learning techniques and a very large repository of prior cases such as jury verdicts and settlements that allow the system to present users with quantitative insights into the likelihood of an instance case having certain outcome properties (or distributions thereof). As such, the system enables users to rely on principled predictions in order to assist in making informed decisions about the legal approaches and principles being used when formulating their arguments. 
     In yet another implementation, the prediction module  138  may generate prediction models by modeling the underlying data and may be associated with prediction scores. 
     Turning to  FIG. 4 , an exemplary graphical user interface (GUI) available through the user interface  174  of access device  170  is disclosed. In one implementation, the user interface  174  includes an application interface  400  with user input field Case Facts Description  402 , Plaintiff claims  404  and Defendant claims  406 , wherein the user may input information in one or more of the fields. 
     In a further implementation, application interface  400  may also include additional selections or options, but not limited to Plaintiff/Defendant  408 ; Jurisdiction—State  410 ; Jurisdiction—County  412 ; Event-type  414 ; Tort  416 ; Case Duration Filter  418  and Classification Taxonomy  420  to be used in the search. 
     Once all the data have been input and optional selections made, the users may select Display Results  422 . 
     Continuing onto  FIG. 5 , an exemplary graphical user interface (GUI) available through the user interface  174  of access device  170  is disclosed. In one implementation, the user interface  174  includes an application interface  500  to present the results of the search. In one implementation, application interface  500  may include one or more sections, i.e.,  502   a - 502   c  for displaying the proposed strategies based on different scenarios, as generated by prediction module  138  as described in step  312  of  FIG. 3 . A listing of related cases  504  is presented along with each of the proposed strategy. An award distribution illustration  506  and additional relevant statistics  508 , such as but not limited to, the mean duration of a trial, shortest and longest trial length, may also be presented. In yet a further implementation, application interface  500  may include section  510  for case feature adjustment, which may be similar to the selections  408 - 420  available in  FIG. 4  and are utilized to further revise the searching parameters. 
     The application interface depicted in  FIG. 6 , illustrates exemplary details behind one of the cases in the legal strategy  502   a  proposed in  FIG. 5 . The details of the case  602  may include but are not limited to: the fact summary, the plaintiff and defendant contentions, the record number, date of incident, date of filing, date of trial, date of settlement, docket number, state, county, court, case type, general description, specific description, primary injury, secondary injury, range amount, award level, damage summary, background description and the like. 
       FIG. 7 a    illustrates an exemplary unstructured textual description in relation to the cases in the Jury Verdict and Settlements data store  182 , which may be processed by segmentation module  130  and segmented into facts of the event  702 , the plaintiffs&#39; claims  704  and the defendants&#39; claims  706 . 
       FIG. 7 b    illustrates another exemplary unstructured textual description in relation to the cases in the Jury Verdict and Settlements data store  182 , which may be processed by segmentation module  130  and segmented into facts of the event  702 , the plaintiffs&#39; claims  704 , the defendants&#39; claims  706  and other information  708 . 
       FIG. 8 a    illustrates an exemplary award levels relative to key numbers as discussed in relation to Award Analysis  204   d  of  FIG. 2 . The exemplary award distributions are associated with clustered sets, in this instance, clustered by key numbers. The topics in this example come from Employment Discrimination cases. However, similar award distributions can be generated for different types of cases and types of clusters as well (i.e., clustering besides using key numbers). In another implementation,  FIG. 8 b    illustrates an exemplary award distribution by defined categories. 
       FIGS. 1 through 8   b  are conceptual illustrations allowing for an explanation of the present disclosure. Various features of the system may be implemented in hardware, software, or a combination of hardware and software. For example, some features of the system may be implemented in one or more computer programs executing on programmable computer. Each program may be implemented in a high level procedural or object-oriented programming language to communicate with a computer system or other machine. Furthermore, each such computer program may be stored on a storage medium such as read-only-memory (ROM) readable by a general or special purpose programmable computer or processor, for configuring and operating the computer to perform the functions described above. 
     Notably, the figures and examples above are not meant to limit the scope of the present disclosure to a single implementation, as other implementations are possible by way of interchange of some or all of the described or illustrated elements. Moreover, where certain elements of the present disclosure can be partially or fully implemented using known components, only those portions of such known components that are necessary for an understanding of the present disclosure are described, and detailed descriptions of other portions of such known components are omitted so as not to obscure the disclosure. In the present specification, an implementation showing a singular component should not necessarily be limited to other implementations including a plurality of the same component, and vice-versa, unless explicitly stated otherwise herein. Moreover, applicants do not intend for any term in the specification or claims to be ascribed an uncommon or special meaning unless explicitly set forth as such.