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Cognitive Legal Research
 
 
Sophia is the genesis of the first cognitive legal research™ database of its kind in the nation.  Sophia natural language processor—artificial—AI—intelligence—is a sophisticated analytical “Deep Question and Answering” computer processor capable of assisting end-user litigants in querying LexisNexis or Westlaw databases for common-law precedents or codified statutes favourable to remedy tortious act.
 

If you don’t like change, you are going to hate extinction.  Scott Klososky

 

 

 

 

 

 

The umbilical connection between the end-user litigant™ and IBM Watson is an infinite algorithmic expression of space and time. The end-user litigant and IBM Watson creätes a well-defined lexical—legal language—of iuris language.  In plain English, IBM Watson algorithmic cognitive ability allows its to anticipate the end-user litigant’s input behaviour—that is, learns to predict the nature of enquiry or query from the end-user litigant, which specified in the form descriptive metadata, structural metadata, and administrative metadata fields. 

 

 

A graphical expression of Euclid's algorithm to find the greatest common divisor for 1599 and 650 is expressed in the model above.

 

1599 = 650×2 + 299
650 = 299×2 + 52
299 = 52×5 + 39
52 = 39×1 + 13
39 = 13×3 + 0

An algorithm is an effective method that can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a functionStarting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing "output" and terminating at a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input.

 

 

 

 

 

 

 

Docket entry and auditing information from Case Management System/eCourt-filing—CMS/ECF—represents a cognitive system,which learns end-user litigants s input behavior by correcting document input fields—that is, learning to predict whether a field’s content is erroneous based on the nature of the document—as specified in the meta-data—and the values of the other fields. As showed in Figure 2, an audit-based error-detecting feature incorporated into the Case Management System significantly reduces error rates, increases compliance, and improves end-user litigants interactions with the Court.