Method for Extracting, Transforming and Loading legal information onto autonomous agents using large language models and computer graph databases

A method of extracting, transforming, and loading legal information to drive the decisions of an autonomous artificial intelligence agent, comprising,

• extracting a logical structure from the legal information, wherein the natural legal information corresponds to jurisprudential, statutory, or regulatory texts,
• transforming the logical structure of the legal information into a formal representation of the logical structure of the legal information, wherein the formal representation is a decision path defined by one or more decision points,
• transforming the formal representation of the logical structure of the legal information into a factor graph document database with source nodes, destination nodes, and edges capable of performing mathematical, logical and statistical operations over the source and destination nodes, using a computer program,
• loading elements of a fact pattern onto the factor graph document database by updating conditional probabilities that map destination nodes onto elements of the fact pattern, wherein the update of the conditional probabilities is done using a method of adding count and normalizing the conditional probability mapping,
• implementing the factor graph document database into the software of an artificial intelligence agent using a computer program, and
• inferring a decision to be enacted by the autonomous artificial intelligence agent using the factor graph document database by inferring all posterior probabilities of the value of all destination nodes and all the logical statements, wherein the inference of the decision is done using a computer program.