Legal Spend Document Processing with Machine Learning

Abstract

A unique user interface for improving legal (and other fields) spend management is described herein. The algorithm may include looping through the lines of an invoice and displaying an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user's input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.

1 . A method of comprising:
 selecting an invoice for review by a computer, said computer electrically connected to a display screen; and   looping, by the computer, through each line of the invoice:
 splitting the line into fields, said fields including a narrative field; 
 processing the narrative field through a machine learning model to derive a machine learning confidence score, wherein the processing by the machine learning model comprises:
 converting the narrative field into word stems that are used by the machine learning model to calculate the machine learning confidence score, 
 searching the narrative field for the word stems that comprised a highest impact in the machine learning confidence score, and 
 adding formatting instructions to at least one word associated with the word stems that comprised the highest impact on the machine learning confidence score; 
 
 displaying on the display screen the fields, including the narrative field with the formatting instructions to indicate reasoning used by the machine learning model in a determination of the machine learning confidence score; and 
 displaying a variable icon on the display screen to graphically indicate the machine learning confidence score, wherein the variable icon displays a different number of items depending upon a magnitude of the machine learning confidence score. 
   
     
     
         2 . The method of  claim 1  wherein the invoice is a legal bill. 
     
     
         3 . The method of  claim 2  further comprising reviewing legal spending of a lawyer as described in the invoice. 
     
     
         4 . The method of  claim 3  wherein the reviewing includes observing the displayed narrative field and the variable icon. 
     
     
         5 . The method of  claim 3  wherein the review is conducted by a user. 
     
     
         6 . The method of  claim 1  wherein the invoice is a court reporter invoice. 
     
     
         7 . The method of  claim 1  wherein the invoice is an expert witness invoice. 
     
     
         8 . The method of  claim 1  wherein the narrative field with the formatting instructions is only displayed on the display screen when a mouse location is over an icon. 
     
     
         9 . The method of  claim 1  wherein a bold instruction is one of the formatting instructions. 
     
     
         10 . The method of  claim 1  wherein an instruction to increase font size is one of the formatting instructions. 
     
     
         11 . The method of  claim 1  wherein the variable icon displays three rays off of a lightbulb based on one range of the machine learning confidence score, and one ray off of the lightbulb based on another range of the machine learning confidence score. 
     
     
         12 . A computer executing non-transitory machine readable media programmed to:
 select an invoice for review by the computer, said computer electrically connected to a display screen; and   loop through each line of the invoice:
 split the line into fields, said fields including a narrative field; 
 process the narrative field through a machine learning model to derive a machine learning confidence score, wherein the machine learning model converts the narrative field into word stems that are used by the machine learning model to calculate the machine learning confidence score, then searches the narrative field for the word stems that comprised a highest impact in the machine learning confidence score, and adds formatting instructions to at least one word associated with the word stems that comprised the highest impact on the machine learning confidence score; 
 display on the display screen the fields, including the narrative field with the formatting instructions to indicate reasoning used by the machine learning model in a determination of the machine learning confidence score; and 
 display a variable icon on the display screen to graphically indicate the machine learning confidence score, wherein the variable icon displays a different number of items depending upon a magnitude of the machine learning confidence score. 
   
     
     
         13 . The computer of  claim 12  wherein the invoice is a legal bill. 
     
     
         14 . The computer of  claim 13  further programmed to review legal spending of a lawyer as described in the invoice. 
     
     
         15 . The computer of  claim 12  wherein the invoice is a court reporter invoice. 
     
     
         16 . The computer of  claim 12  wherein the invoice is an expert witness invoice. 
     
     
         17 . The computer of  claim 12  wherein the narrative field with the formatting instructions is only displayed on the display screen when a mouse location is over an icon. 
     
     
         18 . The computer of  claim 12  wherein a bold instruction is one of the formatting instructions. 
     
     
         19 . The computer of  claim 12  wherein an instruction to increase font size is one of the formatting instructions. 
     
     
         20 . The computer of  claim 12  wherein the variable icon displays three rays off of a lightbulb based on one range of the machine learning confidence score, and one ray off of the lightbulb based on another range of the machine learning confidence score.