IP Library Granted Patent US 11,354,018
Granted Patent B2
US 11,354,018 · App. 17/328,387 · Granted Jun 7, 2022

Visualization of a machine learning confidence score

Inventors: John Canneto (Rye, NY); Flora Kidani (Wilton, CT); Anne Baron (Ansonia, CT); Jonathan Hewitt (Exeter, NH); William Cashman (Durham, NH); Michael Marcinelli (North Andover, MA)
Assignee: Bottomline Technologies, Inc.
G06F3/04817G06F3/0482G06F40/205G06N20/00
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Quick Facts
Patent No.
US 11,354,018
App. No.
17/328,387
Granted
Jun 7, 2022
Kind
B2
Abstract

A unique user interface for improving machine learning algorithms is described herein. The user interface comprises 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.

Claims (42)

1. An apparatus for visualization of a machine learning confidence score, the apparatus comprising:

a display screen;

a special purpose computer electrically connected to the display screen;

a large capacity data storage facility with a machine learning training data set;

a machine learning model built with the machine learning training data set;

a machine learning algorithm programmed to operate on the special purpose computer and to interface with the machine learning model to convert a textual entry into the machine learning confidence score; and

a user interface display algorithm programmed to operate on the special purpose computer and display an indication of the machine learning confidence score on the display screen, wherein the indication is a variable icon that varies a count of icon elements on the variable icon depending on a magnitude of the machine learning confidence score, wherein the variable icon displays a thumb icon and a comment icon when selected.

2. The apparatus of claim 1 wherein the variable icon is a lightbulb icon.

3. The apparatus of claim 2 wherein the icon elements are rays.

4. The apparatus of claim 3 wherein the user interface display algorithm is programmed to display three rays off of the lightbulb icon based on one range of the machine learning confidence score, and one ray off of the lightbulb icon based on a second range of the machine learning confidence score.

5. The apparatus of claim 1 wherein the count of the icon elements is at least three.

6. The apparatus of claim 1 wherein the textual entry is a portion of an invoice.

7. The apparatus of claim 1 wherein a natural language processing algorithm programmed to operate on the special purpose computer and to interface with the user interface display algorithm to convert the textual entry into a table of word stems; and

the machine learning algorithm is programmed to operate on the special purpose computer and to interface with the natural language processing algorithm to convert the table of the word stems into the machine learning confidence score using the machine learning model.

8. A special purpose computer implemented method of visualizing a machine learning confidence score, the method comprising:

building a machine learning model with a machine learning training data set;

operating a machine learning algorithm using the machine learning model to convert a textual entry into the machine learning confidence score;

displaying an indication of the machine learning confidence score on a display screen, wherein the indication is a variable icon;

displaying a thumb icon and a comment icon when the variable icon is selected; and

varying a count of icon elements on the variable icon depending on a magnitude of the machine learning confidence score.

9. The method of claim 8 wherein the variable icon is a lightbulb icon.

10. The method of claim 9 wherein the icon elements are rays.

11. The method of claim 10 further comprises displaying three rays off of the lightbulb icon based on one range of the machine learning confidence score, and one ray off of the lightbulb icon based on a second range of the machine learning confidence score.

12. The method of claim 8 wherein the count of the icon elements is at least three.

13. The method of claim 8 wherein the textual entry is a portion of an invoice.

14. A non-transitory machine-readable media programmed to:

build a machine learning model with a machine learning training data set;

operate a machine learning algorithm using the machine learning model to convert a textual entry into a machine learning confidence score;

display an indication of the machine learning confidence score on a display screen, wherein the indication is a variable icon;

display a thumb icon when the variable icon is selected; and

vary a count of icon elements on the variable icon depending on a magnitude of the machine learning confidence score.

15. The non-transitory machine-readable media of claim 14 wherein the variable icon is a lightbulb icon.

16. The non-transitory machine-readable media of claim 15 wherein the icon elements are rays.

17. The non-transitory machine-readable media of claim 16 further programmed to display three rays off of the lightbulb icon based on one range of the machine learning confidence score, and one ray off of the lightbulb icon based on a second range of the machine learning confidence score.

18. The non-transitory machine-readable media of claim 14 wherein the count of the icon elements is at least three.

19. The non-transitory machine-readable media of claim 14 wherein the textual entry is a portion of an invoice.

20. The non-transitory machine-readable media of claim 14 further programmed to:

parse a narrative section of an invoice into a table of words;

convert the words in the table into stems;

look up each stem in the machine learning model;

place a weight for the stem, as found in the machine learning model, in the table of the words; and

determine the machine learning confidence score by averaging the weights for each word in the table of the words.

Assignments (2)
SECURITY INTEREST Recorded May 13, 2022
From: BOTTOMLINE TECHNOLOGIES, INC.
To: ARES CAPITAL CORPORATION
Reel/Frame 060064/0275 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 26, 2021
From: CANNETO, JOHN; KIDANI, FLORA; BARON, ANNE; HEWITT, JONATHAN; CASHMAN, WILLIAM; MARCINELLI, MICHAEL
To: BOTTOMLINE TECHNOLOGIES, INC.
Reel/Frame 056365/0326 →
Continuity (3)
Continuation 16919580 · Jul 2, 2020
Continuation 16299227 · Mar 12, 2019
Related Publication 20210278957A1 · Sep 9, 2021
Cited By (4)
US 1,061,585 US 1,072,836 US 12,235,941 US 12,235,947