Artificial intelligence assisted review of electronic documents

Patent metadata

Field	Value
Publication number	US-2020293605-A1
Application number	US-201916355145-A
Country	US
Kind code	A1
Filing date	Mar 15, 2019
Priority date	Mar 15, 2019
Publication date	Sep 17, 2020
Grant date	—

Abstract

Artificial intelligence is introduced into document review to identify content suggestions from input to generate suggested annotations for the reviewed document. An approach is provided for receiving an electronic document that contains original content from an original electronic document for review and electronic mark-ups provided by a first user. One or more electronic mark-ups that represent content suggestions proposed by the first user are identified from the electronic document. For each electronic mark-up of the one or more electronic mark-ups identified a document portion of the original content that corresponds to the electronic mark-up is identified, and an annotation is generated for the electronic mark-up comprising the electronic mark-up and a first user ID for the first user and associating the annotation to the document portion identified. The original content with one or more annotations generated from the one or more electronic mark-ups is displayed, in electronic form, within a display window.

First claim

What is claimed is:

1 . An apparatus comprising:
one or more processors; and one or more memories storing instructions which, when processed by the one or more processors, cause:
receiving an electronic document that contains original content from an original electronic document for review and electronic mark-ups provided by a first user;
identifying, from the electronic document, one or more electronic mark-ups that represent content suggestions proposed by the first user;
for each electronic mark-up of the one or more electronic mark-ups identified:
identifying a document portion of the original content that corresponds to the electronic mark-up;
generating an annotation for the electronic mark-up comprising the electronic mark-up and a first user ID for the first user and associating the annotation to the document portion identified; and

displaying, in electronic form within a display window, the original content with one or more annotations generated from the one or more electronic mark-ups.

2 . The apparatus of claim 1 , wherein identifying the one or more electronic mark-ups that represent the content suggestions proposed by the first user, comprises:
identifying the original content within the electronic document; and identifying the one or more electronic mark-ups within the electronic document by identifying content that is separate from the original content identified in the electronic document.

3 . The apparatus of claim 2 , the one or more memories store additional instructions which, when processed by the one or more processors, cause:
using a machine-learning model, for each of the one or more electronic mark-ups, determining electronic mark-up meanings by analyzing combinations of text characters within each of the one or more electronic mark-ups; and using the machine-learning model, determining a content suggestion type for each of the one or more electronic mark-ups, wherein the content suggestion type is one of a comment or a suggested edit.

4 . The apparatus of claim 3 , the one or more memories store additional instructions which, when processed by the one or more processors, cause:
determining that the annotation for the electronic mark-up corresponds to a suggested edit to the document portion; calculating a confidence score for the suggested edit, wherein the confidence score represents a level of confidence that the electronic mark-up corresponds to the suggested edit to the document portion; determining that the confidence score for the suggested edit is above a confidence score threshold for automatically editing the document portion; and automatically editing the document portion to reflect changes proposed in the suggested edit.

5 . The apparatus of claim 1 , wherein generating the annotation for the electronic mark-up, comprises:
determining that the electronic mark-up contains an electronic link to an object, wherein the object is one or a file or a webpage; and generating the annotation for the electronic mark-up comprising the electronic mark-up, the first user ID for the first user, and the electronic link the object, and associating the annotation to the document portion identified.

6 . The apparatus of claim 1 , wherein identifying the document portion of the original content that corresponds to the electronic mark-up, comprises:
using a first machine-learning model, identifying document portions within the original content based upon a determined document type associated with the original content and combinations of words within the original content, wherein the first machine-learning model has been trained using a plurality of documents of different document types; and using a second machine-learning model, correlating the document portion of the document portions to the electronic mark-up based upon a relative position of the electronic mark-up and a text transcription of the electronic mark-up, wherein the second machine-learning model has been trained using a plurality of document portions from a plurality of electronic documents and corresponding content suggestions for the plurality of document portions from the plurality of electronic documents.

7 . The apparatus of claim 1 , the one or more memories store additional instructions which, when processed by the one or more processors, cause:
receiving a second electronic document that contains the original content and second electronic mark-ups provided by a second user; identifying, from the second electronic document, one or more second electronic mark-ups that represent content suggestions proposed by the second user; for each second electronic mark-up of the one or more second electronic mark-ups identified:
identifying a second document portion of the original content that corresponds to the second electronic mark-up; and
generating a second annotation for the second electronic mark-up comprising the second electronic mark-up and a second user ID for the second user and associating the second annotation to the second document portion identified.

8 . One or more non-transitory computer-readable media storing instructions which, when processed by one or more processors, cause:
receiving an electronic document that contains original content from an original electronic document for review and electronic mark-ups provided by a first user; identifying, from the electronic document, one or more electronic mark-ups that represent content suggestions proposed by the first user; for each electronic mark-up of the one or more electronic mark-ups identified:
identifying a document portion of the original content that corresponds to the electronic mark-up;
generating an annotation for the electronic mark-up comprising the electronic mark-up and a first user ID for the first user and associating the annotation to the document portion identified; and
displaying, in electronic form within a display window, the original content with one or more annotations generated from the one or more electronic mark-ups.

9 . The one or more non-transitory computer-readable media of claim 8 , wherein identifying the one or more electronic mark-ups that represent the content suggestions proposed by the first user, comprises:
identifying the original content within the electronic document; and identifying the one or more electronic mark-ups within the electronic document by identifying content that is separate from the original content identified in the electronic document.

10 . The one or more non-transitory computer-readable media of claim 9 , further comprising additional instructions which, when processed by the one or more processors, cause:
using a machine-learning model, for each of the one or more electronic mark-ups, determining electronic mark-up meanings by analyzing combinations of text characters within each of the one or more electronic mark-ups; and using the machine-learning model, determining a content suggestion type for each of the one or more electronic mark-ups, wherein the content suggestion type is one of a comment or a suggested edit.

11 . The one or more non-transitory computer-readable media of claim 10 , further comprising additional instructions which, when processed by the one or more processors, cause:
determining that the annotation for the electronic mark-up corresponds to a suggested edit to the document portion; calculating a confidence score for the suggested edit, wherein the confidence score represents a level of confidence that the electronic mark-up corresponds to the suggested edit to the document portion; determining that the confidence score for the suggested edit is above a confidence score threshold for automaticall

Assignees

• Nelson Steven A
• Kitada Hiroshi
• Wong Lana
• Ricoh Co Ltd

Inventors

• Nelson Steven A
• Kitada Hiroshi
• Wong Lana

Classifications

• G06N20/00

  Machine learning · CPC title

• G06F40/30

  Semantic analysis · CPC title

• G06F40/169

  Annotation, e.g. comment data or footnotes · CPC title

• G06F40/166

  Editing, e.g. inserting or deleting · CPC title

• G06F40/117 Primary

  Tagging; Marking up (details of markup languages G06F40/143); Designating a block; Setting of attributes (style sheets, e.g. eXtensible Stylesheet Language Transformation [XSLT], G06F40/154) · CPC title