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Instaknow’s technology is protected by U.S. patents 6732102, 7073126, 7437342, 7979377, 9443005, 11568666, and additional pending patents.

Instaknow’s Insta-Intelligence® – Competitive Extraction Products Feature Comparison

(compared with offerings from other OCR and Data Extraction solutions)

No.

Feature

Instaknow

Competing Solution

Comments

1

Built-in, full-fledged, multi-system process orchestration using Humanvision AI to interact with browsers, screens, Excels containing data in unknown formats

Yes

No

No competing extraction technology provides process orchestration. Another product must be used and integrated for that. Instaknow has both data extraction and further processing orchestration as an integrated offering.

2

Instaknow does NOT use machine learning. No document examples are needed. Simply define visual rules, similar to the instructions given to a person.

Yes

No

A few competing technologies can follow basic visual rules & instructions (right/left/above/below label). Instaknow (with its unique ability to find “Related” content) can automatically find data related to the label without needing to specify if it is right/left/above/below the label.

3

Detect and separate sub-documents that happen to be together inside a single PDF, regardless of subdocument order.

Yes

No

A physical file may intentionally or unintentionally combine multiple sub-documents. Instaknow detects the beginning and end of each sub-document of interest and process it regardless of its order inside the file

4

Auto-detect all typical labels on all pages, without needing to specify them

Yes

No

Example from a legal document:

5

Detect and ignore page headers and footers – needed to extract details from paragraphs that wrap from the bottom of one page to the top of the next page

Yes

No

Example of a page footer that must be ignored from a legal document to read a list of items:

6

Detect and extract multiple items of interest in middle of paragraphs even when they have unknown occurrences and locations/p>

Yes

No

Example from legal documents showing amounts in a paragraph:

7

Detect and use labels wrapped in the middle of a page, e.g. section descriptors or column headers of a table and look only below that narrow vertical band

Yes

No

Other extraction engines need label words to be in a single line. This limits their practical usage in complex documents. e.g. Holiday Schedule from legal document:

8

Automatically detect and extract entire tables, even when they have unknown number of rows and columns

Yes

No

Examples from court Judgements showing unknown number of rows:

9

Very accurately detect checked/unchecked checkboxes and radio buttons

Yes

Unknown

10

Redact or highlight required data even when it is at unknown locations

Yes

No

11

Built-in Natural Language Processing (NLP) provides semantic (“meaning”) based detection of relevant data

Yes

No

Allows far more flexible detection of data of interest than competing data extraction products

12

Ability of comparing complex clauses with a “standard text” and flagging semantic discrepancies

Yes

No

e.g. detect that the expected clauses are missing from a document

TOTAL SCORE OF 'YES'

12

0