IP Library Granted Patent US 10,839,207
Granted Patent B2
US 10,839,207 · App. 16/510,644 · Granted Nov 17, 2020

Systems and methods for predictive analysis reporting

Inventors: Joseph M. Wood (Broadlands, VA); Robert D. Bailey (Saratoga Springs, UT); Matthew Valley (American Fork, UT); Stewart A. Sintay (Lehi, UT); Stephen W. Shillingford (Salt Lake City, UT); Wacey T. Richards (Midway, UT); Damon A. Darais (Pleasant Grove, UT); Michael E. Kiemel (Lehi, UT); Samuel Z. Shillingford (Salt Lake City, UT)
Assignee: DeepSee.ai Inc.
G06K9/00469G06F40/205G06K9/00463G06K9/6263G06N20/00
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Quick Facts
Patent No.
US 10,839,207
App. No.
16/510,644
Granted
Nov 17, 2020
Kind
B2
Abstract

Systems and methods may utilize a predictive analysis model to analyze a contract or other document. A system may parse a document and/or a repository of information associated with the document. The system may identify one or more terms in the document and corresponding terms in the repository. The system may determine a difference parameter between a first term extracted from the document and a second term extracted from the repository. The system may determine whether the difference between the first term and the second term, represented by the difference parameter, is likely to be acceptable to the user using a predictive analysis model. The system may report a validation parameter indicating a level of acceptability associated with the difference. User feedback on the accuracy of the predictive analysis model is used to train, modify, and improve the predictive analysis model.

Claims (81)

1. A system, comprising:

a term extraction module to:

extract a first contract term from a contract document, and

extract a second contract term from a repository of contract terms based at least in part on the first contract term extracted from the contract document;

a comparison module to:

identify a difference parameter between the first contract term extracted from the contract document and the second contract term extracted from the repository of contract terms, and

determine whether the difference parameter between the first contract term and the second contract term satisfies a threshold based on predictive analysis model;

a validation module to identify a validation parameter based on the determination of whether the difference parameter satisfies the threshold, wherein the validation parameter indicates a quantified level of acceptability of a difference between the first contract term and the second contract term;

a feedback module to receive feedback from a user to validate the validation parameter that was generated using the predictive analysis model;

a predictive analysis module to dynamically modify the predictive analysis model when the received feedback indicates that the comparison module assigned an incorrect different parameter; and

a report module to generate a report indicating the first contract term, the second contract term, the validation parameter, and the feedback from the user.

2. An apparatus, comprising:

means for extracting a first contract term from a contract document;

means for extracting a second contract term from a repository of contract terms based at least in part on the first contract term extracted from the contract document;

means for identifying a difference parameter between the first contract term extracted from the contract document and the second contract term extracted from the repository of contract terms;

means for determining whether the difference parameter between the first contract term and the second contract term satisfies a threshold using a predictive analysis model;

means for generating a validation parameter based at least in part on determining whether the difference parameter satisfies the threshold, wherein the validation parameter indicates a level of acceptability of a difference between the first contract term and the second contract term;

means for requesting feedback from a user to validate the validation parameter generated using the predictive analysis model;

means for receiving the feedback from the user; and

means for generating a report indicating the first contract term, the second contract term, the validation parameter, and the feedback from the user that validated the validation parameter.

3. The apparatus of claim 2 , further comprising:

means for storing the difference parameter between the first contract term and the second contract term, the validation parameter, and the feedback from the user in a database; and

means for performing machine learning on information stored in the database to adjust results of the predictive analysis model, wherein generating the validation parameter using the predictive analysis model is based at least in part on performing the machine learning.

4. The apparatus of claim 2 , further comprising:

means for generating a confidence score associated with the validation parameter generated using the predictive analysis model that indicates a likelihood that the validation parameter is correct for the difference parameter between the first contract term and the second contract term, wherein requesting the feedback from the user is based at least in part on generating the confidence score.

5. The apparatus of claim 2 , further comprising:

means for normalizing the first contract term or the second contract term; and

means for outputting the normalized first contract term or the normalized second contract term to the user, wherein requesting the feedback from the user is based at least in part on outputting the normalized first contract term or the normalized second contract term to the user.

6. The apparatus of claim 2 , further comprising:

means for outputting to the user the first contract term, the second contract term, the validation parameter, a confidence score, or a disposition parameter, or a combination thereof, wherein requesting the feedback from the user is based at least in part on the outputting.

7. The apparatus of claim 2 , further comprising:

means for applying the predictive analysis model to the difference parameter, the first contract term, the second contract term, or a combination thereof, wherein generating the validation parameter is based at least in part on applying the predictive analysis model.

8. The apparatus of claim 2 , further comprising:

means for comparing the difference parameter to one or more difference parameters of validated contract terms stored in a database, wherein determining whether the difference parameter satisfies the threshold is based on the comparison.

9. The apparatus of claim 2 , further comprising:

means for performing one or more risk evaluations on the difference parameter, wherein determining whether the difference parameter satisfies the threshold is based at least in part on performing the one or more risk evaluations.

10. The apparatus of claim 2 , wherein the threshold is an acceptance threshold, a validation threshold, a rejection threshold, a no-prediction threshold, or a combination thereof.

11. An apparatus, comprising:

a processor;

memory in electronic communication with the processor; and

instructions stored in the memory and executable by the processor to cause the apparatus to:

extract a first contract term from a contract document;

extract a second contract term from a repository of contract terms based at least in part on the first contract term extracted from the contract document;

identify a difference parameter between the first contract term extracted from the contract document and the second contract term extracted from the repository of contract terms;

determine whether the difference parameter between the first contract term and the second contract term satisfies a threshold using a predictive analysis model;

generate a validation parameter based at least in part on determining whether the difference parameter satisfies the threshold, wherein the validation parameter indicates a level of acceptability of a difference between the first contract term and the second contract term;

request feedback from a user to validate the validation parameter generated using the predictive analysis model;

receive the feedback from the user;

modify the predictive analysis model based on the feedback from the user; and

generate a report indicating the first contract term, the second contract term, the validation parameter, and the feedback from the user that validated the validation parameter.

12. A non-transitory computer-readable medium storing code, the code comprising instructions executable by a processor to:

extract a first contract term from a contract document;

extract a second contract term from a repository of contract terms based at least in part on the first contract term extracted from the contract document;

determine a difference parameter based on a difference between the first contract term extracted from the contract document and the second contract term extracted from the repository of contract terms;

determine whether the difference parameter satisfies a threshold using a predictive analysis model;

generate a validation parameter that indicates a level of acceptability of differences between the first contract term and the second contract term;

request feedback from a user to validate the validation parameter generated using the predictive analysis model;

receive the feedback from the user indicating that the predictive analysis model is incorrect based on the validation parameter assigned to the different between the first contract term and the second contract term;

update the predictive analysis model based on the feedback from the user; and

generate a report indicating the first contract term, the second contract term, the validation parameter, and the feedback from the user that validated the validation parameter.

13. The non-transitory computer-readable medium of claim 12 , wherein the instructions are further executable to:

store the difference parameter between the first contract term and the second contract term, the validation parameter, and the feedback from the user in a database; and

perform machine learning on information stored in the database to adjust results of the predictive analysis model, wherein generating the validation parameter using the predictive analysis model is based at least in part on performing the machine learning.

14. The non-transitory computer-readable medium of claim 12 , wherein the instructions are further executable to:

generate a confidence score associated with the validation parameter generated using the predictive analysis model that indicates a likelihood that the validation parameter is correct for the difference parameter between the first contract term and the second contract term, wherein requesting the feedback from the user is based at least in part on generating the confidence score.

15. The non-transitory computer-readable medium of claim 12 , wherein the instructions are further executable to:

normalize the first contract term or the second contract term; and

output the normalized first contract term or the normalized second contract term to the user, wherein requesting the feedback from the user is based at least in part on outputting the normalized first contract term or the normalized second contract term to the user.

16. The non-transitory computer-readable medium of claim 12 , wherein the instructions are further executable to:

categorize the first contract term extracted from the contract document; and

categorize the second contract term extracted from the repository of contract terms, wherein identifying the difference parameter is based at least in part on categorizing the first contract term and the second contract term.

17. The non-transitory computer-readable medium of claim 16 , wherein the instructions are further executable to:

determine a validation criterion from a set of validation criteria to apply to the difference parameter based at least in part categorizing the first contract term and the second contract term; and

apply the validation criterion to the difference parameter, wherein determining whether the difference parameter satisfies the threshold is based at least in part on applying the validation criterion.

18. The non-transitory computer-readable medium of claim 12 , wherein the instructions are further executable to:

identify that a second difference parameter between validated contract terms is similar to the difference parameter between the first contract term and the second contract term, wherein the second difference parameter is selected from a database of past validated contract terms; and

identify a second validation parameter associated with the second difference parameter, wherein determining the validation parameter using the predictive analysis model is based at least in part on identifying the second validation parameter associated with the second difference parameter.

19. The non-transitory computer-readable medium of claim 12 , wherein the instructions are further executable to:

receive one or more comments from the user regarding the difference parameter based at least in part on requesting the feedback from the user.

20. The non-transitory computer-readable medium of claim 12 , wherein the instructions are further executable to:

correlate the first contract term extracted from the contract document with the second contract term extracted from the repository of contract terms, wherein identifying the difference parameter is based at least in part on correlating the first contract term and the second contract term.

Assignments (4)
RELEASE OF SECURITY INTEREST Recorded Nov 3, 2025
From: FIRST-CITIZENS BANK & TRUST COMPANY
To: DEEPSEE.AI INC.
Reel/Frame 072766/0875 →
SECURITY INTEREST Recorded Dec 28, 2023
From: DEEPSEE.AI INC.
To: FIRST-CITIZENS BANK & TRUST COMPANY
Reel/Frame 065967/0789 →
CHANGE OF NAME Recorded Nov 12, 2020
From: RESEMBLETECH, INC.
To: DEEPSEE.AI INC.
Reel/Frame 054401/0487 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 18, 2019
From: WOOD, JOSEPH M.; SINTAY, STEWART A.; SHILLINGFORD, STEPHEN W.; RICHARDS, WACEY T.; DARAIS, DAMON A.; KIEMEL, MICHAEL E.; SHILLINGFORD, SAMUEL Z.; BAILEY, ROBERT D.; VALLEY, MATTHEW; VANDENBERG, ROBERT M.
To: RESEMBLETECH, INC.
Reel/Frame 050759/0794 →
Continuity (2)
Provisional Application 62698106 · Jul 14, 2014
Related Publication 20200026916A1 · Jan 23, 2020