Standard exact clause detection

• US 10,185,712 B2
• Filed: 10/02/2017
• Issued: 01/22/2019
• Est. Priority Date: 07/13/2015
• Status: Expired

• Current Assignee: DocuSign International (EMEA) Ltd. (DocuSign Incorporated)
• Original Assignee: Seal Software Ltd.
• Inventors: Gidney, Kevin
• Primary Examiner(s): Serrou, Abdelali
• Application Number:
  US15/723,023
• Publication Number:
  US 20180024992A1
• Time in Patent Office: 477 Days
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/289 Phrasal analysis, e.g. finite state techniques or chunking
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

Embodiments relate to a system and a method for identifying, from contractual documents, (i) standard exact clauses matching clause examples and (ii) non-standard clauses semantically related to but not matching the clause examples. A standard feature data set comprising standard exact clauses matching clause examples is obtained. In addition, a mirror feature data set comprising semantically related clauses of the clause examples is obtained using semantic language analysis, where the mirror feature data set encompasses the standard feature data set. Non-standard clauses are obtained by extracting a difference between the mirror feature data set and the standard exact feature data set.

Claims

1. A non-transitory computer readable medium storing program code for determining a presence of a type of clause within a plurality of documents, the program code comprising instructions that when executed by a processor cause the processor to:

receive a clause example corresponding to the type of clause;

generate a primary policy based upon the received clause example for use in a semantic language evaluator configured to assess a level of semantic similarity between received clauses, the primary policy comprising one or more policy rules and associated with a first threshold value indicating a level of semantic similarity of a clause to the clause example;

analyze, using the semantic language evaluator, the plurality of documents according to the primary policy to automatically provide a first set of clauses corresponding of the plurality of documents, each clause of the first set corresponding to a standard clause matching the clause example;

generate a mirror document based upon the plurality of documents by automatically replacing one or more portions of the plurality of documents having allowable variations with corresponding variables;

parse the mirror document to generate a second set of clauses corresponding to a standard exact feature data set;

generate a secondary policy based upon the primary policy and the clause example for use in the semantic language evaluator, the secondary policy associated with a second threshold value indicating a level of semantic similarity of a clause to the clause example that is lower than the first threshold value;

analyze, using the semantic language evaluator, the plurality of documents according to the secondary policy to automatically provide a third set of clauses comprising non-standard clauses semantically related to but not matching the clause example, wherein the third set of clauses corresponds to a mirror feature data set;

obtain a difference between the mirror feature data set and the standard exact feature data set, the difference corresponding to non-standard clauses of the plurality of documents;

update, automatically, a database to identify the standard and non-standard clauses of the plurality of documents associated with the type of clause based upon the obtained difference, for subsequent usage in analyzing the plurality of documents.

2. The non-transitory computer readable medium of claim 1, further comprising instructions when executed by the processor cause the processor to:

receive one or more features associated with the type of clause; and

generate, using a semantic language evaluator, a plurality of feature replaced clauses by automatically replacing one or more of a plurality of original clauses in the plurality of documents with the one or more features.

3. The non-transitory computer readable medium of claim 1, further comprising instructions when executed by the processor cause the processor to:

identify a portion of the clause example as corresponding to an available variation of the clause example; and

replace the available variation with a variable.

4. The non-transitory computer readable medium of claim 3, further comprising instructions when executed by the processor cause the processor to:

parse the plurality of documents to generate the second set of clauses corresponding to the standard exact feature data set containing clauses matching the clause example based upon the available variation.

5. The non-transitory computer readable medium of claim 1, further comprising instructions when executed by the processor cause the processor to:

replace one or more clauses of the plurality of documents with one or more features, each feature of the one or more features corresponding to a reference or description of a portion of the plurality of documents,

wherein the first policy is generated based upon at least one feature of the one or more features.

6. The non-transitory computer readable medium of claim 1, further comprising instructions when executed by the processor cause the processor to:

obtain a difference between the first set of clauses and the third set of clauses corresponding to one or more non-standard clauses.

7. A computer implemented method for determining a presence of a type of clause within a plurality of documents, the method comprising:

receiving a clause example corresponding to the type of clause;

generating a primary policy based upon the received clause example for use in a semantic language evaluator configured to assess a level of semantic similarity between received clauses, the primary policy comprising one or more policy rules and associated with a first threshold value-indicating a level of semantic similarity of a clause to the clause example;

analyzing, using the semantic language evaluator, the plurality of documents according to the primary policy to automatically provide a first set of clauses corresponding of the plurality of documents, each clause of the first set corresponding to a standard clause matching the clause example;

generating a mirror document based upon the plurality of documents by automatically replacing one or more portions of the plurality of documents having allowable variations with corresponding variables;

parsing the minor document to generate a second set of clauses corresponding to a standard exact feature data set;

generating a secondary policy based upon the primary policy and the clause example for use in the semantic language evaluator, the secondary policy associated with a second threshold value indicating a level of semantic similarity of a clause to the clause example that is lower than the first threshold value;

analyzing, using the semantic language evaluator, the plurality of documents according to the secondary policy to automatically provide a third set of clauses comprising non-standard clauses semantically related to but not matching the clause example, wherein the third set of clauses corresponds to a mirror feature data set;

obtaining a difference between the mirror feature data set and the standard exact feature data set, the difference corresponding to non-standard clauses of the plurality of documents; and

automatically updating a database to identify the standard and non-standard clauses of the plurality of documents associated with the type of clause based upon the obtained difference, for subsequent usage in analyzing the plurality of documents.

8. The method of claim 7, further comprising:

receiving one or more features associated with the type of clause; and

generating, using a semantic language evaluator, a plurality of feature replaced clauses by automatically replacing one or more of a plurality of original clauses in the plurality of documents with the one or more features.

9. The method of claim 7, further comprising:

identifying a portion of the clause example as corresponding to an available variation of the clause example; and

replacing the available variation with a variable.

10. The method of claim 9, further comprising:

parsing the plurality of documents to generate the second set of clauses corresponding to the standard exact feature data set containing clauses matching the clause example based upon the available variation.

11. The method of claim 7, further comprising:

replacing one or more clauses of the plurality of documents with one or more features, each feature of the one or more features corresponding to a reference or description of a portion of the plurality of documents,

wherein the first policy is generated based upon at least one feature of the one or more features.

12. The method of claim 7, further comprising:

obtain a difference between the first set of clauses and the third set of clauses corresponding to one or more non-standard clauses.

13. A system for determining a presence of a type of clause within a plurality of documents, comprising:

a document parsing module configured to receive a clause example corresponding to the type of clause;

a policy definition module configured to;

generate a primary policy based upon the received clause example for use in a semantic language evaluator configured to assess a level of semantic similarity between received clauses, the primary policy comprising one or more policy rules and associated with a first threshold value indicating a level of semantic similarity of a clause to the clause example; and

generate a secondary policy based upon the primary policy and the clause example for use in the semantic language evaluator, the secondary policy associated with a second threshold value indicating a level of semantic similarity of a clause to the clause example that is lower than the first threshold value;

an analysis engine configured to;

analyze, using the semantic language evaluator, the plurality of documents according to the primary policy to automatically provide a first set of clauses corresponding of the plurality of documents, each clause of the first set corresponding to a standard clause matching the clause example;

generate a mirror document based upon the plurality of documents by automatically replacing one or more portions of the plurality of documents having allowable variations with corresponding variables;

parse the mirror document to generate a second set of clauses corresponding to a standard exact feature data set;

analyze, using the semantic language evaluator, the plurality of documents according to the secondary policy to automatically provide a third set of clauses comprising non-standard clauses semantically related to but not matching the clause example, wherein the third set of clauses corresponds to a minor feature data set;

obtain a difference between the mirror feature data set and the standard exact feature data set, the difference corresponding to non-standard clauses of the plurality of documents; and

update, automatically, a database to identify the standard and non-standard clauses of the plurality of documents associated with the type of clause based upon the obtained difference, for subsequent usage in analyzing the plurality of documents.

14. The system of claim 13, wherein the document parsing module is further configured to:

receive one or more features associated with the type of clause; and

generate, using a semantic language evaluator, a plurality of feature replaced clauses by automatically replacing one or more of a plurality of original clauses in the plurality of documents with the one or more features.

15. The system of claim 13, wherein the document parsing module is further configured to:

identify a portion of the clause example as corresponding to an available variation of the clause example; and

replace the available variation with a variable.

16. The system of claim 15, wherein the analysis engine is further configured to:

parse the plurality of documents to generate the second set of clauses corresponding to the standard exact feature data set containing clauses matching the clause example based upon the available variation.

17. The system of claim 13, wherein the document parsing module is further configured to:

replace one or more clauses of the plurality of documents with one or more features, each feature of the one or more features corresponding to a reference or description of a portion of the plurality of documents, and

wherein the policy definition module is configured to generate the first policy based upon at least one feature of the one or more features.

18. The system of claim 13, wherein the analysis engine is further configured to:

obtain a difference between the first set of clauses and the third set of clauses corresponding to one or more non-standard clauses.