DETAILED ACTION
Application No. 19/263,675 filed on 07/09/2025 has been examined. In this Office Action, claims 1-20 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/09/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP § 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto- processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 11,868,400 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because they are cover the same recitations, limitations that are combined or worded slightly differently from the original patented claims but in essence they convey the same subject matter. The instant application is a broader scope of recitation of the claims of the already patented application. The similarities and differences are highlighted in the tabulated comparison below.
Present Application: 19/263675
US PAT: 11,868,400 B1
1. A method implemented by a computer system having one or more processors and one or more memories, comprising: receiving an XBRL document associated with one or more assigned XBRL tags; accessing a trained machine learning model, wherein the trained machine learning model is trained using a first set of XBRL data records and a second set of XBRL data records, wherein the second set of XBRL data records is generated based upon a subset of the first set of XBRL data records by at least modifying the subset of the first set of XBRL data records with a modification, wherein the modification to the subset of the first set of XBRL data records includes at least one selected from a group consisting of a modification to an XBRL tag in the first set of XBRL data records, a modification to filing information associated with the first set of XBRL data records, and a modification to an XBRL outline associated with the first set of XBRL data records; and analyzing the XBRL document using the trained machine learning model to identify a set of outlier XBRL tags in the one or more assigned XBRL tags, each outlier XBRL tag in the set of identified outlier XBRL tags being an uncommon tag for corresponding filing information.
2. The method of claim 1,wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying a first XBRL tag in a first record in the subset of the first set of XBRL data records to generate a second record in the second set of XBRL data records, the first record including first filing information, the second record including a second XBRL tag and the first filing information, the second XBRL tag being the modified first XBRL tag; wherein the second XBRL tag is an outlier tag based at least upon a taxonomy and the first filing information.
3. The method of claim l, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying third filing information in a third record in the subset of the first set of XBRL data records to generate a fourth record in the second set of XBRL data records, the third record including a third XBRL tag and the third filing information, the fourth record including a fourth XBRL tag and fourth filing information, the fourth XBRL tag being same as the third XBRL tag, the fourth filing information being the modified third filing information; wherein the fourth XBRL tag is an outlier tag based at least upon a taxonomy and the fourth filing information.
4. The method of claim l, wherein each XBRL data record in the first set of XBRL data records includes one or more XBRL tags and at least one selected from a group consisting of a classification identifier, a root abstract, and an XBRL outline.
5. The method of claim 4, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying a fifth XBRL outline in a fifth record in the subset of the first set of XBRL data records to generate a sixth record in the second set of XBRL data records, the fifth record including a fifth XBRL tag and fifth filing information, the fifth filing information including the fifth XBRL outline, the sixth record including a sixth XBRL tag and sixth filing information, the sixth XBRL tag being same as the fifth XBRL tag, the sixth filing information including the modified fifth XBRL outline; wherein the sixth XBRL tag is an outlier tag based at least upon a taxonomy and the sixth filing information.
6. The method of claim 1, further comprising: generating an output including the set of identified outlier XBRL tags in the one or more assigned XBRL tags.
7. The method of claim 6, wherein the output includes an indication of a document location for each outlier XBRL tag in the set of identified outlier XBRL tags.
8. The method of claim 7, wherein the document location includes an XBRL outline location for each outlier XBRL tag in the set of identified outlier XBRL tags.
9. The method of claim 6, wherein the output includes a file.
10. The method of claim 1, wherein the trained machine learning model includes a meta-classifier.
11. The method of claim 1, wherein the trained machine learning model is selected based at least in part upon a classification identifier.
12. A computing system comprising: one or more memories having instructions stored thereon; and one or more processors configured to execute the instructions and performs a set of operations comprising: receiving an XBRL document associated with one or more assigned XBRL tags; accessing a trained machine Learning model, wherein the trained machine learning model is trained using a first set of XBRL data records and a second set of XBRL data records, wherein the second set of XBRL data records is generated based upon a subset of the first set of XBRL data records by at least modifying the subset of the first set of XBRL data records with a modification, wherein the modification to the subset of the first set of XBRL data records includes at least one selected from a group consisting of a modification to an XBRL tag in the first set of XBRL data records, a modification to filing information associated with the first set of XBRL data records, and a modification to an XBRL outline associated with the first set of XBRL data records; and analyzing the XBRL document using the trained machine learning model to identify a set of outlier XBRL tags in the one or more assigned XBRL tags, each outlier XBRL tag in the set of identified outlier XBRL tags being an uncommon tag for corresponding filing information.
13. The system of claim 12, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying a first XBRL tag in a first record in the subset of the first set of XBRL data records to generate a second record in the second set of XBRL data records, the first record including first filing information, the second record including a second XBRL tag and the first filing information, the second XBRL tag being the modified first XBRL tag; wherein the second XBRL tag is an outlier tag based at least upon a taxonomy and the first filing information.
14. The system of claim 12, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying third filing information in a third record in the subset of the first set of XBRL data records to generate a fourth record in the second set of XBRL data records, the third record including a third XBRL tag and the third filing information, the fourth record including a fourth XBRL tag and fourth filing information, the fourth XBRL tag being same as the third XBRL tag, the fourth filing information being the modified third filing information; wherein the fourth XBRL tag is an outlier tag based at least upon a taxonomy and the fourth filing information.
15. The system of claim 12, wherein each XBRL data record in the first set of XBRL data records includes one or more XBRL tags and at least one selected from a group consisting of a classification identifier, a root abstract, and an XBRL outline.
16. The system of claim 15, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying a fifth XBRL outline in a fifth record in the subset of the first set of XBRL data records to generate a sixth record in the second set of XBRL data records, the fifth record including a fifth XBRL tag and fifth filing information, the fifth filing information including the fifth XBRL outline, the sixth record including a sixth XBRL tag and sixth filing information, the sixth XBRL tag being same as the fifth XBRL tag, the sixth filing information including the modified fifth XBRL outline; wherein the sixth XBRL tag is an outlier tag based at least upon a taxonomy and the sixth filing information.
17. The system of claim 12, wherein the set of operations further comprises: generating an output including the set of identified outlier XBRL tags in the one or more assigned XBRL tags.
18. The system of claim 17, wherein the output includes an indication of a document location for each outlier XBRL tag in the set of identified outlier XBRL tags.
19. The system of claim 18, wherein the document location includes an XBRL outline location for each outlier XBRL tag in the set of identified outlier XBRL tags.
20. The system of claim 12, wherein the trained machine learning model is selected based at least in part upon a classification identifier.
1. A method implemented by a computer system having one or more processors and one or more memories, comprising: receiving a first set of XBRL data records; generating a second set of XBRL data records based upon a subset of the first set of XBRL data records; training a machine learning model using the first set of XBRL data records and the second set of XBRL data records; receiving an XBRL document associated with one or more assigned XBRL tags; and analyzing the XBRL document using the trained machine learning model to identify a set of outlier XBRL tags in the one or more assigned XBRL tags, each outlier XBRL tag in the set of identified outlier XBRL tags being an uncommon tag for corresponding filing information; wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records comprises: modifying a first XBRL tag in a first record in the subset of the first set of XBRL data records to generate a second record in the second set of XBRL data records, the first record including first filing information, the second record including a second XBRL tag and the first filing information, the second XBRL tag being the modified first XBRL tag; wherein the second XBRL tag is an outlier tag based at least upon a taxonomy and the first filing information.
2. The method of claim 1, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records comprises: modifying third filing information in a third record in the subset of the first set of XBRL data records to generate a fourth record in the second set of XBRL data records, the third record including a third XBRL tag and the third filing information, the fourth record including a fourth XBRL tag and fourth filing information, the fourth XBRL tag being same as the third XBRL tag, the fourth filing information being the modified third filing information; wherein the fourth XBRL tag is an outlier tag based at least upon a taxonomy and the fourth filing information.
3. The method of claim 1, wherein each XBRL data record in the first set of XBRL data records includes one or more XBRL tags and at least one selected from a group consisting of a classification identifier, a root abstract, and an XBRL outline.
4. The method of claim 3, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records comprises: modifying a fifth XBRL outline in a fifth record in the subset of the first set of XBRL data records to generate a sixth record in the second set of XBRL data records, the fifth record including a fifth XBRL tag and fifth filing information, the fifth filing information including the fifth XBRL outline, the sixth record including a sixth XBRL tag and sixth filing information, the sixth XBRL tag being same as the fifth XBRL tag, the sixth filing information including the modified fifth XBRL outline; wherein the sixth XBRL tag is an outlier tag based at least upon a taxonomy and the sixth filing information.
5. The method of claim 1, further comprising: generating an output including the set of identified outlier XBRL tags in the one or more assigned XBRL tags.
6. The method of claim 5, wherein the output includes an indication of a document location for each outlier XBRL tag in the set of identified outlier XBRL tags.
7. The method of claim 6, wherein the document location includes an XBRL outline location for each outlier XBRL tag in the set of identified outlier XBRL tags.
8. The method of claim 5, wherein the output includes a file.
9. The method of claim 1, wherein the trained machine learning model includes a meta-classifier.
10. The method of claim 1, wherein the trained machine learning model is selected based at least in part upon a classification identifier.
11. A method implemented by a computer system having one or more processors and one or more memories, comprising: receiving a filing package including an XBRL document with one or more assigned XBRL tags; analyzing the filing package to determine a classification identifier; selecting a trained machine learning model from a plurality of machine learning models based on the classification identifier; analyzing the XBRL document and the one or more assigned XBRL tags using the selected trained machine learning model; identifying a set of outlier XBRL tags in the one or more assigned XBRL tags, each outlier XBRL tag in the set of identified outlier XBRL tags being an uncommon tag for corresponding filing information in the filing package; receiving a first set of XBRL data records; and generating a second set of XBRL data records based upon a subset of the first set of XBRL data records; wherein the selected trained machine learning model is trained using the first set of XBRL data records and the second set of XBRL data records; wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records comprises: modifying a first XBRL tag in a first record in the subset of the first set of XBRL data records to generate a second record in the second set of XBRL data records, the first record including first filing information, the second record including a second XBRL tag and the first filing information, the second XBRL tag being the modified first XBRL tag; wherein the second XBRL tag is an outlier tag based at least upon a taxonomy and the first filing information.
12. The method of claim 11, further comprising: generating an output including the set of identified outlier XBRL tags in the one or more assigned XBRL tags.
13. The method of claim 12, wherein the output includes an indication of a document location for each outlier XBRL tag in the set of identified outlier XBRL tags.
14. The method of claim 13, wherein the document location includes an XBRL outline location for each outlier XBRL tag in the set of identified outlier XBRL tags.
15. The method of claim 12, wherein the output includes a file.
16. The method of claim 11, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records comprises: modifying third filing information in a third record in the subset of the first set of XBRL data records to generate a fourth record in the second set of XBRL data records, the third record including a third XBRL tag and the third filing information, the fourth record including a fourth XBRL tag and fourth filing information, the fourth XBRL tag being same as the third XBRL tag, the fourth filing information being the modified third filing information; wherein the fourth XBRL tag is an outlier tag based at least upon a taxonomy and the fourth filing information.
17. The method of claim 11, wherein each XBRL data record in the first set of XBRL data records includes one or more XBRL tags and at least one selected from a group consisting of a classification identifier, a root abstract, and an XBRL outline.
18. The method of claim 17, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records comprises: modifying a fifth XBRL outline in a fifth record in the subset of the first set of XBRL data records to generate a sixth record in the second set of XBRL data records, the fifth record including a fifth XBRL tag and fifth filing information, the fifth filing information including the fifth XBRL outline, the sixth record including a sixth XBRL tag and sixth filing information, the sixth XBRL tag being same as the fifth XBRL tag, the sixth filing information including the modified fifth XBRL outline; wherein the sixth XBRL tag is an outlier tag based at least upon a taxonomy and the sixth filing information.
19. The method of claim 11, wherein the selected trained machine learning model includes a meta-classifier.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 of U.S. Patent No. 12,361,064 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because they are cover the same recitations, limitations that are combined or worded slightly differently from the original patented claims but in essence they convey the same subject matter. The instant application is a broader scope of recitation of the claims of the already patented application. The similarities and differences are highlighted in the tabulated comparison below.
Present Application: 19/263675
US PAT: 12,361,064 B2
1. A method implemented by a computer system having one or more processors and one or more memories, comprising: receiving an XBRL document associated with one or more assigned XBRL tags; accessing a trained machine learning model, wherein the trained machine learning model is trained using a first set of XBRL data records and a second set of XBRL data records, wherein the second set of XBRL data records is generated based upon a subset of the first set of XBRL data records by at least modifying the subset of the first set of XBRL data records with a modification, wherein the modification to the subset of the first set of XBRL data records includes at least one selected from a group consisting of a modification to an XBRL tag in the first set of XBRL data records, a modification to filing information associated with the first set of XBRL data records, and a modification to an XBRL outline associated with the first set of XBRL data records; and analyzing the XBRL document using the trained machine learning model to identify a set of outlier XBRL tags in the one or more assigned XBRL tags, each outlier XBRL tag in the set of identified outlier XBRL tags being an uncommon tag for corresponding filing information.
2. The method of claim 1,wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying a first XBRL tag in a first record in the subset of the first set of XBRL data records to generate a second record in the second set of XBRL data records, the first record including first filing information, the second record including a second XBRL tag and the first filing information, the second XBRL tag being the modified first XBRL tag; wherein the second XBRL tag is an outlier tag based at least upon a taxonomy and the first filing information.
3. The method of claim l, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying third filing information in a third record in the subset of the first set of XBRL data records to generate a fourth record in the second set of XBRL data records, the third record including a third XBRL tag and the third filing information, the fourth record including a fourth XBRL tag and fourth filing information, the fourth XBRL tag being same as the third XBRL tag, the fourth filing information being the modified third filing information; wherein the fourth XBRL tag is an outlier tag based at least upon a taxonomy and the fourth filing information.
4. The method of claim l, wherein each XBRL data record in the first set of XBRL data records includes one or more XBRL tags and at least one selected from a group consisting of a classification identifier, a root abstract, and an XBRL outline.
5. The method of claim 4, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying a fifth XBRL outline in a fifth record in the subset of the first set of XBRL data records to generate a sixth record in the second set of XBRL data records, the fifth record including a fifth XBRL tag and fifth filing information, the fifth filing information including the fifth XBRL outline, the sixth record including a sixth XBRL tag and sixth filing information, the sixth XBRL tag being same as the fifth XBRL tag, the sixth filing information including the modified fifth XBRL outline; wherein the sixth XBRL tag is an outlier tag based at least upon a taxonomy and the sixth filing information.
6. The method of claim 1, further comprising: generating an output including the set of identified outlier XBRL tags in the one or more assigned XBRL tags.
7. The method of claim 6, wherein the output includes an indication of a document location for each outlier XBRL tag in the set of identified outlier XBRL tags.
8. The method of claim 7, wherein the document location includes an XBRL outline location for each outlier XBRL tag in the set of identified outlier XBRL tags.
9. The method of claim 6, wherein the output includes a file.
10. The method of claim 1, wherein the trained machine learning model includes a meta-classifier.
11. The method of claim 1, wherein the trained machine learning model is selected based at least in part upon a classification identifier.
12. A computing system comprising: one or more memories having instructions stored thereon; and one or more processors configured to execute the instructions and performs a set of operations comprising: receiving an XBRL document associated with one or more assigned XBRL tags; accessing a trained machine Learning model, wherein the trained machine learning model is trained using a first set of XBRL data records and a second set of XBRL data records, wherein the second set of XBRL data records is generated based upon a subset of the first set of XBRL data records by at least modifying the subset of the first set of XBRL data records with a modification, wherein the modification to the subset of the first set of XBRL data records includes at least one selected from a group consisting of a modification to an XBRL tag in the first set of XBRL data records, a modification to filing information associated with the first set of XBRL data records, and a modification to an XBRL outline associated with the first set of XBRL data records; and analyzing the XBRL document using the trained machine learning model to identify a set of outlier XBRL tags in the one or more assigned XBRL tags, each outlier XBRL tag in the set of identified outlier XBRL tags being an uncommon tag for corresponding filing information.
13. The system of claim 12, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying a first XBRL tag in a first record in the subset of the first set of XBRL data records to generate a second record in the second set of XBRL data records, the first record including first filing information, the second record including a second XBRL tag and the first filing information, the second XBRL tag being the modified first XBRL tag; wherein the second XBRL tag is an outlier tag based at least upon a taxonomy and the first filing information.
14. The system of claim 12, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying third filing information in a third record in the subset of the first set of XBRL data records to generate a fourth record in the second set of XBRL data records, the third record including a third XBRL tag and the third filing information, the fourth record including a fourth XBRL tag and fourth filing information, the fourth XBRL tag being same as the third XBRL tag, the fourth filing information being the modified third filing information; wherein the fourth XBRL tag is an outlier tag based at least upon a taxonomy and the fourth filing information.
15. The system of claim 12, wherein each XBRL data record in the first set of XBRL data records includes one or more XBRL tags and at least one selected from a group consisting of a classification identifier, a root abstract, and an XBRL outline.
16. The system of claim 15, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying a fifth XBRL outline in a fifth record in the subset of the first set of XBRL data records to generate a sixth record in the second set of XBRL data records, the fifth record including a fifth XBRL tag and fifth filing information, the fifth filing information including the fifth XBRL outline, the sixth record including a sixth XBRL tag and sixth filing information, the sixth XBRL tag being same as the fifth XBRL tag, the sixth filing information including the modified fifth XBRL outline; wherein the sixth XBRL tag is an outlier tag based at least upon a taxonomy and the sixth filing information.
17. The system of claim 12, wherein the set of operations further comprises: generating an output including the set of identified outlier XBRL tags in the one or more assigned XBRL tags.
18. The system of claim 17, wherein the output includes an indication of a document location for each outlier XBRL tag in the set of identified outlier XBRL tags.
19. The system of claim 18, wherein the document location includes an XBRL outline location for each outlier XBRL tag in the set of identified outlier XBRL tags.
20. The system of claim 12, wherein the trained machine learning model is selected based at least in part upon a classification identifier.
1. A method implemented by a computer system having one or more processors and one or more memories, comprising: receiving a first set of XBRL data records; generating a second set of XBRL data records based upon a subset of the first set of XBRL data records by at least modifying the subset of the first set of XBRL data records with a modification, the modification to the subset of the first set of XBRL data records including at least one selected from a group consisting of a modification to an XBRL tag in the first set of XBRL data records, a modification to filing information associated with the first set of XBRL data records, and a modification to an XBRL outline associated with the first set of XBRL data records; training a machine learning model using the first set of XBRL data records and the second set of XBRL data records; receiving an XBRL document associated with one or more assigned XBRL tags; and analyzing the XBRL document using the trained machine learning model to identify a set of outlier XBRL tags in the one or more assigned XBRL tags, each outlier XBRL tag in the set of identified outlier XBRL tags being an uncommon tag for corresponding filing information.
2. The method of claim 1, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying a first XBRL tag in a first record in the subset of the first set of XBRL data records to generate a second record in the second set of XBRL data records, the first record including first filing information, the second record including a second XBRL tag and the first filing information, the second XBRL tag being the modified first XBRL tag; wherein the second XBRL tag is an outlier tag based at least upon a taxonomy and the first filing information.
3. The method of claim 1, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying third filing information in a third record in the subset of the first set of XBRL data records to generate a fourth record in the second set of XBRL data records, the third record including a third XBRL tag and the third filing information, the fourth record including a fourth XBRL tag and fourth filing information, the fourth XBRL tag being same as the third XBRL tag, the fourth filing information being the modified third filing information; wherein the fourth XBRL tag is an outlier tag based at least upon a taxonomy and the fourth filing information.
4. The method of claim 1, wherein each XBRL data record in the first set of XBRL data records includes one or more XBRL tags and at least one selected from a group consisting of a classification identifier, a root abstract, and an XBRL outline.
5. The method of claim 4, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying a fifth XBRL outline in a fifth record in the subset of the first set of XBRL data records to generate a sixth record in the second set of XBRL data records, the fifth record including a fifth XBRL tag and fifth filing information, the fifth filing information including the fifth XBRL outline, the sixth record including a sixth XBRL tag and sixth filing information, the sixth XBRL tag being same as the fifth XBRL tag, the sixth filing information including the modified fifth XBRL outline; wherein the sixth XBRL tag is an outlier tag based at least upon a taxonomy and the sixth filing information.
6. The method of claim 1, further comprising: generating an output including the set of identified outlier XBRL tags in the one or more assigned XBRL tags.
7. The method of claim 6, wherein the output includes an indication of a document location for each outlier XBRL tag in the set of identified outlier XBRL tags.
8. The method of claim 7, wherein the document location includes an XBRL outline location for each outlier XBRL tag in the set of identified outlier XBRL tags.
9. The method of claim 6, wherein the output includes a file.
10. The method of claim 1, wherein the trained machine learning model includes a meta-classifier.
11. The method of claim 1, wherein the trained machine learning model is selected based at least in part upon a classification identifier.
12. A method implemented by a computer system having one or more processors and one or more memories, comprising: receiving a filing package including an XBRL document with one or more assigned XBRL tags; analyzing the filing package to determine a classification identifier; selecting a trained machine learning model from a plurality of machine learning models based on the classification identifier, wherein the selected trained machine learning model is trained using a first set of XBRL data records and a second set of XBRL data records, wherein the second set of XBRL data records is generated based at least in part on a modification to a subset of the first set of XBRL data records, the modification to the subset of the first set of XBRL data records including at least one selected from a group consisting of a modification to an XBRL tag in the first set of XBRL data records, a modification to filing information associated with the first set of XBRL data records, and a modification to an XBRL outline associated with the first set of XBRL data records; analyzing the XBRL document and the one or more assigned XBRL tags using the selected trained machine learning model; identifying a set of outlier XBRL tags in the one or more assigned XBRL tags, each outlier XBRL tag in the set of identified outlier XBRL tags being an uncommon tag for corresponding filing information in the filing package.
13. The method of claim 12, further comprising: generating an output including the set of identified outlier XBRL tags in the one or more assigned XBRL tags.
14. The method of claim 13, wherein the output includes an indication of a document location for each outlier XBRL tag in the set of identified outlier XBRL tags.
15. The method of claim 14, wherein the document location includes an XBRL outline location for each outlier XBRL tag in the set of identified outlier XBRL tags.
16. The method of claim 13, wherein the output includes a file.
17. The method of claim 12, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying a first XBRL tag in a first record in the subset of the first set of XBRL data records to generate a second record in the second set of XBRL data records, the first record including first filing information, the second record including a second XBRL tag and the first filing information, the second XBRL tag being the modified first XBRL tag; wherein the second XBRL tag is an outlier tag based at least upon a taxonomy and the first filing information.
18. The method of claim 12, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying third filing information in a third record in the subset of the first set of XBRL data records to generate a fourth record in the second set of XBRL data records, the third record including a third XBRL tag and the third filing information, the fourth record including a fourth XBRL tag and fourth filing information, the fourth XBRL tag being same as the third XBRL tag, the fourth filing information being the modified third filing information; wherein the fourth XBRL tag is an outlier tag based at least upon a taxonomy and the fourth filing information.
19. The method of claim 12, wherein each XBRL data record in the first set of XBRL data records includes one or more XBRL tags and at least one selected from a group consisting of a classification identifier, a root abstract, and an XBRL outline.
20. The method of claim 19, wherein the generating a second set of XBRL data records based upon a subset of the first set of XBRL data records further comprises: modifying a fifth XBRL outline in a fifth record in the subset of the first set of XBRL data records to generate a sixth record in the second set of XBRL data records, the fifth record including a fifth XBRL tag and fifth filing information, the fifth filing information including the fifth XBRL outline, the sixth record including a sixth XBRL tag and sixth filing information, the sixth XBRL tag being same as the fifth XBRL tag, the sixth filing information including the modified fifth XBRL outline; wherein the sixth XBRL tag is an outlier tag based at least upon a taxonomy and the sixth filing information.
21. The method of claim 12, wherein the selected trained machine learning model includes a meta-classifier.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure.
Ritz discloses US 8601367 B1 Systems and Methods for Generating Filing Documents in A Visual Presentation Context with XBRL Barcode Authentication.
Goodman et al discloses US 11087070 B1Systems And Methods for XBRL Tag Suggestion and Validation.
Haila discloses US 9348854 B1 Systems and Methods for Automated Taxonomy Migration in An XBRL Document.
Smith et al discloses US 20130117268 A1 Identifying and Suggesting Classifications for Financial Data According to A Taxonomy.
Bush et al discloses US 20090006472 A1 Automatic Designation of XBRL Taxonomy Tags.
Conclusion
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/Mohammad A Sana/Primary Examiner, Art Unit 2166
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