DETAILED ACTION
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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21-25, 27-35, 37-45, 47-50 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (US 2022/0318573 A1), hereinafter “Smith”, and in view of Nayak et al. (US 2020/0372306 A1), Applicant’s submitted IDS filed 3/13/2026), hereinafter “Nayak”.
As per claim 21, Smith teaches a system for data consolidation comprising: one or more processors; and one or more storage devices storing instructions that, when executed, configure the one or more processors to perform operations including:
“importing, via at least one interface, data from a plurality of sources to a single storage location through at least one iterative import job” at [0044]-[0045] and Fig. 1;
(Smith teaches importing, via the API 134, data from a plurality of data sources 102A, 102B,102C and storing the imported data in the Aggregated Data Store 132)
“transforming, via a first server, the imported data into a plurality of tables” at [0047]-[0048];
(Smith teaches the ingestion engine 136 decrypts the imported data and stores the imported data into a plurality of tables 104, 108, 109 within the aggregated data store 132 as ingested customer data 138. The pre-processing engine 140 preprocesses the elements of ingested customer data 138 to generate consolidated data records 142 (i.e. “plurality of tables”)
“generating, via a second server, two or more data structures by arranging at least a portion of the plurality of tables based on downstream modeling requirements, wherein the downstream modeling requirements specify at least one asset class entity and at least one lifecycle entity associated with the at least one asset class entity” at [0054]-[0061] and Fig. 1B;
(Smith teaches applying one or more filtration criteria (i.e. “downstream modeling requirements”) to the data records of consolidated data records 142 to identify portions of these consolidated data records that are appropriate for a generation of training or validation dataset and storing the filtered subset of the data records as filtered data records 154 (i.e. “two or more data structure”), wherein the filtration criteria includes a product-specific filtration criteria that causes executed filtration engine 152 to exclude, from filtered data record 154, one or more consolidated data records 142 identifying and characterizing a customer that fails to hold an unsecured credit product (i.e. “asset class entities”) or a corresponding customer that fails hold one of unsecured credit product involving in a delinquency event (i.e. “lifecycle entities”))
“storing, via the second server, the two or more data structures in the single storage location” at [0054];
(Smith teaches storing the filtered data records 154 in the consolidated data store 144)
provisioning, via the second server, the two or more data structures for downstream modeling; using, via a third server, the provisioned two or more data structures to build, execute, or train a data model” at [0075] and Fig. 1C;
(Smith teaches provisioning the filtered data records 154 as input to the training engine 172 to train the machine learning model)
Smith does not teach “in response to detecting, based on results of building, executing, or training the data model, a data issue in a data element of the provisioned two or more data structures, correcting the data issue to generate a corrected data element; generating or updating a change history for the corrected data element; and feeding the corrected data element and the change history to the single storage location for integration and subsequent provisioning of at least one of the two or more data structures” as claimed. However, Nayak teaches a method of automatically correcting rejected data including the steps of:
“in response to detecting, based on results of building, executing, or training the data model, a data issue in a data element of the provisioned two or more data structures, correcting the data issue to generate a corrected data element” at [0015], [0017], [0024];
(Nayak teaches the management platform utilizing a machine learning model to receive data from a data source and rejects a portion of the data when the data is unrecognized, unstructured, improperly formatted, and to perform cleansing operation to correct corrupt or inaccurate data points from the rejected data, identifies incomplete, incorrect, in accurate or irrelevant portions of the rejected data and may replace, modify, or delete the identified portion of the rejected data)
“generating or updating a change history for the corrected data element” at [0018]-[0020];
(Nayak teaches generating a training set includes historical rejected data and use the training set to train a machine learning model to correct the rejected data and to generate corrected data. The historical rejected data may include data that is unrecognized, unstructured, improperly formatted)
“feeding the corrected data element and the change history to the single storage location for integration and subsequent provisioning of at least one of the two or more data structure” at [0021]-[0025].
(Nayak teaches the corrected data and the historical rejected data are used to train a machine learning model to integrate with subsequence received data. The trained machine learning model may identify corrections made to the matching historical rejected data and may implement such corrections for the parsed rejected data (i.e., "subsequence provisioning of at least one of the two or more data structure"). The corrections to the parsed rejected data may generate the corrected data)
Thus, it would have been obvious to one of ordinary skill in the art to combine Nayak with Smith's teaching in order to provide an automated method for correcting rejected data utilizing a machine learning model by storing the accepted data and the modified data in a data structure and updating the machine learning model, based on the modified data, to handle the rejected data at a later time, as suggested by Nayak at [0003].
As per claim 22, Smith and Nayak teach the system of claim 21 discussed above. Smith also teaches: wherein “the first server includes an ingestion server; wherein the second server includes an integration server; and wherein the third server includes a consumption server” at [0172] and Fig. 1A, 1B and 1C.
As per claim 23, Smith and Nayak teach the system of claim 21 discussed above. Smith also teaches: wherein “transforming the imported data into a plurality of tables includes generating standardized objects that aggregate, integrate, or consolidate the imported data, and wherein the plurality of tables includes a plurality of object tables, each object table in the plurality of object tables associated with an indexing key and one or more attributes” at [0047]-[0059] and Figs. 1A-1B.
As per claim 24, Smith and Nayak teach the system of claim 21 discussed above. Smith also teaches: wherein “the at least one lifecycle entity includes at least one of loan origination, loan servicing, delinquency, loss mitigation, or loan modification” at [0029]-[0034].
As per claim 25, Smith and Nayak teach the system of claim 21 discussed above. Smith also teaches: wherein “the at least one asset class entity includes at least one of leasing, home equity, mortgage, automobile loans, student loans, credit cards, consumer installment loans, business banking, or unsecured line of credit” at [0029]-[0034].
As per claim 27, Smith and Nayak teach the system of claim 21 discussed above. Smith also teaches: wherein “generating two or more data structures based on downstream modeling requirements includes filtering and formatting the plurality of tables based on the at least one lifecycle entity” at [0054]-[0061] and Fig. 1B.
As per claim 28, Smith and Nayak teach the system of claim 21 discussed above. Smith also teaches: wherein “provisioning the two or more data structures for downstream modeling includes exposing the two or more data structures via at least one of an application programming interface (API), file transfer protocol (FTP), networked drive, server, hypertext transfer protocol (HTTP), memory location, or graphical user interface” at [0111]-[0116].
As per claim 29, Smith and Nayak teach the system of claim 21 discussed above. Smith also teaches: wherein “the data model includes a machine-learning model, analytics model, or regulatory model” at [0071]-[0072].
As per claim 30, Smith and Nayak teach the system of claim 21 discussed above. Smith also teaches: the operations further including: “analyzing a result of building, executing, or training the data model; and generating at least one report based on the analysis” at [0122]-[0125] and Fig 2A.
Claims 31-35, 37-45, 47-50 recite similar limitations as in claims 21-25, 27-30 and are therefore rejected by the same reasons.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 26, 36, 46 are rejected under 35 U.S.C. 103 as being unpatentable over Smith and Nayak, as applied to claims above, and further in view of Dupey et al. (US 2018/0129684 A1), hereinafter “Dupey”.
As per claim 26, Smith-Nayak teach the system of claim 21 discussed above. Smith does not teach “identifying at least one table of the plurality of tables, the at least one table including outlier attributes; modifying the identified at least one table by normalizing or deleting one or more corresponding attributes; and after modifying the identified at least one table, performing a conformity check on the plurality of tables by executing a conformity job, the conformity job including a script that compares the plurality of tables to a control table including control data to ensure data completeness and adjusts attributes in the plurality of tables based on values in the control table” as claimed.
However, Dupey teaches a method for performing data quality functions including the steps of: “identifying at least one table of the plurality of tables, the at least one table including outlier attributes; modifying the identified at least one table by normalizing or deleting one or more corresponding attributes; and after modifying the identified at least one table, performing a conformity check on the plurality of tables by executing a conformity job, the conformity job including a script that compares the plurality of tables to a control table including control data to ensure data completeness and adjusts attributes in the plurality of tables based on values in the control table” at [0023]-[0024] and Fig. 1.
(Dupey teaches performing data quality functions 111 (i.e., “conformity job”) on multiple data records 130 (i.e., “the plurality of integration tables”) received from the source 104. The data quality function 111 is configured to analyze, cleanse, and match customer, suppliers, product, or material data to ensure accurate and complete information is provided. The data quality function 111 can correct components of name and address data and/or fields (i.e., “index key”) and attributes associated with such data. The data quality function 111 can validate name and address data based on reference data sources, such as reference data 112 (i.e., “control table”). The data quality functions 111 includes data cleansing 116, which receives an input such as name or address data and can match (i.e., compare) either or both using any number of matching engine available. For example, the data cleansing 116 may access reference data 112 to verify proper formatting, field entries, and/or attributes. Any number of errors can be corrected (i.e., “adjust”) including but not limited to typographical errors, grammatical errors, country-specific errors, and formatting errors for any of the entered address or name data)
Thus, it would have been obvious to one of ordinary skill in the art to combine Dupey with Wilson’s teaching in order to ensure the data is corrected and in proper format, for further processing of the data.
Claims 36, 46 recite similar limitations as in claim 26 and are therefore rejected by the same reasons.
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 21-50 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 21-50 of co-pending Application No. 19/003,686. Although the claims at issue are not identical, they are not patentably distinct from each other because Claims 21-50 of application number 19/003,686 contain every element of claims 21-50 of the instant application, as detailed in the mapping table below, and as such anticipate claims 21-50 of the instant application.
“A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). “ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Instant Application 19/033,763
Co-pending Application 19/003,686
21. A system for data consolidation comprising: one or more processors; and one or more storage devices storing instructions that, when executed, configure the one or more processors to perform operations including:
importing, via at least one interface, data from a plurality of sources to a single storage location through at least one iterative import job;
transforming, via a first server, the imported data into a plurality of tables;
generating, via a second server, two or more data structures by arranging at least a portion of the plurality of tables based on downstream modeling requirements, wherein the downstream modeling requirements specify at least one asset class entity and at least one lifecycle entity associated with the at least one asset class entity;
storing, via the second server, the two or more data structures in the single storage location; provisioning, via the second server, the two or more data structures for downstream modeling; and
using, via a third server, the provisioned two or more data structures to build, execute, or train a data model;
in response to detecting, based on results of building, executing, or training the data model, a data issue in a data element of the provisioned two or more data structures,
correcting the data issue to generate a corrected data element;
generating or updating a change history for the corrected data element; and
feeding the corrected data element and the change history to the single storage location for integration and subsequent provisioning of at least one of the two or more data structures.
21. A system for domain-centric data consolidation comprising: one or more processors; and one or more storage devices storing instructions that, when executed, configure the one or more processors to perform operations including:
importing data from a plurality of sources to a single storage location through at least one iterative import job;
transforming the imported data into a plurality of integration tables based on a plurality of asset class entities and a plurality of lifecycle entities associated with the plurality of asset class entities through an account;
generating two or more data structures including data by arranging at least a portion of the plurality of integration tables based on one or more downstream requirements, wherein the downstream requirements specify at least one asset class of the plurality of asset class entities;
storing the two or more data structures in the single storage location; and provisioning the two or more data structures for downstream use;
receiving an indication that at least one data element in the provisioned two or more data structures contains a data issue;
correcting the data issue in the data element to create a corrected data element;
creating a change history for the corrected data element, wherein the change history includes the correction; and
feeding the corrected data element and the change history to the single storage location such that the corrected data element and the change history are integrated with the imported data in the single storage location for subsequent provisioning of at least one of the two or more data structures
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Response to Arguments
Applicant’s arguments with respect to the 102 rejections of claims 21-25, 27-35, 37-45 and 47-50 over Smith have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
April 3, 2026