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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/04/2026 has been entered.
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 records 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”))
“wherein generating the two or more data structures comprises filtering the plurality of tables based on the at least one lifecycle entity to extract data corresponding to an application stage for arranging into at least one of the two or more data structure” at [0054]-[0061] and Fig. 1B;
(Smith teaches applying one or more filtration criteria 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, a corresponding customer that fails hold one of unsecured credit product involving in a delinquency event, i.e. “lifecycle entities” corresponding to an application stage
“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 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.
Response to Arguments
Applicant's arguments filed 6/04/2026 have been fully considered but they are not persuasive. The examiner respectfully traverses Applicant’s arguments.
Regarding claim 21, Applicant argued that Smith and Nayak, as combined, does not teach “wherein generating the two or more data structures comprises filtering the plurality of tables based on the at least one lifecycle entity to extract data corresponding to an application stage for arranging into at least one of the two or more data structure” as claimed. On the contrary, Smith teaches at [0054]-[0061] and Fig. 1B the steps of applying one or more filtration criteria 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, a corresponding customer that fails hold one of unsecured credit product involving in a delinquency event, i.e. “lifecycle entities”.
Applicant further argued that “Smith does not teach filtering based on a “lifecycle entity” to “extract data corresponding to an applicant stage for arranging into at least one of the two or more data structures” as recited in amendment claim 21. A delinquency event or product specific criterion is not the same as an application stage, and the Office fails to explain how one of ordinary skill in the art would have equated Smith’s filtering criteria with the claimed “application state”. On the contrary, Applicant’s Specification at [0028] discloses “a data user may select a first table that is structured based on a lifecycle stage of service (e.g., loan origination, loan servicing, delinquency, loss mitigation, or loan modification)”. Smith therefore teaches the filter criteria “delinquency”, which is also a lifecycle stage of a service, as exemplary in Applicant’s specification.
In light of the foregoing arguments, the 35 U.S.C 103 rejections are hereby sustained.
Conclusion
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
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
September 21, 2026
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