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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/22/2026 has been entered. Claims 21, 31, and 41 have been amended.
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 21-24, 26-27, 29-34, 36-37, 39-44, 46-47, 49-50 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (US 2022/0318573 A1), hereinafter “Smith”, in view of Nayak et al. (US 2020/0372306 A1), hereinafter “Nayak”, and further in view of Dupey et al. (US 2018/0107694 A1), hereinafter “Dupey”.
As per claim 21, Smith teaches a system for domain-centric data consolidation comprising: one or more processors; and one or more storage devices storing instructions that, when executed, configured 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” at [0044]-[0047] 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 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” at [0047]-[0053];
(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. “integration tables”) based on the financial products (i.e. “asset class entities”) and occurrences of delinquency events involving corresponding delinquent financial products, past-due balances)
“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” at [0054]-[0061] and Fig. 1B;
(Smith teaches applying one or more filtration criteria (i.e. “downstream 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)
“storing the two or more data structures in the single storage location” at [0054] and Fig. 1B;
(Smith teaches storing the filtered data records 154 in the consolidated data store 144)
“provisioning the two or more data structures for downstream use” 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 “receiving an indication that at least one data element in the provisioned two or more data structure 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; feeding the corrected data element and 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 location for subsequence provisioning of at least one of the two or more data structure” as claimed. However, Nayak teaches a method of automatically correcting rejected data including the steps of:
“receiving an indication that at least one data element in the provisioned two or more data structure contains a data issue” at [0015];
(Nayak teaches the management platform receives data from a data source and rejects a portion of the data when the data is unrecognized, unstructured, improperly formatted)
“correcting the data issue in the data element to create a corrected data element” at [0017], [0024];
(Nayak teaches performing 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)
“creating a change history for the corrected data element, wherein the change history includes the correction” 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 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 location for subsequence provisioning of at least one of the two or more data structure” a t[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].
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 Smith’s teaching in order to ensure the data is corrected and in proper format, for further processing of the data.
As per claim 22, Smith-Nayak and Dupey teach the system of claim 21 discussed above. Smith also teaches: wherein “the one or more downstream requirements further specify at least one lifecycle of the plurality of lifecycle entities, and wherein the at least one lifecycle specifies a lifecycle stage associated with the at least one asset class” at [0029]-[0034].
As per claim 23, Smith-Nayak and Dupey teach the system of claim 22 discussed above. Smith also teaches: wherein “the plurality of asset class entities 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 24, Smith-Nayak and Dupey teach the system of claim 22 discussed above. Smith also teaches: wherein “the plurality of lifecycle entities includes at least one of application, static organization, default, transaction data reporting, origination, servicing, delinquency, loss mitigation, modification, or existing” at [0029]-[0034].
As per claim 26, Smith-Nayak and Dupey teach the system of claim 21 discussed above. Smith also teaches: wherein “the downstream modeling requirements are received from downstream user” at [0054]-[0061] and Fig. 1B.
As per claim 27, Smith-Nayak and Dupey teach the system of claim 21 discussed above. Smith also teaches: wherein “the data in the two or more data structure is the same” at [0054]-[0061] and Fig. 1B.
As per claim 29, Smith-Nayak and Dupey teach the system of claim 21 discussed above. Smith also teaches: wherein “transforming the imported data includes creating an incremental dataset by comparing source with target dates to eliminate outdated sources; the integration tables include profiling tables and conformity tables store data type attributes, and provision the one or more data structures for downstream modeling includes generating persistent tables and exposing the persistent tables to an application programing interfaces configured to be access by downstream users” at [0047]-[0048], [0054]-[0062], [0075]-[0080] and Figs. 1A, 1B and 1C.
As per claim 30, Smith-Nayak and Dupey teach the system of claim 21 discussed above. Smith also teaches: wherein “generating the two or more data structures includes: receiving one or more requirements from a user and filtering the integration tables based on the one or more requirements using filter based on a life cycle event, the life cycle event including one or more application stages” at [0054]-[0061].
Claims 31-34, 36-37, 39-44, 46-47, 49-50 recite similar limitations as in claims 21-24, 26-27, 29-30 and are therefore rejected by the same reasons.
Claims 25, 28, 35, 38, 45, 48 are rejected under 35 U.S.C. 103 as being unpatentable over Smith-Nayak and Dupey, as applied to claims 21-24, 26-27, 29-34, 36-37, 39-44, 46-47, 49-50 above, and further in view of Sigal et al. (US 2023/0351523 A1), hereinafter “Sigal”.
As per claim 25, Smith-Nayak and Dupey teach the system of claim 21 discussed above. Smith does not teach “wherein generating the two or more data structures includes: maintaining a change log storing changes to the plurality of integration tables; and exposing the change log to an application programing interface accessible to users for retrieving the two or more data structures” as claimed. However, Sigal teaches a method for training a machine learning model including the steps of “maintaining a change log storing changes to the plurality of integration tables” at [0026]-[0032];
(Sigal teaches extracting data from expense data 202 and storing the extracted data as training data 206 (i.e., “integration tables”). The training data is used to train a machine learning model. The computer system updates training data comprising comment text for a plurality of submitted expenses that was submitted after the machine learning model was last trained (i.e., “change log”)). Sigal also teaches at [0016]-[0025] and Figs. 1-2 a server system which exposes the changes to an API server 120 accessible to user 106.
Thus, it would have been obvious to one of ordinary skill in the art to combine Sigal with Smith’s teaching in order to improve the accuracy of the machine learning model by logging and monitoring the changes in the training data and using the changes to retrain the machine learning model to generate an updated trained model based on new and updated data received since the machine learning model was last trained, as suggested by Sigal at [0032].
As per claim 28, Smith-Nayak and Dupey teaches the system of claim 21 discussed above. Smith does not explicitly teach “receiving a downstream data model trained on at least one of the two or more data structures; determining that at least one of the plurality of integration tables was modified; and in response to determining at least one of the plurality of integration tables was modified, retraining the data model on modified integration tables” as claimed. However, Sigal teaches at [0026]-[0032] a method for training a machine learning model including the steps of “receiving a downstream data model trained on at least one of the two or more data structures; determining that at least one of the plurality of integration tables was modified; and in response to determining at least one of the plurality of integration tables was modified, retraining the data model on modified integration tables” at [0026]-[0032];
(Sigal teaches extracting data from expense data 202 and storing the extracted data as training data 206 (i.e., “data structures”). The training data is used to train a machine learning model. The computer system updates training data comprising comment text for a plurality of submitted expenses that was submitted after the machine learning model was last trained, monitoring the changes and detecting that the data has changed more than a specified threshold value, retrains the machine learning model based on a trigger that detects how the data was changed since the machine learning model as last trained).
Thus, it would have been obvious to one of ordinary skill in the art to combine Sigal with Smith’s teaching in order to improve the accuracy of the machine learning model by logging and monitoring the changes in the training data and triggering to retrain the machine learning model to generate an updated trained model based on new and updated data received since the machine learning model was last trained, as suggested by Sigal at [0032].
Claims 35, 38, 45, 48 recite similar limitations as in claims 25, 28 and are therefore rejected by the same reasons.
Response to Arguments
Applicant’s arguments with respect to claims 21-50 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
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
July 27, 2026
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