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 .
1. Claims 1, 3-7 and 14-21 are presented for examination.
Claim 2 has been cancelled.
Claims 8-13 have been withdrawn.
Claim Rejections - 35 USC § 103
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.
2. Claim(s) 14 and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kursun, US 2020/0374305 A1, in view of Mishraky, US 11,775,869 B1, and Reddy et al. (hereafter, “Reddy”), US 2023/0206348 A1.
Regarding claim 14, Kursun teaches a non-transitory computer-readable medium storing a set of instructions for verifying quality of a data stream (i.e., a system for machine learning based real-time electronic data quality checks, abstract and Fig. 1), the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device (i.e., the memory device may store any number of programs which comprise computer-executable instructions executed by the processing device, Fig. 1 and page 6 paragraphs [0072]-[0073]), cause the device to:
receive the data stream (i.e., the quality learning module may receive input from a data stream, page 3 paragraph [0048]);
provide the data stream to a machine learning model (i.e., transmit the data quality output 205 to the machine learning module 162, Fig. 2 and page 7 paragraph [0080]) in order to receive an indication of at least one error in the data stream (i.e., the machine learning module should identify an incoming transaction as invalid/error, page 7 paragraph [0081]), wherein the machine learning model is configured to determine the at least one error based on content of the characters (i.e., the transaction request may reference as non-existent city…where the non-existent city does not yet appear in the reference data repositories…determine that the transaction is invalid/error, page 7 paragraph [0080]); and
transmit the updated data stream to a third-party system (i.e., transmit information to the entity computing system 103, Fig. 1 and page 4 paragraph [0051]).
Kursun does not explicitly teach wherein the data stream comprises a sequence of characters; determine validity based on position of the characters; transmit a report, to a user device, including the indication of the at least one error and a recommended change to the data stream based on the indication of the least one error; receive an updated data stream in response to an input that triggers acceptance of the recommended change included in the report.
Mishraky teaches method and system for predicting account action failures (seen in abstract). Mishraky teaches the data stream comprises a sequence of characters (i.e., a character sequence being entered for identifying an account, abstract); determine validity based on position of the character (i.e., determining an initial position in the character sequence where an incorrect character was first entered, claim 19); transmit a report, to a user device, including the indication of the at least one error (i.e., generating a notification that the detected sequence is likely invalid…the notification may be provided to a user, col. 20 lines 17-26); receive an updated data stream in response to the report (i.e., replacement character sequence, col. 20 lines 20-21). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kursun to comprise a sequence of characters; determine validity based on position of the characters; transmit a report, to a user device, including the indication of the at least one error; and receive an updated data stream in response to the report, as taught by Mishraky, in order to reduce error, (i.e., Mishraky, col. 4 lines 63-67).
Reddy teaches transmit a report to a user device including a recommended change to the data stream based on the indication of the at least one error (i.e., users can receive real-time recommendations associated with the one or more first user inputs to correct the at least one anomalous/error transaction, page 6 paragraph [0048]), and receive an updated stream in response to an input that triggers acceptance of the recommended change included in the report (i.e., Reddy, in page 6 paragraph [0050], discloses automatically display the one or more recommended actions to a user of the first computing system and/or the second computing system and receive a selection from a user to correct the least one anomalous transaction. Reddy, in page 3 paragraph [0022], also discloses if the user accepts a real-time recommendation of the system, the system will learn from the user selection and subsequence risk score may be adjusted according). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kursun to transmit a report, to a user device, including the indication of the at least one error and a recommended change to the data stream based on the indication of the least one error; receive an updated data stream in response to an input that triggers acceptance of the recommend change include in the report, as taught by Reddy. One would be motivated to do so to improve the precision and accuracy of the system (i.e., Reddy, page 3 paragraph [0022]).
Regarding claim 16, Kursun teaches the non-transitory computer-readable medium of claim 14, wherein the one or more instructions, that cause the device to receive the data stream, cause the device to: receive the data stream from the user device (i.e., user inputs, paragraph [0042]).
Regarding claim 17, Kursun teaches the non-transitory computer-readable medium of claim 14.
Kursun does not explicitly teach receive the updated data stream from the user device.
Mishraky teaches receive the updated data stream from the user device receive an updated data stream in response to the report (i.e., request for a replacement character sequence…request provided to a user, col. 20 lines 20-21).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kursun to receive the updated data stream from the user device, as taught by Mishraky, in order to reduce error (i.e., Mishraky, col. 4 lines 63-76).
Regarding claim 18, Kursun teaches the non-transitory computer-readable medium of claim 14.
Kursun does not explicitly teach wherein the one or more instructions, when executed by the one or more processors, further cause the device to: receive, from the user device, an indication of a location associated with the updated data stream; transmit a request for the updated data stream based on the location, wherein the updated data stream is received in response to the request.
Mishraky teaches receive, from the user device, an indication of a location associated with the updated data stream (i.e., determining an initial position in the character sequence where an incorrect character was first entered, generating an indication of the initial position, col. 26 lines 39-41); and transmit a request for the updated data stream based on the location (i.e., a request for a replacement character sequence, col. 26 lines 35-38), wherein the updated data stream is received in response to the request (col. 26 lines 35-38).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kursun to receive, from the user device, an indication of a location associated with the updated data stream; transmit a request for the updated data stream based on the location, wherein the updated data stream is received in response to the request, as taught by Mishraky, in order to reduce error (i.e., Mishraky, col. 4 lines 63-76).
3. Claim(s) 1, 3-7 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kursun, in view of Mishraky, Rasti, US 2009/0158030 A1, and Reddy.
Regarding claim 1, Kursun teaches a system for verifying quality of a data stream (i.e., a system for machine learning based real-time electronic data quality checks, abstract and Fig. 1), the system comprising:
one or more memories (i.e., memory device 116); and
one or more processors (i.e., processing device 114), communicatively coupled to the one or more memories (i.e., Fig. 1 and page 5 paragraph [0059]), configured to:
receive the data stream (i.e., the quality learning module may receive input from a data stream, page 3 paragraph [0048]);
provide the data stream to a machine learning model (i.e., transmit the data quality output 205 to the machine learning module 162, Fig. 2 and page 7 paragraph [0080]) in order to receive an indication of at least one error in the data stream (i.e., the machine learning module should identify an incoming transaction as invalid/error, page 7 paragraph [0081]), wherein the machine learning model is configured to determine the at least one error based on content of the characters (i.e., the transaction request may reference as non-existent city…where the non-existent city does not yet appear in the reference data repositories…determine that the transaction is invalid/error, page 7 paragraph [0080]); and
transmit the updated data stream to a third-party system (i.e., transmit information to the entity computing system 103, Fig. 1 and page 4 paragraph [0051]).
Kursun does not explicitly teach wherein the data stream comprises characters encoded according to American Standard Code for Information Interchange (ASCII) standards; determine the at least one error based on position of the characters; transmit a report including the indication of the least one error and a recommended change to the data stream based on the indication of the least at one error; and receive an updated data stream in response to an input that triggers acceptance of the recommended changed included in the report.
Mishraky teaches determine the at least one error based on position of the characters (i.e., determining an initial position in the character sequence where an incorrect character was first entered, claim 19). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kursun to determine validity based on position of the characters, as taught by Mishraky. One would be motivated to do so to allow the invalid character sequences to be quickly and accurately detected (i.e., Mishraky, col. 1 lines 51-54).
Rasti teaches the data stream comprises characters encoded according to American Standard Code for Information Interchange (ASCII) standards (i.e., strings consisting of alpha-numeric characters that may include any character from the ASCII, page 3 paragraph [0034]). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kursun to comprise characters encoded according to American Standard Code for Information Interchange (ASCII) standards, as taught Rasti because it was conventionally employed in the art for standardization and simplicity.
Reddy teaches transmit a report including the indication of the least one error and a recommended change to the data stream based on the indication of the least at one error (i.e., users can receive real-time recommendations associated with the one or more first user inputs to correct the at least one anomalous/error transaction, page 6 paragraph [0048]); and receive an updated data stream in response to an input that triggers acceptance of the recommended changed included in the report (i.e., Reddy, in page 6 paragraph [0050], discloses automatically display the one or more recommended actions to a user of the first computing system and/or the second computing system and receive a selection from a user to correct the least one anomalous transaction. Reddy, in page 3 paragraph [0022], also discloses if the user accepts a real-time recommendation of the system, the system will learn from the user selection and subsequence risk score may be adjusted according). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kursun to transmit a report including the indication of the least one error and a recommended change to the data stream based on the indication of the least at one error; and receive an updated data stream in response to an input that triggers acceptance of the recommended changed included in the report, as taught by Reddy. One would be motivated to do so to improve the precision and accuracy of the system (i.e., Reddy, page 3 paragraph [0022]).
Regarding claim 3, Kursun teaches the system of claim 1, wherein the one or more processors, to provide the data stream to the machine learning model, are configured to: transmit a request including the data stream to a machine learning host (i.e., machine learning computing system 106, Fig. 1) associated with the machine learning model (i.e., provide the input data to the machine learning computing system 106, Fig. 1 and page 5 paragraph [0057]); and receive the indication of the at least one error from the machine learning host in response to the request (i.e., produce an output indicating that the transaction is unauthorized, page 7 paragraph [0076]).
Regarding claim 4, Kursun teaches the system of claim 1, wherein the one or more processors, to selectively transmit the data stream, are configured to: transmit the data stream to the third-party system based on the indication indicating that the data stream is valid (i.e., the machine learning module may provide an output to the entity which indicates whether a transaction request is genuine, page 4 paragraph [0049]).
Regarding claim 5, Kursun teaches the system of claim 1, wherein the one or more processors, to selectively transmit the data stream, are configured to: refrain from transmitting the data stream to the third-party system based on the indication of the at least one error (i.e., the output data 210 may be used as a trigger to automatically block the unauthorized transaction from occurring, pages 7-8 paragraph [0082]).
Regarding claim 6, Kursun teaches the system of claim 1, wherein the one or more processors are further configured to: receive an indication of a location associated with the data stream (i.e., transaction requests may include location of the transaction, sender information, and recipient information, page 8 paragraph [0089]); and transmit a request for the data stream based on the location (i.e., request to execute a transaction in a particular city, page 4 paragraph [0049]), wherein the data stream is received in response to the request (e.g., transaction requests include transaction data, amount of resources to be transferred, location of the transaction, sender information, and recipient information, page 8 paragraph [0089])
Regarding claim 7, Kursun teaches the system of claim 1.
The combination of teachings of Kursun and Mishraky does not explicitly teach wherein the third-party system is associated with a credit bureau, and the data stream comprises an update intended for the credit bureau.
Rasti teaches herein the third-party system is associated with a credit bureau (i.e., credit bureau, page 1 paragraph [000]), and the data stream comprises an update intended for the credit bureau (i.e., send updated identifiers to credit bureaus, page 7 paragraph [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have modified the combination of teachings of Kursun and Mishraky to associate third-party system with a credit bureau, and comprise an update intended for the credit bureau, as taught by Rasti because it was conventionally employed in the art for identify to be authenticated through a trustee.
Regarding claim 19, Kursun teaches the non-transitory computer-readable medium of claim 14.
The combination of teachings of Kursun and Mishraky does not explicitly teach wherein the data stream comprises a sequence of hexadecimals encoding the sequence of characters according to American Standard Code for Information Interchange (ASCII) standards.
Rasti teaches wherein the data stream comprises a sequence of hexadecimals encoding the sequence of characters according to American Standard Code for Information Interchange (ASCII) standards (i.e., strings consisting of alpha-numeric characters that may include any character from the ASCII, page 3 paragraph [0034]). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combination of teachings of Kursun and Mishraky to comprise a sequence of hexadecimals encoding the sequence of characters according to American Standard Code for Information Interchange (ASCII) standards, as taught Rasti because it was conventionally employed in the art for standardization and simplicity.
Regarding claim 20, Kursun teaches the non-transitory computer-readable medium of claim 14.
The combination of teachings of Kursun and Mishraky does not explicitly teach wherein the third-party system is associated with a credit bureau, and the data stream comprises an update intended for the credit bureau.
Rasti teaches wherein the third-party system is associated with a credit bureau (i.e., credit bureau, page 1 paragraph [000]), and the data stream comprises an update intended for the credit bureau (i.e., send updated identifiers to credit bureaus, page 7 paragraph [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have modified the combination of teachings of Kursun and Mishraky to associate third-party system with a credit bureau, and comprise an update intended for the credit bureau., as taught by Rasti because it was conventionally employed in the art for identify to be authenticated through a trustee.
4. Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kursun, Mishraky, Rasti, and Reddy, as applied to claim 1 above, and further in view of Fenichel et al. (hereafter, “Fenichel”), US 11,966,921 B2.
Regarding claim 21, Kursun teaches the system of claim 1.
The combination of teachings of Kursun, Mishraky, Rasti, and Reddy does not explicitly teach the machine learning is further cluster the data stream with similar labeled data streams, and wherein the machine learning model is further configured to determine the at least one error based on which cluster the data stream is classified into.
Fenichel teaches the machine learning is further cluster the data stream with similar labeled data streams (i.e., a cluster generated by a machine-learning model, page 2 liens 60-64), and wherein the model is further configured to determine the at least one error based on which cluster the data stream is classified into (i.e., detecting the error-related association based on a cluster generated by the machine learning model, col. 17 lines 65-67).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combination of teachings of Kursun, Mishraky, Rasti, and Reddy to cluster the data stream with similar labeled data streams, and determine the at least one error based on which cluster the data stream is classified into, as taught by Fenichel because it was conventionally employed in the art for reducing data complexity.
5. Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kursun, Mishraky, and Reddy, as applied to claim 14 above, and further in view of Gerstner et al. (hereafter, “Gerstner”), US 2005/0125321 A1.
Regarding claim 15, Kursun teaches the non-transitory computer-readable medium of claim 14.
The combination of teachings of Kursun, Mishraky, and Reddy does not explicitly teach wherein the one or more instructions, when executed by the one or more processors, further cause the device to: transmit, to the user device, a confirmation that the updated data stream was transmitted to the third-party system.
Gerstner teaches transmit, to the user device, a confirmation that the updated data stream was transmitted to the third-party system (i.e., generate an information output to a customer fulfillment system…the fulfillment system produces a confirmation to be sent to the account holder, page 3 paragraph [0020]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combination of teachings of Kursun, Mishraky, and Reddy to transmit, to the user device, a confirmation that the updated data stream was transmitted to the third-party system, as taught by Gerstner. One would be motivated to do so to allow error handling to be undertaken (i.e., Gerstner, page 3 paragraph [0019]).
Response to Arguments
6. Applicant’s arguments with respect to claim(s) 1 and 3-21 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
7. 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.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to OANH DUONG whose telephone number is (571)272-3983. The examiner can normally be reached Maxiflex Mon-Fri 6:00am-5:00pm.
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/OANH DUONG/Primary Examiner, Art Unit 2441
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