DETAILED ACTION
The following FINAL Office action is in response to Amendment filed on April 29, 2026 for 18589538.
Acknowledgements
Claims 21-30 have been added.
Claims 11-20 have been canceled.
Claims 1-10 and 21-30 have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after December 13, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
In response to the Applicant’s amendments and arguments, the rejection under 35 USC 101 has been withdrawn.
Applicant’s arguments are moot under new grounds of rejection.
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 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 of this title, 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 1-10 and 21-30 are rejected under 35 U.S.C. 103(b) as being unpatentable over Hanson et al. (US 2014/0006275 A1) in view of ELDER et al. (US 2025/0045850 A1) in view of Nicholson et al. (US 2025/0245663 A1) and in further view of Martin et al. (US 8,738,493 B2)
Regarding Claims 1, 24 and 27, Hanson discloses: system comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to (¶0022, ¶0068):
receive a communication from a card holder via one of one or more communication channels (¶0022, ¶0026-¶0028, ¶0032-¶0033, ¶0055, ¶0061)
analyze the communication using [a first machine learning model] to: (¶0034, ¶0036, ¶0042)
determine an identity of the card holder; (¶0034, ¶0036, ¶0042)
detect a mislabeled name associated with an entry; and (¶0034, ¶0035, ¶0039, ¶0042, ¶0055-¶0056, ¶0063)
detect a date associated with the entry; (¶0037, ¶0055-¶0056, ¶0063)
transmit a request to a user device associated with the card holder that asks the card holder to provide an indication of a correct identity associated with the entry; and (¶0042, ¶0044, ¶0056-¶0057, ¶0063)
responsive to receiving a response to the request that includes the indication of the correct identity associated with the entry, alter a statement of the card holder to replace the mislabeled name associated with the entry with the correct identity (¶0044, ¶0046, ¶0051, ¶0052, ¶0057, ¶0064)
Hanson does not disclose: detect, based on the identity of the card holder, historical entry records of the card holder; detect, based on the historical entry records of the card holder, the mislabeled name associated with the entry and the date associated with the entry, raw entry data associated with the entry; generate, using a second machine learning model, a key based on the raw entry data; alter an override list to add the key by: determining whether the key substantially matches one of stored keys in the override list; in response to determining the key does not substantially match one of the stored keys in the override list, storing the key in the override list; and storing an updated name associated with the key in the override list.
ELDER however discloses:
detect, based on the identity of the card holder, historical entry records of the card holder; (¶0015-¶0016)
detect, based on the historical entry records of the card holder, the mislabeled name associated with the entry and the date associated with the entry, raw entry data associated with the entry; (¶0015-¶0016)
generate, using a second machine learning model, a key based on the raw entry data (¶0012, ¶0013, ¶0019, ¶0020, ¶0029)
alter an override list to add the key by: determining whether the key substantially matches one of stored keys in the override list; (¶0012, ¶0013, ¶0027, ¶0029)
in response to determining the key does not substantially match one of the stored keys in the override list… storing the key in the override list; and (¶0013, ¶0019, ¶0026)
storing an updated name associated with the key in the override list (¶0024, ¶0026, ¶0027, ¶0028)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “detect, based on the identity of the card holder, historical entry records of the card holder; detect, based on the historical entry records of the card holder, the mislabeled name associated with the entry and the date associated with the entry, raw entry data associated with the entry; generate, using a second machine learning model, a key based on the raw entry data; alter an override list to add the key by: determining whether the key substantially matches one of stored keys in the override list; in response to determining the key does not substantially match one of the stored keys in the override list, storing the key in the override list; and storing an updated name associated with the key in the override list”, as disclosed in ELDER, in order to provide a technique to prepare a dataset for analysis by removing and/or modifying incorrect, incomplete, irrelevant, duplicated, corrupted, and/or improperly formatted data (see ELDER ¶0001).
The combination of Hanson and Elder does not disclose: a combination of a first machine learning model and a second machine learning model.
Nicholson however discloses a combination of a first machine learning model and a second machine learning model (¶0007-¶0010, ¶0018, ¶0023, ¶0032).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “a combination of a first machine learning model and a second machine learning model”, as disclosed in Nicholson, in order to provide a system using multiple machine learning models to identify from the raw transaction data or text provided for a transaction entity names and
classification categories (see Nicholson ¶0007).
The combination of Hanson, Elder and Nicholson does not disclose: compare the key to cached keys stored in a cache, wherein the cached keys comprise keys from previous comparisons conducted by the system; in response to determining the key substantially matches one of the cached keys, use a corresponding identifier associated with the one of the cached keys; in response to determining the key does not substantially match one of the cached keys.
Martin however discloses:
compare the key to cached keys stored in a cache, wherein the cached keys comprise keys from previous comparisons conducted by the system; (Col. 7 lines 18-37, Col. 8 lines 44-58)
in response to determining the key substantially matches one of the cached keys, use a corresponding identifier associated with the one of the cached keys; (Col. 8 lines 21-30, Col. 8 lines 44-58, Col. 9 lines 1-33)
in response to determining the key does not substantially match one of the cached keys (Col. 8 lines 21-30, Col. 8 lines 44-58, Col. 9 lines 1-33)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “a combination of a first machine learning model and a second machine learning model”, as disclosed in Martin, in order to provide a system to resolve ambiguous merchant data in connection with processing a cashless transaction request in a real-time (see Martin abstract).
Regarding Claim 2, Hanson discloses wherein the one or more communication channels comprise one or more of: emails; messages via a website form; chat messages; text messages; and call logs of service calls (¶0042)
Regarding Claim 3, Hanson discloses wherein the first machine learning model is configured to monitor and analyze communications via the one or more communication channels in near real time (¶0034, ¶0036, ¶0042)
Regarding Claim 4, the combination of Hanson, ELDER, Nicholson and Martin discloses the invention as above.
ELDER further discloses: wherein the first machine learning model comprises a large language model that is configured to process a message of the communication to detect the mislabeled name associated with the entry (¶0029).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “wherein the first machine learning model comprises a large language model that is configured to process a message of the communication to detect the mislabeled name associated with the entry”, as disclosed in ELDER, in order to provide a technique to prepare a dataset for analysis by removing and/or modifying incorrect, incomplete, irrelevant, duplicated, corrupted, and/or improperly formatted data (see ELDER ¶0001).
Regarding Claim 5, the combination of Hanson, ELDER, Nicholson and Martin discloses the invention as above.
Nicholson further discloses wherein the large language model is further configured to process metadata associated with the communication to determine the identity of the card holder (¶0018, ¶0019, ¶0021)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “wherein the large language model is further configured to process metadata associated with the communication to determine the identity of the card holder”, as disclosed in Nicholson, in order to provide a system using multiple machine learning models to identify from the raw transaction data or text provided for a transaction entity names and
classification categories (see Nicholson ¶0007).
Regarding Claim 6, the combination of Hanson, ELDER, Nicholson and Martin discloses the invention as above.
ELDER further discloses: wherein the raw entry data comprises data stored in one or more data fields, wherein the one or more data fields comprises one or more of: a name field; a state field; a zip code field; a country code field; and a category code (¶0012)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “wherein the raw entry data comprises data stored in one or more data fields, wherein the one or more data fields comprises one or more of: a name field; a state field; a zip code field; a country code field; and a category code”, as disclosed in ELDER, in order to provide a technique to prepare a dataset for analysis by removing and/or modifying incorrect, incomplete, irrelevant, duplicated, corrupted, and/or improperly formatted data (see ELDER ¶0001).
Regarding Claim 7, the combination of Hanson, ELDER, Nicholson and Martin discloses the invention as above.
ELDER further discloses: wherein the key is generated based on the data stored in the one or more data fields (¶0012, ¶0013, ¶0019, ¶0020, ¶0029)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “wherein the key is generated based on the data stored in the one or more data fields”, as disclosed in ELDER, in order to provide a technique to prepare a dataset for analysis by removing and/or modifying incorrect, incomplete, irrelevant, duplicated, corrupted, and/or improperly formatted data (see ELDER ¶0001).
Regarding Claim 8, the combination of Hanson, ELDER, Nicholson and Martin discloses the invention as above.
ELDER further discloses: wherein the first machine learning model comprises a transformer model that has been trained using historical communication data regarding historical entries associated with incorrect names and mapping to corresponding raw entry data associated with the historical entries associated with incorrect names (¶0015-¶0016)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “wherein the first machine learning model comprises a transformer model that has been trained using historical communication data regarding historical entries associated with incorrect names and mapping to corresponding raw entry data associated with the historical entries associated with incorrect names.”, as disclosed in ELDER, in order to provide a technique to prepare a dataset for analysis by removing and/or modifying incorrect, incomplete, irrelevant, duplicated, corrupted, and/or improperly formatted data (see ELDER ¶0001).
Regarding Claim 9, the combination of Hanson, ELDER, Nicholson and Martin discloses the invention as above.
Nicholson further discloses wherein the second machine learning model comprises a transformer model that has been trained using historical raw entry data associated with historical entries that were previously associated with incorrect names and corresponding keys stored in the override list (¶0007, ¶0018, ¶0019, ¶0021)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “wherein the second machine learning model comprises a transformer model that has been trained using historical raw entry data associated with historical entries that were previously associated with incorrect names and corresponding keys stored in the override list”, as disclosed in Nicholson, in order to provide a system using multiple machine learning models to identify from the raw transaction data or text provided for a transaction entity names and
classification categories (see Nicholson ¶0007).
Regarding Claim 10, Hanson discloses: wherein the instructions are further configured to cause the system to: receive present entry data, wherein the present entry data comprises entry data originating from use of a card at a device of a first entity and represents an entry that is in the process of attempting to execute (¶0022, ¶0026-¶0028, ¶0032-¶0033, ¶0055, ¶0061); detecting a policy associated with the card, wherein the policy restricts procurements from one or more predetermined entities, wherein the one or more predetermined entities comprise at least the first entity; determining that the present entry data comprises data corresponding to the key, wherein the key is associated with the first entity (¶0022, ¶0026; responsive to the present entry data being received prior to a modification of the override list to add the key, decline the entry (¶0022, ¶0026); and responsive to the present entry data being received after to the modification of the override list to add the key: transmit a request to a user device associated with the card requesting an identification of an entity associated with the entry (¶0044, ¶0046, ¶0051, ¶0052, ¶0057, ¶0064); and responsive to receiving an indication from the user device that the entity associated with the entry is the first entity, approve the entry (¶0044, ¶0046, ¶0051, ¶0052, ¶0057, ¶0064)
Regarding Claims 21 and 28, the combination of Hanson, ELDER, Nicholson and Martin discloses the invention as above.
Martin discloses wherein detecting the raw entry data associated with the entry comprises performing a fuzzy search of the historical entry records to detect entry data comprising raw data that is a fuzzy match to the correct identity and that corresponds to a statement entry associated with the mislabeled name, and wherein the fuzzy search is performed, using the first machine learning model, in response to determining the key does not substantially match one of the cached keys (Col. 7 lines 18-37, Col. 8 lines 44-58)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “wherein detecting the raw entry data associated with the entry comprises performing a fuzzy search of the historical entry records to detect entry data comprising raw data that is a fuzzy match to the correct identity and that corresponds to a statement entry associated with the mislabeled name, and wherein the fuzzy search is performed, using the first machine learning model, in response to determining the key does not substantially match one of the cached keys”, as disclosed in Martin, in order to provide a system to resolve ambiguous merchant data in connection with processing a cashless transaction request in a real-time (see Martin abstract).
Regarding Claim 22, the combination of Hanson, ELDER, Nicholson and Martin discloses the invention as above.
Elder discloses wherein the instructions are further configured to cause the system to: alter, based on the key, one or more statements comprising the mislabeled name to replace the mislabeled name with at least: (i) raw data derived from the raw entry data associated with the entry, or (ii) the correct identity (¶0012, ¶0013, ¶0027, ¶0029)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “wherein the instructions are further configured to cause the system to: alter, based on the key, one or more statements comprising the mislabeled name to replace the mislabeled name with at least: (i) raw data derived from the raw entry data associated with the entry, or (ii) the correct identity”, as disclosed in ELDER, in order to provide a technique to prepare a dataset for analysis by removing and/or modifying incorrect, incomplete, irrelevant, duplicated, corrupted, and/or improperly formatted data (see ELDER ¶0001).
Regarding Claim 23, the combination of Hanson, ELDER, Nicholson and Martin discloses the invention as above.
Elder discloses wherein the instructions are further configured to cause the system to:extract historical entry records; determine whether there is similar entry data to present entry data; verify if the similar entry data includes a mislabeled name; andupdate the mislabeled name in the similar entry data using the correct identity (¶0012, ¶0013, ¶0027, ¶0029)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “wherein the instructions are further configured to cause the system to: extract historical entry records; determine whether there is similar entry data to present entry data; verify if the similar entry data includes a mislabeled name; and update the mislabeled name in the similar entry data using the correct identity”, as disclosed in ELDER, in order to provide a technique to prepare a dataset for analysis by removing and/or modifying incorrect, incomplete, irrelevant, duplicated, corrupted, and/or improperly formatted data (see ELDER ¶0001).
Regarding Claim 25, the combination of Hanson, ELDER, Nicholson and Martin discloses the invention as above.
ELDER further discloses: wherein the raw entry data comprises data stored in one or more data fields, wherein the one or more data fields comprises a city field (¶0012)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “wherein the raw entry data comprises data stored in one or more data fields, wherein the one or more data fields comprises a city field”, as disclosed in ELDER, in order to provide a technique to prepare a dataset for analysis by removing and/or modifying incorrect, incomplete, irrelevant, duplicated, corrupted, and/or improperly formatted data (see ELDER ¶0001).
Regarding Claim 26, the combination of Hanson, ELDER, Nicholson and Martin discloses the invention as above.
ELDER further discloses: wherein detecting the historical entry records comprises detecting historical communications or historical entry records from the card holder or other card holders, and wherein detecting the raw entry data is further based on a plurality of historical entry records (¶0015-¶0016).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “wherein detecting the historical entry records comprises detecting historical communications or historical entry records from the card holder or other card holders, and wherein detecting the raw entry data is further based on a plurality of historical entry records”, as disclosed in ELDER, in order to provide a technique to prepare a dataset for analysis by removing and/or modifying incorrect, incomplete, irrelevant, duplicated, corrupted, and/or improperly formatted data (see ELDER ¶0001).
Regarding Claim 29, the combination of Hanson, ELDER, Nicholson and Martin discloses the invention as above.
Martin discloses wherein using the corresponding identifier associated with the one of the cached keys comprises using a corresponding identifier from the cache for financial statements or to store in a database. (Col. 7 lines 18-37, Col. 8 lines 44-58)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Hanson to include “wherein using the corresponding identifier associated with the one of the cached keys comprises using a corresponding identifier from the cache for financial statements or to store in a database.”, as disclosed in Martin, in order to provide a system to resolve ambiguous merchant data in connection with processing a cashless transaction request in a real-time (see Martin abstract).
Regarding Claim 30, Hanson discloses wherein the communication relates to a transaction with a mislabeled merchant, and wherein the first machine learning model is configured to process a substantive message of the communication to detect the mislabeled name associated with the entry along with the date associated with the entry (¶0034, ¶0035, ¶0039, ¶0042, ¶0055-¶0056, ¶0063)
Conclusion
THIS ACTION IS MADE FINAL. 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 ZEHRA RAZA whose telephone number is (571)272-8128. The examiner can normally be reached 10AM-6:30PM.
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/ZEHRA RAZA/ Examiner, Art Unit 3697
/JOHN W HAYES/ Supervisory Patent Examiner, Art Unit 3697
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