DETAILED ACTION
The following NON-FINAL Office action is in response to Application 18589538 filed on February 28, 2024.
Acknowledgements
Claims 1-10 (Species A) have been elected with traverse.
Claims 11-20 (Species B) have been withdrawn.
Claims 1-10 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 .
Election/Restrictions
Claims 11-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected group, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 12/12/2025.
Applicant's election with traverse of Species A (claims 1-10) in the reply filed on 12/12/2025 is acknowledged. The traversal is on the ground(s) that on the basis that if the search and examination of an entire application can be made without serious burden, the claims must be examined on the merits. This is not found persuasive because claim 1 belongs to a different species described in paragraphs [0025], of the original specification in which another embodiment is described to alter an override list by transmitting a request to a user device associated with the card holder that asks the card holder to provide an indication of a correct identity of the merchant associated with the transaction. Also, claims 11-20 belong to another embodiment described in paragraphs [¶0031] and [0005-0007], of the original specification in which the override identification system alters an override list by modifying, based on the merchant key, a customer statement comprising the mislabeled merchant name to replace the mislabeled merchant name with one of raw merchant data derived from the raw transaction data associated with the transaction; and the correct merchant name associated with the transaction. Both species describe different ways to alter an override list.
The requirement is still deemed proper and is therefore made FINAL.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In the instant case, claims 1-10 are directed to a system. Therefore, these claims fall within the four statutory categories of invention.
The claims recite analyzing data to determine mislabeled merchants which is an abstract idea. Specifically, the claim recites “receive a communication from a card holder; analyze the communication…to determine an identity of the card holder; detect a mislabeled name associated with an entry; detect a date associated with the entry; 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 a [data] based on the raw entry data; alter an override list to add the [data]; determining whether the [data] substantially matches one of stored [data] in the override list; in response to determining the [data] does not substantially match one of the stored [data] in the override list, storing the [data] in the override list; storing an updated name associated with the [data] in the override list; transmit a request to a user associated with the card holder that asks the card holder to provide an indication of a correct identity associated with the entry; and 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.” which is grouped within the “certain methods of organizing human activity” grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test, classified under “fundamental economic principles or practices”, specifically including mitigating risk or fraud in a transaction (See MPEP 2106, specifically 2106.04(a)) because – for example, in this case, the claims involve a series of steps for analyzing communications from a user to determine a correct names or mislabeled merchant names and adding the correct names to a list associated with a transaction. Accordingly, the claim recites an abstract idea (See MPEP 2106, specifically 2106.04(a)).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See MPEP 2106.04(d)), the additional elements of the claims such as the use of a system comprising one or more processors and a memory, communication channels, a first machine learning model, a second machine learning model, a key, stored keys and a user device merely involves using a computer as a tool to perform an abstract idea and/or generally links the use of a judicial exception to a particular technological environment. The use of a system comprising one or more processors and a memory, communication channels, a first machine learning model, a second machine learning model, a key, stored keys and a user device to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment] does not render the claim patent eligible because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. Specifically, a system comprising one or more processors and a memory, communication channels, a first machine learning model, a second machine learning model, a key, stored keys and a user device perform the steps or functions of the “receive a communication from a card holder; analyze the communication…to determine an identity of the card holder; detect a mislabeled name associated with an entry; detect a date associated with the entry; 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 a [data] based on the raw entry data; alter an override list to add the [data]; determining whether the [data] substantially matches one of stored [data] in the override list; in response to determining the [data] does not substantially match one of the stored [data] in the override list, storing the [data] in the override list; storing an updated name associated with the [data] in the override list; transmit a request to a user associated with the card holder that asks the card holder to provide an indication of a correct identity associated with the entry; and 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”. The additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See MPEP 2106, specifically 2106.05), the additional elements of the system comprising one or more processors and a memory, communication channels, a first machine learning model, a second machine learning model, a key, stored keys and a user device, to perform the steps amounts to no more than using the system comprising one or more processors and a memory, communication channels, a first machine learning model, a second machine learning model, a key, stored keys and a user device to automate and/or implement the abstract idea of analyzing data to determine mislabeled merchants. As discussed above, taking the claim elements separately the system comprising one or more processors and a memory, communication channels, a first machine learning model, a second machine learning model, a key, stored keys and a user device perform the steps of Claim 1. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of analyzing data to determine mislabeled merchants. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of the system comprising one or more processors and a memory, communication channels, a first machine learning model, a second machine learning model, a key, stored keys and a user device to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Dependent claims further describe details of processing, monitoring and analyzing communications/messages further elaborating on the abstract idea of analyzing data to determine mislabeled merchants. The dependent claims recite additional elements such as “a large language model and a transformer model”, however, they do not integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, the dependent claims are also not patent eligible.
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 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) and in further view of Nicholson et al. (US 2025/0245663 A1)
Regarding Claims 1, 8 and 15, 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).
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 and Nicholson 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 and Nicholson 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 and Nicholson 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 and Nicholson 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 and Nicholson 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 and Nicholson 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)
Conclusion
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/ZEHRA RAZA/Examiner, Art Unit 3697
/JOHN W HAYES/Supervisory Patent Examiner, Art Unit 3697