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 .
DETAILED ACTION
Applicant filed an amendment on 3/23/26. Claims 28-47 were pending. Claims 28, 44 and 47 are amended. After careful consideration of applicant amendment and arguments, the examiner finds them to be moot and/or non-persuasive. This action is a Final Rejection.
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 28-47 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Claims 28, 44 and 47 respectively are directed to statutory classes of invention which include system, method and non-transitory computer readable medium. (Step 1: Yes)
Here the limitations under their broadest reasonable interpretation cover the performance of the limitation as certain methods of organizing human activity. In this case, a fundamental economic practice, ie. providing payroll services.
Claim 28 which is the representative claim contains the following abstract elements;
system, comprising:
…, to: present, via a … of a payroll service of one or more …, an electronic image associated with an option to execute a transaction pending at a …; receive, responsive to a selection of the electronic image via the …, a request for authorization to access data for the transaction for execution via the payroll service; determine, based on the request for authorization, a context for controlling access to the one or more … for executing the transaction; access, based on the context for controlling access, from an electronic account associated with the transaction, web services data and a history of transactions of the electronic account; classify, based on the … data and the history of transactions input into a … trained with historic web services data and transaction histories, the transaction pending at the … as an allowed transaction; generate, by the … and based on the classification of the transaction as the allowed transaction, an authorization outcome for the request for authorization, the authorization outcome indicative that the request for authorization is approved and cause the payroll service to execute the transaction pending at the …;execute, via the web client and responsive to the classification authorization outcome, the transaction using the payroll service, in accordance with the selection of the …;record, responsive to the execution, the transaction into a ledger associated with the electronic account; and determine a payroll balance for the electronic account based on the transaction recorded in the ledger.
Here the technical elements include the following generic elements. Processors, memory, interface, web (services), image machine learning, web client (assuming its’ not a user)
Claims 44 and 47 are similar to claim 28.
Claim 36 adds a “gateway interfacing”, but generally dependent claims 29-43, 45-46 do not contain any additional elements.
Step 2A -yes, the claims are directed to an abstract idea.
The judicial exception is not integrated into a practical application. In particular the claims recite additional elements of. Processors, memory, interface, web (services), image machine learning.
The computer hardware/software is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computing component. Accordingly these additional elements which considered separately or as an ordered combination do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are a high level of generality. Therefore claims 28, 44 and 47 are directed to an abstract idea without a practical application Step 2A Prong 2 – No the additional elements are not into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately or as an ordered combination they do not add significantly more (known as inventive concept) to the exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of Here the technical elements include the following generic elements. Processors, memory, interface, web (services), image machine learning amounts to no more than mere instructions to apply the exception using a generic computing component. Mere instructions to apply the exception cannot provide inventive concept.
Accordingly there additional elements do not change the outcome of the analysis when considered separately and as an ordered combination. Thus claims 28, 44 and 47 are not patent eligible (Step 2B no) They do not provide significantly more. Dependent claims 29-43 and 45-46, are rejected because they do not include additional elements to integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individual or as an ordered combination. Therefore the dependent claims are directed to an abstract idea.
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.
Claim(s) 28-47 are rejected under 35 U.S.C. 103 as being unpatentable over
US Patent Publication to Clemens 20200005213 in view of US Patent to Parker 11468519 and US Patent 9485265 to Saperstein
As per claim 44, Clemens discloses;
determining, by the one or more processors, based on the request for authorization, a context for controlling access to the one or more web services for executing the transaction; Clemens(0044, 0062, per applicant specification, context for controlling appears to be some form of access controls)
Clemens (0062, generically access control as per objection above) see also Parker col. 8 lines 55-65 describes access) the transaction pending at the web client
as an allowed transaction; (by classification…. See 0052 of applicant specification, Clemens (0104, permit the user to use the system, consistent with applicant specification…. They can conduct transactions)
generating, by the one or more processors using the machine learning model Clemens(0117 machine learning model for risk and mitigation) and based on the
classification of the transaction as the allowed transaction, Clemens(0126, the invention can be used for identity verification for business transactions…. If the transaction were to be completed then it’s allowed…. So to speak, in applicant spec 0046, … in one example context is a security token or cookie… however, this is merely an example) , the transaction using the payroll service, in accordance with the selection of the electronic image; Clemens (0102, again allows the transactions to take place, here image is not specifically defined and the spec. points to 422 of fig. 5 which does not provide more details) via a graphical user interface of a payroll service of one or more web services,
Clemens(0038) Clemens does not explicitly disclose what Parker teaches;
pending at the web client; executing, by the one or more processors, via the web client and responsive to the classification authorization outcome, Parker (col 14 lines 45-60, authorization type activity)
presenting, by one or more processors,
an electronic image associated with an option to execute a transaction pending at a web client;
Parker (col. 2 lines 5-25)
receiving, by the one or more processors, responsive to a selection of the electronic image via the graphical user interface, a request for authorization to access data for the transaction for execution via the payroll service; Parker(col. 2 lines 40-45)
Note applicant spec- 0065-66, perform e commerce transaction using payroll funds.
accessing, by the one or more processors, based on the context for controlling access, from an electronic account associated with the transaction, web services data and a history of transactions of the electronic account; Parker (transaction history col. 7 lines 35-45)
classifying, by the one or more processors, based on the web services data and the history of transactions input into a machine learning model trained with historic web services data and transaction histories, Parker (col. 3 lines 15-15)
recording, by the one or more processors, responsive execution, the transaction into a ledger associated with the electronic account; and determining, by the one or more processors,
a payroll balance for the electronic account based on the transaction recorded in the ledger. Parker(Col. 10 lines 1-5) It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the workforce monitoring of Clemens with the automatic deposit teachings of Parker for the motivation of “allowing the employee to receive payroll funds faster and avoid the use of a physical check” (col. 1 lines 5-15)
Clemens and Parker do not explicitly disclose what Saperstein teaches;
an authorization outcome for the request for authorization,
the authorization outcome indicative that the request for authorization is approved and cause the
payroll service to execute the transaction Saperstein(col. 14, lines 1-20, payroll rule that monitors payroll transactions and approves or disapproves ones that might be suspicious, ie determine fi the payroll transaction is legit)
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the payroll transaction monitoring of Saperstein with the monitoring system of Clemens for the motivation of avoiding fraud and identifying “fraud could take place” (col. 1 lines 30-40)
Claims 28 and 47 are similar to claim 44
As per claim 29 Clemens does not explicitly disclose what Parker teaches; The system of claim 28, comprising one or more processors to determine a risk of the transaction by providing the web services data and the transaction history to a machine learning model to model a probabilistic relationship between the transaction history and risks.
Parker (col. 7 lines 15-20)
The motivation for the combination would be similar to that provided for claim 44.
Claim 45 is similar to claim 29
As per claim 30 Clemens discloses; The system of claim 29, wherein the machine learning model is trained to classify the transaction pending at the web client according to the risk of the transaction.
Clemens(0126 can provide risk assessment for users and transactions)
Claim 46 is similar to claim 30
As per claim 38 Clemens discloses; The system of claim 28, comprising the one or more processors to: access the web services data comprising payroll data that includes historical payroll transactions and one or more account balances; and classify the transaction pending at the web client as the allowed transaction using the historical payroll transactions and the one or more account balances.
Clemens (0104, permit the user to use the system, consistent with applicant specification…. They can conduct transactions)
As per claim 31, Clemens does not explicitly disclose what Parker teaches;
The system of claim 28, wherein determining the context for controlling access includes identifying the electronic account based on a token associated with the transaction.
(per applicant spec 0046, token or cookie are interchangeable, access control feature… , see col. 8 lines 20-25 for cookie)
The motivation would be similar to that provided for claim 44.
As per claim 32 Clemens discloses; The system of claim 31, wherein the token is generated by the payroll service and provided to the web client for accessing the electronic account. (0095-97)
As per claim 33 Clemens does not explicitly disclose what Parker teaches; The system of claim 28, wherein the payroll service is configured to perform payroll operations for an organization associated with the electronic account, the payroll operations including determining an amount of pay for one or more electronic account associated with the organization, the one or more electronic accounts including the electronic account. (Col. 16 lines 50-55) The motivation for the combination would be similar to that provided for claim 44.
As per claim 34, Clemens does not explicitly disclose what Parker teaches; The system of claim 28, comprising the one or more processors to: generate a bank file that identifies a payment of the transaction to a merchant and a payment of the payroll balance to the electronic account.
Parker(col. 5 lines 1-15) The motivation for the combination would be similar to that provided for claim 44
As per claim 35, Clemens does not explicitly disclose what Parker teaches; The system of claim 34, wherein the payroll service forwards the bank file to the payroll service for processing the payroll balance for the electronic account.
Parker(col. 5 lines 40-50)
The motivation for the combination would be similar to that provided for claim 44
As per claim 36, Clemens discloses;
The system of claim 28, wherein the graphical user interface presents the electronic image on a display system of a merchant during a processing of the transaction via a gateway interfacing with the payroll service. Clemens (0038)
As per claim 43, Clemens discloses; The system of claim 28, comprising the one or more processors to generate a notification to the graphical user interface indicating successful execution of the transaction. Clemens (0050)
As per claim 37 Clemens discloses;
The system of claim 36, wherein the gateway facilitates communication between the web client and the payroll service utilizing application programming interface.
Clemens(0041)
As per claim 39 Clemens discloses; The system of claim 28, comprising the one or more processors to: maintain the ledger for all transactions associated with the electronic account; and update the ledger in real-time for transactions executed via the payroll service.
Clemens(0011)
As per claim 40 Clemens discloses; The system of claim 28, comprising the one or more processors to determine the payroll balance for the electronic account based on the transaction recorded in the ledger subtracted from a net pay for a pay period for the electronic account.
Clemens(0038, deductions… to create net pay)
As per claim 41 Clemens discloses; The system of claim 28, comprising the one or more processors to: determine one or more deductions associated with the electronic account; and determine the payroll balance based on the one or more deductions.
Clemens(0038, deductions)
As per claim 42, Clemens discloses; The system of claim 28, comprising the one or more processors to: determine a net pay amount for the electronic account of the one or more accounts associated with an organization; determine the payroll balance for the electronic account based on the net pay amount and the transaction recorded in the ledger; and generate a bank file that identifies a payment of the transaction amount to a merchant and a payment of the payroll balance to the electronic account.
Clemens(0038, net pay)
Response to Arguments
Applicant filed an amendment on 3/23/26. Claims 28-47 were pending. Claims 28, 44 and 47 are amended. After careful consideration of applicant amendment and arguments, the examiner finds them to be moot and/or non-persuasive. This action is a Final Rejection.
Claim objections- moot, however it is noted that “context” is as claimed rather general. The specification gives examples that appear to be allowing access for authorization. However, the examples are not fully limiting because they are examples not definitions.
Claim Rejections Under 35 U.S.C. 101
On page 2 of the Office Action, claims 28-47 were rejected under 35 U.S.C. § 101.
Applicant respectfully traverses this rejection and submits that claims 28-47, as amended, recite patent eligible subject matter in conformance with 35 U.S.C. § 101.
By way of example, independent claim 28 describes a specific computer-implemented AI authorization architecture that includes one or more processors to: (i) present, via a graphical user interface of a payroll service of one or more web services, an electronic image associated with an option to execute a transaction...; (ii) classify, based on the web services data and the history of transactions input into a machine learning model trained with historic web services data and transaction histories, the transaction pending at the web client as an allowed transaction; (iii) generate, by the machine learning model and based on the classification of the transaction as the allowed transaction, an authorization outcome for the request for authorization, the authorization outcome indicative that the request for authorization is approved and cause the payroll service to execute the transaction pending at the web client; and (iv) execute, via the web client and responsive to the authorization outcome, the transaction using the payroll service, in accordance with the electronic image.
These claim elements collectively recite a technical solution to a technical problem,
namely, controlling execution of network-based transactions in a payroll services environment using a machine-learning-driven authorization mechanism that programmatically governs
transaction approval and execution based on payroll-specific data and transaction histories, rather
than relying on static or rule-based authorization logic.
In other words, the claimed ML-based
authorization architecture improves computer transaction processing in a payroll services
environment using adaptive, data-driven classification, authorization, and execution. As such, the
claims integrate any alleged abstract idea into a practical application and improve the operation
of computer-based transaction authorization systems. See MPEP § 2106 and the 2019 PEG (and
October 2019 Update).
Here “AI” is not claimed. Machine learning is claimed but, the claims are at a high level of generality which merely describe activities of the machine learning. Almost an “apply it” type recitation. Applicant does not specific any improvement in the machine learning itself.
For instance, independent claim 28, as amended, recites:
A system, comprising:
one or more processors, coupled with memory, to:
present, via a graphical user interface of a payroll service of one or more
web services, an electronic image associated with an option to execute a transaction
pending at a web client;
receive, responsive to a selection of the electronic image via the graphical
user interface, a request for authorization to access data for the transaction for
execution via the payroll service;
determine, based on the request for authorization, a context for controlling
access to the one or more web services for executing the transaction;
access, based on the context for controlling access, from an electronic
account associated with the transaction, web services data and a history of
transactions of the electronic account;
classify, based on the web services data and the history of transactions input
into a machine learning model trained with historic web services data and transaction histories, the transaction pending at the web client as an allowed
transaction;
generate, by the machine learning model and based on the classification of
the transaction as the allowed transaction, an authorization outcome for the request for authorization, the authorization outcome indicative that the request for authorization is approved and cause the payroll service to execute the transaction
pending at the web client;
execute, via the web client and responsive to the authorization outcome, the
transaction using the payroll service, in accordance with the selection of the
electronic image;
record, responsive to the execution, the transaction into a ledger associated
with the electronic account; and
determine a payroll balance for the electronic account based on the
transaction recorded in the ledger.
Applicant respectfully submits that new claims 28-47, as amended herein: (1) do not
merely amount to an abstract idea without significantly more, and (2) should any such "abstract idea," be found present (which Applicant rejects), these amended claims would still integrate such an abstract idea into a practical application, rendering claims 28-47 patent-eligible under 35 U.S.C. § 101.
Here again, the claim has been presented but, there is no improvement in machine learning.
For instance, claim 28 recites a specific sequence of computer-implemented operations in which a machine-learning model processes payroll-specific web services data and transaction histories to classify a pending transaction, generate an authorization outcome based on that classification, and programmatically control whether the transaction is approved and executed by a payroll service. Here, the claimed authorization outcome is not merely advisory or informational, but is enforced at the web-service execution layer, such that the payroll service itself is prevented from executing the transaction absent a machine-learning-generated authorization outcome indicating approval. The claim further conditions execution of the transaction on the authorization outcome, followed by recording the transaction in a ledger and determining a payroll balance based on the recorded transaction.
Accordingly, claim 28 does not merely recite data analysis or an abstract business practice, but instead recites a distributed authorization and execution control architecture that alters how network-based payroll services operate by dynamically gating service-level execution based on learned transaction patterns and contextual access controls. As such, claim 28 integrates any alleged abstract idea into a practical
application and improves the operation of computer-based transaction authorization systems, rendering claims 28-47 patent-eligible under 35 U.S.C. § 101.
Accordingly, Applicant submits that the subject matter of the above-recited claim, individually or in combination with the remainder of elements of this claim, amounts to significantly more than an abstract idea - and the Office Action does not assert otherwise. Accordingly, reconsideration and withdrawal of this rejection is respectfully requested.
Here applicant is not persuasive because the machine learning is applied at a high level. There is no explicitly improvement in the machine learning as machine learning is known to audit and review records to find fraud (summarized). However, agreeably if the inventive concept or a technical improvement could be claimed, then the examiner would likely agree.
Claim Rejections Under 35 U.S.C. & 103
Claims 28-47 were rejected under 35 U.S.C. § 103 as allegedly unpatentable over U.S. Patent Publication No.: 2020/0005213 ("Clemens") in view of U.S. Patent No. 11,468,519 ("Parker"). (Office Action, page 6).
Independent claim 28 recites: (most of claim 28 cited now redacted)
Claims 44, 47 are argued similar to claim 28.
The Office Action cites to paragraph [0102] of Clemens for the previously presented subject matter of claim 28. Applicant respectfully disagrees.
[0102] Through ongoing and real-time assessment, (using continual API data pulls through currently used HR, Payroll and Risk Management
systems), our metric scoring solution curbs employer negligence through crime or fraud deterrence (as there is transparency to the worker on the status of the score), re-enforcement of policies (through the employer self- audit component which is updated through machine learning risk reduction recommendations), aggregation of information to assist in prevention (employer has a one-stop dashboard with filters to view a snap shot slice of
the organizations risk) and continual recommendations for improvement.
As discussed during the Interview in connection with the above-recited passage of Clemens, the combination of Clemens and Parker does not teach or suggest to execute the transaction using the payroll service, in accordance with the selection of the electronic image.
Instead, this paragraph of Clemens merely discusses that data "pulls through" the HR, Payroll and Risk Management system and other functions and policies, absent any discussion of executing a transaction, much less executing a transaction using a payroll service, or doing so in accordance with a user's selection of an electronic image or responsive to the authorization outcome.
Clemens is silent on both executing a transaction itself, or on the actor that executes the transaction, and thereby failing to teach or suggest the transaction being executed using the payroll service. Meanwhile, as Parker is directed to processing data after transaction information is received, Parker also does not execute any transactions using a payroll service, thus failing to compensate for these deficiencies of Clemens (see Parker col. 2 11. 5-45; col. 5 11. 1-15; col. 7 11. 35-45). Accordingly, the combination of Clemens and Parker does not teach or suggest to execute the transaction using the payroll service, in accordance with the selection of the electronic image - as recited in claim 28.
Further, the combination of Clemens and Parker does not teach or suggest to generate, by the machine learning model and based on the classification of the transaction
as the allowed transaction, an authorization outcome for the request for authorization, the authorization outcome indicative that the request for authorization is approved and cause the payroll service to execute the transaction pending at the web client - and the Office Action does not state otherwise. In addition, because Clemens and Parker fail to teach or suggest the above-recited elements of claim 28,
Here the examiner offers Saperstein. However it is noted that the applicant claims are based on a limiting example of the claims rather than the broadest reasonable interpretation. Also the combination of references is asserted by the examiner not the individual references. However the examiner on careful review understands applicant argument and has countered by adding a reference.
The dependent claims are argued by virtue of dependency.
Conclusion
The prior art made of record from IP.com and not relied upon is considered pertinent to applicant's disclosure.
Secured payment gateway for authorizing E-commerce websites and transactions using Machine Learning Algorithm, IEEE 2020
Evolution of Prepaid Payment Processor's Software Architecture: An Empirical Study, IEEE 2012
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE I EBERSMAN whose telephone number is (571)270-3442. The examiner can normally be reached 8:00 am - 5:00 pm Monday-Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael W Anderson can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRUCE I EBERSMAN/Primary Examiner, Art Unit 3693