DETAILED ACTION
Status of Application
This action is a Non-Final Rejection. This action is in response to the application filed on October 25, 2024.
Claims 1-20 are pending and rejected.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
Priority
This application is a continuation of application number 16/926,459, which was filed on July 10, 2020 and is now abandoned. The effective filing date of this application is July 10, 2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 6, 2024 has been considered by the examiner.
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 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03)
Yes, with respect to claims 1-7, which recite a system and, therefore, are directed to the statutory class of machine or manufacture.
Yes, with respect to claims 8-14, which recite a method and, therefore, are directed to the statutory class of process.
Yes, with respect to claims 15-20, which recite a non-transitory machine-readable medium and, therefore, are directed to the statutory class of manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a))
The following claims identify the limitations that recite the abstract idea in regular text and that recite additional elements in bold:
1. A system, comprising:
a non-transitory memory; and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to:
receive, from an application of a device, a transaction request for processing a transaction between a first account and a second account using a first service;
determine a plurality of parameters associated with the transaction;
determine, using a machine learning-based network and based on the plurality of parameters, a relationship between a first user of the first account and a second user of the second account, wherein the machine learning-based network is (i) trained using transaction data associated with historic transactions conducted between the first account and the second account and (ii) configured to derive the relationship between the first user of the first account and the second user of the second account based on the plurality of parameters and a transaction pattern derived from the transaction data, and wherein the relationship is one of a commercial relationship or a non-commercial relationship;
provide, via the application of the device, a user interface that facilitates a processing of the transaction, wherein the user interface indicates a use of the first service for the processing the transaction;
modify the user interface based on the determined relationship between the first user and the second user, wherein modifying the user interface comprises inserting, on the user interface, a user interface element representing an option for adding a second service for the processing the transaction;
process the transaction based on an input received via the modified user interface, wherein the transaction is processed, based on the input, using (i) the first service and not the second service or (ii) both the first service and the second service; and
further train the machine learning-based network using a parallel processing mechanism based on a result of processing the transaction.
2. The system of claim 1, wherein the plurality of parameters comprises one or more of a currency amount of the transaction, a funding source for the transaction, a transactional history associated with at least one of the first account or the second account, or account characteristics associated with at least one of the first account or the second account.
3. The system of claim 1, wherein the plurality of parameters comprises a narrative generated by the first user and describing the transaction, and wherein the machine learning-based network is further configured to determine the relationship between the first user and the second user further based on the narrative.
4. The system of claim 1, wherein the machine learning-based network comprises a plurality of neural networks, and wherein executing the instructions further causes the system to: select, from the plurality of neural networks, a particular neural network for determining a transaction category for the transaction; and determine, using the particular neural network, the transaction category for the transaction, wherein modifying the user interface is further based on the transaction category determined for the transaction.
5. The system of claim 1, wherein the machine learning-based network comprises a plurality of neural networks, and wherein executing the instructions further causes the system to: determine, using the plurality of neural networks, a transaction category for the transaction, wherein modifying the user interface is further based on the transaction category determined for the transaction.
6. The system of claim 1, wherein the machine learning-based network is further trained using training data associated with second historic transactions between the first account and a plurality of merchants.
7. The system of claim 1, wherein executing the instructions further causes the system to: determine that the second service is not selected based on the input received via the modified user interface, wherein the transaction is processed, based on the input, using the first service and not the second service.
8. A method comprising:
receiving, by a computer system and from an application of a device, a transaction request for processing a transaction between a first account and a second account;
obtaining, by the computer system, a plurality of parameters associated with the transaction;
determining, using a machine learning-based network and based on the plurality of parameters, a relationship between a first user of the first account and a second user of the second account, wherein the machine learning-based network is (i) trained using transaction data associated with historic transactions conducted between the first account and the second account and (ii) configured to derive the relationship between the first user of the first account and the second user of the second account based on the plurality of parameters and a transaction pattern derived from the transaction data, and wherein the relationship is one of a commercial relationship or a non-commercial relationship;
providing, via the application of the device, a user interface that facilitates a processing of the transaction, wherein the user interface indicates a use of a first service for the processing the transaction;
modifying, by the computer system, the user interface based on the determined relationship between the first user and the second user, wherein the modifying comprises inserting, on the user interface, a user interface element representing an option for adding a second service for the processing the transaction;
processing, by the computer system, the transaction based on an input received via the modified user interface, wherein the transaction is processed, based on the input, using at least the first service; and
further training, by the computer system, the machine learning-based network using a parallel processing mechanism based on data associated with the transaction and the input received via the user interface.
9. The method of claim 8, wherein the first service corresponds to a payment processing service for processing the transaction request as a peer-to-peer transaction, and wherein the second service corresponds to a payment protection service.
10. The method of claim 8, wherein the transaction request comprises a textual narrative generated by the first user and describing the transaction, and wherein the method further comprises: determining the plurality of parameters for the transaction based on the textual narrative.
11. The method of claim 8, further comprising: determining that the second service is selected based on the input received via the modified user interface, wherein the transaction is processed, based on the input, using both the first service and the second service.
12. The method of claim 8, wherein the machine learning-based network comprises a plurality of neural networks, and wherein method further comprises: selecting, from the plurality of neural networks, a particular neural network for determining a transaction category for the transaction; and determining, using the particular neural network, the transaction category for the transaction, wherein the modifying the user interface is further based on the transaction category determined for the transaction.
13. The method of claim 8, wherein the machine learning-based network comprises a plurality of neural networks, and wherein method further comprises: determining, using the plurality of neural networks, a transaction category for the transaction, wherein the modifying the user interface is further based on the transaction category determined for the transaction.
14. The method of claim 8, wherein the machine learning-based network is further trained using training data associated with second historic transactions between the first account and a plurality of merchants.
15. A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:
receiving, from an application of a device, a transaction request for processing a transaction;
determining a plurality of parameters associated with the transaction;
determining, using a machine learning-based network and based on the plurality of parameters, a relationship between a first user of a first account and a second user of a second account, wherein the machine learning-based network is (i) trained using transaction data associated with historic transactions conducted between the first account and the second account and (ii) configured to derive the relationship between the first user of the first account and the second user of the second account based on the plurality of parameters and a transaction pattern derived from the transaction data, and wherein the relationship is one of a commercial relationship or a non-commercial relationship;
providing, via the application of the device, a user interface that facilitates a processing of the transaction, wherein the user interface indicates a use of a first service for the processing the transaction;
modifying the user interface based on the determined relationship between the first user and the second user, wherein the modifying comprises inserting, on the user interface, a selectable element representing an option for adding a second service for the processing the transaction;
processing the transaction based on a selection received via the modified user interface, wherein the transaction is processed, based on the selection, using at least the first service; and
further training the machine learning-based network using a parallel processing mechanism based on a result of the processing the transaction.
16. The non-transitory machine-readable medium of claim 15, wherein the transaction request comprises a textual narrative generated by the first user and describing the transaction, and wherein the operations further comprise: determining the plurality of parameters for the transaction based on the textual narrative.
17. The non-transitory machine-readable medium of claim 15, wherein the operations further comprise: determining that the second service is selected based on the selection received via the modified user interface, wherein the transaction is processed, based on the selection, using both the first service and the second service.
18. The non-transitory machine-readable medium of claim 15, wherein the machine learning-based network comprises a plurality of neural networks, and wherein operations further comprise: selecting, from the plurality of neural networks, a particular neural network for determining a transaction category for the transaction; and determining, using the particular neural network, the transaction category for the transaction, wherein the modifying the user interface is further based on the transaction category determined for the transaction.
19. The non-transitory machine-readable medium of claim 15, wherein the machine learning-based network comprises a plurality of neural networks, and wherein operations further comprise: determining, using the plurality of neural networks, a transaction category for the transaction, wherein the modifying the user interface is further based on the transaction category determined for the transaction.
20. The non-transitory machine-readable medium of claim 15, wherein the machine learning-based network is further trained using training data associated with second historic transactions between the first account and a plurality of merchants.
Yes. But for the recited additional elements as shown above in bold, the remaining limitations of the claims recite certain methods of organizing human activity. The claims are directed to processing a transaction. This type of method of organizing human activity is a commercial interaction such as sales activities or behaviors and business relations. Thus, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d))
No. The claims as a whole merely use a computer as a tool to perform the abstract idea. The computing components (i.e., additional elements that are in bold above) are recited at a high level of generality and are merely invoked as a tool to implement the steps. For example, only a programmed general purpose computing device is needed to implement the claimed abstract idea of receiving a transaction request, determining parameters associated with the transaction, determine a relationship between two users, providing a first service for processing the transaction, providing an option for a second service for processing the transaction, and processing the transaction. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, there is no improvement to the functioning of a computer or technology. Therefore, the abstract idea is not integrated into a practical application.
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05)
No. As discussed with respect to Step 2A, Prong 2, the additional elements in the claims, both individually and in combination, amount to no more than tools to perform the abstract idea. Merely performing the abstract idea using a computer cannot provide an inventive concept. Therefore, the claims do not provide an inventive concept.
As such, the claims are not patent eligible.
Prior Art – 35 U.S.C. 102 / 103
The claims filed in this application are similar in scope to the claims that were pending in parent application number 16/926,459 at the time of abandonment. In the parent application, the last Office action prior to abandonment withdrew the rejection under 35 U.S.C. 103 in light of the claim amendments, which are included in the instant claims. An updated search of the prior art did not result in new prior art that necessitated a new rejection. Therefore, the claims are novel and nonobvious for the same reason as the claims in the parent application.
Relevant Prior Art
The following references are relevant to Applicant’s invention:
Al Anbari et al., U.S. Patent Application Publication No. 2018/0082299 A1. This reference teaches risk analysis for fraud detection for electronic transactions. A transaction type is determined and used to determine a transaction processing flow type. See paragraph 0068.
Nosrati et al., U.S. Patent Application Publication Number 2021/0326960 A1. This reference teaches An AI model that analyzes historical transactions and account balances to predict cash shortfalls so that services such as overdraft protection may be offered.
Hirasawa, U.S. Patent Application Publication Number 2019/0102771 A1. This reference teaches a payment system that prevents an unavailable payment method from being selected during a purchase.
Rodriguez et al., U.S. Patent Application Publication Number 2019/0205993 A1. This reference teaches the processing of transaction and payment data to categorize transaction data across different accounts and systems.
Kurani et al., U.S. Patent Number 9,916,577. This reference teaches a selectable list of user accounts for selection of one or more accounts as a payment source by a user. See column 14, lines 16-22.
Griffin et al., U.S. Patent Application Publication Number 2013/0226805 A1. This reference teaches protection for exceeding a credit threshold.
DeLoach, U.S. Patent Number 7,941,355 B1. This reference teaches universal payment protection that is provided upon the occurrence of a trigger event.
Wu, U.S. Patent Application Publication Number 2019/0295158 A1. This reference teaches transaction classification based on transaction time predictions. Specifically, this reference teaches that a machine learning model may be used to classify a user’s transactions as business or personal. See paragraph 0020.
Zhang et al., U.S. Patent Application Publication Number 2023/0142383 A1. This reference states “The calculations performed in tuning the coefficients in the neural network are naturally suited to parallel implementations. Specifically, many machine learning algorithms and software applications have been adapted to use parallel processing hardware within general-purpose graphics product manufacturing message processing devices. It is efficient in processing the calculations associated with training deep neural networks.” See paragraph 0048.
Balasubramanian, U.S. Patent Application Publication Number 2013/0211934 A1. This reference teaches alternative payment implementation for electronic retailers.
Keresman, III et al., U.S. Patent Application Publication Number 2009/0299878 A1. This reference teaches a universal payments dashboard that displays alternative payment button options that consumers can use for payment for a purchase.
Email Communications
Per MPEP 502.03, Applicant may authorize email communications by filing Form PTO/SB/439, available at https://www.uspto.gov/sites/default/files/documents/sb0439.pdf, via the USPTO patent electronic filing system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH H ROSEN whose telephone number is (571) 270-1850 and email address is elizabeth.rosen@uspto.gov. The examiner can normally be reached Monday - Friday, 10 AM ET - 7 PM ET.
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/ELIZABETH H ROSEN/Primary Examiner, 3693
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