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
The following Non-Final office action is in response to application filed on 12/20/2024.
Priority Date: CON of (#18/609,691)(03/19/2024)>Prov[claimed with amended specification]>(05/02/2023)
Claim Status:
Canceled claims: 1-31
Pending [new]claims : 32-67
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 32-67[new] are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
In particular, claims are directed to a judicial exception (Abstract idea) without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, (Step-1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. (Step-2A) If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, (Step-2B) it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
Examples of abstract ideas grouping include: (a) Mental processes; (b) Certain methods of organizing human activities [ i. Fundamental Economic Practice; ii. Commercial or Legal Interaction; iii. Managing Personal behavior or Relations between People]; and (c) Mathematical relationships/formulas. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. (2014).
Analysis is based on the 2019 Revised Patent Eligibility Guidance (2019 PEG)-(see MPEP § 2106.04(II) and 2106.04(d). )-[see MPEP § 2106.04(II), and § 2106.04(d) & MPEP § 2106.05(a),(b),(c),(e )…].
[Step-1] The claims are directed to a method/system/machine, which are a statutory category of invention.
Claim 32 (exemplary) recites a series of steps for Fraud -Risk identification with online Financial Transactions.
[Step-2A]-Prong 1:The claim 32 is then analyzed to determine whether it is directed to a judicial exception:
The claim 32 recites the limitations of:
collecting event data in an event hub with a plurality of microservices;
interpreting the event data from the plurality of microservices with a plurality of topic listeners by:
identifying one or more risk signals based on the event data; and creating or updating one or more composite risk signals based on the event data and the one or more risk signals by:
analyzing the event data using one or more internal logic rules to determine if the one or more composite risk signals need to be triggered or updated;
storing the event data for enrichment of at least one of a party, an account, a device, or an entity;
forming a plurality of discrete source signals based on the event data; and aggregating the plurality of discrete source signals to create or update the one or more composite risk signals; and
generating one or more additional risk signals by: providing the one or more risk signals and the one or more composite risk signals to a machine learning model as input data;
training the machine learning model with training data, wherein the training data includes the event data, one or more risk factors, and one or more series of event data or risk factors;
finding patterns in the training data; and generating the one or more additional risk signals based on the patterns in the training data and the input data and using the machine learning model;
transmitting the event data, the one or more risk signals, the one or more composite risk signals, and the one or more additional risk signals to a fraud application, wherein the fraud application is configured to make a judgement of the event data using one or more internalized logic rules by:
assigning a fraudulent transaction probability score, using a decision engine, wherein the decision engine assigns the fraudulent transaction probability score based on inputs including: the event data; the one or more risk signals; the one or more composite risk signals; and the one or more additional risk signals;
generating a detection event based on the fraudulent transaction probability score; and generating a transaction alert, indicating that a fraudulent activity has occurred, based on the detection event.
The claimed method/system/machine simply describes series of steps for Fraud -Risk identification with online Financial Transactions.
These limitations, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitations via human commercial or business or transactional activities/interactions, but for the recitation of generic computer components. That is, other than reciting one or more servers/processors, devices and computer network nothing in the claim precludes the limitations from practically being performed by organizing human business activity. For example, without the structure elements language, the claim encompasses the activities that can be performed manually between the users and a third party. These limitations are directed to an abstract idea because they are business interaction/sale activity that falls within the enumerated group of “certain methods of organizing human activity” in the 2019 PEG.
[Step-2A]-Prong 2:
Next, the claim is analyzed to determine if it is integrated into a practical application. The claim recites additional limitation of using one or more servers/processors, devices and computer network to perform the steps. The processor in the steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data. This generic processor limitation is no more than mere instructions to apply the exception using generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
[Step-2B]
Next, the claim is analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept).
As discussed above, the recitation of the claimed limitations amounts to mere instructions to implement the abstract idea on a processor (using the processor as a tool to implement the abstract idea). Taking the additional elements individually and in combination, the processor at each step of the process performs purely generic computer functions. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. The same analysis applies here, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at or provide an inventive concept.
Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea itself. Therefore, the claim does not amount to significantly more than the recited abstract idea, and the claim is not patent eligible.
The analysis above applies to all statutory categories of invention including independent claims 32, 44, and 56.
Furthermore, the dependent claims 33-43, 45-55 and 57-67 do not resolve the issues raised in the independent claims.
The dependent claims 33-43, 45-55 and 57-67 are directed towards:
Using, wherein if a single microservice of the plurality of microservices fails, a remaining set of microservices in the plurality of microservices continues to operate; wherein the event data includes one or more business events received from an event source; wherein the event data further includes: additional party transaction data that was published by at least one additional party; and one or more previously generated composite risk signals; wherein the event data is collected in the event hub by configuring one or more upstream systems to publish the event data directly to the event hub; wherein the one or more composite risk signals include at least one of: a recent call, a recent login, a recent device enrollment, a recent demographic change, a recent email risk elevation, a recent device risk elevation, a recent confirmed fraud, a recent beneficiary change, a recent high value transaction, a presence on an internal hotfile, or a presence on a national shared database: and wherein each microservice of the plurality of microservices processes the event data for an individual event.
These limitations are also part of the abstract idea identified in claim 32, and are similarly rejected under same rationale.
Accordingly, the dependent claims 33-43, 45-55 and 57-67 are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis.
The instant claims are rejected under 35 USC 101 in view of The Decision in Alice Corporation Ply. Ltd. v. CLS Bank International, et al. in a unanimous decision, the Supreme Court held that the patent claims in Alice Corporation Pty. Ltd. v. CLS Bank International, et al. ("Alice Corp. ") are not patent-eligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of AIA 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 32-67 are rejected under 35 U.S.C. 103 as being unpatentable over McKenna et al (US 2019/0236695-A1) in view of Abadi et al (US 2023/0098204-A1).
Claims 1–31 (Cancelled).
Ref claims 32 (New), McKenna discloses a computer-implemented method comprising:
collecting event data in an event hub with a plurality of microservices; interpreting the event data from the plurality of microservices with a plurality of topic listeners (para [0034], Fig. 1; via a distributed system for fraud detection …Ex. 100, a distributed system…a first borrower user device 110, a second dealer user device 112, a third lender user device 116, and a fraud detection computer system 120… ) by:
identifying one or more risk signals based on the event data; and creating or updating one or more composite risk signals based on the event data and the one or more risk signals by: analyzing the event data using one or more internal logic rules to determine if the one or more composite risk signals need to be triggered or updated (para [0026]; via The system may implement multiple machine learning [ML] models…may apply the application data to a trained, first ML model…[0027]; via … The output from the second ML model may indicate signals of fraud and/or predict the type of fraud…from the fist ML model…[0031]; via ..the processing may identify a signal of fraud/or predict a type of fraud…);
storing the event data for enrichment of at least one of a party, an account, a device, or an entity (para [0150]; via historical information may be received using consortium data lookup system 326…from one or more data stores [e.g., consortium data store 328] …by storing outputs from system 340/350 and data store 328…);
forming a plurality of discrete source signals based on the event data; and aggregating the plurality of discrete source signals to create or update the one or more composite risk signals; and generating one or more additional risk signals by: providing the one or more risk signals and the one or more composite risk signals to a machine learning model as input data (para [0026]; via The system may implement multiple machine learning [ML] models…may apply the application data to a trained, first ML model…[0027]; via … The output from the second ML model may indicate signals of fraud and/or predict the type of fraud…from the fist ML model…);
training the machine learning model with training data, wherein the training data includes the event data, one or more risk factors, and one or more series of event data or risk factors; finding patterns in the training data; and generating the one or more additional risk signals based on the patterns in the training data and the input data and using the machine learning model (para [0026]; via The system may implement multiple machine learning [ML] models…The system may correlate the application data to a training data set or …may apply the application data to a trained, first ML model…[0027]; via … The output from the second ML model may indicate signals of fraud and/or predict the type of fraud…from the fist ML model…);
transmitting the event data, the one or more risk signals, the one or more composite risk signals, and the one or more additional risk signals to a fraud application, wherein the fraud application is configured to make a judgement of the event data using one or more internalized logic rules by: assigning a fraudulent transaction probability score, using a decision engine, wherein the decision engine assigns the fraudulent transaction probability score based on inputs including: the event data; the one or more risk signals; the one or more composite risk signals; and the one or more additional risk signals (para [0229], Fig.12; via a method of computing a score…the fraud scoring engine 142…other actions…[0250]; via Transmitting data to other systems …to the client devices…);
[[generating a detection event based on the fraudulent transaction probability score; and generating a transaction alert, indicating that a fraudulent activity has occurred, based on the detection event.]]
McKenna does not explicitly disclose the step of: generating a detection event based on the fraudulent transaction probability score; and generating a transaction alert, indicating that a fraudulent activity has occurred, based on the detection event.
However, Abadi being in the same field of invention discloses the step of generating a detection event based on the fraudulent transaction probability score; and generating a transaction alert, indicating that a fraudulent activity has occurred, based on the detection event (para [0011]. Fig. 1; via a system 100 for distinguishing between risky and legitimate transaction…[0015]; System 100 corrects these deficiencies with retailer’s fraud detections system by analyzing transactions/calculating probabilities of actions within the transactions… [0032]; via the scaler value of risk outputted by the model 114 for a given transactions provided by fraud score manager 113 to fraud detection system 124….compared to a threshold by fraud detection system 124 and alerts are raised…and provided by fraud core manager exceeds that threshold.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the features mentioned by McKenna to include the disclosures as taught by Abadi to facilitate generating a transaction alert for fraudulent activity.
Ref claims 33-35 (New), McKenna discloses the method of claim 32, wherein if a single microservice of the plurality of microservices fails, a remaining set of microservices in the plurality of microservices continues to operate, wherein a majority of the event data is still collected by the remaining set of microservices; and wherein the single microservice may be replaced without replacing the remaining set of microservices (para [0038] The first borrower user device 110, second dealer user device 112, and third lender user device 116 may comprise one or more applications that may allow these devices to interact with other computers or devices on a network, including cloud-based software services. The application may be capable of handling requests from many users and posting various webpages. In some examples, the application may help receive and transmit application data or other information to various devices on the network…).
Ref claim 36 (New), McKenna discloses the method of claim 32, wherein the event data includes one or more business events received from an event source (para [0152]; via The borrower information…[053]; via The dealer information…[0154]; The lender information/ a number of loans …[0155]; via The third party information…).
Ref claim 37 (New), McKenna discloses the method of claim 36, wherein the event data further includes: additional party transaction data that was published by at least one additional party; and one or more previously generated composite risk signals. (para [0152]; via The borrower information…[053]; via The dealer information…[0154]; The lender information/ a number of loans …[0155]; via The third party information…).
Ref claim 38 (New), McKenna discloses the method of claim 37, wherein the event data further includes third party data published to the event hub by a third-party provider (para [0152]; via The borrower information…[053]; via The dealer information…[0154]; The lender information/ a number of loans …[0155]; via The third party information…).
Ref claim 39 (New), McKenna discloses the method of claim 32, wherein the event data is collected in the event hub by configuring one or more upstream systems to publish the event data directly to the event hub (para [0042], Fig.1; via the borrower user device 110 to provide application data for a variety of purposes…[0043]; via The application data with one or more segments…).
Ref claim 40 (New), McKenna discloses the method of claim 32, wherein the event data is collected in the event hub by implementing consumers that emit event data to the event hub by leveraging an existing repository in which the event data is already stored (para [0042], Fig.1; via the borrower user device 110 to provide application data for a variety of purposes……[0043]; via The application data with one or more segments…).
Ref claim 41 (New), McKenna discloses the method of claim 36, wherein the one or more business events include at least one of: a login to an online banking account, a login to a mobile banking app, a call to an automated interactive voice response system, a call to a customer care center, a demographic or account data change, an account lifecycle event, a device lifecycle event, a card lock status change, a new contribution to a hotfile, a new contribution to a shared database, or a new contribution from a consortium (para [0152]; via The borrower information…[053]; via The dealer information…[0154]; The lender information/ a number of loans …[0155]; via The third party information…).
Ref claim 42 (New), McKenna discloses the method of claim 32, wherein the one or more composite risk signals include at least one of:
a recent call, a recent login, a recent device enrollment, a recent demographic change, a recent email risk elevation, a recent device risk elevation, a recent confirmed fraud, a recent beneficiary change, a recent high value transaction, a presence on an internal hotfile, or a presence on a national shared database.
Ref claim 43 (New), McKenna discloses the method of claim 32 [new], wherein each microservice of the plurality of microservices processes the event data for an individual event (para [0042], Fig.1; via the borrower user device 110 to provide application data for a variety of purposes……[0043]; via The application data with one or more segments…).
Claim 44 [new] recites similar limitations to claim 32[new] and thus rejected using the same art and rationale in the rejection of claim 32[new] as set forth above.
Claims 45-55 [new] are rejected as per the reasons set forth in claims 33-43[new] respectively.
Claim 56[new] recites similar limitations to claim 32 [new] and thus rejected using the same art and rationale in the rejection of claim 32 [new] as set forth above.
Claims 57-67 [new] are rejected as per the reasons set forth in claims 33-43[new] respectively.
CONCLUSION
The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure.
BIALICK et al (US 2023/0169494 A1) discloses System and Method for Application of Smart Rules to Data Transactions.
Ammatanda et al (US 2024/0144275-A1) discloses Real-Time Fraud Detection using Machine Learning.
YAN et al (KR 2020/0145621 A) discloses Systems and Methods for Real-Time Processing of Data Streams.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HATEM M. ALI whose telephone number is (571) 270-3021, E-mail: Hatem.Ali@USPTO.Gov and FAX (571)270-4021. The examiner can normally be reached Monday-Friday from 8:00 AM to 6:00 PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ABHISHEK VYAS can be reached on (571) 270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HATEM M ALI/
Examiner, Art Unit 3691