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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/09/2026 has been entered.
DETAILED ACTION
The following Non-Final office action is in response to applicant’s Clams-Amendments/Remarks filed on 02/09/2026.
Priority Date: Prov.[05/19/2023]
Claim Status:
Claims amended: 1,10, 16, and 26
Pending claims : 1-31
Claim Rejections - 35 USC § 112(a):
(Withdrawn for new claims-amendments)
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 1-31 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).
[Step-1] The claims are directed to a method/system/machine, which are a statutory category of invention.
Claim 1 (exemplary) recites a series of steps for Continuous Event Monitoring, Identification of Risk Signals, and Acceleration of Fraud Risk Analysis.
[Step-2A]-Prong 1:The claim 1 is then analyzed to determine whether it is directed to a judicial exception:
The claim 1 recites the limitations of:
Obtaining, by the processor, transaction data from a transaction onramp, wherein the transaction data was published to the transaction onramp by a transaction source;
generating, by the processor, a combined standardized transaction data structure based on analysis of the transaction data by:
parsing the transaction data to extract details of the transaction data;
adding to the extracted details of the transaction data one or more supplemental data elements obtained from the transaction source to form a supplemental transaction data element; and
transforming the supplemental transaction data element into the combined standardized transaction data structure to comply with a standard-based data structure;
obtaining, by the processor, event data from an event hub, by a plurality of microservices, wherein each microservice from the plurality of microservices is configured to carry out a single task corresponding to a single event, wherein at least a first microservice and a second microservice of the plurality of microservices forms a first microservice stack and at least a third microservice and a fourth microservice of the plurality of microservices forms a second microservice stack, wherein the first microservice stack supports a first type of software and at least a second microservice of the plurality of microservices supports a second type of software, the event data including:
the transaction data, obtained from the transaction onramp;
one or more business events, which had been published to the event hub from an event source by an event emitter; and
one or more previously generated composite risk signals;
interpreting, by the processor, the event data obtained by the plurality of microservices with a plurality of topic listeners by:
creating or updating one or more composite risk signals based on an analysis of the event data 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;
generating, by the processor, one or more additional risk signals using machine learning based on the combined standardized transaction data structure, the one or more composite risk signals, and the event data;
obtaining, by the processor, one or more policy evaluation and execution results from a policy management module;
obtaining, by a processor, historic transaction data from a historical activities log;
generating, by the processor, a heuristic behavior profile using one or more heuristic analysis methods, based on the historical transaction data;
assigning, by the processor, a fraudulent transaction probability score, using a decision engine, wherein the decision engine assigns the fraudulent transaction probability score based on inputs comprising: the combined standardized transaction data; the one or more composite risk signals; the one or more additional risk signals; the one or more policy evaluation and execution results; and the heuristic behavior profile;
generating, by the processor, a detection event based on the fraudulent transaction probability score;
providing, by the processor, the detection event to the event hub as additional event data; and
generating, by the processor, 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 Continuous Event Monitoring, Identification of Risk Signals, and Acceleration of Fraud Risk Analysis.
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 Revised 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 16, and 26.
Furthermore, the dependent claims 2-15, 17-25 and 27-31 do not resolve the issues raised in the independent claims.
The dependent claims 2-15, 17-25 and 27-31 are directed towards:
Using claims (2-8, 17-19, 27-28), enriching the standard-based data structure with additional party event data; additional party transaction data, obtained from the transaction onramp, and one or more additional party business events, which had been published to the event hub from the at least one additional party; 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 event data is collected in the event hub by implementing consumers that emit the event data to the event hub by leveraging an existing repository in which the event data is already stored, and wherein the event data is collected in the event hub by a plurality of microservices; generating a case management message to detail the fraudulent activity, based on the detection event and the transaction alert, and further comprising: generating a regulatory filing message configured to report the fraudulent activity to a regulatory agency, based on the detection event and the case management message, and wherein the event data further comprises third party data published to the event hub by a third-party; and
in claims (9-15, 20-25, 29-31), a login to an online banking account, a login to a mobile banking app, a call to an automated interactive voice response system, wherein obtaining the one or more additional risk signals by machine learning further comprises: inputting the combined standardized transaction data structures the one or more composite risk signals, and the event data into the machine learning model, wherein the machine learning model is configured to: include a plurality of input nodes, each input node corresponding to one of the combined standardized transaction data structures, the one or more composite risk signals, and the event data; populate the plurality of input nodes with the combined standardized transaction data structures, the one or more composite risk signals, and the event data; and analyze the populated input nodes to produce the one or more additional risk signals; wherein the one or more composite risk signals comprise at least one of: a recent call, further comprising: continually monitoring at least one of: a login to online banking, wherein the demographic or account data change comprises at least one of: a change of address, wherein the demographic or account data change comprises at least one of: a change of address, a change of telephone number, a change of email address, an addition of a new authorized account contact, or a change in notification preferences, wherein the account lifecycle event comprises an application for a new account, a closure of an existing account, an addition of a new beneficiary, or an addition of a new external account, wherein the device lifecycle event comprises a new device registration, a mobile carrier change, a mobile carrier disconnect, a device subscriber identity module (SIM) change, a device unenrollment, or a presence of device malware, wherein the system is a single computer system, wherein the system is a distributed computer system comprising multiple computers, and wherein the system is a cloud computing system or a remote server, wherein the instructions further cause the system to: generate a case management message to detail the fraudulent activity, based on the detection event and the transaction alert, wherein the instructions further cause the system to: generate a regulatory filing message configured to report the fraudulent activity to a regulatory agency, based on the detection event and the case management message.
These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under same rationale.
Accordingly, the dependent claims 2-15, 17-25 and 27-31 are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis.
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 1-31 are rejected under 35 U.S.C. 103 as being unpatentable over McKenna et al (US 2019/0236695 A1), in views of: Modhumita et al (US 20220400058 A1), and Abadi et al (US 2023/0098204 A1).
Ref claim 1, McKenna discloses a computer-implemented method comprising:
Obtaining, by a processor, transaction data from a transaction onramp, wherein the transaction data was published to the transaction onramp by a transaction source (para [0035], FIG.1; a distributed system 100, for fraud detection, includes 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…[0058], the fraud detection computer system 120 comprise an interface engine 132/[API] to receive or transmit data …);
generating, by the processor, a combined standardized transaction data structure based on analysis of the transaction data by:
parsing the transaction data to extract details of the transaction data (para [0060]; System 120 comprise an application engine 136, to receive application data/object….[0061-63];…increased/Appl. score …fraudulent application data…);
adding to the extracted details of the transaction data one or more supplemental data elements obtained from the transaction source to form a supplemental transaction data element; and transforming the supplemental transaction data element into the combined standardized transaction data structure to comply with a standard-based data structure (para [0064]; the appl. engine 136…to apply or adjust a weight to historical appl. data …);
obtaining, by the processor, event data from an event hub, by a plurality of microservices, wherein each microservice from the plurality of microservices is configured to carry out a single task corresponding to a single event, [[ wherein at least a first microservice and a second microservice of the plurality of microservices forms a first microservice stack and at least a third microservice and a fourth microservice of the plurality of microservices forms a second microservice stack, wherein the first microservice stack supports a first type of software and at least a second microservice of the plurality of microservices supports a second type of software, ]] the event data including:
the transaction data, obtained from the transaction onramp; one or more business events, which had been published to the event hub from an event source by an event emitter; and one or more previously generated composite risk signals (para [0027]; ML model may indicate signals of fraud or predict the type of fraud…output from the first ML model….);
interpreting, by the processor, the event data obtained by the plurality of microservices with a plurality of topic listeners by: creating or updating one or more composite risk signals based on an analysis of the event data 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 (para [0034]. FIG. 1; via a distributed system 100 for fraud detection and user devices 110, 112, and 116 and a fraud detection computer system 120 …);
generating, by the processor, one or more additional risk signals using machine learning based on the combined standardized transaction data structure, the one or more composite risk signals, and the event data (para [0024]; a risk-based fraud identification and analysis system/apply application data/machine learning [ML] model [determines the score]…);
obtaining, by the processor, one or more policy evaluation and execution results from a policy management module; obtaining, by the processor, historic transaction data from a historical activities log; generating a heuristic behavior profile using one or more heuristic analysis methods, based on the historical transaction data (para [0067]; the fraud detection system 120 comprise a profiling module 138 to determine a profile of a borrower user or a dealer user…[0186]; via the scoring service 530…a phone number…may be listed/with prior fraud from a third-party data source…);
assigning, by the processor, a fraudulent transaction probability score, using a decision engine, wherein the decision engine assigns the fraudulent transaction probability score based on inputs comprising: the combined standardized transaction data; the one or more composite risk signals; the one or more additional risk signals; the one or more policy evaluation and execution results; and the heuristic behavior profile (para [0024];via a risk-based fraud identification and analysis system/apply application data/machine learning [ML] model [determines the score]…);
generating, by the processor, a detection event based on the fraudulent transaction probability score; providing, by the processor, the detection event to the event hub as additional event data (para [0034]. FIG. 1; via a distributed system 100 for fraud detection and user devices 110, 112, and 116 and a fraud detection computer system 120 …[0024]; a risk-based fraud identification and analysis system/apply application data/machine learning [ML] model [determines the score]…); and
[[generating, by the processor, a transaction alert, indicating that a fraudulent activity has occurred, based on the detection event.]]
McKenna does not explicitly disclose the step of: wherein at least a first microservice and a second microservice of the plurality of microservices forms a first microservice stack and at least a third microservice and a fourth microservice of the plurality of microservices forms a second microservice stack, wherein the first microservice stack supports a first type of software and at least a second microservice of the plurality of microservices supports a second type of software.
However, Modhumita being in the same field of invention discloses the step of: wherein at least a first microservice and a second microservice of the plurality of microservices forms a first microservice stack and at least a third microservice and a fourth microservice of the plurality of microservices forms a second microservice stack, wherein the first microservice stack supports a first type of software and at least a second microservice of the plurality of microservices supports a second type of software (para [0006]; a common software stack execute on each processor while core subsystem functionality is maintained in a microservice software stack…to manage the second device.).
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 Modhumita to facilitate forming microservice stack supporting software.
McKenna does not explicitly disclose the step of: generating, by the processor, 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, by the processor, a transaction alert, indicating that a fraudulent activity has occurred, based on the detection event.
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 to generate a transaction alert.
Ref claim 2, McKenna discloses the method of claim 1, wherein generating the combined standardized transaction data structure further comprises: enriching the standard-based data structure with additional party event data obtained from at least one additional party, wherein the additional party event data comprises: additional party transaction data, obtained from the transaction onramp, wherein the additional party transaction data was published to the transaction onramp by the at least one additional party; and one or more additional party business events, which had been published to the event hub from the at least one additional party (para [0151]; via information used to determine a feature include, borrower, dealer, lender, third party information…)..
Ref claims 3-5, McKenna discloses the method of claim 1, 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 event data is collected in the event hub by implementing consumers that emit the event data to the event hub by leveraging an existing repository in which the event data is already stored, and wherein the event data is collected in the event hub by a plurality of microservices (para [0256]; via Communication subsystem 1424 may receive and transmit data in various forms…in the form of structured/unstructured data feeds 1426, event streams 1428, event updates 1430, and the like. …[0257-258]…).
Ref claims 6-7, McKenna discloses the method of claim 1, further comprising: generating a case management message to detail the fraudulent activity, based on the detection event and the transaction alert, and further comprising: generating a regulatory filing message configured to report the fraudulent activity to a regulatory agency, based on the detection event and the case management message (para [0029]; via the system provide output to a user interface [implied massages] including one or more scores, or actions…to mitigate risk or identity fraud…data…[0035]; via system for fraud detection…devices transmit e-messages via a com. network).
Ref claim 8, McKenna discloses the method of claim 1, wherein the event data further comprises third party data published to the event hub by a third-party provider (para [0256]; via Communication subsystem 1424 may receive and transmit data in various forms…in the form of structured/unstructured data feeds 1426, event streams 1428, event updates 1430, and the like. …web feeds/or real-time updates from one or more third party information sources…).
Ref claim 9, McKenna discloses the method of claim 1, wherein the one or more business events comprise 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 shared database, or a new contribution from a consortium (para [0042], FIG.1; via the first borrower user device 110 provide application data …a request for a loan, lease of purchase of an item from a dealer….).
Ref claim 10, McKenna discloses the method of claim 1, wherein obtaining the one or more additional risk signals by machine learning further includes: inputting the combined standardized transaction data structures the one or more composite risk signals, and the event data into the machine learning model, wherein the machine learning model is configured to: include a plurality of input nodes, each input node corresponding to one of the combined standardized transaction data structures, the one or more composite risk signals, and the event data; populate the plurality of input nodes with the combined standardized transaction data structures, the one or more composite risk signals, and the event data; and analyze the populated input nodes to produce the one or more additional risk signals (para [0026-27]; via The system/ML models…the output from the second ML model indicate signal of fraud/or predict the type of fraud….).
Ref claim 11, McKenna discloses the method of claim 1, wherein the one or more composite risk signals comprise 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 (para [0026-27]; via The system/ML models…the output from the second ML model indicate signal of fraud/or predict the type of fraud….).
Ref claim 12, McKenna discloses the method of claim 1, further comprising: continually monitoring at least one of: a login to online banking, a login to mobile banking, a call to automated Interactive Voice Response (IVR) 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 third party change, a confirmed fraud, a new contribution to a shared database, a new contribution from a consortium, or a transaction lifecycle change (para [0042], FIG.1; via the first borrower user device 110 provide application data …a request for a loan, lease of purchase of an item from a dealer….).
Ref claim 13, McKenna discloses the method of claim 12, wherein the demographic or account data change comprises at least one of: a change of address, a change of telephone number, a change of email address, an addition of a new authorized account contact, or a change in notification preferences (para [0186]; via the scoring service 530…a phone number…may be listed/compared…an email address may be put/with prior fraud from a third-party data source….).
Ref claim 14, McKenna discloses the method of claim 12, wherein the account lifecycle event comprises an application for a new account, a closure of an existing account, an addition of a new beneficiary, or an addition of a new external account (para [0042], FIG.1; via the first borrower user device 110 provide application data …a request for a loan, lease of purchase of an item from a dealer….).
Ref claim 15, McKenna discloses the method of claim 12, wherein the device lifecycle event comprises a new device registration, a mobile carrier change, a mobile carrier disconnect, a device subscriber identity module (SIM) change, a device unenrollment, or a presence of device malware (para [0042], FIG.1; via the first borrower user device 110 provide application data …a request for a loan, lease of purchase of an item from a dealer….).
Claim 16 recites similar limitations to claim 1 and thus rejected using the same art and rationale in the rejection of claim 1 as set forth above.
Claims 17-19 are rejected as per the reasons set forth in claims 3-5 respectively.
Claim 20 is rejected as per the reasons set forth in claim 10.
Ref claims 21-23, McKenna discloses the system of claim 16, wherein the system is a single computer system, wherein the system is a distributed computer system comprising multiple computers, and wherein the system is a cloud computing system or a remote server (para [0035]; FIG. 1, illustrate a distributed system for fraud detection… in some examples, devices comprise a mixture of physical and cloud computing components…).
Ref claims 24-25, McKenna discloses the system of claim 16, wherein the instructions further cause the system to: generate a case management message to detail the fraudulent activity, based on the detection event and the transaction alert, wherein the instructions further cause the system to: generate a regulatory filing message configured to report the fraudulent activity to a regulatory agency, based on the detection event and the case management message (para [0029]; via the system provide output to a user interface [implied massages] including one or more scores, or actions…to mitigate risk or identity fraud…data…[0035]; via system for fraud detection…devices transmit e-messages via a com. network).
Claim 26 recites similar limitations to claim 1 and thus rejected using the same art and rationale in the rejection of claim 1 as set forth above.
Claims 27-28 are rejected as per the reasons set forth in claims 3-4 respectively.
Claim 29 is rejected as per the reasons set forth in claim 10.
Claims 30-31 are rejected as per the reasons set forth in claims 24-25 respectively.
Response to Arguments
Applicant's arguments filed on 02/09/2026 have been fully considered and they are deemed to be non-persuasive:
Response to Applicant’s arguments with respect to the 35 USC 103 rejection is addressed in the above rejection.
Applicant's arguments filed with respect to the 35 USC 101 rejection of the previous action have been fully considered but they are not persuasive.
Applicant argues further in substance that "The claims are Not Directed to an Abstract Idea and the claims Recite ‘Significantly More’ than abstract idea” and noted PEG-2019 [Step-2A-Prong One-Prong two-2B].
Applicant also cited analogy with the court cases such as, Finjan, Berkheimer memo.
In addition to 101 rejections Applicant noted about 103 rejections with applied prior arts.
In response:
Examiner respectfully disagrees. Updated claim analysis as a whole including amended features are provided above/again based on the latest Patent Eligibility Guidance [2019-PEG>Step 2A-Prong 1 & Prong 2-Step-2B].
Claims 1-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more. The rejection of the previous action was a direct result of the Supreme Court's decision in Alice Corp. Pty. Ltd v. CLS Bank I'ntl. 573 U.S. (2014); Under Alice.
APPLICANT’s REMARKS:
Status of the Claims:
“In the Final Office Action dated November 7, 2025, the Office:
i. rejected claims 1-31 under 35 U.S.C. § 112, first paragraph, as allegedly
failing to comply with the written description requirement;
ii. rejected claims 1-31 under 35 U.S.C. § 101 as allegedly directed to a non-
statutory subject matter; and
iii. rejected claims 1-31 under 35 U.S.C. § 103 as allegedly being unpatentable over U.S. Patent Application Pub. No. 2019/0236695 ("McKenna") in view of U.S. Patent Application Pub. No. 2023/0169494 ("Bialick") and U.S. Patent Application Pub. No. 2023/0098204 ("Abadi'). Claims 1-31 are currently pending in this application, of which claims 1, 16, and 26 are independent. By this response, Applicant amends claims 1, 10, 16, and 26. …;…;…;” (Noted)
II. Rejection of claims 1-31 under 35 U.S.C. § 112(a)
Claims 1-31 are rejected under 35 U.S.C. §112(a), as allegedly failing to comply with the written description requirement.
(Withdrawn).
III. Rejection of claims 1-31 under 35 U.S.C. § 101:
The Office rejected claims 1-31 under 35 U.S.C. § 101 as allegedly being
directed towards an abstract idea without significantly more. Final Office Action at 3. Applicant respectfully traverses the rejection, for at least the reasons that amended claims 1, 16, and 26 (i) do not recite an abstract idea, (ii) integrate the judicial exception into a practical application, and (iii) the claims, as a whole, recite "significantly more" than the alleged abstract idea.
(i) The Office Overlooks Several Claim Elements at Step 2A, Prong 1:
The Office failed to consider the pending claims as a whole, and instead characterized the claims based on 1-2 words at the beginning of the clam limitations. Office Action at 5. The Office then concluded that its own generalization of the claims can "practically be[] performed by organizing human business activity." Id. This is improper because, at step 2A, prong 1, the Office must look to "the 'focus' of the claims [and] their 'character as a whole"' to determine whether the claims are directed to a law of nature, natural phenomenon, or abstract idea. Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (citations omitted). "Examiners should ... be careful to distinguish claims that recite an exception (which require further eligibility-20-analysis) and claims that merely involve an exception (which are eligible and do not require further eligibility analysis)." MPEP § 2106.04(II)(1). …;… ;…; withdraw the rejection.
(ii) The Claim Recite Actionable Real-World Results and Demonstrate a Practical Application Under Step 2A, Prong 2:
The claims recite elements directed to how a computer performs operations of the alleged "mental process," and how the claimed embodiments integrate the pending claims into a practical application.
Under Step 2A, Prong 2, the key is whether the claims recite "largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves those results."…;…;…; In Finjan, Inc. v. Blue Coat Systems, Inc., by contrast, the Federal Circuit found claims directed to creation of a security profile not abstract because they resulted in the creation of a deliverable that administrators could use to "craft security policies with highly granular rules and to alter those security policies in response to evolving threats." 879 F.3d 1299, 1304 (Fed. Cir. 2018). …;…;The claims are also patent-eligible under step 2A, prong 2.
(iii) Non-Generic Software Elements and Steps Improve Computer Functionality at Step 2B:
To the extent the Office reaches the Step 2B inquiry despite the explanations above, the claims are patent eligible because they recite significantly more than a mere mental process. …;…;…;…;
Instead, the claims recite specific and unconventional steps and data structures like those the Federal Circuit has found to "provide[] benefits that improve computer functionality" and are thus eligible under step 2B. Berkheimer v. HP Inc., 881 F.3d 1360, 1370 (Fed. Cir. 2018).
IN RESPONSE to III [101 Rejection]: Examiner Disagree:
Under Alice-Step (2A)-Prong 1: A method for deriving financial information from transaction data accounts is akin to the abstract idea subject matter grouping of: (Certain Methods of Organizing Human Activity as ‘Fundamental economic practice to Managing personal behavior or relationships or interactions between people including, teaching, and following rules or instructions). As such, the claims include an abstract idea. The specific limitations of the invention are identified to encompass the abstract idea include:
(obtaining a transaction data…;generating a combined standardized transaction data structure…;parsing… adding…;transmitting the supplemental…;obtaining event data…hub, by a plurality of microservices,.. microservices forms a second microservices stack…; interpreting the event data…by: creating or updating…by:analyzing…;storing….;forming…;aggregating…;generating…;providing…;generating transaction alert,…detection event.)
As stated above, this abstract idea falls into the subject matter grouping (b) of: (Certain Methods of Organizing Human Activity as ‘Fundamental economic practice to Managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions’).
Under Alice-Step (2A)-Prong 2: When considered individually and in combination, the instant claims do not integrate the exception into a practical application because the steps of:
(obtaining a transaction data…;generating a combined standardized transaction data structure…;parsing…; adding…;transmitting the supplemental…;obtaining…event data…hub, by a plurality of …….microservices forms a second microservices stack,..; interpreting the event data…by: creating or updating…by:analyzing…;storing….;forming…;aggregating…;generating…;providing…;generating transaction alert,…detection event.)
do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception (i.e. the abstract idea).
The instant recited claims including additional elements (i.e. “obtaining…;generating,,,;parsing…;transmiting…;obtaining…microservices stack ;interpreting…;creating…;storing…;aggregating….;providing…;generating…detection event”)
do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The limitations merely use a generic computing technology (Specification [0006/61]: processor, memory, instructions, storage medium, and electrical communication) as tools to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception. (MPEP § 2106.05 (f) (g)). Therefore, the claims are directed to an abstract idea.
Under Alice-Step (2B): Additionally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (Claims: e.g., processor, machine learning, equations, instructions, memory, electrical communication, and storage medium) amount to no more than generally linking the use of the judicial exception to a particular technological environment or merely using generic components as tool to perform an abstract idea. In conclusion, merely “linking/applying” the exception using generic computer components does not constitute ‘significantly more’ than the abstract idea. (MPEP § 2106.05 (f) (h)). Therefore, the claims are not patent eligible under 35 USC 101.
In support of “Mental Process ”or “Method of organizing Human activity”: It is to be noted that “the claimed invention is similar to Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) (mental processes: “a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind ). MPEP 2106.04(a)(2), 2106.05(a)(g)(h)”
Moreover, Applicant further cites Berkheimer, presuming that Examiner took the position that the claims describe well-understood, routine and conventional activities.
Examiner respectfully disagrees. Examiner did not indicate that the claims describe well-understood, routine and conventional activities in the 101 rejection in this particular case. Rather, Examiner identified the focus of the invention, and determined that it is directed to an abstract idea and the use of a generic computer to implement the abstract idea.
Furthermore, Examiner relies on what the courts have recognized, or those of ordinary skill in the art would recognize, as elements that describe well-understood, routine, and conventional activity in particular fields. For example, receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (Emphasis added)).
In this case, the use of a computer processor is described at a high level of generality, or as an insignificant extra-solution activity that cannot be considered as an improvement to network/computer technology.
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299 (Fed. Cir. 2018), the claimed invention involves a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. The claims were held patent eligible because the court concluded that the claimed method recites specific steps that accomplish a result that realizes an improvement in computer functionality. In particular, the method generates a security profile that identifies both hostile and potentially hostile operations, and can protect the user against both previously unknown viruses and "obfuscated code." This is an improvement over traditional virus scanning, which only recognized the presence of previously-identified viruses. The method also enables more flexible virus filtering and greater user customization.
IV. Rejection of claims 1-31 under 35 U.S.C. § 103
“The Office rejected claims 1-31 under 35 U.S.C. § 103 as being unpatentable over McKenna in view of Bialick and Abadi. Final Office Action 9. Applicant respectfully traverses the rejection. …:…;…;”
IN RESPONSE to IV ( 103 Rejection)-Examiner Disagrees with Applicant’s assertions:
However, McKenna discloses all limitations [obviously] including amended elements, in views of: Modhumita [[Bialick]] and Abadi as stated above with 103 rejections.
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 Method for Application of Smart Rules to Data Transactions.
Albright et al (US 20200211021 A1) discloses System and Method for Early Detection of Network Fraud Events.
Anbukkarasu et al (US 20190026716 A1) discloses System and Method for Providing Services to Smart Devices connected in an IOT Platform.
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.
Examiner interviews are available via telephone, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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
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