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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 August 10, 2026, has been entered.
Status of Claims
Applicant filed an amendment on August 10, 2026. Claims 1-27 were pending in the Application. Claims 1, 13, and 21 are amended. No new claims have been added. No new claims have been canceled, with claims 8, 10, 14, 19, and 22-23 remaining canceled. Claims 1, 13, and 21 are the independent claims, the remaining claims depend on claims 1, 13, and 21. Thus claims 1-7, 9, 11-13, 15-18, 20-21, and 24-27 are currently pending. After careful and full consideration of Applicant arguments and amendments, the Examiner finds them to be moot and/or not persuasive.
Response to Arguments
In the context of 35 U.S.C. §101, Applicant respectfully traverses the rejection. Applicant is of the opinion that the claims are statutory and respectfully asserts the following:
The claims recite additional elements that integrate any alleged judicial exception into a practical application (Step 2A, Prong Two).
As amended, claim 1 recites that the computing system automatically initiates a vulnerability scan on an account, and/or prevents a transaction from committing to a database, in direct response to a risk score generated by the unsupervised machine learning process exceeding a threshold - without further input from an administrator or other human intervention. These are not generic outputs of a business process; they are technical security and database-management operations performed on the computing system itself. Neither operation is a step in "assessing commercial risk" - both are actions taken on the computing system in response to that assessment, which is a materially different thing.
Applying the Ex Parte Desjardins guidance here, the claimed clustering and centroid calculations are not the end of the claimed invention - they are part of an unsupervised machine learning process that automatically and without human intervention triggers a specific technical security response (vulnerability scanning) and a specific technical database operation (commit-blocking). This is a materially different situation from a claim that merely computes a number and displays it, or that computes a number and uses it to make a business decision such as approving a loan or setting a price.
The claimed operations are performed on the computing system and database themselves and change how those systems behave in response to the machine learning process's output. The database performance is improved by more efficiently and effectively preventing and mitigating attacks.
This integrates any alleged judicial exception into a practical application.
The claims recite significantly more than any alleged abstract idea (Step 2B).
Even under Step 2B, the ordered combination of limitations recites significantly more than any alleged abstract idea. The absence of this combination from the cited art of record is itself evidence that the claimed combination of elements is not well-understood, routine, or conventional activity previously known in the industry.
The claims recite this technical response occurring automatically and without administrator input, closing the loop between an unsupervised model's detection of anomalous cross-cluster behavior and a concrete, immediate technical remediation.
This ordered combination - adapting to patterns in inter and cross-cluster deviations to automatically trigger a specific security or database operation - is not merely the abstract idea of risk assessment implemented on a generic computer; it is a specific technical architecture for automated threat response that finds no counterpart in the cited art.
RecogniCorp does not control.
The additional claim elements are not themselves abstract concepts. The claimed operations of automatically initiating a vulnerability scan and preventing a database transaction from committing are concrete technical operations automatically executed to improve the performance of a computing system (i.e., the database system).
Unlike the claim in RecogniCorp, the claim does not merely combine one abstract concept with another; it recites a particular technological operation that imposes a specific constraint on how the computing system and database operate.
RecogniCorp does not establish that the recited mathematical concept renders the claim abstract where, as here, the remaining claim limitations are directed to a concrete technological operation rather than another abstract idea.
The claims are not directed to organizing human activity.
The Office Action characterizes the claims as directed to commercial or legal interactions because the underlying data relates to customer transactions, however, this characterization does not resolve the eligibility inquiry, because the claims are not directed to the commercial significance of that data - they are directed to specific technical operations performed by a computing system on that data, and specific technical actions automatically triggered by the output of an unsupervised machine learning process, including preventing a database transaction from committing and initiating a vulnerability scan.
These are technical operations that improve the performance of a computing system (a database system), not steps in organizing a commercial or legal interaction, regardless of the subject matter of the underlying transaction data.
Dependent claims 2-7, 9, and 11-12 further limit independent claim 1 and are patent eligible for at least the reasons set forth above with respect to claim 1, as well as for the additional limitations recited therein. Claims 13 and 21 recite limitations substantively parallel to those of claim 1, including the automatic vulnerability-scan and database-transaction-blocking limitations, and are patent eligible for the same reasons. Dependent claims 15-18, 20, and 24-27 are patent eligible for at least the reasons set forth above with respect to claims 13 and 21, respectively, as well as for the additional limitations recited therein.
The claims are not directed to an abstract idea and recite significantly more than any abstract idea, as the claims recite specific technical operations performed by a computing system and a database in automatic response to the output of an unsupervised machine learning process.
Applicant respectfully requests reconsideration and withdrawal of the rejection.
Initially, the Examiner would like to point out that the basis of the rejection is Alice, by applying the subject matter eligibility analysis and flowchart according to MPEP § 2106, which applies a two-step framework, earlier set out in Mayo Collaborative Services v. Prometheus Laboratories, Inc., 566 U.S. 66 (2012), "for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts." Alice, 573 U.S. at 217.
Under the two-step framework, it must first be determined if "the claims at issue are directed to a patent-ineligible concept." If the claims are determined to be directed to a patent-ineligible concept, e.g., an abstract idea, then the second step of the framework is applied to determine if "the elements of the claim ... contain an "inventive concept" sufficient to 'transform' the claimed abstract idea into a patent-eligible application." (citing Mayo, 566 U.S. at 72-73, 79).
With regard to step one of the Alice framework, we apply a "directed to" two-prong test: 1) evaluate whether the claim recites a judicial exception, and 2) if the claim recites a judicial exception, evaluate whether the claim "applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception," i.e., whether the claim integrates the judicial exception into a practical application. (MPEP §2106.04 II.A.1. and II.B.2.).
The Specification, (PG Pub US 20250094989 A1, para 1), provides evidence as to what the claimed invention is directed. In this case, the specification, (‘989 A1, para 1), discloses that the invention relates to transaction monitoring providing cross cluster transaction risk assessment, and is grouped under “Certain Methods of Organizing Human Activity, commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)” and grouped under “Certain Methods of Organizing Human Activity, fundamental economic principles or practices (including hedging, insurance, mitigating risk)”, in prong one of step 2A. (MPEP §2106.04 II.A.1.).
Claim 1 provides additional evidence, and recites the limitations “obtaining, by an unsupervised machine learning process executing on a computing system, customer transaction data comprising a plurality of transaction details; clustering, by the unsupervised machine learning process executing on the computing system, the customer transaction data into clusters of transactions based on the transaction details; calculating, by the unsupervised machine learning process executing on the computing system, a centroid for each cluster of transactions, the centroid representing a transaction corresponding to a mean value within the corresponding cluster; clustering, by the unsupervised machine learning process executing on the computing system, transactions across multiple customers within a posting period to determine a centroid for each of the customers; calculating, by the unsupervised machine learning process executing on the computing system, a risk score for each transaction, wherein the unsupervised machine learning process calculates the risk score for the transaction as a function of (a) a relationship score determined based on a distance between the transaction and a centroid of the cluster of transactions to which the transaction is assigned and (b) cross-cluster deviations between other transactions and the centroid for other clusters to which the other transactions are assigned, wherein the unsupervised machine learning process adapts to evolving patterns in both intra-cluster and cross-cluster deviations such that the unsupervised machine learning process flags at least one transaction that is not anomalous in an isolated context of an account; filtering, by the computing system, a list of accounts based on the risk score for each transaction; and selectively enabling, by the computing system, a security measure on an account-by-account basis based on the output of the unsupervised machine learning process, wherein the computing system locks the account and blocks the at least one transaction that was flagged by the unsupervised machine learning process, wherein the computing system automatically initiates a vulnerability scan on the account for which the at least one transaction that was flagged by the unsupervised machine learning process was not anomalous in the isolated context of the account”, which represent the abstract idea of “transaction risk assessment”. The abstract idea is in italics, and the additional elements are in bold. (MPEP §2106.04 II.A.1.).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (MPEP §2106.04 II.A.2.), the additional elements of the claim, such as “an unsupervised machine learning process executing on a computing system”, “the unsupervised machine learning process adapts to evolving patterns in both intra-cluster and cross-cluster deviations such that the unsupervised machine learning process flags at least one transaction that is not anomalous in an isolated context of an account”, and “the unsupervised machine learning process, wherein the computing system locks the account and blocks the at least one transaction that was flagged by the unsupervised machine learning process, wherein the computing system automatically initiates a vulnerability scan on the account for which the at least one transaction that was flagged by the unsupervised machine learning process was not anomalous in the isolated context of the account”, amount to merely “apply it”, as they represent the use of a computer as a tool to perform an abstract idea. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to implementing the acts of “transaction risk assessment”.
Examiner notes the basis of the rejection was, and is not as any mental process covering performance in the mind, but classified as an abstract idea, “transaction risk assessment”, grouped under “Certain Methods of Organizing Human Activity, commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)” and grouped under “Certain Methods of Organizing Human Activity, fundamental economic principles or practices (including hedging, insurance, mitigating risk).
With respect to the additional elements operating in a non-conventional and non-generic way and reflecting an improvement to a particular technological environment, the cited additional elements represent the use of a computer as a tool to perform the abstract idea. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to implementing the acts of “transaction risk assessment”. The claim is not directed to improving computer functionality nor improving another technology or technical field but improving the method for “transaction risk assessment”. For potential improvement in an abstract idea, “transaction risk assessment”, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a transaction risk assessment concept) is not an improvement in technology. (MPEP § 2106.04(d)(1)). Therefore, claim 1 is non-statutory.
Claim 13 also recites the abstract idea of “transaction risk assessment “, as well as the additional elements of “a system comprising: at least one device including one or more hardware processors; an unsupervised machine learning process; one or more non-transitory computer-readable media storing instructions that, when executed by the one or more hardware processors, cause operations comprising: …”, “the unsupervised machine learning process adapts to evolving patterns in both intra-cluster and cross-cluster deviations such that the unsupervised machine learning process flags at least one transaction that is not anomalous in an isolated context of an account”, and “the unsupervised machine learning process, wherein the computing system locks the account and blocks the at least one transaction that was flagged by the unsupervised machine learning process, wherein the computing system automatically initiates a vulnerability scan on the account for which the at least one transaction that was flagged by the unsupervised machine learning process was not anomalous in the isolated context of the account”, which amount to merely “apply it”, as they represent the use of a computer as a tool to perform an abstract idea. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to implementing the acts of “transaction risk assessment”.
When analyzed under step 2B (MPEP 2106.05 I.A.), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claim merely describes the concept of “transaction risk assessment” using computer technology (e.g., “at least one device including one or more hardware processors” and “an unsupervised machine learning process”). Therefore, these additional elements do no more than employ a computer as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve computer functionality or improve another technology or technical field. Therefore, claim 13 is non-statutory.
Claim 21 also recites the abstract idea of “transaction risk assessment “, as well as the additional elements of “a non-transitory computer-readable medium storing instructions that, when executed by one or more hardware processors, cause performance of operations comprising: …”, “the unsupervised machine learning process executing on the computing system”, “the unsupervised machine learning process adapts to evolving patterns in both intra-cluster and cross-cluster deviations such that the unsupervised machine learning process flags at least one transaction that is not anomalous in an isolated context of an account”, and “the unsupervised machine learning process, wherein the computing system locks the account and blocks the at least one transaction based on at least one risk score for at least one associated transaction exceeding a threshold that was flagged by the unsupervised machine learning process, wherein the computing system automatically initiates a vulnerability scan on the account for which the at least one transaction that was flagged by the unsupervised machine learning process was not anomalous in the isolated context of the account”, which amount to merely “apply it”, as they represent the use of a computer as a tool to perform an abstract idea. Therefore, these additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to implementing the acts of “transaction risk assessment”.
When analyzed under step 2B (MPEP 2106.05 I.A.), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claim merely describes the concept of “transaction risk assessment” using computer technology (e.g., “a non-transitory computer-readable medium storing instructions” and “an unsupervised machine learning process”). Therefore, these additional elements do no more than employ a computer as a tool to implement the abstract idea. And as the computer does no more than serve as a tool to implement the abstract idea, they do not improve computer functionality nor improve another technology or technical field. Therefore, claim 21 is non-statutory.
Finally, Examiner notes the basis of the rejection is Alice, by applying the subject matter eligibility analysis and flowchart according to MPEP § 2106. And, based on this standard, the claims are non-statutory, and correctly rejected under 35 U.S.C. § 101.
In the context of 35 U.S.C. § 112(b), Antecedent Basis, in the Final Rejection Office Action dated May 11, 2026, paragraph 22, Applicant has adequately amended to overcome the rejection under 35 U.S.C. § 112(b), Antecedent Basis. Applicant has amended the claim 13 claim language “filtering, by the computing system” to recite “filtering, by the system” to eliminate the Antecedent Basis issue. Examiner hereby rescinds the rejection under 35 U.S.C. § 112(b), Antecedent Basis, of the Final Rejection Office Action dated May 11, 2026, paragraph 22, for claim 13, as well as for the dependent claims 15-18 and 20, which depend from claim 13.
In the context of 35 U.S.C. § 112(b), Unclear Scope, in the Final Rejection Office Action dated May 11, 2026, paragraph 23, Applicant has not adequately amended to overcome the rejection under 35 U.S.C. § 112(b), Unclear Scope. It is still not clear whether the one or more hardware processors or the system or some combination thereof are performing the limitations “filtering” and “selectively enabling”. MPEP § 2173.02 I recites “For example, if the language of a claim, given its broadest reasonable interpretation, is such that a person of ordinary skill in the relevant art would read it with more than one reasonable interpretation, then a rejection under 35 U.S.C. § 112(b) or pre-AIA 35 U.S.C. § 112, second paragraph is appropriate.” Therefore, the scope of claim 13 is unclear, and remains rejected under 35 U.S.C. § 112(b), Unclear Scope. Dependent claims 15-18 and 20, which depend from claim 13, are also similarly rejected under 35 U.S.C. § 112(b), Unclear Scope. The rejection under 35 U.S.C. § 112(b), Unclear Scope, is maintained. (MPEP § 2173.02 I and In re Zletz, 893 F.2d 319,321 (Fed. Cir. 1989)).
In the context of 35 U.S.C. § 112(b), Unclear Scope, in the Final Rejection Office Action dated May 11, 2026, paragraph 24, Applicant has adequately amended to overcome the rejection under 35 U.S.C. § 112(b), Unclear Scope. Applicant has amended the claim 21 claim language “by an unsupervised machine learning process executing on a computing system” to recite “via an unsupervised machine learning process executing on a computing system” to make it clear that the limitations of claim 21 are being performed by the one or more hardware processors. Examiner hereby rescinds the rejection under 35 U.S.C. § 112(b), Unclear Scope, of the Final Rejection Office Action dated May 11, 2026, paragraph 24, for claim 21, and dependent claims 24-27, which depend from claim 21.
In the context of 35 U.S.C. § 103, after further consideration and search, no prior art was found that teaches, suggests, and/or discloses these limitations and/or some combination thereof:
"calculating, by the unsupervised machine learning process executing on the computing system, a risk score for each transaction, wherein the unsupervised machine learning process calculates the risk score for the transaction as a function of (a) a relationship score determined based on a distance between the transaction and a centroid of the cluster of transactions to which the transaction is assigned and (b) cross-cluster deviations between other transactions and the centroid for other clusters to which the other transactions are assigned, wherein the unsupervised machine learning process adapts to evolving patterns in both intra-cluster and cross-cluster deviations such that the unsupervised machine learning process flags at least one transaction that is not anomalous in an isolated context of an account";
" filtering, by the computing system, a list of accounts based on the risk score for each transaction ";
“selectively enabling, by the computing system, a security measure on an account-by-account basis based on the output of the unsupervised machine learning process, wherein the computing system locks the account and blocks the at least one transaction that was flagged by the unsupervised machine learning process, wherein the computing system automatically initiates a vulnerability scan on the account for which the at least one transaction that was flagged by the unsupervised machine learning process was not anomalous in the isolated context of the account”.
Therefore, the rejection under 35 U.S.C. § 103 being rescinded is maintained.
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 1-7, 9, 11-13, 15-18, 20-21, and 24-27 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
In the instant case, claims 1-7, 9, and 11-12 are directed to “a method”, claims 13, 15-18, and 20 are directed to “a system”, and claims 21 and 24-27 are directed to “a non-transitory computer-readable medium”. Therefore, these claims are directed to one of the four statutory categories of invention.
Claim 1 recites “transaction risk assessment”, which is a form of commercial or legal interactions and a form of fundamental economic principles or practices (i.e., organizing human activity), and therefore, is an abstract idea. Specifically, the claim recites “obtaining, by an unsupervised machine learning process executing on a computing system, customer transaction data comprising a plurality of transaction details; clustering, by the unsupervised machine learning process executing on the computing system, the customer transaction data into clusters of transactions based on the transaction details; calculating, by the unsupervised machine learning process executing on the computing system, a centroid for each cluster of transactions, the centroid representing a transaction corresponding to a mean value within the corresponding cluster; clustering, by the unsupervised machine learning process executing on the computing system, transactions across multiple customers within a posting period to determine a centroid for each of the customers; calculating, by the unsupervised machine learning process executing on the computing system, a risk score for each transaction, wherein the unsupervised machine learning process calculates the risk score for the transaction as a function of (a) a relationship score determined based on a distance between the transaction and a centroid of the cluster of transactions to which the transaction is assigned and (b) cross-cluster deviations between other transactions and the centroid for other clusters to which the other transactions are assigned, wherein the unsupervised machine learning process adapts to evolving patterns in both intra-cluster and cross-cluster deviations such that the unsupervised machine learning process flags at least one transaction that is not anomalous in an isolated context of an account; filtering, by the computing system, a list of accounts based on the risk score for each transaction; and selectively enabling, by the computing system, a security measure on an account-by-account basis based on the output of the unsupervised machine learning process, wherein the computing system locks the account and blocks the at least one transaction that was flagged by the unsupervised machine learning process, wherein the computing system automatically initiates a vulnerability scan on the account for which the at least one transaction that was flagged by the unsupervised machine learning process was not anomalous in the isolated context of the account”. The abstract idea is in italics, and the additional elements are in bold. (MPEP §2106.04 II.A.1.).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (MPEP §2106.04 II.A.2.), the additional elements of the claim, such as “an unsupervised machine learning process executing on a computing system”, “the unsupervised machine learning process adapts to evolving patterns in both intra-cluster and cross-cluster deviations such that the unsupervised machine learning process flags at least one transaction that is not anomalous in an isolated context of an account”, and “the unsupervised machine learning process, wherein the computing system locks the account and blocks the at least one transaction that was flagged by the unsupervised machine learning process, wherein the computing system automatically initiates a vulnerability scan on the account for which the at least one transaction that was flagged by the unsupervised machine learning process was not anomalous in the isolated context of the account”, amount to merely “apply it”, as they represent the use of a computer as a tool to perform an abstract idea. Therefore, these additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to implementing the acts of “transaction risk assessment”.
When analyzed under step 2B (MPEP 2106.05 I.A.), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claims merely describes the concept of “transaction risk assessment” using computer technology (e.g., “a computing system” and “an unsupervised machine learning process”). Therefore, these additional elements do no more than employ a computer as a tool to implement the abstract idea. And as the computer does no more than serve as a tool to implement the abstract idea, they do not improve computer functionality nor improve another technology or technical field. Therefore, claim 1 is non-statutory.
Claim 13 also recites the abstract idea of “transaction risk assessment “, as well as the additional elements of “a system comprising: at least one device including one or more hardware processors; an unsupervised machine learning process; one or more non-transitory computer-readable media storing instructions that, when executed by the one or more hardware processors, cause operations comprising: …”, “the unsupervised machine learning process adapts to evolving patterns in both intra-cluster and cross-cluster deviations such that the unsupervised machine learning process flags at least one transaction that is not anomalous in an isolated context of an account”, and “the unsupervised machine learning process, wherein the computing system locks the account and blocks the at least one transaction that was flagged by the unsupervised machine learning process, wherein the computing system automatically initiates a vulnerability scan on the account for which the at least one transaction that was flagged by the unsupervised machine learning process was not anomalous in the isolated context of the account”, which amount to merely “apply it”, as they represent the use of a computer as a tool to perform an abstract idea. Therefore, these additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to implementing the acts of “transaction risk assessment”.
When analyzed under step 2B (MPEP 2106.05 I.A.), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claim merely describes the concept of “transaction risk assessment” using computer technology (e.g., “at least one device including one or more hardware processors” and “an unsupervised machine learning process”). Therefore, these additional elements do no more than employ a computer as a tool to implement the abstract idea. And as the computer does no more than serve as a tool to implement the abstract idea, they do not improve computer functionality nor improve another technology or technical field. Therefore, claim 13 is non-statutory.
Claim 21 also recites the abstract idea of “transaction risk assessment “, as well as the additional elements of “a non-transitory computer-readable medium storing instructions that, when executed by one or more hardware processors, cause performance of operations comprising: …”, “the unsupervised machine learning process executing on the computing system”, “the unsupervised machine learning process adapts to evolving patterns in both intra-cluster and cross-cluster deviations such that the unsupervised machine learning process flags at least one transaction that is not anomalous in an isolated context of an account”, and “the unsupervised machine learning process, wherein the computing system locks the account and blocks the at least one transaction based on at least one risk score for at least one associated transaction exceeding a threshold that was flagged by the unsupervised machine learning process, wherein the computing system automatically initiates a vulnerability scan on the account for which the at least one transaction that was flagged by the unsupervised machine learning process was not anomalous in the isolated context of the account”, which amount to merely “apply it”, as they represent the use of a computer as a tool to perform an abstract idea. Therefore, these additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to implementing the acts of “transaction risk assessment”.
When analyzed under step 2B (MPEP 2106.05 I.A.), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claim merely describes the concept of “transaction risk assessment” using computer technology (e.g., “a non-transitory computer-readable medium storing instructions” and “an unsupervised machine learning process”). Therefore, these additional elements do no more than employ a computer as a tool to implement the abstract idea. And as the computer does no more than serve as a tool to implement the abstract idea, they do not improve computer functionality nor improve another technology or technical field. Therefore, claim 21 is non-statutory.
Dependent claims 2-7, 9, 11-12, 15-18, 20, and 24-27 further describe the abstract idea of “transaction risk assessment”, which is insufficient to overcome the rejections of claims 1, 13, and 21, above.
Dependent claims 2-3, 6-7, 9, 11-12, 15-18, 20, and 24-27 do not recite any new additional elements that integrate the abstract idea into a practical application, and that do no more than represent a computer performing functions that correspond to implementing the acts of “transaction risk assessment”, when analyzed under Step 2A, Prong Two. And, as they do no more than employ a computer as a tool to implement the abstract idea, they do not improve computer functionality nor improve another technology or a technical field, when analyzed under Step 2B.
Dependent claim 4 recites a new additional element of “a logarithmic transformation”, which does no more than employ a computer as a tool to implement the abstract idea. And, as it does no more than employ a computer as a tool to implement the abstract idea, it does not improve computer functionality or improve another technology or technical field.
Dependent claim 5 recites a new additional element of “a standardized scaling technique”, which does no more than employ a computer as a tool to implement the abstract idea. And, as it does no more than employ a computer as a tool to implement the abstract idea, it does not improve computer functionality or improve another technology or technical field.
Hence, claims 1-7, 9, 11-13, 15-18, 20-21, and 24-27 are not patent eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. § 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13, 15-18, and 20-25 are rejected under 35 U.S.C. § 112(b) or 35 U.S.C. § 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Unclear Scope
Claim 13 recites “A system comprising: at least one device including one or more hardware processors; an unsupervised machine learning process; one or more non-transitory computer-readable media storing instructions that, when executed by the one or more hardware processors, cause operations comprising: obtaining, by an unsupervised machine learning process executing on the system, …; clustering, by the unsupervised machine learning process executing on the system, …; calculating, by the unsupervised machine learning process executing on the system, …; clustering, by the unsupervised machine learning process executing on the system, …; calculating, by the unsupervised machine learning process on the system, …; filtering, by the system, a list of accounts …; selectively enabling, by the system, a security measure …” It is not clear whether the one or more hardware processors or the unsupervised machine learning process executing on the system or the system or some combination thereof, are performing the limitations of claim 13.
MPEP § 2173.02 I recites “For example, if the language of a claim, given its broadest reasonable interpretation, is such that a person of ordinary skill in the relevant art would read it with more than one reasonable interpretation, then a rejection under 35 U.S.C. § 112(b) or pre-AIA 35 U.S.C. § 112, second paragraph is appropriate.” Therefore, the scope of claim 13 is unclear. Dependent claims 15-18 and 20, which depend from claim 13, are also similarly rejected. (MPEP § 2173.02 I and In re Zletz, 893 F.2d 319,321 (Fed. Cir. 1989)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Nick et al (U. S. Patent Application Publication No. 20190114639 A1) – Anomaly Detection In Data Transactions
Nick discloses computing systems and methods for detecting anomalies in a distribution of one or more attributes associated with data transactions. In the embodiments, data transactions are accessed that each include various attributes. The data transactions are grouped into a first subset associated with a first sub-type of a first attribute and a second subset including any remaining sub-types of the first attribute. Second attributes in the first and second subsets are compared to determine differences in the proportion of the second attributes between the first and second subsets, where the differences are indicative of an anomaly in an expected distribution of the second attributes. Based at least on a determination that there are differences in the proportion, subsequently accessed data transactions that are associated with attributes similar to the data transactions of the first subset are rejected or subjected to a further review process.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN R CHISM whose telephone number is (571)272-5915. The examiner can normally be reached on Monday-Friday 9:00 AM – 4:00 PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan D Donlon can be reached at (571) 270-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEVEN R CHISM/ Examiner, Art Unit 3692
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695
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