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
Status of Claims
Claims 21-38, 41 and 42 are pending. Claims 21, 29, and 37 are amended. Claim 38 is canceled and claim 43 is new. Claims 21-37, and 41-43 are pending.
Response to Arguments
Applicant’s arguments regarding the 101 rejection have been considered but are not persuasive.
Applicant argues:
1 – The claims are similar to those of example 47, claim 3 which address network security issues.
The Office asserts that the present claims are not analogous to improving network security but to detecting fraudulent transactions. Fraudulent transactions pose no risk to the network in which they are processed but only to the payor of the transaction. The network security is not affected.
2 – The claims recite technical improvements in monitoring repeating data and use AI in a way that cannot practically be performed in the human mind. The system is able to identify inconsistent data patterns and block transactions from being processed.
The Office asserts that humans are also able to identify inconsistent data patterns and block transactions from being processed. Training a machine learning model to do the same is a matter of adding the words “apply it”, or the like to the abstract idea.
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 21-37, and 41-43 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s):
21. A computing system comprising at least one processor in communication with at least one memory, wherein the at least one processor is configured to:
train a machine learning model to identify discrepancies in repeating data
plans associated with a particular merchant by;
training the machine learning model in a first stage using a first training set comprising historical transaction data associated with a plurality of merchants, wherein the historical transaction data includes historical repeating data elements comprising historical repeating amounts, historical repeating frequencies, and historical lengths of time; and
training the machine learning model in a second stage using a second training set comprising historical transaction data associated with the particular merchant, wherein the historical transaction data associated with the particular merchant includes second historical repeating data elements comprising second historical repeating amounts, second historical repeating frequencies, and
second historical lengths of time;
store the trained machine learning model in the at least one memory;
receive a first instance of data from a payment interchange computer network, wherein the first instance of data is associated with a user account and the particular merchant, and includes first repeating data elements defining a first repeating data plan;
receive a second instance of data from the payment interchange computer network, wherein the second instance of data is associated with the user account and the particular merchant, and includes second repeating data elements;
input at least part of the second instance of data to the trained machine learning model based at least in part upon the trained machine learning model being trained using the historical transaction data being associated with the merchant and the first instance of data and the second instance of data being associated with the merchant;
receive an output from the trained machine learning model that identifies an inconsistency in at least the part of the second instance of data;
automatically cause a transaction associated with the second instance of data to be blocked in response to the output from the trained machine learning model identifying the inconsistency.
The underlined portions of the claims recite certain methods of organizing human activity, fundamental economic principles or practices of mitigating risk of making payments on incorrect billing data.
This judicial exception is not integrated into a practical application because the process may be practiced by hand or by a human with pen and paper and is only augmented by generic devices, adding the words “apply it”, via the computing system and the machine learning model, the payment network and memory. Training, storing and updating the machine learning model is an act of “applying” a computer to implement an abstract idea. Per MPEP 2106.05(f)(2), “Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).”
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim elements are either the abstract idea or the computer implementation of the abstract idea.
Claims 29 and 37 are similar and are similarly rejected.
The dependent claims merely narrow the abstract idea. For example, claim 22 recites the use of user preference data used to train the machine learning model. Per the specification, the preference data may apply to thresholds used to determine the inconsistency. The preference data merely narrows the abstract idea of determining mitigating risk. Claim 24 is similar but uses issuer data. Claims 2-28 relate to notifications received by the user regarding the analysis, again narrowing the abstract idea. As a whole and in combination the claims comprise the abstract idea with mere instructions to apply the abstract idea and are not significantly more or an integration into a practical application as the claims generally recite comparing data for inconsistencies and taking appropriate action based on the comparison. The computer is used in its ordinary capacity and for the sake of efficiency.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to William E. Rankins whose telephone number is 571-270-3465. The examiner can normally be reached on M-F 7:30 AM - 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bennett Sigmond can be reached on 303-297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WILLIAM E RANKINS/Primary Examiner, Art Unit 3694
Prosecution Insights