DETAILED ACTION
This Final Office Action is in response to the application filed on 01/11/2024 and the Amendment & Remark filed on 03/02/2026.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 2-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc).
While the Applicant specifies in claims 2, 9 and 17 that “… each of the plurality of models trained using training transaction data of purchase histories of multiple customers to generate an output of a vote indicating whether a transaction is fraudulent or non-fraudulent based on an input of transaction data of a current transaction; wherein the plurality of models is trained using at least fuzzy logic, wherein the fuzzy logic is configured to manipulate a subset of the training transaction data to have a different location or different transaction value than an actual location or amount of the transaction data”, there is no written content as to how or what specific fuzzy logic are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to train a plurality of models to generate output indicating whether transaction is fraudulent. The examiner noted that the Specification 0077 and 0078 discloses that a fuzzy logic “may make small changes to locations of purchases … “ and “may modify one value of one percent of the transaction …”. However, no written content can be found regarding what specific logic is used or how the nominal function is achieved by the fuzzy logic described only by the nominal function. There is also no logic connection between the manipulation of training transaction data and how the models with specific functions are trained. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
The written description requirement can be satisfied if the particular steps, i.e., algorithm, necessary to perform the claimed function were “described in the specification.” In re Hayes Microcomputer Prods, Inc. Patent Litigation, 982 F.2d 1527, 1533-34, 25 USPQ2d 1241, (Fed. Cir. 1992).
As such, claims 2-21 are rejected as failing the written description requirement.
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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As an initial matter, the claims as a whole are to an apparatus, a process and a manufacture, which falls within one or more statutory categories. (Step 1: YES) The recitation of the claimed invention is then further analyzed as follow, in which the abstract elements are boldfaced.
The claims recite:
An apparatus, comprising at least one processor; and
a memory coupled to the at least one processor,
the memory comprising instructions that, when executed by the at least one processor, cause the at least one processor to:
receive a plurality of votes generated by a plurality of models, each of the plurality of models being executed one of a plurality of mobile devices in communication with the apparatus, each of the plurality of mobile devices being associated with a customer of a plurality of customers associated with an entity that provides fraud detection services, each of the plurality of models trained using training transaction data of purchase histories of multiple customers to generate an output of a vote indicating whether a transaction is fraudulent or non-fraudulent based on an input of transaction data of a current transaction, wherein the plurality of models is trained using at least fuzzy logic, wherein the fuzzy logic is configured to manipulate the training transaction data to have a different location or different transaction value than an actual location or amount of the transaction data;
determine whether the current transaction is fraudulent or non-fraudulent based on a fraudulent portion of the plurality of votes indicating that the transaction is fraudulent and a non-fraudulent portion of the plurality of votes indicating that the transaction is non-fraudulent; and
send a request comprising the transaction data to a mobile device associated with the customer and the transaction, the request requesting that the customer confirm whether the transaction is fraudulent or not fraudulent.
the instructions, when executed by the at least one processor, to cause the at least one processor to: receive approval from the customer to participate in the fraud detection services, determine a customer identifier for the customer, and assign the customer identifier to one of the plurality of models for association with the customer; wherein the apparatus further comprises obscuring logic configured to hash or encrypt the transaction data before it is sent to the plurality of models.
the instructions, when executed by the at least one processor, to cause the at least one processor to transmit an instance of the one of the plurality of models to a mobile device associated with the customer; and wherein the request to the mobile device includes a set of digital links for the customer to select, a first of the set of digital links associated with the transaction being fraudulent and a second of the set of the digital links associated with the transaction being non-fraudulent.
the instructions, when executed by the at least one processor, to cause the at least one processor to assign each customer of the plurality of customers to one of a plurality of clusters based on at least one characteristic of the customer, wherein the apparatus further comprises random logic configured to form random sets of transactions from the multiple customers, wherein the training transaction data comprises the random sets of transactions.
wherein each of the plurality of clusters is associated with a differently trained version of the plurality of models.
wherein the differently trained versions of the plurality of models are trained based on transaction data of customers belonging to each of the plurality of clusters.
the instructions, when executed by the at least one processor, to cause the at least one processor to: determine a current cluster of the plurality of clusters of a current customer of the current transaction; and transmit transaction data of the current transaction only to the plurality of models associated with customers belonging to the current cluster;
A computer-implemented method, comprising, via at least one processor of a computing device:
receiving a plurality of votes for a transaction from a plurality of fraud detection models, each of the plurality of fraud detection models assigned to one of a plurality of customers, wherein: the plurality of fraud detection models are trained to generate a vote indicating whether a transaction is fraudulent or non-fraudulent based on an input of transaction data of a current transaction, wherein the plurality of models is trained using at least fuzzy logic, wherein the fuzzy logic is configured to manipulate the training transaction data to have a different location or different transaction value than an actual location or amount of the transaction data and at least a portion of the plurality of fraud detection models are trained differently based on at least one characteristic of the plurality of customers assigned to the portion of the plurality of fraud detection models; and
determining whether the transaction is fraudulent or non-fraudulent based on the plurality of votes generated by the plurality of fraud detection models;
sending a request comprising the transaction data to a mobile device associated with the customer and the transaction, the request requesting that the customer confirm whether the transaction is fraudulent or not fraudulent.
wherein the plurality of fraud detection models are trained using training transaction data of purchase histories of multiple customers of the plurality of customers; wherein the apparatus further comprises obscuring logic configured to hash or encrypt the transaction data before it is sent to the plurality of models.
wherein each of the plurality of fraud detection models comprises an instance of a model transferred to mobile computing devices of the plurality of customers; and wherein the request to the mobile device includes a set of digital links for the customer to select, a first of the set of digital links associated with the transaction being fraudulent and a second of the set of the digital links associated with the transaction being non-fraudulent.
wherein the plurality of models are maintained at the computing device for access via mobile computing devices of the plurality of customers; wherein the computing device further comprises random logic, and the computer-implemented method further includes: forming random sets of transactions from the multiple customers and using the random sets of transactions as the training transaction data.
for each customer of the plurality of customers: receiving approval from the customer to participate in fraud detection services, determining a customer identifier for the customer; and assigning the customer identifier to one of the plurality of models for association with the customer.
assigning each customer of the plurality of customers to one of a plurality of clusters based on the at least one characteristic of the customer.
wherein each of the plurality of clusters is associated with a differently trained version of the plurality of fraud detection models.
wherein the differently trained versions of the plurality of models are trained based on transaction data of customers belonging to each of the plurality of clusters.
A non-transitory computer-readable medium storing instructions configured to cause one or more processors of at least one computing device to:
transmit transaction data of a transaction to a plurality of fraud detection models, each of the plurality of fraud detection models assigned to one of a plurality of customers, wherein the plurality of fraud detection models are trained to identify fraudulent transactions, wherein the plurality of models is trained using at least fuzzy logic, wherein the fuzzy logic is configured to manipulate the training transaction data to have a different location or different transaction value than an actual location or amount of the transaction data; and
determine whether the transaction is fraudulent or non-fraudulent based on a plurality of votes generated by the plurality of fraud detection models, wherein each of the plurality of fraud detection models comprises an instance of a model transferred to mobile computing devices of the plurality of customers;
send a request comprising the transaction data to a mobile device associated with the customer and the transaction, the request requesting that the customer confirm whether the transaction is fraudulent or not fraudulent.
the instructions configured to cause the one or more processors of the at least one computing device to, for each customer of the plurality of customers: receive approval from the customer to participate in fraud detection services, determine a customer identifier for the customer; and assign the customer identifier to one of the plurality of models for association with the customer; wherein the apparatus further comprises obscuring logic configured to hash or encrypt the transaction data before it is sent to the plurality of models.
the instructions configured to cause the one or more processors of the at least one computing device to assign each customer of the plurality of customers to one of a plurality of clusters based on at least one characteristic of the customer; wherein the at least one computing device further comprises random logic configured to: form random sets of transactions from the multiple customers; and use the random sets of transactions as the training transaction data.
wherein each of the plurality of clusters is associated with a differently trained version of the plurality of fraud detection models; and wherein the request to the mobile device includes a set of digital links for the customer to select, a first of the set of digital links associated with the transaction being fraudulent and a second of the set of the digital links associated with the transaction being non-fraudulent.
wherein the differently trained versions of the plurality of models are trained based on transaction data of customers belonging to each of the plurality of clusters.
The ordered combination of the recited limitations is a process that, under its broadest reasonable interpretation, covers fraud detection from collected data but for the recitation of generic computer components. That is, other than reciting generic computing language such as “An apparatus, comprising at least one processor; and a memory coupled to the at least one processor, the memory comprising instructions that, when executed by the at least one processor, cause the at least one processor to:”, “associated with one of a plurality of mobile devices”, “a computer-implemented method, comprising, via at least one processor of a computing device:”, “a non-transitory computer-readable medium storing instructions configured to cause one or more processors of at least one computing device to:”, “to a mobile device”, “a set of digital links … to select” nothing in the claim elements that precludes the steps from that of a commercial interaction of detecting transaction fraud using collected data.
For example, the limitation “receive a plurality of votes generated by a plurality of models, each of the plurality of models associated with one of a plurality of mobile devices, each of the plurality of mobile devices corresponding to a customer of a plurality of customers associated with an entity that provides fraud detection services, each of the plurality of models trained using training transaction data of purchase histories of multiple customers to generate an output of a vote indicating whether a transaction is fraudulent or non-fraudulent based on an input of transaction data of a current transaction, wherein the plurality of models is trained using at least fuzzy logic, wherein the fuzzy logic is configured to manipulate the training transaction data to have a different location or different transaction value than an actual location or amount of the transaction data” encompasses no more than invoking one or more generic computing element to perform the Judicial Exception step of receiving the plurality of votes generated by the plurality of models and manipulating the training transaction data;
the limitation “determine whether the current transaction is fraudulent or non-fraudulent based on a fraudulent portion of the plurality of votes indicating that the transaction is fraudulent and a non-fraudulent portion of the plurality of votes indicating that the transaction is non-fraudulent” encompasses no more than invoking one or more generic computing element to perform the Judicial Exception step of determining whether the current transaction is fraudulent based on the plurality of votes;
the limitation “send a request comprising the transaction data to a mobile device associated with the customer and the transaction, the request requesting that the customer confirm whether the transaction is fraudulent or not fraudulent” encompasses no more than invoking one or more generic computing element to perform the Judicial Exception step of sending a request to the customer to confirm whether the transaction is fraudulent or not fraudulent;
the limitation “the instructions, when executed by the at least one processor, to cause the at least one processor to: receive approval from the customer to participate in the fraud detection services, determine a customer identifier for the customer, and assign the customer identifier to one of the plurality of models for association with the customer; wherein the apparatus further comprises obscuring logic configured to hash or encrypt the transaction data before it is sent to the plurality of models” encompasses no more than invoking one or more generic computing element to perform the Judicial Exception step of receiving the approval from the customer and hashing or encrypting the transaction;
the limitation “the instructions, when executed by the at least one processor, to cause the at least one processor to transmit an instance of the one of the plurality of models to a mobile device associated with the customer” encompasses no more than invoking one or more generic computing element to perform the Judicial Exception step of sending the instance of the one of the plurality of model to the customer;
the limitation “wherein the request to the mobile device includes a set of digital links for the customer to select, a first of the set of digital links associated with the transaction being fraudulent and a second of the set of the digital links associated with the transaction being non-fraudulent” encompasses no more than invoking one or more generic computing element to perform the Judicial Exception step of requesting a customer to select whether the transaction is fraudulent or non-fraudulent;
the limitation “the instructions, when executed by the at least one processor, to cause the at least one processor to assign each customer of the plurality of customers to one of a plurality of clusters based on at least one characteristic of the customer, wherein the apparatus further comprises random logic configured to form random sets of transactions from the multiple customers, wherein the training transaction data comprises the random sets of transactions” encompasses no more than invoking one or more generic computing element to perform the Judicial Exception step of assigning each customer to one or the plurality of clusters and forming random sets of transaction from multiple customers;
the limitation “wherein each of the plurality of clusters is associated with a differently trained version of the plurality of models; wherein the differently trained versions of the plurality of models are trained based on transaction data of customers belonging to each of the plurality of clusters” encompasses no more than invoking one or more generic computing element to perform the Judicial Exception step of training the plurality of models based on the cluster;
the limitation “the instructions, when executed by the at least one processor, to cause the at least one processor to: determine a current cluster of the plurality of clusters of a current customer of the current transaction; and transmit transaction data of the current transaction only to the plurality of models associated with customers belonging to the current cluster” encompasses no more than invoking one or more generic computing element to perform the Judicial Exception step of determining a current cluster of the a current customer and send transaction data only to the customers belonging to the current cluster;
the limitation “receiving a plurality of votes for a transaction from a plurality of fraud detection models, each of the plurality of fraud detection models assigned to one of a plurality of customers, wherein: the plurality of fraud detection models are trained to generate a vote indicating whether a transaction is fraudulent or non-fraudulent based on an input of transaction data of a current transaction, and at least a portion of the plurality of fraud detection models are trained differently based on at least one characteristic of the plurality of customers assigned to the portion of the plurality of fraud detection models” encompasses no more than invoking one or more generic computing element to perform the Judicial Exception step of receiving a plurality of votes for a transaction from a plurality of fraud detection models;
the limitation “transmit transaction data of a transaction to a plurality of fraud detection models, each of the plurality of fraud detection models assigned to one of a plurality of customers, wherein the plurality of fraud detection models are trained to identify fraudulent transactions” encompasses no more than invoking one or more generic computing element to perform the Judicial Exception step of transmitting transaction data of a transaction to a plurality of fraud detection models;
the limitation “determine whether the transaction is fraudulent or non-fraudulent based on a plurality of votes generated by the plurality of fraud detection models, wherein each of the plurality of fraud detection models comprises an instance of a model transferred to mobile computing devices of the plurality of customers” encompasses no more than invoking one or more generic computing element to perform the Judicial Exception step of determining whether the transaction is fraudulent or non-fraudulent based on a plurality of votes generated by the plurality of fraud detection models;
If a claim, under its broadest reasonable interpretation, covers a commercial interaction of detecting transaction fraud but for the recitation of certain generic computing components, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. As such, the claim recites an abstract idea. (Step 2A prong one: Yes)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional element of processor as a mere tool to perform the … steps of the Judicial Exception. The processor in the above steps is recited at a high-level of generality, without technological detail of how the particular steps are performed technologically, such that it amounts no more than mere instruction to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claims are directed to an abstract idea.
The claims, when considered both individually and as an ordered combination, 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 element of using a computer processor to detect fraud amounts to no more than mere instructions to apply the exception using generic computer component. Mere instruction to apply an exception using a generic computer cannot provide an inventive concept. Such additional elements are determined to not contain an inventive concept according to MPEP 2106.05(f). It should be noted that (1) the “recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not provide significantly more because this type of recitation is equivalent to the words “apply it””, and (2) “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more”. (Step 2A prong two: No)
Additional elements that require no more than a generic computer to perform generic computer functions includes receiving and transmitting data from/to a plurality of mobile computing device (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec), determining a result of a vote. (Performing repetitive calculations, Flook) and digital links associated with selectable option. (A Web browser’s back and forward button functionality, Internet Patent Corp. v. Active Network, Inc.) These generic computer functions are factually determined to be well-understood, routine and conventional activities previously known to the industry as referenced by MPEP 2106.05(d) II according the USPTO Memorandum on Changes in Examination Procedure Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility Decision (Berkheimer v. HP, Inc.) dated April 19 2018. The recited ordered combination of additional elements includes a generically recited processor performing steps of the Judicial Exception. No additional element currently recited in the claims amount the claims to be significantly more than the cited abstract idea. (Step 2B: No)
Therefore, claims 2-21 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 03/02/2026 have been fully considered but they are not persuasive.
Regarding the applicant argument that the written description provides an adequate disclosure, the examiner respectfully disagrees. As noted in the rejection, the claims recite generic algorithm to perform specific functions, such as 1) “each of the plurality of models trained using training transaction data of purchase histories of multiple customers to generate an output of a vote indicating whether a transaction is fraudulent or non-fraudulent based on an input of transaction data of a current transaction, wherein the plurality of models is trained using at least fuzzy logic …”. It should be noted there is no standard fuzzy logic commonly understood by one skilled in art to perform the desired function of training a plurality of models “to generate an output of a vote indicating whether a transaction is fraudulent or non-fraudulent based on an input of transaction data of a current transaction”. Without disclosing how specific the models with desired function are trained, the examiner does not find such description “reasonably conveys to those skilled in the art that the inventor had possession of the fuzzy logic” that would train a plurality of model to perform the desired fraud detection function. The applicant further asserted “those having ordinary skill in the art of machine learning and neural network understand how to train a model using historic data”. However, general skill in the art does not substitute for actual written description defining the invention in functional language specifying a desired result – training a plurality of fraud detection models. As such, the argument is not persuasive.
Regarding the applicant’s argument that claims do not recite a Judicial Exception, the examiner respectfully disagrees. Conducting a vote to determine whether a transaction is fraudulent involves at least a voting process between multiple individuals, so it is an interaction between humans. Since the voting is used to determine whether a transaction is fraudulent, the nature of the voting activity is commercial. As such, the claims clearly recites a Judicial Exception.
Regarding the applicant’s argument that claims integrate the Judicial Exception into practical application, the examiner respectfully disagrees. The applicant contended that the claims “improve the robustness of the fraud detection model and increase security by reducing the ability to identify customers based the transaction data” (See Remark Page 22). However, the examiner noted that transaction fraud detection is a commercial interaction while a model is mathematical concept. Improving the fraud detection model improves the Judicial Exception, which cannot furnish any eligibility. As such, the argument is not persuasive.
Regarding the applicant’s argument that claims amount to significantly more, the examiner respectfully disagrees. The applicant contended that the recitation of fuzzy logic manipulating training transaction data “providing a technical solution to the “Privacy-Accuracy Paradox” that was not well-understood or conventional at the time of filing”. However, invoking a generically recited logic to perform a data modification step of the Judicial Exception is at best adding masking mathematical operation to the commercial interaction of fraud detection, both of which are ineligible subject matter. Adding an ineligible mathematical concept to an ineligible commercial interaction cannot furnish any eligibility. As such, the argument is not persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 CHO KWONG whose telephone number is (571)270-7955. The examiner can normally be reached 9am - 5pm EST M-F.
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/CHO YIU KWONG/Primary Examiner, Art Unit 3693