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
1. This office action is in response to an amendment received on 2/27/26 for patent application 18/733,811.
2. Claims 21,28,35 are amended.
3. Claims 21-40 are pending.
RESPONSE TO ARGUMENTS
Applicant argues#1
Applicant submits that the claims as amended are both directed to (i) an improvement to the technical field of automated fraud detection and mitigation and (ii) a technical improvement by improving the functioning of a machine learning model, as per Desjardins.
Examiner Response
Examiner respectfully disagrees.
The claims of the Ex Parte Desjardins decision analyzed eligibility to determine whether
the claims were directed to an improvement in the functioning of the computer or an
improvement to other technology or technical field. It was in step 2a prong 2, it was
determined that the specification identified improvements and was reflected in the
claims as to how the machine learning model itself operates.
The specification of the Desjardins application identified the improvement to machine
learning technology.
Whereas the claims and the specification of the instant application, do not reflect the
improvement to the machine learning models, see the Response to Applicant argues#2 below.
Therefore, the claims are unlike the claims in Ex Parte Desjardins.
The rejection is maintained.
Applicant argues#2
As an initial matter, the claims, as amended, are directed to a particular improvement
in the field of automated fraud detection and mitigation. For example, as described in the specification (see, e.g., paragraph [0003]), one issue in legacy systems is the fragmented use of data from disparate sources including unstructured data whereby agents must review disparate unstructured data (e.g., notes, voice analytics) alongside structured data to come to a conclusion as to the validity of fraud.
Often such fragmented review is inconsistent without any way to comprehend
unstructured inputs into usable features ([0003], [0033]-[0035]). Furthermore, such systems rely heavily on user-input, which can be highly variable and may be a lengthy process (and may thus slow down fraud mitigation/resolution). The claims recite features including automatically extracting additional data from sources and using the additional data to generate new metadata elements such as in a format that can be ingested and used, e.g., in downstream models to automatically determine fraud (See, e.g., "extract additional data related to the potential fraudulent account event from a plurality of sources generate metadata elements by encoding one or more instances of the additional data search the metadata elements by inputting at least
a portion of the metadata elements into a pattern recognition function of one or more machine learning models").
That is, this approach enables the system to automatically identify and extract data from different sources into a format that can be ingested by a model and thus enables automatic detection of fraud which is faster, thereby avoiding unnecessary delays in identifying/mitigating fraud events and improving consistency in the decision-making process. Accordingly, the claims are directed to improving a technical field of electronic fraud detection and are therefore patent- eligible under Step 2A.
Examiner Response
Examiner respectfully disagrees.
The specification paras that applicant refers to are reproduced below:
[0003] Currently, detecting fraud and processing fraud cases may comprise a detection engine and a primarily manual risk assessment process of validating a probability of actual fraud and determining a resolution path. The current risk assessment process may be characterized as based on one or more judgmental decisions, for example, after a review of structured and unstructured data from an account history and third party information. Thus, such legacy fraud processing may typically involve one or more manual operations.
[0033] In embodiments of the invention, the fraud risk assessment process may involve, for example, collecting both structured and unstructured data. Such structured data may include, for example, a number of transactions attempted or made by a client over a particular time period, a number of contacts made by the client to service the clients' account, the nature of the client's spending habits, and / or the client's payment activities. Unstructured data, which may typically be found, for example, in a client's historical servicing notes, may by useful in attempting to ascertain the nature of the client's interactions.
[0034] A fraud risk assessment process for embodiments of the invention may require
evaluation of structured data, such as account information and third party data, as well as evaluation of unstructured data, such as notes regarding a client's service history, voice analytics, and other free-form information, to make a decision to further investigate and determine a resolution method. Embodiments of the invention employ a unique approach to fraud risk assessment that employs artificial intelligence and machine learning to mine the unstructured data and convert that data into structured data, which can be used with pattern recognition engines to eliminate a need for human decisions.
[0035] An aspect of embodiments of the invention may use such structured and unstructured data and employ artificial intelligence to search for specifically known fraud patterns. Such patterns may be revealed, for example, in velocity of contacts between a client and the business, the nature or subject matter of such contacts, as well as a number of failed attempts in authenticating by the client. In embodiments of the invention, the risk assessment process may be automated via methods and processes, such as system communication protocols, intelligent data mining with machine learning and artificial intelligence, and pattern recognition with machine learning.
As can be seen from the spec paras cited, the additional elements in the claim (artificial intelligence and machine learning) are recited at a high level of generality and are being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f).
Applicant argued the claims present a technical improvement. Examiner does not find this argument persuasive. Applicant’s claims do not improve technology; the underlying technology remains unaffected by the claims. Applicant is addressing a business problem (identifying potential fraud associated with transactional accounts) with a business solution. Applicant is merely using existing technology (for its intended purpose) to implement the business solution. Any improvements lie in the abstract idea itself, not in underlying technology.
The rejection is maintained.
Applicant argues#3
Furthermore, Applicant submits that, as amended, the claim language reflects the
technical improvements analogous to those in Ex parte Desjardins, Appeal No. 2024-000567, Application No. 16/319,040, at *7 (P.T.A.B. Sept. 26, 2025) (Appeals Review Panel). In particular, in the Desjardins decision, the Panel determined that improvements "that constitute[] an improvement to how the machine learning model itself operates" were found to be technical improvements that are patent-eligible under Step 2A, prong 2 (p. 8-9). Similarly, as described in the specification, the invention improves the functioning of the machine learning model by enriching and enhancing the data input for input into the machine learning model, e.g., for detecting fraud.
In particular, the claims discuss extracting additional data from a plurality of sources and
encoding the additional data which is described in the specification to enhance the data for input into the model. For example, the amended claims include limitations including:
(a) "extract additional data related to the potential fraudulent account event from
a plurality of sources
(b) "generate metadata elements by encoding one or more instances of the
additional data and
(c) "search the metadata elements by inputting at least a portion of the metadata
elements into a pattern recognition function of one or more machine learning models
for automatically detecting one or more data patterns indicative of whether the potential
fraudulent account event is fraudulent."
Applicant submits that similarly, enriching and enhancing data for input to a machine
learning model constitutes "an improvement to how the machine learning model works." Ex parte Desjardins, Appeal 2024-000567 (PTAB Sept. 26, 2025). Thus, the amendments to the claims recite limitations that reflect technical improvements to the usage of a machine learning model, which the Panel in Desjardins found to be patent-eligible under Step 2A, prong 2.
Accordingly, the claims recite a particular arrangement and workflow that provides a
concrete improvement in the field of fraud detection and mitigation. Therefore, the claims do not merely recite an abstract idea or a mental process, but rather an improvement to a technical field.
Examiner Response
Examine respectfully disagrees.
The argument pertaining to Desjardins, has been addressed above, see the Response to Applicant argues#2 above.
The limitations ( (a) "extract additional data related to the potential fraudulent account event from a plurality of sources; (b) "generate metadata elements by encoding one or more instances of the additional data and (c) "search the metadata elements; detecting one or more data patterns indicative of whether the potential fraudulent account event is fraudulent.") are part of the identified abstract idea.
The additional elements, outside of the abstract idea (the pattern recognition function of one or more machine learning models) is recited at a high level of generality, and is operating in its ordinary capacity as a tool to implement the steps of the identified abstract idea.
Therefore there are no additional elements that are indicative of integration into a practical application.
The rejection is maintained.
Claim Rejections- 35 U.S.C § 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.
1. Claims 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 21, 28, 35 are directed to a system, method and computer readable medium which are statutory categories of invention. (Step 1: YES).
Representative claim 21 recites the limitations of:
A system for account fraud detection, comprising: one or more processors coupled to memory, the one or more processors being programmed to execute application code instructions that are stored in a storage device, the application code instructions cause the one or more processors to:
receive data regarding a potential fraudulent account event from a fraud detection platform processor;
segregate the data regarding the potential fraudulent account event into (1) a portfolio type grouping comprising at least one of branded cards type, retail services type, or retail bank type and (2) a fraud type grouping comprising at least one of account takeover fraud type, never received issues fraud type, transaction fraud type, or identification fraud type;
extract additional data related to the potential fraudulent account event from plurality of sources using intelligent search engines using at least one machine learning model;
generate metadata elements by encoding one or more instances of the additional data based on characteristics and data patterns of the additional data;
search the metadata elements by inputting a least a portion of the metadata elements into a pattern recognition function of one or more machine learning models for automatically detecting one or more data patterns indicative of whether the potential fraudulent account event is fraudulent;
responsive to detecting that at least one data pattern of the one or more data pattern indicates that the potential fraudulent account is fraudulent, identify a resolution path comprising one or more actions; and
causing execution of the one or more actions comprised in the resolution path to resolve the potential fraudulent account event.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity.
The claim recites elements that are in bold above, which covers performance of the limitation as a fundamental economic practice (mitigating risk), steps for identifying potential fraud associated with transactional accounts (e.g., receive data regarding a potential fraudulent account event; segregate the data regarding the potential fraudulent account event into (1) a portfolio type grouping comprising at least one of branded cards type, retail services type, or retail bank type and (2) a fraud type grouping comprising at least one of account takeover fraud type, never received issues fraud type, transaction fraud type, or identification fraud type; extract additional data related to the potential fraudulent account event from plurality of sources; generate metadata elements by encoding one or more instances of the additional data based on characteristics and data patterns of the additional data; search the metadata elements for detecting one or more data patterns indicative of whether the potential fraudulent account event is fraudulent; responsive to detecting that at least one data pattern of the one or more data pattern indicates that the potential fraudulent account is fraudulent, identify a resolution path comprising one or more actions; and causing execution of the one or more actions comprised in the resolution path to resolve the potential fraudulent account event)
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a Fundamental Economic Practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Claims 28, 35 are abstract for similar reasons.
(Step 2A-Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Claims 21,28, 35 includes the following additional elements:
-One or more processors coupled to memory
-A storage device
-An intelligent search engine
-A pattern recognition function of the one or more machine learning models
-A non-transitory computer readable medium
The one or more processors coupled to memory, storage device, intelligent search engine, pattern recognition function of the one more machine learning models and non-transitory computer readable medium are recited at a high level of generality and are being used in their ordinary capacity and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f), where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
Therefore claims 21, 28, 35 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited in the claim beyond the judicial exception.
Mere instructions to implement an abstract idea, on or with the use of generic computer components, or even without any computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claims 21, 28, 35 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 22-27, 29-34, 36-40 further define the abstract idea that is present in their respective independent claims 21, 28, 35 and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above.
Therefore, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims (22-27, 29-34, 36-40) are directed to an abstract idea. Thus, the claims 21-40 are not patent-eligible.
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 MOHAMMAD Z SHAIKH whose telephone number is (571)270-3444. The examiner can normally be reached M-T, 9-600; Fri, 8-11, 3-5.
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/MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694 5/16/2026
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