DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 8/5/25 has been entered.
Status of Claims
Claims 2-22 are pending in this application.
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 2-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 2-22 are directed to a system, method, or product, which are/is one of the statutory categories of invention. (Step 1: YES).
The examiner has identified independent method claim 2 as the claim that represents the claimed invention for analysis and is similar to independent system claim 9 and product claim 16. Claim 2 recites the limitations of creating/refining classification model that can help classify imbalanced data by using and modifying loss functions to penalize errors and minimize such losses. Note, “imbalanced data” refers to a classification problem where the classes are not represented in an equal manner. See specification, paragraph 19.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Receiving transaction data for evaluating machine learning model programmed on a programmable electronic circuit; machine learning model trained using (a) imbalanced data set AND; (b) modified loss function to minimize quantifiable losses resulting from misclassification of different data; “modified loss function” generated by modifying an original loss function based on a loss resulting from false positive or negative classification; generating an “initial set of quantifiable losses” related to the initial set of classifications; “initial set of quantifiable losses” is generated using the modified loss function; “initial set of quantifiable losses” results from transaction data misclassifications; determining that “initial set of quantifiable losses” is above a loss threshold; Iteratively retraining/updating machine learning model by evaluating an additional loss function to identify a new set of quantifiable losses; “additional loss function” measures the miscalculation in an imbalanced data set; receiving the new set of quantifiable losses (from the updated machine learning model); determining that “initial set of quantifiable losses” is below a loss threshold; receiving additional imbalanced transaction data; additional imbalanced transaction data relates to additional transaction; processing the additional imbalanced transaction data; generating output that minimizes quantifiable losses resulting from misclassifications of the additional imbalanced transaction data; transmitting a notification to continue using the updated ML model; and the ML model identifying fraudulent transactions and block transaction account associate with fraudulent transaction, – specifically, the claim recites “receiving… transaction data associated with an existing machine-learning model programmed… trained using an imbalanced data set and a modified loss function to minimize quantifiable losses resulting from misclassification of data, and wherein the modified loss function is generated by modifying an original loss function based on a loss resulting from false positive classifications or false negative classifications; generating… an initial set of quantifiable losses corresponding to an initial set of classifications of the transaction data, wherein the initial set of quantifiable losses is generated using the modified loss function, and wherein the initial set of quantifiable losses results from one or more misclassifications of the transaction data; determining… that the initial set of quantifiable losses is above a loss threshold stored in a memory: creating… an updated machine-learning model by iteratively retraining the existing machine-learning model and evaluating outputs using an additional loss function to identify a new set of quantifiable losses, wherein the additional loss function provides a quantifiable measure resulting from misclassification of data in an additional imbalanced data set; receiving… the new set of quantifiable losses from the updated machine-learning model; determining… that the new set of quantifiable losses is below the loss threshold; receiving… additional imbalanced transaction data based on the determination, wherein the additional imbalanced transaction data corresponds to one or more additional transactions associated with a user; processing… the additional imbalanced transaction data through the updated machine-learning model; generating… a model output that minimizes quantifiable losses resulting from misclassifications of the additional imbalanced transaction data; and transmitting… a notification… to indicate to… continue to use the updated machine learning model, wherein when the notification is received… identifies a fraudulent transaction using the updated machine-learning model and blocks the fraudulent transaction or an account associated with the fraudulent transaction”, recites a fundamental economic practice, directed to mitigating risk (dealing with “transaction data”). See also, specification paragraph 18 – “the transaction classification system may evaluate this set of classifications using a modified loss function that differentiates and weighs classification errors resulting in false positives ( e.g., an authentic transaction being classified as being fraudulent) and false negatives ( e.g., fraudulent transactions being classified as being authentic)”); and claims 8, 15, and 22 (disclosing usage relating to approving credit applications).
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. Accordingly, the claim recites an abstract idea.
The “a communications interface”, “an existing machine-learning model”, “a programmable electronic circuit”, “a memory”, “an updated machine-learning model”, and “a transaction processing system” in claim 2; the additional technical element of “a system” and “one or more processors” in claim 9; and the additional technical element of “a non-transitory, computer-readable storage medium” in claim 16, are just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claims 9 and 16 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of: a computer such as a programmable electronic circuit, a transaction processing system, a system, and one or more processors; a communication device such as a communications interface; a storage unit such as a memory and a non-transitory, computer-readable storage medium; and software module and algorithm such as an existing machine-learning model, and an updated machine-learning model. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 and are at a high level of generality. Therefore, claims 2, 9, and 16 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, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 2, 9, and 16 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims further define the abstract idea that is present in their respective independent claims 2, 9, and 16 and thus correspond to Certain Methods of Organizing Human Activity, and hence are abstract for the reasons presented above.
Dependent claim 3 discloses the limitation of the additional imbalanced transaction data is received with a new loss threshold, and wherein the new loss threshold is used to modify the one or more criteria related to misclassification of different data, which further narrows the abstract idea.
Dependent claim 4 discloses the limitation of the initial set of quantifiable losses corresponds to overall costs resulting from the false positive classifications and the false negative classifications from the additional imbalanced transaction data generated using the existing machine-learning model, which further narrows the abstract idea. Note that the technical element “the existing machine-learning model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 5 discloses the limitation of processing the additional imbalanced transaction data includes: dividing the additional imbalanced transaction data into one or more data subsets; and performing a set of evaluations of the existing machine-learning model using the one or more data subsets, wherein the set of evaluations are performed to generate the initial set of quantifiable losses, which further narrows the abstract idea. Note that the technical element “the existing machine-learning model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 discloses the limitation of the one or more criteria include a requirement whereby the initial set of quantifiable losses is not to exceed resulting losses derived from processing of the additional imbalanced transaction data using one or more other machine-learning models, which further narrows the abstract idea. Note that the technical element “one or more other machine-learning models” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 7 discloses the limitation of wherein iteratively retraining includes iteratively updating one or more model coefficients and the one or more model coefficients are iteratively updated using gradient descent to generate new cutoff values usable to classify data points associated with the additional imbalanced transaction data, which further narrows the abstract idea.
Dependent claim 8 discloses the limitation of the additional imbalanced transaction data includes approved credit applications, and wherein the approved credit applications are classified as being authentic or fraudulent, which further narrows the abstract idea.
Dependent claim 10 discloses the limitation of the additional imbalanced transaction data is received with a new loss threshold, and wherein the new loss threshold is used to modify the one or more criteria, which further narrows the abstract idea.
Dependent claim 11 discloses the limitation of wherein the initial set of quantifiable losses corresponds to overall costs resulting from the false positive classifications and the false negative classifications from the additional imbalanced transaction data generated using the existing machine-learning model, which further narrows the abstract idea. Note that the technical element “the existing machine-learning model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 12 discloses the limitation of the instructions that cause the system to process the transaction data further cause the system to: divide the additional imbalanced transaction data into one or more data subsets; and perform a set of evaluations of the existing machine-learning model using the one or more data subsets, wherein the set of evaluations are performed to generate the initial set of quantifiable losses, which further narrows the abstract idea. Note that the technical elements “the system” and “the existing machine-learning model”, are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Dependent claim 13 discloses the limitation of the one or more criteria include a requirement whereby the initial set of quantifiable losses is not to exceed resulting losses derived from processing of the new transaction data using one or more other machine-learning models, which further narrows the abstract idea. Note that the technical element “one or more other machine-learning models” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 14 discloses the limitation of wherein iteratively retraining includes iteratively updating one or more model coefficients and wherein the one or more model coefficients are iteratively updated using gradient descent to generate new cutoff values usable to classify data points associated with the new transaction data, which further narrows the abstract idea.
Dependent claim 15 discloses the limitation of the additional imbalanced transaction data includes approved credit applications, and wherein the approved credit applications are classified as being authentic or fraudulent, which further narrows the abstract idea.
Dependent claim 17 discloses the limitation of the additional imbalanced transaction data is received with a new loss threshold, and wherein the new loss threshold is used to modify the one or more criteria, which further narrows the abstract idea.
Dependent claim 18 discloses the limitation of the initial set of quantifiable losses corresponds to overall costs resulting from the false positive classifications and the false negative classifications from the additional imbalanced transaction data generated using the existing machine-learning model, which further narrows the abstract idea. Note that the technical element “the existing machine-learning model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 19 discloses the limitation of the executable instructions that cause the computer system to process the additional imbalanced transaction data further cause the computer system to: divide the transaction data into one or more data subsets; and perform a set of evaluations of the existing machine-learning model using the one or more data subsets, wherein the set of evaluations are performed to generate the initial set of quantifiable losses, which further narrows the abstract idea. Note that the technical elements “the executable instructions”, “the computer system”, and “the existing machine-learning model”, are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Dependent claim 20 discloses the limitation of the one or more criteria include a requirement whereby the initial set of quantifiable losses is not to exceed resulting losses derived from processing of the additional imbalanced transaction data using one or more other machine-learning models, which further narrows the abstract idea. Note that the technical element “one or more other machine-learning models” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 21 discloses the limitation of wherein iteratively retraining includes iteratively updating one or more model coefficients and wherein the one or more model coefficients are iteratively updated using gradient descent to generate new cutoff values usable to classify data points associated with the additional imbalanced transaction data, which further narrows the abstract idea.
Dependent claim 22 discloses the limitation of the additional imbalanced transaction data includes approved credit applications, and wherein the approved credit applications are classified as being authentic or fraudulent, which further narrows the abstract idea.
Thus, 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 are directed to an abstract idea. Thus, the claims 2-22 are not patent-eligible.
Response to Arguments
Applicant's arguments filed 8/5/25 have been fully considered but they are not persuasive.
In response to applicant's argument that:
“35 U.S.C. § 101… the claims are amended as noted above. Withdrawal of the rejection under 35 U.S. C. §101 is therefore respectfully requested,”
the examiner respectfully disagrees. The applicant added elements “transmitting, via the communications interface, a notification, to a transaction processing system to indicate to the transaction processing system to continue to use the updated machine learning model, wherein when the notification is received, the transaction processing system identifies a fraudulent transaction using the updated machine-learning model and blocks the fraudulent transaction or an account associated with the fraudulent transaction” may, perhaps, make the claim more like example 47, claim 3. Similar to example 47, claim 3, a machine learning model is trained (here, using and modifying loss function). Also similar to example 47, claim 3, the machine learning model identifies fraudulent transactions and block transaction accounts associate with the fraudulent transaction.
However, in example 47, claim 3, the trained machine learning model carries out specific technical functions – i.e., the machine learning model detects and drops “malicious network packets”, and blocks future traffic from the source address. Here, the machine learning model identifies fraudulent transactions and block transaction account associate with the fraudulent transaction. While sounding similar, it is not clear how is the system blocking future transactions? Is it by flagging/labeling the account? Is it by deleting the account (thus preventing it from being used again)? Those steps are not necessarily technical. The specification paragraph 99 talks about “blocking”; but nothing technical is specifically disclosed. As written, this step seems to be not a technical step like example 47, claim 3.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK H GAW whose telephone number is (571)270-0268. The examiner can normally be reached Mon-Fri: 9am -5pm.
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/MARK H GAW/Examiner, Art Unit 3693