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 .
Status of Claims
Claims 1-20 are pending.
Priority
This application 19/123,351, filed 04/22/2025 claims priority to:
371 of PCT/US2024/012198, filed 01/19/2024 (effective filing date)
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 04/22/2025 and 08/22/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) has/have been considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Step 1 of the eligibility analysis asks is the claim to a process, machine, manufacture or composition of matter (See MPEP § 2106.03, subsections I and II). Claims 1-7 are directed to a computer-implemented method (i.e., process). Claims 8-15 are directed to a computer-implemented system (i.e., machine, and manufacture). Claims 16-20 are directed to a non-transitory computer-readable storage medium (i.e., manufacture). Therefore, these claims fall within the four statutory categories of invention.
Step 2A, Prong One
Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon (MPEP § 2106.04(II)(A)(1)). Claims 1, 8 and 16 under a broadest reasonable interpretation recite an abstract idea because the claims describe classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” grouping of abstract ideas (MPEP § 2106.04(a)(2), subsection II). The claim limitations reciting the abstract idea are grouped within the “certain methods of organizing human activity” grouping of abstract ideas because the limitations describe fundamental economic principles or practices, including mitigating risk, and describe commercial or legal interactions, including advertising, marketing or sales activities or behaviors. The abstract idea is also grouped within the “mental processes” grouping of abstract ideas (See MPEP § 2106.04(a)(2), subsection III). The claim limitations reciting the abstract idea are grouped within the “mental processes” grouping of abstract ideas because the limitations describe concepts that can practically be performed in the human mind, with or without the use of a physical aid. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
Claim 1:
A computer-implemented method comprising:
obtaining transaction data for a transaction;
providing the transaction data as input data to a machine learning model that has been trained to classify transactions using a set of labels, wherein the set of labels includes a first label indicating a normal transaction type, a second label indicating an attack transaction type, and a third label indicating a transaction of uncertain type, wherein the machine learning model includes:
a plurality of generative units including a first generative unit associated with the normal transaction type and a second generative unit associated with the attack transaction type, wherein each of the generative units receives the input data and outputs a reconstruction of the input data, wherein the generative units operate independently of each other;
a join gate that produces intermediate data by combining respective reconstruction outputs from the plurality of generative units with the input data; and
a multi-label classifier unit that determines, based on the intermediate data, a probability score for each of the labels in the set of labels; and
classifying the transaction as a normal transaction or an attack transaction based at least in part on the probability score for each of the labels in the set of labels.
Claim 8:
A computer system comprising:
a communication interface to communicate with one or more server systems;
a memory to
store transaction data for a plurality of previous transactions including a plurality of normal transactions and a plurality of attack transactions; and
a processor coupled to the memory and configured to
implement a machine learning model that includes:
a plurality of generative units including a first generative unit associated with a normal transaction type and a second generative unit associated with an attack transaction type, wherein each of the generative units receives input data representing a transaction and outputs a reconstruction of the input data, wherein the generative units operate independently of each other;
a join gate that produces intermediate data by combining respective outputs from the plurality of generative units with the input data; and
a multi-label classifier unit that determines, based on the intermediate data, a probability score for each label in a set of labels, wherein the set of labels includes a first label indicating the normal transaction type, a second label indicating the attack transaction type, and a third label indicating a transaction of uncertain type,
wherein the processor is further configured to:
train the machine learning model using the stored transaction data;
receive, via the communication interface, new transaction data from one of the one or more server systems;
use the trained machine learning model to determine, for the new transaction data, a probability score for each of the labels in the set of labels; and
classifying the transaction as a normal transaction or an attack transaction based at least in part on the probability score for each of the labels in the set of labels.
Claim 16:
A computer-readable storage medium having stored therein program code instructions that, when executed by a processor in a computer system, cause the processor to perform a method comprising:
obtaining transaction data for a transaction;
providing the transaction data as input data to a machine learning model that has been trained to classify transactions using a set of labels, wherein the set of labels includes a first label indicating a normal transaction type, a second label indicating an attack transaction type, and a third label indicating a transaction of uncertain type,
wherein the machine learning model includes:
a plurality of generative units including a first generative unit associated with the normal transaction type and a second generative unit associated with the attack transaction type, wherein each of the generative units receives the input data and outputs a reconstruction of the input data, wherein the generative units operate independently of each other;
a join gate that produces intermediate data by combining respective outputs from the plurality of generative units with the input data; and
a multi-label classifier unit that determines, based on the intermediate data, a probability score for each of the labels in the set of labels; and
classifying, based at least in part on the probability score for each of the labels in the set of labels, the transaction as a normal transaction or an attack transaction.
Step 2A, Prong Two
Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application (MPEP § 2106.04(II)(A)(2)). Here, the additional elements individually and in combination, are recited at a high level of generality as generic and conventional elements merely serving as a tool to perform the abstract idea (MPEP § 2106.05(f)) and generally linking the use of the abstract idea to a particular technological environment (MPEP § 2106.05(h)). The description of the additional elements evidences that they are generic and conventional elements used as tools to perform the abstract idea (See Spec. 0034-0037, 0040, 0046, 0050-0055, 0068, 0080, 0083, 0087-0090). These additional elements do not improve the functioning of computers, another technology, or a technical field (MPEP §§ 2106.04(d)(1) and 2106.05(a)). They do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)). They do not implement the abstract idea with a particular machine or manufacture that is integral to the claim (MPEP § 2106.05(b)). They do not transform or reduce a particular article to a different state or thing (MPEP § 2106.05(c)). Nor do they apply the abstract idea in a meaningful way or impose a meaningful limit on it beyond linking its use to a particular technological environment (MPEP § 2106.05(e)). Such a generic computer implementation does not make the abstract idea patent eligible because a wholly generic computer implementation is not generally the sort of additional feature that provides any practical assurance that the process is more than a drafting effort designed to monopolize the abstract idea itself. The Specification and the claim language provide evidence that the focus of the claim is not on a specific improvement in technology but rather on a scheme, for which generic and conventional elements are invoked merely as a tool to implement the abstract idea and link it to a particular field of use. Even if the Specification describes technical improvements, they are not claimed. Thus, the additional elements do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to the abstract idea identified above.
Step 2B
Step 2B determines whether the claim as a whole amount to significantly more than the abstract idea itself (MPEP § 2106.05). Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the abstract idea itself. Individually, the additional elements do not amount to significantly more than the abstract idea. As discussed previously, the description of the additional elements evidences that they are generic and conventional elements used as tools to perform the abstract idea (See Spec. 0034-0037, 0040, 0046, 0050-0055, 0068, 0080, 0083, 0087-0090). There is nothing in the Specification to indicate that the operations recited in the claims require any specialized hardware or inventive computer components or that the claimed invention is implemented using other than generic computer components to perform generic computer functions. As such, the additional elements merely serve as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment. The ordered combination recites no more than the individual elements do. Thus, the additional elements are not significantly more than the abstract idea. Accordingly, the claims are directed to the abstract idea identified above without significantly more. The claims are not eligible, warranting a rejection for lack of subject matter eligibility and concluding the eligibility analysis.
Dependent Claims
Claim 2 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
obtaining a training data set comprising transaction data for a plurality of transactions, wherein at least some of the transaction data in the training data set is initially unlabeled; and
using the training data set to train the machine learning model,
wherein training the machine learning model includes:
directing transaction data having the first label to the first generative unit;
directing transaction data having the second label to the second generative unit; and
directing unlabeled transaction data and transaction data having the third label randomly to one or more of the generative units.
Claim 3 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein some of the transaction data in the training data set is initially labeled.
Claim 4 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein training of the machine learning model includes a plurality of training epochs and wherein at the end of each training epoch, an updated label is assigned to the transaction data for at least one of the transactions in the training data set based on the probability scores determined by the multi-label classifier unit.
Claim 5 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein classifying the transaction includes:
determining which label of the set of labels has a highest probability score;
in the event that the first label has the highest probability score, classifying the transaction as a normal transaction;
in the event that the second label has the highest probability score, classifying the transaction as an attack transaction; and
in the event that the third label has the highest probability score:
determining which label of the set of labels has a second-highest probability score;
in the event that the first label has the second-highest probability score, classifying the transaction as a normal transaction; and
in the event that the second label has the second-highest probability score, classifying the transaction as an attack transaction.
Claim 6 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
assigning an uncertainty score to the classification of the transaction as a normal transaction or an attack transaction based on the probability score for the third label.
Claim 7 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein the transaction data is received while a transaction is in progress and wherein the method further comprises:
determining whether to allow or reject the transaction based at least in part on whether the transaction is classified as a normal transaction or an attack transaction.
Claim 9 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein at least one of the generative units includes a variational autoencoder.
Claim 10 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein the multi-label classifier unit includes a feed-forward neural network having one or more layers.
Claim 11 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein the transaction data for each transaction includes an account credential provided by a client system to the server system,
wherein the normal transaction type corresponds to an authorized use of the account credential and wherein the attack transaction type corresponds to an attempted or successful unauthorized use of the account credential.
Claim 12 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein the processor is further configured such that training the machine learning model includes:
defining a training data set using at least a portion of the stored transaction data, wherein the training data set initially includes at least some transactions having the first label, at least some transactions having the second label, at least some transactions having the third label and at least some unlabeled transactions;
directing transaction data for transactions having the first label to the first generative unit; and
directing transaction data having the second label to the second generative unit.
Claim 13 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein the processor is further configured such that training the machine learning model includes:
randomly directing each of the transactions having the third label to one or the other of the first generative unit or the second generative unit; and
randomly directing each of the unlabeled transactions to one or the other of the first generative unit or the second generative unit.
Claim 14 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein the processor is further configured such that training the machine learning model includes:
directing a randomly selected subset of the transactions having the third label to both of the first generative unit and the second generative unit; and
directing a randomly selected subset of the unlabeled transactions to both of the first generative unit and the second generative unit.
Claim 15 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein training of the machine learning model includes a plurality of training epochs and
wherein the processor is further configured such that,
at the end of each training epoch, updated labels are determined for transactions in the training data set that have the third label and for unlabeled transactions, wherein the updated label for a transaction is determined based on the probability scores determined by the multi-label classifier unit.
Claim 17 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein the method further comprises:
obtaining a training data set comprising transaction data for a plurality of transactions, wherein at least some of the transaction data in the training data set is initially unlabeled; and
using the training data set to train the machine learning model,
wherein training the machine learning model includes a plurality of training epochs and wherein, during each epoch:
transaction data having the first label is directed to the first generative unit;
transaction data having the second label is directed to the second generative unit; and
unlabeled transaction data and transaction data having the third label is directed randomly to zero or more of the generative units.
Claim 18 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein the method further comprises, after each training epoch:
applying the machine learning model to unlabeled transaction data and transaction data having the third label to determine probability scores for each of the labels in the set of labels; and
determining updated labels for the unlabeled transaction data and transaction data having the third label based on the probability scores for each of the labels in the set of labels.
Claim 19 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein the transaction data is received from a server computer and wherein the method further comprises:
transmitting a report to the server computer, the report indicating whether the transaction was classified as a normal transaction or an attack transaction.
Claim 20 recites an abstract idea because the claim describes classifying transactions using labels, and classifying the transactions based on probability scores generated for each label using produced intermediate data, grouped within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. The additional elements do not integrate the abstract idea into a practical application because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. The additional elements are not significantly more than the abstract idea because individually and in combination, the additional elements are recited at a high level of generality as generic and conventional computers and components merely serving as a tool to perform the abstract idea and generally linking the use of the abstract idea to a particular technological environment. Therefore, the claim is not eligible. The following underlined claim limitations recite the abstract idea. The non-underlined claim limitations recite additional elements.
wherein the report further includes an uncertainty score based on the probability score for the third label.
Claims Free of Art
Claims 1-20 are free of art. The closest prior art of record is US 2023/0351383 A1 by Bar Eliyahu et al. (hereinafter “Bar Eliyahu”). Bar Eliyahu teaches:
obtaining transaction data for a transaction; (Fig.4 items 410-430; paras 0124-0127)
a multi-label classifier unit that determines, based on the intermediate data, a probability score for each of the labels in the set of labels; and (Fig.4 items 440-450, Fig.5 item 530; paras 0128-0129, 0134)
classifying the transaction as a normal transaction or an attack transaction based at least in part on the probability score for each of the labels in the set of labels. (Fig.5 item 540; paras 0135)
Therefore, the prior art does not teach, neither singly nor in combination the following:
providing the transaction data as input data to a machine learning model that has been trained to classify transactions using a set of labels, wherein the set of labels includes a first label indicating a normal transaction type, a second label indicating an attack transaction type, and a third label indicating a transaction of uncertain type, wherein the machine learning model includes: a plurality of generative units including a first generative unit associated with the normal transaction type and a second generative unit associated with the attack transaction type, wherein each of the generative units receives the input data and outputs a reconstruction of the input data, wherein the generative units operate independently of each other; a join gate that produces intermediate data by combining respective reconstruction outputs from the plurality of generative units with the input data; and
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2017/0161635 A1 to Oono et al. discloses: In various embodiments, the systems and methods described herein relate to generative models. The generative models may be trained using machine learning approaches, with training sets comprising chemical compounds and biological or chemical information that relate to the chemical compounds. Deep learning architectures may be used. In various embodiments, the generative models are used to generate chemical compounds that have desired characteristics, e.g. activity against a selected target. The generative models may be used to generate chemical compounds that satisfy multiple requirements.
US 2021/0390385 A1 to Saleh et al. discloses: Techniques are disclosed relating to improving machine learning classification using both labeled and unlabeled data, including electronic transactions. A computing system may train a machine learning module using a first set of transactions (of any classifiable data) with label information that indicates designated classifications for those transactions and a second set of transactions without label information. This can allow for improved classification error rates, particularly when additional labeled data may not be present (e.g., if a transaction was disallowed, it may not be later labeled as fraudulent or not). The training process may include generating first error data based on classification results for the first set of transactions, generating second error data based on reconstruction results for both the first and second sets of transactions, and updating the machine learning module based on the first and second error data.
US 2023/0096895 A1 to Stokes et al. discloses: The techniques disclosed herein enable systems to train a machine learning model to classify malicious command line strings and select anomalous and uncertain samples for analysis. To train the machine learning model, a system receives a labeled data set containing command line inputs that are known to be malicious or benign. Utilizing a term embedding model, the system can generate aggregated numerical representations of the command line inputs for analysis by the machine learning model. The aggregated numerical representations can include various information such as term scores that represent a probability that an individual term of the command line string is malicious as well as numerical representations of the individual terms. The system can subsequently provide the aggregated numerical representations to the machine learning model for analysis. Based on the aggregated numerical representations, the machine learning model can learn to distinguish malicious command line inputs from benign inputs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ari Shahabi whose telephone number is (571)272-2565. The examiner can normally be reached M-F: 8:00-5:00.
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/ARI SHAHABI/Primary Examiner, Art Unit 3697