DETAILED ACTION
The present application, filed on 3/10/2023 is being examined under the AIA first inventor to file provisions.
The following is a FINAL Office Action in response to Applicant’s amendments filed on 3/10/2026.
a. Claims 1-6, 8-9, 11, 14-18, 20 are amended
Overall, claims 1-20 are pending and have been considered below.
Claim Rejections - 35 USC § 101
35 USC 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 USC 101 because the claimed invention is not directed to patent eligible subject matter. The claimed matter is directed to a judicial exception, i.e. an abstract idea, not integrated into a practical application, and without significantly more.
Per Step 1 of the multi-step eligibility analysis, claims 1-7 are directed to a system, claims 8-14 are directed to a computer implemented method, and claims 15-20 are directed to computer executable instructions stored on a non-transitory storage medium.
Thus, on its face, each independent claim and the associated dependent claims are directed to a statutory category of invention.
[INDEPENDENT CLAIMS]
Per Step 2A.1. Independent claim 1, (which is representative of independent claims 15) is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 1 (which is representative of independent claims 15) recite an abstract idea, shown in bold below:
[A] A system, comprising: a non-transitory memory; and one or more hardware processors coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations
[B] receiving a request for classifying a transaction;
[C] providing a set of attribute values associated with the transaction to an
efficacy determination model external to a cascade machine learning (ML) model system,
[D] wherein the cascade ML model system comprises a plurality of ML models and is configured to classify the transaction according to an execution scheme that specifies an order of executing a first ML model in the plurality of ML models before executing a second ML model in the plurality of ML models, and
[E] wherein the efficacy determination model is configured to evaluate an efficacy of each ML model in the cascade ML model system for classifying the transaction;
[F] obtaining one or more efficacy output from the efficacy determination model,
[G] wherein the efficacy output indicates a predicted accuracy of the cascade ML model system in classifying the transaction;
[H] based on the one or more efficacy outputs, reconfiguring the cascade ML
model system to bypass the first ML model during a classification of the transaction;
[I] classifying, using the cascade ML model system, the transaction based on the set of attribute values;
[J] wherein the classifying comprises using the second ML model, but not the first ML model, and
[K] processing the transaction based on the classifying.
Independent claim 1 (which is representative of independent claims 15) recites: providing the set of attribute values to an efficacy determination model and obtaining an efficacy output from the efficacy determination model ([C], [F]); reconfiguring the cascade ML model and classifying the transactions based on the model ([H], [I]), and processing the transaction based on the new classification ([K]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: “classifying transactions (i.e., purchases, payments) into legitimate and fraudulent transactions, and processing the legitimate ones”.
This is a combination that, under its broadest reasonable interpretation, covers agreements in the form of marketing, sales activities or behaviors, business relationships, which falls under Certain Methods of Organizing Human Activity, i.e., Commercial or Legal Interactions grouping of abstract ideas (see MPEP 2106.04(a)(2)).
Accordingly, it is concluded that independent claim 1 (which is representative of independent claims 15) recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – Additional Elements]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the added elements “non-transitory memory,” “processors,” recite computing elements at a high level of generality, generally linking the use of a judicial exception to a particular technological environment (see MPEP 2106.05(h)), or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Further, the additional elements “wherein the cascade ML model system comprises a plurality of ML models and is configured to classify the transaction according to an execution scheme that specifies an order of executing a first ML model in the plurality of ML models before executing a second ML model in the plurality of ML models”, “wherein the efficacy determination model is configured to evaluate an efficacy of each ML model in the cascade ML model system for classifying the transaction;”, “wherein the efficacy output indicates a predicted accuracy of the cascade ML model system in classifying the transaction;”, “wherein the classifying comprises using the second ML model, but not the first ML model” as applied to the output of the efficacy determination model, are nothing more than (a) descriptive limitations of claim elements, such as describing the nature, structure and/or content of other claim elements, or (b) general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These additional elements of the independent claims do not preclude from carrying out the identified abstract idea “classifying transactions (i.e., purchases, payments) into legitimate and fraudulent transactions, and processing the legitimate ones”, and do not serve to integrate the identified abstract idea into a practical application.
The additional steps in the independent claims, shown not bolded above, recite: receiving a request for classifying a transaction ([B]). When considered individually, they amount to nothing more than receiving data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (“classifying transactions (i.e., purchases, payments) into legitimate and fraudulent transactions, and processing the legitimate ones”) into a practical application (see MPEP 2106.05(f)(2)).
Therefore, the additional claim elements of independent claim 1 (which is representative of independent claims 15) do not integrate the identified abstract idea into a practical application and the claims remain a judicial exception.
Per Step 2B. Independent claim 1 (which is representative of claims independent 15) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claims 1, 15 are deemed ineligible.
Per Step 2A.1. Independent claim 8 is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 8 recite an abstract idea, shown in bold below:
[A] A method, comprising:
[B] receiving a request for processing a transaction;
[C] accessing a cascade machine learning (ML) model system for classifying the transaction,
[D] wherein the cascade ML model system comprises a plurality of ML models and is configured to classify the transaction according to an execution scheme that specifies an order of executing a first ML model in the plurality of ML models before executing a second ML model in the plurality of ML models;
[E] determining, using an efficacy determination model configured to evaluate an efficacy of each ML model of the cascade ML model system, one or more efficacy outputs based on transaction data associated with the transaction,
[F] wherein the one or more efficacy outputs indicate a predicted accuracy of each ML model in the plurality of ML models of the cascade ML model system in classifying the transaction
[G] wherein the modified execution scheme indicates a bypass of the first ML model in classifying the transaction;
[H] modifying the execution scheme of the cascade ML model system for
classifying the transaction based on the one or more efficacy outputs,
[I] wherein the modified execution scheme indicates a bypass of the first ML model in classifying the transaction
[J] subsequent to the modifying the execution scheme characteristics, classifying, using
the cascade ML model system, the transaction based on the transaction data,
[K] wherein the classifying comprises using the second ML model, but not the first ML model; and
[L] processing the transaction based on the classifying.
Independent claim 8 recites: accessing a cascade machine learning (ML) model system for classifying the transaction and determining efficacy outputs ([C], [E]), ] modifying the execution scheme of the cascade ML model system and classifying the transaction ([H], [J]), processing the transaction ([L]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: “classifying transactions (i.e., purchases, payments) into legitimate and fraudulent transactions, and processing the legitimate ones”.
This is a combination that, under its broadest reasonable interpretation, covers agreements in the form of marketing, sales activities or behaviors, business relationships, which falls under Certain Methods of Organizing Human Activity, i.e., Commercial or Legal Interactions grouping of abstract ideas (see MPEP 2106.04(a)(2)).
Accordingly, it is concluded that independent claim 8 recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – Additional Elements]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the additional elements “wherein the cascade ML model system comprises a plurality of ML models and is configured to classify the transaction according to an execution scheme that specifies an order of executing a first ML model in the plurality of ML models before executing a second ML model in the plurality of ML models;”, “wherein the one or more efficacy outputs indicate a predicted accuracy of each ML model in the plurality of ML models of the cascade ML model system in classifying the transaction”, “wherein the modified execution scheme indicates a bypass of the first ML model in classifying the transaction;”, “wherein the modified execution scheme indicates a bypass of the first ML model in classifying the transaction”, “wherein the classifying comprises using the second ML model, but not the first ML model”, as applied to the output of the efficacy model, are nothing more than (a) descriptive limitations of claim elements, such as describing the nature, structure and/or content of other claim elements, or (b) general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These additional elements of the independent claims do not preclude from carrying out the identified abstract idea “classifying transactions (i.e., purchases, payments) into legitimate and fraudulent transactions, and processing the legitimate ones”, and do not serve to integrate the identified abstract idea into a practical application.
The additional steps in the independent claim, shown not bolded above, recite: receiving a request for processing a transaction ([B]). When considered individually, they amount to nothing more than receiving data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (“classifying transactions (i.e., purchases, payments) into legitimate and fraudulent transactions, and processing the legitimate ones”) into a practical application (see MPEP 2106.05(f)(2)).
Therefore, the additional claim elements of independent claim 8 do not integrate the identified abstract idea into a practical application and the claims remain a judicial exception.
Per Step 2B. Independent claim 8 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claims 8 is deemed ineligible.
[DEPENDENT CLAIMS]
Dependent claim 2 recites:
classifying, using the second ML model in the cascade ML model system, the transaction into a first classification of a plurality of classifications.
Dependent claim 2 merely elaborates on the abstract idea identified in the independent claim(s). It does not recite any new additional elements for further consideration under Step 2A2 or Step 2B and, therefore, is ineligible for the same reasons that make the independent claim(s) ineligible.
Therefore, dependent claim 2 is deemed ineligible.
Dependent claim 3 recites:
in response to determining that the first classification corresponds to the predetermined classification, re-classifying, using a second ML model in the cascade ML model system, the transaction into a second classification of the plurality of classifications,
wherein the transaction is processed based on the second classification.
Dependent claim 3 merely elaborates on the abstract idea identified in the independent claim(s). It does not recite any new additional elements for further consideration under Step 2A2 or Step 2B and, therefore, is ineligible for the same reasons that make the independent claim(s) ineligible.
Therefore, dependent claim 3 is deemed ineligible.
Dependent claim 4 recites:
selecting, from the plurality of ML models, the third ML model for re-classifying the
transaction based on the execution scheme
Dependent claim 4 merely elaborates on the abstract idea identified in the independent claim(s). It does not recite any new additional elements for further consideration under Step 2A2 or Step 2B and, therefore, is ineligible for the same reasons that make the independent claim(s) ineligible.
Therefore, dependent claim 4 is deemed ineligible.
Dependent claim 6, which is representative of dependent claims 12-14, recites:
wherein the operations further comprise:
determining a plurality of prediction accuracy categories based on possible prediction accuracy outcomes from different ML models in the plurality of ML models of the cascade ML model system,
wherein each prediction accuracy category in the plurality of prediction accuracy categories represents a corresponding combination of prediction accuracy outcomes associated with the plurality of ML models;
determining, for a previously conducted transaction, a particular prediction accuracy category based on prediction accuracy outcomes associated with the plurality of ML models in classifying the previously conducted transaction;
labeling the previously conducted transaction with the particular prediction accuracy category;
generating training data for the efficacy determination model based at least on the previously conducted transaction labeled with the particular prediction accuracy category; and
training the efficacy determination model using the training data.
Dependent claim 6, which is representative of dependent claims 12-14, merely elaborates on the abstract idea identified in the independent claim(s). It does not recite any new additional elements for further consideration under Step 2A2 or Step 2B and, therefore, is ineligible for the same reasons that make the independent claim(s) ineligible.
Therefore, dependent claim 6 (which is representative of dependent claims 12-14) is deemed ineligible.
Dependent claim 9, which is representative of dependent claims 18, recites:
configuring the plurality of ML models in the cascade ML model system as a whole.
Dependent claim 9, which is representative of dependent claims 18, merely elaborates on the abstract idea identified in the independent claim(s). It does not recite any new additional elements for further consideration under Step 2A2 or Step 2B and, therefore, is ineligible for the same reasons that make the independent claim(s) ineligible.
Therefore, dependent claim 9 (which is representative of dependent claims 18) is deemed ineligible.
Dependent claim 10, which is representative of dependent claims 19, recites:
determining a set of configurations for the cascade ML model system, wherein each configuration in the set of configurations represents a different set of hyperparameter values for the plurality of ML models in the cascade ML model system;
generating a plurality of instances of the cascade ML model system, wherein each instance in the plurality of instances of the cascade ML model system is configured based on a distinct configuration from the set of configurations;
testing the plurality of instances of the cascade ML model system;
selecting, from the set of configurations, a particular configuration for configuring the cascade ML model system based on the testing; and
configuring the cascade ML model system using the particular configuration.
Dependent claim 10, which is representative of dependent claims 19, merely elaborates on the abstract idea identified in the independent claim(s). It does not recite any new additional elements for further consideration under Step 2A2 or Step 2B and, therefore, is ineligible for the same reasons that make the independent claim(s) ineligible.
Therefore, dependent claim 10 (which is representative of dependent claims 19) is deemed ineligible.
Dependent claims 5, 7, 11, 14, 17 which are representative of dependent claim 20, recite:
wherein the one or more efficacy outputs indicate an efficacy score associated with the first ML model below a threshold.
wherein the plurality of prediction accuracy categories comprises a first prediction accuracy category indicating an accurate prediction for each ML model in the plurality of ML models, a second prediction accuracy category indicating an accurate prediction for one of the plurality of ML models, and a third prediction accuracy category indicating an inaccurate prediction for each ML model in the plurality of ML models.
wherein a particular instances of the cascade ML model system configured using the particular configuration yields a highest accuracy result among the plurality of instances based on the testing.
wherein the one or more efficacy outputs indicate a prediction accuracy of the first ML model below a threshold
wherein the first ML model is implemented using a different computer model architecture than the second ML model
Dependent claim 5, 7, 11, 14, 17 which are representative of dependent claim 20, merely elaborates on the abstract idea identified in the independent claim(s). It does not recite any new additional elements for further consideration under Step 2A2 or Step 2B and, therefore, is ineligible for the same reasons that make the independent claim(s) ineligible.
Therefore, dependent claim 5, 7, 11, 14, 17 which are representative of dependent claim 20, is deemed ineligible.
When the dependent claims are considered as a whole, as an ordered combination, the claim elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense. The most significant elements, which form the abstract concept, are set forth in the independent claims. The fact that the computing devices and the dependent claims are facilitating the abstract concept is not enough to confer statutory subject matter eligibility, since their individual and combined significance do not transform the identified abstract concept at the core of the claimed invention into eligible subject matter. Therefore, it is concluded that the dependent claims of the instant application, considered individually, or as a as a whole, as an ordered combination, do not amount to significantly more (see MPEP 2106.07(a)II).
In sum, claims 1-20 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claims 8-14 are directed to non-statutory subject matter because the system disclosed is software per se. The applicant is advised to implement a particular hardware component such as a computer in the body of the claim language to the claimed system in order to overcome the rejections.
Claims 8-14 recite a method directed to purely mental steps. In order for a method to be considered a "process" under §101, a claimed process must either: (1) be tied to another statutory class (such as a particular apparatus) or (2) transform underlying subject matter (such as an article or materials). Diamond v. Diehr, 450 U.S. 175, 184 (1981); Parker v. Flook, 437 U.S. 584, 588 n.9 (1978); Gottschalk v. Benson, 409 U.S. 63, 70 (1972); Cochrane v. Deener, 94 U.S. 780, 787-788 (1876). If neither of these requirements is met by the claim, the method is not a patent eligible process under §101 and is non-statutory subject matter. Thus, to qualify as a statutory process, the claim should positively recite the other statutory class (the thing or product) to which it is tied, for example, by identifying the apparatus that accomplishes the method steps, or positively recite the subject matter that is being transformed, for example, by identifying the material that is being changed to a different state. The applicant is advised to implement a particular apparatus such as a computer in the body of the claim language to perform the claimed method in order to overcome the rejections.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the difference between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
i. Determining the scope and contents of the prior art.
ii. Ascertaining the differences between the prior art and the claims at issue.
iii. Resolving the level of ordinary skill in the pertinent art.
iv. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alon et al (US 2022/0156524), in view of Zhou e al (US 2022/0309359), in further view of Miguel et al (US 2021/0240787).
Regarding Claims 1, 15: Alon discloses: A system, comprising: a non-transitory memory; and one or more hardware processors coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
providing a set of attribute values associated with the transaction to an efficacy determination model external to a cascade machine learning (ML) model system, {see at least fig1B, rc201b, [0024] providing input data (based on the BRI (MPEP 2111), reads on attribute values; based on the BRI (MPEP 2111), efficacy is regarded as a particular version of efficiency)}
wherein the efficacy determination model is configured to evaluate an efficacy of each ML model in the cascade ML model system for classifying the transaction; {see at least fig5, fig6, fog7A, fig7B, [0076]-[0082] cascaded ensembles; ML efficiency models; EfficientNet models (reads on evaluating efficacy; based on the BRI (MPEP 2111), efficacy is regarded as a particular version of efficiency))
obtaining one or more efficacy output from the efficacy determination model, wherein the one or more efficacy outputs indicate a predicted accuracy of each ML model in the plurality of ML models of the cascade ML model system in classifying the transaction; {see at least fig1B, rc210b, [0024]-[0027] the model generates a first output}
wherein the classifying comprises using the second ML model, but not the first ML model {see at least [0022] accurately predicting classes; selecting predicted classes (reads on classifying)}
Alon dos not disclose, however, Zhou discloses:
receiving a request for classifying a transaction; {see at least [0051]-[0053] transaction request}
classifying, by the cascade ML model system, the transaction based on the set of attribute values {see at least [0019] risk associated with the transaction type (reads on classifying transactions); [0025] fraudulent or legitimate transactions (reads on classifying transactions); [0053]-[0059]; fig1, rc210, [0071] classification of transactions; [0076], [0085] classification of transactions}
processing the transaction based on the classifying. {see at least [0085] transaction processed if legitimate (reads on processing transaction)}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Alon to include the elements of Zhou. One would have been motivated to do so, in order to perform the processing of the classified transactions. In the instant case, Alon evidently discloses classifying transactions. Zhou is merely relied upon to illustrate the functionality of processing the classified transactions in the same or similar context. Since both classifying transactions, as well as processing the classified transactions are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Alon, as well as Zhou would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Alon / Zhou.
Alon, Zhou does not disclose, however, Miguel discloses:
wherein the cascade ML model system comprises a plurality of ML models and is configured to classify the transaction according to an execution scheme that specifies an order of executing a first ML model in the plurality of ML models before executing a second ML model in the plurality of ML models, and {see at least fig4, rc410, rc420, rc430, [0132]-[0135] sequence of executing the first, second, third, fourth machine learning models (reads on specified order)}
wherein the classifying comprises using the second ML model, but not the first ML model when {see at least fig4, rc410, rc420, rc430, [0132]-[0135] any one of the first, second, third models are executed, but the fourth model is not executed, as it is not shown in fig4;}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Alon, Zhou to include the elements of Miguel. One would have been motivated to do so, in order to improve the execution results. In the instant case, Alon, Zhou evidently discloses performing the processing of classified transactions. Miguel is merely relied upon to illustrate the functionality of selecting the ML models and the sequencing of those ML models in the same or similar context. Since both performing the processing of classified transactions, as well as selecting the ML models and the sequencing of those ML models are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Alon, Zhou, as well as Miguel would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Alon, Zhou / Miguel.
Regarding Claims 2: Alon, Zhou, Miguel discloses the limitations of Claims. Alon further discloses: wherein the classifying the transaction comprises:
classifying, using the second ML model in the cascade ML model system, the transaction into a first classification of a plurality of classifications. {see at least [0002] model used to classify inputs; [0080]-[0081], [table1] classifying inputs}
Regarding Claims 3: Alon, Zhou, Miguel discloses the limitations of Claims 2. Zhou further discloses: wherein the classifying the transaction further comprises:
in response to determining that the transaction is classified into first classification using the second ML model, re-classifying, using a third ML model in the cascade ML model system, the transaction into a second classification of the plurality of classifications, wherein the transaction is processed based on the second classification. {see at least fig6, rc600, [0082] cutoff threshold (based on the BRI (MPEP 2111) the reclassification occurs by changing the cutoff threshold)}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Alon, Zhou, Miguel to include additional elements of Zhou. One would have been motivated to do so, in order to create a improved classification of transactions. In the instant case, Alon, Zhou evidently discloses performing the processing of classified transactions. Zhou is merely relied upon to illustrate the additional functionality of re-classifying transactions in the same or similar context. Since the subject matter is merely a combination of old elements, and in the combination each element would have performed the same function it performed separately, one having ordinary skill in the art before the effective filing date would have recognized that the results of the combination were predictable.
Regarding Claims 4: Alon, Zhou, Miguel discloses the limitations of Claims 3. Miguel further discloses: wherein the operation further comprises:
selecting, from the plurality of ML models, the third ML model for re-classifying the transaction based on the execution scheme {see at least fig4, rc410, rc420, rc430, [0132]-[0135] sequence of executing the first, second, third, fourth machine learning models (reads on specified order); any one of the first, second, third models are executed, but the fourth model is not executed, as it is not shown in fig4}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Alon, Zhou, Miguel to include additional elements of Miguel. One would have been motivated to do so, in order to create classification criteria for a more efficient execution. In the instant case, Alon, Zhou, Miguel evidently discloses processing classified transactions. Miguel is merely relied upon to illustrate the additional functionality of selecting a different model for reclassifying in the same or similar context. Since the subject matter is merely a combination of old elements, and in the combination each element would have performed the same function it performed separately, one having ordinary skill in the art before the effective filing date would have recognized that the results of the combination were predictable.
Regarding Claims 5: Alon, Zhou, Miguel discloses the limitations of Claims 1, 2. Zhou further discloses:
wherein the one or more efficacy outputs indicate an efficacy score associated with the first ML model below a threshold. {see at least fig6, rc600, [0082] cutoff threshold (based on the BRI (MPEP 2111) the reclassification occurs by changing the cutoff threshold)}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Alon, Zhou, Miguel to include additional elements of Zhou. One would have been motivated to do so, in order to create classification criteria for a more efficient execution. In the instant case, Alon, Zhou, Miguel evidently discloses processing classified transactions. Zhou is merely relied upon to illustrate the additional functionality of a threshold score in the same or similar context. Since the subject matter is merely a combination of old elements, and in the combination each element would have performed the same function it performed separately, one having ordinary skill in the art before the effective filing date would have recognized that the results of the combination were predictable.
Regarding Claims 6, 12-14: Alon, Zhou, Miguel discloses the limitations of Claims 1, 8, 10. Alon further discloses:
wherein the operations further comprise:
determining a plurality of prediction accuracy categories based on possible prediction accuracy outcomes from different ML models in the plurality of ML models of the cascade ML model system, wherein each prediction accuracy category in the plurality of prediction accuracy categories represents a corresponding combination of prediction accuracy outcomes associated with the plurality of ML models; {see at least fig1B, rc210b, rc220b, [0024]-[0027] different ML models; the tow ML models are concatenated for improved results}
determining, for a previously conducted transaction, a particular prediction accuracy category based on prediction accuracy outcomes associated with the plurality of ML models in classifying the previously conducted transaction; {see at least fig1B, rc210b, rc220b, [0024]-[0027] different ML models; the tow ML models are concatenated for improved results; prediction accuracy is improved}
labeling the previously conducted transaction with the particular prediction accuracy category; {see at least fig1B, rc210b, rc220b, [0026] less or greater than threshold (based on BRI (MPEP 2111), reads on accuracy category}
generating training data for the efficacy determination model based at least on the previously conducted transaction labeled with the particular prediction accuracy category; and {see at least fig1B, rc210b, rc220b, [0026] less or greater than threshold (based on BRI (MPEP 2111), reads on accuracy category; adding the second ML model, (e.g., rc220b), creates a particular prediction accuracy category)}
training the efficacy determination model using the training data. {see at least [0003], [0019]-[0020] training data set}
Regarding Claims 7: Alon, Zhou, Miguel discloses the limitations of Claims 6. Alon further discloses:
wherein the plurality of prediction accuracy categories comprises a first prediction accuracy category indicating an accurate prediction for each ML model in the plurality of ML models, a second prediction accuracy category indicating an accurate prediction for one of the plurality of ML models, and a third prediction accuracy category indicating an inaccurate prediction for each ML model in the plurality of ML models. {see at least fig1B, rc210b, rc220b. Alon discloses the claimed invention except for a third ML model. It would have been obvious to one having ordinary skill in the art at the time the invention was filed to add a third ML model to the already disclosed two ML models, since it has been held that mere duplication of the essential working parts of a device involves only routine skill in the art. St. Regis Paper Co. v Bemis Co., 193 USPQ 8; In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960).}
Regarding Claims 8, 16-17: Alon discloses: A method, comprising:
determining, using an efficacy determination model configured to evaluate an efficacy of each ML model of the cascade ML model system, one or more efficacy outputs based on the transaction data associated with the transaction, {see at least fig1B, rc210b, [0024]-[0027] the model generates a first output}
wherein the one or more efficacy outputs indicates a predicted accuracy of each ML model in the plurality of ML models of the cascade ML model system in classifying the transaction
modifying the execution scheme of the cascade ML model system for classifying the transaction based on the one or more efficacy outputs, {see at least fig1B, rc210b, rc220b, [0024]-[0027] improving accuracy through cascading ML models (based on BRI (MPEP 2111), reads on predicted, desired accuracy}
wherein the modified execution scheme indicates a bypass of the first ML model in classifying the transaction; {see at least fig1, rc210b, [0024]-[0026] only first ML model used (reads on bypassing the second model)}
subsequent to the modifying the execution scheme,
classifying, using the cascade ML model system, the transaction based on the transaction data; {see at least [0022] accurately predicting classes; selecting predicted classes (reads on classifying)}
wherein the classifying comprises using the second ML model, but not the first ML model; {see at least [0022] accurately predicting classes; selecting predicted classes (reads on classifying)}
Alon does not disclose, however, Zhou discloses:
receiving a request for processing a transaction; {see at least [0051]-[0053] transaction request}
processing the transaction based on the classifying. {see at least [0085] transaction processed if legitimate (reads on processing transaction)}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Alon to include the elements of Zhou. One would have been motivated to do so, in order to perform the processing of the classified transactions. In the instant case, Alon evidently discloses classifying transactions. Zhou is merely relied upon to illustrate the functionality of processing the classified transactions in the same or similar context. Since both classifying transactions, as well as processing the classified transactions are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Alon, as well as Zhou would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Alon / Zhou.
Alon, Zhou does not disclose, however, Miguel discloses:
accessing a cascade machine learning (ML) model system for classifying the transaction, wherein the cascade ML model system comprises a plurality of ML models and is configured to classify the transaction according to an execution scheme that specifies an order of executing a first ML model in the plurality of ML models before executing a second ML model in the plurality of ML models; {see at least fig4, rc410, rc420, rc430, [0132]-[0135] sequence of executing the first, second, third, fourth machine learning models (reads on specified order)}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Alon, Zhou to include the elements of Miguel. One would have been motivated to do so, in order to improve the execution results. In the instant case, Alon, Zhou evidently discloses performing the processing of classified transactions. Miguel is merely relied upon to illustrate the functionality of selecting the ML models and the sequencing of those ML models in the same or similar context. Since both performing the processing of classified transactions, as well as selecting the ML models and the sequencing of those ML models are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Alon, Zhou, as well as Miguel would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Alon, Zhou / Miguel.
Regarding Claims 9, 18: Alon, Zhou, Miguel discloses the limitations of Claims 8, 15. Alon further discloses: further comprising:
configuring the plurality of ML models in the cascade ML model system as a whole. {see at least fig1B, rc210b, rc215b, rc220b, [0024]-[0027] ML models are concatenated (cascaded)}
Regarding Claims 10, 19: Alon, Zhou, Miguel discloses the limitations of Claims 9, 18. Alon further discloses: wherein the configuring the plurality of ML models comprises:
determining a set of configurations for the cascade ML model system, wherein each configuration in the set of configurations represents a different set of hyperparameter values for the plurality of ML models in the cascade ML model system; {see at least fig1B, rc210b, rc220b, [0024]-[0027]}
generating a plurality of instances of the cascade ML model system, wherein each instance in the plurality of instances of the cascade ML model system is configured based on a distinct configuration from the set of configurations; {see at least fig1B, rc210b, rc215b, rc220b, [0024]-[0027] (based on BRI (MPEP 2111), reads on distinct configurations}
testing the plurality of instances of the cascade ML model system; {see at least fig1B, rc210b, rc220b, [0024]-[0027] – with or without rc220b (based on the BRI (MPEP 2111), reads on testing configurations}
selecting, from the set of configurations, a particular configuration for configuring the cascade ML model system based on the testing; and {see at least fig1B, rc210b, rc220b, [0024]-[0027] – rc210b and rc220b (based on the BRI (MPEP 2111), reads on selecting a particular configuration}
configuring the cascade ML model system using the particular configuration. {see at least fig1B, rc210b, rc220b, [0024]-[0027] – rc210b and rc220b (based on the BRI (MPEP 2111), reads on selecting a particular configuration, i.e., configuring}
Regarding Claims 11, 20: Alon, Zhou, Miguel discloses the limitations of Claims 10, 19. Alon further discloses:
wherein a particular instance of the cascade ML model system configured using the particular configuration yields a highest accuracy result among the plurality of instances based on the testing. {see at least [claim9] concatenating (i.e., cascading) the ML models, yields highest accuracy}
The prior art made of record and not relied upon which, however, is considered pertinent to applicant's disclosure:
US 20240273339 A1 Zhao; Wei et al. MIXTURE-OF-EXPERT BASED NEURAL NETWORKS Methods and systems are presented for configuring, training, and utilizing a machine learning model that includes different experts corresponding to different domains, such that the machine learning model may facilitate transfer of knowledge acquired from one domain to another domain and to use different mixtures of experts to perform tasks across the different domains. The machine learning model includes individual domain experts configured to process input values corresponding to features that are unique to the corresponding domains. The machine learning model also includes a common expert configured to process input values corresponding to features that are common to the different domains. By training the machine learning model using training data associated with a first domain, both a first domain expert and the common expert are trained. The knowledge acquired by the common expert can then be utilized when processing tasks associated with a second domain.
US 20090119172 A1 Soloff; David L. Advertising Futures Marketplace Methods and Systems Methods and systems provide information products relating to past, present and future advertising transactions (i.e., contracts to place advertisements in various media) to enable a marketplace in advertising products. Information regarding a plurality of advertising transactions are gathered. Data is analyzed to determine its attributes. Some attribute values are transformed and the attribute values are stored in a database. Attributes are organized or indexed according to a taxonomy of attributes to provide indexes to advertising transaction records. Indexes and benchmarks for various selected types of advertising transactions can be generated by selecting certain records from the database and aggregating the data or otherwise synthesizing information products, such as benchmarks and market entities for the selected types of advertising transactions. Information products may be published and syndicated as market indexes and benchmarks.
US 20240095738 A1 Arulmozhi; Suraj et al. DATA MINING FRAMEWORK FOR SEGMENT PREDICTION Methods and systems are presented for mining data in association with predicting occurrences of events. Upon detecting an occurrence of an event associated with a transaction, a data mining system accesses data associated with different transactions, and generates a decision tree for predicting occurrences of the event based on the data. Using a classification specification, the data mining system traverses the decision tree and prunes at least a portion of the decision tree that does not satisfy the classification specification. The data mining system then extracts data relevant to predicting occurrences of the event from the pruned decision tree. The extracted data includes attributes and/or criteria that are relevant to predicting occurrences of the event. Based on the extracted data, one or more actions can be performed to improve the event prediction process and/or reduce the frequency of the occurrences of the event.
US 6985878 B1 Yamazaki; Hiroshi et al. Integrated finance risk manager and financial transaction modeling device A class corresponding to a plurality of transaction entities are collectively processed to realize a virtual transaction. A cash flow type transaction entity is managed as a set of cash flow elements (CashFlowLet) for each unit transaction period on each of the receipt side and the payment side, and a current price evaluating operation for each element is commonly operated. An option transaction is realized by a class storing a class of an original asset transaction as a container. A financial curve definition function and a function of realizing a virtual curve by combining a plurality of financial curves are implemented. A user interface capable of easily changing a parameter for use in risk management and displaying a simulation result thereof can be provided.
US 20210304204 A1 Ramesh; Venkatesh J. et al. MACHINE LEARNING MODEL AND NARRATIVE GENERATOR FOR PROHIBITED TRANSACTION DETECTION AND COMPLIANCE There are provided systems and methods for a machine learning model and narrative generator for prohibited transaction detection and compliance. A service provider server, such as an electronic transaction processor, may generate a machine learning model using a supervised training technique, which may detect transactions that may be money laundering. The model may be iteratively trained by detecting flagged transactions and outputting those transactions to an agent for identification of false positives, which may be used to retrain the model. When outputting the flagged transactions, a narrative may be generated using an explainer graph and a machine learning prediction explainer that identifies the features of the transaction data that caused the transactions to be flagged. Further, once the model is trained additional transactions may be processed to determine whether the features of those transactions indicate prohibited behavior.
US 20220309359 A1 Zhou; Yanzan et al. ADVERSE FEATURES NEUTRALIZATION IN MACHINE LEARNING Methods and systems are presented for identifying and neutralizing adverse input features that negatively impact accuracy of a machine learning model. A machine learning model is configured to produce an output based on parameter values corresponding to input features. Each input feature is evaluated with respect to its impact on producing a correct output by the machine learning model. One or more adverse input features that have a negative impact on accuracy of the machine learning model are determined. When a request to assess a data is received, input values associated with the data and corresponding to the set of input features are obtained. One or more input values corresponding to the adverse input features are identified. The one or more input values are altered, and the altered input values along with other unaltered input values are used to generate a more accurate output by the machine learning model.
US 11727506 B2 Cella; Charles Howard Systems and methods for automated loan management based on crowdsourced entity information Systems and methods for automated servicing of a subsidized loan are disclosed. An example system may include a crowdsourcing services circuit to collect information related to a set of entities involved in a set of subsidized loan transactions and a condition classifying circuit including a model and an artificial intelligence services circuit to classify a set of parameters of the set of subsidized loans based on information from the crowdsourcing services circuit, where the model is trained using a training data set of outcomes related to subsidized loans. The example system may further include a smart contract circuit for automatically modifying a term or a condition of the subsidized loan based on the classified set of parameters from the condition classifying circuit.
Response to Amendments/Arguments
Applicant’s submitted remarks and arguments have been fully considered.
Applicant disagrees with the Office Action conclusions and asserts that the presented claims fully comply with the requirements of 35 U.S.C. § 101 regrading judicial exceptions. Further, Applicant is of the opinion that the prior art fails to teach Applicant’s invention.
Examiner respectfully disagrees in both regards.
With respect to Applicant’s Remarks as to the Claim Objection
The objection is withdrawn, as a result of the amendments.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 101.
Applicant submits:
a. The pending claims are not directed to an abstract idea.
b. The identified abstract idea is integrated into a practical application.
c. The pending claims amount to significantly more.
Furthermore, Applicant asserts that the Office has failed to meet its burden to identify the abstract idea and to establish that the identified abstract idea is not integrated into a practical application and that the pending claims do not amount to significantly more.
Examiner responds – The arguments have been considered in light of Applicants’ amendments to the claims. The arguments ARE NOT PERSUASIVE. Therefore, the rejection is maintained.
The pending claims, as a whole, are directed to an abstract idea not integrated into a practical application. This is because (1) they do not effect improvements to the functioning of a computer, or to any other technology or technical field (see MPEP 2106.05 (a)); (2) they do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or a medical condition (see the Vanda memo); (3) they do not apply the abstract idea with, or by use of, a particular machine (see MPEP 2106.05 (b)); (4) they do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05 (c)); (5) they do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the identified abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designated to monopolize the exception (see MPEP 2106.05 (e) and the Vanda memo).
In addition, the pending claims do not amount to significantly more than the abstract idea itself.
As such, the pending claims, when considered as a whole, are directed to an abstract idea not integrated into a practical application and not amounting to significantly more.
More specific:
Applicant submits “Here, even if it is determined that amended claim 1 recites an abstract idea, Applicant respectfully submits that when viewed as a whole, amended claim 1 integrates the abstract idea into a practical application under Step 2A, Prong Two of the Alice/Mayo analysis.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
MPEP 2106.04(d)(1) discloses:
An important consideration to evaluate when determining whether the claim as a whole integrates a judicial exception into a practical application is whether the claimed invention improves the functioning of a computer or other technology .... In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art .... Second, if the specification sets forth an improvement in technology. the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. (Emphasis added)
That is, the claimed invention may integrate the judicial exception into a practical application by demonstrating that it improves the relevant existing technology although it may not be an improvement over well-understood, routine, conventional activity. (Emphasis added)
Thus, the rejection is proper and has been maintained.
Applicant submits “As such, Applicant respectfully submits that the claims, as a whole, provide an improvement to the technical field of machine learning (see MPEP 2106.05(a) and DDR Holdings, LLC v. Hotels.com). Specifically, using the techniques recited in the claims, the latency of performing a task by a machine learning model can be reduced while maintaining acceptable accuracy performance of the task.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
Regarding applicant’s argument making reference to the DDR Holdings, LLC v Hotels.com L.P., 773 F.3d 1245 (Fed. Cir.) (2014) decision: “… the claims, as a whole, provide an improvement to the technical field of machine learning (see MPEP 2106.05(a) and DDR Holdings, LLC v. Hotels.com).” Examiner responds as follows:
Applicant appears to argue that the claims are patent eligible and significantly more, as they are similar to the claims that were found to be eligible in the DDR Holdings decision. However, in the DDR decision, the Court sided with the applicant/patent holder not only because, as quoted, “[T]he claimed solution is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks.” DDR Holdings, 773 F.3d at 1257.
This rationale from the DDR decision, cannot be taken in a vacuum, as the court made clear that the fundamental ultimate reasoning for their decision hinged upon, as they explain, that “It is also clear that the claims at issue do not attempt to preempt every application of the idea of increasing sales by making two web pages look the same, or of any other variant suggested by [defendants]. Rather, they recite a specific way to automate the creation of a composite web page by an “outsource provider” that incorporates elements from multiple sources in order to solve a problem faced by websites on the Internet.” So, the computer in the DDR case doing more than just fundamental, routine functioning, but it is doing what is described, creating a website that has the look and feel of the competitor website using a composite of information from multiple sources. Such capability for the computer to dynamically generate a composite webpage not from predetermined instructions, but from a dynamic composite of several sources of dynamic information, was considered a technical improvement of the computer by the Court. The Court further points to the technical improvement of the hyperlink, in which the hyperlink is not used in the traditional manner of simply transferring the user to a corresponding URL, but instead the engagement of the hyperlink triggers the process of dynamic webpage generation. It was ultimately these reasons that took the claims beyond simply generic functioning, not only that it was solving a problem that specifically rose in the realm of computer networks.
By contrast, the instant application discloses a computer that utilizes a series of machine learning models that can be configures to achieve a certain objective. Unlike the limitations in the DDR Holdings case, the limitations of the instant application contain elements constitute an abstract idea, with the additional elements not interacting the abstract idea into a practical application and not constituting “significant more” for they do not improve the technical field (see MPEP 2106.059(a)), does not apply the judicial exception with or uses a particular machine (see MPEP 2106.059(b)), does not apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designated to monopolize the exception (see MPEP 2106.05(e)).
Thus, the rejection is proper and has been maintained.
Applicant submits “Since the claims, as amended herein, recite specific steps of changing the configuration of a cascade machine learning model system (e.g., modifying the order and/or the specific machine learning models to be executed based on characteristics of a task, etc.), the flow of information (e.g., transaction data, etc.) is changed dynamically based on such a modification/re-configuration of the cascade machine learning model system.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
The Supreme Court has supported that choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success is sufficient to determine an invention obvious over such combination (see KSR International Co. v. Teleflex Inc. (KSR), 550 U.S.,82 USPQ2d 1385 (2007) & MPEP 2143(E)).
Thus, the rejection is proper and has been maintained.
It follows from the above that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. Therefore, the rejection under 35 U.S.C. § 101 is maintained.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 103.
Applicant submits remarks and arguments geared toward the amendments. Examiner has carefully reviewed and considered Applicant’s remarks; however, they ARE MOOT in light of the fact that they are geared towards the amendments. Nevertheless, Miguel discloses:
wherein the cascade ML model system comprises a plurality of ML models and is configured to classify the transaction according to an execution scheme that specifies an order of executing a first ML model in the plurality of ML models before executing a second ML model in the plurality of ML models, and {see at least fig4, rc410, rc420, rc430, [0132]-[0135] sequence of executing the first, second, third, fourth machine learning models (reads on specified order)}
wherein the classifying comprises using the second ML model, but not the first ML model when {see at least fig4, rc410, rc420, rc430, [0132]-[0135] any one of the first, second, third models are executed, but the fourth model is not executed, as it is not shown in fig4;}
Therefore, Miguel discloses the amended claim limitation.
The other arguments presented by Applicant continually point back to the above arguments as being the basis for the arguments against the other 103 rejections, as the other arguments are presented only because those claims depend from the independent claims, and the main argument above is presented against the independent claims. Therefore, it is believed that all arguments put forth have been addressed by the points above.
Examiner has reviewed and considered all of Applicant’s remarks. The changes of the grounds for rejection, if any, have been necessitated by Applicant’s extensive amendments to the claims. Therefore, the rejection is maintained, necessitated by the extensive amendments and by the fact that the rejection of the claims under 35 USC § 101 has not been overcome.
Conclusion
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 extension fee 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.
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/Radu Andrei/
Primary Examiner, AU 3698