Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/05/2026 has been entered.
Response to Amendment
The office action is responsive to the amendment filed on 01/05/2026 . As directed by the amendments claims 1, 18 and 20 are amended. Claims 1 and 4-20 are pending for examination.
Response to Arguments
Regarding the 35 U.S.C § 112(a) rejection:
Applicant's further arguments see pg. 11-12 filed 01/05/2026, have been fully considered but they are not persuasive.
APPLICANT ARGUMENT:
Applicant argues, “Contrary to the Examiner's assertions, the specification states "embodiments provide techniques and systems that enable automatic evaluation and/or selection of features not only based on the impact of each feature on the performance of the MTL model, but also based on the impact of each feature on the performance of each individual task." Published Application,' para. [0079]. For example, "if the issuer system has a sufficiently high degree of certainty... that a transaction can be posted early... posting the transaction may... improve accuracy." Id. The specification explains a "performance score" can be, e.g., "F score, F1 score, accuracy, and/or the like," and "the feature score for each respective feature may be determined based on the performance score." Published Application, paras. [0114], [0118]. Thus, features are selected based on feature scores, which are determined based on performance scores (e.g., accuracy scores), and the features that have the greatest impact on accuracy can be selected”.
For the reasons stated above, applicant submits that independent claim 1 complies with the written description requirement. Similar, independent claims 18 and 20 are allowable for at least the reasons set forth above regarding independent claim 1. Claims 4-17 and 19 are allowable at least for depending on claims 1, 18and 20. Therefore, applicant requests that the rejection of claims 1 and 4-20 under 35 U.S.C. § 112(a) be withdrawn and that the claims be allowed.
EXAMINER RESPONSE: Examiner respectfully disagree. Claims 1 recites the limitation “training, with the at least one processor, a second multi-task learning model based on the subset of the plurality of features of the testing data set to improve accuracy of the second multi-task learning model”. However, the specification does not contain support in how the accuracy of the model is being improved based on the subset of the plurality of features. To be specific, paragraphs [0009], [0011], [0030], [0032] teach training a second multi-task learning model based on a subset of the testing data set, however it does not teach that training the “second multi-task learning model” with the subset of the plurality of features of the testing data in order to improves accuracy of the model. In addition, as stated in paragraph [0079] of the instant application the only recitation of accuracy improvement occurring is related to posting a transaction to “ improve accuracy (of the balance and/or available funds of the consumer's account)”, this does not teach or suggest, that the “second multi-task learning model” is being improved as claimed in claim 1. Therefore, the specification of the instant application does not teach or suggest the second multi-task learning model being improved based on the subset of the plurality of features of the testing data set. Claims 18 and 20 are independent and recites similar limitation to claim 1.
Therefore, the rejection of claim 1 applies. Claims 4-17 and 19 are dependent on claims 1 and 18. Therefore, the rejection of claims 1 and 18 applies.
Regarding the 35 U.S.C § 101 rejection:
Applicant's further arguments see pg. 12-19 filed 01/05/2026, have been fully considered but they are not persuasive.
APPLICANT ARGUMENT:
Applicant argues, the claims are not “directed to” an abstract idea – “While Applicant maintains that the claims are clearly eligible under any standard, Applicant respectfully submits that the rejections cannot be sustained under the instructions for examination under the revised MPEP and the 2024 Guidance, the August 2025 Memo, and the Desjardins Memo”. Applicant submits that the claims are directed to a “...practical application including systems and techniques for improved multi-task learning” in particular applicant argues “the claims are not directed to any abstract idea, but rather to a practical application that is tied to a particular machine/system (e.g., first and second multi-task learning models) and includes improvements to technology, including improvements that are necessarily rooted in computer technology”. Applicant further argues in pg. 17, “The recent decision by the Appeals Review Panel (ARP) is in Ex pane Desjardins, Appeal No. 2024-000567 (September 26, 2025) is instructive here”.
Moreover, applicant argues the claims recites an “inventive concept” that is significantly more than any alleged abstract idea – “Even if it is found that the claims are directed to an abstract idea, the claims are still eligible under 35 U.S.C. § 101 at least because the claim elements, taken alone or in combination, have not been shown to be well-understood, routine, and conventional and, instead, include elements and/or combinations of elements that, as a whole, amount to significantly more than a judicial exception and provide an improvement to technology”.
EXAMINER RESPONSE: Examiner respectfully disagree; applicant argument is not persuasive. The claims are directed toward an abstract idea. Amended independent claim 1 is rejected under 35 US.C. § 101 because the claims recites the limitation of that can be performed by a human with the aid of pen and paper. For example, a human can group data such as feature, can determine overall accuracy score, can adjust scores based on the grouping and can select subset of features from multiple features. Further, the additional elements as disclosed alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, amended claim 1 when viewed as whole recites abstract idea of grouping and selecting data based on scored that are being adjusted.
In addition, the amended claims as presented do not recites "an improvement in the functioning of a computer, or an improvement to other technology or technical field", though amended claim 1 recites the subset of the plurality of features are used for training to “improve the accuracy of the second multi-task learning model” the specification lacks of support for this limitation, the claim does not specify how the accuracy of the model is being the improved and the applicant does not provide details in how the claims as whole provides an improvement. To add, the only improvement being mentioned in the specification can be found in paragraph [0079] which are related to costumer experience, balance and/or available funds and transparency, which are not evident improvement of accuracy of the second multi-task learning model as recited in amended claim 1. Furthermore, as stated in the “ the USPTO Memo on "Advance notice of change to the MPEP in light of Ex Parte Desjardins" of December 5, 2025 (hereinafter referred to as the "USPTO Memo") the “updates are not intended to announce any new USPTO practice or procedure and are meant to be consistent with existing USPTO guidance”. Regarding applicant argument, applicant does not provide detail in how the claims of the instant applicant are analogues to the USPTO Memo other than stating “The recent decision by the Appeals Review Panel (ARP) is in Ex pane Desjardins, Appeal No. 2024-000567 (September 26, 2025) is instructive here”.
Nonetheless, the claims presented for examination are not analogues to the Ex Parte Desjardins, Appeal No. 2024-000567 (hereinafter Ex Parte Desjardins) as the claim as presented for examination, do not specify the technical features or details for how the computer functionality is being improved, in the Ex Parte Desjardins, the claims were found to be patent eligible because when evaluating the claim as whole, the limitation of independent claim 1, constituted an improvement to how the machine learning model itself operated, and not, for example, the identified mathematical calculation. Thus, the claims of the instant applicant do not reflect such improvement to the machine learning model, instead independent claims 1,18 and 20 recites claim language in a generic manner which appear to be an improvements of an abstract idea. The following paragraph from the USPTO Memo regarding the Ex Parte Desjardins, recites:
“The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO)”. (emphasis added, see MPEP § 2106.04(d), subsection III).
Per contra, independent claim 1 when view as whole do not appear to recites features which provide concrete benefits, for example, the claim does not provide details in how for example the machine learning model is being optimize, how deployment times are being decreased or how lower computational cost for training in the target domain are being achieved. Further, applicant is reminder that while “the claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”)”, if the specification sets forth an improvement in technology or a technical field, the claims must “includes the components or steps of the invention that provide the improvement described in the specification” (see MPEP § 2106.04(d)(1)).
Moreover, regarding the arguments that “the claims are directed to a practical application”, examiner respectfully disagree. The amended claims as presented do not integrates the judicial exception into a practical application since the claimed limitations are recited as a mental process of reducing features in a generic manner without any technical detail or feature that requires specific use or improvement or tied to a particular machine rather than performing the mental process with generic use of a computer (see MPEP 2106.04(d)). Rather the claims merely recites the words "apply it" (or an equivalent) as discussed in MPEP § 2106.05(f) and it adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) which the courts have also identified limitations that did not integrate a judicial exception into a practical application (see MPEP 2106.04(d)(1)).
Lastly, regarding applicant argument, regarding the arguments “the claims recites an “inventive concept” that is significantly more than any alleged abstract idea”, examiner respectfully disagree. The amended claims as presented include limitations that the courts have identified not to be enough to qualify as “significantly more” when recited in a claim with a judicial exceptions this includes: adding the words “apply it” (or equivalent) with the judicial exception; simply appending well-understood, routine, conventional activities previously known to the industry, specified at high level of generality, to the judicial exception; adding insignificant extra solution activity to the judicial exception and generally linking the use of the judicial exception to a particular environment or field of use (see MPEP 2106.05 (I)(A)).
Therefore for the above reason, claims 1 and 4-20 are not directed to patent-eligible subject matter under 35 U.S.C § 101.
Regarding the 35 U.S.C § 103 rejection:
Applicant’s arguments, see pg. 20-23, filed 01/05/2026, with respect to claims 1 and 4-20 have been fully considered and are persuasive. The rejection of claims 1 and 4-20 under 35 U.S.C § 103 rejection has been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 4-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 recites the limitation “training, with the at least one processor, a second multi-task learning model based on the subset of the plurality of features of the testing data set to improve accuracy of the second multi-task learning model”. However, the specification does not contain support in how the accuracy of the model is improved based on the selection of the features. To be specific, [0079] of the specification teaches “Additionally or alternatively, such embodiments provide techniques and systems that enable automatic evaluation and/or selection of features not only based on the impact of each feature on the performance of the MTL model...” and Fig. 6 teaches “F1s = Overall Accuracy Score of the Model” but neither the specification nor the drawing explicitly teach how the “accuracy” of the model is being improved as suggested by the amended claim 1. In conclusion, the specification does not provides details in how the model accuracy is being improved as result of selecting the subset of the plurality of features of the testing data set as disclosed in the amended claim 1.
Claims 18 and 20 are independent and recites similar limitation to claim 1. Therefore, the rejection of claim 1 applies.
Claims 4-17 and 19 are dependent on claims 1 and 18. Therefore, the rejection of claims 1 and 18 applies.
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 and 4-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1, 4-19 are a method type claim. Claim 20 is a system claim. Therefore, claims 1 and 4-20 are directed to either a process, machine, manufacture or composition of matter.
Regarding claim 1: Step 2A Prong 1:
grouping... the plurality of features into a plurality of groups based on an impact of each feature of the plurality of features on the first task and the at least one second task; (mental process – of grouping features into groups can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... an overall accuracy score, a first task accuracy score, and at least one second task accuracy score based on inputting the testing data set to the first multi-task learning model; (mental process – of determining an overall accuracy score and accuracy score of the task can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
adjusting... the feature score of each respective feature of the plurality of features based on a respective group of the plurality of groups associated with the respective feature and at least one of the overall accuracy score, the first task accuracy score, the at least one second task accuracy score, or a combination thereof to provide an adjusted feature score for the respective feature; (mental process – of adjusting the feature score of each feature based on the grouping can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
selecting... a subset of the plurality of features based on the adjusted feature score for each respective feature of the plurality of features; and (mental process – of selecting subset of the plurality of features based on the adjusted feature score can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A computer-implemented method, comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
receiving, with at least one processor, a first multi-task learning model associated with a first task and at least one second task; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
receiving, with the at least one processor, a testing data set comprising a plurality of testing data items for the first multi-task learning model, each testing data item comprising a plurality of elements, each element of the plurality of elements associated with a respective feature of a plurality of features; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
applying, with the at least one processor, feature reduction evaluation (FRE) based on the first multi-task learning model and the testing data set to provide a feature score for each feature of the plurality of features; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
training, with the at least one processor, a second multi-task learning model based on the subset of the plurality of features of the testing data set to improve accuracy of the second multi- task learning model (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A computer-implemented method, comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
receiving, with at least one processor, a first multi-task learning model associated with a first task and at least one second task; ( This is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
receiving, with the at least one processor, a testing data set comprising a plurality of testing data items for the first multi-task learning model, each testing data item comprising a plurality of elements, each element of the plurality of elements associated with a respective feature of a plurality of features; ( This is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
applying, with the at least one processor, feature reduction evaluation (FRE) based on the first multi-task learning model and the testing data set to provide a feature score for each feature of the plurality of features; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
training, with the at least one processor, a second multi-task learning model based on the subset of the plurality of features of the testing data set to improve accuracy of the second multi- task learning model (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 4: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
further comprising communicating, with the at least one processor, the adjusted feature score for each respective feature of the plurality of features to a remote computing device(This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 5: Step 2A Prong 1:
wherein grouping the plurality of features into a plurality of groups comprises: (mental process – of grouping features into groups can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
grouping... the plurality of features into the plurality of groups based on the first impact score and the at least one second impact score (mental process – of grouping features into groups can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
training, with the at least one processor, a third multi-task learning model based on a subset of the testing data set; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
applying, with the at least one processor, FRE based on the third multi-task learning model and the subset of the testing data set to provide a first impact score for each feature of the plurality of features on the first task and at least one second impact score for each feature of the plurality of features on the at least one second task; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 6: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the third multi-task learning model comprises an input layer, a first plurality of hidden layers associated with the first task, an output layer associated with the first task, at least one second plurality of hidden layers associated with the at least one second task, and at least one output layer associated with the at least one second task (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
Regarding claim 7: Step 2A Prong 1:
wherein grouping the plurality of features into the plurality of groups based on the first impact score and the at least one second impact score comprises: (mental process – of grouping the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
ranking... the plurality of features based on the first impact score of each feature of the plurality of features to provide a first ranking of the plurality of features; (mental process – of ranking the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... a first subset of features based on a first top portion of the first ranking of the plurality of features; (mental process – of determining a first subset of features based on a first top portion of the first ranking of the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... a second subset of features comprising features of the plurality of features not in the first subset of features; (mental process – of determining a second subset of features comprising features of the plurality of features not in the first subset of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
ranking... the plurality of features based on the at least one second impact score of each feature of the plurality of features to provide at least one second ranking of the plurality of features; (mental process – of ranking the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... at least one third subset of features based on at least one second top portion of the at least one second ranking of the plurality of features; (mental process – of determining the third subset of features based on at least one second top portion of the at least one second ranking of the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... at least one fourth subset of features comprising features of the plurality of features not in the at least one third subset of features; and (mental process – of determining the fourth subset of features comprises features of the plurality of features not in the third subset of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
grouping... the plurality of features based on the first subset of features, the second subset of features, the at least one third subset of features, and the at least one fourth subset of features (mental process – of grouping features based on the first, second, third, fourth subset of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 8: Step 2A Prong 1:
wherein grouping the plurality of features based on the first subset of features, the second subset of features, the at least one third subset of features, and the at least one fourth subset of features comprises: (mental process – of grouping the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... a first group of the plurality of features based on the first subset and the at least one third subset; (mental process – of determining a first group of the plurality of features based on the first and third subset can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... a second group of the plurality of features based on the first subset and the at least one fourth subset; (mental process – of determining a second group of the plurality of features based on the first and fourth subset can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... a third group of the plurality of features based on the second subset and the at least one third subset; and (mental process – of determining a third group of the plurality of features based on the second and third subset can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... a fourth group of the plurality of features based on the second subset and the at least one fourth subset (mental process – of determining a fourth group of the plurality of features based on the second and fourth subset can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 9: Step 2A Prong 1:
wherein adjusting the feature score of each respective feature of the plurality of features comprises: (mental process – of adjusting the feature score of each respective feature of the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
adjusting... the feature score of each respective feature of the first group of the plurality of features based on the overall accuracy score to provide the adjusted feature score for the respective feature of the first group of the plurality of features; (mental process – of adjusting the feature score can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
adjusting... the feature score of each respective feature of the second group of the plurality of features based on the overall accuracy score and the at least one second task accuracy score to provide the adjusted feature score for the respective feature of the second group of the plurality of features; (mental process – of adjusting the feature score can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
adjusting... the feature score of each respective feature of the third group of the plurality of features based on the overall accuracy score and the first task accuracy score to provide the adjusted feature score for the respective feature of the third group of the plurality of features; and (mental process – of adjusting the feature score can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
adjusting... the feature score of each respective feature of the fourth group of the plurality of features based on the overall accuracy score, the first task accuracy score, and the at least one second task accuracy score to provide the adjusted feature score for the respective feature of the fourth group of the plurality of features (mental process – of adjusting the feature score can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 10: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the first task comprises generating, based on an authorization request, a first prediction associated with a likelihood of a first transaction amount in the authorization request matching a second transaction amount in at least one clearing message corresponding to the authorization request (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 11: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the at least one second task comprises at least one of generating, based on the authorization request, a second prediction associated with when the at least one clearing message will be received after the authorization request, generating, based on the authorization request, a third prediction associated with a number of clearing request of the at least one clearing message, or any combination thereof (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 12: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the first prediction comprises a first score (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
Regarding claim 13: Step 2A Prong 1:
generating, ...the first score associated with the likelihood of the first transaction amount in the authorization request matching the second transaction amount in the at least one clearing message corresponding to the authorization request; (mental process – of generating scores associated with a probability can be performed by the human mind with the help of pen and paper (e.g., judgement )).
Steps 2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
receiving, with the at least one processor, the authorization request from at least one of a merchant system or an acquirer system; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
..with the at least one processor, based on the authorization request... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
inserting, with the at least one processor, the first score into at least one field of the authorization request to provide an enhanced authorization request; and (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
communicating, with the at least one processor, the enhanced authorization request to an issuer system (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
receiving, with the at least one processor, the authorization request from at least one of a merchant system or an acquirer system; ( This is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
..with the at least one processor, based on the authorization request... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
inserting, with the at least one processor, the first score into at least one field of the authorization request to provide an enhanced authorization request; and ( This is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
communicating, with the at least one processor, the enhanced authorization request to an issuer system ( This is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 14: Step 2A Prong 1:
wherein generating the first score comprises: (mental process – of generating a first score can be performed by the human mind with the help of pen and paper (e.g., judgement)).
determining... a first plurality of elements based on the authorization request, each element of the first plurality of elements associated with a first respective feature of the plurality of features; and (mental process – of determine a first plurality of elements can be performed by the human mind with the help of pen and paper (e.g., judgement)).
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
inputting, with the at least one processor, the first plurality of elements to the first multi-task learning model to generate the first score associated with the likelihood of the first transaction amount in the authorization request matching the second transaction amount in the at least one clearing message corresponding to the authorization request (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 15: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
further comprising determining, with the at least one processor, based on the authorization request, that the issuer system is enrolled in a program before generating the first score (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 16: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein generating the first score, inserting the first score into the at least one field of the authorization request to provide the enhanced authorization request... (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
...and communicating the enhanced authorization request are in response to determining that the issuer is enrolled in the program (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 17: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the issuer system determines to post a transaction associated with the authorization request to an account before receiving the clearing message corresponding to the authorization request based on the first score in the enhanced authorization request satisfying a threshold (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 18: 2A Prong 1:
generating, ...and a machine learning model, a first score associated with a likelihood of a first transaction amount in the authorization request matching a second transaction amount in at least one clearing message corresponding to the authorization request,... (mental process – of generating scores associated with a probability can be performed by the human mind with the help of pen and paper (e.g., judgement )).
grouping the plurality of features into a plurality of groups based on an impact of each feature of the plurality of features on the first task and the at least one second task; (mental process – of grouping features into groups can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining an overall accuracy score, a first task accuracy score, and at least one second task accuracy score based on inputting the testing data set to the first multi-task learning model; (mental process – of determining an overall accuracy score and accuracy score of the task can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
adjusting, ...(mental process – of adjusting the feature score of each feature based on the grouping can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
selecting, ...a subset of the plurality of features based on the adjusted feature score for each respective feature of the plurality of features,... (mental process – of selecting subset of the plurality of features based on the adjusted feature score can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A computer-implemented method, comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
receiving, with at least one processor, an authorization request from at least one of a merchant system or an acquirer system; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
...with the at least one processor, based on the authorization request... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...the machine learning model trained based on a subset of a plurality of features selected by: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
receiving a first multi-task learning model associated with a first task and at least one second task; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
receiving a testing data set comprising a plurality of testing data items for the first multi-task learning model, each testing data item comprising a plurality of elements, each element of the plurality of elements associated with a respective feature of the plurality of features; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
applying feature reduction evaluation (FRE) based on the first multi- task learning model and the testing data set to provide a feature score for each feature of the plurality of features; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...with the at least one processor,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...wherein training the machine learning model based on the subset of the plurality of features of the testing data set improves accuracy of the machine learning model; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
inserting, with the at least one processor, the first score into at least one field of the authorization request to provide an enhanced authorization request; and (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
communicating, with the at least one processor, the enhanced authorization request to an issuer system (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A computer-implemented method, comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
receiving, with at least one processor, an authorization request from at least one of a merchant system or an acquirer system; ( This is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
...with the at least one processor, based on the authorization request... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...the machine learning model trained based on a subset of a plurality of features selected by: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
receiving a first multi-task learning model associated with a first task and at least one second task; ( This is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
receiving a testing data set comprising a plurality of testing data items for the first multi-task learning model, each testing data item comprising a plurality of elements, each element of the plurality of elements associated with a respective feature of the plurality of features; ( This is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
applying feature reduction evaluation (FRE) based on the first multi- task learning model and the testing data set to provide a feature score for each feature of the plurality of features; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...with the at least one processor,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...wherein training the machine learning model based on the subset of the plurality of features of the testing data set improves accuracy of the machine learning model; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
inserting, with the at least one processor, the first score into at least one field of the authorization request to provide an enhanced authorization request; and ( This is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
communicating, with the at least one processor, the enhanced authorization request to an issuer system ( This is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 19: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the machine learning model comprises at least one of a deep neural network (DNN), a multi- task learning model, or any combination thereof (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
Regarding claim 20: it is rejected under the same rationality as claim 1. Claim 20 only recites the additional elements of A system, comprising: at least one processor; and at least one non-transitory computer-readable medium including one or more instructions that, when executed by the at least one processor, direct the at least one processor to... which is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GISEL G FACCENDA whose telephone number is (703)756-1919. The examiner can normally be reached Monday - Friday 8:00 am - 4:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571) 270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/G.G.F./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
