Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is responsive to the following communication: Amendment filed Dec. 24, 2025. This Action is made Final.
Claims 1-20 are pending in the case. Claims 1, 8 and 15 are independent claims.
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 differences 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 in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (hereinafter Zheng) U.S. Patent Publication No. 2021/0103957 in view of Hearty et al. (hereinafter Hearty) U.S. Patent Publication No. 2022/0172215 and in further view of Shaked et al. (hereinafter Shaked) U.S. Patent Publication No. 2020/0151614.
With respect to independent claim 1, Zheng teaches a method for generating a machine learning model comprising:
Generating, using one or more processors, a machine learning feature template, the machine learning feature template comprising a first grouping of first machine learning feature variables and a second grouping of second machine learning feature variables (see e.g., Fig. 2 Claim 21 Para [41]-[47] – “The online banner features 210 include a template data feature that identifies a template for generating an online banner image, a frequency feature that identifies how frequently the online banner image should be presented to the user, a result feature that indicates whether a certain online banner image is associated with a desired result”);
generating, using one or more processors, a plurality of machine learning features by combining a respective one of each of the first machine learning feature variables of the first grouping with a respective one of each of the second machine learning feature variables of the second grouping (see e.g., Para [38] – “ the machine-learning algorithms utilize the training data 212 to find correlations among identified features 202 (or combinations of features 202) that affect the outcome … an administrator of the machine learning system 400 determines which of the various combinations of features are great (e.g., lead to desired results), and which ones are not. The combinations of features determined to be (e.g., classified as) successful are input into a machine learning algorithm for the machine learning algorithm to learn which combinations of features (also referred to as “patterns”) are “good” (e.g., a user will select the online banner image), and which patterns are “bad.””);
training, using one or more processors, a first machine learning model utilizing the plurality of machine learning features and first training data to generate a machine learning output (see e.g., Para [46] [47] – “the machine-learning tool is trained at operation 214. The machine-learning tool appraises the value of the features 202 as they correlate to the training data 212. The result of the training is the trained machine-learning program 216.”);
analyzing, using one or more processors, the machine learning output to determine a prediction accuracy of the plurality of machine learning features; based on the prediction accuracy of the plurality of machine learning features, selecting a subset of the plurality of machine learning features (see e.g., Para [46] [47] – “the machine-learning program 216 generates the assessment 220 as output. For example, when a user selects a particular online banner image displayed for the user in a user interface of a client device of the user, a machine learning program, trained with various combinations of features used to generate various online banner images, updates the features 202 with one or more additional features (e.g., a feature that indicates that the user selected the particular online banner image) which will be used in further training of the machine-learning program, and in the generating of future personalized online banner images for the particular user and possibly other users.”).
Zheng does not expressly show training, using one or more processors, a second machine learning model based on the subset of the plurality of machine learning features and the first training data; and providing, using one or more processors, a network transaction to the second machine learning model to generate a classification of the network transaction. However, Hearty teaches the above feature (see e.g. para [41] – “the model training network 310 according to some embodiments. The model training network 310 is configured to use a labeled training dataset (prepared or generated by the feature generation network 305) for training a model (for example, a supervised classification model), tuning the model, and outputting model artifacts to a storage service” and claim 1 “determining, with an electronic processor, a fraud prediction for the request based on the application of the fraud prediction model to the set of features; and generating and transmitting, with the electronic processor, a response to the request, the response including the fraud prediction.”). Both Zheng and Hearty are directed to machine training. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Zheng and Hearty in front of them to modify the system of Zheng to include the above feature. The motivation to combine Zheng and Hearty comes from Hearty. Hearty discloses the motivation to provide network based training so that a larger number of customers can benefit from the training (see e.g. Hearty Abstract and Para [4]).
Zheng-Hearty does not expressly show at least one of the first grouping of the first machine learning feature variables or the second grouping of the second machine learning feature variables of the machine learning feature template is modified, based on the prediction accuracy of the plurality of machine learning features and model data associated with a merchant, to generate a modified machine learning feature template. However, Shaked teaches the above feature (see e.g. Abstract para [14]-[28] – “a machine learning system may be exploring 3 base templates {A, B, C} to be included in an empty machine learning model. Beginning with the empty model, first, the base templates {A, B, C} may each be scored. Base template B may be the highest scoring template and, as a result, may be added to the model. In the next round of template exploration, all base templates not added to the model plus all possible extensions (e.g., by one more base template) to the templates in the model may be explored. In this case the templates that may be scored may include {A, C, A X B, B X C}. “ “Implementations of the disclosed subject matter may be used in machine learning models that may contain millions of billions of features in templates. A model based on a single template often is not informative enough to provide accurate predictions; instead, an aggregate of features is more helpful for predictions, as such, it is advantageous to construct cross templates that include multiple templates. Since exploring the space of all of the 100s of billions of feature templates is infeasible in such large-scale machine learning systems, it may be desirable to efficiently explore the space of templates based on estimating the performance gain of a model including a cross-template that contains a combination of multiple templates. With each iteration of the techniques described herein, a selection of a template is based on an assessment of the performance gain of the model with each new template addition. This technique may be used to optimize performance of a machine learning model ”). Both Zheng and Shaked are directed to machine training. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Zheng and Shaked in front of them to further modify the modified system of Zheng to include the above feature. The motivation to combine Zheng and Shaked comes from Shaked. Shaked discloses the motivation to improve/update feature template to improve machine learning model prediction accuracy (Abstract para [14]-[28]).
With respect to dependent claim 2, the modified Zheng teaches obtaining second training data; generating a second plurality of machine learning features based on the modified first and second groupings of the modified machine learning feature template; and training a third machine learning model using the second plurality of machine learning features and the second training data (see e.g., Para [47] – “when a user selects a particular online banner image displayed for the user in a user interface of a client device of the user, a machine learning program, trained with various combinations of features used to generate various online banner images, updates the features 202 with one or more additional features (e.g., a feature that indicates that the user selected the particular online banner image) which will be used in further training of the machine-learning program, and in the generating of future personalized online banner images for the particular user and possibly other users.”).
With respect to dependent claim 3, the modified Zheng teaches the first grouping of machine learning feature variables comprises a plurality of characteristics associated with a plurality of network transactions and the second grouping of the machine learning feature variables comprises a plurality of categories into which one or more of the characteristics are to be grouped (see e.g., Para [41]-[44] – “ the features 202 may be of different types and include one or more of user features 204, item features 206, preference features 208, and online banner features 210.” and Hearty para [44] – “the learning engine 610 may identify one or more unique characteristics or trends of the features and develop a model that maps the one or more unique characteristics or trends to a particular classification. ” ).
With respect to dependent claim 4, the modified Zheng teaches the machine learning feature template is a first machine learning feature template of a plurality of machine learning feature templates, wherein the first machine learning feature template is associated with a first prediction category, and wherein the method further comprises: obtaining second training data associated with a second prediction category; selecting the first machine learning feature template based on the second prediction category matching the first prediction category, generating a second plurality of machine learning features by combining a respective one of each of the first machine learning feature variables of the first grouping with a respective one of each of the second machine learning feature variables of the second grouping; and training a third machine learning model utilizing one or more of the second plurality of machine learning features and the second training data (see e.g., Para [45]-[46] - ”The machine-learning algorithms utilize the training data 212 to find correlations among the identified features 202 that affect the outcome or assessment 220. In some example embodiments, the training data 212 includes known data for one or more identified features 202 and one or more outcomes, such as combinations of features in an online banner image which lead to users selecting the online banner image, combinations of features in an online banner image which lead to users purchasing the product displayed in a product image included in the online banner image, etc. With the training data 212 and the identified features 202, the machine-learning tool is trained at operation 214.
”).
With respect to dependent claim 5, the modified Zheng teaches the first training data comprises a first plurality of transactions for a first merchant and the second training data comprises a second plurality of transactions for a second merchant, and wherein selecting the first machine learning feature template is further based on a comparison of a first profile of the first merchant and a second profile of the second merchant (see e.g., Hearty para [36] – “A feature may include, for example, a number of unique users, devices, browsers, operating systems, or the like associated with an IP of the request over a specific time-window (for example, last 6 months, 1 day, or the like). Alternatively or in addition, a feature may include, for example, a transaction volume, and its growth, associated for the user associated with the prediction request over a specific time-window, an average, standard deviation, and/or median of the historical risk score for the user associated with the request” The motivation to combine is discussed above with respect to claim 1.).
With respect to dependent claim 6, the modified Zheng teaches prior to generating the second plurality of machine learning features, modifying at least one of the first machine learning feature variables of the first grouping or the second machine learning feature variables of the second grouping based on characteristics of the second training data (see e.g., Para [45]-[47]– “The machine-learning algorithms utilize the training data 212 to find correlations among the identified features 202 that affect the outcome or assessment 220. In some example embodiments, the training data 212 includes known data for one or more identified features 202 and one or more outcomes, such as combinations of features in an online banner image which lead to users selecting the online banner image, combinations of features in an online banner image which lead to users purchasing the product displayed in a product image included in the online banner image, etc.”).
With respect to dependent claim 7, the modified Zheng teaches the network transaction is one of a plurality of network transactions, and wherein the second machine learning model is configured to predict fraudulent transactions in the plurality of network transactions (see e.g., Hearty Para [25] – “The fraud prediction service 110 is configured to provide or support one or more security or anti-fraud functions, services, or applications, such as fraud detection and monitoring services.” Hearty teaches the motivation to provide fraud prediction. Therefore, it would have been obvious to include this feature. Also, see discussion above with respect to claim 1).
Claim 8 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 9 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 10 is rejected for the similar reasons discussed above with respect to claim 3.
Claim 11 is rejected for the similar reasons discussed above with respect to claim 4.
Claim 12 is rejected for the similar reasons discussed above with respect to claim 5.
Claim 13 is rejected for the similar reasons discussed above with respect to claim 6.
Claim 14 is rejected for the similar reasons discussed above with respect to claim 7.
Claim 15 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 16 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 17 is rejected for the similar reasons discussed above with respect to claim 3.
Claim 18 is rejected for the similar reasons discussed above with respect to claim 4.
Claim 19 is rejected for the similar reasons discussed above with respect to claim 5.
Claim 20 is rejected for the similar reasons discussed above with respect to claim 6.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141
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