Detailed Action
Continued Examination Under 37 CFR 1.114
A request for continued examination (RCE) 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 February 26, 2026 has been entered.
Acknowledgements
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the RCE filed on February 26, 2026.
Claims 10-11 are cancelled.
Claims 1-9 and 12-20 are pending.
Claims 1-9 and 12-20 are examined.
This Office Action is given Paper No. 20260813 for references purposes only.
Claim Objections
Claim 13 is objected to because it recites “one more.” Examiner assumes that Applicant intended “one or more.” Appropriate correction is required.
Claim Rejections - 35 USC § 112b
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 8 and 20 recite “the weighted first initial fraud result” and “the weighted second initial fraud result.” There is lack of antecedent basis for these terms. For purposes of applying the prior art only, Examiner will interpret as “a weighted first initial fraud result” and “a weighted second initial fraud result.”
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 1-9 and 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 2A Prong 1: The claims recite an abstract idea of rejecting or approving transactions based on training model data, which is a certain method of organizing human activity (e.g. fundamental economic principles or practices including hedging, insurance, mitigating risk; commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, business relations; managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions).
Claim 1, representative of claims 9 and 13, includes the following limitations:
Training a first model trained on relevant data including a first set of features, which include one or more dominant features, the first model receiving the transaction request data and producing a first initial fraud result;
Training a second model trained on the relevant data including a second set of features, which exclude the one or more dominant features, the second model receiving the transaction request data and producing a second initial fraud result;
Combining the first and second initial fraud results to generate a final fraud score;
Rejecting or allowing the transaction request based on the final fraud score.
Step 2A Prong 2: The claim limitations recite the following additional elements that are beyond the judicial exception:
receiving an electronic payment card transaction request including transaction request data.
These additional elements are not indicative of integration into a practical application because:
They add insignificant extra-solution activity to the judicial exception. Note that “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity can include both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process. An example of post-solution activity is an element that is not integrated into the claim as whole. See MPEP 2106.05(g).
Step 2B: The claim limitations do not recite additional elements, or an ordered combination of additional elements, that are sufficient to amount to significantly more than the judicial exception.
As discussed with respect to step 2A prong 2 above, the additional element is extra solution activity that does not integrate a judicial exception into a practical application at step 2A or provide an inventive concept at step 2B.
According to the 2019 PEG, a conclusion that an additional element is insignificant extra solution activity under step 2A should be re-evaluated at step 2B. The limitation “receiving an electronic payment card transaction request including transaction request data” is re-evaluated to determine whether it constitutes well-understood, routine, and conventional activity in the field. The “receiving of data” is well-understood, routine, and conventional in the field. See Intellectual Ventures v. Symantec, 838 F.3d 1307, 1321 and MPEP 2106.05(d). Thus, a conclusion that the limitation “receiving an electronic payment card transaction request including transaction request data” is well-understood, routine, and conventional is supported under Berkheimer.
Therefore, when considering all the additional claim elements both individually and as an ordered combination, Examiner finds that the claim does not amount to significantly more than the exception.
The dependent claims fail to cure this deficiency and are rejected accordingly.
Claim 2 recites the first and second modeling technologies are the same, which is merely describing data and further defining the abstract idea.
Claim 3 recites the first and second modeling technologies are different, which is merely describing data and further defining the abstract idea.
Claim 4 recites determining a first weight and second weight to apply and then combining the weighted first result and weighted second result, which is insignificant extra-solution activity (e.g. selecting a particular data source or type of data to be manipulated). See Electric Power Group, and MPEP 2106.05(g).
Claim 5 recites combining the first and second fraud results, which is insignificant extra-solution activity (e.g. mere data gathering). See CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, and MPEP 2106.05(g).
Claim 6 recites the model is based on a boosted tree technology, which is merely describing data and further defining the abstract idea.
Claim 7 recites determining a first weight and second weight to apply and then combining the weighted first result and weighted second result, which is insignificant extra-solution activity (e.g. selecting a particular data source or type of data to be manipulated). See Electric Power Group, and MPEP 2106.05(g).
Claim 8 recites receiving one or more fields from the models and generating a final fraud score, which is insignificant extra-solution activity (e.g. selecting a particular data source or type of data to be manipulated). See Electric Power Group, and MPEP 2106.05(g).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9 and 12-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Hu et al. (US 2023/0186164) in view of Ameisen et al. (US 2024/0095741).
Claims 1, 13
Hu discloses:
a non-transitory memory (memory, see [0080-0082]);
and one or more hardware processors (processor, see [0079-0080]) coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the computer system to perform operations comprising:
training a first model (first model, see [0013]) on relevant data (training data, see [0013]) and including a first set of features (first subset of features, see [0013]) which are relevant to detecting fraud in electronic payment card (card, see [0035, 0043]) transactions (payment transaction, see [0015, 0031]), the first set of features including one or more dominant features (e.g. feature A, see figure 3), and the first model receiving an electronic payment card transaction request (transaction request, see [0013]) including an electronic payment card transaction request data from a requesting entity, evaluating the electronic payment card transaction request data for fraud, and producing a first initial fraud result (collective error for the first layer, see [0018]);
training a second model (second model, see [0014]) on the relevant data and including a second set of features (second subset of features, see [0014]) which are relevant to detecting fraud in the electronic payment card transactions, the second set of features excluding the one or more dominant features (second subset of features is different from the first subset of features, see [0014]), and the second model receiving the electronic payment card transaction request data (transaction request, see [0014]), evaluating the electronic payment card transaction request data for fraud, and producing a second initial fraud result (collective error for the second layer, see [0020]);
combining, with a score generator, the first and second initial fraud results to generate a final fraud score (collective error, see [0059]); and
receiving, at a response module, the final fraud score and taking an action (response, see [0072]) based on the final fraud score, wherein the action includes rejecting (deny, see [0072]) or allowing (authorize, see [0072]) the electronic payment card transaction request from the requesting entity.
Hu does not explicitly disclose:
including… entity.
Ameisen teaches:
including an electronic payment card transaction request data (e.g. transaction identifiers, amount, time, see [0037]) from a requesting entity.
Hu discloses training a first model, training a second model, combining the first and second initial fraud results, receiving a final fraud score, and taking an action. Hu does not explicitly disclose transmitting a transaction request including transaction data, but Ameisen does. It would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to combine the enhanced gradient boosting tree for risk and fraud modeling of Hu with the transmitting a transaction request including transaction data of Ameisen because 1) a need exists for reducing the time required to build certain types of machine learning models without sacrificing their accuracy performance (see Hu [0004]); and 2) a need exists for deploying updated fraudulent transaction detection modules that improve fraud detection without disrupting merchant expectations (see Ameisen [0005]). Transmitting a transaction request including transaction data can be used for fraudulent transaction detection modules.
Claims 2, 14
Furthermore, Hu discloses:
the first model is based on a first modeling technology and the second model is based on a second modeling technology which is the same (e.g. decision tree, see [0017]) as the first modeling technology.
Claims 3, 15
Furthermore, Hu discloses:
the first model is based on a first modeling technology and the second model is based on a second modeling technology which is different (e.g. regression model, a neural network, see [0017]) from the first modeling technology.
Claims 4, 16
Furthermore, Ameisen teaches:
the score generator is a combination module further determining a first weight (e.g. w1) to apply to the first initial fraud result (initial fraud score, see [0029]) to produce a weighted first initial fraud result (adjusted weight score, see [0029]), determining a second weight (e.g. w2, see [0078]) to apply to the second initial fraud result (additional score, see [0078]) to produce a weighted second initial fraud result, and then combining the weighted first initial fraud result and the weighted second initial fraud result to generate the final fraud score (final fraud detection score, see [0078]).
Claims 5, 17
Furthermore, Hu discloses:
the score generator is a combination model which is trained, receives the first (collective error for the first layer, see [0018]) and second (collective error for the second layer, see [0020]) initial fraud results, and combines the first and second initial fraud results to generate the final fraud score (collective error, see [0059]).
Claims 6, 18
Furthermore, Hu discloses:
the combination model is based on a boosted tree modeling technology (gradient boosting tree, see [0012]).
Claims 7, 12, 19
Furthermore, Ameisen teaches:
determining a first weight (e.g. w1) to apply to the first initial fraud result (initial fraud score, see [0029]) to produce a weighted first initial fraud result (adjusted weight score, see [0029]), determining a second weight (e.g. w2, see [0078]) to apply to the second initial fraud result (additional score, see [0078]) to produce a weighted second initial fraud result, and then combining the weighted first initial fraud result and the weighted second initial fraud result to generate the final fraud score (final fraud detection score, see [0078]).
Claims 8, 12, 20
Furthermore, Ameisen teaches:
receiving one or more fields (e.g. threshold value, see [0078]) from the first and second models and generating the final fraud score (final fraud detection score, see [0078]) by combining the one or more fields, the weighted first initial fraud result, and the weighted second initial fraud result.
Claim 9
Hu discloses:
a non-transitory memory (memory, see [0080-0082]);
and one or more hardware processors (processor, see [0079-0080]) coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the computer system to perform operations comprising:
training a first model (first model, see [0013]) on relevant data (training data, see [0013]) and including a first set of features (first subset of features, see [0013]) which are relevant to detecting fraud in electronic payment card (card, see [0035, 0043]) transactions (payment transaction, see [0015, 0031]), the first set of features including one or more dominant features (e.g. feature A, see figure 3), and the first model receiving an electronic payment card transaction request (transaction request, see [0013]) including an electronic payment card transaction request data from a requesting entity, evaluating the electronic payment card transaction request data for fraud, and producing a first initial fraud result (collective error for the first layer, see [0018]);
training a second model (second model, see [0014]) on the relevant data and including a second set of features (second subset of features, see [0014]) which are relevant to detecting fraud in the electronic payment card transactions, the second set of features excluding the one or more dominant features (second subset of features is different from the first subset of features, see [0014]), and the second model receiving the electronic payment card transaction request data (transaction request, see [0014]), evaluating the electronic payment card transaction request data for fraud, and producing a second initial fraud result (collective error for the second layer, see [0020]);
wherein the first model and the second model are based on the same modeling technology (e.g. decision tree, see [0017]);
combining, with a score generator, the first and second initial fraud results to generate a final fraud score (collective error, see [0059]), wherein the score generator is a combination model is based on a boosted tree modeling technology (gradient boosting tree, see [0012]) and which is trained, receives the first (collective error for the first layer, see [0018]) and second (collective error for the second layer, see [0020]) initial fraud results, and combines the first and second initial fraud results to generate the final fraud score (collective error, see [0059]); and
receiving, at a response module, the final fraud score and taking an action (response, see [0072]) based on the final fraud score, wherein the action includes rejecting (deny, see [0072]) or allowing (authorize, see [0072]) the electronic payment card transaction request from the requesting entity.
Hu does not explicitly disclose:
including… entity.
Ameisen teaches:
including an electronic payment card transaction request data (e.g. transaction identifiers, amount, time, see [0037]) from a requesting entity.
Hu discloses training a first model, training a second model, the models are the same modeling technology, combining the first and second initial fraud results, receiving a final fraud score, and taking an action. Hu does not explicitly disclose transmitting a transaction request including transaction data, but Ameisen does. It would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to combine the enhanced gradient boosting tree for risk and fraud modeling of Hu with the transmitting a transaction request including transaction data of Ameisen because 1) a need exists for reducing the time required to build certain types of machine learning models without sacrificing their accuracy performance (see Hu [0004]); and 2) a need exists for deploying updated fraudulent transaction detection modules that improve fraud detection without disrupting merchant expectations (see Ameisen [0005]). Transmitting a transaction request including transaction data can be used for fraudulent transaction detection modules.
Response to Arguments
112 arguments
Examiner has withdrawn the 112 rejection corresponding to the 112f interpretation because the claims now recite structure.
101 arguments
Applicant argues that the claimed invention is directed to an improvement in computer technology.
Examiner disagrees. Applicant has not identified the improvement in computer technology (e.g. faster processing times, less memory storage, etc). The claimed invention is directed to training a first model to produce a first fraud result, training a second model to produce a second fraud result, combing the two fraud results to generate a final fraud score, and either rejecting or approving a transaction request based on the final fraud score. There is no identified technical solution, and as such, the 101 rejection remains.
103 arguments
Applicant argues that Hu is directed to first and second layers of models that use independent feature sets, but the claimed invention is directed to using a single layer of models that are dependent (i.e. the feature set of the first model partially determines the feature set of the second model).
Examiner disagrees. The claim limitations do not recite that the feature set of the first model partially determines the feature set of the second model. The claims simply recite training a first model on a first set of features including one or more dominant features, and training a second model on a second set of features excluding the one or more dominant features. Hu discloses training a first model (first model, see [0013]) on a first set of features including dominant features (first subset of features, see [0013]), and training a second model (second model, see [0014]) on a second set of features (second subset of features, see [0014]) that exclude the dominant features (second subset of features is different from the first subset of features, see [0014]). Having different subsets ensures that the first features are excluded from the second features.
Claim Interpretation
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure (see attached form PTO-892).
Raj et al. (US 12,555,114) discloses fraud detection using multi-task learning and/or deep learning.
Conclusion
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from Examiner should be directed to Chrystina Zelaskiewicz whose telephone number is 571-270-3940. Examiner can normally be reached on Monday-Friday, 9:30am-5:00pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Neha Patel can be reached at 571-270-1492.
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/CHRYSTINA E ZELASKIEWICZ/Primary Examiner, Art Unit 3699
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