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 .
Claim Rejections - 35 USC § 112
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 21-39 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In the independent claims – an online resource – is vague and indefinite – as in receive, from an electronic device configured to manage an online resource. An online resource is not defined or shown in the specification and or drawings (see figure 2) . Within the context of the invention, it is not clear what apparatus is being managed.
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In claim 24 -
determine a rate at which TPP transactions are being determined fraudulent; a rate suggests that a time dependent ratio. For example, transactions per hour. A fraudulent rate however isn’t as clear as it could, for example be either fraudulent transactions per hour, e.g., 5 fraudulent transaction per hour. Given a second construction, the rate could be a percentage of the transaction rate itself, e.g., 10% of the any transaction rate.
Claims with two distinct claim constructions to a PHOSITA are indefinite. Therefor the claims are rejected under this paragraph.
compare the rate to a desired rate; A desired rate is indefinite or the reasons above. Moreover, “desired” is indefinite given the limitations that follow:
when the rate is a predetermined amount greater than the desired rate, increase the adjustable predetermined threshold; Its is not clear if the “predetermined amount” is the antecedent “the rate” or is some new non-rate amount, e.g., an integer.
when the rate is the predetermined amount less than the desired rate, decrease the
adjustable predetermined threshold.
Lastly, it is not clear to what “predetermined threshold” is pointed to. Is it rate or a non- rate and how the “adjustability” further limits a predetermined threshold from a “adjustable predetermined threshold. Moreover, the conditional statements seem to be the opposite of the reaction of fraudulent activity.
In Claim 28. (New) The server according to claim 21, wherein the electronic processor is further
configured to: by performing value transformation is indefinite in scope as to what and how is being transformed.
Claim Rejections - 35 USC § 101
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 21-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
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The claims are either directed to a system, method, and computer readable medium which are one of the statutory categories of invention.
The abstract idea is determining scoring a transaction for fraud. The steps that make up the abstract idea (Representative, claim 21) include the following (in bold) limitations:
1) an electronic processor, the electronic processor configured to receive, from an electronic device configured to manage [[an]]a plurality of user accounts online resource, a request to generate a fraud risk score for a TPP transaction with respect to one user account from the plurality of user accounts;
2) determine a frequency-recency-monetary value feature, a reputation feature,and a rule feature for the TPP transaction, wherein the frequency-recency-monetary value feature includes at least one sub-feature indicating a rate at which purchases associated with a user authorizing the TPP transaction were made in a predetermined temporal period preceding the TPP transaction;
3) using the frequency-recency-monetary value feature, the reputation feature, and
the rule feature as input for a machine learning model, execute the machine learning
model to generate a blended fraud risk score;
4) when the blended fraud risk score is above an adjustable predetermined threshold,
determine that the TPP transaction is fraudulent; and
5) send an indication of whether the TPP transaction is fraudulent to the electronic
device, wherein the electronic device allows or denies access to the online resource based
on the indication of whether the TPP transaction is fraudulent.
Limitations 1-2 are pre-solution activity.
Limitations 3-4 under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim recites elements that are in bold above, which covers performance of the limitation as a commercial interaction (steps for determining fraud in an electronic transaction).
Limitation 5 is post solution activity.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a commercial interaction then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, claim 1 recites an abstract idea.
This judicial exception is not integrated into a practical application. The elements in addition to the abstract idea are one or more electronic processors and electronic devices.
Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
The database and the server including an electronic processor and memory are recited at a high level of generality and being used in its ordinary capacity and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f), where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 2 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use. Generally linking the use of the judicial exception to a particular technological environment or field of use, with the use of generic computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claims 20 is not patent eligible.
Dependent claims 22-28 which further define the abstract idea that is present in their respective independent claims 21 thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, claims 21-28 are not patent-eligible. Claims 29-40 are the method and computer readable equivalents to the apparatus of claims 21-28 and are rejected based on the reasons above.
Response to Arguments
The applicant argues conventional systems often misclassify legitimate transactions as fraudulent because lacking a purchase frequency factor and that by integrating a time-aware sub-features into a machine learning model executed on a server, the claims provide a concrete technical solution that enhances fraud detection accuracy and efficiency in a distributed computing environment.
The examiner is not persuaded by this argument as the inclusion. The system using only rule features and reputational features is not improved by using frequency-recency-monetary value features (as well as rule features and reputation features) in the analysis of these TPP transactions. While the output of the model may offer improved fraud analysis, this by itself does not amount to a patent eligible invention for the reasons stated above. features, and frequency-recency-monetary value features used by the embodiments described herein) is more effective than an analysis using only one or two types of features.
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 (i.e., changing from AIA to pre-AIA ) 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 21-27 and 29-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Piel US20200193443A1.
Claim(s) 21-40 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by
1) an electronic processor, the electronic processor configured to receive, from an electronic device configured to manage [[an]]a plurality of user accounts online resource, a request to generate a fraud risk score for a TPP transaction with respect to one user account from the plurality of user accounts; p[0005] (the limitations directed to the device are not claim limiting to the electronic processor)
2) determine a frequency-recency-monetary value feature, a reputation feature,and a rule feature for the TPP transaction, wherein the frequency-recency-monetary value feature includes at least one sub-feature indicating a rate at which purchases associated with a user authorizing the TPP transaction were made in a predetermined temporal period preceding the TPP transaction; frequency [0035] reputation [0044] reputation , rule based [0005] geolocation (Spec teaches [0027], For example, a rule may be triggered when the transaction involves the user making a payment to their own account, a rule may be triggered when the transaction involves the user making a payment using a VPN, a rule may be triggered when the transaction involves the user typing an unrealistic number of words per minute, and the like. In some embodiments, each rule may be associated with a numerical scoring value.
Claim 21/29/35
A server method and/or medium for generating a fraud risk score for a third party provider (TPP)
transaction, the server comprising:
an electronic processor, the electronic processor configured to receive, from an electronic device configured to manage an online resource, a request to generate a fraud risk score for a TPP transaction;[0005] The AA computer device is also configured to generate a model associating each of the plurality of cardholder authentication types with a corresponding set of values for transaction parameters derived from the historical transaction data, wherein the transaction parameters include at least one of a transaction amount range, a transaction geographic location, a merchant identifier, and a merchant category. [0035] The Spec teaches “a TPP may be an incumbent bank, a fintech organization, or a merchant”.
2) determine a frequency-recency-monetary value feature, a reputation feature,and a rule feature for the TPP transaction, wherein the frequency-recency-monetary value feature includes at least one sub-feature indicating a rate at which purchases associated with a user authorizing the TPP transaction were made in a predetermined temporal period preceding the TPP transaction ; recency /frequency [0035] reputation [0044] reputation , rule based [0005] geolocation (Spec teaches [0027], For example, a rule may be triggered when the transaction involves the user making a payment to their own account, a rule may be triggered when the transaction involves the user making a payment using a VPN, a rule may be triggered when the transaction involves the user typing an unrealistic number of words per minute, and the like. In some embodiments, each rule may be associated with a numerical scoring value.
The reference fails to expressly teach
the rule feature as input for a machine learning model, execute the machine learning model to generate a blended fraud risk score;. The reference the execution of a simulation of the model provides a dynamic prediction of an authentication type preferred by the cardholder, personalized to the cardholder [0048]. The reference teaches a model the scores the transaction and correlates that score to a second step of verification – including one of a PIN, a one-time password, a pattern code, a passcode, a digital signature, a signature capture, a biometric signature, a biometric sample, a challenge question, a low-energy infrared retinal scan, a finger vein scan, a near infrared iris scan, an optical fingerprint scan, a three-dimensional (3D) fingerprint scan, an optical palm print, a 3D facial scan, an optical facial scan, and a speech recognition sample. The aggregated data may be organized by parameters associated with payment transactions such as frequency of engaging in payment transactions, payment amounts, frequency of fraudulent activity, etc.
The reference with this teaching is evidence that a PHOSITA would have had a reasonable expectation of success in scoring a model to a preset reference value and in doing so make a determination as to whether the transaction is fraudulent, where fraudulent would mean that one or more of the second factors including one of a PIN, a one-time password, a pattern code, a passcode, a digital signature, a signature capture, a biometric signature, a biometric sample, a challenge question, a low-energy infrared retinal scan, a finger vein scan etc. are required.
Accordingly, the reference tenders the evidence that a PHOSITA would have had a reasonable expectation of success when the blended fraud risk score is above an adjustable predetermined threshold, determine that the TPP transaction is fraudulent;
and send an indication of whether the TPP transaction is fraudulent to the electronic device, wherein the electronic device allows or denies access to the online resource based on the indication of whether the TPP transaction is fraudulent. {0074] When cardholder 120 tenders payment for a purchase with a transaction card, merchant 130 requests authorization from a merchant bank 140 for the amount of the purchase. The request may be performed over the telephone, but may also be performed through the use of a POS terminal, which reads cardholder's 120 account information from a magnetic stripe, a chip, or embossed characters on the transaction card and communicates electronically with the transaction processing computers of merchant bank 140. Alternatively, merchant bank 140 may authorize a third party to perform transaction processing on its behalf. In this case, the POS terminal will be configured to communicate with the third party. Such a third party may be called a “merchant processor,” an “acquiring processor,” or a “third party processor.” [0003] Authentication procedures also apply to secure systems involving financial transactions that require multiple parties, such as an issuing bank, a merchant, a payment processing network, the user of the payment card, and an acquirer bank.
.
Claim 22/30/36. (New) The server according to claim 21, wherein the electronic processor is further
configured to:
determine a volume score for the TPP transaction; and when the volume score is above a first predetermined threshold, determine that the TPP transaction is fraudulent.
The Specification teaching on volume score is {0022} Determining a volume score includes checking whether automated attacks are being performed against or via the TPP associated with the TPP transaction. For example, the electronic processor 300 may determine the volume score by determining the number of TPP transactions received during a predetermined time frame that are associated with a common useragent string, IP address, country, persistent device identification token, geo location, or the like.
Volume Score anticipated by the “long history” [0055] e.g., if there is a long history of similar transactions between the merchant and suspect cardholder, etc.) in which case the AA computer device may approve the transaction without any further authentication steps. For example, the RBD component may identify one or more pieces of information about the transaction that are used to “score” the transaction for risk (e.g., potential fraud)
Claim 23. (New) The server according to claim 21, wherein the machine learning model is a linear
regression model or a tree-based model. [0053] the machine learning programs may also utilize deep learning algorithms that may be primarily focused on pattern recognition, and may be trained after processing multiple examples. In some embodiments, the machine learning programs may include Bayesian Program Learning (BPL), voice recognition and synthesis, image or object recognition, optical character recognition, and/or natural language processing—either individually or in combination. The machine learning programs may also include natural language processing, semantic analysis, automatic reasoning, and/or machine learning. [0054] In supervised machine learning, a processing element may be provided with example inputs and their associated outputs, and may seek to discover a general rule that maps inputs to outputs, so that when subsequent novel inputs are provided, the processing element may, based upon the discovered rule, accurately predict the correct output. For example, cardholder-defined preferences may be input by the cardholder that defines authentication types for various circumstances (e.g., transaction amount, merchant location, etc.). The examiner takes official notice that supervised learning algorithms are synonymous with regression and decision trees.
Claim 24/31/37. (New) The server according to claim 21, wherein the electronic processor is further
configured to:
determine a rate at which TPP transactions are being determined fraudulent;
compare the rate to a desired rate; [0076] AA computer device 110 generates a model using at least the history of authentications. The model represents optimal authentication types for cardholder 120 over a period of time (e.g., 3 months, 6 months, 1 year, etc.)
The reference fails to teach the following:
when the rate is a predetermined amount greater than the desired rate, increase the adjustable predetermined threshold; when the rate is the predetermined amount less than the desired rate, decrease the adjustable predetermined threshold.
The reference does however teach that a PHOSITA would have had a reasonable expectation of success in raising or lowering the values that the model should compared to when making a determination as to whether the transaction is fraudulent. [0113] In the example embodiment, AA computer device 110 determines 714, using the model (The model represents optimal authentication types for cardholder 120 over a period of time (e.g., 3 months, 6 months, 1 year, etc.), an authentication type to use to authenticate cardholder 120 for the pending transaction. AA computer device 110 may base determination 714 of the one or more authentications on at least one generated 712 model. In some embodiments, the model will be used to determine whether or not an authentication is warranted. If an authentication is warranted, AA computer device 110 determines 714 which authentication type is needed. [0004] The same authentication types may be presented regardless of transaction amounts or proximity to a central location, such as a user residence. In addition, frequent visits to the same merchant may unnecessarily require authentications for each visit. In other situations, a visit to a place with higher potential for fraud may not require any authentication. Alerting users to heightened risks through elevated and more sophisticated authentications may potentially reduce the likelihood of users engaging in risky transactions. [0005] The AA computer device is configured to retrieve, from a database, (i) historical transaction data for a plurality of historical transactions processed over a payment processing network, and (ii) a respective one of a plurality of cardholder authentication types used for each of the historical transactions. Therefor a PHOSITA would have had a reasonable expectation of success in raising or lowering thresholds for fraudulent activity and it would have been obvious based on the preponderance of the evidence.
Claim 25/32/38. (New) The server according to claim 21, wherein the electronic processor is further
adjust the adjustable predetermined threshold when an anomalous event occurs.
[0108} For example, cardholder 120 (shown in FIG. 1) may configure AA computer device 110 to initiate an authentication using a text message for payment transactions at restaurants while configuring AA computer device 110 to initiate an authentication using a phone call at gas stations. In some embodiments, the method of communication used to initiate an authentication may be determined at least partially using the model as described herein. The event being the authentication at a gas station and the threshold being the type of 2nd factor authentication.
Claim 26/35/39 (New) The server according to claim 21, wherein the frequency-recency-monetary value feature is a time aware feature based on a user's pre-existing behavior compared to the TPP
transaction that the user has authorized. [0059] (iii) retrieving a history of authentications; (iv) generating a model representing the preferred authentication type of the cardholder;
Claim 27/34/40. (New) The server according to claim 21, wherein the reputation feature is related to data collected across multiple TPPs regarding a user authorizing the TPP transaction. [0047] In some embodiments, the default model may be determined by analyzing a history of authentications performed by other cardholders at the particular merchant location (e.g., using an average, median, moving average, statistical analysis, probability distribution, etc.)
Response to Arguments
The applicant argues the following newly amended is not taught -"determine a frequency-recency-monetary value feature, a reputation feature, and a rule feature for the TPP transaction, wherein the frequency-recency-monetary value feature includes at least one sub-feature indicating a rate at which purchases associated with a user authorizing the TPP transaction were made in a predetermined temporal period preceding the TPP transaction."
The examiner disagrees as the reference teaches a fraud model based on a history of payment transactions and whether the suspect cardholder has engaged in previous, similar transactions with the merchant. e.g., if there is a long history of similar transactions between the merchant and suspect cardholder, etc.) in which case the AA computer device may approve the transaction without any further authentication steps. P[0035]
Claim(s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Piel US20200193443A1 in view of Lai US Patent Pub 20170286962 .
Claim 28. (New) The server according to claim 21, wherein the electronic processor is further
configured to:
The reference fail to teach the step
preprocess the frequency-recency-monetary value feature, the reputation feature, and the
rule feature by performing value transformation, data cleaning, or both. Lai teaches preprocessing {0032] The model generation module may then do some data cleaning and transforming. In practice, for each chargeback, the model generation module may not have every attribute available. For example, if a chargeback has no fraud alert, the attribute value may be empty. The model generation module may preprocess the data by replacing each empty value with a special string “Unknown”. The model generation module may also get rid of any outliers for the numeric variables to avoid any clutter to the training. For categorical variables, such as payment instrument and bank identification number, the model generation module may convert a categorical variable into a continuous numerical variable by applying a weight-of-evidence (WOE) transform.
The patent publication is analogous art as both use machine learning models for financial transactions. It would have been obvious to clean the data of the primary reference as a PHOSITA would improve the models by being able to use both ordinal and nominal data.
Provisional Obviousness Type Double Patenting Rejection
4. Claims 21-39 in this application are provisionally rejected on the ground of provisional nonstatutory obvious type double patenting as being unpatentable over independent claim 1 of US Patent
5. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
6. A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
7. The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp
Response to Arguments
. The argument is not persuasive as it is conclusory and fails to particularly point out the claimed feature not rendered obvious by the parent patent.
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 RICHARD C WEISBERGER whose telephone number is (571)272-6753. The examiner can normally be reached Monday - Thursday 10AM-8PM PCT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Anderson can be reached at 571-270-0580. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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RICHARD C. WEISBERGER
Examiner
Art Unit 3693
/RICHARD C WEISBERGER/Primary Examiner, Art Unit 3693
Prosecution Insights