DETAILED ACTION
This action is in reply to the amendment filed on 08/12/2026.
Claims 1, 5, 9, 10, 14 and 19 have been amended.
Claim 7-8 were previously canceled.
Claims 4, 6, 13, 15, 18 and 20 have been canceled.
Claims 1-3, 5, 9-12, 14, 16-17, 19 have been examined.
Claims 1-3, 5, 9-12, 14, 16-17, 19 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Remarks
With regard to the 101 rejection, the arguments have been considered and they are not persuasive. The applicant asserted in page 12 that “[by] comparing the gradient direction with the normal direction, the method identifies which input feature descriptions are most influential in the model’s risk determination . . . is at a high level and ignores the specific, unconventional technical steps that are integral to the claimed invention. The claims are therefore not directed to a judicial exception under step 2A.” However, the use of a neural network and training model to optimize and classify transactions helps to expedite the business process rather than improving the technology per se. The overall idea is to determine a risky transaction which is still a commercial activity. Hence, the claim is directed to a judicial exception.
Additionally, the applicant asserted that “ the independent claims 1, 9 and 10 include additional elements that amount to significantly more than the abstract idea itself.” However, the additional elements do not render the claims significantly more because the claim still uses the technology to help expedite the business process. The Limitations that are not indicative of an inventive concept (aka “significantly more”): 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 - see MPEP 2106.05(f). Therefore, the claim is not patent eligible.
With regard to the 103 rejection, it should be noted that previously the Examiner used the name Gardner for one of the references, the correct name should have been Equifax (Applicant’s name). The Examiner corrected the name to be consistent with the cited reference.
With regard to the arguments, the arguments have been considered but they are not persuasive. The Applicant amended Claim 1 significantly. However, the majority of the features are still disclosed by Equifax, formerly cited as Gardner. It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of performing an optimization modeling as disclosed by Equifax with the invention as taught by Poli in view of Dickie to better adjust the neural network to generate predict the risk level changes (Equifax, abstract). Therefore, the combination is obvious.
Please refer to the rejection below for further details.
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 1-3, 5, 9-12, 14, 16-17, 19 are directed to a system, a method, or product which are one of the statutory categories of invention. (Step 1: YES).
Claims 1-3, 5, 9-12, 14, 16-17, 19 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. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide generic computer functions that do not add meaningful limits to practicing the abstract idea.
Claims 1, 9 and 10 are grouped together, Claim 10, for instance , recite in part, A non-transitory computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed on a computer, the computer is enabled to perform: obtain at least one group of risk transaction data for risk transaction prediction, wherein each group of risk transaction data satisfy one risk feature description; obtain at least one group of random transaction data from transaction record data of a historical risk control event; separately input the at least one group of risk transaction data and the at least one group of random transaction data into a pre-trained risk transaction prediction model, to obtain at least one risk transaction representation corresponding to the risk transaction data and at least one random transaction representation corresponding to the random transaction data that are output by a first neuron layer of the risk transaction prediction model, wherein the risk transaction prediction model is used to predict whether a transaction has a risk, and the first neuron layer is not an output layer of the risk transaction prediction model; and determine, based on a difference between importance of the at least one risk transaction representation in determining whether a risk transaction has a risk and importance of the at least one random transaction representation in determining whether a risk transaction has a risk, a risk feature description capable of being used to perform transaction risk determining in all risk feature descriptions; training a linear model based on the risk transaction representation and the random transaction representation, wherein the linear model is used to classify the risk transaction representation and the random transaction representation into two different spaces; determining an orthogonal direction corresponding to the linear model as a normal direction of an interface for distinguishing the risk transaction representation and the random transaction representation; and for each of the risk transaction representations, performing the following operations: obtaining a final representation that is of a current risk transaction representation and that is output by the output layer of the risk transaction prediction model; computing a partial derivative that is of the current risk transaction representation and that is obtained based on the final expression; determining, based on a partial derivative obtained based on each risk transaction representation and the normal direction of the interface, a risk feature description capable of being used to perform transaction risk determining; wherein the normal direction of the interface points to a direction of a space in which the risk transaction representation is located; separately determining whether a direction of the partial derivative obtained based on each risk transaction representation is consistent with the normal direction of the interface; and upon determining that a direction of a partial derivative obtained based on a risk transaction representation is consistent with the normal direction of the interface, determining a risk feature description corresponding to the risk transaction representation as a risk feature description capable of being used to perform transaction risk determining.. The limitations are directed to determining risky transaction while transacting (commercial interactions). Hence, they fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements such as a non-transitory computer-readable medium, one or more processors, a memory, a computer, a first neuron layer and other generic computer components to perform receiving, authenticating, translating, and transmitting. The generic computer components are recited at a high-level of generality (obtaining, inputting, and determining) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, the claim is directed to an abstract idea
Next the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure the claim amounts to significantly more than an abstract idea. Claims 1, 9, 10 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of at least a computing device to perform receiving, adding and communicating data are merely additional elements performing the abstract idea on a generic device i.e., abstract idea and apply it. See MPEP 2106.05(f). There is no improvement to computer technology or computer functionality MPEP 2106.05(a) nor a particular machine MPEP 2106.05(b) nor a particular transformation MPEP 2106.05(c). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) see MPEP 2106.05(d). Furthermore, the limitations are not indicative of integration into a practical application because they are merely adding the words “apply it” to a judicial exception on a generic computing device. See MPEP 2106.05(f). Given the above reasons, a generic processing device associated with determining risky transaction within a set of transactions is not an Inventive Concept. Thus, the claim is not patent eligible.
The dependent claims have been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The Dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because for the same reasoning as above and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional limitations of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea.
Claims 2, 11, 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) obtaining at least one group of risk transaction data for risk transaction prediction. This judicial exception is not integrated into a practical application because the limitations are 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 - see MPEP 2106.05(f). The claim(s) does/do not include additional elements (such as one or more processors, a memory) that are sufficient to amount to significantly more than the judicial exception because the limitations are 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 - see MPEP 2106.05(f).
Claims 3, 12 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) an abstract idea of obtaining risk transaction data corresponding to each risk feature description. This judicial exception is not integrated into a practical application because the limitations are 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 - see MPEP 2106.05(f). The claim(s) does/do not include additional elements (one or more processors, a memory) that are sufficient to amount to significantly more than the judicial exception because the limitations are 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 - see MPEP 2106.05(f).
Claims 5, 14 and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) an abstract idea of computing, based on the following computing formula, the partial derivative. This judicial exception is not integrated into a practical application because the limitations are 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 - see MPEP 2106.05(f). The claim(s) does/do not include additional elements (one or more processors, a memory) that are sufficient to amount to significantly more than the judicial exception because the limitations are 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 - see MPEP 2106.05(f).
Therefore, Claims 1-3, 5, 9-12, 14, 16-17, 19 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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.
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 1-3, 5, 9-12, 14, 16-17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Poli et al. (US 2022/0027750 A1) in view of Dickie et al. (US 2022/0207430 A1) in further view of Equifax (WO 2018/084867 A1).
Claims 1, 9 and 10 are grouped together. Claim 1, for instance, is taught: Poli teaches: A risk feature description extraction method, comprising: obtaining at least one group of risk transaction data for risk transaction prediction, wherein each group of risk transaction data satisfy one risk feature description (Poli, see at least par. [0064] “. . . The process 400 begins by obtaining (at step 405) a first set of input data values corresponding to a feature over a first period of time (e.g., the past 6 hours, 12 hours, 24 hours, etc.). For example, the feature selection module 204 may select one or more input features for examining. The risk manager 202 may then obtain input values corresponding to the input features (e.g., amount input feature, address input feature, etc.) from the account database 136 and/or the service application 138 . . .” & par. [0065] describes the first group of risk transaction data with anomalies;
obtaining at least one group of random transaction data from transaction record data of a historical risk control event (Poli et al. (US 2022/0027750 A1), see at least par. [0047] “. . . he risk determination module 132 may be configured to dynamically modify the risk model 212 based on detected shifts of input features to accommodate for any sudden shift in transaction behavior patterns. In some embodiments, the feature selection module 204 may select one or more input features from the input features used by the risk model 212 for performing risk predictions. The risk manager 202 may then establish benchmark statistics corresponding to the one or more input features based on past transactions (e.g., transactions that were conducted during the past 6 months, 12 months, etc.) The risk manager 202 may obtain values corresponding to a first input feature (e.g., the amount input feature) from the past transactions from the account database 136. The risk manager 202 may then determine benchmark statistics for the amount input feature based on the obtained values, such as from periods with similar conditions. The risk manager 202 may determine distribution statistics for the amount input feature based on the obtained values, such as a minimum amount, a maximum amount, a standard deviation, a skewness value, a kurtosis value, a cardinality value (e.g., a number of different amounts).”) The system uses past transaction record as benchmark;
training a linear model based on the risk transaction representation and the random transaction representation, wherein the linear model is used to classify the risk transaction representation and the random transaction representation into two different spaces (Poli, see at least par. [0016] “. For example, the risk determination system may adjust one or more parameters within the risk model in determining a risk of a transaction request (or classify the transaction request as legitimate or fraudulent). In some embodiments, the risk model may be configured and/or trained to recognize that a transaction from a particular vendor having a transaction amount over a threshold amount is considered high risk based on training data that indicates that a large portion of past transactions with this particular vendor are associated with amounts below the threshold.”) two different spaces are non-fraudulent and fraudulent transactions;
Poli does not disclose the following; however, Dickie teaches:
separately inputting the at least one group of risk transaction data and the at least one group of random transaction data into a pre-trained risk transaction prediction model, to obtain at least one risk transaction representation corresponding to the risk transaction data and at least one random transaction representation corresponding to the random transaction data that are output by a first neuron layer of the risk transaction prediction model, wherein the risk transaction prediction model is used to predict whether a transaction has a risk, and the first neuron layer is not an output layer of the risk transaction prediction model (Dickie et al. (US 2022/0207430 A1, see par. [0023] “ . . . certain of these exemplary processes, which generate training, validation, and input datasets that include feature values obtained from, or derived from, elements of contextual data that characterize purchase transactions initiated by customers of the financial institution, may enable the one or more of the FI computing systems to adaptively train and validate the gradient-boosted, decision-tree process using, and to apply the trained and validated gradient-boosted, decision-tree process to, data characterizing real-time changes, or real-time patterns, in the counterparty-specific purchasing or spending habits of these customers. The data characterizing the real-time changes or patterns in the counterparty-specific purchasing or spending habits of the customers may, for example, capture a real-time transition in customer purchasing or spending between certain types of counterparties, or certain type-specific counterparty categories, and may be associated with a contribution to a predicted risk of future customer default that exceeds comparable contributions by other feature values of the training, validation, and input datasets (e.g., extracted, or derived, from elements of customer profile, account, delinquency, and/or reporting data). Additionally, one or more of the exemplary processes described herein may provide, to the financial institution, a real-time indication of the likelihood of a future default event involving one or more customers, which may inform a determination of not only an initial set of terms and conditions associated with a newly issued credit product, but also a subsequent modification of an existing set of terms and conditions associated with a previously issued credit product . . .”) The cited portion discloses inputting the datasets which include purchase transactions that are associated with predicted risk of future default and to generate potential future default;
and determining, based on a difference between importance of the at least one risk transaction representation in determining whether a risk transaction has a risk and importance of the at least one random transaction representation in determining whether a risk transaction has a risk, a risk feature description capable of being used to perform transaction risk determining in all risk feature descriptions (Dickie, par. [0017] & see at least par. [0021] “. . . Further, the trained machine-learning or artificial-intelligence process (e.g., the trained gradient-boosted, decision-tree process described herein) may further ingest input datasets associated with one or more customers of the financial institution, and based on an application of the trained gradient-boosted, decision-tree process to the input datasets, the one or more FI computing systems may generate elements of output data indicative of a likelihood of an occurrence of a default event involving corresponding ones of the customers during a future temporal interval, such a three-month interval disposed between three and six months from a prediction date.”) the machine learning model helps to predict default event.
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of determining risk transaction representation as disclosed by Dickie with the invention as taught by Poli to better predict likelihood of an occurrence of an event during temporal interval (Dickie, abstract). Therefore, the combination is obvious.
Poli in view of Dickie does not teach the following; however, Equifax teaches:
determining an orthogonal direction corresponding to the linear model as a normal direction of an interface for distinguishing the risk transaction representation and the random transaction representation (Equifax, par. [0058] “ The optimization module 212 can also determine whether a degree of multicollinearity among the relevant factors is sufficiently low. For instance, degree of multicollinearity among the relevant factors is sufficiently low if the relevant factors are orthogonal or near-orthogonal. Orthogonality can include a change in one factor impacting the risk indicator without impacting the value of another factor. (By contrast, an absence of orthogonality among factors may negatively impact the explanatory capability of the model.)” ;
and for each of the risk transaction representations, performing the following operations: obtaining a final representation that is of a current risk transaction representation and that is output by the output layer of the risk transaction prediction model (Equifax, par. [0053] “In one example, the model development engine can generate a feed-forward neural network. A feed-forward neural network can include a neural network in which every node of the neural network propagates an output value to a subsequent layer of the neural network”;
computing a partial derivative that is of the current risk transaction representation and that is obtained based on the final expression (Equifax, see at least par. [0061] “The optimization module 212 can include instructions for causing the model development engine 108 to determine a rate of change (e.g., a derivative or partial derivative) of the risk indicator with respect to each relevant factor. The rate of change is determined through every path in the neural network that each relevant factor can follow to affect the risk indicator”; determining, based on a partial derivative obtained based on each risk transaction representation and the normal direction of the interface, a risk feature description capable of being used to perform transaction risk determining (Equifax, see at least par. [0053] “data may move in one direction (forward) from one node to the next node in a feed-forward neural network. The feed-forward neural network can include one or more hidden layers of interconnected nodes that can share input data and exchange signals between one another. The layers may be considered hidden because they may not be directly observable in the normal functioning of the neural network”;
wherein the normal direction of the interface points to a direction of a space in which the risk transaction representation is located (see at least par. [0140] “. . . For example, a data set used to generate credit scores may involve financial records associated with millions of consumers. Numerically approximating the location of each consumer's global maximum score is computationally intractable using current technology in a run-time environment”);
separately determining whether a direction of the partial derivative obtained based on each risk transaction representation is consistent with the normal direction of the interface (see at least par. [0054] “. Optimizing the neural network (or other model) can allow both the risk indicator and the impact of a factor associated with a predictor variable to be identified using the same neural network. Examples of optimizing the neural network or other model are described herein with respect to FIG. 3. & [0055] In some aspects, the outputs from an optimization process can be used to adjust the neural network or other model. For example, if an exploratory data analysis indicates that a positive relationship exists between an odds ratio (e.g., an odds index) and a predictor variable, and a neural network shows a negative relationship between a credit score and a factor positively associated with the predictor variable, the neural network can be modified. For example, the architecture of the neural network can be changed (e.g., by adding or removing a node from a hidden layer or increasing or decreasing the number of hidden layers), a factor analysis can be modified (e.g., by modifying the number of factors used), one or more of the predictor variables can be modified through transformations, or one or more of the predictor variables can be eliminated from the neural network.”) The positive relationship corresponds to going in the same direction;
and upon determining that a direction of a partial derivative obtained based on a risk transaction representation is consistent with the normal direction of the interface, determining a risk feature description corresponding to the risk transaction representation as a risk feature description capable of being used to perform transaction risk determining (see at least par. [0055]) Removing or adding node corresponds to using risk feature description to perform transaction risk detemrining.
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of performing an optimization modeling as disclosed by Equifax with the invention as taught by Poli in view of Dickie to better adjust the neural network to generate predict the risk level changes (Equifax, abstract). Therefore, the combination is obvious.
Claims 2, 11, 16 are grouped together. Claim 2, for instance, is disclosed: Poli in view of Dickie in further view of Equifax discloses: The method according to claim 1. Poli further teaches: wherein the obtaining at least one group of risk transaction data for risk transaction prediction comprises: pre-determining at least one initial risk feature description that describes a transaction event, wherein each initial risk feature description comprises at least one variable first parameter, and a first parameter comprises at least one of a time, a place, and a transaction amount of a transaction(Poli, see at least par. [0064] “. . . The process 400 begins by obtaining (at step 405) a first set of input data values corresponding to a feature over a first period of time (e.g., the past 6 hours, 12 hours, 24 hours, etc.). For example, the feature selection module 204 may select one or more input features for examining. The risk manager 202 may then obtain input values corresponding to the input features (e.g., amount input feature, address input feature, etc.) from the account database 136 and/or the service application 138 . . .” & par. [0065] describes the first group of risk transaction data with anomalies; for each initial risk feature description, performing value traversal on the first parameter in an initial risk feature description based on a preset parameter traversal range, to generate a traversal risk feature description; and obtaining risk transaction data corresponding to each risk feature description based on the initial risk feature description and each traversal risk feature description (Poli, see at least par. [0047] “. . . he risk determination module 132 may be configured to dynamically modify the risk model 212 based on detected shifts of input features to accommodate for any sudden shift in transaction behavior patterns. In some embodiments, the feature selection module 204 may select one or more input features from the input features used by the risk model 212 for performing risk predictions. The risk manager 202 may then establish benchmark statistics corresponding to the one or more input features based on past transactions (e.g., transactions that were conducted during the past 6 months, 12 months, etc.) The risk manager 202 may obtain values corresponding to a first input feature (e.g., the amount input feature) from the past transactions from the account database 136. The risk manager 202 may then determine benchmark statistics for the amount input feature based on the obtained values, such as from periods with similar conditions. The risk manager 202 may determine distribution statistics for the amount input feature based on the obtained values, such as a minimum amount, a maximum amount, a standard deviation, a skewness value, a kurtosis value, a cardinality value (e.g., a number of different amounts).”) The system uses past transaction record as benchmark;
Claims 3, 12, 17 are grouped together. Claim 3, for instance, is disclosed: Poli in view of Dickie in further view of Equifax teaches: The method according to claim 2. Dickie further teaches: wherein the obtaining risk transaction data corresponding to each risk feature description based on the initial risk feature description and each traversal risk feature description comprises: converting the initial risk feature description and the traversal risk feature description into an SQL statement (Dickie, see at least par. [0044] “Executed pre-processing engine 140 may perform operations that consolidate the one or more obtained data records and generate a corresponding one of consolidated data records 142 that includes the customer identifier and temporal identifier, and that is associated with, and characterizes, the particular customer of the financial institution across the temporal intervals. By way of example, executed pre-processing engine 140 may consolidate the obtained data records, which include the pair of customer and temporal identifiers, through an invocation of an appropriate Java-based SQL “join” command (e.g., an appropriate “inner” or “outer” join command, etc.). Further, executed pre-processing engine 140 may perform any of the exemplary processes described herein to generate another one of consolidated data records 142 for each additional, or alternate, customer of the financial institution during the temporal interval (e.g., as represented by a corresponding customer identifier and the temporal interval”.) Temporal interval could correspond to traversal risk feature; and querying a transaction database based on the SQL statement, to obtain risk transaction data that satisfy each risk feature description; and/or randomly generating risk transaction data that satisfy the initial risk feature description; and randomly generating risk transaction data that satisfy the traversal risk feature description (par. [0044] “some instances, executed pre-processing engine 140 may perform further operations that, for a particular customer of the financial institution during the temporal interval (e.g., represented by a pair of the customer and temporal identifiers described herein), obtain one or more data records of profile data 104A, account data 104B, delinquency data 104C and/or credit-bureau data 108A that include the pair of customer and temporal identifiers. Executed pre-processing engine 140 may perform operations that consolidate the one or more obtained data records and generate a corresponding one of consolidated data records 142 that includes the customer identifier and temporal identifier, and that is associated with, and characterizes, the particular customer of the financial institution across the temporal intervals.”).
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of using a transaction database for querying function as disclosed by Dickie with the invention as taught by Poli in view of Dickie to better predict likelihood of an occurrence of an event during temporal interval. Therefore, the combination is obvious.
Claims 5, 14 and 19 are grouped together. Poli in view of Dickie in further view of Equifax teaches: The method according to claim 1. Furthermore, Equifax teaches: wherein the computing a partial derivative that is of the current risk transaction representation and that is obtained based on the final expression comprises: computing, based on the following computing formula, the partial derivative that is of the current risk transaction representation and that is obtained based on the final expression: S = Vh(f(x)) S is used to represent the partial derivative that is of the current risk transaction representation and that is obtained based on the final expression of the current risk transaction representation, h is used to represent the final expression of the current risk transaction representation, f(x) is used to represent the current risk transaction representation, and x is used to represent a risk feature description corresponding to the current risk transaction representation (Equifax, see par. [0061] “In some aspects, the optimization module 212 includes instructions for causing the model development engine 108 to perform a test process for determining an effect or an impact of each predictor variable or factor driving a certain predictor variables on the risk indicator after the iteration is terminated. For example, the model development engine 108 can use a neural network or other optimized model to implicitly incorporate non-linearity into one or more modeled relationships between each predictor variable and the risk indicator. The optimization module 212 can include instructions for causing the model development engine 108 to determine a rate of change (e.g., a derivative or partial derivative) of the risk indicator with respect to each relevant factor. The rate of change is determined through every path in the neural network that each relevant factor can follow to affect the risk indicator. Each path includes one or more predictor variables associated with the factor.”).
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of determining a transaction risk as disclosed by Gardner with the invention as taught by Poli in view of Dickie in further view of Strong Force to better calculate a risk associated transaction. Therefore, the combination is obvious.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/TOAN DUC BUI/Examiner, Art Unit 3693
/CHO YIU KWONG/Primary Examiner, Art Unit 3693
