DETAILED ACTION
This action is in reply to the application filed on 12/06/2024.
Claim 7-8 have been canceled.
Claims 1-6, 9-20 have been examined.
Claims 1-6, 9-20 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 .
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 1-6, 9-20 are directed to a system, a method, or product which are one of the statutory categories of invention. (Step 1: YES).
Claims 1-6, 9-20 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. 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 importance of the at least one risk transaction representation and 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. 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 4, 13 and 18 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 a partial derivative that is of the current risk transaction representation and that is obtained based on the final expression. 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).
Claims 6, 15 and 20 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 normal direction of the interface points to a direction of a space in which the risk transaction representation is located. 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 1-6, 9-20 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, 9-12, 16-17 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).
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;
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 importance of the at least one risk transaction representation and 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.
Claims 2, 11, 16 are grouped together. Claim 2, for instance, is disclosed: Poli in view of Dickie 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 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;
Claims 3, 12, 17 are grouped together. Claim 3, for instance, is disclosed: Poli in view of Dickie 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 4, 13 and 18 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 Strong Force, (WO 2022/133210 A2)
Claims 4, 13 and 18 are grouped together. Claim 4 is disclosed: Poli in view of Dickie teaches: The method according to claim 1. However, Strong Force teaches: wherein the determining, based on importance of the at least one risk transaction representation and 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 comprises: 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 (Strong Force, WO 2022/133210 A2, see at least par. [01904] “. . . In pairs trading strategies where similar stocks are paired and a short position is taken on the top (potentially overpriced) asset and a long position is taken on the bottom (potentially underpriced) asset (which may optionally involve pairing similar stocks and using a linear combination (or other combination) of their price to generate a stationary time-series, computing a set of scores, such as z-scores, for the stationary signal and trading on the spread assuming reversion to the mean) where input data sources and feature vectors may include trading data that indicates trades of similar size and timing in pairs of similar assets . . .”); 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; and 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 (see at least par. [00511] “In some embodiments, the machine learning model 13702 may be and/or include a Bayesian network. The Bayesian network may be a probabilistic graphical model configured to represent a set of random variables and conditional independence of the set of random variables. The Bayesian network may be configured to represent the random variables and conditional independence via a directed acyclic graph. The Bayesian network may include one or both of a dynamic Bayesian network and an influence diagram.”) Bayesian model incorporates partial derivatives.
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 Strong Force with the invention as taught by Poli in view of Dickie to better calculate a risk associated transaction. Therefore, the combination is obvious.
Claims 5, 6, 14, 15, 19 and 20 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 Strong Force, (WO 2022/133210 A2) in further view of Gardner (WO 2018/084867 A1).
Claims 5, 14 and 19 are grouped together. Poli in view of Dickie in further view of Strong Force teaches: The method according to claim 4. However, Gardner 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 (Gardner (WO 2018/084867 A1)), 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.
Claims 6, 15 and 20 are grouped together. Claim 6, for instance is taught: Poli in view of Dickie in further view of Strong Force teaches: The method according to claim 4, wherein the normal direction of the interface points to a direction of a space in which the risk transaction representation is located; and the 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 comprises: 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 [[if]]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 (Gardner, see par. [0061]-[0062]) the cited portions disclose the direction, or risk indicators, when using partial derivative as applied to risk factors.
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
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/TOAN DUC BUI/Examiner, Art Unit 3693
/BRUCE I EBERSMAN/Primary Examiner, Art Unit 3693
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