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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/24/2026 has been entered.
Response to Arguments
Applicant’s arguments filed 04/24/2026 have been fully considered.
Regarding the rejection of claims under 35 U.S.C. 103, amendments to the claims have necessitated new grounds of rejection, which are given below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
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 1–21 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Regarding claim 1:
Examiner finds that “generating, by one or more processors, a plurality of feature tuple sets using Taylor expansion based on the plurality of features and a plurality of non-constant interaction levels” is not enabled by the disclosure. Examiner has analyzed the limitation under the guidelines of MPEP 2164.01.
Examiner finds that “Taylor expansion” is a term of art, also called the “Taylor series”: (“In mathematics, the Taylor series of a function is an infinite sum of terms that are expressed in terms of the function's derivatives at a single point. For most common functions, the function and the sum of its Taylor series are equal near this point,” Wikipedia, “Taylor Series,” entry dated 09/03/2021).
Examiner’s search for prior art has not produced any examples of using Taylor expansion (also known as a Taylor series) in the generation of feature tuple sets or in model feature selection more generally.
A feature tuple set is essentially a table of data, here drawn from “a plurality of features from a plurality of datasets respectively associated with a plurality of external computing entities.” A Taylor series is a mathematical expression consisting of an infinite sum of terms. The connection between a table of data or a selection of data columns, and a mathematical expression or infinite series of sums, is unclear.
The specification provides no such method. The closest mention is in paragraph 0016, “Aspects of the present invention group multiple similar proxies into ensembles. Ensembles can be varied by what representation variable is used (e.g., a member may proxied by social security number, patient ID, demographic key, and/or the like) as well as central values used to represent a variable ( e.g., count of investigation may be proxied by mean, median, total, and/or the like). Then, by using Taylor expansion, a proposed system can establish single variable ensembles, two variable ensembles, and/or the like.” Examiner is unsure whether the “ensembles” of this paragraph are related to the “plurality of feature tuple sets” of the claim; in any case, the paragraph is insufficient to explain how Taylor expansion could be used to establish the ensembles.
Examiner finds that a reasonable level of experimentation would not allow a person having ordinary skill in the art to generate “a plurality of feature tuple sets using Taylor expansion based on the plurality of features and a plurality of non-constant interaction levels” in order to make and use the claimed invention. The claim therefore fails to comply with the enablement requirement.
Regarding claims 8 and 15:
Claims 8 and 15 are analogous to claim 1 and are rejected by the same arguments.
Regarding claims 2–7, 9–14, and 16–21:
Claims 2–7, 9–14, and 16–21 are dependent on claim 1, 8, or 15, and are rejected by the same arguments.
Claims 1–21 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.
Regarding claim 1:
Examiner finds the phrase “using Taylor expansion” in the limitation “generating, by one or more processors, a plurality of feature tuple sets using Taylor expansion based on the plurality of features and a plurality of non-constant interaction levels” indefinite in the context of the claim.
As stated in the 35 U.S.C. 112(a) rejection above, the application does not disclose a method for using Taylor expansion in the generation of feature tuple sets, and the concept is not found in prior art. The connection between a table of data or a selection of data columns, and a mathematical expression or infinite series of sums, is unclear. A person having ordinary skill in the art would therefore be unable to determine what is intended by “using Taylor expansion” in the context of this claim to determine the metes and bounds of the claim.
In further examination below, the limitation “generating, by one or more processors, a plurality of feature tuple sets using Taylor expansion based on the plurality of features and a plurality of non-constant interaction levels” is interpreted to mean only that Taylor expansion or a Taylor series is used in the processing or generation of an input value that could be used as a model feature.
Regarding claims 8 and 15:
Claims 8 and 15 are analogous to claim 1 and are rejected by the same arguments.
Regarding claims 2–7, 9–14, and 16–21:
Claims 2–7, 9–14, and 16–21 are dependent on claim 1, 8, or 15, and are rejected by the same arguments.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-21 rejected under 35 U.S.C. 103 over Peng et al., “A Deep Multi-View Framework for Anomaly Detection on Attributed Networks,” 2020, https://ieeexplore.ieee.org/document/9162509 (hereafter Peng) in view of Shekhar et al., US Pre-Grant Publication No. 2021/0233080 (hereafter Shekhar), Raman et al., US Pre-Grant Publication No. 2020/0242611 (hereafter Raman), and Kuo et al., US Pre-Grant Publication No. 2022/0020134 (hereafter Kuo).
Regarding claim 1 and analogous claims 8 and 15:
Peng teaches:
(bold only) “generating, by one or more processors, a plurality of feature tuple sets using Taylor expansion based on the plurality of features and a plurality of non-constant interaction levels, wherein (i) the plurality of feature tuple sets comprise different feature counts defined by a plurality of different non-constant interaction levels ranging from a feature count of zero or one to a feature count equivalent to a number of the plurality of datasets and (ii) a feature tuple of at least one feature tuple set of the plurality of feature tuple sets comprises a first feature value of a first dataset of the plurality of datasets and a second feature value of a second dataset of the plurality of datasets;”: Peng, Fig. 1,
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[showing a set of features f1-10, and four selections of those features, shown as perspectives 1-4, hence, generating, …, a plurality of feature tuple sets … based on the plurality of features and a plurality of non-constant interaction levels. These perspectives have varying numbers of features, the number being in the range of one to the total number of available features, hence, wherein (i) the plurality of feature tuple sets comprise different feature counts defined by a plurality of different non-constant interaction levels ranging from a feature count of zero or one to a feature count equivalent to a number of the plurality of datasets, non-constant interaction level interpreted as an indicator of the number of feature values in a tuple].
(bold only) “generating, by one or more processors, a predicted anomaly score based on a plurality of interaction level anomaly scores respectively generated for the plurality of feature tuple sets”: Peng, Fig. 3, “
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”; Peng, section 4.3, paragraph 3, ”Weighted Aggregation - giving each view-based vector a different weight and then summing them up, which means that each view has a distinct proportion in the final representation. To be more specific, the combined results Z ∈ ℝn x h2 is selected from the following set:
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[hence, Z is generated using a plurality of feature tuple sets]”; Peng, section 4.4, paragraph 3, “For another, the attribute decoder aims at approximating the original node attributes from the encoded embeddings. To be more specific, we leverage a simple fully-connected layer to reconstruct the attribute information as follows:
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[the attribute decoder produces output based on the original feature tuple sets, each associated, as shown above, with varying number of feature values, hence the output is based on a plurality of interaction level anomaly scores] ”; Peng, section 4.5, “After the iterative optimization processs [sic], the abnormal score for the ith node can be computed by
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where the first term and the second term report the degree of deviation in structure and attribute, respectively, and λ is a trade-off parameter. Since nodes with high scores are more likely to be anomalous, we can rank anomalies according to abnormal scores [abnormality score based on a difference between original feature tuple sets and their reconstructions from a weighted aggregate, hence, generating … a predicted anomaly score based on a plurality of interaction level anomaly scores respectively generated for the plurality of feature tuple sets].”
“and wherein generating an interaction level anomaly score fora non-constant interaction level, of the plurality of different non-constant interaction levels, associated with the at least one feature tuple set comprises: determining a feature tuple anomaly score that indicates a measure of observed anomalous behavior associated with the feature tuple”: Peng, section 4.5, “After the iterative optimization processs [sic], the abnormal score for the ith node can be computed by
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where the first term and the second term report the degree of deviation in structure and attribute, respectively, and λ is a trade-off parameter. Since nodes with high scores are more likely to be anomalous, we can rank anomalies according to abnormal scores [determining a feature tuple anomaly score that indicates a measure of observed anomalous behavior associated with the feature tuple].”
“determining a feature tuple weight for the feature tuple that indicates an estimated contribution of the feature tuple to the predicted anomaly score; determining, for the at least one feature tuple set associated with the non-constant interaction level and comprising the feature tuple, a plurality of weighted feature tuple anomaly scores, wherein determining the plurality of weighted feature tuple anomaly scores comprises determining a first weighted feature tuple anomaly score for the feature tuple based at least in part on the feature tuple anomaly score and the feature tuple weight; and determining the interaction level anomaly score for the non-constant interaction level based at least in part on the plurality of weighted feature tuple anomaly scores”: Peng, Fig. 3, “
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”; Peng, section 4.3, paragraph 3, ”Weighted Aggregation - giving each view-based vector a different weight and then summing them up, which means that each view has a distinct proportion in the final representation [the final representation is used to determine feature set anomaly, hence, determining a feature tuple weight for the feature tuple that indicates an estimated contribution of the feature tuple to the predicted anomaly score]. To be more specific, the combined results Z ∈ ℝn x h2 is selected from the following set:
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”; Peng, section 4.4, paragraph 3, “For another, the attribute decoder aims at approximating the original node attributes from the encoded embeddings. To be more specific, we leverage a simple fully-connected layer to reconstruct the attribute information as follows:
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”; Peng, section 4.5, “After the iterative optimization processs [sic], the abnormal score for the ith node can be computed by
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where the first term and the second term report the degree of deviation in structure and attribute, respectively, and λ is a trade-off parameter [feature set anomaly determined by comparison to set regenerated from weighted composite, hence, determining, for the at least one feature tuple set associated with the non-constant interaction level and comprising the feature tuple, a plurality of weighted feature tuple anomaly scores, wherein determining the plurality of weighted feature tuple anomaly scores comprises determining a first weighted feature tuple anomaly score for the feature tuple based at least in part on the feature tuple anomaly score and the feature tuple weight, non-constant interaction level interpreted as an indicator of the number of feature values in a tuple] Since nodes with high scores are more likely to be anomalous, we can rank anomalies according to abnormal scores [and determining the interaction level anomaly score for the non-constant interaction level based at least in part on the plurality of weighted feature tuple anomaly scores, as each non-constant interaction level is an indicator of feature count associated with a particular feature set, this is interpreted as determining the anomalousness for the particular feature set using multiple weighted feature set scores].”
Peng does not explicitly teach:
“A computer-implemented method comprising”
“extracting, by one or more processors, a plurality of features from a plurality of datasets respectively associated with a plurality of external computing entities”
(bold only) “generating, by one or more processors, a plurality of feature tuple sets using Taylor expansion based on the plurality of features and a plurality of non-constant interaction levels, wherein (i) the plurality of feature tuple sets comprise different feature counts defined by a plurality of different non- constant interaction levels ranging from a feature count of zero or one to a feature count equivalent to a number of the plurality of datasets and (ii) a feature tuple of at least one feature tuple set of the plurality of feature tuple sets comprises a first feature value of a first dataset of the plurality of datasets and a second feature value of a second dataset of the plurality of datasets.”
(bold only) “generating, by one or more processors, a predicted anomaly score based on a plurality of interaction level anomaly scores respectively generated for the plurality of feature tuple sets”
“wherein the predicted anomaly score indicates a likelihood that a user is represented by multiple identities within the plurality of datasets, as a super entity”
“and providing, by the one or more processors and using a communication network, the generated predicted anomaly score to at least one of the plurality of external computing entities”
Shekhar teaches:
“A computer-implemented method comprising”: Shekhar, paragraph 0114, “Each of the components 802-816 of the fraudulent transaction detection system 106 can include software, hardware, or both. For example, the components 802-816 can include one or more instructions stored on a computer readable storage medium and executable by processors of one or more computing devices, such as a client device or server device. When executed by the one or more processors, the computer-executable instructions of the fraudulent transaction detection system 106 can cause the computing device(s) to perform the methods [computer-implemented method] described herein.”
“extracting, by one or more processors, a plurality of features from a plurality of datasets respectively associated with a plurality of external computing entities” and (bold only) “generating, by one or more processors, a predicted anomaly score based on a plurality of interaction level anomaly scores respectively generated for the plurality of feature tuple sets”: Shekhar, paragraph 0114, “Each of the components 802-816 of the fraudulent transaction detection system 106 can include software, hardware, or both. For example, the components 802-816 can include one or more instructions stored on a computer readable storage medium and executable by processors [by one or more processors] of one or more computing devices, such as a client device or server device”; Shekhar, paragraph 0044, “As mentioned above, the system 100 includes the server(s) 102. The server(s) can generate, store, receive, and/or transmit data, including data regarding digital transactions. For example, the server(s) 102 can receive payment information related to payment of a product or service from one or more client devices (e.g., one or more of the client devices 110a-110n) [a plurality of datasets respectively associated with a plurality of external computing entities]. The server(s) 102 can further receive information related to digital identities associated with digital transactions”; Shekhar, paragraph 0046, “Additionally, the server(s) 102 can include the fraudulent transaction detection system 106. In particular, in one or more embodiments, the fraudulent transaction detection system 106 utilizes the server(s) 102 to identify digital identities that correspond to fraudulent transactions”; Shekhar, paragraph 0047, “For example, in one or more embodiments, the fraudulent transaction detection system 106, via the server(s) 102, identifies a plurality of digital identities corresponding to a plurality of digital transactions. The fraudulent transaction detection system 106 can, via the server(s) 102, generate a transaction map that includes edge connections between a plurality of nodes that correspond to the plurality of digital identities [extracting, by one or more processors, a plurality of features].”
“wherein the predicted anomaly score indicates a likelihood that a user is represented by multiple identities within the plurality of datasets, as a super entity”: Shekhar, paragraph 0022, “As further mentioned, in one or more embodiments, the fraudulent transaction detection system determines whether a digital identity is associated with a fraudulent transaction. Indeed, using a similarity probability between a pair of digital identities generated by the time dependent graph convolutional neural network, the fraudulent transaction detection system can determine that a digital identity from the pair of digital identities is associated with a fraudulent entity (e.g., a fraudulent user). More particularly, knowing a given node is associated with a fraudulent transaction or entity, the fraudulent transaction detection system can compare other nodes to the given node using similarity scores to determine if the other nodes are the same entity as the given node [wherein the predicted anomaly score indicates a likelihood that a user is represented by multiple identities within the plurality of datasets, as a super entity]. Accordingly, the fraudulent transaction detection system can determine that a digital transaction associated with that digital identity is a fraudulent transaction.”
“providing, by the one or more processors and using a communication network, the generated predicted anomaly score to at least one of the plurality of external computing entities”: Shekhar, paragraph 0056, “In some embodiments, the fraudulent transaction detection system 106 transmits a notification indicating that the digital identity corresponds to a fraudulent transaction. For example, the fraudulent transaction detection system 106 can transmit a notification to an administrator or manager of the online retail website from which the fraudulent transaction originated [providing, by the one or more processors and using a communication network, the generated predicted anomaly score to at least one of the plurality of external computing entities].”
Shekhar and Peng are analogous arts as they are both related to anomaly detection. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the detection of shared identity in Shekhar with the teachings of Peng to arrive at the present invention, in order to better detect fraudulent activity, as stated in Shekhar, paragraph 0022, “More particularly, knowing a given node is associated with a fraudulent transaction or entity, the fraudulent transaction detection system can compare other nodes to the given node using similarity scores to determine if the other nodes are the same entity as the given node. Accordingly, the fraudulent transaction detection system can determine that a digital transaction associated with that digital identity is a fraudulent transaction.”
Raman teaches (bold only) “generating, by one or more processors, a plurality of feature tuple sets using Taylor expansion based on the plurality of features and a plurality of non-constant interaction levels, wherein (i) the plurality of feature tuple sets comprise different feature counts defined by a plurality of different non- constant interaction levels ranging from a feature count of zero or one to a feature count equivalent to a number of the plurality of datasets and (ii) a feature tuple of at least one feature tuple set of the plurality of feature tuple sets comprises a first feature value of a first dataset of the plurality of datasets and a second feature value of a second dataset of the plurality of datasets”: Raman, paragraph 0077, “For example, DR engine [by one or more processors] 410 may select the top five features recommended by each algorithm, or a subset of features that make to the top 50 percent of features as ranked by each algorithm [a feature tuple of at least one feature tuple set of the plurality of feature tuple sets comprises a first feature value of a first dataset of the plurality of datasets and a second feature value of a second dataset of the plurality of datasets]. In some examples, a user selects the selection criteria from user interface 205 using an I/O device 203. In some examples, DR engine 410 selects a maximum number of features, such as the top few features from each algorithm.”
Raman and Peng are analogous arts as they are both related to anomaly detection. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the feature selecting schemes of Raman to the teachings of Peng to arrive at the present invention, in order to base fraud detection on the most relevant features, as stated in Raman, paragraph 0077, “For example, DR engine 410 may select the top five features recommended by each algorithm, or a subset of features that make to the top 50 percent of features as ranked by each algorithm. In some examples, a user selects the selection criteria from user interface 205 using an I/O device 203. In some examples, DR engine 410 selects a maximum number of features, such as the top few features from each algorithm.”
Kuo teaches (bold only) “generating, by one or more processors, a plurality of feature tuple sets using Taylor expansion based on the plurality of features and a plurality of non-constant interaction levels”: Kuo, paragraph 0100, “In example embodiments, prior to describing the feature points, the method further comprises: obtaining the offset of coordinates of each feature point in the feature point set of the image block by using a Taylor series interpolation method [using Taylor expansion]; and correcting the coordinates of the corresponding feature points in the discrete space by using the coordinate offset to obtain the coordinates of all the feature points of the image block in the continuous space, and determining the positions of the corresponding feature points based on the coordinates.”
Kuo and Peng are analogous arts as they are both related to feature analysis. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the Taylor series interpolation of Kuo with the teachings of Peng to arrive at the present invention, in order to apply the methods to image inputs, as stated in Kuo, paragraph 0100, “In example embodiments, prior to describing the feature points, the method further comprises: obtaining the offset of coordinates of each feature point in the feature point set of the image block by using a Taylor series interpolation method; and correcting the coordinates of the corresponding feature points in the discrete space by using the coordinate offset to obtain the coordinates of all the feature points of the image block in the continuous space, and determining the positions of the corresponding feature points based on the coordinates.”
Regarding claim 2 and analogous claims 9 and 16:
Peng as modified by Shekhar, Raman and Kuo teaches “the computer-implemented method of claim 1.”
Raman further teaches “wherein determining the feature tuple anomaly score comprises: determining a partial derivative measure of an anomaly distribution measure with respect to the feature tuple, and determining the feature tuple anomaly score based at least in part on the partial derivative measure”: Raman, paragraph 0059, “Strategy expansion engine 406 obtains strategy data 416 from initial strategy engine 404, and generates a modified strategy, which is identified and characterized by modified strategy data 316. The modified strategy may be generated based on the same set of features used to generate the initial strategy as identified by strategy data or may be based on a different set of features, as identified by training data 420. In some examples, the modified strategy is based on the application of one or more discrete stochastic gradient descent (DSGD) algorithms by DSGD engine 408”; Raman, paragraph 0071, “The gain for each ith dimension indicates a change in average bad probability of the action space after the in average bad probability of the action space after the dimension value is changed from Θi to Θi-ai. DSGD engine 408 evaluates, for each iteration, the gains of all thresholds Θ1 , . . . , Θk according to equation (8), and updates the threshold Θi whose partial derivative is the largest [determining a partial derivative measure of an anomaly distribution measure with respect to the feature tuple, and determining the feature tuple anomaly score based at least in part on the partial derivative measure].”
Raman and Peng are both related to the same field of endeavor (anomaly detection). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the use of gradient descent in Raman to the teachings of Peng to arrive at the present invention, in order to optimize parameter values in the fraud detection system, as stated in Raman, paragraph 0062, “DSGD engine 408 may generate optimal threshold values for the action space expansion in new strategy S' as follows.”
Regarding claim 3 and analogous claims 10 and 17:
Peng as modified by Shekhar, Raman and Kuo teaches “the computer-implemented method of claim 1.”
Peng further teaches “wherein determining the interaction level anomaly score comprises: combining the plurality of weighted feature tuple anomaly scores for the feature tuple using a summation operation to generate the interaction level anomaly score”: Peng, Fig. 3, “
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”; Peng, section 4.3, paragraph 3, ”Weighted Aggregation - giving each view-based vector a different weight and then summing them up, which means that each view has a distinct proportion in the final representation. To be more specific, the combined results Z ∈ ℝn x h2 is selected from the following set:
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[combining the plurality of weighted feature tuple anomaly scores for the feature tuple using a summation operation to generate the interaction level anomaly score]."
Regarding claim 4 and analogous claims 11 and 18:
Peng as modified by Shekhar, Raman and Kuo teaches “the computer-implemented method of claim 1.”
Raman further teaches:
“wherein determining a weighted feature tuple anomaly score of the plurality of weighted feature tuple anomaly scores for the feature tuple comprises: determining a first feature weight associated with the first feature value and a second feature weight associated with the second feature value based at least in part on the feature tuple weight”: Raman, paragraph 0083, “At this step, the transformed features from the feature transformation step are weighted based on characteristics of the transactions ( e.g., a type of transaction) the transformed features are associated with. For example, to create a wider separation effect between good (e.g., not fraudulent) and bad (e.g., fraudulent) transactions, DR engine 410 may weigh the transformed features based on whether they are associated with a good, or bad, transaction. DR engine 410 generates a Multiplication Factor or Index Mj for the normalized features. Mj is defined as the bad (e.g., fraudulent) rate of ith bin for any normalized feature Xj[determining a first feature weight associated with the first feature value and a second feature weight associated with the second feature value based at least in part on the feature tuple weight].”
“determining a first weight deviation measure associated with the first feature value and a second weight deviation measure associated with the second feature value based at least in part on the first feature value, the second feature value, the first feature weight, and the second feature weight”: Raman, paragraph 0069, “For example, taking the initial strategy defined in equation (3) above, DSGD engine 408, may generate learning rates α1 = σ(C(x)), α2 = σ(x1), α3 = σ(x5), α4 = σ(x4), where α stands for the standard deviation of the acting variable (e.g., feature) calculated based on the training set (e.g., training data 420) [determining a first weight deviation measure associated with the first feature value and a second weight deviation measure associated with the second feature value based at least in part on the first feature value, the second feature value, the first feature weight, and the second feature weight].”
“and determining the weighted feature tuple anomaly score based at least in part on the feature tuple anomaly score for the feature tuple and the first weight deviation measure and the second weight deviation measure”: Raman, paragraph 0069, “For example, taking the initial strategy defined in equation (3) above, DSGD engine 408, may generate learning rates α1 = σ(C(x)), α2 = σ(x1), α3 = σ(x5), α4 = σ(x4), where α stands for the standard deviation of the acting variable (e.g., feature) calculated based on the training set (e.g., training data 420)”; Raman, paragraphs 0096-0097, “Proceeding to step 608, an intermediate strategy is generated based on applying at least one discrete stochastic gradient descent (DSGD) algorithm to the output of the trained classifier and the initial strategy. For example, DSGD engine 408 may apply on one or more discrete stochastic gradient descent algorithms to strategy data 416. At step 610, a new strategy is generated based on applying at least one dimensionality reduction (DR) algorithm to the output of the trained classifier and the intermediate strategy. For example, DR engine 410 may apply one or more dimensionality reduction algorithms to a strategy generated by DSGD engine 408 to provide modified strategy data 316. At step 612, a determination is made as to whether all fraudulent transactions of the transaction data were identified as fraud by the new strategy. For example, each transaction of the training data may be identified as fraudulent or not. Fraud detection computing device 102 may compare the fraud identification of each transaction to a fraud determination based on the new strategy [and determining the weighted feature tuple anomaly score based at least in part on the feature tuple anomaly score for the feature tuple and the first weight deviation measure and the second weight deviation measure].”
Raman and Peng are analogous arts as they are both related to anomaly detection. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the feature selecting and weighting schemes of Raman to the teachings of Shekhar to arrive at the present invention, in order to base fraud detection on the most relevant features, as stated in Raman, paragraph 0085, “Thus, feature bins with better odds ratios (probability of good over probability of bad) than others will have higher Multiplication Factors. In other words, in those feature bins with high Multiplication Factors, the transactions labelled as bad ( e.g., fraudulent transactions) are better separated from the transactions labelled as good (e.g., non fraudulent transactions).”
Regarding claim 5 and analogous claims 12 and 19:
Peng as modified by Shekhar, Raman and Kuo teaches “the computer-implemented method of claim 1.”
Peng further teaches “for a pth-level non-constant defined interaction level of the feature tuple, the feature tuple may comprise p feature values, where p is a positive integer”: Peng, Fig. 1,
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[showing different perspectives of the same global feature set F, each perspective having p feature values, where p is positive, hence, for a pth-level non-constant defined interaction level of the feature tuple, the feature tuple may comprise p feature values, where p is a positive integer].
Regarding claim 6 and analogous claims 13 and 20:
Peng as modified by Shekhar, Raman and Kuo teaches “the computer-implemented method of claim 5.”
Peng further teaches “wherein the feature tuple is associated with at least one of an ensemble, an ensemble element, or an ensemble element enumeration”: Peng, Fig. 1,
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[showing perspectives as feature tuples, associated with global feature set F, interpreted as an ensemble, hence, wherein the feature tuple is associated with at least one of an ensemble, an ensemble element, or an ensemble element enumeration].
Regarding claim 7 and analogous claims 14 and 21:
Peng as modified by Shekhar, Raman and Kuo teaches “the computer-implemented method of claim 5.”
Peng further teaches (bold only) “wherein the feature tuple is associated with an assigned weight value that is determined based on an output of processing the first feature value and the second feature value using a trained regression-based machine learning model”: Peng, section 4.3, paragraph 3, ”Weighted Aggregation - giving each view-based vector a different weight and then summing them up, which means that each view has a distinct proportion in the final representation [the feature tuple is associated with an assigned weight value]. To be more specific, the combined results Z ∈ ℝn x h2 is selected from the following set:
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when αi = 0, the ith view will be ignored. The aggregation weights { αi}ki=1 play a role similar to the attention mechanism, here we show three ways to obtain this set of weights: Randomly generate a vector e ∈ ℝk and then create the aggregation weights using the softmax function
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Utilizing the error backpropagation algorithm, αi can be continuously optimized and updated []that is determined based on an output of processing the first feature value and the second feature value using a trained regression-based machine learning model. The resulting weights represent the optimal view aggregation ratio considered by the proposed model.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yu et al., US Pre-Grant Publication No. 2011/0286507, discloses a method for processing an input that has been generated using a Taylor series expansion.
Mazumdar et al., US Pre-Grant Publication No. 2012/0223847, discloses a method to synthesize digital input signals using Taylor series expansion.
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/VAS/Examiner, Art Unit 2129
/HAL SCHNEE/Primary Examiner, Art Unit 2129
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