DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-9 are directed to a method and claims 10-18 and 19-20 are directed to a machine claim. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1:
2A Prong 1:
A method for evaluating a classification model(This step for evaluating the classification model is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
analyzing(This step for analyzing the first information to determine second information is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
generating(This step for generating a missingness graph is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
decomposing(This step for decomposing an expression is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
(This step for generating respective weights for the recoverable terms is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
and calculating (This step for calculating the bounds on the metric is based on mathematical equations Para [99-100].)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
receiving, by the at least one processor, first information that relates to first data to be used for training and evaluating a performance of a first classification model that is designed to make a determination with respect to a predetermined query; (The step directed to receiving information, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
“implementing, receiving, analyzing, generating, decomposing, training, and calculating, by the at least one processor” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f).)
training, by the at least one processor, a second classification model to generate (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
receiving, by the at least one processor, first information that relates to first data to be used for training and evaluating a performance of a first classification model that is designed to make a determination with respect to a predetermined query; (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity as identified by the court (MPEP 2106.05(d)(ll)(IV)))))
“implementing, receiving, analyzing, generating, decomposing, training, and calculating, by the at least one processor” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f).)
training, by the at least one processor, a second classification model to generate (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 10: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “A computing apparatus for evaluating a classification model, the computing apparatus comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to:” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 19
2A Prong 1: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “A non-transitory computer readable storage medium storing instructions for evaluating a classification model, the storage medium comprising a set of executable code which, when executed by a processor, causes the processor to:” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claims 2, 11, 20
2A Prong 1: None
2A Prong 2:
wherein the first data includes a set of ordered pairs, each ordered pair including a respective set of features and a respective label that is associated with the respective set of features. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
2B: wherein the first data includes a set of ordered pairs, each ordered pair including a respective set of features and a respective label that is associated with the respective set of features. (The specification of data is directed be a field of use limitation, which is understood to be field of use and technological environment (MPEP2106.05(h)))
Regarding Claims 3 and 12
2A Prong 1: None
2A Prong 2: wherein when the second information indicates that there is no missing data, the performance of the first classification model is evaluatable by using a subset of the first data within which the respective label is removed from each corresponding ordered pair. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
2B: wherein when the second information indicates that there is no missing data, the performance of the first classification model is evaluatable by using a subset of the first data within which the respective label is removed from each corresponding ordered pair. (The specification of data is directed be a field of use limitation, which is understood to be field of use and technological environment (MPEP2106.05(h))
Regarding Claims 4 and 13
2A Prong 1: None
2A Prong 2:
wherein the missing data has a non-random distribution with respect to the
respective sets of features and the respective labels included in the first data. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
2B:
wherein the missing data has a non-random distribution with respect to the
respective sets of features and the respective labels included in the first data. (The specification of data is directed be a field of use limitation, which is understood to be field of use and technological environment (MPEP2106.05(h))
Regarding Claims 5 and 14
2A Prong 1:
wherein the calculating of the lower bound corresponds to the first classification model incorrectly determining a set of classifications for all non-recoverable terms, and wherein the calculating of the upper bound corresponds to the first classification model correctly determining the set of classifications for all non-recoverable terms. (This step for calculating the bounds on the metric is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 6 and 15
2A Prong 1:
wherein the calculating of the upper bound and the lower bound comprises assuming that respective labels for non-recoverable terms all have an equal constant label and interpolating the equal constant label from zero to one. (This step for calculating the bounds on the metric is based on mathematical equations Para [99-100].)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 7 and 16
2A Prong 1: None
2A Prong 2 & 2B:
wherein the generating of the missingness graph comprises describing the first information based on a probabilistic graphical model (PGM) and using random variables to represent the missing data. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic graphical model as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
Regarding Claims 8 and 17
2A Prong 1: None
2A Prong 2:
wherein the classification metric includes at least one from among a value detection rate, a precision, and a recall. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
2B:
wherein the classification metric includes at least one from among a value detection rate, a precision, and a recall. (The specification of data is directed be a field of use limitation, which is understood to be field of use and technological environment (MPEP2106.05(h)))
Regarding Claims 9 and 18
2A Prong 1: None
2A Prong 2:
wherein the predetermined query relates to at least one from among a financial fraud detection query, an infectious disease classification query, and an e-commerce data stream query. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
2B: wherein the predetermined query relates to at least one from among a financial fraud detection query, an infectious disease classification query, and an e-commerce data stream query. (The specification of data is directed be a field of use limitation, which is understood to be field of use and technological environment (MPEP2106.05(h)))
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claim(s) 1, 2, 4, 6, 10, 11, 13, 15, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiaxin Zhang et al. ("Recoverability and estimation of causal effects under typical multivariable missingness mechanisms", hereinafter " Zhang") in view of Karthika Mohana et al. ("Graphical Models for Processing Missing Data," hereinafter "Mohan") and in view of Jurijs Nazarovs et al. (“Cordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series,” hereinafter “Nazarovs.”)
Regarding Claim 1
Zhang discloses:
A method for evaluating a classification model, the method being implemented by at least one processor, the method comprising: ([Section 2, Para 1, Lines 1-7] discloses a classification model. [Section 5, Para 1, Lines 1-4] discloses evaluating a classification model. [Section 4, Para 1] discloses simulation and data generation, which implies the use of a processor.);
receiving, by the at least one processor, first information that relates to first data to be used for training and evaluating a performance of a first classification model that is designed to make a determination with respect to a predetermined query; ([Section 2, Para 1, Lines 1-7] discloses a classification model and a data being received for training and evaluating the model. The first information that relates to the first data is the data being received. [Section 2, Para 1, Lines 5-7] discloses making a determination with respect to a predetermined query. The predetermined query is the estimation the model makes.);
analyzing, by the at least one processor, the first information to determine second information that relates to missing data ([Section 3.1, Para 1, Lines 1-3] discloses a second information as a vector of confounders with missing data. It analyzes the first information, which is the data used for the model, to determine the second information.);
generating, by the at least one processor based on the first information and the second information, a missingness graph that relates to a description of how the missing data has come to be missing; ([Page 2, Introduction, Para 2, Lines 1-4] discloses missingness graphs to describe how missing data has come to be: "These graphs allow clear depiction of the assumed causes of missingness in each incomplete variable." [Page 3, Section 3.1, Para 1] discloses generating the missingness graphs as shown in [see also in Fig 1].)
decomposing, by the at least one processor based on the missingness graph, an expression that relates to a predetermined classification metric into recoverable terms and non-recoverable terms; ([Appendix A.4, Equation 5] discloses an expression used for recoverability of terms. [Appendix A.4, Lines 4-15] discloses decomposing an expression to obtain recoverable terms. [Appendix A.6, Lines 4-11] discloses the same expression being decomposed to obtain non-recoverable terms.)
Zhang does not explicitly disclose:
training, by the at least one processor, a second classification model to generate respective weights for the recoverable terms;
However, Mohan discloses in the same field of endeavor:
training, by the at least one processor, a second classification model to generate respective weights for the recoverable terms; ([Page 1027, Col 1 & Tables 3, 4] where Eq(2), RHS is a model to generate the weights P(G,O,A) for the recoverable terms; “RHS of Equation 2 is expressed in terms of variables in the observed-data distribution. Therefore, P(G,A,O) can be consistently estimated (i.e. recovered) from the available data … samples in which the obesity is missing is used to update … p1,…,p12” denotes training the model based on observed data.)
Zhang and Mohan are both analogous art to the present invention because both are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the method for receiving the first information for training and evaluation of a first classification model, analyzing the first information to determine second information that relates to missing data, generating a missingness graph, and decomposing an expression into recoverable terms and non-recoverable terms disclosed by Zhang with the method for generating weights for the recoverable terms disclosed by Mohan. One of ordinary skill in the art would have been motivated to make this modification in order to have the model make consistent estimations from incomplete data. ([Page 1026, Section 3, Para 1, Lines 1-4], Mohan).
Zhang in view of Mohan does not explicitly disclose:
calculating, by the at least one processor based on the non-recoverable terms, an upper bound and a lower bound on the predetermined classification metric.
However, Nazarovs discloses in the same field of endeavor:
calculating, by the at least one processor based on the non-recoverable terms, an upper bound and a lower bound on the predetermined classification metric. ([Page 7, Col 1, Para 5; Col 2, Para 1-6] discloses calculating confidence intervals for the performance metric of accuracy [see also Page 6, Fig. 6] which displays the calculated upper and lower confidence bounds on the calculated performance metric of accuracy)
Zhang, Mohan, and Nazarovs are analogous art to the present invention because both are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the method for calculating an upper bound and a lower bound by Nazarovs with the method for training and evaluating a classification model using missingness graphs disclosed by Zhang in view of Mohan. One of ordinary skill in the art would have been motivated to make this modification in order to evaluate the performance of a model in the presence of missingness and further improve accuracy based on evaluation results. (Abstract, Lines 9-15, Nazarovs).
Regarding Claim 2
Zhang in view of Mohan and Nazarovs discloses:
wherein the first data includes a set of ordered pairs, each ordered pair including a respective set of features and a respective label that is associated with the respective set of features.([Page 4, Col 1, Para 4, Lines 5-7] Nazarovs discloses the first data including a set of features, denoted as feature space f(xte) , and a label, denoted as f̅ with ∆ and + as the possible labels. The first data is entered into the model, denoted as an encoder, as an ordered pair: “sssIf our encoder perfectly matches relation between labels into the feature space, i.e., D(f(xte), f̅∆)=D(f(xte), f̅+)…”)
Regarding Claim 4
Zhang in view of Mohan and Nazarovs discloses: The method of claim 2, wherein the missing data has a non-random distribution with respect to the respective sets of features and the respective labels included in the first data. ([Page 1025, Para 3, Lines 6-9] Mohan discloses missingness data has a non-random distribution with respect of the respective set of features and respective labels, as seen in Table 2 and Figure 1 (c) on obesity for the features, age and gender, and their label obesity: “Missingness can be caused by random processes (i.e., caused by variables that are not correlated with other variables in the model) or can depend on other variables in the dataset… Teenagers rebelling and not reporting their weight is an example of missingness caused by a fully observed variable. This is depicted in Figure 1 (c) by an edge between A and RO.”)
Regarding Claim 6
Zhang in view of Mohan and Nazarovs discloses: The method of claim 2, wherein the calculating of the upper bound and the lower bound comprises assuming that respective labels for non-recoverable terms all have an equal constant label and interpolating the equal constant label from zero to one. ([Page 7, Col 2, Para 4-6] Nazarovs discloses calculating confidence intervals [see also Page 6, Fig. 6] which displays the calculated upper and lower confidence bounds on the calculated performance metric of accuracy. [Page 6, Fig. 6] Nazarovs shows the non-recoverable terms are the number of missing classes on the x-axis, which is an equal constant label. The label is interpolated from zero to one as seen on the y-axis, see [Fig. 6, Lines 1-3] for interpolation description.)
Regarding Claim 10
Claim 10 is an apparatus claim having similar limitations of method Claim 1, therefore it is rejected under the same rational as of Claim 1. Additionally, Claim 10 includes additional limitations below that are rejected in view of Zhang.
Zhang teaches
a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to ([Section 4, Para 1] discloses simulation and data generation, which implies the use of a processor, memory, and communication interface coupled to each the processor and the memory.)
Regarding Claim 11
(Claim 11 recites analogous limitations to Claim 2 and therefore is rejected on the same ground as Claim 2.)
Regarding Claim 13
(Claim 13 recites analogous limitations to Claim 4 and therefore is rejected on the same ground as Claim 4.)
Regarding Claim 15
(Claim 15 recites analogous limitations to Claim 6 and therefore is rejected on the same ground as Claim 6.)
Regarding Claim 19
Claim 19 is a non-transitory computer readable storage medium claim having similar limitations of method Claim 1, therefore it is rejected under the same rational as of Claim 1. Additionally, Claim 19 includes additional limitations below that are rejected in view of Zhang.
Zhang teaches
A non-transitory computer readable storage medium storing instructions for evaluating a classification model, the storage medium comprising a set of executable code which, when executed by a processor, causes the processor to ([Section 4, Para 1] discloses simulation and data generation, which implies the use of a processor and non-transitory computer readable storage medium storing instructions executed by a processor.)
Regarding Claim 20
(Claim 20 recites analogous limitations to Claim 2 and therefore is rejected on the same ground as Claim 2.)
Claim(s) 3 and 12 is/are rejected under 35 U.S.C 103 as being unpatentable over Zhang in view of Mohan, Nazarovs, and Avital Oliver et al. (“Realistic Evaluation of Deep Semi-Supervised Learning Algorithms”, hereinafter “Oliver”).
Regarding Claim 3
Zhang in view of Mohan and Nazarovs discloses:
The method of claim 2, wherein when the second information indicates that there is no missing data,
Zhang in view of Mohan and Nazarovs does not explicitly disclose:
the performance of the first classification model is evaluatable by using a subset of the first data within which the respective label is removed from each corresponding ordered pair.
However, Oliver discloses in the same field of endeavor:
the performance of the first classification model is evaluatable by using a subset of the first data within which the respective label is removed from each corresponding ordered pair. ([Section 4.1, Para 3, Lines 1-2] Oliver discloses using a subset of the first data in which the respective label is removed for evaluating the model: “We tested each SSL approach on the widely-reported image classification benchmarks of SVHN [40] with all but 1000 labels discarded and CIFAR-10 [31] with all but 4,000 labels discarded.”)
Zhang, Mohan, Nazarovs, and Oliver are analogous art to the present invention because both are from the same field of endeavor directed to machine learning.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to test a classification model using a subset of the first data in which the respective label is removed disclosed by Oliver into the method of Zhang in view of Mohan and Nazarovs to evaluate and train a classification model. One of ordinary skill in the art would have been motivated to make this modification in order to test the accuracy of a model more efficiently and realistically without using a large dataset. ([Introduction, Para 1] Oliver)
Regarding Claim 12
(Claim 12 recites analogous limitations to Claim 3 and therefore is rejected on the same ground as Claim 3.)
Claim(s) 5 and 14 is/are rejected under 35 U.S.C 103 as being unpatentable over Zhang in view of Mohan, Nazarovs, and Markus Dominik Mueck et al. (WO 2023014985 A1, hereinafter “Mueck”).
Regarding Claim 5
Zhang in view of Mohan and Nazarovs discloses:
wherein the calculating of the lower bound corresponds to the first classification model incorrectly determining a set of classifications for ([Page 7, Col 1, Para 5; Col 2, Para 1-6] Nazarovs discloses calculating confidence intervals for the performance metric of accuracy for a classification model [see also Page 6, Fig. 6] which displays the calculated upper and lower confidence bounds on the calculated performance metric of accuracy, where the lower bound corresponds to incorrectly determining a set of classifications for non-recoverable terms and the upper bound corresponds to correctly determining a set of classifications for non-recoverable terms. The non-recoverable terms are the number of missing classes shown in Fig. 6.)
Zhang in view of Mohan and Nazarovs does not explicitly disclose:
wherein the calculating of the lower bound corresponds to the first classification model incorrectly determining a set of classifications for all
However, Mueck discloses in the same field of endeavor:
wherein the calculating of the lower bound corresponds to the first classification model incorrectly determining a set of classifications for all ([0207] Mueck discloses calculating an upper bound for when a classification model correctly determines a set of classification for all predictions and calculating a lower bound for when a classification model incorrectly determines a set of classification for all predictions: “In one example, the quality metric 3010 is set to 0 when the accuracy metrics indicate that all of the decisions/predictions are incorrect, and the quality metric 3010 is set to 99 when the accuracy metrics indicate that all of the decisions/predictions are correct.”)
Zhang, Mohan, Nazarovs, and Mueck are analogous art to the present invention because both are from the same field of endeavor directed to machine learning.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to calculate an upper bound for all correct determinations and a lower bound for all incorrect determinations by Mueck into the method of Zhang in view of Mohan and Nazarovs to evaluate and train a classification model using recoverable and non-recoverable terms. One of ordinary skill in the art would have been motivated to make this modification in order to test AI systems for quality, accuracy, and robustness. ([Abstract] Mueck)
Regarding Claim 14
(Claim 14 recites analogous limitations to Claim 5 and therefore is rejected on the same ground as Claim 5.)
Claim(s) 7 and 16 is/are rejected under 35 U.S.C 103 as being unpatentable over Zhang in view of Mohan, Nazarovs, and Gerald Kerth et al. (“Discovering Latent Structure in High-Dimensional Healthcare Data: Toward Improved Interpretability”, hereinafter “Kerth”).
Regarding Claim 7
Zhang in view of Mohan and Nazaros discloses:
The method of claim 1,
Zhang in view of Mohan and Nazaros does not explicitly disclose:
wherein the generating of the missingness graph comprises describing the first information based on a probabilistic graphical model (PGM) and using random variables to represent the missing data.
However, Kerth discloses in the same field of endeavor:
wherein the generating of the missingness graph comprises describing the first information based on a probabilistic graphical model (PGM) and using random variables to represent the missing data. ([Page 16, Section 3.3, Para 2] discloses using PGM and random variables to represent missingness graph, which is described as graphs that modeled missingness: “In probabilistic graphical models (PGM) each random variable Xi is associated with a node i in a graph… PGMs are advantageous for healthcare problems in many ways: They are statistically grounded, they allow to inherently model uncertainty or missingness and its effects…”)
Zhang, Mohan, Nazarovs, and Kerth are analogous art to the present invention because both are from the same field of endeavor directed to machine learning.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to use PGM and random variables to generate missingness graphs disclosed by Kerth into the method of Zhang in view of Mohan and Nazarovs to evaluate a classification model. One of ordinary skill in the art would have been motivated to make this modification in order to model missingness and its effects for probabilistic reasoning. ([Page 16, Section 3.3, Para 2] Kerth)
Regarding Claim 16
(Claim 16 recites analogous limitations to Claim 7 and therefore is rejected on the same ground as Claim 7.)
Claim(s) 8, 9, 17, and 18 is/are rejected under 35 U.S.C 103 as being unpatentable over Zhang in view of Mohan, Nazarovs, and Chang-jun Jiang et al. (CN 109886284 A, hereinafter “Jiang”).
Regarding Claim 8
Zhang in view of Mohan and Nazaros discloses:
The method of claim 1,
Zhang in view of Mohan and Nazaros does not explicitly disclose:
wherein the classification metric includes at least one from among a value detection rate, a precision, and a recall.
However, Jiang discloses in the same field of endeavor:
wherein the classification metric includes at least one from among a value detection rate, a precision, and a recall. ([Page 11, Para 10-11] discloses classification evaluation metric that includes at least one from among detection rate, precision, and recall: “...according to the table 1, (Recall) calculating the recall rate, precision rate (Precision) and a weighted average of the two (F1), the calculation formula is as follows.”)
Zhang, Mohan, Nazarovs, and Jiang are analogous art to the present invention because both are from the same field of endeavor directed to machine learning.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to use detection rate, precision, and/or recall for classification metrics disclosed by Jiang into the method of Zhang in view of Mohan and Nazarovs to evaluate a classification model. One of ordinary skill in the art would have been motivated to make this modification in order to evaluate and improve the performance of a classification model. ([Page 2, Para 2, Lines 4-8] Jiang)
Regarding Claim 9
Zhang in view of Mohan, Nazarovs, and Jiang discloses: The method of claim 1, wherein the predetermined query relates to at least one from among a financial fraud detection query, an infectious disease classification query, and an e-commerce data stream query. ([page 1, last par; Page 2, para 1 and Para 5, Lines 1-8] Jiang discloses a predetermined query from financial fraud detection query: “…the present invention is to provide a fraud detection method... A fraud detection method based on hierarchical clustering, comprising: obtaining and analyzing transaction characteristic information to obtain the feature analysis data, according to the data characteristic analyzing selected clustering model, obtaining the sample data set.”)
Regarding Claim 17
(Claim 17 recites analogous limitations to Claim 8 and therefore is rejected on the same ground as Claim 8.)
Regarding Claim 18
(Claim 18 recites analogous limitations to Claim 9 and therefore is rejected on the same ground as Claim 9.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Amanda D. Nguyen whose telephone number is (571)270-1854. The examiner can normally be reached M-F, 7:00am to 4:30pm ET 1st Fridays off, 2nd Friday 7:00 am - 3:30 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AMANDA D NGUYEN/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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