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 .
This action is responsive to the Amendment filed on April 28, 2026. Claims 1 and 13 are amended. Claims 3, 7, and 12 are cancelled. Claims 1, 2, 4-6, 8-11, and 13-18 are pending in the case. Claim 1 is the independent claim.
This action is final.
Applicant’s Response
In the Amendment filed on April 28, 2026, Applicant amended and provided arguments in response to the rejections of the claims under 35 USC 101, 102, and 103 in the previous office action.
Response to Argument/Amendment
Applicant’s amendments to the claims in response to the rejection of the claims under 35 USC 101 are acknowledged, and Applicant’s associated arguments have been fully considered. Applicant argues that claim 1 as a whole relates to fraud detection using a positive and unlabeled set of financial transactions, an estimated fraud rate, and a trained Hellinger decision tree, and improves operation of computerized fraud detection system because the use of the Hellinger distance enables the Hellinger decision tree to handle highly imbalanced data sets effectively. Applicant further argues that claim 1 cannot be performed in the human mind because the concepts of claim 1 “collectively require machine learning functionality that is not executable by a human mind…the claim requires node-level computation during operation of a trained decision tree. Such an operation includes iterative and distribution-based computations that exceed the scope of human mental reasoning and require a processor executing machine-learning algorithms. Humans cannot reliably consider unlabeled data at scale….the recited operations are computationally intensive and model-specific, not conceptual or judgment-based….the concepts in claim 1 are applied in a manner that improves the functioning of a fraud-detection computing system itself….The Hellinger computation is embedded in the operation of the decision tree. The trained Hellinger decision tree is applied in the context of fraud detection.”
Applicant’s arguments are not persuasive. The majority of the features argued by Applicant are not reflected in the claims. For example, Applicant argues that claim 1 requires machine learning functionality, provides improvements in handling highly imbalanced data sets effectively, performing node-level computation during operation of a decision tree using iterative and distribution based computations requiring a processor executing machine-learning algorithms, and “unlabeled data at scale,” etc. However, as presently recited, the claims merely recite that method steps are performed using a processor. The claims do not recite any limitation indicating that the dataset is imbalanced, or any indication regarding the level of imbalance, such as the dataset being “highly” imbalanced. The claims also provide no limitation indicating the scale of the dataset or the decision tree, and therefore do not appear to recite any benefit with respect to processing “unlabeled data at scale.” Further, although the claims do recite that the Hellinger decision tree is “trained,” no claim limitation is directed to the actual training of the Hellinger decision tree, and no claim limitation recites any other use of machine learning algorithms executed by a processor. Although the claims do recite determining Hellinger distance “at tree nodes,” the claims provide no indication regarding whether this step involves a large scale of tree nodes and provide no indication regarding the complexity of the determination itself. Therefore, it does not appear that the claim limitations as actually recited “collectively require machine learning functionality that is not executable by a human mind…node-level computation during operation of a trained decision tree [which] includes iterative and distribution-based computations that exceed the scope of human mental reasoning and require a processor executing machine-learning algorithms [and involves] consider[ing] unlabeled data at scale [and] operations [that] are computationally intensive and model-specific.” Instead, the claim appears to recite applying a Hellinger decision tree and estimated fraud rate, including determining a Hellinger distance quantifying divergence between class distributions at tree nodes, with no apparent limitation or definition regarding how the applying is to be performed and how the determining of the Hellinger distance is to be performed, and no meaningful limitation on the scale of the Hellinger tree itself or the corresponding data set which would appear to indicate that determining at a tree node is so intensive that it cannot be performed in a human mind. To the extent that the claim recites that the Hellinger decision tree is “trained,” there are no other limitations related to the training, and there are no details indicating that the “trained” Hellinger tree has any particular characteristics or attributes such that “applying” it “using the processor” would amount to more than merely applying the Hellinger tree using generic computer components. The remainder of the claim limitations appear to recite either insignificant extra solution activity (i.e. receiving the data set, receiving the estimated fraud rate) or are used in a descriptive way to further describe the field of use and/or technological environment (i.e. the data set being “positive and unlabeled” and including transactions labeled as fraudulent and transactions lacking fraud labels, the fraud rate being “estimated,” the Hellinger distance quantifying “divergence between positive and negative transaction class distributions without being dominated by class imbalance”). Applicant’s arguments do not appear to consider any of these considerations, and appear to only discuss whether the claim as a whole can be performed mentally.
Applicant’s amendments to the claims in response to the rejection of the claims under 35 USC 102 and 103 are acknowledged, and Applicant’s associated arguments have been fully considered. Applicant argues that Pozzolo does not teach or suggest unlabeled data handling or PU learning, and that it would not be obvious to combine it with another reference that uses unlabeled data handling because this would improperly alter the principle of operation of Pozzolo.
However, Pozzolo does appear to teach use of unlabeled data (e.g. page 589, second column, fourth paragraph, set of labeled observations available at time t, plus new unlabelled instance; page 590, first column, section B. Hellinger distance as a weighting ensemble strategy; using two consecutive data batches of a data stream; batch at time t used for training and subsequent testing batch; for testing batch, labels are not provided; i.e. the dataset is collectively formed of two batches, including a first set/batch corresponding to a time t, having labelled observations, used as a training batch, and a second set/batch, corresponding to a subsequent time, having unlabelled observations/data, used as a testing batch). Therefore, it is unclear how combining Pozzolo with another reference that uses unlabeled data handling would improperly alter any principle of operation of Pozzolo. In addition, nothing in Pozzolo or any other cited reference appears to actually criticize, disparage, or otherwise teach away from use of unlabeled data. Therefore, this argument is not persuasive.
Applicant further argues that Pozzolo does not teach “receiving, at a processor, an estimated fraud rate for the data set, and while Pozzolo may teach probabilities of fraud from empirical frequencies, it does not teach an estimated fraud rate for the data set.
As cited in the previous office action, Pozzolo teaches receiving an estimated fraud rate for the data set (e.g. page 590, second column, second paragraph, indicating that the number of frauds in each chunk of streaming data is usually less than 1% (i.e. where this indicates an estimated fraud rate); page 591, second column, first paragraph, indicating that the fraud rate of the credit card dataset is known to be 0.15% of the transactions, i.e., prior to the use of the Hellinger decision tree with the dataset). As cited, the use of the decision tree can be performed in conjunction with knowledge that, in general, the number of frauds for a given data chunk will probably/usually be 1% or less. Under the broadest reasonable interpretation, as indicated by the fact that this is the probable/usual value, and that the value is a range (1 or less), this percentage of frauds (indicative of a rate of fraud) appears to be analogous to an “estimated fraud rate of the dataset.” Therefore, this argument is not persuasive.
Applicant further argues that Pozzolo does not teach or suggest applying a Hellinger decision tree and estimated fraud rate to detect fraudulent transactions in the data set. However, Pozzolo clearly teaches applying a Hellinger decision tree and estimated fraud rate to detect fraudulent transactions in the data set (e.g. page 588, second column, first and second full paragraphs, batch ensemble model combination based on Hellinger Distance and Information Gain, tested with different types of datasets including unbalanced credit card fraud dataset; page 589, first column, first and second paragraphs, majority class is negative and minority class is positive; Hellinger distance quantifies similarity between two probability distributions; page 589, first column, first full paragraph, Hellinger distance as splitting criteria in decision trees to improve accuracy in unbalanced problems; page 589, first column, seventh paragraph (titled “IV. Hellinger Distance Decision Trees”), for each feature f calculating distance between the classes over all of the feature’s partitions; Hellinger distance between positive class and negative class; page 589, first column, final paragraph, describing Hellinger distance decision trees; page 589, second column, first paragraph, class imbalance ratio does not influence the distance calculation; page 590, second column, second and third paragraphs, number of frauds occurring in each chunk usually less than 1%; in Gao’s framework, positive examples accumulated along the stream until they represent 40% of the observations, then oldest positive examples are replaced by new minority class observations; page 591, first column, describing experimental setup in section VI using Hellinger Distance Decision Tree (HDDT); using Gao’s propagation method for reare class instances and undersampling; framework implemented in Java, using Weka implementation of HDDT; page 591, second column, first paragraph, experimental setup includes using real-world credit card dataset which is highly unbalanced and whose frauds are changing in type and distribution). Therefore, this argument is not persuasive.
New grounds of rejection, necessitated by Applicant’s amendments to the claims, are provided below.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental steps) without significantly more. This judicial exception is not integrated into a practical application because any additional elements amount to implementing the abstract idea on a generic computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding independent claim 1, and relying on the evaluation flowchart in MPEP 2106:
Step 1 (Is the claim to a process, machine, manufacture, or composition of matter?): Yes. Claim 1 is a method of fraud detection (process).
Step 2a Prong One (Does the claim recite an abstract idea?): Yes. Claim 1 recites:
applying a Hellinger decision tree and the estimated fraud rate to detect fraudulent transactions in the data set of financial transactions wherein applying the Hellinger decision tree includes determining, at tree nodes, a Hellinger distance that quantifies divergence between positive and negative transaction class distributions without being dominated by class imbalance (a mental process involving performing a mathematical calculation, with or without the aid of pen and paper, such as a human mentally applying a decision tree and fraud rate to detect fraudulent transactions in the data set of financial transactions, including mentally performing calculations necessary to use/apply the Hellinger distance when applying the decision tree and fraud rate to determine at tree nodes Hellinger distance/divergence between positive and negative distributions).
Under the broadest reasonable interpretation, these steps may be performed mentally, using mental observation and mental determination, including by a human using a physical aid such as pen and paper, including a human mentally performing observations and mentally performing mathematical calculations, and therefore correspond to the Mental Processes grouping.
Step 2a Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?): No. Claim 1 additionally recites:
receiving a positive and unlabeled data set of financial transactions, the data set including transactions labeled as fraudulent and transactions lacking fraud labels (insignificant extra-solution activity of transmitting data over a network as discussed in MPEP 2106.05(g));
receiving an estimated fraud rate for the data set (insignificant extra-solution activity of transmitting data over a network as discussed in MPEP 2106.05(g));
that the method is computer implemented, the receiving is at a processor, the Hellinger decision tree is trained, and that the applying of the Hellinger decision tree is using the processor (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Moreover, to the extent that the claims recite that the method is for fraud detection, that the data set is of financial transactions, and that the Hellinger decision tree is trained and applied to detect fraudulent transactions, these limitations also describe a field of use and technological environment as discussed in MPEP 2106.05(h).
Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are merely implementing the abstract idea using generic computer components.
Step 2b (Does the claim recite additional elements that amount to siqnificantly more than the judicial exception): No. Relying on the same analysis as Step 2a Prong Two (see MPEP 2106.05.I.A: Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:…Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP 2106.05(f));…Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception...; Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g);…)), claim 1 does not recite any additional elements that amount to significantly more than the abstract idea. As discussed above, Claim 1 recites:
receiving a positive and unlabeled data set of financial transactions, the data set including transactions labeled as fraudulent and transactions lacking fraud labels (insignificant extra-solution activity as discussed in MPEP 2106.05(g) which can be reevaluated as well-understood, routine, conventional activity such as transmitting data over a network as discussed in MPEP 2106.05(d));
receiving an estimated fraud rate for the data set (insignificant extra-solution activity of transmitting data over a network as discussed in MPEP 2106.05(g) which can be reevaluated as well-understood, routine, conventional activity such as transmitting data over a network as discussed in MPEP 2106.05(d));
that the method is computer implemented, that the receiving is at a processor, the Hellinger decision tree is trained, and that the applying of the Hellinger decision tree is using the processor (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Moreover, to the extent that the claims recite that the method is for fraud detection, that the data set is of financial transactions, and that the Hellinger decision tree is trained and applied to detect fraudulent transactions, these limitations also describe a field of use and technological environment as discussed in MPEP 2106.05(h).
The additional elements as discussed above, in combination with the abstract idea, are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea.
Regarding dependent claim 2:
Step 2a Prong One: incorporates the rejection of claim 1.
Step 2a Prong Two: the claims additionally recite wherein the Hellinger decision tree is part of a machine learning algorithm (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Step 2b: the claims additionally recite wherein the Hellinger decision tree is part of a machine learning algorithm (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Regarding dependent claim 4:
Step 2a Prong One: incorporates the rejection of claim 1; the claims further recite wherein the Hellinger decision tree is configured to use class prior to estimate counts of positives and negatives in each node (a mental process involving performing a mathematical calculation, with or without the aid of pen and paper, such as a human mentally configuring the decision tree based on calculations to use class prior to estimate counts of positives and negatives in each node).
Step 2a Prong Two: the claims do not recite any other limitations in addition to the abstract idea discussed above.
Step 2b: the claims do not recite any other limitations in addition to the abstract idea discussed above.
Regarding dependent claim 5:
Step 2a Prong One: incorporates the rejection of claim 1; the claim further recite limiting a size of the Hellinger decision tree after a tree node reaches a maximum height thereby avoiding overfitting (a mental process of evaluation, such as a human mentally determining to limit the size of the decision tree using a maximum height to avoid overfitting).
Step 2a Prong Two: the claim additionally recites using the processor (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Step 2b: the claim additionally recites using the processor (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Regarding dependent claim 6:
Step 2a Prong One: incorporates the rejection of claim 1; the claim further recite wherein the Hellinger decision tree is a positive and unbalanced Hellinger decision tree, and wherein the data set of fraudulent transactions is imbalanced positive and unlabeled data (a mental process of evaluation, such as a human mentally determining to configure the decision tree as a positive and unbalanced decision tree and to use a data set of positive and unlabeled data).
Step 2a Prong Two: the claims do not recite any other limitations in addition to the abstract idea discussed above.
Step 2b: the claims do not recite any other limitations in addition to the abstract idea discussed above.
Regarding dependent claim 8:
Step 2a Prong One: incorporates the rejection of claim 1.
Step 2a Prong Two: the claim additionally recite wherein the Hellinger decision tree is used as a base learner in a modified random forest (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)).
Step 2b: the claim additionally recite wherein the Hellinger decision tree is used as a base learner in a modified random forest (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)).
Regarding dependent claim 9:
Step 2a Prong One: incorporates the rejection of claims 1 and 8; the claim additionally recites wherein the Hellinger decision tree is a positive and unbalanced Hellinger decision tree, and wherein the data set of fraudulent transactions is imbalanced positive and unlabeled data (a mental process of evaluation, such as a human mentally determining to configure the decision tree as a positive and unbalanced decision tree and to use a data set of imbalanced positive and unlabeled data).
Step 2a Prong Two: the claims do not recite any other limitations in addition to the abstract idea discussed above.
Step 2b: the claims do not recite any other limitations in addition to the abstract idea discussed above.
Regarding dependent claim 10:
Step 2a Prong One: incorporates the rejection of claim 1, 8, and 9; the claims further recite wherein the Hellinger decision tree is configured to consider random feature selection when initializing a tree node (a mental process of evaluation, such as a human mentally determining to configure the decision tree to consider random feature selection when initializing a tree node).
Step 2a Prong Two: the claims do not recite any other limitations in addition to the abstract idea discussed above.
Step 2b: the claims do not recite any other limitations in addition to the abstract idea discussed above.
Regarding dependent claim 11:
Step 2a Prong One: incorporates the rejection of claim 1, 8, and 9; the claims further recite wherein the Hellinger decision tree is configured to use a size of a stratified bootstrap sample and a class prior (a mental process of evaluation, such as a human mentally determining to configure the decision tree to use a size of a stratified bootstrap sample and a class prior).
Step 2a Prong Two: the claims do not recite any other limitations in addition to the abstract idea discussed above.
Step 2b: the claims do not recite any other limitations in addition to the abstract idea discussed above.
Regarding dependent claim 13:
Step 2a Prong One: incorporates the rejection of claim 1.
Step 2a Prong Two: the claim further recites training the Hellinger decision tree with the positive and unlabeled dataset (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)).
Step 2b: the claim further recites training the Hellinger decision tree with the positive and unlabeled dataset (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)).
Regarding dependent claim 14:
Step 2a Prong One: incorporates the rejection of claim 1.
Step 2a Prong Two: the claim further recites wherein the positive and unlabeled dataset is unbalanced (a field of use and technological environment as discussed in MPEP 2106.05(h)).
Step 2b: the claim further recites wherein the positive and unlabeled dataset is unbalanced (a field of use and technological environment as discussed in MPEP 2106.05(h)).
Regarding dependent claim 15:
Step 2a Prong One: incorporates the rejection of claim 1.
Step 2a Prong Two: the claim further recites wherein the financial transactions are credit card transactions (a field of use and technological environment as discussed in MPEP 2106.05(h)).
Step 2b: the claim further recites wherein the financial transactions are credit card transactions (a field of use and technological environment as discussed in MPEP 2106.05(h)).
Regarding dependent claim 16:
Step 2a Prong One: incorporates the rejection of claim 1.
Step 2a Prong Two: the claim further recites wherein the financial transactions are insurance transactions (a field of use and technological environment as discussed in MPEP 2106.05(h)).
Step 2b: the claim further recites wherein the financial transactions are insurance transactions (a field of use and technological environment as discussed in MPEP 2106.05(h)).
Regarding dependent claim 17:
Step 2a Prong One: incorporates the rejection of claim 1.
Step 2a Prong Two: the claim further recites wherein the system is a computer or a server (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Step 2b: the claim further recites wherein the system is a computer or a server (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Regarding dependent claim 18:
Step 2a Prong One: incorporates the rejection of claim 1.
Step 2a Prong Two: the claim further recites a non-transitory computer readable medium storing a program configured to instruct the processor to execute the method of claim 1 (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Step 2b: the claim further recites a non-transitory computer readable medium storing a program configured to instruct the processor to execute the method of claim 1 (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as recited in the dependent claims discussed above alone or in combination do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity, combined with implementing the abstract idea using generic computer components, and limitations describing a field of use or technological environment. The additional elements as discussed above, in combination with the abstract idea, are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea, and limitations describing a field of use or technological environment.
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.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102€, (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1, 2, 6, and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over A. Dal Pozzolo, R. Johnson, O. Caelen, S. Waterschoot, N. V. Chawla and G. Bontempi, "Using HDDT to avoid instances propagation in unbalanced and evolving data streams," 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China, 2014, pp. 588-594, doi: 10.1109/IJCNN.2014.6889638. (hereinafter Pozzolo) in view of C. Phua, V. Lee, K. Smith and R. Gayler, "A Comprehensive Survey of Data Mining-based Fraud Detection Research," Arxiv.org, arXiv:1009.6119. (hereinafter Phua).
With respect to claim 1, Pozzolo teaches a computer implemented method of fraud detection comprising:
receiving, at a processor, a positive data set of financial transactions (e.g. page 588, second column, first and second full paragraphs, highly unbalanced credit card fraud dataset with concept drift; page 589, first and second lines, indicating that the majority class refers to negative and the minority class refers to positive data in the dataset; page 589, first column final paragraph, describing HDDT as being based on distances between positive and negative classes over all of a feature’s partitions; page 591, second column, first paragraph, experimental setup includes using real-world credit card dataset which is highly unbalanced and whose frauds are changing in type and distribution; dataset including 0.15% of transactions as being fraudulent (i.e. positive); page 593, first column, final paragraph indicating that the fraud dataset is extremely unbalanced and exhibiting concept drift within the minority class, where the HDDT performs very well on the dataset; i.e. the dataset is unbalanced and positive (includes positive data)),
the data set including transactions labeled as fraudulent and transactions lacking fraud labels (e.g. page 589, second column, fourth paragraph, set of labeled observations available at time t, plus new unlabelled instance; page 590, first column, section B. Hellinger distance as a weighting ensemble strategy; using two consecutive data batches of a data stream; batch at time t used for training and subsequent testing batch; for testing batch, labels are not provided; i.e. the dataset is collectively formed of two batches, including a first set/batch corresponding to a time t, having labelled observations, used as a training batch, and a second set/batch, corresponding to a subsequent time, having unlabelled observations/data, used as a testing batch);
receiving, at a processor, an estimated fraud rate for the data set (e.g. page 590, second column, second paragraph, indicating that the number of frauds in each chunk of streaming data is usually less than 1% (i.e. where this indicates an estimated fraud rate); page 591, second column, first paragraph, indicating that the fraud rate of the credit card dataset is known to be 0.15% of the transactions, i.e., prior to the use of the Hellinger decision tree with the dataset); and
applying a trained Hellinger decision tree and the estimated fraud rate, using the processor, to detect fraudulent transactions in the data set of financial transactions, wherein applying the trained Hellinger decision tree includes determining, at tree nodes, a Hellinger distance that quantifies divergence between positive and negative transaction class distributions without being dominated by class imbalance (e.g. page 588, second column, first and second full paragraphs, batch ensemble model combination based on Hellinger Distance and Information Gain, tested with different types of datasets including unbalanced credit card fraud dataset; page 589, first column, first and second paragraphs, majority class is negative and minority class is positive; Hellinger distance quantifies similarity between two probability distributions; page 589, first column, first full paragraph, Hellinger distance as splitting criteria in decision trees to improve accuracy in unbalanced problems; page 589, first column, seventh paragraph (titled “IV. Hellinger Distance Decision Trees”), for each feature f calculating distance between the classes over all of the feature’s partitions; Hellinger distance between positive class and negative class; page 589, first column, final paragraph, describing Hellinger distance decision trees; page 589, second column, first paragraph, class imbalance ratio does not influence the distance calculation; page 590, second column, second and third paragraphs, number of frauds occurring in each chunk usually less than 1%; in Gao’s framework, positive examples accumulated along the stream until they represent 40% of the observations, then oldest positive examples are replaced by new minority class observations; page 591, first column, describing experimental setup in section VI using Hellinger Distance Decision Tree (HDDT); using Gao’s propagation method for reare class instances and undersampling; framework implemented in Java, using Weka implementation of HDDT; page 591, second column, first paragraph, experimental setup includes using real-world credit card dataset which is highly unbalanced and whose frauds are changing in type and distribution).
As discussed above, Pozzolo teaches that the data set may include both labeled and unlabeled data (i.e. a data stream including a data batch for training which includes labels and a data batch for testing which does not include labels as cited above). However, assuming arguendo that Pozzolo does not explicitly disclose that the data set is an unlabeled dataset, Phua teaches that the data set is an unlabeled dataset (e.g. page 5 second column, second paragraph, indicating that some research recommends use of unlabelled data in fraud detection use cases; page 8, first column, third and fourth paragraphs, describing unsupervised approaches with unabelled data, including in conjunction with Hellinger distance for comparing probability distributions and giving suspicions scores, and detecting statistical outliers using Hellinger distance).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo and Phua in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams), to incorporate the teachings of Phua (directed to fraud detection research) to include the capability to utilize an unlabeled dataset. One of ordinary skill would have been motivated to perform such a modification in order to address criticisms associated with using labelled data to detect fraud as described in Phua (page 5, second column, second paragraph).
With respect to claim 2, Pozzolo in view of Phua teaches all of the limitations of claim 1 as previously discussed, and Pozzolo further teaches wherein the Hellinger decision tree is part of a machine learning algorithm (e.g. page 588, first column, final paragraph, static learning setting; learning from non-stationary data streams; using HDDT as base learner; page 588, second column, first and second full paragraphs, batch ensemble model combination based on Hellinger Distance and Information Gain; page 591, first full paragraph, using decision tree as base learner (comparing C4.5 and HDDT)).
With respect to claim 15, Pozzolo in view of Phua teaches all of the limitations of claim 1 as previously discussed, and Pozzolo further teaches wherein the financial transactions are credit card transactions (e.g. page 588, second column, first and second full paragraphs, highly unbalanced credit card fraud dataset with concept drift; page 591, second column, first paragraph, experimental setup includes using real-world credit card dataset which is highly unbalanced and whose frauds are changing in type and distribution).
With respect to claim 6, Pozzolo in view of Phua teaches all of the limitations of claim 1 as previously discussed, and Pozzolo further teaches wherein the Hellinger decision tree is a positive and unbalanced Hellinger decision tree, and wherein the data set of fraudulent transactions is imbalanced positive data (e.g. page 589, first and second lines, indicating that the majority class refers to negative and the minority class refers to positive data in the dataset; page 589, first column final paragraph, describing HDDT as being based on distances between positive and negative classes over all of a feature’s partitions; page 591, first column, second paragraph, indicating the HDDT is used as a base learner on the unbalanced data; page 591, second column first paragraph, describing real-world credit card dataset as being highly unbalanced but including 0.15% of transactions as being fraudulent (i.e. positive); page 593, first column, final paragraph indicating that the fraud dataset is extremely unbalanced and exhibiting concept drift within the minority class, where the HDDT performs very well on the dataset; i.e. the dataset is unbalanced and positive (includes positive data), and the HDDT is trained on this unbalanced and positive dataset).
Hellinger does not explicitly disclose that the dataset is unlabeled data. However, Phua teaches that the dataset is unlabeled data (e.g. page 5 second column, second paragraph, indicating that some research recommends use of unlabelled data in fraud detection use cases; page 8, first column, third and fourth paragraphs, describing unsupervised approaches with unabelled data, including in conjunction with Hellinger distance for comparing probability distributions and giving suspicions scores, and detecting statistical outliers using Hellinger distance).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo and Phua in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams), to incorporate the teachings of Phua (directed to fraud detection research) to include the capability to utilize an unlabeled dataset. One of ordinary skill would have been motivated to perform such a modification in order to address criticisms associated with using labelled data to detect fraud as described in Phua (page 5, second column, second paragraph).
With respect to claim 13, Pozzolo in view of Phua teaches all of the limitations of claim 1 as previously discussed, and Pozzolo further teaches the method further comprising: training the Hellinger decision tree with the positive dataset (e.g. page 589, first and second lines, indicating that the majority class refers to negative and the minority class refers to positive data in the dataset; page 589, first column final paragraph, describing HDDT as being based on distances between positive and negative classes over all of a feature’s partitions; page 591, first column, second paragraph, indicating the HDDT is used as a base learner on the unbalanced data; page 591, second column first paragraph, describing real-world credit card dataset as being highly unbalanced but including 0.15% of transactions as being fraudulent (i.e. positive); page 593, first column, final paragraph indicating that the fraud dataset is extremely unbalanced and exhibiting concept drift within the minority class, where the HDDT performs very well on the dataset; i.e. the dataset is unbalanced and positive (includes positive data), and the HDDT is trained on this unbalanced and positive dataset).
Pozzolo does not explicitly disclose that the data set is an unlabeled dataset. However, Phua teaches that the data set is an unlabeled dataset (e.g. page 5 second column, second paragraph, indicating that some research recommends use of unlabelled data in fraud detection use cases; page 8, first column, third and fourth paragraphs, describing unsupervised approaches with unabelled data, including in conjunction with Hellinger distance for comparing probability distributions and giving suspicions scores, and detecting statistical outliers using Hellinger distance).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo and Phua in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams), to incorporate the teachings of Phua (directed to fraud detection research) to include the capability to utilize an unlabeled dataset. One of ordinary skill would have been motivated to perform such a modification in order to address criticisms associated with using labelled data to detect fraud as described in Phua (page 5, second column, second paragraph).
With respect to claim 14, Pozzolo in view of Phua teaches all of the limitations of claim 13 as previously discussed, and Pozzolo further teaches wherein the positive dataset is unbalanced (e.g. page 589, first and second lines, indicating that the majority class refers to negative and the minority class refers to positive data in the dataset; page 589, first column final paragraph, describing HDDT as being based on distances between positive and negative classes over all of a feature’s partitions; page 591, first column, second paragraph, indicating the HDDT is used as a base learner on the unbalanced data; page 591, second column first paragraph, describing real-world credit card dataset as being highly unbalanced but including 0.15% of transactions as being fraudulent (i.e. positive); page 593, first column, final paragraph indicating that the fraud dataset is extremely unbalanced and exhibiting concept drift within the minority class, where the HDDT performs very well on the dataset; i.e. the dataset is unbalanced and positive (includes positive data), and the HDDT is trained on this unbalanced and positive dataset).
Pozzolo does not explicitly disclose that the dataset is unlabeled. However, Phua teaches that the dataset is unlabeled (e.g. page 5 second column, second paragraph, indicating that some research recommends use of unlabelled data in fraud detection use cases; page 8, first column, third and fourth paragraphs, describing unsupervised approaches with unabelled data, including in conjunction with Hellinger distance for comparing probability distributions and giving suspicions scores, and detecting statistical outliers using Hellinger distance).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo and Phua in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams), to incorporate the teachings of Phua (directed to fraud detection research) to include the capability to utilize an unlabeled dataset. One of ordinary skill would have been motivated to perform such a modification in order to address criticisms associated with using labelled data to detect fraud as described in Phua (page 5, second column, second paragraph).
With respect to claim 16 Pozzolo in view of Phua teaches all of the limitations of claim 1 as previously discussed. Pozzolo does not explicitly disclose wherein the financial transactions are insurance transactions. However, Phua teaches wherein the financial transactions are insurance transactions (e.g. page 8, first column, first paragraph, indicating fraud detection based on analyzing twelve months worth of insurance claims; page 8, first column, fourth paragraph, detecting statistical outliers using Hellinger distance on medical insurance data).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo and Phua in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams), to incorporate the teachings of Phua (directed to fraud detection research) to include the capability to utilize as, the dataset of financial transactions, a dataset of insurance transactions. One of ordinary skill would have been motivated to perform such a modification in order to address criticisms associated with using labelled data to detect fraud as described in Phua (page 5, second column, second paragraph).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Pozzolo in view of Phua, further in view of Gribelyuk et al. (US 10754946 B1).
With respect to claim 5, Pozzolo in view of Phua teaches all of the limitations of claim 1 as previously discussed. Pozzolo does not explicitly disclose limiting a size of the Hellinger decision tree after a tree node reaches a maximum height using the processor thereby avoiding overfitting. However, Gribelyuk teaches limiting a size of the Hellinger decision tree after a tree node reaches a maximum height using the processor thereby avoiding overfitting (e.g. col. 12 lines 19-46, discussing parameters for decision trees, including setting a maximum depth (i.e. height) in order to prevent overfitting).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo, Phua, and Gribelyuk in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams) and Phua (directed to fraud detection research), to incorporate the teachings of Gribelyuk (directed to a machine learning approach to modeling entity behavior, including using decision trees) to include the capability to limit the size of the decision tree to a maximum height/depth in order to avoid overfitting. One of ordinary skill would have been motivated to perform such a modification in order to avoid overfitting as described in Gribelyuk (col. 12 lines 19-46).
Claims 8, 9, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Pozzolo in view of Phua, further in view of Guo et al. (US 10754946 B1).
With respect to claim 8, Pozzolo in view of Phua teaches all of the limitations of claim 1 as previously discussed, and Pozzolo further teaches wherein the Hellinger decision tree is used as a base learner in a modified random forest (e.g. page 588, first column, final paragraph, static learning setting; learning from non-stationary data streams; using HDDT as base learner; page 588, second column, first and second full paragraphs, batch ensemble model combination based on Hellinger Distance and Information Gain; page 591, first full paragraph, using decision tree as base learner (comparing C4.5 and HDDT)).
Hellinger does not explicitly disclose wherein the decision tree is used in a modified random forest. However, Guo teaches wherein the decision tree is used in a modified random forest (e.g. paragraph 0043, generating decision trees in modified random forest model).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo, Phua, and Guo in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams) and Phua (directed to fraud detection research), to incorporate the teachings of Guo (directed to facilitating automatic handling of incomplete data in a random forest model) to include the capability to implement the Hellinger decision tree (i.e. of Pozzolo) in a modified random forest. One of ordinary skill would have been motivated to perform such a modification in order to address challenges in handling missing data values in a dataset as described in Guo (paragraph 0023).
With respect to claim 17, Pozzolo in view of Phua teaches all of the limitations of claim 1 as previously discussed. Assuming arguendo that Pozzolo does not explicitly disclose a system configured to perform the method of claim 1, wherein the system is a computer or a server, Guo teaches a system configured to perform the method of claim 1, wherein the system is a computer or server (e.g. paragraph 0027, Fig. 1, system including a computing device 102, including modified random forest component that can facilitate automatically analyzing one or more datasets).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo, Phua, and Guo in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams) and Phua (directed to fraud detection research), to incorporate the teachings of Guo (directed to facilitating automatic handling of incomplete data in a random forest model) to include the capability to implement the Hellinger decision tree (i.e. of Pozzolo) using a computing device. One of ordinary skill would have been motivated to perform such a modification in order to address challenges in handling missing data values in a dataset as described in Guo (paragraph 0023).
With respect to claim 18, Pozzolo in view of Phua teaches all of the limitations of claim 1 as previously discussed. Assuming arguendo that Pozzolo does not explicitly disclose a non-transitory computer readable medium storing a program configured to instruct the processor to execute the method of claim 1, Guo teaches a non-transitory computer readable medium storing a program configured to instruct the processor to execute the method of claim 1 (e.g. paragraph 0028, memory 108 storing computer executable components including modified random forest component 104 and associated components, along with processor 106 that executes the computer executable components stored in memory 108).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo, Phua, and Guo in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams) and Phua (directed to fraud detection research), to incorporate the teachings of Guo (directed to facilitating automatic handling of incomplete data in a random forest model) to include the capability to implement the Hellinger decision tree (i.e. of Pozzolo) via a program stored in a computer memory. One of ordinary skill would have been motivated to perform such a modification in order to address challenges in handling missing data values in a dataset as described in Guo (paragraph 0023).
With respect to claim 9, Pozzolo in view of Phua, further in view of Guo teaches all of the limitations of claim 8 as previously discussed, and Pozzolo further teaches wherein the Hellinger decision tree is a positive and unbalanced Hellinger decision tree, and wherein the data set of fraudulent transactions is imbalanced positive data (e.g. page 589, first and second lines, indicating that the majority class refers to negative and the minority class refers to positive data in the dataset; page 589, first column final paragraph, describing HDDT as being based on distances between positive and negative classes over all of a feature’s partitions; page 591, first column, second paragraph, indicating the HDDT is used as a base learner on the unbalanced data; page 591, second column first paragraph, describing real-world credit card dataset as being highly unbalanced but including 0.15% of transactions as being fraudulent (i.e. positive); page 593, first column, final paragraph indicating that the fraud dataset is extremely unbalanced and exhibiting concept drift within the minority class, where the HDDT performs very well on the dataset; i.e. the dataset is unbalanced and positive (includes positive data), and the HDDT is trained on this unbalanced and positive dataset).
Pozzolo does not explicitly disclose that the data set is unlabeled data. However, Phua teaches that the data set is unlabeled data (e.g. page 5 second column, second paragraph, indicating that some research recommends use of unlabelled data in fraud detection use cases; page 8, first column, third and fourth paragraphs, describing unsupervised approaches with unabelled data, including in conjunction with Hellinger distance for comparing probability distributions and giving suspicions scores, and detecting statistical outliers using Hellinger distance).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo, Guo, and Phua in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams) and Guo (directed to facilitating automatic handling of incomplete data in a random forest model), to incorporate the teachings of Phua (directed to fraud detection research) to include the capability to utilize an unlabeled dataset. One of ordinary skill would have been motivated to perform such a modification in order to address criticisms associated with using labelled data to detect fraud as described in Phua (page 5, second column, second paragraph).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Pozzolo in view of Phua, further in view of Guo, further in view of Patel et al. (US 20190213605 A1).
With respect to claim 10, Pozzolo in view of Phua, further in view of Guo teaches all of the limitations of claim 9 as previously discussed. Pozzolo and Phua do not explicitly disclose wherein the Hellinger decision tree is configured to consider random feature selection when initializing a tree node.
However, Patel teaches wherein the Hellinger decision tree is configured to consider random feature selection when initializing a tree node (e.g. paragraph 0124, building decision tree in random forest; each node splits on a feature selected from a random subset of the full feature set).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo, Guo, Phua, and Patel in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams), Guo (directed to facilitating automatic handling of incomplete data in a random forest model), and Phua (directed to fraud detection research), to incorporate the teachings of Patel (directed to prediction of automotive warranty fraud) to include the capability to configure the decision tree to consider random feature selection when initializing nodes. One of ordinary skill would have been motivated to perform such a modification in order to implement the tree such that it can be trained quickly, while also being resistant to overfitting and providing a good estimate of generalization error as described in Patel (paragraph 0124).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Pozzolo in view of Phua, further in view of Barrow et al. (US 20060167655 A1).
With respect to claim 4, Pozzolo in view of Phua teaches all of the limitations of claim 1 as previously discussed. Pozzolo does not explicitly disclose wherein the Hellinger decision tree is configured to use class prior to estimate counts of positives and negatives in each node. However, Barrow teaches wherein the Hellinger decision tree is configured to use class prior to estimate counts of positives and negatives in each node (e.g. paragraph 0105, every probability estimate of class membership for every possible combination of probability range bines at each node of the tree is the prior probability of class membership; throughout probability range bin combinations at each node of tree, count of total number of instances is set to 1 and the count of the number of positive outcome instances is set to the prior probability estimate).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo, Phua, and Barrow in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams) and Phua (directed to fraud detection research), to incorporate the teachings of Barrow (directed to classification using probability estimate resampling) to include the capability to use, in the decision tree, class prior to estimate counts of positives and negatives in each node. One of ordinary skill would have been motivated to perform such a modification in order to ensure the model will output the prior probability for any new instances to be classified which happen to fall in probability range bin combinations with no observed instances in the training data, as described in Barrow (paragraph 0105).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Pozzolo in view of Phua, further in view of Guo, further in view of Barrow, further in view of Miranda et al. (US 20180357299 A1).
With respect to claim 11, Pozzolo in view of Phua, further in view of Guo teaches all of the limitations of claim 9 as previously discussed. Pozzolo does not explicitly disclose wherein the Hellinger decision tree is configured to use a class prior. However, Barrow teaches wherein the Hellinger decision tree is configured to use a class prior (e.g. paragraph 0105, every probability estimate of class membership for every possible combination of probability range bines at each node of the tree is the prior probability of class membership; throughout probability range bin combinations at each node of tree, count of total number of instances is set to 1 and the count of the number of positive outcome instances is set to the prior probability estimate).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo, Phua, Guo, Phua, and Barrow in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams), Phua (directed to fraud detection research), Guo (directed to facilitating automatic handling of incomplete data in a random forest model), and Phua (directed to fraud detection research), to incorporate the teachings of Barrow (directed to classification using probability estimate resampling) to include the capability to use, in the decision tree, a class prior. One of ordinary skill would have been motivated to perform such a modification in order to ensure the model will output the prior probability for any new instances to be classified which happen to fall in probability range bin combinations with no observed instances in the training data, as described in Barrow (paragraph 0105).
Pozzolo and Barrow do not explicitly disclose wherein the Hellinger decision tree is configured to use a size of a stratified bootstrap sample. However, Miranda teaches wherein the Hellinger decision tree is configured to use a size of a stratified bootstrap sample (e.g. paragraph 0068, performing statistical sampling; choice based stratified sampling of log entries; paragraphs 0087-0088, assignment of category identifiers to log entries based on groupings determined by classifier which may be a random forest classifier based on a decision tree; fitting decision trees on sub-samples of the dataset; sub-sample size may be the same as the original input sample size; samples drawn with replacement if bootstrap=True; i.e. the system uses a bootstrap sampling method of stratified samples, based on a size of the samples).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Pozzolo, Guo, Phua, Barrow, and Miranda in front of him to have modified the teachings of Pozzolo (directed to using Hellinger distance decision trees to avoid instances propagation in unbalanced and evolving data streams), Guo (directed to facilitating automatic handling of incomplete data in a random forest model), Phua (directed to fraud detection research), and Barrow (directed to classification using probability estimate resampling), to incorporate the teachings of Miranda (directed to assignment of category identifiers to log entries based on decision trees) to include the capability to use a size of a stratified bootstrap sample. One of ordinary skill would have been motivated to perform such a modification in order to achieve accuracy of learning algorithms available while also allowing processing of high dimensional space using a large number of training examples as described in Miranda (paragraph 0088).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain,” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting in re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (GCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co, v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert, denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F,3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir, 2005): Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L STANLEY whose telephone number is (469)295-9105. The examiner can normally be reached on Monday-Friday from 9:00 AM to 5:00 PM CST.
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/JEREMY L STANLEY/
Primary Examiner, Art Unit 2127
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