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 .
DETAILED ACTION
1. This Office Action is in response to the application filed on 04/16/2024.
Claims 1-20 are pending.
Priority
2. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
3. The information disclosure statement (IDS) filed on 05/10/2024 complies with the provisions of M.P.E.P. 609. The examiner has considered it.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
At Step 1:
Independent claims 1, 10 and 19 are directed to a "method", a “system” and a “program product” and thus directed to a statutory category
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
• " receiving a training data set" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 9 and paragraphs 89-90 where one can mentally visualizing receiving a training dataset.
• " initiating an imbalance analysis …" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 9 and paragraph 91 where one can mentally visualizing initiating an imbalance analysis.
• " initiating a mutual information (MI) analysis …" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 9 and paragraph 92 where one can mentally visualizing initiating a mutual information analysis.
• " generating a set of matrices …" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 9 and paragraph 93 where one can mentally visualizing generating a set of matrices.
• " generating a baseline reference data …" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 9 and paragraph 94 where one can mentally visualizing generating a baseline reference data.
• " providing a baseline reference data …" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 9 and paragraph 95 where one can mentally visualizing providing a baseline reference data.
At Step 2A, Prong Two:
• The claim recites no additional elements. At most one might consider that a "a memory; and a processor" as claimed might be considered to represent a computer-implemented system and method consistent with Fig. 1 even though the claim does not recite any computer. At most this would be a high-level recitation of a generic computer components and represents mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
• Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
• The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
• Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Dependent Claims 2-9, 11-18 and 20
Claims 2 and 12 recite, “wherein the training dataset comprises data in a format corresponding with time series data, text data, tabular data, or image data, and wherein the imbalance analysis on the training dataset is unchanged based on the format of the data” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claims 3 and 13 recite, “wherein the training dataset comprises time series data, text data, tabular data, or image data absent adjusting other portions or data structures associated with implementing the method” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claim 4 recites, “wherein the imbalance analysis also computes a frequency distribution of data points across the set of classes, and wherein a binning process is initiated to maintain the frequency distribution of data points in the set of matrices” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
The limitations as recited in dependent claims 5, 14 and 20 recite, “initiating an imbalance correction process, the imbalance correction process comprising a boundary limiting process that uses MI values to bound class bins” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claims 6 and 15 recite, “wherein the boundary limiting process complements a Synthetic Minority Over-sampling Technique (SMOTE)” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
The limitations as recited in dependent claims 7 and 16 recite, “initiating an imbalance correction process that calculates neighbors based on a calculated Euclidean distance between neighbors of a central MI value determined from the MI analysis” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claims 8 and 17 recite, “wherein the first data element and the second data element in the MI analysis are a same number of rows in tabular data or time series data” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claims 9 and 18 recite, “wherein the first data element and the second data element in the MI analysis are images” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claim 11 recites, “wherein the binning process categorizes the data points associated with a specific type using a one-dimensional clustering technique that segregates MI values into bins ranging from a minimum MI value to a maximum MI value” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Examiner’s Note
6. Imbalance analysis on a training dataset (According to Google): “Imbalance analysis on a training dataset is the process of evaluating the distribution of target classes to identify if certain categories are vastly underrepresented. Identifying this skew is critical because training a model on imbalanced data often causes it to become heavily biased toward the majority class.”
A mutual information analysis measures the statistical dependence between two random variables (According to Google): “A mutual information analysis measures the statistical dependence between two random variables by calculating how much observing one variable reduces the uncertainty about the other. Within a set of classes (categories), this analysis evaluates how strongly class memberships align, revealing non-linear relationships and shared information.”
Madakasira Ramakrishna et al, US 20240428050, [Ramakrishna: Paragraph 75 (“ach convolution may comprise an array of weights, which represents part of the input. While each may vary in size, the filter size may comprise a matrix that determines the size of the receptive field. The filter is then applied to an area of the input, and a dot product is calculated between the input pixels and the filter. This dot product is then fed into an output array”)] [Ramakrishna: Paragraph 13 (“The system may use a machine learning model to classify the user (e.g., the digital representation of the user), based on current state characteristics, into a class of users. In particular, the system may determine, for the entity using the plurality of current state characteristics as input into a clustering machine learning model, an entity cluster within a plurality of entity clusters. The clustering machine learning model may have been trained to classify the entity based on an entity's current state characteristics, into the entity cluster of the plurality of entity clusters. Furthermore, each entity cluster within the plurality of entity clusters may be defined by a corresponding set of current state characteristics. For example, each entity may represent a user. Thus, the clustering machine learning model may classify a user based on the user's demographic and other data (e.g., age, gender, income, risk tolerance, etc.).”)] [Ramakrishna: Paragraph 14 (“The clustering machine learning model may be trained using a training dataset. Thus, the system may receive a training dataset that includes a corresponding set of current state characteristics for a plurality of entities. Each set of current state characteristics may include the entity characteristics and item representations associated with each corresponding entity. The clustering machine learning model may then be trained, using the training dataset to classify a given entity based on a given entity's current state characteristics into a corresponding entity cluster of a plurality of entity clusters. In some embodiments, each entity cluster within the plurality of entity clusters may be defined by the corresponding set of current state characteristics.”)].
Everet et al, US 20240303503, [Everett: Paragraph 68 (“In some embodiments, x for each data file may represent a row in a matrix X, where each row represents a data file having one or more features (e.g., columns in matrix X) and y represents a vector of ground truth features (e.g., target features) for which a machine learning model should indicate and/or predict (e.g., generate as signal outputs) once trained using matrix X. Thus, for example, a training dataset may include at least one matrix of feature values and ground truth values associated with the matrix of feature values.”)] [Everett: Abstract and paragraph 4 (“uncertainty estimates in machine learning may affect accuracy and/or reliability of machine learning systems in some safety-critical applications. For example, machine learning may be used in the medical field. Such systems may be unable to reject out-of-distribution (OOD) data to send the OOD data back to human experts for review. OOD data and/or OOD samples may include data that is outside of a distribution of data used to train a machine learning model (e.g., training data). That is, OOD data and/or OOD samples may include data that is outside of a training dataset for a machine learning model. In some instances, uncertainty estimates may be used to improve a rate at which a machine learning model is able to detect an OOD sample”)].
Wolf et al, US 20240242083, [Wolf: Paragraph 77 (“It should be noted that while some examples disclosed in details are applied on a vector, it would be obvious to the person skilled in the art to extend the disclosed method to matrices, tensors, vectors comprising vectors and/or matrices, and the likes. Similarly, it would be obvious to the person skilled in the art to use a triplet or a larger set of vector parts rather than a pair, and the likes”)] [Wolf: Abstract (“mprove machine learning models by cleaning training data using anomaly detection, as well as anomaly detection per se. The method considers the task of finding out-of-class samples in tabular data, where little may be safely assumed about the structure of the data. The method captures the structure of the samples of the single training class, by learning mappings that maximize the mutual information between each sample and a part that is masked out. The mappings are learned by employing a contrastive loss that considers only one sample at a time. Once learned, the disclosure may score a test sample by measuring whether the learned mappings lead to a small contrastive loss using the masked parts of this sample. The experiments show accuracy advantage in comparison to the literature using the same set of hyperparameters as the state of the art results across benchmarks”)].
Karpovsky et al, US 20230418948, [Karpovsky: Paragraph 55 (“FIG. 4 illustrates an embodiment of a confusion matrix 400. As illustrated in FIG. 4, the confusion matrix 400 includes two columns and two rows. The first column represents data from a monitored dataset that are actually an anomaly 410. The second column represents data from the monitored dataset that are actually a non-anomaly 420. The first row represents the data from the monitored dataset that are classified by an IDS as an anomaly 430. The second row represents the data from the monitored dataset that are classified by the IDS to be a non-anomaly 440. As such, the top-left block represents true positive results 450, which include classifications of data that are correctly predicted to be an anomaly. The top-right block represents false-positive results 460, which include classifications of data that are incorrectly classified as being non-anomalous when in fact such data is anomalous. The bottom-left block represents false-negative results 470, which include classifications of data that are incorrectly classified as non-anomalous, and that are actually anomalous. The bottom-right block represents true negative results 480, which include classifications of data that are correctly classified as non-anomalous”)] [Karpovsky: Paragraph 7 (“training one or more machine-learning models to perform anomaly detection. A training dataset is accessed. An overall sensitivity score is determined that indicates an amount of sensitive data in the training dataset. Machine-learning models are trained based on the training dataset and the overall sensitivity score. The machine-learning models use the overall sensitivity score to determine a threshold. The threshold is relatively low for datasets having a large amount of sensitive data and is relatively high for dataset having a small among of sensitive data. When executed, the machine-learning models determine if a probability score of features extracted from a received dataset are above the determined threshold when a second overall sensitivity score of the received dataset is substantially similar to the overall sensitivity score. When the probability score is above the determined threshold, the machine-learning models cause an alert to be generated”)].
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to [Hung D. Le], whose telephone number is [571-270-1404]. The examiner can normally be communicated on [Monday to Friday: 9:00 A.M. to 5:00 P.M.].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on [571-272-4080]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Hung Le
07/07/2026
/HUNG D LE/Primary Examiner, Art Unit 2161
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