DETAILED ACTION
The present application, filed on 6/7/2024 is being examined under the AIA first inventor to file provisions.
The following is a non-final First Office Action on the Merits. Claims 1-20 are pending and have been considered below.
Information Disclosure Statement (IDS)
The information disclosure statement (IDS) submitted on 6/7/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, such IDS is being considered by Examiner.
Claim Rejections - 35 USC § 101
35 USC 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 are rejected under 35 USC 101 because the claimed invention is not directed to patent eligible subject matter. The claimed matter is directed to a judicial exception, i.e. an abstract idea, not integrated into a practical application, and without significantly more.
Per Step 1 of the multi-step eligibility analysis, claims 1-7 are directed to a computer implemented method, claims 8-14 are directed to a system, and claims 15-20 are directed to computer executable instructions stored on a non-transitory storage medium.
Thus, on its face, each independent claim and the associated dependent claims are directed to a statutory category of invention.
[INDEPENDENT CLAIMS]
Per Step 2A.1. Independent claim 1, (which is representative of independent claims 8, 15) is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 1 (which is representative of independent claims 8, 15) recite an abstract idea, shown in bold below:
[A] A computer-implemented method for generating training data for a machine learning model
[B] receiving, by one or more processors and from one or more data sources, a first machine learning training data set that includes a plurality of data points;
[C] determining, by the one or more processors, an input outlier score of a first data point of the plurality of data points;
[D] determining, by the one or more processors, an output outlier score of the first data point;
[E] generating, by the one or more processors, a total output score of the first data point based on the input outlier score of the first data point and the output outlier score of the first data point, the total output score of the first data point representing a likelihood that the first data point is an inconsistently annotated data point;
[F] comparing, by the one or more processors, the total output score of the first data point with a pre-determined threshold;
[G] based on the comparison of the total output score of the first data point with the pre-determined threshold, generating, by the one or more processors, a second machine learning training data set that excludes the first data point; and
[H] inputting, by the one or more processors and into the machine learning model, the second machine learning training data set to train the machine learning model.
Independent claim 1 (which is representative of independent claims 8, 15) recites: determining an input outlier and an output outlier ([C], [D]); generating an output score and comparing it with a threshold ([E], [F]); and inputting a new training data set ([H]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: generating training data for a machine learning model.
This is a combination that, under its broadest reasonable interpretation, covers performance of limitations expressing mathematical concepts like mathematical relationships, mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
Accordingly, it is concluded that independent claim 1 (which is representative of independent claims 8, 15) recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – Additional Elements]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the added elements “processor,” and “storing medium” recite computing elements at a high level of generality, generally linking the use of a judicial exception to a particular technological environment (see MPEP 2106.05(h)), or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Further, the additional elements, like ‘the nature of the data point’ are nothing more than (a) descriptive limitations of claim elements, such as describing the nature, structure and/or content of other claim elements, or (b) general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These additional elements of the independent claims do not preclude from carrying out the identified abstract idea generating training data for a machine learning model, and do not serve to integrate the identified abstract idea into a practical application.
The additional elements in the independent claims, shown not bolded above, recite: receiving a data set ([B]). When considered individually, they amount to nothing more than receiving data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (generating training data for a machine learning model) into a practical application (see MPEP 2106.05(f)(2)).
Therefore, the additional claim elements of independent claim 1, (which is representative of independent claims 8, 15), evaluated individually, as well as a whole, as an ordered combination, do not integrate the identified abstract idea into a practical application and the claims are directed to the recited judicial exception.
Per Step 2B. Independent claim 1 (which is representative of claims independent 8, 15) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claims 1, 8, 15 are deemed ineligible.
[DEPENDENT CLAIMS]
Dependent claim 2, which is representative of dependent claims 9, 16, recites: wherein determining the input outlier score of the first data point of the plurality of data points comprises:
determining, using the one or more processors, a local outlier factor for the first data point using a first k-nearest neighbor (KNN) algorithm;
generating, using the one or more processors and based on the local outlier factor for the first data point, a first value using a scaling function of the local outlier factor for the first data point; and
equating, using the one or more processors, the input outlier score of the first data point to the first value.
The elements in these dependent claims are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claims continue to recite the identified abstract idea.
The dependent claims elements have the same relationship to the underlying abstract idea as outlined in the independent claims analysis above. It is readily clear that the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claims further elaborate on the previously identified abstract idea (generating training data for a machine learning model).
Therefore, dependent claim 2 (which is representative of dependent claims 9, 16) is deemed ineligible. As a result, it is concluded that the dependent claim elements do not integrate the identified abstract idea into a practical application (see MPEP 2106.05(f)(2)).
Dependent claim 3, which is representative of dependent claims 10, 17, recites: wherein determining the output outlier score of the first data point of the plurality of data points comprises:
determining, using the one or more processors, a centroid of a k-nearest neighborhood for the first data point using a second KNN algorithm;
calculating, using the one or more processors, a Euclidean distance between the centroid and the first data point;
generating, using the one or more processors and based on the calculated Euclidean distance, a second value between zero and one using a min-max scaling function of the calculated Euclidean distance; and
equating, using the one or more processors, the output outlier score of the first data point to the second generated value.
The elements in these dependent claims are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claims continue to recite the identified abstract idea.
The dependent claims elements have the same relationship to the underlying abstract idea as outlined in the independent claims analysis above. It is readily clear that the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claims further elaborate on the previously identified abstract idea (generating training data for a machine learning model).
Therefore, dependent claim 3 (which is representative of dependent claims 10, 17) is deemed ineligible. As a result, it is concluded that the dependent claim elements do not integrate the identified abstract idea into a practical application (see MPEP 2106.05(f)(2)).
Dependent claim 4, which is representative of dependent claims 11, 18, recites: wherein generating the total output score of the first data point of the plurality of data points comprises:
applying, using the one or more processors, a transformation to a total outlier score of the first data point,
wherein the transformation assigns higher output scores to data points that have a greater likelihood of being inconsistently annotated data points and assigns lower output scores to data points that have a lower likelihood of being inconsistently annotated data points.
The elements in these dependent claims are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claims continue to recite the identified abstract idea.
The dependent claims elements have the same relationship to the underlying abstract idea as outlined in the independent claims analysis above. It is readily clear that the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claims further elaborate on the previously identified abstract idea (generating training data for a machine learning model).
Therefore, dependent claim 4 (which is representative of dependent claims 11, 18) is deemed ineligible. As a result, it is concluded that the dependent claim elements do not integrate the identified abstract idea into a practical application (see MPEP 2106.05(f)(2)).
Dependent claim 5, which is representative of dependent claims 12, 19, recites: wherein: determining the input outlier score of the first data point includes
applying, using the one or more processors, a first KNN algorithm;
determining the output outlier score of the first data point includes applying, using the one or more processors, a second KNN algorithm; and
generating the total output score of the first data point of the plurality of data points comprises applying, using the one or more processors, a Monte Carlo approximation to the first KNN algorithm and to the second KNN algorithm.
The elements in these dependent claims are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).. When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claims continue to recite the identified abstract idea.
The dependent claims elements have the same relationship to the underlying abstract idea as outlined in the independent claims analysis above. It is readily clear that the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claims further elaborate on the previously identified abstract idea (generating training data for a machine learning model).
Therefore, dependent claim 5 (which is representative of dependent claims 12, 19) is deemed ineligible. As a result, it is concluded that the dependent claim elements do not integrate the identified abstract idea into a practical application (see MPEP 2106.05(f)(2)).
Dependent claim 6, which is representative of dependent claims 13, 20, recites:
determining, by the one or more processors, an input outlier score of a second data point of the plurality of data points;
determining, by the one or more processors, an output outlier score of the second data point;
generating, by the one or more processors, a total output score of the second data point based on the input outlier score of the second data point and the output outlier score of the second data point, the total output score of the second data point representing a likelihood that the second data point is an inconsistently annotated data point;
comparing, by the one or more processors, the total output score of the second data point with the pre-determined threshold;
based on the comparison of the total output score of the second data point with the pre-determined threshold, generating, by the one or more processors, a third machine learning training data set that excludes the second data point; and
inputting, by the one or more processors and into the machine learning model, the third machine learning training data set to train the machine learning model.
The elements in these dependent claims are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).. When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claims continue to recite the identified abstract idea.
The dependent claims elements have the same relationship to the underlying abstract idea as outlined in the independent claims analysis above. It is readily clear that the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claims further elaborate on the previously identified abstract idea (generating training data for a machine learning model).
Therefore, dependent claim 6 (which is representative of dependent claims 13, 20) is deemed ineligible. As a result, it is concluded that the dependent claim elements do not integrate the identified abstract idea into a practical application (see MPEP 2106.05(f)(2)).
Dependent claim 7, which is representative of dependent claims 14, recites: based on the comparison of the total output score of the first data point with the pre-determined threshold,
generating, by the one or more processors, an alert indicating that the first data point is an inconsistently annotated data point.
The elements in these dependent claims are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claims continue to recite the identified abstract idea.
The dependent claims elements have the same relationship to the underlying abstract idea as outlined in the independent claims analysis above. It is readily clear that the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claims further elaborate on the previously identified abstract idea (generating training data for a machine learning model).
Therefore, dependent claim 7 (which is representative of dependent claims 14) is deemed ineligible. As a result, it is concluded that the dependent claim elements do not integrate the identified abstract idea into a practical application (see MPEP 2106.05(f)(2)).
When the dependent claims are considered as a whole, as an ordered combination, the claim elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense. The most significant elements, which form the abstract concept, are set forth in the independent claims. The fact that the computing devices and the dependent claims are facilitating the abstract concept is not enough to confer statutory subject matter eligibility, since their individual and combined significance do not transform the identified abstract concept at the core of the claimed invention into eligible subject matter. Therefore, it is concluded that the dependent claims of the instant application, considered individually, or as a as a whole, as an ordered combination, do not amount to significantly more (see MPEP 2106.07(a)II).
In sum, claims 1-20 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Examiner Remarks
No art rejection has been applied to the instant set of claims. The identified prior art does not disclose:
“generating, by the one or more processors, a total output score of the first data point based on the input outlier score of the first data point and the output outlier score of the first data point, the total output score of the first data point representing a likelihood that the first data point is an inconsistently annotated data point;”
The prior art made of record and not relied upon which, however, is considered pertinent to applicant's disclosure:
US 20190370681 A1 OBA; Tatsumi CLUSTERING METHOD, CLASSIFICATION METHOD, CLUSTERING APPARATUS, AND CLASSIFICATION APPARATUS A clustering method for clustering packets is provided. The clustering method calculates similarities between packets, and clusters the packets using the calculated similarities. The present disclosure relates to a clustering method which clusters packets.
US 20250299208 A1 Smith; Barbara Sue et al. SYSTEM AND METHODS FOR VARYING OPTIMIZATION SOLUTIONS USING CONSTRAINTS An apparatus for generating a market analysis plan, the apparatus including at least a processor; a memory communicatively connected to the at least a processor, the memory containing instructions configuring the processor to: receive user data; generate an interface query data, wherein the interface query data structure configures a remote display device to: display the input field to the user; receive at least a user-input datum into the input field; retrieve data related to the at least a user-input data from a database communicatively connected to the processor; and refine the interface query data structure; generate multiple data multipliers based on the at least a user-input datum; identify at least an improvement datum as a function of the achievement plan; generate a goal report as a function of the at least an improvement datum.
US 20210065024 A1 Kudo; Jumma et al. NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR STORING DETERMINATION PROCESSING PROGRAM, DETERMINATION PROCESSING METHOD, AND DETERMINATION PROCESSING APPARATUS A non-transitory computer-readable storage medium for storing a determination processing program which causes a processor to perform processing that includes: obtaining an importance degree vector for a plurality of feature amounts by training a first machine learning model based on machine-learning data, the machine-learning data including pieces of training data, each of the pieces of training data including the plurality of feature amounts and being associated with a corresponding determination result; training a second machine learning model of a k-nearest neighbors algorithm in accordance with the machine-learning data and the importance degree vector; and determining, from among the pieces of training data, a piece of data that is similar to data to be predicted, by using the trained second machine learning model and the data to be predicted.
US 20240362571 A1 Smith; Barbara Sue et al. METHOD AND AN APPARATUS FOR ROUTINE IMPROVEMENT FOR AN ENTITY A method for routine improvement for an entity may include receiving an entity profile, generating, by a first datum, and receiving a second datum. The method may include generating at least an entity-specific improvement recommendation as a function of the second datum. Further, the method may include determining at least a user interface element as a function of the at least an entity-specific improvement recommendation. Moreover, the method may include transmitting the at least a user interface element to a display.
US 20220129772 A1 LEE; Weng Fook et al. SYSTEM AND METHOD HAVING THE ARTIFICIAL INTELLIGENCE (AI) ALGORITHM OF K-NEAREST NEIGHBORS (K-NN) The present invention relates to a system and method having the Artificial Intelligence (AI) algorithm of k-Nearest Neighbors (k-NN) as logic gates and SRAM/DRAM/non-volatile memory. One of the advantages of the system is that it utilizes a low power consumption. This is compared to few watts power consumption for existing AI platform available in the market The system of the present invention is also very efficient as it does not need CPU or GPU to do the intensive calculation, as it is fully logic design. In addition, the system of the present invention is also low in cost due to small die size as it does not require any CPU or GPU to perform the intensive computation.
US 20230080553 A1 Dharmasiri; Yakupitiyage Don Thanuja Samodhye et al ADJUSTING OUTLIER DATA POINTS FOR TRAINING A MACHINE-LEARNING MODEL Techniques for adjusting outlier datasets for training chatbot systems in natural language processing are disclosed. In one particular aspect, a method is provided that includes receiving a dataset that includes training or inference data. An initial set of outlier data points can be identified within the dataset based on a score of the outlier data points being above or below a threshold. The initial set can be adjusted by identifying one or more nearest neighbors, which can be included in the dataset. Outlier data points that include a label that matches a number of labels of the nearest neighbors that exceeds a predetermined threshold can be removed from the initial set of outlier data points to generate a final set. Outlier data points of the final set can be adjusted with respect to the dataset to generate a set of training data that is used to train a machine-learning model.
US 20240184858 A1 Lindner; Peter J. et al. METHODS AND MECHANISMS FOR AUTOMATIC SENSOR GROUPING TO IMPROVE ANOMALY DETECTION An electronic device manufacturing system configured to obtain, by a processor, a plurality of datasets associated with a process recipe, wherein each dataset of the plurality of datasets comprises data generated by a plurality of sensors during a corresponding process run performed using the process recipe. The processor is further configured to determine, using the plurality of data sets associated with the process recipe, a correlation value between two or more sensors of the plurality of sensors. Responsive to the correlation value satisfying a threshold criterion, the processor assigns the two or more sensors to a cluster. During a subsequent process run, the processor generates an anomaly score associated with the cluster and indicative of an anomaly associated with at least one step of the subsequent process run.
US 20250165591 A1 Lindner; Peter J. et al. METHODS AND MECHANISMS TO PERFORM AUTOMATED CLASSIFICATIONS OF ANOMALOUS TRACE SHAPES A system configured to obtain current trace data associated with a substrate processing system and provide the trace data as input to a first predictive subsystem trained to detect anomalies using a first technique. Responsive to detecting an anomaly in the trace data, the system provides the trace data as input to a second predictive subsystem trained to detect anomalies using a second technique. Output data obtained from the first predictive subsystem and the second predictive subsystem is provided to a third predictive subsystem. Output data from the third predictive subsystem is obtained. The output data is reflective of a trace shape associated with the anomaly. Based on the trace shape, a type of issue that caused the anomaly in the trace data is identified.
US 11868859 B1 Smith; Barbara Sue et al. Systems and methods for data structure generation based on outlier clustering Disclosed herein are systems and methods for determining data structures. In some embodiments, a classifier may be used to determine one or more attributes of an entity. In some embodiments, a clustering algorithm may be used to determine an attribute cluster. In some embodiments, an impact metric machine learning model may be used to determine an outlier cluster. In some embodiments, an outlier process may be determined as a function of the outlier cluster. In some embodiments, a visual element may be determined as a function of an outlier process and may be displayed to a user.
US 12182178 B1 Smith; Barbara Sue et al. System and methods for varying optimization solutions using constraints based on an endpoint A system for varying optimization solutions using constraints based on an endpoint, the system including at least a processor, a memory communicatively connected to the at least a processor, the memory containing instructions configuring the processor to receive process data including a plurality of impediments, generate an endpoint using a module configured to, analyze the plurality of impediments by extracting a feature from each impediment of the plurality of impediments, classify a plurality of impediments using the extracted features to a plurality of identifiers, rank the plurality of identifiers based on severity score, output the endpoint based on an identifier severity score, identify a plurality of nodes, receive at least a constraint, locate in the plurality of nodes an outlier cluster based on the endpoint, determine an outlier process as a function of the outlier cluster, and determine a visual element data structure as a function of the outlier process.
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/Radu Andrei/
Primary Examiner, AU 3697
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