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
This action is a responsive to the application filed on 01/31/2023
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 11 are respectively drawn to a system, method, and non-transitory computer readable storage medium, hence each falls under one of four categories of statutory subject matter (Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1 and 11 recite the following, or analogous, limitations “loading a training data set, the training data set including a first bin and a second bin, applying an under-sampling technique to elements of the first bin to generate an updated first bin, applying an over-sampling technique to elements of the second bin to generate an updated second bin, generating an updated training data set by merging the updated first bin and the updated second bin…; generating input variables by: assigning alphanumeric strings to elements of a raw data set, tokenizing each alphanumeric string, converting the tokenized strings to scalar values, performing frequency filtering to emphasize scalar values based on a frequency the scalar values appear in a set of data objects while de-emphasizing scalar values based on a frequency the scalar values appear in a group of sets of data objects, and saving the filtered scalar values as input variables; and providing the input variables to the…model to generate output variables”. These limitations, as claimed, under its broadest reasonable interpretation, can be evaluated in a human mind, except for the recitation of generic computer components (using artificial intelligence/machine learning, a computer including one or more microprocessors, and a non-transitory computer readable storage medium) (Step 2A). Other than reciting “memory hardware”, “processing hardware”, “training a machine learning model by”, “and training the machine learning model with the updated training data set”, and “trained machine learning model” to perform the exceptions, nothing in the claims preclude the steps from practically being performed in the human mind. For example, a human expert can:
mentally/with the aid of pen and paper loading a training data set, the training data set including a first bin and a second bin (e.g. by thinking of/writing out remembering a set of samples and dividing them into two categories based on majority and minority),
mentally/with the aid of pen and paper applying an under-sampling technique to elements of the first bin to generate an updated first bin (e.g. by thinking of/writing out selecting a first subset of the majority category of samples by choosing fewer samples than the total),
mentally/with the aid of pen and paper applying an over-sampling technique to elements of the second bin to generate an updated second bin (e.g. by thinking of/writing out selecting a second subset of the minority category of samples by choosing more that the first subset),
mentally/with the aid of pen and paper generating an updated training data set by merging the updated first bin and the updated second bin… (e.g. by thinking of/writing out combining the first and second subset),
mentally/with the aid of pen and paper generating input variables by: assigning alphanumeric strings to elements of a raw data set, tokenizing each alphanumeric string, converting the tokenized strings to scalar values (e.g. by thinking of/writing out associating numbers and letters labels to remembered samples, chunking the labels, and turning the chunks into scalar values),
mentally/with the aid of pen and paper performing frequency filtering to emphasize scalar values based on a frequency the scalar values appear in a set of data objects while de-emphasizing scalar values based on a frequency the scalar values appear in a group of sets of data objects, and saving the filtered scalar values as input variables (e.g. by thinking of/writing out weighting the scalars by determining a count of appearance for each category in a sample dataset).
mentally/with the aid of pen and paper providing the input variables to the…model to generate output variables (e.g. by thinking of/writing out a calculation to input the values for acquiring output data).
Thus, the claims recite a mental process (Step 2A, Prong 1).
Claims 1, 9, and 17 include additional elements, “memory hardware”, “processing hardware”, “training a machine learning model by”, “and training the machine learning model with the updated training data set”, and “trained machine learning model”, however the recitations of these elements are at a high level of generality, and amount to adding the words “apply it” (or an equivalent) with the judicial exception (i.e., “training a machine learning model by”, “and training the machine learning model with the updated training data set”), or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., “memory hardware”, “processing hardware”) (see MPEP 2106.05(f)); and generally linking the user of the judicial exception to a particular technological environment or field of use (i.e., “trained machine learning model”) (see MPEP 2106.05(h)). Hence, each of the additional limitations or in combination do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2; see MPEP 2106.05(f)). The additional elements in the claim do not amount to significantly more than an abstract idea. Furthermore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using “memory hardware”, “processing hardware”, “training a machine learning model by”, “and training the machine learning model with the updated training data set”, and “trained machine learning model” to perform the steps of the independent claims amounts to no more than mere adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea; and generally linking the user of the judicial exception to a particular technological environment or field of use; as these cannot provide an inventive concept. (STEP 2B). As such, claims 1 and 11 are not patent eligible.
Dependent claims 2-10, and 12-20 are also ineligible for the same reasons given with respect to claims 1 and 11. The dependent claims describe additional mental processes:
mentally/with the aid of pen and paper automatically determining optimal hyperparameters for the…model; and configuring the…model with the optimal hyperparameters (claims 2 and 12) (e.g. by mentally/writing out determining and applying coefficients for the calculation)
mentally/with the aid of pen and paper wherein determining the optimal hyperparameters includes: loading baseline hyperparameters; configuring the…model with the baseline hyperparameters; running the configured…model to determine baseline metrics; and in response to the baseline metrics being above a threshold, saving the baseline hyperparameters as the optimal hyperparameters (claims 3 and 13) (e.g. by mentally/writing out the calculation with initial coefficients, determining the output loss being greater than a predetermined value, and remembering the calculation coefficients)
mentally/with the aid of pen and paper wherein determining the optimal hyperparameters includes, in response to the baseline metrics being at or below the threshold: adjusting the baseline hyperparameters, reconfiguring the…model with the adjusted hyperparameters, running the reconfigured…model to determine updated metrics, and in response to the updated metrics being more optimal than the baseline metrics, saving the updated metrics as the optimal hyperparameters (claims 4 and 14) (e.g. by mentally/writing out determining the calculation output loss being less than a predetermined value, changing the calculation coefficients, determining the updated output loss being greater than a predetermined value, and remembering the changed calculation coefficients)
mentally/with the aid of pen and paper in response to determining the output variables are above a threshold: loading a second…model, and providing the input variables to the second…model to generate second output variables (claims 5 and 15) (e.g. by thinking of/writing out determining the output loss being greater than a predetermined value, replacing the calculation with a second calculation, and determining a second output)
mentally/with the aid of pen and paper in response to determining the output variables are not above the threshold: loading a third…model, and providing the input variables to the third…model to generate third output variables (claims 6 and 16) (e.g. by thinking of/writing out determining the output loss being less than a predetermined value, replacing the calculation with a third calculation, and determining a third output)
mentally/with the aid of pen and paper wherein input variables include a third bin and a fourth bin (claims 9 and 19) (e.g. by thinking of/writing out the remembered samples having third and fourth categories)
mentally/with the aid of pen and paper wherein the input variables of the third bin are assigned to fixed effects and the input variables of the fourth bin are assigned to mixed effects (claims 10 and 20) (e.g. by thinking of/writing out the third category and fourth category being fixed and mixed effects)
Again, the dependent claims continued to cover the performance of the limitation in the mind as inherited from the independent claims (Step 2A, Prong 1). The dependent claims 2-6 and 12-16 recitation of “trained machine learning model” and “machine learning”; claims 7 and 17 recitation of “wherein the trained machine learning model includes a light gradient-boosting machine model”; claims 8 and 18 recitation of “wherein the trained machine learning model includes a mixed effects random forests model with a light gradient-boosting machine regressor” are again recited at a high level and amount to generally linking the user of the judicial exception to a particular technological environment or field of use, and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2). The additional element in the claims do not amount to significantly more than an abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements to perform the steps of in the dependent claims and perform the steps of the claims amount to no more than generally linking the user of the judicial exception to a particular technological environment or field of use, and this cannot provide an inventive concept. (STEP 2B). As such, dependent claims 2-10, and 12-20 additional elements or combination of elements do not amount to significantly more than an abstract idea nor provide any inventive concept, nor impose a meaningful limit to integrate the elements into a practical application or significantly more than the judicial exceptions; therefore, the dependent claims are not deemed patent eligible.
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 the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 7, 11-12, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Farrugia et al (“A Real‑Time Prescriptive Solution for Explainable Cyber‑Fraud Detection Within the iGaming Industry”, 2021) hereinafter Farrugia, in view of He et al (“ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning”, 2008) hereinafter He, in view of Bykov et al (US Pub 20210065126) hereinafter Bykov.
Regarding claims 1 and 11, Farrugia teaches a computer-implemented method comprising; and a system comprising: memory hardware configured to store instructions and processing hardware configured to execute the instructions (sections “Experimental Setup” teaches “We conducted our experiments using Jupyter Notebooks on a 16GB RAM, 64-bit Unix system” that includes one or more processors communicatively coupled to one or more memories executing code for performing the embodiments of the disclosure), wherein the instructions include:
training a machine learning model by:
loading a training data set, the training data set including a first bin and a second bin (section “Data Preparation” teaches “Our dataset consisted of 451,123 players which included a total of 13,591 confirmed fraudsters, with the remaining players (437,532) not being previously flagged for fraud”.),
applying an under-sampling technique to elements of the first bin to generate an updated first bin (section “Data Preparation” teaches a labeled dataset of “non-fraud” and “fraud” samples, and “we filtered out those non-fraud players (first bin) who had no activity recorded, bringing down the total number of players to 197,733 (184,142 of which not previously flagged as fraud). Besides reducing noise, this process also acted as an under-sampling strategy and helped improve our class imbalance issue slightly” and bringing down the majority class.),
applying an over-sampling technique to elements of the second bin to generate an updated second bin (sections “Data Preparation” and “Predictive Modeling” teach “We also experimented with over-sampling the minority class [fraud class] (second bin) using Synthetic Minority Over-sampling TEchnique (SMOTE) to balance both classes further”),
generating an updated training data set by merging the updated first bin and the updated second bin (section “Data Preparation”-“Predictive Modelling” teach over and under sampling the different classes in the training dataset, then training machine learning “RF, LGB, DT, and LR [models] on our dataset”), and
training the machine learning model with the updated training data set (section “Data Preparation”-“Predictive Modelling” teach over and under sampling the different classes in the training dataset, then training machine learning “RF, LGB, DT, and LR [models] on our dataset”);
generating input variables by:
assigning alphanumeric strings to elements of a raw data set (section “Data Processing” teaches “binary classification dataset (‘non-fraud’ and ‘fraud’)”),
tokenizing each alphanumeric string (section “Data Preparation” teaches “We monitored over 1000 dimensions which we later reduced to 25 features based on the following attributes (we further discuss this process in Sect. 3.3): 1. Multi-session behavioural aggregates, 2. Gaming patterns, 3. Session identification and geolocation, 4. Demographics, 5. Payment information”; thus converting the labeled data into feature values),
converting the tokenized strings to scalar values (section “Data Pre-processing” teaches “we categorised our features into three groups: Boolean’s, scalars, and highly-skewed scalar”),
However, while Farrugia teaches categorizing features into scalar values by scaling the features “within its respective inter-quartile range” and removing features based on a contribution threshold, Farrugia does not explicitly teach performing frequency filtering to emphasize scalar values based on a frequency the scalar values appear in a set of data objects while de-emphasizing scalar values based on a frequency the scalar values appear in a group of sets of data objects, and saving the filtered scalar values as input variables; and providing the input variables to the trained machine learning model to generate output variable.
He teaches performing frequency filtering to emphasize scalar values based on a frequency the scalar values appear in a set of data objects while de-emphasizing scalar values based on a frequency the scalar values appear in a group of sets of data objects (section 2 teaches “Our objective here is similar to those in SMOTEBoost [16] and DataBoost-IM [17] algorithms: providing different weights (emphasize) for different minority examples (based on a frequency) to compensate for the skewed distribution” of the converted data), and
saving the filtered scalar values as input variables (section 2 teaches weighting the training and testing data sets according to majority and minority of appearance); and
providing the input variables to the trained machine learning model to generate output variable (sections 2 and 3C teach using the training and testing set on “a decision tree as the base classifier”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement He’s teachings of weighting the training and testing data sets according to majority and minority of appearance into Farrugia’s teaching of over and under sampling of different data types for training models and input data conversion in order to acquire “improved accuracy” for predictions on the minority and majority classes (He, sections 2 and 3C).
Further, Farrugia at least implies tokenizing each alphanumeric string (see mappings above); however, Bykov teaches tokenizing each alphanumeric string (paragraphs 0092-0094 teach “clean the string (i.e., collapse multiple spaces), surround alphanumeric characters with spaces, tokenize the string, identify skill variants and wrap them in tags”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify over and under sampling of different data types for training models and input data conversion, as taught by Farrugia as modified by weighting the training and testing data sets according to majority and minority of appearance as taught by He, to include string data tokenization as taught by Bykov in order to more efficiently train the model from data conversion and segmentation (Bykov, paragraphs 0092-0099).
Regarding claims 2 and 12, the combination of Farrugia, He, and Bykov teach all the claim limitations of claims 1 and 11 above; and further teach automatically determining optimal hyperparameters for the trained machine learning model; and configuring the trained machine learning model with the optimal hyperparameters (Farrugia, section “Predictive Modelling” teach “We evaluated all models using their default hyper-parameters (from the scikit-learn Python package). We further performed hyper-parameter tuning using Bayesian optimization and distributed Tree of Parzen Estimators (TPE) of the best performing model”).
Regarding claims 7 and 17, the combination of Farrugia, He, and Bykov teach all the claim limitations of claims 1 and 11 above; and further teach wherein the trained machine learning model includes a light gradient-boosting machine model (Farrugia, sections “Data Pre-processing”-“Predictive Modelling” teach using a “LightGBM (LGB) model”).
Claims 3-4 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Farrugia et al (“A Real‑Time Prescriptive Solution for Explainable Cyber‑Fraud Detection Within the iGaming Industry”, 2021) hereinafter Farrugia, in view of He et al (“ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning”, 2008) hereinafter He, in view of Bykov et al (US Pub 20210065126) hereinafter Bykov, in view of Mostafa et al (US Pub 20220284353) hereinafter Mostafa.
Regarding claims 3 and 13, the combination of Farrugia, He, and Bykov teach all the claim limitations of claims 2 and 12 above; and further teach wherein determining the optimal hyperparameters includes: loading baseline hyperparameters; configuring the trained machine learning model with the baseline hyperparameters; running the configured machine learning model to determine baseline metrics (Farrugia, section “Predictive Modelling” teach “We evaluated all models using their default hyper-parameters (from the scikit-learn Python package).);
However, the combination does not explicitly teach and in response to the baseline metrics being above a threshold, saving the baseline hyperparameters as the optimal hyperparameters.
Mostafa teaches and in response to the baseline metrics being above a threshold, saving the baseline hyperparameters as the optimal hyperparameters (paragraphs 0027-0029 and 0043 teach loading previously determined hyperparameters that satisfy a threshold “to configure a training algorithm which is then used to train on a local dataset” and determine if the model’s “loss has improved by an amount larger than a threshold (baseline metrics being above a threshold) compared to the loss of the aggregate model from the previous round”, and using the hyperparameters further in future rounds (saving the baseline hyperparameters as the optimal hyperparameters)).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify over and under sampling of different data types for training models and input data conversion, as taught by Farrugia as modified by weighting the training and testing data sets according to majority and minority of appearance as taught by He, as modified by string data tokenization as taught by Bykov, to include hyperparameter performance comparison against a threshold for saving parameters for future use as taught by Mostafa in order to more efficiently use a computer system when training machine learning models (Mostafa, paragraphs 0027-0029, 0043, and 0110).
Regarding claims 4 and 14, the combination of Farrugia, He, Bykov, and Mostafa teach all the claim limitations of claims 3 and 13 above; and further teach wherein determining the optimal hyperparameters includes, in response to the baseline metrics being at or below the threshold: adjusting the baseline hyperparameters, reconfiguring the trained machine learning model with the adjusted hyperparameters, running the reconfigured machine learning model to determine updated metrics, and in response to the updated metrics being more optimal than the baseline metrics, saving the updated metrics as the optimal hyperparameters (Farrugia, section “Predictive Modelling” teach “We evaluated all models using their default hyper-parameters (from the scikit-learn Python package). We further performed hyper-parameter tuning using Bayesian optimization and distributed Tree of Parzen Estimators (TPE) of the best performing model”).
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Farrugia et al (“A Real‑Time Prescriptive Solution for Explainable Cyber‑Fraud Detection Within the iGaming Industry”, 2021) hereinafter Farrugia, in view of He et al (“ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning”, 2008) hereinafter He, in view of Bykov et al (US Pub 20210065126) hereinafter Bykov, in view of Alon et al (US Pub 20220156524) hereinafter Alon.
Regarding claims 5 and 15, the combination of Farrugia, He, and Bykov teach all the claim limitations of claims 1 and 11 above; however, the combination does not explicitly teach in response to determining the output variables are above a threshold: loading a second trained machine learning model, and providing the input variables to the second trained machine learning model to generate second output variables.
Alon teaches in response to determining the output variables are above a threshold: loading a second trained machine learning model, and providing the input variables to the second trained machine learning model to generate second output variables (paragraph 0004 teaches determining a first model output and the output’s “correctness metric”, and “responsive to determining that the correctness metric exceeds the threshold: (a) applying the second machine learning model to the input to generate a second model output”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify over and under sampling of different data types for training models and input data conversion, as taught by Farrugia as modified by weighting the training and testing data sets according to majority and minority of appearance as taught by He, as modified by string data tokenization as taught by Bykov, to include model outputs compared to thresholds for model replacement as taught by Alon in order to obtain more accurate model predictions based on a performance metric (Alon, 0004 and 0027).
Regarding claims 6 and 16, the combination of Farrugia, He, Bykov, and Alon teach all the claim limitations of claims 5 and 15 above; and further teach in response to determining the output variables are not above the threshold: loading a third training machine learning model, and providing the input variables to the third trained machine learning model to generate third output variables (Alon, paragraph 0027 teaches “second correctness metric could be generated by second machine learning model 220b and/or determined from its output and the second correctness metric could be used to determine whether to conditionally execute further machine learning models (e.g., a third machine learning model). This could include comparing the second correctness metric to a second threshold”).
Farrugia, He, Bykov, and Alon are combinable for the same rationale as set forth above with respect to claims 5 and 15.
Claims 8-10 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Farrugia et al (“A Real‑Time Prescriptive Solution for Explainable Cyber‑Fraud Detection Within the iGaming Industry”, 2021) hereinafter Farrugia, in view of He et al (“ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning”, 2008) hereinafter He, in view of Bykov et al (US Pub 20210065126) hereinafter Bykov, in view of Provencher et al (US Pub 20230077428) hereinafter Provencher.
Regarding claims 8 and 18, the combination of Farrugia, He, and Bykov teach all the claim limitations of claims 1 and 11 above; however, the combination does not explicitly teach wherein the trained machine learning model includes a mixed effects random forests model with a light gradient-boosting machine regressor.
Provencher teaches wherein the trained machine learning model includes a mixed effects random forests model with a light gradient-boosting machine regressor (paragraphs 0062-0068 teach a “MERF model” utilizing regression gradient boosting trees).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify over and under sampling of different data types for training models and input data conversion, as taught by Farrugia as modified by weighting the training and testing data sets according to majority and minority of appearance as taught by He, as modified by string data tokenization as taught by Bykov, to include a specific model type for data processing as taught by Provencher in order to obtain more accurate predictions based on MERF model data advantageous “cluster data” processing performance (Provencher, 0004 and 0027).
Regarding claims 9 and 19, the combination of Farrugia, He, Bykov, and Provencher teach all the claim limitations of claims 8 and 18 above; and further teach wherein input variables include a third bin and a fourth bin (He, sections 2 and 3C teach using the training (third bin) and testing set (fourth bin) on “a decision tree as the base classifier”).
Farrugia, He, Bykov, and Provencher are combinable for the same rationale as set forth above with respect to claims 8 and 18.
Regarding claims 10 and 20, the combination of Farrugia, He, Bykov, and Provencher teach all the claim limitations of claims 9 and 19 above; and further teach wherein the input variables of the third bin are assigned to fixed effects and the input variables of the fourth bin are assigned to mixed effects (Provencher, paragraphs 0062-0068 teach a “MERF model” processing datasets of “fixed effect” and datasets of mixed random and fixed effect data).
Farrugia, He, Bykov, and Provencher are combinable for the same rationale as set forth above with respect to claims 8 and 18.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
El Youssoufi et al (US Pub 20200320433) teach “Responsive to a confidence level for a first model being below a first threshold of confidence level, the behavior prediction system 140 may determine to use a second model that is less sensitive to occlusions, and therefore makes predictions associated with a higher confidence level that is higher than a threshold level”.
Conclusion
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/C.M./Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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