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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 25, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 missing rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP 2106.04(a)(2)(III) "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.
Further, the MPEP recites "The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide run) to perform the claim limitation.
MPEP 2106.04(a)(2)(I) "The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations."
With respect to claim 1:
Step 2A, Prong 1:
A judicial exception is recited in this claim as it recites a mental process:
Determining, by one or more hardware processors, a first set of optimum clusters within a tabular base data using Gaussian Mixture Model (GMM) technique, wherein the tabular based data associated with a Machine Learning (ML) model comprises a protected attribute, a plurality of categorical features and a plurality of continuous features; Generating a set of optimum clusters can be practically performed in the mind.
Determining, by the one or more hardware processors, a first cluster among the first set of optimum clusters to which a local instance of the ML model belongs to, based on the plurality of categorical features and the plurality of continuous features associated with the local instance; Determining a local cluster from a set of clusters can be practically performed in the mind.
Generating, by the one or more hardware processors, a subset of the tabular base data comprising i) the local instance, and ii) a perturbed dataset obtained by perturbing the tabular base data associated with the first cluster, wherein boundaries of perturbation are obtained using a constrained perturbation technique, wherein the protected attribute in the subset is flipped from an unprivileged group to a privileged group; Generating a subset of data by perturbing data can be practically performed in the mind.
Determining, by the one or more hardware processors, a second set of optimum clusters by clustering the subset using the GMM technique to identify a second cluster among the second set of optimum of clusters comprising the local instance; Determining a set of optimum clusters can be practically performed in the mind.
Selecting, by the one or more hardware processors, data points within the perturbed dataset that fall withing the second cluster, wherein the local instance is excluded from the selected datapoints of the second cluster; Selecting data points can be practically performed in the mind.
Determining, by the one or more hardware processors, a local fairness of the ML model with a degree of fairness by: computing, an individual similarity score for each of the selected datapoints by determining a cosine similarity between the probability of the local instance with the probability of each of the selected data points, wherein a value of the individual similarity score is i) positive if the class of the local instance maps to the class of a selected datapoint from among the selected datapoints, and ii) negative if the class of the local instance varies from the class of the selected datapoint; and Determining a local fairness can be practically performed in the mind while computing a similarity score is a mathematical operation.
Obtaining, an average similarity score by averaging the individual similarity score of each of the selected datapoints, wherein the ML model decision is: fair if the average similarity score is greater than zero, wherein a value of the average similarity score indicates the degree of fairness, and unfair if the average similarity score is equal to or less than zero, wherein the value of the average similarity score indicates a degree of unfairness. Obtaining a score by averaging scores is a mathematical operation.
Step 2A, Prong 2:
The recited additional elements:
One or more hardware processors; (this amounts to nothing more than a generic computer element as per MPEP 2106.05(f))
Obtaining, by the one or more hardware processors, a class, and a probability of i) the selected datapoints, and ii) the local instance; (obtaining data step adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g). This element represents generic computer functions).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
One or more hardware processors; (this amounts to nothing more than a generic computer element as per MPEP 2106.05(f))
Obtaining, by the one or more hardware processors, a class, and a probability of i) the selected datapoints, and ii) the local instance; (obtaining data step is well understood routine and conventional, See MPEP 2106.05(d)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
The claim is ineligible.
With respect to claim 2:
Step 2A Prong 1:
Creates perturbations to each of the plurality of continuous features of the first cluster constrained by Coefficient of Variation (CV) score of each feature of the plurality of continuous features derived from feature distribution of a percentage of sample data selected from the first cluster, and Creating perturbations can be practically performed in the mind.
Creates perturbations to each of the plurality of categorical features of the first cluster by random sampling from set of feature values of the sample data selected from the first cluster such that it covers 90% of the percentage of sample data. Creating perturbations can be practically performed in the mind.
There are no additional elements in the claims.
With respect to claim 3:
Step 2A Prong 1:
A feature distribution of the of the perturbed data lies within the feature distribution of the first cluster and in proximity of the local instance. The limitation only further defines what the perturbed data includes.
There are no additional elements in the claims.
With respect to claim 4:
Step 2A Prong 1:
A feature distribution of the second cluster is in proximity to the feature distribution of the local instance. The limitation only further defines what the perturbed data includes.
There are no additional elements in the claims.
With respect to claim 5:
Step 2A Prong 1:
There is no abstract idea.
Step 2A Prong 2:
The GMM technique trains a plurality of GMMs using a first local maxima of a Silhouette score technique to identify main clusters as optimum clusters. (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 - see MPEP 2106.05(f) - Examiner's note: high level recitation of training a machine learning model.)
Step 2B:
The GMM technique trains a plurality of GMMs using a first local maxima of a Silhouette score technique to identify main clusters as optimum clusters. (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 - see MPEP 2106.05(f) - Examiner's note: high level recitation of training a machine learning model.)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
The claim is ineligible.
With respect to claim 6:
Step 2A Prong 1:
There is no abstract idea.
Step 2A Prong 2:
The ML model is a pretrained classification model. (amounts to nothing more than a generic element as per MPEP 2106.05(f))
Step 2B
The ML model is a pretrained classification model. (a pretrained classification model is well understood routine and conventional)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
The claim is ineligible.
Claims 7-18 are rejected according to claims 1-6 above.
Allowable Subject Matter
Claims 1-18 are allowable in view of prior art. The closest prior art is as follows:
US Patent 11/928730 to Feng et al, teaches training a machine learning model with a fairness improvement and generating a risk score based on executing the trained machine learning model.
US PG Pub 2025/0139418 to Saplicki et al, teaches generating statistically fair training data as to allow a model to be fairer by scoring independent variables.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIELA D REYES whose telephone number is (571)270-1006. The examiner can normally be reached Monday-Friday, 7:30 am -5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Wiley can be reached at (571) 272-4150. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights