DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 5/30/2023 for application number 18/203,146. The Office acknowledges receipt of the following: Specification, Drawings, Abstract, Oath/Declaration, and Claims.
Claims 1-20 are presented for examination.
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 .
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. Independent claim 1 (representative of independent claim 11) recites:
A method for generating and training a fuzzy machine learning model, the method comprising: receiving, by one or more processors, a set of input data for the fuzzy machine learning model; encoding, by the one or more processors, the set of input data into fuzzy concepts, wherein the fuzzy concepts are representative of approximate logical relationships between variables; determining, by one or more processors and based on the fuzzy concepts, a ruleset for the fuzzy machine learning model, wherein rules of the ruleset are based on a piecewise categorizing function; and training, by the one or more processors, the ruleset for the fuzzy machine learning model based on the set of input data by: approximating, using tropical geometry, a continuous representation of the piecewise categorizing function, and generating, based on at least the continuous representation of the piecewise categorizing function, a trained fuzzy ruleset.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically mathematical calculations and formulas. Applicant’s specification states that the steps of encoding the set of input data, determining the ruleset, and training the ruleset using tropical geometry are carried out using specific calculations and formulas (see paragraphs 0069-72, as filed, which discuss the calculations and formulas).
(2A, prong 2) This judicial exception is not integrated into a practical application. The claims recite the additional elements of (a) receiving a set of input data and (b) generic computer hardware components like a processor and memory. Additional element (a) is insignificant extra-solution activity because it is mere necessary data gathering for the abstract idea. Additional element (b) is a mere instruction to apply the exception because it merely adds generic computer components after-the-fact to the abstract idea. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because they only add insignificant extra-solution activity and mere instructions to apply the exception to the abstract idea.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional element (a) is well-understood, routine, and conventional activity, analogous to storing and retrieving information in memory, see MPEP 2106.05(d) citing Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Additional element (b) is a mere instruction to apply the exception as explained above. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements only add insignificant extra-solution activity that is well-understood, routine, and conventional and mere instructions to apply the exception to the abstract idea. In other words, the claim as a whole is directed to mathematical calculations with a generic computer to receive data for and perform the calculations.
The Examiner notes that he has considered whether the claims are an improvement to the function of a computer or technical field. Here, the specification sets forth a technical improvement of creating a human-interpretable ML model (spec. para. 0003-04) with fuzzy rules that allows for faster training convergence compared to prior techniques (spec. para. 0049-54). However, the claim itself must reflect the disclosed improvement, and “the judicial exception alone cannot provide the improvement.” See MPEP § 2106.05(a). Here, the mathematical calculations in the claim reflect the disclosed improvement (to the degree that they reflect the disclosed math that allow for faster convergence), but there are no additional elements that provide an improvement, even when all of the additional element are considered in combination with the abstract idea. In other words, the claims are directed to improved mathematical calculations, which are not patentable subject matter.
With respect to dependent claims 2-9, 12-19, these claims recite additional mathematical calculations and formulas. Claims 2 and 12 recite determining a distance matrix based on the continuous representation of the piecewise categorizing functions, clustering rules using the distance matrix, and calculating a representative rule from the cluster, which are mathematical calculations (see spec. para. 0099-0100 as filed). Claims 3-5 and 13-15 recite the input and ruleset both include a rule, determining using the continuous function the rule can be improved, training the rule, and the training is agnostic to the accuracy of the rule; this training process is disclosed as being further mathematical calculations (see spec. para. 0083-91 as filed). Claims 6-7 and 16-17 recite determining, during training, a firing strength for each rule and each parameter of each rule, and then determining a contribution of a rule to an outcome based on the determined firing strengths, which are mathematical calculations (see spec. para. 0081-87, 0106-09). Claims 8 and 18 recite iteratively training using gradient descent to find a local minimum or maximum to determine the ruleset, which are mathematical calculations. Claims 9 and 19 recite the piecewise categorizing function defines low, medium, and high membership functions, which are mathematical formulas (see spec. para. 0069 as filed).
With respect to dependent claims 10 and 20, these claims recite the additional element of the input data being ordinal, continuous, or categorical variable data. (2A, prong 2) This additional element does not integrate the abstract idea into a practical application because it is insignificant extra-solution activity because it is mere necessary data gathering for the abstract idea. (2B) This additional element does not amount to significantly more than the abstract idea itself because it is well-understood, routine, and conventional activity, analogous to storing and retrieving information in memory, see MPEP 2106.05(d) citing Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements only add insignificant extra-solution activity that is well-understood, routine, and conventional and mere instructions to apply the exception to the abstract idea.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. These claims recite, “the fuzzy machine learning model is configured to receive input data as each of ordinal variable data, continuous variable data, or categorical variable data.” The “each of” language indicates that all three of ordinal, continuous, and categorical data are required. However, the “or” language indicates that just one of the data types are required. It is unclear if all of the data types or just one of the data types is required. For prior art purposes, the Examiner assumes the broader “or” language was intended.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 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.
Claim(s) 1, 3-11, 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jang (ANFIS : Adaptive-Network-Based Fuzzy Inference System, NPL [13] cited in IDS of 7/12/2024) in view of Maragos et al. (Tropical Geometry and Machine Learning, NPL [W]).
In reference to claim 1, Jang teaches a method for generating and training a fuzzy machine learning model, the method comprising: receiving, by one or more processors, a set of input data for the fuzzy machine learning model (set of fuzzy rules is received, A. Fuzzy If-Then Rules, page 665 and A. ANFIS Architecture, page 670); encoding, by the one or more processors, the set of input data into fuzzy concepts, wherein the fuzzy concepts are representative of approximate logical relationships between variables (rules are turned into nodes in model, A. ANFIS Architecture page 670 and fig. 4, page 668); determining, by one or more processors and based on the fuzzy concepts, a ruleset for the fuzzy machine learning model, wherein rules of the ruleset are based on a piecewise categorizing function (piecewise membership functions are determined for the nodes, A. ANFIS Architecture page 670); and training, by the one or more processors, the ruleset for the fuzzy machine learning model based on the set of input data by: … and generating, based on [a] continuous representation the piecewise categorizing function, a trained fuzzy ruleset (a trained fuzzy inference system is generated by training parameters of the model, B. Hybrid Learning Algorithm, page 670-671; membership function may be continuous, A. ANFIS Architecture, page 670 and C. Fuzzy Inference Systems with Simplified Fuzzy If-Then Rules, page 673).
However, Jang does not explicitly teach approximating, using tropical geometry, a continuous representation of the piecewise categorizing function; and generating, based on at least the continuous representation of the piecewise categorizing function, a trained fuzzy ruleset.
Maragos teaches approximating, using tropical geometry, a continuous representation of the piecewise categorizing function (see VII. Tropical Regression, pages 747-751, showing how to calculate a continuous representation of piecewise functions using tropical geometry).
It would have been obvious to one of ordinary skill in art, having the teachings of Jang and Maragos before the earliest effective filing date, to modify the piecewise functions of Jang to include the continuous function of Maragos.
One of ordinary skill in the art would have been motivated to modify the piecewise functions of Jang to include the continuous function of Maragos because it can make parameter estimation for fitting data easier (Maragos, A. PWL Function Representation and Data Fitting, pages 747-748).
In reference to claim 3, Jang teaches the method of claim 1, wherein the set of input data for the fuzzy machine learning model includes at least one rule, and wherein the ruleset for the fuzzy machine learning model includes the at least one rule (A. ANFIS Architecture, page 670).
In reference to claim 4, Jang teaches the method of claim 3, wherein generating the trained fuzzy ruleset includes: determining, based on at least the continuous representation of the piecewise categorizing function, the at least one rule can be improved; and training the at least one rule (forward pass determines parameters of rule can be improved, and backward pass trains the rules, B. Hybrid Learning Algorithm, page 670-671).
In reference to claim 5, Jang teaches the method of claim 4, wherein training the ruleset is agnostic to an accuracy of the at least one rule (training is agnostic to the accuracy of rules in that the rules are adapted to fit the training data, B. Hybrid Learning Algorithm, page 670-671).
In reference to claim 6, Jang teaches the method of claim 1, further comprising: determining, responsive to training the ruleset, a firing strength for each parameter of at least one rule of the trained fuzzy ruleset; and determining, responsive to training the ruleset, a firing strength for each rule of the trained fuzzy ruleset (firing strengths of each parameter and rule are trained, A. ANFIS Architecture and B. Hybrid Learning Algorithm, page 670-671).
In reference to claim 7, Jang teaches the method of claim 6, further comprising: determining, responsive to determining the firing strength of each parameter and the firing strength of each rule, the contribution of each rule to one or more outcome classes (firing strength of rule shows contribution to final output, A. Fuzzy Inference Systems, pages 665-666).
In reference to claim 8, Jang teaches the method of claim 1, wherein generating the trained fuzzy ruleset includes: iteratively training one or more parameters of the ruleset to find a local minimum output and/or a local maximum output of the ruleset using a gradient descent algorithm; and generating the trained fuzzy ruleset based on the local minimum output and/or the local maximum output of the ruleset (training performed through iterations of gradient descent, B. Hybrid Learning Algorithm, page 670-671).
In reference to claim 9, Jang teaches the method of claim 1, wherein the piecewise categorizing function defines whether a received variable follows a high membership function, a medium membership function, or a low membership function (membership function can be small, large, page 670, medium, page 673).
In reference to claim 10, Jang teaches the method of claim 1, wherein the fuzzy machine learning model is configured to receive input data as each of ordinal variable data, continuous variable data, or categorical variable data (continuous time series data, pages 679-682).
In reference to claim 11, this claim is directed to a system associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 13, this claim is directed to a system associated with the method claimed in claim 3 and is therefore rejected under a similar rationale.
In reference to claim 14, this claim is directed to a system associated with the method claimed in claim 4 and is therefore rejected under a similar rationale.
In reference to claim 15, this claim is directed to a system associated with the method claimed in claim 5 and is therefore rejected under a similar rationale.
In reference to claim 16, this claim is directed to a system associated with the method claimed in claim 6 and is therefore rejected under a similar rationale.
In reference to claim 17, this claim is directed to a system associated with the method claimed in claim 7 and is therefore rejected under a similar rationale.
In reference to claim 18, this claim is directed to a system associated with the method claimed in claim 8 and is therefore rejected under a similar rationale.
In reference to claim 19, this claim is directed to a system associated with the method claimed in claim 9 and is therefore rejected under a similar rationale.
In reference to claim 20, this claim is directed to a system associated with the method claimed in claim 10 and is therefore rejected under a similar rationale.
Claim(s) 2 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jang (ANFIS : Adaptive-Network-Based Fuzzy Inference System, NPL [13] cited in IDS of 7/12/2024) in view of Maragos et al. (Tropical Geometry and Machine Learning, NPL [W]) as applied to claims 1 and 11 above, and in further view of Destercke et al. (Building an interpretable fuzzy rule base from data using Orthogonal Least Squares—Application to a depollution problem, NPL [U]).
In reference to claim 2, Jang and Maragos do not explicitly teach the method of claim 1, wherein generating the trained fuzzy ruleset includes: determining, based on at least the continuous representation of the piecewise categorizing function, a distance matrix, wherein the distance matrix is representative of similarity between one or more rules of the ruleset, determining, based on the distance matrix, clusters of rules for the one or more rules of the ruleset, and generating the trained fuzzy ruleset by determining representative rules from each cluster of the clusters of rules.
Destercke teaches the method of claim 1, wherein generating the trained fuzzy ruleset includes: determining, based on at least the continuous representation of the piecewise categorizing function, a distance matrix, wherein the distance matrix is representative of similarity between one or more rules of the ruleset, determining, based on the distance matrix, clusters of rules for the one or more rules of the ruleset, and generating the trained fuzzy ruleset by determining representative rules from each cluster of the clusters of rules (see method on pages 2080-2085 and particularly fig. 3 on page 2083: a modified orthogonal least squares algorithm is used to first initialize N rules, one for each pair of data points, then a NxN matrix is used to cluster the data points using k-means clustering and select a number of representative rules).
It would have been obvious to one of ordinary skill in art, having the teachings of Jang, Maragos, and Destercke before the earliest effective filing date, to modify the ruleset generation of Jang to include the distance matrix of Destercke.
One of ordinary skill in the art would have been motivated to modify the ruleset generation of Jang to include the distance matrix of Destercke because it can help automatically build interpretable rules for a fuzzy model (Destercke, 1. Introduction, pages 2078-79).
In reference to claim 12, this claim is directed to a system associated with the method claimed in claim 2 and is therefore rejected under a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Notice of References Cited: [A]-[D] and [X] each generally teach fuzzy inferencing and fuzzy neural networks; [V] which teaches using tropical geometry for fuzzy control.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
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