NON-FINAL REJECTION, FIRST DETAILED ACTION
Status of Prosecution
The present application, 18/606,802 filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The application was filed in the Office on March 15, 2024 and is a continuation of PCT/JP21/35299 filed September 27, 2021.
Claims 1-18 are pending and are rejected. Claims 1, 7 and 13 are independent.
Status of Claims
Claims 1-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 17 is objected to.
Claims 1-5, 7-11 and 13-17 are rejected under 35 U.S.C. §103 as being unpatentable over non-patent literature, Ming et al. (“Ming”), “RuleMatrix: Visualizing and Understanding Classifiers with Rules,” published in 2019 in view of non-patent literature, Vilone et al. (“Vilone”), “Explainable Artificial Intelligence: A Systematic Review,” published in 2020.
Claims 6, 12 and 18 are rejected under 35 U.S.C. §103 as being unpatentable over Ming in view of Vilone and in further view of non-patent literature, Carmona et al. (“Carmona”), “FRIwE: Fuzzy Rule Identification With Exceptions,” published in 2004.
Claim Objection – Multiple Dependency
Claim 17 is objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim is recited, claiming “The machine learning explanation method according to any one of claim [sic] 13 to claim 16…”. See MPEP § 608.01(n). Accordingly, the claim has not been further treated on the merits.
Claim Rejections – 35 USC § 101 – Subject Matter Eligibility
Claims 1-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding representative claim 1, at step 1, the claim recites a non-transitory computer readable medium, and therefore is a maufacture, which is a statutory category of invention. See MPEP § 2106.03.
At step 2A, prong one, the claim recites a system that is capable of making assessments.
The following limitations are the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C):
generating, based on pieces of training data used for training of a machine learning model, a first plurality of rules that each include a condition and a conclusion for a case where the condition is satisfied; when a first plurality of pieces of data that satisfy a first condition included in a first rule of the first plurality of rules among the pieces of training data and a second plurality of pieces of data that satisfy at least one of a plurality of conditions included in a second plurality of rules of the first plurality of rules among the pieces of training data agree, selecting one or a plurality of rules from the second plurality of rules based on a result of comparison between a value that indicates a probability of satisfaction of the first rule based on the pieces of training data and a plurality of values that indicate respective probabilities of satisfaction of the second plurality of rules.
At step 2A prong 2, the claim language is analyzed to determine whether it recites additional elements that integrate the judicial exception into a practical application. See MPEP § 2106.04(d).
The limitation:
outputting, for an inference result of the machine learning model, explanatory information that includes the first rule and another rule other than the one or a plurality of rules among the second plurality of rules.
This that are related to display information which is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Next, at step 2B of the analysis, the claim is considered if it recites additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The additional element of a user interface for displaying information is one the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
Therefore, claim 1 is ineligible.
As to dependent claims 2-4, the analysis of the respective parent claim is incorporated. In the step 2A, prong one analysis, the additional limitations deal with calculations and values of probability of satisfaction are all the abstract idea of a mathematical calculation or are mental processes. See MPEP § 2106.04(a)(2).
The claims are also ineligible.
As to dependent claim 5-6, the analysis of the respective parent claim is incorporated. In the step 2A, prong two analysis, the additional limitation deals with the presentation of additional information on a display. Similarly to claim 1, the additional element of a display is one the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
The claims are also ineligible.
As to independent claims 7 and 13, they are rejected for similar reasons as claim 1. Their dependent claims are rejected similarly to their corresponding dependent claims.
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.
A.
Claims 1-5, 7-11 and 13-17 are rejected under 35 U.S.C. §103 as being unpatentable over non-patent literature, Ming et al. (“Ming”), “RuleMatrix: Visualizing and Understanding Classifiers with Rules,” published in 2019 in view of non-patent literature, Vilone et al. (“Vilone”), “Explainable Artificial Intelligence: A Systematic Review,” published in 2020.
As to Claim 1, Ming teaches: A non-transitory computer-readable recording medium storing a machine learning explanation program for causing a computer to execute processing comprising:
generating, based on pieces of training data used for training of a machine learning model (Ming: Fig. 2, the original model), a first plurality of rules that each include a condition and a conclusion for a case where the condition is satisfied (Ming: Sec. 5.1.1, a decision rule is a logical statement consisting of an antecedent (i.e. condition) and a consequent (i.e. a conclusion));
selecting one first rule based on the pieces of training data and a plurality of values that indicate respective probabilities of satisfaction of the of rules (Ming: Sec. 5.2.1, the filtering of the rules allows for a rule to be selected and the ; and
outputting, for an inference result of the machine learning model, explanatory information that includes the first rule and another rule other than the one or a plurality of rules among the second plurality of rules (Ming: Fig. 2, Sec. 5 the visual interface displays explanatory information related to the rules).
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Ming may not explicitly teach: when a first plurality of pieces of data that satisfy a first condition included in a first rule of the first plurality of rules among the pieces of training data and a second plurality of pieces of data that satisfy at least one of a plurality of conditions included in a second plurality of rules of the first plurality of rules among the pieces of training data agree, selecting one or a plurality of rules from the second plurality of rules based on a result of comparison between a value that indicates a probability of satisfaction of the first rule based on the pieces of training data and a plurality of values that indicate respective probabilities of satisfaction of the second plurality of rules.
Vilone teaches in general concepts related to explainable artificial intelligence (XAI)’s development and growth (Vilone: Abstract). Specifically, the Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) model is one that is a fuzzy rule-based model engineered to classify genetic expression from microarray technologies (Vilone: p. 36). Generated rules are checked to determine redundant and inconsistent rules (Vilone: p. 36).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Ming disclosures and teachings by comparing various rules appropriately to determine which may likely satisfy similarity (satisfaction of a ule based on respective probabilities) as taught and suggested by Violone. Such a person would have been motivated to do so with a reasonable expectation of success to allow for optimizing performance of the models.
As to Claim 2, Ming and Vilone teach the elements of claim 1.
Ming and Vilone may not explicitly teach: wherein the selecting of the one or a plurality of rules is executed when a number of the second plurality of rules is larger than a value obtained by adding a predetermined value to a number of patterns of values that indicate a probability of satisfaction of the first rule and each of the second plurality of rules.
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have further modified the Ming and Vilone disclosures and teachings by using a threshold to determine that there is a similarity between two rules by comparing the sum. Such a person would have been motivated to do so with a reasonable expectation of success to allow for optimizing performance of the models.
As to Claim 3, Ming and Vilone teach the elements of claim 1.
Ming and Vilone may not explicitly teach: wherein the selecting of the one or a plurality of rules includes, when a difference between a value that indicates a probability of satisfaction of the first rule and a value that indicates a probability of satisfaction of a second rule included in the second plurality of rules is less than a predetermined threshold, selecting the one or a plurality of rules that include the second rule.
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have further modified the Ming and Vilone disclosures and teachings by using a threshold to determine that there is a similarity between two rules by comparing the difference. Such a person would have been motivated to do so with a reasonable expectation of success to allow for optimizing performance of the models.
As to Claim 4, Ming and Vilone teach the elements of claim 1.
Ming and Vilone may not explicitly teach: wherein the value that indicates a probability of satisfaction is a value based on, among the pieces of training data, a number of pieces of training data that satisfy a condition included in a rule and a number of pieces of training data that satisfy a condition included in a rule and in which a conclusion included in a rule is a predetermined conclusion.
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have further modified the Ming and Vilone disclosures and teachings by using a threshold to determine that there is a similarity between two rules by comparing against a predetermine conclusion. Such a person would have been motivated to do so with a reasonable expectation of success to allow for optimizing performance of the models.
As to Claim 5, Ming and Vilone teach the elements of claim 1.
Ming further teaches: wherein the explanatory information includes values that indicate a probability of satisfaction of each of the first rule and the another rule included in the explanatory information (Ming: Fig. 2, Sec. 5 the visual interface displays explanatory information related to the rules, including the likelihood of the rules).
As to Claim 7, it is rejected for similar reasons as claim 1.
As to Claim 8, it is rejected for similar reasons as claim 2.
As to Claim 9, it is rejected for similar reasons as claim 3.
As to Claim 10, it is rejected for similar reasons as claim 4.
As to Claim 11, it is rejected for similar reasons as claim 5.
As to Claim 13, it is rejected for similar reasons as claim 1.
As to Claim 14, it is rejected for similar reasons as claim 2.
As to Claim 15, it is rejected for similar reasons as claim 3.
As to Claim 16, it is rejected for similar reasons as claim 4.
As to Claim 17, it is rejected for similar reasons as claim 5
B.
Claims 6, 12 and 18 are rejected under 35 U.S.C. §103 as being unpatentable over non-patent literature, Ming et al. (“Ming”), “RuleMatrix: Visualizing and Understanding Classifiers with Rules,” published in 2019 in view of non-patent literature, Vilone et al. (“Vilone”), “Explainable Artificial Intelligence: A Systematic Review,” published in 2020 and in further view of non-patent literature, Carmona et al. (“Carmona”), “FRIwE: Fuzzy Rule Identification With Exceptions,” published in 2004.
As to Claim 6, Ming and Vilone teach the elements of claim 1.
Ming and Vilone may not explicitly teach: wherein the outputting of the explanatory information includes outputting the first rule as a rule of principle and outputting the another rule as a rule of exception.
Carmona in general discusses identifying fuzzy models from examples and to also simplify rules by reducing, merging and exceptions to the rules in models (Carmona: Abstract). Exceptions may be determined and added to a resulting compound rule (Sec. III, Algorithm Step. 1.6).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have further modified the Ming and Vilone disclosures and teachings by applying an exception in the rule simplification as suggested and taught by Carmona. Such a person would have been motivated to do so with a reasonable expectation of success to allow for optimizing performance of the models.
As to Claim 12, it is rejected for similar reasons as claim 6.
As to Claim 18, it is rejected for similar reasons as claim 6.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES T TSAI whose telephone number is (571)270-3916. The examiner can normally be reached M-F 8-5 Eastern.
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/JAMES T TSAI/ Primary Examiner, Art Unit 2147
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