Response to Amendment
The amendment filed on August 11, 2025 has been entered. Claims 1, 14, 20 have been amended. Claims 1-20 are currently pending in the application.
Allowable Subject Matter
Claims 21-26 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
35 U.S.C 101
Regarding independent claims 1, 14, and 20, Applicant argues that the amendments of a “biological sample” recites significantly more than the abstract idea.
Examiner has carefully considered Applicant’s argument and respectfully disagrees. The examiner maintains that a biological sample does not represent an integration into a practical application beyond generally linking the use of the judicial exception to a particular technological environment or field of use. In this scenario, the field of use is a biological field.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Claim 1 recites the following limitations directed to an abstract idea “inputting a learning data set including a known sample group belonging to a given class, which is a target, and a biological feature set of the known biological sample group; and selecting a biological feature group needed for class determination for an unknown biological sample of which a belonging class is unknown, from the biological feature set based on the learning data set, wherein the selection step includes a quantification step of, by a pairwise coupling that combines two classes among the N classes, quantifying a discrimination possibility between the two classes in accordance with each biological feature of the selected biological feature group by using the learning data set, an optimization step of totalizing the quantified discrimination possibilities for all the pairwise couplings and selecting a subset of the biological features for which a result of the totalization is to be optimized, a base class designation step of designating one or more base classes from the N classes in advance in a separate frame, and a totalization step of, for a pairwise coupling of a first class and a second class which do not include the base class among the N classes, further totalizing a discrimination possibility of pairwise between the first class and the base class and a discrimination possibility of pairwise between the second class and the base class for a biological feature having the discrimination possibility quantified in the quantification step, and in the optimization step, a balance step, which is measured by distance of target class from the base class in the biological feature space, of a result of the totalization in the totalization step is evaluated to select a combination of the biological feature groups, thereby suppressing a measurement cost using the biological feature selection method”. These steps describe a mental process that may be performed in the human mind including observing data points and evaluating those observations.
Furthermore the claim does not recite limitations that integration into a practical application or are “significantly more” than the abstract idea because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the user of an abstract idea to a particular technological environment of a biological feature. It should be noted the limitations of the current claims are performed by the generically recited computer/processor. The limitations are merely instructions to implement the abstract idea on a computer and require no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry. Therefore, claim 1 is directed to non-statutory subject matter.
Claims 2-13, which depend on claim 1 and include all the limitations of claim 1, recite the additional elements of “a first marking step of marking a part of the given classes as first discrimination unneeded class groups that do not need to be discriminated from each other, and a first exclusion step of excluding the pairwise coupling of the marked first discrimination unneeded class groups from pairwise couplings to be expanded, wherein a class belonging to the N classes and being designated as a class group that does not need to be discriminated from the base class is excluded from a target of balance selection; a non-transitory, computer-readable tangible recording medium which records thereon a program for causing a computer to execute the feature selection method; an acquisition step of acquiring, based on a feature group selected by using the feature selection method, a feature value of the selected feature group; and a multi-class classification step of performing multi-class classification based on the acquired feature value, which includes a binary-class classification step using a binary- class classifier associated with a pairwise coupling marked in the selection of the feature group, wherein the multi-class classification step further includes a base class designation step of designating one or more base classes from the N classes in advance in a separate frame, and a first evaluation step of, in the binary-class classification step of the base class and a first class which is any class other than the base class, in a case in which a feature of a given sample is close to the first class, performing weighting of the feature such that a case in which a discrimination result of the multi-class classification is the first class is increased; a marking step of marking a part of given classes as discrimination unneeded class groups that do not need to be discriminated from each other; and an exclusion step of excluding the pairwise coupling of the marked discrimination unneeded class groups from pairwise couplings to be expanded, wherein the multi-class classification step is performed by using a class belonging to the N classes and being designated as a class group that does not need to be discriminated as the base class; a reference step of, for a feature having a discrimination possibility in a pairwise coupling for any second class and third class belonging to the N classes, further referring to a discrimination possibility of pairwise of the second class and the base class and a discrimination possibility of pairwise of the third class and the base class; a second evaluation step of, as a result of the reference, for the second class, in a case in which there is the discrimination possibility of the pairwise of the second class and the base class and a value of the feature is close to the second class, performing weighting such that a case in which a discrimination result of the binary-class classification step is the second class is increased; and a third evaluation step of, as a result of the reference, for the third class, in a case in which there is the discrimination possibility of the pairwise of the third class and the base class and a value of the feature is close to the third class, performing weighting such that a case in which a discrimination result of the binary-class classification step is the third class is increased; a configuration step of configuring a multi-class classifier from the binary-class classifier by a target value setting step of setting a target value of a misclassification probability of the sample, a first probability evaluation step of evaluating a first misclassification probability which is a probability in which a sample, which originally belongs to the base class, is misclassified into any different class other than the base class by the weighting, a second probability evaluation step of evaluating a second misclassification probability which is a probability in which a sample, which originally belongs to the different class, is misclassified into the base class, and a weighting adjustment step of adjusting the weighting such that the first misclassification probability and the second misclassification probability fall within the target value or such that deviation amounts of the first misclassification probability and the second misclassification probability from the target value are decreased, wherein, in the multi-class classification step, the multi-class classification is performed by using the configured multi-class classifier; a configuration step of configuring a multi-class classifier from the binary-class classifier by an evaluation parameter setting step of setting a misclassification evaluation parameter which is a part or all of the target value of the misclassification probability of the sample, the number of features having a discrimination possibility for a pairwise coupling of any first class other than the base class and the base class, reliability of the feature, and an assumed defective rate of the feature, and a weighting setting step of setting the weighting within a weighting range calculated by the misclassification evaluation parameter, wherein, in the multi-class classification step, the multi-class classification is performed by using the configured multi-class classifier; wherein, in the weighting setting step, the weighting is set by learning a part or all of the misclassification evaluation parameters from any first learning data set; wherein, in the weighting setting step, the weighting is set such that a performance of the multi-class classification is improved based on any second learning data set; a first warning step of issuing a warning to a user in a case in which an amount of the weighting does not allow a performance of the multi-class classification to fall within a performance target or a second warning step of issuing a warning to the user in a case in which the performance target is predicted to be achievable without performing the weighting; a multi-class classification device that determines, in a case in which N is an integer of 2 or more, which of N classes a sample belongs to, from a feature of the sample, the multi-class classification device comprising: a processor, wherein the processor executes acquisition processing of acquiring, based on a feature group selected by using the feature selection method according to claim 1, a feature value of the selected feature group, and multi-class classification processing of performing multi-class classification based on the acquired feature value, which includes binary-class classification processing using a binary-class classifier associated with a pairwise coupling marked in the selection of the feature group, and the multi-class classification processing further includes base class designation processing of designating one or more base classes from the N classes in advance in a separate frame, and first evaluation processing of, in the binary-class classification processing of the base class and a first class which is any class other than the base class, in a case in which a feature of a given sample is close to the first class, performing weighting of the feature such that a case in which a discrimination result of the multi-class classification is the first class is increased”. These limitations do not amount to significantly more than the abstract idea of a mental process.
Therefore, claims 2-13 are directed to an abstract idea without significantly more.
Claims 14-20 recite similar limitations and are also directed to the abstract idea of a mental process of analyzing information. These claims are rejected using the same rationale used in claims 1-13 above.
Conclusion
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/TONY WU/ Primary Examiner, Art Unit 2166