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 .
Status of Claims
Claims 1-15 are currently pending and have been examined.
Claims 1-15 have been amended.
Claims 1-15 have been rejected.
Priority and Formal Matters
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed for Application No. EP23305938.5 on 14 Nov. 2024.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 13 June 2023 claiming benefit to EP23305938.5.
The preliminary amendments to the claims, received on 13 June 2023 have been received and are accepted.
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 1-15 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.
Step 1 – Statutory Categories of Invention:
Claims 1-15 are drawn to a method or device, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method for constructing a decision tree to diagnose a system.
Independent claim 13 recites a device for contrasting a decision tree to diagnose a system.
Independent claim 15 recites a computer-readable recording medium for constructing a decision tree to diagnose a system.
These independent claims recite the following steps best characterized as a mental process under MPEP § 2106.04(a)(2)(III) citing the abstract idea grouping for mental processes in general:
obtaining a training data set comprising pairs, wherein one pair of said pairs comprising
a vector of measured values of observable variables representing an operation of the system, and an associated label wherein the associated label belongs to a group of labels comprising
a label representing a nominal operating state of said system, and a plurality of labels each representing a failure state [of said system]
each failure state of each label of said labels associated with at least one component of said plurality of components [of said system]
processing a current node [of the decision tree] associated with a subset of the training data set comprising a current data set, derived from the training data set, said processing comprising, when at least one splitting criterion is satisfied
splitting the current node into a first child node and a second child node by applying a classification function obtained from the current data set and defined to associate with a plurality of said observable variables
a first class as a nominal class, representing the nominal operating state of the system or
a second class as a failure class, representing said failure state of said system, and
classifying the pairs of the current data set comprising a first label of the group of labels in the nominal class and the pairs of the current data set comprising a second label of the group of labels in the failure class, and
propagating said pairs [that are] classified in the nominal class in a first data subset of the first child node and said pairs classified in the failure class in a second data subset of the second child node, and
providing a decision tree to diagnose said system
Under the broadest reasonable interpretation of the limitations, these limitations are best characterized as applying a mental process to a generic computing environment - see MPEP § 2106.04(a)(2)(III)(c)(2).
Dependent claim 2 recites, in part, wherein said at least one splitting criterion comprises an impurity criterion and said method further comprises checking the impurity criterion, comprising determining a ratio between a number of pairs in the current data set associated with a given label in the group of labels and a total number of pairs in said current data set, the impurity criterion being checked when the ratio is below a given purity threshold.
Dependent claim 3 recites, in part, wherein the processing of the current node further comprises, when the impurity criterion is satisfied, selecting the first label and the second label in the group of labels, as a label pair, the first label and the second label being distinct and represented in the pairs of the current data set, determining a search data set using at least some of the current data set and based on the label pair that is selected, and searching for the classification function using symbolic classification based on a given set of operators and the pairs in the search data set.
Dependent claim 4 recites, in part, wherein said at least one splitting criterion comprises a classification performance criterion of the classification function obtained, and in that the method further comprises, prior to said splitting, verifying said classification performance criterion, said verifying comprising determining a first ratio between a number of pairs in the current data set comprising the label representing the nominal operating state classified by the classification function in the nominal class out of a total number of pairs in the current data set comprising said label, determining a second ratio between a number of pairs in the search data set classified by the classification function in a class from the nominal class and the failure class which corresponds to their label and a total number of pairs in the search data set, and comparing the first ratio and the second ratio respectively with a first given threshold and a second given threshold, the classification performance criterion being verified when the first given threshold and the second given threshold are crossed.
Dependent claim 5 recites, in part, wherein, after said splitting the current node and as long as at least one next node remains unprocessed according to a given sequence of the decision tree, the method further comprises iterating the processing for the at least one next node.
Dependent claim 6 recites, in part, wherein, when the classification performance criterion has not been verified, the processing of the current node further comprises selecting a new pair of labels as long as there remains one pair of labels not yet selected in the current data set.
Dependent claim 7 recites, in part, wherein, when the current data set comprises pairs comprising the label representing the nominal operating state, the label pair that is selected comprises said label as the first label and a label representing a fault state as the second label.
Dependent claim 8 recites, in part, wherein the search data set comprises all of the pairs in the current data set comprising the label, from the first label and the second label, which is least represented in number in the current data set, and as many pairs of the current data set comprising another label thereof.
Dependent claim 9 recites, in part, when the current data set does not comprise any pairs comprising the label representing the nominal operating state, the current data set comprising a first number of pairs comprising the first label and a second number of pairs comprising the second label, said first number of pairs being greater than the second number of pairs, the search data set is formed of a third number of pairs comprising the first label, less than or equal to the second number of pairs, the second number of pairs and a fourth number of pairs comprising the label representing the nominal operating state of the system, the fourth number being equal to a difference between the second number of pairs and the third number of pairs.
Dependent claim 10 recites, in part, wherein the searching for the classification function by symbolic classification comprises implementation of a genetic algorithm that comprises randomly generating a plurality of candidate functions associating an actual classification value with several of said observable variables, for each candidate function that is generated, evaluating the each candidate function comprising applying said each candidate function to the search data set, for the actual classification value obtained, and applying a transformation function to said actual classification value of said each candidate function, with binary transformed values being obtained, and determining a fitness score for the each candidate function of the search data set from said binary transformed values, selecting at least one candidate function from the plurality of candidate functions generated using the fitness score, said at least one candidate function being associated with at least one best fitness score according to a given criterion, mutating the at least one candidate function that is selected and iterating the evaluating and the selecting on at least one candidate function that is mutated, until at least one stopping criterion is not satisfied.
Dependent claim 11 recites, in part, wherein said at least one stopping criterion comprises at least determining a stagnation of the fitness score during a given number of iterations, and a maximum number of iterations of the evaluating and the selecting that reached.
Dependent claim 12 recites, in part, obtaining said measured values of said observable variables representing an operation of said system via sensors, said vector comprising the measured values of said observable variables being formed, applying to said vector said decision tree that is constructed, said applying comprising propagating the vector in the decision tree up to an unsplit node comprising a leaf node, said leaf node being associated with at least one label belonging to the group of labels comprising the label representing the nominal operating state of the system and said plurality of labels each representing the failure state of the system, providing a diagnosis result, comprising said at least one label associated with the leaf node of the decision tree comprising said vector.
Dependent claim 14 recites, in part, the device according to claim 13, apply to said vector said decision tree that is constructed, wherein said apply comprises propagating the vector in the decision tree up to an unsplit node comprising a leaf node, said leaf node being associated with the label belonging to the group comprising the label representing the nominal operating state of the system and the plurality of labels each representing the failure state of said one component of the system, provide a diagnosis result, comprising said at least one label associated with the leaf node of the decision tree comprising said vector.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 13 accordingly, and hence are nonetheless directed towards fundamentally the same mental process abstract idea grouping as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 13 recites a device comprising at least one memory and at least one processor. The specification provides that the instant claims may implemented by hardware (for example one or more electronic circuits, and/or any other hardware component) in the Detailed Description in ¶ 0147-150. The use of a device comprising at least one memory and at least one processor, in this case to construct a decision tree to diagnose a system, only recites the device comprising at least one memory and at least one processor as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claim 14 recites a non-transitory computer-readable recording medium on which is stored a computer program product. The specification provides that the instant claims may implemented by hardware (for example one or more electronic circuits, and/or any other hardware component) in the Detailed Description in ¶ 0147-150. The use of a non-transitory computer-readable recording medium on which is stored a computer program product, in this case to construct a decision tree to diagnose a system, only recites the non-transitory computer-readable recording medium on which is stored a computer program product as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 13 recites a device comprising at least one memory and at least one processor. Claim 14 recites a non-transitory computer-readable recording medium on which is stored a computer program product.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-15 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 5, 12-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hiruta et al. (US Patent App No 20190370668)[hereinafter Hiruta].
Claim 1 is rejected because Hiruta teaches on all elements of the claim:
a method of constructing a decision tree to diagnose a system comprising a plurality of components, said method comprising is taught in the Detailed Description in ¶ 0067-68 and ¶ 0076 (teaching on a computer implemented method for training a decision tree model to classify abnormal time between failure (TBF) durations for equipment)
obtaining a training data set comprising pairs, wherein one pair of said pairs comprising a vector of measured values of observable variables representing an operation of the system, and an associated label wherein the associated label belongs to a group of labels comprising a label representing a nominal operating state of said system, and is taught in the Detailed Description in ¶ 0068-69 and ¶ 0077-78 (teaching on training a machine learning decision tree classification model utilizing a labeled historical training set of sensor value features corresponding to normal TBF values)
a plurality of labels each representing a failure state of said system, each failure state of each label of said labels associated with at least one component of said plurality of components of said system is taught in the Detailed Description in ¶ 0068-69 and ¶ 0077-78 (teaching on training a machine learning decision tree classification model utilizing a labeled historical training set of sensor value features corresponding abnormal TBF values assigned to each sensor (sensor A, B, etc.))
processing a current node of the decision tree associated with a subset of the training data set comprising a current data set, derived from the training data set, said processing comprising, when at least one splitting criterion is satisfied is taught in the Detailed Description in ¶ 0069 (teaching on generating the decision tree via node splitting conditions)
when at least one splitting criterion is satisfied, splitting the current node into a first child node and a second child node by applying a classification function obtained from the current data set and defined to associate with a plurality of said observable variables is taught in the Detailed Description in ¶ 0070-73 and ¶ 0077-78 (teaching on splitting the node into two children nodes by applying a node termination algorithm for classifying the new observed sensor data)
a first class as a nominal class, representing the nominal operating state of the system or a second class as a failure class, representing said failure state of said system, and classifying the pairs of the current data set comprising a first label of the group of labels in the nominal class and the pairs of the current data set comprising a second label of the group of labels in the failure class, and is taught in the Detailed Description in ¶ 0068-69 and ¶ 0077-78 (teaching on classifying the sensor operation as normal TBF values and abnormal TBF values assigned to each sensor (sensor A, B, etc.))
propagating said pairs classified in the nominal class in a first data subset of the first child node and said pairs classified in the failure class in a second data subset of the second child node, and is taught in the Detailed Description in ¶ 0070-75 and ¶ 0077-78 (teaching on generating a data path for each sensor wherein the propagation of the nodes to a terminal leaf represents the normal TBF and abnormal TBF classification parameter ranges)
providing a decision tree to diagnose said system is taught in the Detailed Description in ¶ 0076 (teaching on providing the decision tree for a plurality of sensors/operating parameter variables for a system to determine operational status)
Independent claims 13 and 15 are rejected under the same rational.
As per claim 2, Hiruta discloses all of the limitations of claim I#. Hiruta also discloses the following:
the method of constructing a decision tree according to claim 1, wherein said at least one splitting criterion comprises an impurity criterion and said method further comprises checking the impurity criterion, comprising determining a ratio between a number of pairs in the current data set associated with a given label in the group of labels and a total number of pairs in said current data set, the impurity criterion being checked when the ratio is below a given purity threshold is taught in the Detailed Description in ¶ 0068-69 (teaching on utilizing a Gini Index impurity criterion for data partitioning wherein a Gini Index is a probability (treated as synonymous to a ratio) of a cleanly split class between abnormal and normal values)
As per claim 5, Hiruta discloses all of the limitations of claim I#. Hiruta also discloses the following:
the method of constructing a decision tree according to claim 1, wherein, after said splitting the current node and as long as at least one next node remains unprocessed according to a given sequence of the decision tree, the method further comprises iterating the processing for the at least one next node is taught in the Detailed Description in ¶ 0070-75 and ¶ 0077-78 (teaching on generating a data path for each sensor wherein the propagation of the nodes to a terminal leaf represents the normal TBF and abnormal TBF classification parameter ranges)
As per claim 12, Hiruta discloses all of the limitations of claim I#. Hiruta also discloses the following:
the method of constructing a decision tree according to claim 1, further comprising obtaining said measured values of said observable variables representing an operation of said system via sensors, said vector comprising the measured values of said observable variables being formed, applying to said vector said decision tree that is constructed, said applying comprising propagating the vector in the decision tree up to an unsplit node comprising a leaf node, said leaf node being associated with at least one label belonging to the group of labels comprising the label representing the nominal operating state of the system and said plurality of labels each representing the failure state of the system is taught in the Detailed Description in ¶ 0070-75 and ¶ 0077-78 (teaching on generating a data path for each sensor wherein the propagation of the nodes to a terminal leaf represents the normal TBF and abnormal TBF classification parameter ranges)
providing a diagnosis result, comprising said at least one label associated with the leaf node of the decision tree comprising said vector is taught in the Detailed Description in ¶ 0076 (teaching on providing the decision tree for a plurality of sensors/operating parameter variables for a system to determine operational status)
Dependent claim 14 is rejected under the same rational.
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.
Claims 3, 4, 6-11 are rejected under 35 U.S.C. 103 as being unpatentable over Hiruta et al. (US Patent App No 20190370668)[hereinafter Hiruta] in view of Randerath et al. (US Patent Application No. 2014/0163812)[hereinafter Randerath].
As per claim 3, Hiruta discloses all of the limitations of claim 1. Hiruta also discloses the following:
the method of constructing a decision tree according to claim 2, wherein the processing of the current node further comprises, when the impurity criterion is satisfied, selecting the first label and the second label in the group of labels, as a label pair, the first label and the second label being distinct and represented in the pairs of the current data set, determining a search data set using at least some of the current data set and based on the label pair that is selected, and is taught in the Detailed Description in ¶ 0068-69 (teaching on utilizing a Gini Index impurity criterion for data partitioning wherein a Gini Index is a probability (treated as synonymous to a ratio) of a cleanly split class between abnormal and normal values wherein a Gini index necessarily tests pairs of features and for every pair, it finds the Gini score of the resulting groups)
Hiruta fails to teach the following limitation of claim 1. Randerath, however, does teach the following:
searching for the classification function using symbolic classification based on a given set of operators and the pairs in the search data set is taught in the Detailed Description in ¶ 0076-79 (teaching on parameter optimization utilizing a genetic algorithm to determine a break-off criterion for the decision tree)
One of ordinary skill in the art would combine the Gini Index node impurity measure of Hiruta with genetic algorithm search classification of Randerath with the motivation of applying the “simplest evolutionary optimization method[] which can also be implemented very quickly and can be adapted to new problems” (Randerath in the Detailed Description in ¶ 0077).
As per claim 4, the combination of Hiruta and Randerath discloses all of the limitations of claim 3. Hiruta also discloses the following:
the method of constructing a decision tree according to claim 3, wherein said at least one splitting criterion comprises a classification performance criterion of the classification function obtained, and in that the method further comprises, prior to said splitting, verifying said classification performance criterion, said verifying comprising determining a first ratio between a number of pairs in the current data set comprising the label representing the nominal operating state classified by the classification function in the nominal class out of a total number of pairs in the current data set comprising said label is taught in the Detailed Description in ¶ 0074-75 and ¶ 0083 (teaching on calculating a R score performance index for each segment and ranking the scores wherein higher scores R scores indicate more important or otherwise influential operating parameters in the system )
determining a second ratio between a number of pairs in the search data set classified by the classification function in a class from the nominal class and the failure class which corresponds to their label and a total number of pairs in the search data set, and comparing the first ratio and the second ratio respectively with a first given threshold and a second given threshold, the classification performance criterion being verified when the first given threshold and the second given threshold are crossed is taught in the Detailed Description in ¶ 0074-75 and ¶ 0083 (teaching on calculating a R score performance index for each segment/sensors (treated as synonymous the first and second pairs) and ranking the scores wherein higher R scores (treated as synonymous to a threshold) indicate more important or otherwise influential operating parameters in the system )
As per claim 6, the combination of Hiruta and Randerath discloses all of the limitations of claim 4. Hiruta also discloses the following:
the method of constructing a decision tree according to claim 4, wherein, when the classification performance criterion has not been verified, the processing of the current node further comprises selecting a new pair of labels as long as there remains one pair of labels not yet selected in the current data set is taught in the Detailed Description in ¶ 0074-75 and ¶ 0083 (teaching on calculating a R score performance index for each segment - the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (see MPEP 2111.04(II) on contingent limitations). Because verification condition for performing the new pair selection contingent step is not satisfied, the performance recited by the step need not be carried out in order for the claimed method to be performed)
As per claim 7, the combination of Hiruta and Randerath discloses all of the limitations of claim 3. Hiruta also discloses the following:
the method of constructing a decision tree according to claim 3, wherein, when the current data set comprises pairs comprising the label representing the nominal operating state, the label pair that is selected comprises said label as the first label and a label representing a fault state as the second label is taught in the Detailed Description in ¶ 0068-69 and ¶ 0077-78 (teaching on training a machine learning decision tree classification model utilizing a labeled historical training set of sensor value features corresponding to normal TBF values and abnormal TBF values utilizing a Gini index wherein a Gini index necessarily tests pairs of features and for every pair, it finds the Gini score of the resulting groups)
As per claim 8, the combination of Hiruta and Randerath discloses all of the limitations of claim 7. Hiruta also discloses the following:
the method of constructing a decision tree according to claim 7, wherein the search data set comprises all of the pairs in the current data set comprising the label, from the first label and the second label, which is least represented in number in the current data set, and as many pairs of the current data set comprising another label thereof is taught in the Detailed Description in ¶ 0068-69 and ¶ 0077-78 (teaching on training a machine learning decision tree classification model utilizing a labeled historical training set of sensor value features corresponding to normal TBF values and abnormal TBF values utilizing a Gini index wherein a Gini index necessarily tests pairs of features and for every pair, it finds the Gini score of the resulting groups and picks the feature-and-threshold pair that gives the lowest total Gini impurity score)
As per claim 9, the combination of Hiruta and Randerath discloses all of the limitations of claim 3. Hiruta also discloses the following:
the method of constructing a decision tree according to claim 3, wherein, when the current data set does not comprise any pairs comprising the label representing the nominal operating state, the current data set comprising a first number of pairs comprising the first label and a second number of pairs comprising the second label, said first number of pairs being greater than the second number of pairs, the search data set is formed of a third number of pairs comprising the first label, less than or equal to the second number of pairs, the second number of pairs and a fourth number of pairs comprising the label representing the nominal operating state of the system, the fourth number being equal to a difference between the second number of pairs and the third number of pairs is taught in the Detailed Description in ¶ 0068-69 (teaching on utilizing a Gini Index impurity criterion for data partitioning - the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (see MPEP 2111.04(II) on contingent limitations). Because no nominal operating state pair condition for performing the new pair selection contingent step is not satisfied, the performance recited by the step need not be carried out in order for the claimed method to be performed)
As per claim 10, the combination of Hiruta and Randerath discloses all of the limitations of claim 3. Hiruta fails to teach the following; Randerath, however, does disclose:
the method of constructing a decision tree according to claim 3, wherein the searching for the classification function by symbolic classification comprises implementation of a genetic algorithm that comprises randomly generating a plurality of candidate functions associating an actual classification value with several of said observable variables, for each candidate function that is generated, evaluating the each candidate function comprising applying said each candidate function to the search data set, for the actual classification value obtained, and applying a transformation function to said actual classification value of said each candidate function, with binary transformed values being obtained, and determining a fitness score for the each candidate function of the search data set from said binary transformed values, selecting at least one candidate function from the plurality of candidate functions generated using the fitness score, said at least one candidate function being associated with at least one best fitness score according to a given criterion, mutating the at least one candidate function that is selected and iterating the evaluating and the selecting on at least one candidate function that is mutated, until at least one stopping criterion is not satisfied is taught in the Detailed Description in ¶ 0076-79 (teaching on parameter optimization utilizing a genetic algorithm to determine a break-off criterion for the decision tree)
One of ordinary skill in the art would combine the Gini Index node impurity measure of Hiruta with genetic algorithm search classification of Randerath with the motivation of applying the “simplest evolutionary optimization method[] which can also be implemented very quickly and can be adapted to new problems” (Randerath in the Detailed Description in ¶ 0077).
As per claim 11, the combination of Hiruta and Randerath discloses all of the limitations of claim 10. Hiruta also discloses the following:
the method of constructing a decision tree according to claim 10, wherein said at least one stopping criterion comprises at least determining a stagnation of the fitness score during a given number of iterations, and a maximum number of iterations of the evaluating and the selecting that reached is taught in the Detailed Description in ¶ 0070-75 and ¶ 0077-78 (teaching on generating a data path for each sensor wherein the propagation of the nodes to a terminal leaf according to a maximum r samples represents the normal TBF and abnormal TBF classification parameter ranges)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Barros et al., A Survey of Evolutionary Algorithms for Decision-Tree Induction, 42 IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 291-312 (June 23, 2011) teaching on the state of the art for utilizing evolutionary algorithms to improve particular components of decision-tree classifiers in the § VIII. APPLICATIONS OF EVOLUTIONARY ALGORITHMS FOR DECISION-TREE INDUCTION on p. 306-307
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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