DETAILED ACTION
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 .
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.
Claims 1-20 are subject to review.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/07/2024 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without reciting significantly more.
Step 1 – is the claim directed to a process, machine, manufacture, or composition of matter?
Claims 1-7 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Claims 8-14 are directed to a “system” which describes one of the four statutory categories of patentable subject matter, i.e., a machine.
Claims 15-20 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Regarding Claim 1
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 1 recites an abstract idea, substantially as follows:
“identifying at least one characteristic to be studied of the learning data;” – is directed to the abstract idea of a mental process i.e., observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)) and may be performed with the aid of pen and paper, or using a computer as a tool.
“identifying ranges of values for each characteristic from the extracted values;” – is directed to the abstract idea of a mental process i.e., observation, evaluation, judgement, opinion are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)) and may be performed with the aid of pen and paper, or using a computer as a tool.
“assigning a class to each cell of the classification table according to a number of occurrences of the learning data of each group according to their extracted value of each characteristic with respect to the ranges of values defined for each studied characteristic; and” – is directed to the abstract idea of a mental process i.e., assigning a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 1 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“obtaining at least one group of learning data, each group of learning data being associated with an indicated class;” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
“extracting a value of each characteristic defined for all learning data;” – is merely selecting a particular data source of type of data to be manipulated, considered an insignificant extra-solution activity (see MPEP 2106.06(g)).
“creating a classification table having a number of dimensions corresponding to the number of studied characteristics, each dimension having a size equal to the number of ranges of values defined for the characteristic associated with this dimension, each cell of the classification table being associated with a range of values of each studied characteristic;” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
“generating the classification model comprising the classification table.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 1 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“obtaining at least one group of learning data, each group of learning data being associated with an indicated class;” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“extracting a value of each characteristic defined for all learning data;” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“creating a classification table having a number of dimensions corresponding to the number of studied characteristics, each dimension having a size equal to the number of ranges of values defined for the characteristic associated with this dimension, each cell of the classification table being associated with a range of values of each studied characteristic;” – the broadest reasonable interpretation of this imitation is found to be merely outputting data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“generating the classification model comprising the classification table.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 2
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 2 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 2 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the classification model further includes a minimum value and a maximum value of each studied characteristic.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 2 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the classification model further includes a minimum value and a maximum value of each studied characteristic.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 3
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 3 recites an abstract idea, substantially as follows:
“wherein the class assigned to a cell of the classification table corresponds to the class of the group of learning data having the largest number of occurrences of learning data over the range of values of each characteristic associated with the cell of the classification table.” – is directed to the abstract idea of a mental process i.e., assigning a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 3 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 3 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 4
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 4 recites an abstract idea, substantially as follows:
“wherein the class assigned to a cell of the classification table corresponds to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is the same and non-zero for each group of learning data.” – is directed to the abstract idea of a mental process i.e., assigning a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 4 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 4 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 5
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 5 recites an abstract idea, substantially as follows:
“wherein the class assigned to a cell of the classification table corresponds to an undetermined class or to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is zero for each group of learning data.” – is directed to the abstract idea of a mental process i.e., assigning a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 5 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 5 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 6
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 6 recites an abstract idea, substantially as follows:
“wherein the ranges of values for each characteristic are set using Sturges’s rule.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing performing a calculation using a formula, which is considered to be a mathematical calculation
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 6 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 6 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 7
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 7 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 7 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“further comprising developing a classification computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the generated classification model.” – is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 7 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“further comprising developing a classification computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the generated classification model.” – is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 8
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 8 recites an abstract idea, substantially as follows:
“identify at least one characteristic to be studied of the learning data;” – is directed to the abstract idea of a mental process i.e., observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)) and may be performed with the aid of pen and paper, or using a computer as a tool.
“identify ranges of values for each characteristic from the extracted values;” – is directed to the abstract idea of a mental process i.e., observation, evaluation, judgement, opinion are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)) and may be performed with the aid of pen and paper, or using a computer as a tool.
“assign a class to each cell of the classification table according to a number of occurrences of the learning data of each group according to their extracted value of each characteristic with respect to the ranges of values defined for each studied characteristic; and” – is directed to the abstract idea of a mental process i.e., assigning a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 8 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“a memory comprising a computer program, the computer program comprising instructions to:” – is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
“obtain at least one group of learning data, each group of learning data being associated with an indicated class;” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
“extract a value of each characteristic defined for all learning data;” – is merely selecting a particular data source of type of data to be manipulated, considered an insignificant extra-solution activity (see MPEP 2106.06(g)).
“create a classification table having a number of dimensions corresponding to the number of studied characteristics, each dimension having a size equal to the number of ranges of values defined for the characteristic associated with this dimension, each cell of the classification table being associated with a range of values of each studied characteristic;” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
“generate the classification model comprising the classification table.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
“a processor configured to execute the computer program.” – is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 8 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“a memory comprising a computer program, the computer program comprising instructions to:” – is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
“obtaining at least one group of learning data, each group of learning data being associated with an indicated class;” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“extracting a value of each characteristic defined for all learning data;” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“creating a classification table having a number of dimensions corresponding to the number of studied characteristics, each dimension having a size equal to the number of ranges of values defined for the characteristic associated with this dimension, each cell of the classification table being associated with a range of values of each studied characteristic;” – the broadest reasonable interpretation of this imitation is found to be merely outputting data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“generating the classification model comprising the classification table.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
“a processor configured to execute the computer program.” – is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 9
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 9 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 9 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the classification model further includes a minimum value and a maximum value of each studied characteristic.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 9 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the classification model further includes a minimum value and a maximum value of each studied characteristic.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 10
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 10 recites an abstract idea, substantially as follows:
“wherein the class assigned to a cell of the classification table corresponds to the class of the group of learning data having the largest number of occurrences of learning data over the range of values of each characteristic associated with the cell of the classification table.” – is directed to the abstract idea of a mental process i.e., assigning a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 10 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 10 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 11
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 11 recites an abstract idea, substantially as follows:
“wherein the class assigned to a cell of the classification table corresponds to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is the same and non-zero for each group of learning data.” – is directed to the abstract idea of a mental process i.e., assigning a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 11 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 11 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 12
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 12 recites an abstract idea, substantially as follows:
“wherein the class assigned to a cell of the classification table corresponds to an undetermined class or to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is zero for each group of learning data.” – is directed to the abstract idea of a mental process i.e., assigning a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 12 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 12 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 13
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 13 recites an abstract idea, substantially as follows:
“wherein the ranges of values for each characteristic are set using Sturges’s rule.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing performing a calculation using a formula, which is considered to be a mathematical calculation
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 13 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 13 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 14
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 14 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 14 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“further comprising developing a classification computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the generated classification model.” – is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 14 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“further comprising developing a classification computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the generated classification model.” – is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 15
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 15 recites an abstract idea, substantially as follows:
“identifying at least one characteristic to be studied of the learning data;” – is directed to the abstract idea of a mental process i.e., observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)) and may be performed with the aid of pen and paper, or using a computer as a tool.
“identifying ranges of values for each characteristic from the extracted values;” – is directed to the abstract idea of a mental process i.e., observation, evaluation, judgement, opinion are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)) and may be performed with the aid of pen and paper, or using a computer as a tool.
“assigning a class to each cell of the classification table according to a number of occurrences of the learning data of each group according to their extracted value of each characteristic with respect to the ranges of values defined for each studied characteristic; and” – is directed to the abstract idea of a mental process i.e., assigning a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
“determining the class of the data to be classified from the classification table of the classification model indicating the class assigned for the extracted values.” – is directed to the abstract idea of a mental process i.e., determining a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 15 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“obtaining at least one group of learning data, each group of learning data being associated with an indicated class;” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
“extracting a value of each characteristic defined for all learning data;” – is merely selecting a particular data source of type of data to be manipulated, considered an insignificant extra-solution activity (see MPEP 2106.06(g)).
“creating a classification table having a number of dimensions corresponding to the number of studied characteristics, each dimension having a size equal to the number of ranges of values defined for the characteristic associated with this dimension, each cell of the classification table being associated with a range of values of each studied characteristic;” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
“generating a classification model comprising the classification table.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
“obtaining data to be classified;” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
“extracting values from the data to be classified for each characteristic defined in the classification model; and” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 15 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“obtaining at least one group of learning data, each group of learning data being associated with an indicated class;” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“extracting a value of each characteristic defined for all learning data;” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i). “creating a classification table having a number of dimensions corresponding to the number of studied characteristics, each dimension having a size equal to the number of ranges of values defined for the characteristic associated with this dimension, each cell of the classification table being associated with a range of values of each studied characteristic;” – the broadest reasonable interpretation of this imitation is found to be merely outputting data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“generating a classification model comprising the classification table.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
“obtaining data to be classified;” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“extracting values from the data to be classified for each characteristic defined in the classification model; and“ – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 16
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 16 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 16 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the classification model includes a minimum value and a maximum value of each studied characteristic, and wherein the class assigned for the data to be classified is found in the classification table from the values extracted from the data to be classified, the minimum and maximum values of each studied characteristic and the size of each dimension of the classification table.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 16 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the classification model includes a minimum value and a maximum value of each studied characteristic, and wherein the class assigned for the data to be classified is found in the classification table from the values extracted from the data to be classified, the minimum and maximum values of each studied characteristic and the size of each dimension of the classification table.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 17
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 17 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 17 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the classification model further includes a minimum value and a maximum value of each studied characteristic.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 17 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the classification model further includes a minimum value and a maximum value of each studied characteristic.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 18
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 18 recites an abstract idea, substantially as follows:
“wherein the class assigned to a cell of the classification table corresponds to the class of the group of learning data having the largest number of occurrences of learning data over the range of values of each characteristic associated with the cell of the classification table.” – is directed to the abstract idea of a mental process i.e., assigning a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 18 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 18 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 19
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 19 recites an abstract idea, substantially as follows:
“wherein the class assigned to a cell of the classification table corresponds to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is the same and non-zero for each group of learning data.” – is directed to the abstract idea of a mental process i.e., assigning a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 19 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 19 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 20
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 20 recites an abstract idea, substantially as follows:
“wherein the class assigned to a cell of the classification table corresponds to an undetermined class or to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is zero for each group of learning data.” – is directed to the abstract idea of a mental process i.e., assigning a class or label is equivalent to observation, evaluation, and judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 20 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 20 does not include additional limitations that amount to significantly more than the judicial exception.
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.
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.
Claims 1-3, 7-10, 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Tsutsui, Hiroaki (US 6510245 B1), hereinafter referred to as Tsutsui, in view of NPL reference Ghamisi et al, “Spectral-Spatial Classification based on Integrated Segmentation”, hereinafter referred to Ghamisi.
Regarding Claim 1
Tsutsui discloses:
“obtaining at least one group of learning data, each group of learning data being associated with an indicated class;” (Tsutsui at Col 8, Lines 58-65: The user inputs the collected data as multi-dimensional data characterized by the n types of variates [Examiner Note: mapped to at least one group of learning data](i.e., data whose positions in the feature space are specified by the n types of variates) to the computer (step 103). Assume that there is one class A characterized by two variates x1 and x2, and a plurality of data D belonging to class A [Examiner Note: mapped to associated with an indicated class] are distributed in a two-dimensional feature space 3 defined by the variates x1 and x2, as shown in FIG. 3)
“identifying at least one characteristic to be studied of the learning data;” (Tsutsui at Col 6, Lines 57-61: According to the present invention, n types of variates [Examiner Note: mapped to characteristics] regarded as suitable for situation classification or good in separability of classes from each other are selected, and the range of the minimum value to the maximum value of each variate is normalized)
“extracting a value of each characteristic defined for all learning data;” (Tsutsui at Col 7, Lines 36-39: Letting x1, x2, . . . , xn be the values of variates representing first data, and x1′, x2′, . . . , xn′ be the values of variates representing the second data)
“identifying ranges of values for each characteristic from the extracted values;” (Tsutsui at Col 6, Lines 57-61: According to the present invention, n types of variates [Examiner Note: mapped to characteristics] regarded as suitable for situation classification or good in separability of classes from each other are selected, and the range of the minimum value to the maximum value of each variate is normalized)
“creating a classification table having a number of dimensions corresponding to the number of studied characteristics, each dimension having a size equal to the number of ranges of values defined for the characteristic associated with this dimension, each cell of the classification table being associated with a range of values of each studied characteristic;” (Tsutsui at Col 3, Lines 8-14: a classification model generating method of the present invention comprises the steps of, when n-dimensional data which belongs to one class in an n-dimensional feature space defined by n types of variates and whose position is specified by the variates is input, dividing the feature space into m.sup.n divided areas by performing m-part division for each of the variates; Col 9, Lines 2-5: FIG. 4 shows the result obtained by dividing each of the full ranges of the variates x1 and x2 by 16. With this operation, the feature space S is divided into 256 areas E;
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[Examiner Note: the feature space is divided according the ranges of the variates which is equivalent to creating a classification table having a number of dimensions corresponding to a number of characters, each dimension equal to the number of ranges. Further, the full ranges of each variate is divided into smaller ranges to create 256 areas in the feature space, and is equivalent to each cell of the table being associated with a range of values of each characteristic.]
“assigning a class to each cell of the classification table […] according to their extracted value of each characteristic with respect to the ranges of values defined for each studied characteristic; and” (Tsutsui at Col 9, Lines 6-13: The computer associates the divided areas E generated by m-part division with the data D to perform classification in the feature space S (step 105). More specifically, if the data D is present in a given divided area E, the computer recognizes this area E as an area belonging to class A. [Examiner Note: mapped to assigning a class to each cell of the classification table] Each divided area E determined as an area belonging to class A will be referred to as a learning area Ea hereinafter. FIG. 5 shows these learning areas Ea; [Examiner Note: the feature space S is divided into areas labeled E based on the ranges of variates x1 and x2 (Tsutsui at Col 3, Lines 8-14) and then classifies each area E which is equivalent to assigning a class to each cell with respect to the values of each characteristic]
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“generating the classification model comprising the classification table.” (Tsutsui at Col 12, Lines 3-8: Upon reception of these data, the computer classifies the feature space S into three areas, namely a learning area Ea belonging class A, a learning area Eb belonging to class B, and a non-learning area E, thereby generating a classification model like the one shown in FIG. 13(b));
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However, Tsutsui does not disclose:
“assigning a class […] according to a number of occurrences of the learning data of each group”
On the other hand, Ghamisi discloses:
“assigning a class […] according to a number of occurrences of the learning data of each group” (Ghamisi at 2.3 Majority Voting within each object: Fig. 2 shows the general idea of majority voting within each object.
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[...] To perform the MV on the output of the segmentation and classification steps, a counting on the number of pixels with different class labels in each object is first carried out. Subsequently, all the pixels in each object are assigned to the most frequent class label for the object.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tsutsui with the above teachings of Ghamisi by using a method of generating a classification model via a classification table where is cell is classified according to values of characteristics of learning data as taught by Tsutsui, and determining a classification based on a number of occurrences of group within learning data as taught by Ghamisi. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve classification accuracy as suggested by Ghamisi at 3.3. Result: “Results confirm the spectral and spatial classification methods, using majority voting within each object, are able to improve the result of the pixel-wise classification (SVM) accuracy significantly, especially when the spatial structures in the data set are large.”
Referring to Claim 8, it is rejected on the same basis as Claim 1, mutatis mutandis, since both are analogous claims.
Regarding Claim 2
The combination of Tsutsui and Ghamisi discloses, “The method according to claim 1,”.
The combination of Tsutsui and Ghamisi further discloses:
“wherein the classification model further includes a minimum value and a maximum value of each studied characteristic.” (Tsutsui at Col 6, Lines 57-61: According to the present invention, n types of variates regarded as suitable for situation classification or good in separability of classes from each other are selected, and the range of the minimum value to the maximum value of each variate is normalized)
Referring to Claims 9 and 17, they are rejected on the same basis as Claim 2, mutatis mutandis, since both are analogous claims.
Regarding Claim 3
The combination of Tsutsui and Ghamisi discloses, “The method according to claim 1,”.
The combination of Tsutsui and Ghamisi further discloses:
“wherein the class assigned to a cell of the classification table corresponds to the class of the group of learning data having the largest number of occurrences of learning data over the range of values of each characteristic associated with the cell of the classification table.” (Ghamisi at 2.3 Majority Voting within each object: Fig. 2 shows the general idea of majority voting within each object.
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[...] To perform the MV on the output of the segmentation and classification steps, a counting on the number of pixels with different class labels in each object is first carried out. Subsequently, all the pixels in each object are assigned to the most frequent class label for the object.)
The same motivation utilized to combine Tsutsui with Ghamisi, as set in forth in Claim 1, is equally applicable to Claim 3.
Referring to Claims 10 and 18, they are rejected on the same basis as Claim 3, mutatis mutandis, since both are analogous claims.
Regarding Claim 7
The combination of Tsutsui and Ghamisi discloses, “The method according to claim 1,”.
The combination of Tsutsui and Ghamisi further discloses:
“further comprising developing a classification computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the generated classification model.” (Tsutsui at Col 3, Lines 2-5: and a recording medium on which a program for making a computer execute the classification model generating method is recorded)
Referring to Claim 14, it is rejected on the same basis as Claim 7, mutatis mutandis, since both are analogous claims.
Regarding Claim 15
Tsutsui discloses:
“obtaining at least one group of learning data, each group of learning data being associated with an indicated class;” (Tsutsui at Col 8, Lines 58-65: The user inputs the collected data as multi-dimensional data characterized by the n types of variates [Examiner Note: mapped to at least one group of learning data](i.e., data whose positions in the feature space are specified by the n types of variates) to the computer (step 103). Assume that there is one class A characterized by two variates x1 and x2, and a plurality of data D belonging to class A [Examiner Note: mapped to associated with an indicated class] are distributed in a two-dimensional feature space 3 defined by the variates x1 and x2, as shown in FIG. 3)
“identifying at least one characteristic to be studied of the learning data;” (Tsutsui at Col 6, Lines 57-61: According to the present invention, n types of variates [Examiner Note: mapped to characteristics] regarded as suitable for situation classification or good in separability of classes from each other are selected, and the range of the minimum value to the maximum value of each variate is normalized)
“extracting a value of each characteristic defined for all learning data;” (Tsutsui at Col 7, Lines 36-39: Letting x1, x2, . . . , xn be the values of variates representing first data, and x1′, x2′, . . . , xn′ be the values of variates representing the second data)
“identifying ranges of values for each characteristic from the extracted values;” (Tsutsui at Col 6, Lines 57-61: According to the present invention, n types of variates [Examiner Note: mapped to characteristics] regarded as suitable for situation classification or good in separability of classes from each other are selected, and the range of the minimum value to the maximum value of each variate is normalized)
“creating a classification table having a number of dimensions corresponding to the number of studied characteristics, each dimension having a size equal to the number of ranges of values defined for the characteristic associated with this dimension, each cell of the classification table being associated with a range of values of each studied characteristic;” (Tsutsui at Col 3, Lines 8-14: a classification model generating method of the present invention comprises the steps of, when n-dimensional data which belongs to one class in an n-dimensional feature space defined by n types of variates and whose position is specified by the variates is input, dividing the feature space into m.sup.n divided areas by performing m-part division for each of the variates; Col 9, Lines 2-5: FIG. 4 shows the result obtained by dividing each of the full ranges of the variates x1 and x2 by 16. With this operation, the feature space S is divided into 256 areas E;
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[Examiner Note: the feature space is divided according the ranges of the variates which is equivalent to creating a classification table having a number of dimensions corresponding to a number of characters, each dimension equal to the number of ranges. Further, the full ranges of each variate is divided into smaller ranges to create 256 areas in the feature space, and is equivalent to each cell of the table being associated with a range of values of each characteristic.]
“assigning a class to each cell of the classification table […] according to their extracted value of each characteristic with respect to the ranges of values defined for each studied characteristic; and” (Tsutsui at Col 9, Lines 6-13: The computer associates the divided areas E generated by m-part division with the data D to perform classification in the feature space S (step 105). More specifically, if the data D is present in a given divided area E, the computer recognizes this area E as an area belonging to class A. [Examiner Note: mapped to assigning a class to each cell of the classification table] Each divided area E determined as an area belonging to class A will be referred to as a learning area Ea hereinafter. FIG. 5 shows these learning areas Ea; [Examiner Note: the feature space S is divided into areas labeled E based on the ranges of variates x1 and x2 (Tsutsui at Col 3, Lines 8-14) and then classifies each area E which is equivalent to assigning a class to each cell with respect to the values of each characteristic]
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“generating the classification model comprising the classification table.” (Tsutsui at Col 12, Lines 3-8: Upon reception of these data, the computer classifies the feature space S into three areas, namely a learning area Ea belonging class A, a learning area Eb belonging to class B, and a non-learning area E, thereby generating a classification model like the one shown in FIG. 13(b));
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“obtaining data to be classified;” (Tsutsui at Col 8, Lines 58-61: The user inputs the collected data as multi-dimensional data characterized by the n types of variates (i.e., data whose positions in the feature space are specified by the n types of variates) to the computer (step 103))
“extracting values from the data to be classified for each characteristic defined in the classification model; and” (Tsutsui at Col 7, Lines 36-39: Letting x1, x2, . . . , xn be the values of variates representing first data, and x1′, x2′, . . . , xn′ be the values of variates representing the second data; )
“determining the class of the data to be classified from the classification table of the classification model indicating the class assigned for the extracted values.” (Tsutsui at Col 10, Lines 5-12: The user inputs the variates x1 and x2 [Examiner Note: mapped to extracted values] representing the situation to be classified. In accordance with this operation, the computer performs classification processing by using the generated classification model (step 109). More specifically, the computer classifies the situation depending on whether the data whose position on the classification mode is designated by the input variates x1 and x2 belongs to the learning area Ea" or the area E.)
However, Tsutsui does not disclose:
“assigning a class […] according to a number of occurrences of the learning data of each group”
On the other hand, Ghamisi discloses:
“assigning a class […] according to a number of occurrences of the learning data of each group” (Ghamisi at 2.3 Majority Voting within each object: Fig. 2 shows the general idea of majority voting within each object.
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[...] To perform the MV on the output of the segmentation and classification steps, a counting on the number of pixels with different class labels in each object is first carried out. Subsequently, all the pixels in each object are assigned to the most frequent class label for the object.)
The same motivation utilized to combine Tsutsui and Ghamisi, as set forth in Claim 1, is equally applicable to Claim 15.
Regarding Claim 16
The combination of Tsutsui and Ghamisi discloses, “The method according to claim 15,” and the limitations are shown in the rejection above.
The combination of Tsutsui and Ghamisi further discloses:
“wherein the classification model includes a minimum value and a maximum value of each studied characteristic, and wherein the class assigned for the data to be classified is found in the classification table from the values extracted from the data to be classified, the minimum and maximum values of each studied characteristic and the size of each dimension of the classification table.” (Tsutsui at Col 6, Lines 57-61: According to the present invention, n types of variates regarded as suitable for situation classification or good in separability of classes from each other are selected, and the range of the minimum value to the maximum value of each variate [Examiner Note: mapped to studied characteristic] is normalized; Tsutsui at Col 9, Lines 2-5: "FIG. 4 shows the result obtained by dividing each of the full ranges of the variates x1 and x2 by 16. With this operation, the feature space S is divided into 256 areas E [Examiner Note: mapped to size of each dimension]; Tsutsui at Col 9, Lines 6-13: The computer associates the divided areas E generated by m-part division with the data D to perform classification in the feature space S (step 105). More specifically, if the data D is present in a given divided area E, the computer recognizes this area E as an area belonging to class A. [Examiner Note: mapped to class assigned for the data to be classified is found in the classification table] Each divided area E determined as an area belonging to class A will be referred to as a learning area Ea hereinafter. FIG. 5 shows these learning areas Ea)
Claims 4, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Tsutsui in view Ghamisi, and further in view of NPL reference Harris et al, “A robust, cross-validation classification method (RCM) for improved mapping accuracy and confidence metrics”, hereinafter referred to as Harris.
Regarding Claim 4
The combination of Tsutsui and Ghamisi discloses, “The method according to claim 1,”.
However, the combination of Tsutsui and Ghamisi does not disclose:
“wherein the class assigned to a cell of the classification table corresponds to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is the same and non-zero for each group of learning data.”
On the other hand, Harris discloses:
“wherein the class assigned to a cell of the classification table corresponds to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is the same and non-zero for each group of learning data.” (Harris at Pg. 72, Col 2, Paragraph 2 - Pg. 73, Col 1, Paragraph 1: A map showing pixels that had a tied value for the majority classification is also produced, as well as a map that shows the number of times (as a percentage) each pixel was assigned the same class throughout the process. […] The class to which tied pixels are ultimately assigned on the majority classification map is selected based on which mean rule value for the competing classes is highest [Examiner Note: mapped to class having the highest probability])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tsutsui and Ghamisi with the above teachings of Harris by using a method of generating a classification model as taught by Tsutsui and Ghamisi, and assigning a label based on the highest probability in the case the class occurrences for each group of learning data are the same, as taught by Harris. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve classification accuracy as suggested by Harris at Discussion: "RCM can also result in improved classification accuracies, similar to boosting, bagging, and ensemble classification methods, by determining a sub-set of the training areas that provide the highest classification accuracy".
Referring to Claims 11 and 19, they are rejected on the same basis as Claim 4, mutatis mutandis, since both are analogous claims.
Claims 5, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tsutsui in view Ghamisi, and further in view of NPL reference Zhang et al, “Subpopulation-specific confidence designation for more informative biomedical classification”, hereinafter referred to as Zhang.
Regarding Claim 5
The combination of Tsutsui and Ghamisi discloses, “The method according to claim 1,”.
However, the combination of Tsutsui and Ghamisi does not disclose:
“wherein the class assigned to a cell of the classification table corresponds to an undetermined class or to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is zero for each group of learning data.”
On the other hand, Zhang discloses:
“wherein the class assigned to a cell of the classification table corresponds to an undetermined class or to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is zero for each group of learning data.” (Zhang at 2.1 Classification Algorithm: This “selective voting” by members of the ensemble gives different vote totals among the test samples. A simple majority of voters decides the classification of a test sample, and some test samples may remain unclassified [Examiner Note: mapped to undetermined class] due to receiving zero votes or tied votes)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tsutsui and Ghamisi with the above teachings of Zhang by using a method of generating a classification model, as taught by Tsutsui and Ghamisi, and assigning a label as undetermined in the case the class occurrences for each group of learning data are the zero, as taught by Zhang. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve classification performance as suggested by Zhang at 2.4.: “Assessing subpopulation-specific confidence: Estimates of individual class probabilities can be important in their own right, and when averaged in an ensemble can lead to improved classification performance compared to simple majority voting in ensembles like bagged classification trees”.
Referring to Claims 12 and 20, they are rejected on the same basis as Claim 5, mutatis mutandis, since both are analogous claims.
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Tsutsui in view Ghamisi, and further in view of Cheng et al (US 20130159226 A1) hereinafter referred to as Cheng.
Regarding Claim 6
The combination of Tsutsui and Ghamisi discloses, “The method according to claim 1,”.
However, the combination of Tsutsui and Ghamisi does not disclose:
“wherein the ranges of values for each characteristic are set using Sturges’s rule.”
On the other hand, Cheng discloses:
“wherein the ranges of values for each characteristic are set using Sturges’s rule.” (Cheng at [0043]: After all of the WED.sub.i(i=1, 2, . . . , n), are obtained, they are first plotted as a histogram. Then, the Sturge's rule is applied to calculate the desired number (C) of clusters [Examiner Note: mapped to ranges])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tsutsui and Ghamisi with the above teachings of Zhang by using a method of generating a classification model, as taught by Tsutsui and Ghamisi, and using Sturge’s rule to define ranges of values, as taught by Cheng. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve model prediction accuracy as suggested by Cheng at [0017]: “Hence, with the application of the embodiments of the present invention, enough important model-building sample data can be obtained and kept, and thus the prediction accuracy of the prediction model can be assured”.
Referring to Claims 13, it is rejected on the same basis as Claim 6, mutatis mutandis, since both are analogous claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20120054129 A1 – recites a method for classification of objects that are associated with class labels which are used to assign a class label of its corresponding subgraph.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMIYAH KABIR whose telephone number is (571)270-0722. The examiner can normally be reached Monday-Thursday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAMIYAH KABIR/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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