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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119
(a)-(d). The certified copy has been filed in parent Application No. JP2021-213871, filed on 12/28/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/11/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement 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.
Regarding claim 1, in Step 1 of the 101 analyses set forth in MPEP 2106, the claim recites A machine learning device used in an exhaust gas analysis device that irradiates a combustion exhaust gas with light, performs a detection of light transmitted through the combustion exhaust gas, and analyzes the combustion exhaust gas based on a detection signal of the detection, the machine learning device comprising. An system is one of the four statutory categories.
In Step 2a Prong 1 of the 101 analyses set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a [ mental process/mathematical concept] but for recitation of generic computer components:
an arithmetic value of a specific component concentration calculated using the individual component concentration in the element balance formula, (an arithmetic value generated by calculation is represents a mathematical calculation which falls within the mathematical concept grouping of abstract ideas (MPEP 2106.04(a)(2), Further, a person can mentally generate an arithmetic value by doing a calculation by a process of simply evaluating the formula and making a judgement on what the arithmetic value is. (MPEP 2106))
learning on a relationship between a reference value of the specific component concentration, and at least one of the spectrum data, the individual component concentration, or the arithmetic value of the specific component concentration to generate specific component correlation data. (A person can mentally learn a relationship between a reference value of the specific component concentration and at least one spectrum data by a process of simply evaluating the values and making a judgement on what the relationships are.)
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a [ mental process/mathematical concept] but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analyses set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
a training data reception unit that receives training data; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))).
and a machine learning unit that performs machine learning using the training data, wherein the training data reception unit receives training data including: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
a reference value of a specific component concentration that is at least one of an H2 concentration or an 02 concentration obtained by an analyzer different from the exhaust gas analysis device; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))).
and at least one of spectrum data obtained by irradiating the combustion exhaust gas with light, or an individual component concentration selected based on an element balance formula for determining the specific component concentration, or (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))).
and the machine learning unit performs machine (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analyses set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
a training data reception unit that receives training data; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g)) , Furthermore, the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).).
and a machine learning unit that performs machine learning using the training data, wherein the training data reception unit receives training data including: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
a reference value of a specific component concentration that is at least one of an H2 concentration or an 02 concentration obtained by an analyzer different from the exhaust gas analysis device; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g)), Furthermore, the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).).
and at least one of spectrum data obtained by irradiating the combustion exhaust gas with light, or an individual component concentration selected based on an element balance formula for determining the specific component concentration, or (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g)), Furthermore, the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).).
and the machine learning unit performs machine (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) Furthermore, the additional element is directed to application of a computer tool (machine learning model), which is not indicative of significantly more (MPEP 2106.05(f)).)
Claims 12 and 13 are rejected on the same grounds as Claim 1.
Regarding claim 2 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites wherein the training data reception unit receives training data including the reference value of the specific component concentration (In step 2A, prong 2, this recites insignificant extra solution activity of mere data gathering, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites receiving data over a network which is a well-understood, routine and conventional activity, which is not indicative of significantly more.) and the spectrum data, and the machine learning unit performs machine learning on a relationship between the reference value of the specific component concentration and the spectrum data to generate the specific component correlation data. (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally learn a relationship between a reference value of the specific component concentration and at least one spectrum data by a process of simply evaluating the values and making a judgement on what the relationships are.)
Regarding claim 3 it is dependent upon claim 2, and thereby incorporates the limitations of, and corresponding analysis applied to claim 2. Further, claim 3 recites wherein the training data reception unit further receives the individual component concentration as training data, (In step 2A, prong 2, this recites insignificant extra solution activity of mere data gathering, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites receiving data over a network which is a well-understood, routine and conventional activity, which is not indicative of significantly more.) and the machine learning unit performs machine learning on a relationship among the reference value of the specific component concentration, the spectrum data, and the individual component concentration to generate the specific component correlation data. (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally learn a relationship between a reference value of the specific component concentration and at least one spectrum data by a process of simply evaluating the values and making a judgement on what the relationships are.)
Regarding claim 4 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 4 recites wherein the machine learning unit includes:
a first correlation data generation unit that calculates a minimum error value obtained by minimizing an error between the reference value of the specific component concentration and the arithmetic value of the specific component concentration, (Calculating a minimum error value is directed to a mathematical calculation and thus is not patent eligible (MPEP 2106).)
and generates, as a part of the specific component correlation data, first correlation data indicating a correlation between the minimum error value and a parameter used to calculate the minimum error value; (first correlation data indicating a correlation between the minimum error is directed to a mathematical calculation and thus is not patent eligible (MPEP 2106).)
and a second correlation data generation unit that performs machine learning on a relationship between the spectrum data and the minimum error value to generate, as a part of the specific component correlation data, second correlation data indicating a correlation between the spectrum data and the minimum error value. (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally learn a relationship between a the spectrum data and the minimum error value by a process of simply evaluating the values and making a judgement on what the relationships are.)
Regarding claim 5 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 5 recites wherein the training data reception unit receives training data including the reference value of the specific component concentration and the individual component concentration, (In step 2A, prong 2, this recites insignificant extra solution activity of mere data gathering, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites receiving data over a network which is a well-understood, routine and conventional activity, which is not indicative of significantly more.) and the machine learning unit performs machine learning on a relationship between the reference value of the specific component concentration and the individual component concentration to generate the specific component correlation data. (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally learn a relationship between the reference value of the specific component concentration and the individual component concentration by a process of simply evaluating the values and making a judgement on what the relationships are.)
Regarding claim 6 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 6 recites wherein in a case where machine learning is performed on H2 correlation data as the specific component correlation data, the individual component concentration is at least one of a CO2 concentration, a CO concentration, an H20 concentration, or a THC concentration, and in a case where machine learning is performed on 02 correlation data as the specific component correlation data, the individual component concentration is at least one of a CO2 concentration, a CO concentration, an H20 concentration, a THC concentration, or an NO concentration. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 7 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 7 recites wherein the training data reception unit receives training data including a reference value of a THC concentration obtained by an analyzer different from the exhaust gas analysis device and the spectrum data, and the machine learning unit performs machine learning on a relationship between the reference value of the THC concentration and the spectrum data to generate THC correlation data. (In step 2A, prong 2, this recites insignificant extra solution activity of mere data gathering, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites receiving data over a network which is a well-understood, routine and conventional activity, which is not indicative of significantly more.)
Regarding claim 8 it is dependent upon claim 7, and thereby incorporates the limitations of, and corresponding analysis applied to claim 7. Further, claim 8 recites wherein the individual component concentration includes a THC concentration, and the THC concentration is obtained from the spectrum data and the THC correlation data. (In step 2A, prong 2, this recites insignificant extra solution activity of mere data gathering, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites receiving data over a network which is a well-understood, routine and conventional activity, which is not indicative of significantly more.)
Regarding claim 9 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 9 recites An exhaust gas analysis device that analyzes combustion exhaust gas, the exhaust gas analysis device comprising: a light source that irradiates the combustion exhaust gas with light; a photodetector that detects light transmitted through the combustion exhaust gas; a specific component correlation data storage unit that stores specific component correlation data generated by the machine learning device according to claim 1; and a specific component concentration calculation unit (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
that calculates a specific component concentration in the combustion exhaust gas from at least one of the spectrum data, the individual component concentration, or the arithmetic value of the specific component concentration, and the specific component correlation data. (calculating a specific component concentration in the combustion is directed to a mathematical calculation and thus is not patent eligible (MPEP 2106).)
Regarding claim 10 it is dependent upon claim 9, and thereby incorporates the limitations of, and corresponding analysis applied to claim 9. Further, claim 10 recites wherein the combustion exhaust gas is an exhaust gas of an automobile. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 11 it is dependent upon claim 9, and thereby incorporates the limitations of, and corresponding analysis applied to claim 9. Further, claim 11 recites wherein Fourier transform infrared spectroscopy is used. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 14 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 14 recites An exhaust gas analysis method of analyzing a combustion exhaust gas using a light source that irradiates the combustion exhaust gas with light and a photodetector that detects light transmitted through the combustion exhaust gas, the exhaust gas analysis method comprising, by using specific component correlation data generated by the machine learning device according to claim 1,calculating a specific component concentration in the combustion exhaust gas from at least one of the spectrum data, the individual component concentration, or the arithmetic value of the specific component concentration, and the specific component correlation data. (calculating a specific component concentration in the combustion exhaust gas is directed to a mathematical calculation and thus is not patent eligible (MPEP 2106).)
Regarding claim 15 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 15 recites An exhaust gas analysis program used in an exhaust gas analysis device using a light source that irradiates combustion exhaust gas with light and a photodetector that detects light transmitted through the combustion exhaust gas, the exhaust gas analysis program causing a computer to have: a function as a specific component correlation data storage unit that stores specific component correlation data generated by the machine learning device according to claim 1; and a function as a specific component concentration calculation unit (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)that calculates a specific component concentration in the combustion exhaust gas from at least one of the spectrum data, the individual component concentration, or the arithmetic value of the specific component concentration, and the specific component correlation data. (calculating a specific component concentration in the combustion exhaust gas is directed to a mathematical calculation and thus is not patent eligible (MPEP 2106).)
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5, and 9-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mori et al. Pub No.: US 20200386678 A1.
Regarding claim 1 Mori teaches A machine learning device used in an exhaust gas analysis device that irradiates a combustion exhaust gas with light, performs a detection of light transmitted through the combustion exhaust gas, and analyzes the combustion exhaust gas based on a detection signal of the detection, the machine learning device comprising: (Mori, paragraph 0033-0036, teaches the use of an combustion gas anaylizer device that irradiates combustion gas with light and detects the light that is transmitted through the gas.)
a training data reception unit that receives training data; (Mori, paragraph 0023, teaches and acquisition part of a machine learning system to receive training data (i.e. a reception unit.)
and a machine learning unit that performs machine learning using the training data, (Mori, paragraph 0023, teaches the use of a machine learning system to perform machine learning using the training data.)
wherein the training data reception unit receives training data including:
a reference value of a specific component concentration that is at least one of an H2 concentration or an 02 concentration obtained by an analyzer different from the exhaust gas analysis device; (Mori, paragraph 0018-0020, teaches the use of a original O2 concentration value that is calculated by an equation and used by the analyzer to help in the later concentration predictions.)
and at least one of spectrum data obtained by irradiating the combustion exhaust gas with light, or an individual component concentration selected based on an element balance formula for determining the specific component concentration, or an arithmetic value of a specific component concentration calculated using the individual component concentration in the element balance formula, (Mori, paragraph 0039-0043, teaches the use of a spectrum data generation part that irradiated the combustion gas with light to obtain spectrum data.)
and the machine learning unit performs machine learning on a relationship between
a reference value of the specific component concentration,
and at least one of the spectrum data, the individual component concentration, or the arithmetic value of the specific component concentration to generate specific component correlation data. (Mori, paragraph 0071, 0083-0086, teaches the ability of a machine leaning device to learn the relationships between the reference value and the spectrum data to generate component correlation data.)
Claims 12 and 13 are rejected on the same grounds as Claim 1.
Regarding claim 2 Mori teaches The machine learning device according to claim 1, wherein the training data reception unit receives training data including the reference value of the specific component concentration and the spectrum data, and the machine learning unit performs machine learning on a relationship between the reference value of the specific component concentration and the spectrum data to generate the specific component correlation data. (Mori, paragraph 0065-0071, 0083-0086, teaches the machine learning model receiving the O2 concentration as a reference value and the spectrum data and the ability of a machine leaning device to learn the relationships between the reference value and the spectrum data to generate component correlation data.)
Regarding claim 3 Mori teaches The machine learning device according to claim 2, wherein the training data reception unit further receives the individual component concentration as training data, and the machine learning unit performs machine learning on a relationship among the reference value of the specific component concentration, the spectrum data, and the individual component concentration to generate the specific component correlation data. (Mori, paragraph 0065-0071, 0083-0086, teaches the machine learning model receiving the O2 concentration as a reference value, the spectrum data and the specific CO2 concentration value and the ability of a machine leaning device to learn the relationships between the reference value and the spectrum data to generate component correlation data.)
Regarding claim 5 Mori teaches The machine learning device according to claim 1, wherein the training data reception unit receives training data including the reference value of the specific component concentration and the individual component concentration, and the machine learning unit performs machine learning on a relationship between the reference value of the specific component concentration and the individual component concentration to generate the specific component correlation data. (Mori, paragraph 0065-0071, 0083-0086, teaches the machine learning model receiving the O2 concentration as a reference value, the spectrum data and the specific CO2 concentration value and the ability of a machine leaning device to learn the relationships between the reference value and the spectrum data to generate component correlation data.)
Regarding claim 9 Mori teaches An exhaust gas analysis device that analyzes combustion exhaust gas, the exhaust gas analysis device comprising: a light source that irradiates the combustion exhaust gas with light; a photodetector that detects light transmitted through the combustion exhaust gas; a specific component correlation data storage unit that stores specific component correlation data generated by the machine learning device according to claim 1; and a specific component concentration calculation unit that calculates a specific component concentration in the combustion exhaust gas from at least one of the spectrum data, the individual component concentration, or the arithmetic value of the specific component concentration, and the specific component correlation data. (Mori, paragraph 0040-0068, teaches the use of various specific component concentration calculations to calculate specific component concentrations based on spectrum data, concentrations of components, arithmetic values of concentrations and specific component correlation data.)
Regarding claim 10 Mori teaches The exhaust gas analysis device according to claim 9, wherein the combustion exhaust gas is an exhaust gas of an automobile. (Mori, paragraph 0032, teaches the combustion exhaust gas coming from a vehicle (i.e. automobile))
Regarding claim 11 Mori teaches The exhaust gas analysis device according to claim 9 or 10, wherein Fourier transform infrared spectroscopy is used. . (Mori, paragraph 0021, teaches the use of Fourier transform infrared spectroscopy.)
Regarding claim 14 Mori teaches An exhaust gas analysis method of analyzing a combustion exhaust gas using a light source that irradiates the combustion exhaust gas with light and a photodetector that detects light transmitted through the combustion exhaust gas, the exhaust gas analysis method comprising, by using specific component correlation data generated by the machine learning device according to claim 1,calculating a specific component concentration in the combustion exhaust gas from at least one of the spectrum data, the individual component concentration, or the arithmetic value of the specific component concentration, and the specific component correlation data. (Mori, paragraph 0040-0068, teaches the use of various specific component concentration calculations to calculate specific component concentrations based on spectrum data, concentrations of components, arithmetic values of concentrations and specific component correlation data.)
Regarding claim 15 Mori teaches An exhaust gas analysis program used in an exhaust gas analysis device using a light source that irradiates combustion exhaust gas with light and a photodetector that detects light transmitted through the combustion exhaust gas, the exhaust gas analysis program causing a computer to have: a function as a specific component correlation data storage unit that stores specific component correlation data generated by the machine learning device according to claim 1; and a function as a specific component concentration calculation unit that calculates a specific component concentration in the combustion exhaust gas from at least one of the spectrum data, the individual component concentration, or the arithmetic value of the specific component concentration, and the specific component correlation data. (Mori, paragraph 0040-0068, teaches the use of various specific component concentration calculations to calculate specific component concentrations based on spectrum data, concentrations of components, arithmetic values of concentrations and specific component correlation data.)
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.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Mori et al. Pub No.:US 20200386678 A1 in view of Yamada et al. Pub No.: US 20070234781 A1.
Regarding claim 4 Mori teaches The machine learning device according to claim 1, wherein the machine learning unit includes:
a first correlation data generation unit that1…, and generates, as a part of the specific component correlation data, first correlation data indicating a correlation between the 2…; (Mori, paragraph 0018-0020, teaches the use of a original O2 concentration value that is calculated by an equation and used by the analyzer to help in the later concentration predictions.)
and a second correlation data generation unit that performs machine learning on a relationship between the spectrum data and the 3…to generate, as a part of the specific component correlation data, second correlation data indicating a correlation between the spectrum data and the 4….(Mori, paragraph 0065-0071, 0083-0086, teaches the machine learning model receiving the O2 concentration as a reference value and the spectrum data and the ability of a machine leaning device to learn the relationships between the reference value and the spectrum data to generate component correlation data.)
Mori does not teach 1…calculates a minimum error value obtained by minimizing an error between the reference value of the specific component concentration and the arithmetic value of the specific component concentration2… minimum error value and a parameter used to calculate the minimum error value3… minimum error value4… minimum error value. However, Yamada in analogous art teaches this limitation (Yamada, paragraph 0170, teaches the minimization of an error between NOx and CO concentrations in an exhaust gas analysis system.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Yamada’s teaching of minimizing an error between concentrations of gases in exhaust gas analysis with Mori’s teaching of exhausts gas analysis. The motivation to do so would be to get a more accurate analysis of how the gases interact with each other and how they are effect each other in the system.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Mori et al. Pub No.:US 20200386678 A1 in view of Ando et al. Pub No.: JPWO2019031331A1.
Regarding claim 6 Mori teaches The machine learning device according to claim 1,wherein in a case where machine learning is performed on … correlation data as the specific component correlation data, the individual component concentration is at least one of a CO2 concentration, a CO concentration, an H20 concentration, or a THC concentration, (Mori, paragraph 0065-0071, 0083-0086, teaches the machine learning model receiving the O2 concentration as a reference value, the spectrum data and the specific CO2 concentration value and the ability of a machine leaning device to learn the relationships between the reference value and the spectrum data to generate component correlation data.) and in a case where machine learning is performed on 02 correlation data as the specific component correlation data, the individual component concentration is at least one of a CO2 concentration, a CO concentration, an H20 concentration, a THC concentration, or an NO concentration. (Mori, paragraph 0065-0071, 0083-0086, teaches the machine learning model receiving the O2 concentration as a reference value, the spectrum data and the specific CO2 concentration value and the ability of a machine leaning device to learn the relationships between the reference value and the spectrum data to generate component correlation data.)
Mori does not teach …H2… However, Ando in analogous art teaches this limitation (Ando, paragraph 0035, teaches the use of H2 in the analysis of exhaust gases in an exhaust gas analysis apparatus.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Ando’s teaching of analyzing H2 concentrations in exhaust gas analysis with Mori’s teaching of exhausts gas analysis. The motivation to do so would be to get a more accurate picture of all the gases present and their effects on the other concentrations of gas in the exhaust gases.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Mori et al. Pub No.:US 20200386678 A1 in view of Ando et al. Pub No.: US 20150330868 A1.
Regarding claim 7 Mori teaches The machine learning device according to claim 1,wherein the training data reception unit receives training data including a reference value of a … concentration obtained by an analyzer different from the exhaust gas analysis device and the spectrum data, and the machine learning unit performs machine learning on a relationship between the reference value of the … concentration and the spectrum data to generate … correlation data. (Mori, paragraph 0065-0071, 0083-0086, teaches the machine learning model receiving the O2 concentration as a reference value, the spectrum data and the specific CO2 concentration value and the ability of a machine leaning device to learn the relationships between the reference value and the spectrum data to generate component correlation data.)
Mori does not teach …THC… THC… THC… However, Fukami in analogous art teaches this limitation (Fukami, paragraph 0035, teaches the use of a THC analyzer that continuously measures the concentration of hydrocarbons for use in an exhaust gas analysis apparatus.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Fukami’s teaching of analyzing THC concentrations in exhaust gas analysis with Mori’s teaching of exhausts gas analysis. The motivation to do so would be to get a more accurate picture of all the gases present and their effects on the other concentrations of gas in the exhaust gases.
Regarding claim 8 the combination of Mori and Fukami teaches The machine learning device according to claim 7, wherein the individual component concentration includes a …concentration, and the … concentration is obtained from the spectrum data and the … correlation data. (Mori, paragraph 0065-0071, 0083-0086, teaches the machine learning model receiving the O2 concentration as a reference value, the spectrum data and the specific CO2 concentration value and the ability of a machine leaning device to learn the relationships between the reference value and the spectrum data to generate component correlation data.)
…THC… THC… THC…(Fukami, paragraph 0035, teaches the use of a THC analyzer that continuously measures the concentration of hydrocarbons for use in an exhaust gas apparatus.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS B LANE whose telephone number is (571)272-1872. The examiner can normally be reached M-Th: 7:20am-5:20pm; F: Out of Office.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MARIELA REYES can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THOMAS BERNARD LANE/Examiner, Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142
