DETAILED ACTIONS
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/17/2024, 06/16/2025, and 08/25/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections- 35 USC §101
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 therefore, subject to the conditions and requirements of this title.
Claims 1-8, 10-18 are rejected under 35 U.S.C.§101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding Claim 1,
A method for computerized thermal analysis of a sample of a substance, said method comprising:
providing first data as an input to a first software module stored at one or more non-transitory electronic storage devices of one or more computers, said first data representing an observable response signal of said sample subjected to an excitation which generates an observable response and said excitation as a function of time, said response signal being representative of a thermal effect due to said sample.
calculating, by way of the first software module, a thermo-analytical measurement curve from the first data, said thermos-analytical measurement curve allowing the identification of said thermal effect;
outputting, by way of the first software module, second data suitable for representing said measurement curve as an input to a second software module stored at the one or more non-transitory electronic storage devices of the one or more computers, said second software module comprising an artificial intelligence engine;
automatically identifying, by way of the artificial intelligence engine, the thermal effect from the second data; and outputting, by way of the second software module,
The claim limitations underlined above is abstract idea, and the remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “ “ calculating, by way of the first software module, a thermo-analytical measurement curve from the first data”,” and “automatically identifying, by way of the artificial intelligence engine, the thermal effect from the second data;” and represents the mathematical concepts manipulation of thermal data using known software/algorithm/ (Specification, Page 2, STARe software of Mettler Toledo) by a computer processor to generate thermo-analytical curve. See (Specification, page 2, middle and bottom paragraph, and Pages 4- 7). The thermal effect calculation or determination is done using known method in the art using Machine learning method. These steps encompass under its broadest reasonable interpretation a mathematical concept/ mathematical manipulation by a processor to predict/estimate/ making evaluation/judgement based on the detected and historical data.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 1 recites additional elements “providing first data as an input to a first software module stored at one or more non-transitory electronic storage devices of one or more computers, said first data representing an observable response signal of said sample subjected to an excitation which generates an observable response and said excitation as a function of time, said response signal being representative of a thermal effect due to said sample.” are data gathering steps for the particular technological environment or field of use and describing types of data. Obtaining thermal excitation response data by means of exciting sample data as a time series data at a particular point or position of time are means of obtaining data. and step of “outputting, by way of the first software module, second data suitable for representing said measurement curve as an input to a second software module stored at the one or more non-transitory electronic storage devices of the one or more computers, said second software module comprising an artificial intelligence engine;” represents the post solution activity outputting the result of mathematical manipulations of trained data by using software/ algorithm of the AI engine See (Specification, Pages 6-7) represent post solution activity and only add an insignificant extra-solution activity to the judicial exception. The above additional elements considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and neither integrate the judicial exception into a practical application. Furthermore, nothing in the claim reasonably indicates that the predicted value is displayed to user (as is disclosed in Claim 9) or implemented the abstract idea in practical use as is disclosed in specifications pages 14, and nothing in the claim discloses implementation of the output result in practical use. Therefore, the claims are directed at a judicial exception and require further analysis under Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring thermal excitation data and evaluate using algorithm. See Prior art ref. Denner et al (US 2015/0019157 A1, CN 105823863 A ). Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
claims 2-8, Claim 10-11, and 16-17 are rejected under 35 U.S.C. 101 because claims depend on claim 1, therefore, has the abstract idea of claim 1 and also has the routine and conventional structure above of claim 1. In addition, claims 2-8 and Claim 10-11 and 16-17 further recite the elements which are simply more standard computational, mathematical calculation to data gathering /generate data and/ or a model, and. Describe type of data, and data gathering process. Furthermore, claims claims 2-8 and Claim 10-11 and 16-17 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 12,
A system for thermal analysis of a sample of a substance, said system comprising:
a measurement means operative to measure a response signal of a sample subjected to an excitation which generates an observable response, said response signal being representative of a thermal effect due to said sampler and
to output first data representing the response signal and said excitation as a function of time; and
a data processing means comprising a first software module configured to receive the first data as an input, to calculate a thermos-analytical measurement curve from said first data, said thermos-analytical measurement curve allowing the identification of said thermal effects and
to output second data suitable for representing said measurement curve, said data processing means further comprising a second software module comprising an artificial intelligence engine configured for automatic identification of thermal effects, said second software module being configured to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine.
The claim limitations underlined above is abstract idea, and the remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “to calculate a thermos-analytical measurement curve from said first data, said thermos-analytical measurement curve allowing the identification of said thermal effect and “ automatic identification of thermal effects,” represents the mathematical concepts manipulation of thermal data using known software/algorithm/ (Specification, Page 2, STARe software of MettlerToledo) by a computer processor to generate thermo-analytical curve. See (Specification, page 2, middle and bottom paragraph, and Pages 4- 7). The thermal effect calculation or determination is done using known method in the art using Machine learning method. These steps encompass under its broadest reasonable interpretation a mathematical concept/ mathematical manipulation by a processor to predict/estimate/ making evaluation/judgement based on the detected and historical data.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 12 recites additional elements “A system for thermal analysis of a sample of a substance, said system comprising: a measurement means operative to measure a response signal of a sample subjected to an excitation which generates an observable response, said response signal being representative of a thermal effect due to said sampler and to output first data representing the response signal and said excitation as a function of time; and a data processing means comprising a first software module configured to receive the first data as an input. “are data gathering steps for the particular technological environment or field of use and describing types of data. Obtaining thermal excitation response data by means of exciting sample data as a time series data at a particular point or position of time are means of obtaining data. and step of “to output second data suitable for representing said measurement curve, said data processing means further comprising a second software module comprising an artificial intelligence engine” , “second software module being configured to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine” represents the post solution activity outputting the result of mathematical manipulations of trained data by using software/ algorithm of the AI engine See (Specification, Pages 6-7) represent post solution activity and only add an insignificant extra-solution activity to the judicial exception. The above additional elements considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and neither integrate the judicial exception into a practical application. Furthermore, nothing in the claim reasonably indicates that the predicted value is displayed to user (as is disclosed in Claim 9) or implemented the abstract idea in practical use as is disclosed in specifications pages 14, and nothing in the claim discloses implementation of the output result in practical use. Therefore, the claims are directed at a judicial exception and require further analysis under Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring thermal excitation data and evaluate using algorithm. See Prior art ref. Denner et al (US 2015/0019157 A1). Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
Regarding Claim 13,
One or more non-transitory electronic storage devices comprising a computer program for thermal analysis of a sample of a substance, said computer program comprising;
a first software module configured to when executed, configure one or more processors to:
receive first data representing a response signal of said sample subjected to an excitation which generates an observable response and said excitation as a function of time as an input, said response signal being representative of a thermal effect due to said sample; and
calculate a thermos-analytical measurement curve which allows the identification of said thermal effect and to output second data suitable for representing said measurement curve;
a second software module comprising an artificial intelligence engine configured automatically identify
wherein said second software module, when executed, configures the one or more processors to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine.
The claim limitations underlined above is abstract idea, and the remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “calculate a thermos-analytical measurement curve which allows the identification of said thermal effect;” and step of
“automatically identify of thermal effects,” represents the mathematical concepts manipulation of thermal data using known software/algorithm/ (Specification, Page 2, STARe software of MettlerToledo) by a computer processor to generate thermo-analytical curve. See (Specification, page 2, middle and bottom paragraph, and Pages 4- 7). The thermal effect calculation or determination is done using known method in the art using Machine learning method. These steps encompass under its broadest reasonable interpretation a mathematical concept/ mathematical manipulation by a processor to predict/estimate/ making evaluation/judgement based on the detected and historical data.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 13 recites additional elements “One or more non-transitory electronic storage devices comprising a computer program for thermal analysis of a sample of a substance, said computer program comprising;a first software module configured to when executed, configure one or more processors to:receive first data representing a response signal of said sample subjected to an excitation which generates an observable response and said excitation as a function of time as an input, said response signal being representative of a thermal effect due to said sample; a second software module comprising an artificial intelligence engine configured to, when executed, configure the one or more processors, wherein said second software module, are data gathering steps for the particular technological environment or field of use and describing type of data. Obtaining thermal excitation response data by means of exciting sample data as a time series data at a particular point or position of time and Using Machine learning (AI engine) to process data are routine data gathering processing step in a particular technology or field of technology.These steps represent mere routine data gathering steps and only add an insignificant extra-solution activity to the judicial exception. Step of when executed, configures the one or more processors to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine.” represents the post solution activity outputting the result of mathematical manipulations of trained data by using software/ algorithm of the AI engine See (Specification, Pages 6-7) represent post solution activity and only add an insignificant extra-solution activity to the judicial exception. The above additional elements considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and neither integrate the judicial exception into a practical application. Furthermore, nothing in the claim reasonably indicates that the predicted value is displayed to user (as is disclosed in Claim 9) or implemented the abstract idea in practical use as is disclosed in specifications pages 14, and nothing in the claim discloses implementation of the output result in practical use. Therefore, the claims are directed at a judicial exception and require further analysis under Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring thermal excitation data and evaluate using algorithm. See Prior art ref. Denner et al (US 2015/0019157 A1, CN 105823863 A ). Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
Regarding Claim 14,
One or more non-transitory electronic storage devices comprising a training software module of a computer program for thermal analysis of a substance, wherein said training software module, when executed, configures the one or more processors to;
receive expert training data comprising data sets of second and third training data, said second training data being suitable for representing a training measurement curve due to a training sample, said training measurement curve allowing the identification of at least one thermal training effect due to said training sample, and said third training data being representative of said thermal training effecti;
and/or to create said expert training data by receiving second data suitable for representing a thermos-analytical measurement curve which allows the identification of at least one thermal training effect from a first software module and creating an associated third data from a human input representative of a human identification of the at least one thermal training effect, wherein said first software module comprises software instructions stored at the one or more non-transitory electronic storage devices, which when executed, configures the one or more processors to:
receive first data representing a response signal of said sample subjected to an excitation which generates an observable response and said excitation as a function of time as an input, where said response signal is representative of a thermal effect due to said sample; and calculate a thermos-analytical measurement curve which allows the identification of said thermal effect and to output second data suitable for representing said measurement curve;
create a trained sub engine based on the training data, the trained sub engine being suitable to be deployed to a second software module comprising an artificial intelligence engine, which when executed, configures the one or more processors to automatically identify thermal effects, wherein said second software module, when executed, configures the one or more processors to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine a trained sub engine based on the training data, the trained sub engine being suitable to be deployed to a second software module comprising an artificial intelligence engine, which when executed, configures the one or more processors to automatically identify thermal effects, wherein said second software module, when executed, configures the one or more processors to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine.
The claim limitations underlined above is abstract idea, and the remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “create said expert training data;” and step of
calculate a thermos-analytical measurement curve which allows the identification of said thermal effect”, automatically identify thermal effects, create a trained sub engine based on the training data, the trained sub engine being suitable to be deployed to a second software module comprising an artificial intelligence engine” represents the mathematical concepts manipulation of thermal data using known software/algorithm/ (Specification, Page 2, STARe software of MettlerToledo) by a computer processor to generate thermo-analytical curve. See (Specification, page 2, middle and bottom paragraph, and Pages 4- 7). The thermal effect calculation or determination is done using known method in the art using Machine learning method. The step of “creating an associated third data from a human input representative of a human identification of the at least one thermal training effect,,” is a mental step. process steps capable of being done in the human mind by observing the output data.
These steps encompass under its broadest reasonable interpretation a mathematical concept/ mathematical manipulation by a processor to predict/estimate/ making evaluation/judgement based on the detected and historical data.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 13 recites additional elements “One or more non-transitory electronic storage devices comprising a training software module of a computer program for thermal analysis of a substance, wherein said training software module, when executed, configures the one or more processors to;receive expert training data comprising data sets of second and third training data, said second training data being suitable for representing a training measurement curve due to a training sample, said training measurement curve allowing the identification of at least one thermal training effect due to said training sample, and said third training data being representative of said thermal training effecti; receiving second data suitable for representing a thermos-analytical measurement curve which allows the identification of at least one thermal training effect from a first software module; wherein said second software module, when executed, configures the one or more processors to receive said second data as an input.” are data gathering steps for the particular technological environment or field of use and describing type of data. Obtaining thermal excitation response data by means of exciting sample data as a time series data at a particular point or position of time and Using Machine learning (AI engine) to process data are routine data gathering processing step in a particular technology or field of technology.These steps represent mere routine data gathering steps and only add an insignificant extra-solution activity to the judicial exception. Step of and to output third data representative of said thermal effect identified by said artificial intelligence engine create a trained sub engine based on the training data, the trained sub engine being suitable to be deployed to a second software module comprising an artificial intelligence engine, wherein said second software module, when executed, configures the one or more processors to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine.” represents the post solution activity outputting the result of mathematical manipulations of trained data by using software/ algorithm of the AI engine See (Specification, Pages 6-7) represent post solution activity and only add an insignificant extra-solution activity to the judicial exception. The above additional elements considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and neither integrate the judicial exception into a practical application. Furthermore, nothing in the claim reasonably indicates that the predicted value is displayed to user (as is disclosed in Claim 9) or implemented the abstract idea in practical use as is disclosed in specifications pages 14, and nothing in the claim discloses implementation of the output result in practical use. Therefore, the claims are directed at a judicial exception and require further analysis under Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring thermal excitation data and evaluate using algorithm. See Prior art ref. Denner et al (US 2015/0019157 A1, CN 105823863 A ). Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Denner et al. (US 2015/0019157 A1, hereinafter Denner) and in view of Wang et al. (CN105823863 A, hereinafter Wang, IDS ref an original copy with translation is uploaded by the examiner).
Regarding Claim 1, Denner teaches,
A method for computerized thermal analysis of a sample of a substance, said method comprising: (Denner, [0075] The method represented in FIG. 1 for the evaluation of a measurement result of a thermal analysis”)
providing first data as an input to a first software module stored at one or more non-transitory electronic storage devices of one or more computers, (Denner, [0009] “objective is solved by a method for evaluating a measurement result of a thermal analysis, wherein a program-controlled computer unit is used to calculate at least one probability of the agreement of the measurement result with at least one
dataset previously stored in the computer unit, wherein this calculation is based on a comparison of effect data previously extracted from a measurement curve of the thermal analysis with corresponding stored effect data of the dataset”) said first data representing an observable response signal of said sample subjected to an excitation which generates an observable response and said excitation as a function of time, said response signal being representative of a thermal effect due to said sample. (Denner, [104] With regard to FIG. 1, there then takes place in stepS4 a comparison of the effects which have been found in the DSC sample measurement curve (step S2) and more precisely quantified (step S3) with the effects which have previously been stored for "stored measurement results" in the form of datasets in a database of the computer unit used.”)
calculating, by way of the first software module, a thermo-analytical measurement curve from the first data, said thermos-analytical measurement curve allowing the identification of said thermal effect; (Denner, [0009] “wherein this calculation is based on a comparison of effect data previously extracted from a measurement curve of the thermal analysis with corresponding stored effect data of the dataset”. [0014] "Effect data" in the sense of the invention are a quantitative description of the initially mentioned, characteristic and locally (i.e. in relatively limited temperature ranges) occurring signal changes that arise in the thermal analysis”).
Denner is silent on outputting, by way of the first software module, second data suitable for representing said measurement curve as an input to a second software module stored at the one or more non-transitory electronic storage devices of the one or more computers, said second software module comprising an artificial intelligence engine;
automatically identifying, by way of the artificial intelligence engine, the thermal
However, Wang teaches outputting, by way of the first software module, second data suitable for representing said measurement curve as an input to a second software module stored at the one or more non-transitory electronic storage devices of the one or more computers,(Wang, Figure 4, Page 4, paragraph 5, 3rd step: automatically analyzed ature of coal and caloric value by thermogravimetric curve, constant temperature thermogravimetric curve is automatically analyzed, therefrom calculate the input data needed for coal analysis, using calculating system based on neutral net to obtain ature of coal parameter, actual moisture, volatile matter, fixed carbon, ash and the caloric value of coal is as the output data of the
system of calculating. said second software module comprising an artificial intelligence engine. automatically identifying, by way of the artificial intelligence engine, the thermal effect from the second data; and outputting, by way of the second software module, third data representative of said thermal effect automatically identified by said artificial intelligence engine;( Wang, Figure 4, Page 8, paragraph 1, Forecast model uses neural network model as shown in Figure 4, including input layer, hidden layer and output layer, by data above as input parameter, i.e. YB, YC, YD, YE, XB, XC is the input of neutral net,, different weights in neutral net, between each node, can be there are in the output data with actual moisture, volatile matter, fixed carbon, ash and the caloric value of coal as neutral net.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Denner’s thermal analysis method to incorporate a machine learning network based thermal analysis steps as taught by Wang and obtain an accurate thermal effect measurement (Wang, abstract). It would have been obvious to a person of ordinary skill to include the well-known neural network forecast model with input layer, hidden layer and output layer along with the other machine learning network, in order to yield the predicted results of generating accurate quick thermal effect result, yet with higher accuracy (KSR).
Regarding Claim 2, combination of Denner and Wang teaches the method according to claim 1,
Denner is silent on wherein; said artificial intelligence engine
However, Wang teaches wherein; said artificial intelligence engine comprises at least one neural network the automatic identification of thermal effects, said at least one neural network comprising an input layer of input neurons for receiving said second data and an output layer of output neurons for outputting said third data-;land said second data is provided to said input layer. ;( Wang, Figure 4, Page 8, paragraph 1, Forecast model uses neural network model as shown in Figure 4, including input layer, hidden layer and output layer, by data above as input parameter, i.e. YB, YC, YD, YE, XB, XC is the input of neutral net,, different weights in neutral net, between each node, can be there are in the output data with actual moisture, volatile matter, fixed carbon, ash and the caloric value of coal as neutral net.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Denner’s thermal analysis method to incorporate a machine learning network based thermal analysis steps as taught by Wang and obtain an accurate thermal effect measurement (Wang, abstract). It would have been obvious to a person of ordinary skill to include the well-known neural network forecast model with input layer, hidden layer and output layer along with the other machine learning network, in order to yield the predicted results of generating accurate quick thermal effect result, yet with higher accuracy (KSR).
Regarding Claim 3, combination of Denner and Wang teaches the method according to claim 1,
Denner is silent on wherein said artificial intelligence engine comprises at least two sub engines for the automatic identification of thermal effects, and
receiving, at the second software module, selection data for selecting one of said sub engines for the automatic identification of thermal effects; and providing the second data to an input of the selected sub engine.
However, Wang teaches ;( Wang, Figure 4, Page 8, paragraph 1, Forecast model uses neural network model as shown in Figure 4, including input layer, hidden layer and output layer, by data above as input parameter, i.e. YB, YC, YD, YE, XB, XC is the input of neutral net,, different weights in neutral net, between each node, can be there are in the output data with actual moisture, volatile matter, fixed carbon, ash and the caloric value of coal as neutral net.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Denner’s thermal analysis method to incorporate a machine learning network based thermal analysis steps as taught by Wang and obtain an accurate thermal effect measurement (Wang, abstract). It would have been obvious to a person of ordinary skill to include the well-known neural network forecast model with input layer, hidden layer and output layer along with the other machine learning network, in order to yield the predicted results of generating accurate quick thermal effect result, yet with higher accuracy (KSR).
Regarding Claim 4, combination of Denner and Wang teaches the method according to claim 2,
Denner is silent on further comprising, by way of the one or more computers:
deploying at least one sub engine to said second software module and/or removing at least one sub engine from said second software module and/or deactivating at least one sub engine in said second software module.
However Wang teaches further comprising, by way of the one or more computers: deploying at least one sub engine to said second software module ;( Wang, Figure 4, Page 8, paragraph 1, Forecast model uses neural network model as shown in Figure 4, including input layer, hidden layer and output layer”) and/or removing at least one sub engine from said second software module and/or deactivating at least one sub engine in said second software module.
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Denner’s thermal analysis method to incorporate a machine learning network based thermal analysis steps as taught by Wang and obtain an accurate thermal effect measurement (Wang, abstract). It would have been obvious to a person of ordinary skill to include the well-known neural network forecast model with input layer, hidden layer and output layer along with the other machine learning network, in order to yield the predicted results of generating accurate quick thermal effect result, yet with higher accuracy (KSR).
Regarding Claim 5, combination of Denner and Wang teaches the method according to claim 1,
Denner is silent on further comprising, by way of the one or more computers: deploying, to said second software module, a training software module to create a trained sub by; using expert training data associated to at least one training sample of a training substance, said expert training data comprising data sets of second and third training data, said second training data being suitable for representing a training measurement curve due to said training sample; using said training measurement curve to identify at least one thermal training effect due to that training sample, and said third training data being representative of said thermal training effect providing a sub engine template which links an input data to output data in a matter specified by a set of engine parameters, and determining the engine parameters such that, for most of the data sets, the third data outputted by the trained sub engine using the engine parameters is essentially equal to the third training data of one of the data sets when the second training data of said data set is inputted into said trained sub engine.
However, Wang teaches further comprising, by way of the one or more computers: deploying, to said second software module, a training software module to create a trained sub by; using expert training data associated to at least one training sample of a training substance, said expert training data comprising data sets of second and third training data, said second training data being suitable for representing a training measurement curve due to said training sample; using said training measurement curve to identify at least one thermal training effect due to that training sample, and said third training data being representative of said thermal training effect providing a sub engine template which links an input data to output data in a matter specified by a set of engine parameters, and determining the engine parameters such that, for most of the data sets, the third data outputted by the trained sub engine using the engine parameters is essentially equal to the third training data of one of the data sets when the second training data of said data set is inputted into said trained sub engine. (Wang, page 8, top paragraph, Forecast model uses neural network model as shown in Figure 4, including input layer, hidden layer and output layer, by data above as input parameter, i.e. YB, YC, YD, YE, XB, XC is the input of neutral net,, different weights in neutral net, between each node, can be there are in the output data with actual moisture, volatile matter, fixed carbon, ash and the caloric value of coal as neutral net. First with the multiple weight-loss curve determining coal sample of apparatus measures specified and Industrial Analysis and caloric value numerical value in the case of laboratory determines, use randomly draw 80% data as training sample for training neutral net, with remaining sample as test samples, use the accuracy of specific test rating inspection training, and reach the data such as the moisture of neural network prediction coal, volatile matter, fixed carbon, ash and caloric value of required precision after using training”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Denner’s thermal analysis method to incorporate a machine learning network based thermal analysis steps as taught by Wang and obtain an accurate thermal effect measurement (Wang, abstract). It would have been obvious to a person of ordinary skill to include the well-known neural network forecast model with input layer, hidden layer and output layer along with the other machine learning network, in order to yield the predicted results of generating accurate quick thermal effect result, yet with higher accuracy (KSR).
Regarding Claim 6, combination of Denner and Wang teaches the method according to claim 5,
Denner further teaches wherein; said training measurement curve is obtained by applying a thermos-analytical measurement to said training sample and/or said training measurement curve is a theoretical measurement curve corresponding to said training sample, ( Denner, [0131] FIG. 10 shows, similar to FIG. 9, a comparison of the DATAsample effect data with previously stored datasets DATArefl, DATAref2, DATAref3, which again are defined in part as belonging to classes Cl and C2, wherein, in contrast with the embodiment according to FIG. 9, the DATAsample effect data are based on a "virtual measurement curve" or literature values of effect data for a known sample”)said third training data is obtained by human identification of the thermal training effect present in said training measurement curve. (Denner, [0133] As a result of the grouping by the user, the method can be increasingly trained in respect of the classification with suitable configuration of the software, without the basic mathematical parameters having to be modified”).
Regarding Claim 7, combination of Denner and Wang teaches the method according to claim 1,
Denner further teaches wherein said third data comprises an excitation range and a type of said thermal effect. (Denner, [0014] "Effect data" in the sense of the invention are a quantitative description of the initially mentioned, characteristic
and locally (i.e. in relatively limited temperature ranges) signal changes occurring that arise in the thermal analysis. [0015] The method according to the invention can thus be referred to as an "effect-based" evaluation method, from which a large number of advantages arise in particular for the application to measurement results of the thermal analysis.”).
Regarding Claim 8, combination of Denner and Wang teaches the method according to claim 1,
Denner is silent on further teaches further comprising: providing said third data and fourth data suitable for representing said measurement curve to an evaluation software module stored at the one or more non- transitory electronic storage devices of the one or more computers as an input; and calculating, by way of said evaluation software module, a value of at least one characteristic quantity associated with said at least one thermal effect in accordance with said fourth data.
However, Wang teaches further teaches further comprising: providing said third data and fourth data suitable for representing said measurement curve to an evaluation software module stored at the one or more non- transitory electronic storage devices of the one or more computers as an input; and calculating, by way of said evaluation software module, a value of at least one characteristic quantity associated with said at least one thermal effect in accordance with said fourth data. .(Wang, page 8, top paragraph, Forecast model uses neural network model as shown in Figure 4, including input layer, hidden layer and output layer, by data above as input parameter, i.e. YB, YC, YD, YE, XB, XC is the input of neutral net,, different weights in neutral net, between each node, can be there are in the output data with actual moisture, volatile matter, fixed carbon, ash and the caloric value of coal as neutral net.First with the multiple weight-loss curve determining coal sample of apparatus measures specified and Industrial Analysis and caloric value numerical value in the case of laboratory determines, use randomly draw 80% data as training sample for training neutral net, with remaining sample as test samples, use the accuracy of specific test rating inspection training, and reach the data such as the moisture of neural network prediction coal, volatile matter, fixed carbon, ash and caloric value of required precision after using training”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Denner’s thermal analysis method to incorporate a machine learning network based thermal analysis steps as taught by Wang and obtain an accurate thermal effect measurement (Wang, abstract). It would have been obvious to a person of ordinary skill to include the well-known neural network forecast model with input layer, hidden layer and output layer along with the other machine learning network, in order to yield the predicted results of generating accurate quick thermal effect result, yet with higher accuracy (KSR).
Regarding Claim 9, combination of Denner and Wang teaches the method according to claim 1,
Denner further teaches further comprising: displaying a graphical representation of the thermos-analytical measurement curve and/or data associated with the excitation range of the thermal effect and/or data associated with the type of said thermal effect and/or the value of the at least one characteristic quantity on a display means associated with the one or more computers. (Denner, [0022] The computer unit preferably comprises a memory unit, in which a large number (e.g. more than 20, in particular more than 100) datasets of effect data are stored for given known materials or samples. The number of stored datasets can expediently be provided in a variable manner, for example by corresponding user inputs to add or delete individual datasets. In particular, an addition of datasets in a database of the computer unit can take place, said datasets being generated on the basis of an extraction of the required effect data from a measurement curve which has been
recorded by a thermal analysis of a sample of known composition”)
Regarding Claim 10, combination of Denner and Wang teaches the method according to claim 1,
Denner further teaches wherein said excitation comprises an excitation quantity which corresponds to a variable temperature (Denner, [0050] The second sub-step "evaluation of the effects" (extraction of the features) can also be carried out partially or completely automated by means of an extraction algorithm
running on the computer unit. In this sub-step, the effects identified in the preceding sub-step and demarcated on the basis of their respective temperature ranges are evaluated in respect of their (more precise) "properties" and features" are formed by the quantification of these properties. In other words, the effect data are generated (calculated) by this substep. These features thus obtained can then form vector components of the already mentioned feature vector”).and/or an excitation quantity which corresponds to a variable power and/or an excitation quantity which corresponds to a variable pressure and/or an excitation quantity which corresponds to a variable amount of radiation and/or a an excitation quantity which corresponds to a variable stress or strain and/or an excitation quantity which corresponds to a variable atmosphere of gas and/or an excitation quantity which corresponds to a variable magnetic field.
Regarding Claim 11, combination of Denner and Wang teaches the method according to claim 1,
Denner further teaches wherein the response comprises a response quantity which corresponds to a temperature difference of a dynamic thermos-analytical method and/or a response quantity which corresponds to a heat flow of a dynamic thermos-analytical method, said dynamic thermos-analytical method comprising a heat flow constituted by a difference between heat flows to a sample of the substance and to a known reference and/or a response quantity which corresponds to a difference in heating power of a dynamic power-compensating thermos-analytical method (Denner, The effect data thus obtained then indicate, for each effect, at least ( or only) the effect type and a temperature-related expansion of the effect (e.g. defined by a start temperature and an end temperature). The effect data then indicate, as it were, essentially only the presence of an effect (together with an effect type) at specific points of the measurement curve”. [0019]” In an embodiment, the effect data relate to a DSC signal, i.e. can be regarded as a quantitative description of the locally occurring characteristic changes in the DSC signal. “[0004], “the so-called DCS signal ( corresponding to the heat flow rate), the mass or change in mass and the length or change in length of the sample may be mentioned as possible measurement signals”).
and/or a response quantity which corresponds to a change in length of a dynamic thermo- mechanical analytical method and/or a response quantity which corresponds to a change in weight of a dynamic thermo-gravimetric analytical method and/or a response quantity which corresponds to a force of a dynamic mechanical analytical method and/or a response quantity which corresponds to a change in length of a dynamic.
Regarding Claim 12, Denner teaches,
A system for thermal analysis of a sample of a substance, said system comprising: (Denner, [0075] The method represented in FIG. 1 for the evaluation of a measurement result of a thermal analysis”)
a measurement means operative to measure a response signal of a sample subjected to an excitation which generates an observable response, said response signal being representative of a thermal effect due to said sampler (Denner, [104] With regard to FIG. 1, there then takes place in stepS4 a comparison of the effects which have been found in the DSC sample measurement curve (step S2) and more precisely quantified (step S3) with the effects which have previously been stored for "stored measurement results" in the form of datasets in a database of the computer unit used.”) and to output first data representing the response signal and said excitation as a function of time;and a data processing means comprising a first software module configured to receive the first data as an input, to calculate a thermos-analytical measurement curve from said first data, said thermos-analytical measurement curve allowing the identification of said thermal effects and to output second data suitable for representing said measurement curve, ; (Denner, [0009] “wherein this calculation is based on a comparison of effect data previously extracted from a measurement curve of the thermal analysis with corresponding stored effect data of the dataset”. [0014] "Effect data" in the sense of the invention are a quantitative description of the initially mentioned, characteristic and locally (i.e. in relatively limited temperature ranges) occurring signal changes that arise in the thermal analysis”)
Denner is silent on said data processing means further comprising a second software module comprising an artificial intelligence engine configured for automatic identification of thermal effects. said second software module being configured to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine.
However, Wang teaches said data processing means further comprising a second software module comprising an artificial intelligence engine configured for automatic identification of thermal effects, (Wang, Figure 4, Page 4, paragraph 5, 3rd step: automatically analyzed ature of coal and caloric value by thermogravimetric curve, constant temperature thermogravimetric curve is automatically analyzed, therefrom calculate the input data needed for coal analysis, using calculating system based on neutral net to obtain ature of coal parameter, actual moisture, volatile matter, fixed carbon, ash and the caloric value of coal is as the output data of the system of calculating”) said second software module being configured to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine ;( Wang, Figure 4, Page 8, paragraph 1, Forecast model uses neural network model as shown in Figure 4, including input layer, hidden layer and output layer, by data above as input parameter, i.e. YB, YC, YD, YE, XB, XC is the input of neutral net,, different weights in neutral net, between each node, can be there are in the output data with actual moisture, volatile matter, fixed carbon, ash and the caloric value of coal as neutral net.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Denner’s thermal analysis method to incorporate a machine learning network based thermal analysis steps as taught by Wang and obtain an accurate thermal effect measurement (Wang, abstract). It would have been obvious to a person of ordinary skill to include the well-known neural network forecast model with input layer, hidden layer and output layer along with the other machine learning network, in order to yield the predicted results of generating accurate quick thermal effect result, yet with higher accuracy (KSR).
Regarding Claim 13, Denner teaches,
One or more non-transitory electronic storage devices comprising a computer program for thermal analysis of a sample of a substance, said computer program (Denner, [0009] “objective is solved by a method for evaluating a measurement result of a thermal analysis, wherein a program-controlled computer unit is used to calculate at least one probability of the agreement of the measurement result with at least one dataset previously stored in the computer unit”), comprising;
a first software module configured to when executed, configure one or more processors to: receive first data representing a response signal of said sample subjected to an excitation which generates an observable response (Denner, “wherein this calculation is based on a comparison of effect data previously extracted from a measurement curve of the thermal analysis with corresponding stored effect data of the dataset”), and said excitation as a function of time as an input, said response signal being representative of a thermal effect due to said sample(Denner, [104] With regard to FIG. 1, there then takes place in stepS4 a comparison of the effects which have been found in the DSC sample measurement curve (step S2) and more precisely quantified (step S3) with the effects which have previously been stored for "stored measurement results" in the form of datasets in a database of the computer unit used.”) and calculate a thermos-analytical measurement curve which allows the identification of said thermal effect and to output second data suitable for representing said measurement curve(Denner, [0009] “wherein this calculation is based on a comparison of effect data previously extracted from a measurement curve of the thermal analysis with corresponding stored effect data of the dataset”. [0014] "Effect data" in the sense of the invention are a quantitative description of the initially mentioned, characteristic and locally (i.e. in relatively limited temperature ranges) occurring signal changes that arise in the thermal analysis”).;
Denner is silent on a second software module comprising an artificial intelligence engine configured to, when executed, configure the one or more processors to automatically identify
wherein said second software module, when executed, configures the one or more processors to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine.
However, Wang teaches a second software module comprising an artificial intelligence engine configured to, when executed, configure the one or more processors to automatically identify ,(Wang, Figure 4, Page 4, paragraph 5, “3rd step: automatically analyzed ature of coal and caloric value by thermogravimetric curve, constant temperature thermogravimetric curve is automatically analyzed, therefrom calculate the input data needed for coal analysis, using calculating system based on neutral net to obtain ature of coal parameter, actual moisture, volatile matter, fixed carbon, ash and the caloric value of coal is as the output data of the system of calculating”).
wherein said second software module, when executed, configures the one or more processors to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine;
( Wang, Figure 4, Page 8, paragraph 1, “Forecast model uses neural network model as shown in Figure 4, including input layer, hidden layer and output layer, by data above as input parameter, i.e. YB, YC, YD, YE, XB, XC is the input of neutral net,, different weights in neutral net, between each node, can be there are in the output data with actual moisture, volatile matter, fixed carbon, ash and the caloric value of coal as neutral net.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Denner’s thermal analysis method to incorporate a machine learning network based thermal analysis steps as taught by Wang and obtain an accurate thermal effect measurement (Wang, abstract). It would have been obvious to a person of ordinary skill to include the well-known neural network forecast model with input layer, hidden layer and output layer along with the other machine learning network, in order to yield the predicted results of generating accurate quick thermal effect result, yet with higher accuracy (KSR).
Regarding Claim 14, Denner teaches,
One or more non-transitory electronic storage devices comprising a training software module of a computer program for thermal analysis of a substance, wherein said training software module, when executed, configures the one or more processors (Denner, [0009] “objective is solved by a method for evaluating a measurement result of a thermal analysis, wherein a program-controlled computer unit is used to calculate at least one probability of the agreement of the measurement result with at least one dataset previously stored in the computer unit, wherein this calculation is based on a comparison of effect data previously extracted from a measurement curve of the thermal analysis with corresponding stored effect data of the dataset”) to;
receive first data representing a response signal of said sample subjected to an excitation which generates an observable response and said excitation as a function of time as an input, where said response signal is representative of a thermal effect due to said sample; ( Denner, [0131] FIG. 10 shows, similar to FIG. 9, a comparison of the DATAsample effect data with previously stored datasets DATArefl, DATAref2, DATAref3, which again are defined in part as belonging to classes Cl and C2, wherein, in contrast with the embodiment according to FIG. 9, the DATAsample effect data are based on a "virtual measurement curve" or literature values of effect data for a known sample”) and calculate a thermos-analytical measurement curve which allows the identification of said thermal effect and to output second data suitable for representing said measurement curve; (Denner, [0133] As a result of the grouping by the user, the method can be increasingly trained in respect of the classification with suitable configuration of the software, without the basic mathematical parameters having to be modified”).
Denner is silent on receive expert training data comprising data sets of second and third training data, said second training data being suitable for representing a training measurement curve due to a training sample, said training measurement curve allowing the identification of at least one thermal training effect due to said training sample, and said third training data being representative of said thermal training effect;
and/or to create said expert training data by receiving second data suitable for representing a thermos-analytical measurement curve which allows the identification of at least one thermal training effect from a first software module and creating an associated third data from a human input representative of a human identification of the at least one thermal training effect, wherein said first software module comprises software instructions stored at the one or more non-transitory electronic storage devices, which when executed, configures the one or more processors to:
create a trained sub engine based on the training data, the trained sub engine being suitable to be deployed to a second software module comprising an artificial intelligence engine, which when executed, configures the one or more processors to automatically identify thermal effects, wherein said second software module, when executed, configures the one or more processors to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence enginemore processors to automatically identify thermal effects, wherein said second software module, when executed, configures the one or more processors to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine.
However, Wang teaches receive expert training data comprising data sets of second and third training data, said second training data being suitable for representing a training measurement curve due to a training sample, said training measurement curve allowing the identification of at least one thermal training effect due to said training sample, and said third training data being representative of said thermal training effect; and/or to create said expert training data by receiving second data suitable for representing a thermos-analytical measurement curve which allows the identification of at least one thermal training effect from a first software module and creating an associated third data from a human input representative of a human identification of the at least one thermal training effect, wherein said first software module comprises software instructions stored at the one or more non-transitory electronic storage devices, which when executed, configures the one or more processors to:
create a trained sub engine based on the training data, the trained sub engine being suitable to be deployed to a second software module comprising an artificial intelligence engine, which when executed, configures the one or more processors to automatically identify thermal effects, wherein said second software module, when executed, configures the one or more processors to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence enginetrained sub engine being suitable to be deployed to a second software module comprising an artificial intelligence engine, which when executed, configures the one or more processors to automatically identify thermal effects, wherein said second software module, when executed, configures the one or more processors to receive said second data as an input and to output third data representative of said thermal effect identified by said artificial intelligence engine.( Wang, page 8, top paragraph, “Forecast model uses neural network model as shown in Figure 4, including input layer, hidden layer and output layer, by data above as input parameter, i.e. YB, YC, YD, YE, XB, XC is the input of neutral net,, different weights in neutral net, between each node, can be there are in the output data with actual moisture, volatile matter, fixed carbon, ash and the caloric value of coal as neutral net. First with the multiple weight-loss curve determining coal sample of apparatus measures specified and Industrial Analysis and caloric value numerical value in the case of laboratory determines, use randomly draw 80% data as training sample for training neutral net, with remaining sample as test samples, use the accuracy of specific test rating inspection training, and reach the data such as the moisture of neural network prediction coal, volatile matter, fixed carbon, ash and caloric value of required precision after using training” NOTE: it is known in neural network having sub layers, hidden layers and output layers. No of layers depend on application number of input data and desire output result. It is a model design choice depending on application).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Denner’s thermal analysis method to incorporate a machine learning network based thermal analysis steps as taught by Wang and obtain an accurate thermal effect measurement (Wang, abstract). It would have been obvious to a person of ordinary skill to include the well-known neural network forecast model with input layer, hidden layer and output layer along with the other machine learning network, in order to yield the predicted results of generating accurate quick thermal effect result, yet with higher accuracy (KSR).
Regarding Claim 16, combination of Denner and Wang teaches the method according to claim 3,
Denner is silent on the at least two sub engines are neural networks such that the selection data indicates selection of one of the neural networks; and
the second data is provided as the input of the selected sub engine by providing the second data to the input layer of the selected neural network.
However, Wang teaches the at least two sub engines are neural networks such that the selection data indicates selection of one of the neural networks; and the second data is provided as the input of the selected sub engine by providing the second data to the input layer of the selected neural network. (Wang, page 8, top paragraph, Forecast model uses neural network model as shown in Figure 4, including input layer, hidden layer and output layer by data above as input parameter, i.e. YB, YC, YD, YE, XB, XC is the input of neutral net,”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Denner’s thermal analysis method to incorporate a machine learning network based thermal analysis steps as taught by Wang and obtain an accurate thermal effect measurement (Wang, abstract). It would have been obvious to a person of ordinary skill to include the well-known neural network forecast model with input layer, hidden layer and output layer along with the other machine learning network, in order to yield the predicted results of generating accurate quick thermal effect result, yet with higher accuracy (KSR).
Regarding Claim 17, combination of Denner and Wang teaches the method according to claim 5,
Denner is silent on wherein: the trained sub engine is a trained neural network;
the sub engine template is a blank neural network comprising an input layer for receiving said second data, an output layer for outputting said third data and one or more layers and weights describing the connection between the input layer, the output layer and preferably intermediate layer; and
the engine parameters are weights, which are determined such that, for most of the data sets, the third data outputted by the output layer is essentially equal to the third training data of one of the data sets when the second training data of said data set is received by the input layer and whereby the determined weights specify the trained neural network.
However, Wang teaches wherein: the trained sub engine is a trained neural network; the sub engine template is a blank neural network comprising an input layer for receiving said second data, an output layer for outputting said third data and one or more layers and weights describing the connection between the input layer, the output layer and preferably intermediate layer; andthe engine parameters are weights, which are determined such that, for most of the data sets, the third data outputted by the output layer is essentially equal to the third training data of one of the data sets when the second training data of said data set is received by the input layer and whereby the determined weights specify the trained neural network. .(Wang, page 8, top paragraph, Forecast model uses neural network model as shown in Figure 4, including input layer, hidden layer and output layer, by data above as input parameter, i.e. YB, YC, YD, YE, XB, XC is the input of neutral net,, different weights in neutral net, between each node, can be there are in the output data with actual moisture, volatile matter, fixed carbon, ash and the caloric value of coal as neutral net.First with the multiple weight-loss curve determining coal sample of apparatus measures specified and Industrial Analysis and caloric value numerical value in the case of laboratory determines, use randomly draw 80% data as training sample for training neutral net, with remaining sample as test samples, use the accuracy of specific test rating inspection training, and reach the data such as the moisture of neural network prediction coal, volatile matter, fixed carbon, ash and caloric value of required precision after using training”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Denner’s thermal analysis method to incorporate a machine learning network based thermal analysis steps as taught by Wang and obtain an accurate thermal effect measurement (Wang, abstract). It would have been obvious to a person of ordinary skill to include the well-known neural network forecast model with input layer, hidden layer and output layer along with the other machine learning network, in order to yield the predicted results of generating accurate quick thermal effect result, yet with higher accuracy (KSR).
Regarding Claim 18, combination of Denner and Wang teaches the method according to claim 14,
Denner is silent on wherein: the trained sub engine is a trained neutral network; and the training software module is part of the computer program of claim 13
However, Wang teaches wherein: the trained sub engine is a trained neutral network; and the training software module is part of the computer program of claim 13. Wang, page 8, top paragraph, Forecast model uses neural network model as shown in Figure 4, including input layer, hidden layer and output layer”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Denner’s thermal analysis method to incorporate a machine learning network based thermal analysis steps as taught by Wang and obtain an accurate thermal effect measurement (Wang, abstract). It would have been obvious to a person of ordinary skill to include the well-known neural network forecast model with input layer, hidden layer and output layer along with the other machine learning network, in order to yield the predicted results of generating accurate quick thermal effect result, yet with higher accuracy (KSR).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Byvank et al. (US 20180058953 A1) describes “ An example apparatus can comprise an emitter to emit radio frequency radiation, an absorber that changes temperature based on emissions from the emitter, and one or more sensors to measure a temperature difference between a sample and a reference coupled to the absorber”.(abstract)
Beyer et al. (WO 2021/032616 A1) The invention provides “The invention relates to a device and a method for determining a material characteristic variable, in particular for a plastic material or a process. In the method a combination of input variables (S1,..., Sxx) for a model (106) is provided (202), and the material characteristic variable is determined (204) on the basis of the model (106). The model (106) maps the combinations of input variables (S1,..., Sxx) onto material characteristic variables, wherein the model (106) is trained on the basis of training data defined by a plurality of combinations of input variables (S1,..., Sxx) and their respective assignment to a target material characteristic variable. On the basis of the result of a comparison (206) of a material characteristic variable determined for one of the combinations of training data of the model (106) with the target material characteristic variable assigned to said combination in the training data, the model (106) is either further trained or a modified model (106) is determined (210) by adding a module (A,..., Z) to the model (106) and/or by removing at least one module (A,..., Z) from the model (106), and the modified model (106) is trained” (abstract)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILARA SULTANA whose telephone number is (571)272-3861. The examiner can normally be reached Mon-Fri, 9 AM-5:30 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s
supervisor, EMAN ALKAFAWI can be reached on (571) 272-4448. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DILARA SULTANA/Examiner, Art Unit 2858
09/16/2026
/SON T LE/Primary Examiner, Art Unit 2858
Prosecution Insights