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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C 101.
Claim 1:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
analyzing, … the sets of coordinates indicative of the plurality of characteristics for the candidate material; and; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses evaluating the different data points related to the new material.
based on the analyzing, outputting … at least one predicted thermal endurance characteristic for the candidate material.; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses using the earlier evaluation of data points related to the new material to make a thermal related prediction about the new material.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
by the at least one processor; which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
training, a machine learning model using a set of training data, which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
wherein the set of training data (i) comprises training sets of coordinates indicative of a plurality of characteristics for a plurality of materials, and (ii) is labeled with at least one thermal endurance characteristic for each material of the plurality of materials, wherein the at least one thermal endurance characteristic is associated with each training set of coordinates, of the training sets of coordinates, for that material; which amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
using the machine learning model that was trained, which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
by the at least one processor;
Computer element(s) are recited at a high-level of generality such and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
training, a machine learning model using a set of training data,
Training is recited at a high-level of generality with no detail of the training process such and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
wherein the set of training data (i) comprises training sets of coordinates indicative of a plurality of characteristics for a plurality of materials, and (ii) is labeled with at least one thermal endurance characteristic for each material of the plurality of materials, wherein the at least one thermal endurance characteristic is associated with each training set of coordinates, of the training sets of coordinates, for that material;
As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.
using the machine learning model that was trained,
The machine learning model is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The training of the machine learning model is recited at a high-level of generality with no detail of the training process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the set of training data is labeled with at least one of an electrical relative thermal index (RTI), a mechanical impact RTI, or a mechanical strength RTI. which amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the set of training data is labeled with at least one of an electrical relative thermal index (RTI), a mechanical impact RTI, or a mechanical strength RTI.
As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 3:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
training …the machine learning model using the set of training data and a set of validation data, which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
wherein the set of validation data (i) comprises validation sets of coordinates indicative of the plurality of characteristics for the plurality of materials, and (ii) is labeled with the at least one thermal endurance characteristic for each material of the plurality of materials, wherein the at least one thermal endurance characteristic is associated with each validation set of coordinates, of the validation sets of coordinates, for that material. which amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
by the at least one processor, which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
training …the machine learning model using the set of training data and a set of validation data,
Training is recited at a high-level of generality with no detail of the training process such and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
wherein the set of validation data (i) comprises validation sets of coordinates indicative of the plurality of characteristics for the plurality of materials, and (ii) is labeled with the at least one thermal endurance characteristic for each material of the plurality of materials, wherein the at least one thermal endurance characteristic is associated with each validation set of coordinates, of the validation sets of coordinates, for that material.
Training is recited at a high-level of generality with no detail of the training process such and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
by the at least one processor,
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 4:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
accessing … the sets of coordinates; which amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
coordinates indicative of at least one of: an infrared analysis, a thermogravimetric analysis, or a differential scanning calorimetry for the candidate material.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
accessing … the sets of coordinates indicative of at least one of: an infrared analysis, a thermogravimetric analysis, or a differential scanning calorimetry for the candidate material.;
As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 4:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
accessing … the sets of coordinates; which amounts to extra-solution activity of transmitting data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
coordinates indicative of at least one of: an infrared analysis, a thermogravimetric analysis, or a differential scanning calorimetry for the candidate material.; which amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
accessing … the sets of coordinates
As discussed above, the additional element of accessing amounts to extra-solution activity of transmitting data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
coordinates indicative of at least one of: an infrared analysis, a thermogravimetric analysis, or a differential scanning calorimetry for the candidate material.;
As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 5:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
accessing … the sets of coordinates which amounts to extra-solution activity of transmitting data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
coordinates indicative of at least one of: an infrared analysis, a thermogravimetric analysis, or a differential scanning calorimetry for the candidate material.; which amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
by the at least one processor, which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
updating … the machine learning model with (i) the sets of coordinates; which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
coordinates indicative of the plurality of characteristics for the candidate material, and (ii) the at least one actual thermal endurance characteristic for the candidate material. which amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
accessing … the sets of coordinates
As discussed above, the additional element of accessing amounts to extra-solution activity of transmitting data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
coordinates indicative of at least one of: an infrared analysis, a thermogravimetric analysis, or a differential scanning calorimetry for the candidate material.;
As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.
by the at least one processor,
Computer components are recited at a high-level of generality such and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
updating … the machine learning model with (i) the sets of coordinates
Updating the model reads on training and is recited at a high-level of generality with no detail of the training process such and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
coordinates indicative of the plurality of characteristics for the candidate material, and (ii) the at least one actual thermal endurance characteristic for the candidate material.
As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 6:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
outputting … (i) the at least one predicted … characteristic for the candidate material and (ii) a confidence level for each of the at least one predicted … characteristic for the candidate material.; which amounts to extra-solution activity of transmitting data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
thermal endurance characteristic; which amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
by the machine learning model, which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
outputting … (i) the at least one predicted … characteristic for the candidate material and (ii) a confidence level for each of the at least one predicted … characteristic for the candidate material.;
This limitation is directed to transmitting input at an interface on a computing device, wherein the input comprises a dataset, an analysis for the dataset, and an output medium which amounts to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
thermal endurance characteristic;
As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.
by the machine learning model,
The machine learning model is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The training of the machine learning model is recited at a high-level of generality with no detail of the training process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 7:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
accessing … the sets of coordinates, wherein each of the sets of coordinates comprises (x,y) coordinates indicative of a respective characteristic of the plurality of characteristics.; which amounts to extra-solution activity of transmitting data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
by the at least one processor, which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
accessing … the sets of coordinates, wherein each of the sets of coordinates comprises (x,y) coordinates indicative of a respective characteristic of the plurality of characteristics.;
As discussed above, the additional elements of accessing coordinates amounts to extra-solution activity of transmitting data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
by the at least one processor, which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 8:
Claim 8 recites substantially similar limitations for claim 1 and is therefore rejected on the same basis.
Claim 9:
Claim 9 recites substantially similar limitations for claim 2 and is therefore rejected on the same basis.
Claim 10:
Claim 10 recites substantially similar limitations for claim 3 and is therefore rejected on the same basis.
Claim 11:
Claim 11 recites substantially similar limitations for claim 4 and is therefore rejected on the same basis.
Claim 12:
Claim 12 recites substantially similar limitations for claim 5 and is therefore rejected on the same basis.
Claim 13:
Claim 13 recites substantially similar limitations for claim 6 and is therefore rejected on the same basis.
Claim 14:
Claim 14 recites substantially similar limitations for claim 7 and is therefore rejected on the same basis.
Claim 15:
Claim 15 recites substantially similar limitations for claim 1 and is therefore rejected on the same basis.
Claim 16:
Claim 16 recites substantially similar limitations for claim 2 and is therefore rejected on the same basis.
Claim 17:
Claim 17 recites substantially similar limitations for claim 3 and is therefore rejected on the same basis.
Claim 18:
Claim 18 recites substantially similar limitations for claim 4 and is therefore rejected on the same basis.
Claim 19:
Claim 19 recites substantially similar limitations for claim 5 and is therefore rejected on the same basis.
Claim 20:
Claim 20 recites substantially similar limitations for claim 6 and is therefore rejected on the same basis.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-3,7-10, 14-17 are rejected under 35 U.S.C 103 as being unpatentable over Ghaderi et al. (Ghaderi, Aref, Georges Ayoub, and Roozbeh Dargazany. , "Constitutive behavior and failure prediction of crosslinked polymers exposed to concurrent fatigue and thermal aging: a reduced-order knowledge-driven machine-learned model.", hereinafter Ghaderi) in view of Sarabi et al. (WO2014095519A1, hereinafter Sarabi).
Regarding Claim 1:
Ghaderi teaches:
A … method of using machine learning to assess thermal endurance characteristics of materials, the … method comprising:
Ghaderi [Abstract, pg 5066] The suggested model showed a satisfactory correlation with the experimental results, demonstrating its potential for predicting the long-term durability and reliability of materials subjected to thermal aging and cyclic fatigue. Overall, our approach provides a valuable tool for designing and optimizing materials that are subjected to complex aging conditions.
training … a machine learning model using a set of training data, wherein the set of training data (i) comprises training sets of coordinates indicative of a plurality of characteristics for a plurality of materials, and (ii) is labeled with at least one thermal endurance characteristic for each material of the plurality of materials, wherein the at least one thermal endurance characteristic is associated with each training set of coordinates, of the training sets of coordinates, for that material;
Ghaderi [Paragraph above Validation and results; pg 5077] In this approach, tensile strength and elongation at break for various aged conditions are determined using an inverse problem, where the stress–strain point of the unaged material and several aged conditions are used for training.
[Table 1 in Experimental observations, pg 5071]
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EN: “various aged conditions” reads on including thermal aging conditions shown in Table 1
accessing … sets of coordinates indicative of the plurality of characteristics for a candidate material;
EN: this reads on fetching from a database
Ghaderi [Algorithm 1 in Failure model, pg 5077]
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EN: “import[ing]” data reads on “accessing” from a database; “deformation, time, temperature and number of cycles” reads on plurality of characteristics
analyzing, … using the machine learning model that was trained, the sets of coordinates indicative of the plurality of characteristics for the candidate material; and
Ghaderi [Validation and results, col 2, pg 5078] The deep neural network was trained and then used to make predictions.
EN: this reads on the inputs of the NN being the same for training and predict
based on the analyzing, outputting, by the machine learning model, at least one predicted thermal endurance characteristic for the candidate material.
Ghaderi [Validation and results, pg 5078-9] The model’s predictions were validated against experimental data collected at two different temperatures, namely 60 and 80◦C , for varying aging periods and number of cycles, as depicted in Figs. 6 and 7. The results of the comparison demonstrate that the proposed model accurately captured the elastomer response to both softening due to fatigue damage and hardening due to thermal aging. Furthermore, the model successfully predicted the behavior of the material when both damage processes were combined. The model has a reasonable accuracy and can predict both constitutive behavior and the failure point (see Fig. 8).
[Fig 8 in Validation and results, pg 5080]
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EN: the prediction of stress due to thermal aging reads on the “outputting” of “at least one predicted thermal endurance characteristic”
Ghaderi does not explicitly teach that the method is computer-implemented or that the training, accessing, and analyzing are done by at least one processor:computer-implemented method
by the at least one processor
However, Sarabi teaches:
computer-implemented method
Sarabi [pg 8] In a further aspect, the invention relates to a computer program product having instructions for carrying out the above-described method, wherein said instructions can be implemented by a processor. In a further aspect, the invention relates to a system for determining the ageing characteristics of a material, wherein the ageing characteristics are described by means of parameters, wherein the parameters comprise a physical test characteristic of the material, a storage temperature, and a storage time of the material, wherein the physical test characteristic is given in association with the storage temperature and the storage time, wherein the system comprises a neural network and an Arrhenius model,
by the at least one processor
Sarabi [pg 8] In a further aspect, the invention relates to a computer program product having instructions for carrying out the above-described method, wherein said instructions can be implemented by a processor.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the method of predicting thermal aging characteristics of Ghaderi with the method of predicting thermal aging characteristics using RTS of Sarabi in order to use more relevant data for certain materials for training.
Sarabi [pg 1-2] Many materials become brittle over time, for instance, and so a material that was originally reversibly, elastically flexible becomes incapable, over a certain period of time, of absorbing bending forces. Instead, the material breaks. It has become commonplace to utilize the so-called relative temperature index (RTI) in order to describe the ageing characteristics of materials, in particular with respect to the mechanical properties.
[pg 4] In determining the ageing characteristics of the material, instead of being limited to stating the RTI as the usual 50% value for the mechanical loadability, for example, it is now also possible to apply an 80% value of the RTS or a 30% value of the RTS without performing a measurement in advance. Higher values of the RTS can be highly relevant in particular for materials that are used at safety-critical points. It is therefore feasible, for example, to use the same material at different points of a motor vehicle, for example, at the bumper and at a covering in the vehicle interior. Since it is substantially more critical for the bumper if the material loses the mechanical properties thereof over a longer period of time, the 80% value of the RTI could be used for bumpers, while a 20% value of the RTI could be used for the covering parts in the vehicle interior.
Regarding Claim 2:
The combination of Ghaderi and Sarabi teaches all of the limitations of claim 1 as cited above, but Ghaderi does not explicitly teach:
wherein the set of training data is labeled with at least one of an electrical relative thermal index (RTI), a mechanical impact RTI, or a mechanical strength RTI.
However, Sarabi further teaches:
wherein the set of training data is labeled with at least one of an electrical relative thermal index (RTI), a mechanical impact RTI, or a mechanical strength RTI.
Sarabi [pg 2] The RTI is defined in the international standard CEl [EC 60216-1 as follows: The RTI is defined as the temperature at which the amount of time required for a specified property to drop to 50% of the original value thereof, in the case of a material stored in air, is equivalent to that of a comparable, other material at the already-known RTI temperature thereof. In other words, a reference material is initially defined, about which the time period after which this material has lost 50% of a predetermined physical property such as dielectric strength, tensile strength, or impact strength is known.
[pg 2] After various periods of time, the standard bodies are removed, cooled, and subjected to a mechanical test in order to determine, for example, the tensile strength or the relative tensile strength (RTS) as a percent of the mechanical loadability as compared to before the start of the temperature-controlled storage. The goal thereof is typically to determine the storage time, at a given temperature, at which the RTS is 50%.
EN: tensile strength reads on mechanical strength
[pgs 3-4] Embodiments of the invention could have the advantage that the ageing
characteristics of the material can be determined after the neural network is trained accordingly using measured values for the TS, the storage temperature, and the storage time for subsequently arbitrary values of RTS, storage temperature, and storage time. For example, it could be possible to supply only the RTS and the storage temperature, as the parameters, to the neural network and the Arrhenius model, on the basis of which the associated storage time is obtained after a related calculation is performed according to the Arrhenius model. It is advantageous in this case that the use of a hybrid neural network, i.e. a combination of a neural network with an analytical component (the Arrhenius model) also makes it possible to formulate predictions for parameter ranges for which the neural network was not trained, i.e. for which the neural network was not supplied with measurement data. The reason is that a pure neuronal network cannot extrapolate. Embodiments of the invention could therefore make it possible to make predictions regarding the ageing characteristics of a material for nearly any value ranges of parameters without the need for actual measurement methods extending across the entire value range of the parameters. In determining the ageing characteristics of the material, instead of being limited to stating the RTI as the usual 50% value for the mechanical loadability, for example, it is now also possible to apply an 80% value of the RTS or a 30% value of the RTS without performing a measurement in advance.
[pg 2] Determining the RTS for various temperatures therefore makes it possible to determine the RTI of this material and, therefore, to determine a measure of the ageing characteristics of this material.
[pg 4] Nevertheless, according to the embodiments of the invention, it is sufficient if measurement data related to 50% of the RTS and, therefore, 50% of the RTI, were actually acquired once, the neural network was trained using these data, and the above-described method for determining the ageing characteristics was then carried out.
EN: these passages read on multiple RTS values being used as training data which means that the RTI value was used as the mechanical strength RTI is when the material strength drops to 50% of its original RTS;
Regarding Claim 3:
The combination of Ghaderi and Sarabi teaches all of the limitations of claim 1 as cited above, and Ghaderi further teaches:
training … the machine learning model using the set of training data and a set of validation data, wherein the set of validation data (i) comprises validation sets of coordinates indicative of the plurality of characteristics for the plurality of materials, and (ii) is labeled with the at least one thermal endurance characteristic for each material of the plurality of materials, wherein the at least one thermal endurance characteristic is associated with each validation set of coordinates, of the validation sets of coordinates, for that material.
Ghaderi [Abstract, pg 5066] The model is validated using a wide range of experimental data based on sequential aging. The suggested model showed a satisfactory correlation with the experimental results, demonstrating its potential for predicting the long-term durability and reliability of materials subjected to thermal aging and cyclic fatigue.
[pg 5078] The model’s predictions were validated against experimental data collected at two different temperatures, namely 60 and 80◦C , for varying aging periods and number of cycles, as depicted in Figs. 6 and 7.
EN: this reads on validation data
Ghaderi does not explicitly teach that the training is performed by at least one processor:
by the at least one processor
However, Sarabi teaches:
by the at least one processor
Sarabi [pg 8] In a further aspect, the invention relates to a computer program product having instructions for carrying out the above-described method, wherein said instructions can be implemented by a processor.
Regarding Claim 7:
The combination of Ghaderi and Sarabi teaches all of the limitations of claim 1 as cited above, and Ghaderi further teaches:
wherein accessing the sets of coordinates indicative of the plurality of characteristics for the candidate material comprises:
accessing, by the at least one processor, the sets of coordinates, wherein each of the sets of coordinates comprises (x,y) coordinates indicative of a respective characteristic of the plurality of characteristics.
Ghaderi [Validation and results, col 2, pg 5078] The deep neural network was trained and then used to make predictions.
[Algorithm 1 in Failure model, pg 5077]
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EN: deformation ‘F’ reads on y, and the other variables (time, temperature and number of cycles) reads on x; each set would be one of the ’x’ paired with y (deformation);
Claim 8:
Claim 8 recites substantially similar limitations for claim 1 and is there taught by the combination of Ghaderi and Sarabi. The computer-implemented method from claim 1 is instead a system in claim 8.
Ghaderi does not distinctly disclose:
A system … comprising: a memory storing a set of computer-readable instructions; and one or more processors interfaced with the memory, and configured to execute the set of computer-readable instructions to cause the one or more processors to:
However, Sarabi teaches:
A system … comprising: a memory storing a set of computer-readable instructions; and one or more processors interfaced with the memory, and configured to execute the set of computer-readable instructions to cause the one or more processors to:
Sarabi [pg 1] The invention relates to a method for determining the ageing characteristics of a material, a related computer program product, and a system for determining the ageing characteristics of a material.
[pg 8] In a further aspect, the invention relates to a computer program product having instructions for carrying out the above-described method, wherein said instructions can be implemented by a processor.
EN: computer program product having instruction reads on storing in memory
Therefore, claim 8 is rejected on the same basis in addition to the system.
Claim 9:
Claim 9 recites substantially similar limitations for claim 2 and is therefore rejected on the same basis.
Claim 10:
Claim 10 recites substantially similar limitations for claim 3 and is therefore rejected on the same basis.
Claim 14:
Claim 14 recites substantially similar limitations for claim 7 and is therefore rejected on the same basis.
Claim 15:
Claim 15 recites substantially similar limitations for claim 1 and is there taught by the combination of Ghaderi and Sarabi. The computer-implemented method from claim 1 is instead a non-transitory computer-readable storage medium in claim 15.
Ghaderi does not distinctly disclose:
A non-transitory computer-readable storage medium configured to store instructions executable by one or more processors, the instructions comprising:
However, Sarabi teaches:
A non-transitory computer-readable storage medium configured to store instructions executable by one or more processors, the instructions comprising:
Sarabi [pg 1] The invention relates to a method for determining the ageing characteristics of a material, a related computer program product, and a system for determining the ageing characteristics of a material.
[pg 8] In a further aspect, the invention relates to a computer program product having instructions for carrying out the above-described method, wherein said instructions can be implemented by a processor.
EN: computer program product reads on non-transitory computer-readable storage medium
Therefore, claim 15 is rejected on the same basis in addition to the non-transitory computer-readable storage medium.
Claim 16:
Claim 16 recites substantially similar limitations for claim 2 and is therefore rejected on the same basis.
Claim 17:
Claim 17 recites substantially similar limitations for claim 3 and is therefore rejected on the same basis.
Claim 4,11, and 18 are rejected under 35 U.S.C 103 as being unpatentable over Ghaderi in view of Sarabi in further of view Wust et al. (US 20250060327 A1, hereinafter Wust).
Regarding Claim 4:
The combination of Ghaderi and Sarabi teaches all of the limitations of claim 1 as cited above, and Ghaderi does not distinctly disclose:
by the at least one processor
However, Sarabi teaches:
by the at least one processor
Sarabi [pg 8] In a further aspect, the invention relates to a computer program product having instructions for carrying out the above-described method, wherein said instructions can be implemented by a processor.
The combination of Ghaderi and Sarabi teaches accessing the sets of coordinates indicative of the plurality of characteristics for the candidate material comprises as cited above, but does not distinctly disclose:
accessing … the sets of coordinates indicative of at least one of: an infrared analysis, a thermogravimetric analysis, or a differential scanning calorimetry for the candidate material.
Wust [0022] The first data may comprise data obtained by subjecting said sample to a thermoanalytical measurement comprising Differential Scanning calorimetry (DSC), Differential Thermal Analysis (DTA), Thermogravimetric Analysis (TGA), Evolved Gas Analysis (EGA), Thermomechanical Analysis (TMA) or Dynamic Mechanical Analysis (DMA), but it is not limited to this. The first data may be obtained by applying only one of these measurements, or it may be obtained applying more than one of these measurements simultaneously.
[0031] The second data is provided to the second software module. The second software module comprises an artificial intelligence engine configured for automatic identification of thermal effects. The artificial intelligence engine may be operative to detect a characteristic feature of the thermal effect reflected in the second data and/or the measurement curve. The artificial intelligence engine may comprise at least one artificial intelligence algorithm, in particular a machine learning algorithm. A machine learning algorithm may be trained using training data. I. e., the machine learning algorithm may be first trained using training data and then be used for the identification of thermal effects.
[Fig 2, Sheet 2]
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EN: DSC and TGA are included in the first data which is processed through the first module and outputted as second data which is inputted into an ML for training
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the method of predicting thermal aging characteristics of Ghaderi and Sarabi with the training data of DSC and TGA for a thermal effect prediction AI engine of Wust in order to have training data that better helps the identification of the thermal effect.
Wust [0002] Thermal analysis studies physical and chemical properties of a substance as they change with temperature. According to the International Confederation for Thermal Analysis and calorimetry, thermal analysis is a group of techniques in which a physical property of a substance is measured as a function of temperature whilst the substance is subjected to a controlled temperature program. I.e., in thermal analysis a sample of said substance is subjected to an excitation which generates an observable response. The response signal corresponding to said observable response is measured with measurement means. First data comprising the response signal and the excitation, both as a function of time (i.e., a time series of the excitation and the response signal), is used to calculate a thermoanalytical measurement curve which allows the identification of said thermal effect.
Claim 11:
Claim 11 recites substantially similar limitations for claim 4 and is therefore rejected on the same basis.
Claim 18:
Claim 18 recites substantially similar limitations for claim 4 and is therefore rejected on the same basis.
Claim 5-6, 12-13, 19-20 are rejected under 35 U.S.C 103 as being unpatentable over Ghaderi in view of Sarabi in further of view Zhang et al. (US 20220108132 A1, hereinafter Zhang).
Regarding Claim 5:
The combination of Ghaderi and Sarabi teaches all of the limitations of claim 1 as cited above, and Ghaderi further teaches:
accessing … at least one actual thermal endurance characteristic for the candidate material; and
Ghaderi [Algorithm 1 in Failure model, pg 5077]
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EN: “import[ing]” data reads on “accessing” from a database; “deformation, time, temperature and number of cycles” reads on plurality of characteristics
Ghaderi does not explicitly teach:
by the at least one processor
However, Sarabi teaches:
by the at least one processor
Sarabi [pg 8] In a further aspect, the invention relates to a computer program product having instructions for carrying out the above-described method, wherein said instructions can be implemented by a processor.
The combination of Ghaderi and Sarabi does not explicitly teach:
updating … the machine learning model
However, Zhang teaches:
updating … the machine learning model
Zhang [0006] The system may train the classifier upon exceeding a convergence threshold. At decision 361, the system may determine if the network as met a convergence threshold. If the system has not met the convergence threshold, it will continue to train the network. However, if convergence is met, the system will output the trained network. At step 363, the output may be a trained network. Thus, the trained network may be able to identify certain perturbations or adversarial images (e.g., during an adversarial attack) that may not have been previously identifiable.
[0080] In a scenario where an adversarial attack may occur, the system described above may be further trained to better detect
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the method of predicting thermal aging characteristics of Ghaderi and Sarabi with the updating of the ML model of Zhang as it would be obvious to one of ordinary skill in the art of machine learning to allow retraining of the algorithm to update for new data.
Regarding Claim 6:
The combination of Ghaderi and Sarabi teaches all of the limitations of claim 1 as cited above, and Ghaderi further teaches:
wherein outputting, by the machine learning model, the at least one predicted thermal endurance characteristic for the candidate material comprises:
outputting, by the machine learning model, (i) the at least one predicted thermal endurance characteristic for the candidate material and
Ghaderi [Validation and results, pg 5078-9] The model’s predictions were validated against experimental data collected at two different temperatures, namely 60 and 80◦C , for varying aging periods and number of cycles, as depicted in Figs. 6 and 7. The results of the comparison demonstrate that the proposed model accurately captured the elastomer response to both softening due to fatigue damage and hardening due to thermal aging. Furthermore, the model successfully predicted the behavior of the material when both damage processes were combined. The model has a reasonable accuracy and can predict both constitutive behavior and the failure point (see Fig. 8).
[Fig 8 in Validation and results, pg 5080]
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EN: the prediction of stress due to thermal aging reads on the “outputting” of “at least one predicted thermal endurance characteristic”
Ghaderi teaches the outputting of at least one predicted thermal endurance characteristic but does not explicitly teach the outputting of the corresponding confidence level with that characteristic:
(ii) a confidence level for each of the at least one predicted … characteristic….
However, Zhang teaches:
(ii) a confidence level for each of the at least one predicted … characteristic….
Zhang [0036] A machine-learning algorithm 310 may generate a confidence level or factor for each output generated.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the method of predicting thermal aging characteristics of Ghaderi and Sarabi with the confidence level outputs of Zhang as it would be obvious to one of ordinary skill in the art of machine learning to output a confidence level for each output of a ML model.
Zhang [0036] In the example, the machine-learning algorithm 310 may process raw source data 315 and output an indication of a representation of an image. The output may also include augmented representation of the image. A machine-learning algorithm 310 may generate a confidence level or factor for each output generated. For example, a confidence value that exceeds a predetermined high-confidence threshold may indicate that the machine-learning algorithm 310 is confident that the identified feature corresponds to the particular feature. A confidence value that is less than a low-confidence threshold may indicate that the machine-learning algorithm 310 has some uncertainty that the particular feature is present.
Claim 12:
Claim 12 recites substantially similar limitations for claim 5 and is therefore rejected on the same basis.
Claim 13:
Claim 13 recites substantially similar limitations for claim 6 and is therefore rejected on the same basis.
Claim 19:
Claim 19 recites substantially similar limitations for claim 5 and is therefore rejected on the same basis.
Claim 20:
Claim 20 recites substantially similar limitations for claim 6 and is therefore rejected on the same basis.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIAHE NIU whose telephone number is (571)270-0152. The examiner can normally be reached 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.N./Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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