DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Title
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Examiner believes that the title of the invention is imprecise. A descriptive title indicative of the invention will help in proper indexing, classifying, searching, etc. See MPEP 606.01. However, the title of the invention should be limited to 500 characters. Examiner suggests including the aspect(s) of the claims which Applicant believes to be novel or nonobvious over the prior art.
Drawings
The drawings are objected to because of the following informalities.
Figures 6-10 contain illegible text. Additionally, numbers, letters, and reference characters must measure at least .32 cm. (1/8 inch) in height. See 37 CFR 1.84(p)(3).
Figures 2, 3a-d, 4c-f, 5a-b contain terms in formats such as “dataset(s),” “property(s),” and “class(s).” For examination purposes, these are treated as “one or more datasets,” “one or more properties,” and “one or more classes.” Similar uses of “(s)” are treated likewise. These instances and others like it should be amended to prevent potential interpretation issues.
Corrected drawings in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
The specification uses “(s)” in several places. For examination purposes, these are treated as “one or more” of whatever precedes it. These instances and others like it should be amended to prevent potential interpretation issues.
Appropriate correction is required.
Claim Objections
The following claims are objected to because of the following informalities:
The claims use “(s)” in several places. For examination purposes, these are treated as “one or more” of whatever precedes it. These instances and others like it should be amended to prevent potential interpretation issues.
Claim 6 recites one or more of the training dataset(s) but claim 1 introduced this element as one or more of training datasets. Elements referring to the same element should use the identical identifiers for clarity of the claim. Examiner recommends amending claim 6 to recite in part, one or more of the training datasets.
Claim 8 does not end in a period.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Specifically, claims 1, 2, 4,14, and 15 recite extracting target-relevant material properties. It is unclear by what standard to judge if a property is target-relevant or not.
Specifically, claims 1, 14 and 15 recite one or more machine learning algorithms twice. It is unclear if this is the same element, a different element, or related elements. Additionally, claims 1, 14, and 15 recite the specific target material property(s) without clear antecedent basis. Additionally, claims 1, 14, and 15 recite the plurality of training datasets and the plurality of machine learning algorithms. However, these lack clear antecedent basis, since they were both introduced as one or more.
Specifically, claim 2 recites the material and property classes without clear antecedent basis. Additionally, claim 2 recites values of the extracted target-relevant material properties was previously introduced in claim 1. It is unclear if this is the same element, a different element, or related elements. Additionally, claim 2 recites value(s) of the specific target material property without clear antecedent basis since claim 1 introduces values of the specific target material property.
Specifically, claim 3 recites one or more of the classes twice. It is unclear if this is the same element, a different element, or related elements.
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.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a signal per se which is non-statutory subject matter according to MPEP 2106, which states that a signal per se is not directed to one of the four categories of statutory subject matter listed in 35 U.S.C. 101. Claim 15 recites a computer program product comprising instructions. The scope of this term includes transitory signals.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a program per se (i.e. software per se) which is non-statutory subject matter according to MPEP 2106, which states that a computer program per se is not directed to one of the four categories of statutory subject matter listed in 35 U.S.C. 101. Claim 15 recites a computer program product comprising instructions. The scope of this term includes software per se.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a method claim. Claim 14 is a system claim. Claim 15 is a computer program product claim. Therefore, claims 1 and 14 are directed to either a process, machine, manufacture or composition of matter. Claim 15 is not directed to a statutory subject matter; however, it can be amended to fall into one of the enumerated categories, if proper support exists in the original disclosure.
With respect to Claim 1:
Step 2A Prong 1:
generating one or more training datasets for training one or more machine learning algorithms for determining values of one or more target material properties of assessment materials, a training dataset being generated specifically for each of the one or more target material properties (mental process – user can manually generate one or more training datasets for training one or more machine learning algorithms for determining values of one or more target material properties of assessment materials, a training dataset being generated specifically for each of the one or more target material properties)
classifying the data records into a plurality of classes according to the input value(s) of one or more of the material properties, at least according to the values of chemical composition and processing parameter(s) (mental process – user can manually classify the data records into a plurality of classes according to the input value(s) of one or more of the material properties, at least according to the values of chemical composition and processing parameter(s))
within each of the plurality of classes, extracting target-relevant material properties from the data records having a dependency relationship with the specific target material property(s) (mental process – user can manually within each of the plurality of classes, extract target-relevant material properties from the data records having a dependency relationship with the specific target material property(s))
generating at least one training dataset corresponding to one or more of the plurality of classes using values of the extracted target-relevant material properties and corresponding values of the specific target material property(s) (mental process – user can manually generate at least one training dataset corresponding to one or more of the plurality of classes using values of the extracted target-relevant material properties and corresponding values of the specific target material property(s))
determining output value(s) of the specific target material property(s) of an assessment material using one of the plurality of machine learning algorithms trained using a training dataset corresponding to the class of the assessment material and trained for determining the specific target material property (mental process – user can manually determine output value(s) of the specific target material property(s) of an assessment material)
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Additional elements:
providing a database comprising data records indicative of input values of material properties of a multitude of structural materials, comprising at least chemical composition and processing parameter(s) of the multitude structural materials (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
training of one or more machine learning algorithms for one or more of the plurality of classes, each machine learning algorithm being trained for determining a specific target material property of assessment materials using one of the plurality of training datasets (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 - see MPEP 2106.05(f))
determining output value(s) of the specific target material property(s) of an assessment material using one of the plurality of machine learning algorithms trained using a training dataset corresponding to the class of the assessment material and trained for determining the specific target material property (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 - see MPEP 2106.05(f))
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. Additional elements:
providing a database comprising data records indicative of input values of material properties of a multitude of structural materials, comprising at least chemical composition and processing parameter(s) of the multitude structural materials (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving information in memory” or “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer)
training of one or more machine learning algorithms for one or more of the plurality of classes, each machine learning algorithm being trained for determining a specific target material property of assessment materials using one of the plurality of training datasets (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 - see MPEP 2106.05(f))
determining output value(s) of the specific target material property(s) of an assessment material using one of the plurality of machine learning algorithms trained using a training dataset corresponding to the class of the assessment material and trained for determining the specific target material property (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 - see MPEP 2106.05(f))
Conclusion: The claim is not patent eligible.
Claims 14 and 15 are rejected on the same grounds as claim 1. Additionally for claims 14 and 15: Claim 14 has the additional elements of a data storage device for storing a database comprising data records indicative of input values of material properties of a multitude of structural materials, and for storing training dataset(s); a computing device communicatively connected to the data storage device; an input interface for receiving input indicative of value(s) of a first set of material properties of the assessment material and/or for receiving a selection of a machine learning model; and an output interface for outputting output data indicative of values of target material property(s) of an assessment material. These elements are mere instructions to apply the exception using a generic computer component under Step 2A prong 2 and Step 2B. Claim 15 has the additional element of a computer program product. This element is mere instructions to apply the exception using a generic computer component under Step 2A prong 2 and Step 2B.
Regarding Claim 2: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually determining validation and/or test dataset(s) corresponding to one or more of the material and property classes using values of the extracted target-relevant material properties and corresponding value(s) of the specific target material property(s).
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 3: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein splitting one or more of the classes into training, validation and/or test dataset(s) is performed using a rational split algorithm, such as selection of data records within one or more of the classes having a uniform distribution of values of one or more of the material properties over a regression space.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 4: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually applying a mutual information based algorithm, in particular a partial mutual information algorithm and/or
applying a selection algorithm such as a Markov blanket algorithm onto the data records within one or more of the classes to identify a subset of target-relevant material properties of the data records having a dependency relationship with the target material property(s).
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 5: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein generating training dataset(s) further comprises normalizing the data records within one or more of the classes, wherein normalizing the data records within one or more of the classes comprises:
removal of data records with missing values of material properties; and/or
removal of data records comprising extreme values of material properties; and/or
removal of data records comprising inconsistent values of material properties; and/or
transforming non-numerical values of material properties into corresponding numerical representations.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 6: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually determining respective applicability domain(s) of one or more of the training dataset(s).
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 7: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) includes the additional elements of wherein the training of the machine learning algorithm comprises computing a regression model representative of a relationship between values of two or more of material properties within the training dataset(s).
These judicial exceptions are not integrated into a practical application. The additional element(s) of wherein the training of the machine learning algorithm comprises computing a regression model representative of a relationship between values of two or more of material properties within the training dataset(s) recite merely 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 - see MPEP 2106.05(f). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of wherein the training of the machine learning algorithm comprises computing a regression model representative of a relationship between values of two or more of material properties within the training dataset(s) recite 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 - see MPEP 2106.05(f). Accordingly, the claims are not patent eligible.
Regarding Claim 8: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein the machine learning algorithm comprises a neural network-based machine learning algorithm.
The limitation(s) includes the additional elements of wherein the training of the machine learning algorithm further comprises the steps of:
a) receiving a first set of material properties as an input, a second set of material properties as an expected output associated with the input;
b) determining a generated output of the neural network by inputting the input into the neural network;
c) determining value(s) of a selected cost function based on a comparison of the expected output and the generated output; and
d) adapting the neural network based on the value(s) of the selected cost function.
These judicial exceptions are not integrated into a practical application. The additional element(s) of wherein the training of the machine learning algorithm further comprises the steps of: b) determining a generated output of the neural network by inputting the input into the neural network; c) determining value(s) of a selected cost function based on a comparison of the expected output and the generated output; and d) adapting the neural network based on the value(s) of the selected cost function recite merely 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 - see MPEP 2106.05(f). The additional element(s) of a) receiving a first set of material properties as an input, a second set of material properties as an expected output associated with the input recite adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of wherein the training of the machine learning algorithm further comprises the steps of: b) determining a generated output of the neural network by inputting the input into the neural network; c) determining value(s) of a selected cost function based on a comparison of the expected output and the generated output; and d) adapting the neural network based on the value(s) of the selected cost function recite 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 - see MPEP 2106.05(f). The additional element(s) of a) receiving a first set of material properties as an input, a second set of material properties as an expected output associated with the input recite merely “storing and retrieving information in memory” or “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim) (MPEP 2106.05(d)(II)). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer. Accordingly, the claims are not patent eligible.
Regarding Claim 9: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein the machine learning algorithm comprises a decision tree-based machine learning algorithm, in particular a gradient-boosted decision tree-based machine learning algorithm.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 10: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) includes the additional elements of preventing overtraining of the machine learning algorithm using the validation dataset(s) and/or further comprising evaluating the performance of the machine learning algorithm using the test dataset(s).
These judicial exceptions are not integrated into a practical application. The additional element(s) of preventing overtraining of the machine learning algorithm using the validation dataset(s) and/or further comprising evaluating the performance of the machine learning algorithm using the test dataset(s)recite merely 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 - see MPEP 2106.05(f). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of preventing overtraining of the machine learning algorithm using the validation dataset(s) and/or further comprising evaluating the performance of the machine learning algorithm using the test dataset(s)recite 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 - see MPEP 2106.05(f). Accordingly, the claims are not patent eligible.
Regarding Claim 11: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually determining value(s) of the specific target material property(s) of the assessment material using the machine learning algorithm trained using the training dataset(s).
The limitation(s) includes the additional elements of receiving input indicative of value(s) of a first set of material properties of the assessment material, and determining value(s) of the specific target material property(s) of the assessment material using the machine learning algorithm trained using the training dataset(s).
These judicial exceptions are not integrated into a practical application. The additional element(s) of using the machine learning algorithm trained using the training dataset(s)recite merely 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 - see MPEP 2106.05(f). The additional element(s) of receiving input indicative of value(s) of a first set of material properties of the assessment material recite adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of using the machine learning algorithm trained using the training dataset(s)recite 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 - see MPEP 2106.05(f). The additional element(s) of receiving input indicative of value(s) of a first set of material properties of the assessment material recite merely “storing and retrieving information in memory” or “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim) (MPEP 2106.05(d)(II)). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer. Accordingly, the claims are not patent eligible.
Regarding Claim 12: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein determining value(s) of target material property(s) using the machine learning algorithm further comprises selecting a machine learning algorithm trained with a training dataset for the specific target material property(s).
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 13: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) includes the additional elements of receiving a selection of a machine learning model, wherein:
the machine learning algorithm is generated and trained in accordance with the selection of a machine learning model; and
the values of specific target material property(s) of the assessment material is/are determined using the machine learning algorithm according to the selected machine learning model.
These judicial exceptions are not integrated into a practical application. The additional element(s) of the machine learning algorithm is generated and trained in accordance with the selection of a machine learning model; and the values of specific target material property(s) of the assessment material is/are determined using the machine learning algorithm according to the selected machine learning model recite merely 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 - see MPEP 2106.05(f). The additional element(s) of receiving a selection of a machine learning model recite adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of the machine learning algorithm is generated and trained in accordance with the selection of a machine learning model; and the values of specific target material property(s) of the assessment material is/are determined using the machine learning algorithm according to the selected machine learning model recite 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 - see MPEP 2106.05(f). The additional element(s) of receiving a selection of a machine learning model recite merely “storing and retrieving information in memory” or “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim) (MPEP 2106.05(d)(II)). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer. Accordingly, the claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 5-8, 10-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Merayo et al. (hereinafter Merayo), Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys.
Regarding Claim 1, Merayo discloses a computer implemented method for processing material properties of structural materials, the method comprising:
generating one or more training datasets for training one or more machine learning algorithms for determining values of one or more target material properties of assessment materials, a training dataset being generated specifically for each of the one or more target material properties [“input dataset creation” §2 ¶1; Fig. 1; “data employed to carry out this study comprised both wrought and casting alloys and were obtained from Matmatch (Munich, Germany), which is an open-access online materials library, which is comprised of thousands of entries” §2.1 ¶1], the generation of training datasets comprising the steps:
providing a database comprising data records indicative of input values of material properties of a multitude of structural materials [“data employed to carry out this study comprised both wrought and casting alloys and were obtained from Matmatch (Munich, Germany), which is an open-access online materials library, which is comprised of thousands of entries” §2.1 ¶1], comprising at least chemical composition and processing parameter(s) of the multitude structural materials [“records containing information on its Brinell hardness (HB), its yield stress (YS), and its ultimate stress (UTS) were considered” §2.1 ¶3; “Only alloys whose chemical composition is defined at more than 95%” §2.1 ¶3];
classifying the data records into a plurality of classes according to the input value(s) of one or more of the material properties, at least according to the values of chemical composition and processing parameter(s) [“construction of the input dataset requires several processes aimed at guaranteeing the quality of the final corpus of information: reading the datasheets and organizing them as a table” §2.1 ¶2; Fig, 2; “records containing information on its Brinell hardness (HB), its yield stress (YS), and its ultimate stress (UTS) were considered” §2.1 ¶3; “Only alloys whose chemical composition is defined at more than 95%” §2.1 ¶3; “different treatments” §2.1 ¶3];
within each of the plurality of classes, extracting target-relevant material properties from the data records having a dependency relationship with the specific target material property(s) [“HB” “Temper” “Chemical composition” Fig. 3; “Brinell hardness (HB), its yield stress (YS), and its ultimate stress (UTS)” §2.1 ¶3]; and
generating at least one training dataset corresponding to one or more of the plurality of classes using values of the extracted target-relevant material properties and corresponding values of the specific target material property(s) [“input dataset creation” §2 ¶1; Fig. 1; “data employed to carry out this study comprised both wrought and casting alloys and were obtained from Matmatch (Munich, Germany), which is an open-access online materials library, which is comprised of thousands of entries” §2.1 ¶1; Fig. 2];
training of one or more machine learning algorithms for one or more of the plurality of classes, each machine learning algorithm being trained for determining a specific target material property of assessment materials using one of the plurality of training datasets [“start the training and prediction phase” §2.3 ¶1; “yield strength (YS) and ultimate tensile strength (UTS)” §2.3 ¶1; Fig. 4]; and
determining output value(s) of the specific target material property(s) of an assessment material using one of the plurality of machine learning algorithms trained using a training dataset corresponding to the class of the assessment material and trained for determining the specific target material property [“The prediction and training process generates a large amount of information that, after a detailed analysis, makes it possible to estimate the performance and capabilities of the methodology” §2.3 ¶5; Fig. 4].
Regarding Claim 2, Merayo discloses the computer implemented method for processing material properties of structural materials according to claim 1. Merayo further discloses further comprising determining validation and/or test dataset(s) corresponding to one or more of the material and property classes using values of the extracted target-relevant material properties and corresponding value(s) of the specific target material property(s) [“The input dataset is randomly split into two subsets, which comprise, respectively, 80% (training subset) and 20% (testing subset) of the records.” §2.3 ¶2].
Regarding Claim 5, Merayo discloses the computer implemented method for processing material properties of structural materials according to claim 1. Merayo further discloses wherein generating training dataset(s) further comprises normalizing the data records within one or more of the classes, wherein normalizing the data records within one or more of the classes comprises:
removal of data records with missing values of material properties [“Only records containing information on its Brinell hardness (HB), its yield stress (YS), and its ultimate stress (UTS) were considered” §2.1 ¶3; “Only alloys whose chemical composition is defined at more than 95% are taken into account” §2.1 ¶3; “Most of the discarded records were eliminated because the information they contained was imprecise (the definition of the chemical composition was poor) or incomplete (some of the relevant properties were missing, especially the Brinell hardness).” §2.1 ¶4]; and/or
removal of data records comprising extreme values of material properties; and/or
removal of data records comprising inconsistent values of material properties; and/or
transforming non-numerical values of material properties into corresponding numerical representations.
Regarding Claim 6, Merayo discloses the computer implemented method for processing material properties of structural materials according to claim 1. Merayo further discloses further comprising determining respective applicability domain(s) of one or more of the training dataset(s) [“any aluminum alloy” Abstract].
Regarding claim 7, Merayo discloses the computer implemented method for processing material properties of structural materials according to claim 1. Merayo further discloses wherein the training of the machine learning algorithm comprises computing a regression model representative of a relationship between values of two or more of material properties within the training dataset(s) [“A multilayer ANN is a supervised learning algorithm capable of learning a nonlinear function by training on a labeled input dataset that can be used to perform classifications and regressions” §1.3 ¶4; Fig. 5, 9].
Regarding Claim 8, Merayo discloses the computer implemented method for processing material properties of structural materials according claim 1. Merayo further discloses wherein the machine learning algorithm comprises a neural network-based machine learning algorithm [“main objective of this work is to develop an ANN capable of making precise predictions” §1.3 ¶6; Abstract], and
wherein the training of the machine learning algorithm further comprises the steps of:
a) receiving a first set of material properties as an input, a second set of material properties as an expected output associated with the input [“the training subset” §2.3 ¶2];
b) determining a generated output of the neural network by inputting the input into the neural network [“ANN training with the training subset” §2.3 ¶2];
c) determining value(s) of a selected cost function based on a comparison of the expected output and the generated output [“Training stops when a training error of less than 0.1 is reached” §2.3 ¶4]; and
d) adapting the neural network based on the value(s) of the selected cost function [“ANN training with the training subset” §2.3 ¶2]
Regarding Claim 10, Merayo discloses the computer implemented method for processing material properties of structural materials according to claim 1. Merayo further discloses further comprising preventing overtraining of the machine learning algorithm using the validation dataset(s) and/or further comprising evaluating the performance of the machine learning algorithm using the test dataset(s) [“The input dataset is randomly split into two subsets, which comprise, respectively, 80% (training subset) and 20% (testing subset) of the records. Using disjoint groups to carry out these two steps (training and prediction) ensures that unwanted effects such as bias or overfitting will not occur” §2.3 ¶2].
Regarding Claim 11, Merayo discloses the computer implemented method for processing material properties of structural materials according to claim 1. Merayo further discloses wherein determining value(s) of the specific target material property(s) using the machine learning algorithm comprises the steps:
receiving input indicative of value(s) of a first set of material properties of the assessment material [“Input layer” Fig. 3, 5]; and
determining value(s) of the specific target material property(s) of the assessment material using the machine learning algorithm trained using the training dataset(s) [“Output layer” Fig. 3, 5].
Regarding Claim 12, Merayo discloses the computer implemented method for processing material properties of structural materials according to claim 11. Merayo further discloses wherein determining value(s) of target material property(s) using the machine learning algorithm further comprises selecting a machine learning algorithm trained with a training dataset for the specific target material property(s) [“Tools based on artificial neural networks have shown high potential for predicting the behavior and properties of industrial components.” Abstract; §1.3; “Input Dataset Creation” §2.1].
Regarding Claim 13, Merayo discloses the computer implemented method for processing material properties of structural materials according to claim 11. Merayo further discloses wherein determining value(s) of target material property(s) using the machine learning algorithm further comprises receiving a selection of a machine learning model [“Tools based on artificial neural networks have shown high potential for predicting the behavior and properties of industrial components.” Abstract; §1.3], wherein:
the machine learning algorithm is generated and trained in accordance with the selection of a machine learning model [“Training, Prediction, and Analysis” §2.3]; and
the values of specific target material property(s) of the assessment material is/are determined using the machine learning algorithm according to the selected machine learning model [“Training, Prediction, and Analysis” §2.3].
Claim 14 is rejected on the same grounds as claim 1. Merayo further discloses a data storage device for storing a database comprising data records indicative of input values of material properties of a multitude of structural materials [“data employed to carry out this study comprised both wrought and casting alloys [9,10] and were obtained from Matmatch” §2.1 ¶1], and for storing training dataset(s); a computing device communicatively connected to the data storage device; an input interface for receiving input indicative of value(s) of a first set of material properties of the assessment material and/or for receiving a selection of a machine learning model; and an output interface for outputting output data indicative of values of target material property(s) of an assessment material [“a computer-aided tool is developed” Abstract; “a computer-aided methodology is developed” §1.1 ¶6; Fig. 3].
Claim 15 is rejected on the same grounds as claim 1. Merayo further discloses instructions which, when executed by a computing device, cause the computing device to carry out a method [“a computer-aided tool is developed” Abstract; “a computer-aided methodology is developed” §1.1 ¶6].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Merayo in view of Saptoro et al. (hereinafter Saptoro), A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models.
Regarding Claim 3, Merayo discloses the computer implemented method for processing material properties of structural materials according to claim 2.
However, Merayo fails to explicitly disclose wherein splitting one or more of the classes into training, validation and/or test dataset(s) is performed using a rational split algorithm, such as selection of data records within one or more of the classes having a uniform distribution of values of one or more of the material properties over a regression space.
Saptoro discloses wherein splitting one or more of the classes into training, validation and/or test dataset(s) is performed using a rational split algorithm [“MDKS (Kennard-Stone algorithm based on Mahalanobis distance), to divide the data into training and testing subsets for developing artificial neural network (ANN) models. This method is a modified version of the Kennard-Stone (KS) algorithm.” Abstract], such as selection of data records within one or more of the classes having a uniform distribution of values of one or more of the material properties over a regression space.
It would have been obvious to one having ordinary skill in the art, having the teachings of Merayo and Saptoro before him before the effective filing date of the claimed invention, to modify the method of Merayo to incorporate the data splitting technique of MDKS of Saptoro.
Given the advantage of better performance for data splitting for developing artificial neural network models, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Merayo in view of Darudi et al. (hereinafter Darudi), Partial Mutual Information Based Algorithm For Input Variable Selection.
Regarding Claim 4, Merayo discloses the computer implemented method for processing material properties of structural materials according to claim 1,
However, Merayo fails to explicitly disclose wherein extracting target-relevant material properties from the data records indicative of material properties of one or more of the classes comprises:
applying a mutual information based algorithm, in particular a partial mutual information algorithm and/or
applying a selection algorithm such as a Markov blanket algorithm onto the data records within one or more of the classes to identify a subset of target-relevant material properties of the data records having a dependency relationship with the target material property(s).
Darudi discloses wherein extracting target-relevant material properties from the data records indicative of material properties of one or more of the classes comprises:
applying a mutual information based algorithm, in particular a partial mutual information algorithm [“an IVS algorithm based on partial mutual information” Abstract] and/or
applying a selection algorithm such as a Markov blanket algorithm onto the data records within one or more of the classes to identify a subset of target-relevant material properties of the data records having a dependency relationship with the target material property(s).
It would have been obvious to one having ordinary skill in the art, having the teachings of Merayo and Darudi before him before the effective filing date of the claimed invention, to modify the method of Merayo to incorporate an IVS algorithm based on partial mutual information of Darudi.
Given the advantage of using partial mutual information as a reliable measure to evaluate linear/nonlinear dependency and redundancy among variables, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Merayo in view of Neelakandan et al. (hereinafter Neelakandan), A gradient boosted decision tree-based sentiment classification of twitter data.
Regarding Claim 9, Merayo discloses the computer implemented method for processing material properties of structural materials according to claim 7.
However, Merayo fails to explicitly disclose wherein the machine learning algorithm comprises a decision tree-based machine learning algorithm, in particular a gradient-boosted decision tree-based machine learning algorithm.
Neelakandan discloses wherein the machine learning algorithm comprises a decision tree-based machine learning algorithm, in particular a gradient-boosted decision tree-based machine learning algorithm [“features are provided as input to the GBDT classifier. Gradient boosting (GB) is the utmost propitious machine learning methodology for classification and regression problems which generates a prediction framework in the sort of integration of weak prediction frameworks, mainly decision trees.” §3.6 ¶1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Merayo and Neelakandan before him before the effective filing date of the claimed invention, to modify the method of Merayo to incorporate gradient-boosted decision tree-based machine learning algorithm of Neelakandan.
Given the advantage of being an extremely effective technique for learning linear as well as nonlinear function by a linear combination of a series of DTs, one having ordinary skill in the art would have been motivated to make this obvious modification.
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Conclusion
Any prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is reminded that in amending in response to a rejection of claims, the patentable novelty must be clearly shown in view of the state of the art disclosed by the references cited and the objections made. Applicant must also show how the amendments avoid such references and objections. See 37 CFR §1.111(c). Additionally when amending, in their remarks Applicant should particularly cite to the supporting paragraphs in the original disclosure for the amendments.
The following references were found during the examination of this patent application and were found to be relevant to patentability. Applicant is advised to review these references prior to responding to this Office action.
Merayo et al. (Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks) discloses a computer-aided tool is developed to predict relevant physical and mechanical properties that are involved in the selection tasks of metallic materials.
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/R.B./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148
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