DETAILED ACTION
Claims 1-20 are currently presented for examination.
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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 717 and 725. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). 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.
Claim Objections
Claim 1 is objected to because of the following informalities: the claim recites “second set of stress value” when each subsequent recitation is “… stress values”. Appropriate correction is required.
Claim 5 is objected to because of the following informalities: the claim recites “stress values” when this is not the first recitation. Appropriate correction is required.
Claim 5 is objected to because of the following informalities: the claim recites “strain values” when this is not the first recitation. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: the claim recites “determined stress values” when this is not the first recitation. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: the claim recites “determined strain values” when this is not the first recitation. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: the claim recites “the first node” when it is the first recitation. Appropriate correction is required.
Claim 7 is objected to because of the following informalities: the claim recites “determined stress values” when this is not the first recitation. Appropriate correction is required.
Claim 7 is objected to because of the following informalities: the claim recites “determined strain values” when this is not the first recitation. Appropriate correction is required.
Claim 7 is objected to because of the following informalities: the claim recites “the set of” twice. Appropriate correction is required.
Claim 8 is objected to because of the following informalities: the claim recites “stress value” when each other recitation is “stress values”. Appropriate correction is required.
Claim 11 is objected to because of the following informalities: the claim recites “second set of stress value” when each subsequent recitation is “… stress values”. Appropriate correction is required.
Claim 15 is objected to because of the following informalities: the claim recites “stress values” when this is not the first recitation. Appropriate correction is required.
Claim 15 is objected to because of the following informalities: the claim recites “strain values” when this is not the first recitation. Appropriate correction is required.
Claim 16 is objected to because of the following informalities: the claim recites “determined stress values” when this is not the first recitation. Appropriate correction is required.
Claim 16 is objected to because of the following informalities: the claim recites “determined strain values” when this is not the first recitation. Appropriate correction is required.
Claim 16 is objected to because of the following informalities: the claim recites “the first node” when it is the first recitation. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: the claim recites “determined stress values” when this is not the first recitation. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: the claim recites “determined strain values” when this is not the first recitation. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: the claim recites “the set of” twice. Appropriate correction is required.
Claim 18 is objected to because of the following informalities: the claim recites “stress value” when each other recitation is “stress values”. Appropriate correction is required.
Claim 20 is objected to because of the following informalities: the claim recites “second set of stress value” when each subsequent recitation is “… stress values”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Regarding claims 1-20, are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-10 are directed to a method, which is a process, which is a statutory category of invention. Claims 11-19 are directed to an apparatus, which is a machine, which is a statutory category of invention. Claims 18-20 are directed to a non-transitory computer readable storage medium, which is a manufacture, which is a statutory category of invention. Therefore, claims 1-20 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 11 and 20 recite the abstract idea of multiple sets of stress and strain values, constituting an abstract idea based on Mathematical Concepts including mathematical formulas or equations as well as calculations or alternatively Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper. The limitation of "obtaining a set of graphs for a set of material structures, wherein: each graph of the set of graphs is associated with a material structure of the set of material structures; each graph comprises nodes representing finite elements of a corresponding material structure;” covers mental processes including observing a dataset where the dataset is in the form of graphs. Additionally, the limitation of “determining first sets of stress values and first sets of strain values for the set of graphs based on a machine learning model;” covers mathematical concepts in the form of a series of calculations, alternatively this can be viewed as mental processes including evaluating a labeled dataset and determining stress and strain values this includes values that are previously known to the user and calculated values, in view of Specification [0027] and [0036]. But for the inclusion of the machine learning model, there is nothing that precludes operation of the limitation in the human mind. This follows for each subsequent recitation. Additionally, the limitation of “obtaining second sets of stress value and second sets of strain values for a subset of the set of material structures;” covers mathematical concepts in the form of a second series of calculations, alternatively this can be viewed as mental processes including determining stress and strain values this includes values that are previously known to the user and calculated values, in view of Specification [0027] and [0036]. Additionally, the limitation of “determining other stress values and other strain values for other material structures based on the updated machine learning model” covers mathematical concepts in the form of another series of calculations, alternatively this can be viewed as mental processes including determining stress and strain values this includes values that are previously known to the user and calculated values, in view of Specification [0027] and [0036]. Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper.
Dependent claims 2-10 and 12-19 further narrow the abstract ideas, identified in the independent claims.
Step 2A, Prong 2: The judicial exception is not integrated into a practical application. In Claims 1, 11 and 20, the additional element of “a machine learning model”, as well as “graph neural network, a recurrent neural network, a convolutional neural network, and a deep neural network” in claim 10, “a memory” and “a processing device”, in claim 12, as well as “non-transitory computer-readable storage medium” and “a processing device” in claim 20, as well as “a hydrophone” in claims 11, 17 and 19, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitations of “updating the machine learning model based on the first sets of stress values, the first sets of strain values, the second sets of stress values, and the second sets of strain values; and” in claims 1, 11 and 20 merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The training of the machine learning model are mere instructions to apply an exception as set forth in MPEP2106.05(f). Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-10 and 12-19 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above.
Step 2B: Claims 1, 11 and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In Claims 1, 11 and 20, the additional element of “a machine learning model”, as well as “graph neural network, a recurrent neural network, a convolutional neural network, and a deep neural network” in claim 10, “a memory” and “a processing device”, in claim 12, as well as “non-transitory computer-readable storage medium” and “a processing device” in claim 20, as well as “a hydrophone” in claims 11, 17 and 19, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitations of “updating the machine learning model based on the first sets of stress values, the first sets of strain values, the second sets of stress values, and the second sets of strain values; and” in claims 1, 11 and 20 merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The training of the machine learning model are mere instructions to apply an exception as set forth in MPEP2106.05(f). Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claims 2 and 12 are directed to further defining the generation of materials, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 3 and 13 are directed to further defining the generation of material by removal, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 4 and 14 are directed to further defining the generation of materials by addition, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 5 and 15 are directed to further defining the selection of a node, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 6 and 16 are directed to further defining the determination of a percentage, which further narrows the abstract idea identified in the independent claim, which is directed to “Mathematical Concepts” or alternatively “Mental Processes.”
Dependent claims 7 and 17 are directed to further defining the determination of counts, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 8 and 18 are directed to further defining the use of finite element analysis for calculation of stress values, which further narrows the abstract idea identified in the independent claim, which is directed to “Mathematical Concepts” or alternatively “Mental Processes.”
Dependent claims 9 and 19 are directed to further defining the identification of a subset, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
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.
Claims 1-6, 8-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Friemann “Predicting the elasto-plastic response of short fiber composites using deep neural networks trained on micro-mechanical simulations” in view of Heller et al. “A gradientless finite element procedure for shape optimization.”
Regarding claim 1, Friemann teaches obtaining a set of graphs for a set of material structures, wherein: each graph of the set of graphs is associated with a material structure of the set of material structures; (Section 2.2.1-2.2.5, Figures 2.3, 2.4 and 2.5, graphs of materials that are associated with the structure are used)
determining first sets of stress values and first sets of strain values for the set of graphs based on a machine learning model; (Section 2.2.1-2.2.5, stress and strain values are calculated)
obtaining second sets of stress value and second sets of strain values for a subset of the set of material structures; (Section 2.2.1-2.2.5, stress and strain values are calculated, this includes multiple sets)
updating the machine learning model based on the first sets of stress values, the first sets of strain values, the second sets of stress values, and the second sets of strain values; and (Section 2.3-2.3.6, Figure 2.7, a supervised machine learning model uses all sets of stress and strain values to updated the machine learning model)
determining other stress values and other strain values for other material structures based on the updated machine learning model. (Sections 2.3-5, other stress values are calculated using the trained machine learning model)
Friemann does not explicitly recite each graph comprises nodes representing finite elements of a corresponding material structure;
Heller teaches each graph comprises nodes representing finite elements of a corresponding material structure; (Figures 2, 8 and 10-12, a graph of a finite element model containing the material structure is used)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Friemann with Heller as the references deal determining the stress of an object due to strain applied, in order to implement a system that performs a finite element analysis to add and remove material based on the determined stress and strain, in order to optimize the material structure using percentages. Heller would modify Friemann by performs a finite element analysis to add and remove material based on the determined stress and strain, in order to optimize the material structure using percentages. The benefit of doing so is a reinforced structure with a focus on minimizing stress while maintaining the effectiveness of the reinforcement can be created. Additionally, stress reduction can be achieved by way of the local shape changes due to the optimization. The created method is also an effective and robust alternative to the use of more expensive and complex gradient-based finite element optimization software. (Heller Abstract)
Regarding claim 2, the combination of Friemann and Heller teach the limitations of claim 1. Friemann does not explicitly teach generating the set of material structures based on an initial set of material structures wherein generating the set of material structures based on the initial set of material structures comprises one or more of: removing material from the initial set of material structures; and adding material to the initial set of material structures.
Heller teaches generating the set of material structures based on an initial set of material structures wherein generating the set of material structures based on the initial set of material structures comprises one or more of: removing material from the initial set of material structures; and adding material to the initial set of material structures. (Sections 1 and 3.1 the shape is optimized, creating its structure, by adding or removing material)
See motivation of claim 1
Regarding claim 3, the combination of Friemann and Heller teach the limitations of claim 2. Friemann does not explicitly teach removing material from areas of the set of material structures with stress values below a threshold stress value and strain values below a threshold strain value.
Heller teaches removing material from areas of the set of material structures with stress values below a threshold stress value and strain values below a threshold strain value. (Sections 1 and 3.1 the shape is optimized by removing material when the stress is less than the maximum stress which is directly tied to the applied strain)
See motivation of claim 1
Regarding claim 4, the combination of Friemann and Heller teach the limitations of claim 2. Friemann does not explicitly teach adding material to areas of the set of material structures with stress values above a threshold stress value and strain values above a threshold strain value.
Heller teaches adding material to areas of the set of material structures with stress values above a threshold stress value and strain values above a threshold strain value. (Sections 1 and 3.1 the shape is optimized by adding material when the stress is more than the minimum stress which is directly tied to the applied strain)
See motivation of claim 1
Regarding claim 5, the combination of Friemann and Heller teach the limitations of claim 1. Friemann teaches selecting a first node from a first set of nodes of a first graph based on determined stress values and determined strain values of other nodes connected to the first set of nodes, wherein the first set of nodes are not associated with stress values and strain values. (Section2.3-2.3.2, Figure 2.7, for the nodes that are not yet calculated, a node is selected from the set for the stress and the strain values to be determined)
Regarding claim 6, the combination of Friemann and Heller teach the limitations of claim 5. Friemann does not explicitly teach determining a set of percentages for the first set of nodes, wherein each percentage indicates a percentage of neighbor nodes with determined stress values and determined strain values for a respective node of the first set of nodes; selecting the first node based on a highest percentage of the set of the set of percentages.
Heller teaches determining a set of percentages for the first set of nodes, wherein each percentage indicates a percentage of neighbor nodes with determined stress values and determined strain values for a respective node of the first set of nodes; selecting the first node based on a highest percentage of the set of the set of percentages. (Section 5.2, figure 10, a percentage of nodes with the best determined stress and strain values among the nodes are selected to optimize the structure)
See motivation of claim 1.
Regarding claim 8, the combination of Friemann and Heller teach the limitations of claim 1. Friemann does not explicitly recite wherein obtaining the second sets of stress value and the second sets of strain values for the subset of the set of material structures comprises: performing finite element analyses for the subset of the set of material structures to obtain the second sets of stress value and the second sets of strain values.
Heller teaches wherein obtaining the second sets of stress value and the second sets of strain values for the subset of the set of material structures comprises: performing finite element analyses for the subset of the set of material structures to obtain the second sets of stress value and the second sets of strain values. (Abstract, Section 5.2, figures 10-12, a small subset of the total structure has a plurality of stress and strain values computed with a finite element analysis)
See motivation of claim 1
Regarding claim 9, the combination of Friemann and Heller teach the limitations of claim 1. Friemann teaches identifying the subset of the set of material structures based on the first sets of stress values and the first sets of strain values. (Section 2.2.1-2.2.5, Figures 2.3-2.5 a subset of the material is pulled out based on the calculated stress and strain values)
Regarding claim 10, the combination of Friemann and Heller teach the limitations of claim 1. Friemann teaches wherein the machine learning model comprises one or more of a graph neural network, a recurrent neural network, a convolutional neural network, and a deep neural network. (Sections 2.3-2.3.6, a recurrent neural network and a deep neural network can be used)
In regards to claim 11, it is the system embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1.
Examiner’s Note: Friemann teaches the additional computer components by using Matlab.
In regards to claim 12, it is the system embodiment of claim 2 with similar limitations to claim 2, and is such rejected using the same reasoning found in claim 2.
In regards to claim 13, it is the system embodiment of claim 3 with similar limitations to claim 3, and is such rejected using the same reasoning found in claim 3.
In regards to claim 14, it is the system embodiment of claim 4 with similar limitations to claim 4, and is such rejected using the same reasoning found in claim 4.
In regards to claim 15, it is the system embodiment of claim 5 with similar limitations to claim 5, and is such rejected using the same reasoning found in claim 5.
In regards to claim 16, it is the system embodiment of claim 6 with similar limitations to claim 6, and is such rejected using the same reasoning found in claim 6.
In regards to claim 18, it is the system embodiment of claim 8 with similar limitations to claim 8, and is such rejected using the same reasoning found in claim 8.
In regards to claim 19, it is the system embodiment of claim 9 with similar limitations to claim 9, and is such rejected using the same reasoning found in claim 9.
In regards to claim 20, it is the computer readable medium embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1.
Examiner’s Note: Friemann teaches the additional computer components by using Matlab.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Friemann in view of Heller, and in further view of Cai et al. “Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium.”
Regarding claim 7, the combination of Friemann and Heller teach the limitations of claim 5. The combination of Friemann and Heller does not explicitly recite determining a set of counts for the first set of nodes, wherein each count indicates a number of neighbor nodes with determined stress values and determined strain values for a respective node of the first set of nodes; selecting the first node based on a highest count of the set of the set of counts.
Cai teaches determining a set of counts for the first set of nodes, wherein each count indicates a number of neighbor nodes with determined stress values and determined strain values for a respective node of the first set of nodes; selecting the first node based on a highest count of the set of the set of counts. (Abstract, Sections 2.2.2-2.2.3, 3.2 and 4, Figure 5, Tables 2 and 6, the highest number of 10 nearest neighbors is used in a KNN model)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Friemann and Heller with Cai as the references deal determining the stress of an object due to strain applied, in order to implement a selection of the highest neighbor count in calculations. Cai would modify Friemann and Heller by selecting the highest neighbor count in calculations. The benefit of doing so is the KNN model is a fast algorithm to generalize N-point problems. (Cai Section 2.3)
In regards to claim 17, it is the system embodiment of claim 7 with similar limitations to claim 7, and is such rejected using the same reasoning found in claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Murugan et al. “Finite element simulation of residual stresses and their measurement by contour method”: Also teaches the calculation of multiple stresses on a material using the finite element method.
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/MICHAEL EDWARD COCCHI/Primary Examiner, Art Unit 2188
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