DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 14-25 have been presented for examination based on the application filed on 7/7/2022.
Claims 14-25 are rejected under 35 U.S.C. 101.
Claims 14-18, 20-25 rejected under 35 U.S.C. 102(a)(1) as being anticipated by US PGPUB No. 20180341246 A1 by Coffman; Valerie R. et al.
Claim(s) 19 is rejected under 35 U.S.C. 103 as being unpatentable over Claims 14-18, 20-25 rejected under 35 U.S.C. 102(a)(1) as being anticipated by US PGPUB No. 20180341246 A1 by Coffman; Valerie R. et al., in view of US PGPUB No. 20210357555 by LIU; Wing Kam et al.
This action is made Non-Final.
Claim Objection
The claims 14-25 are objected for being literal translation lacking proper punctuations and sometimes rendering them unclear to interpretation. Best effort is made to map and reject the claims. Applicant is encouraged to grammatically correct the claims according to US English standards and split the limitations to be in better form. Applicant are welcome to request interview if they need further clarification.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d). The certified copy has been filed in this application, with priority date of 01/08/2020.
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 14-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process without any additional elements that provide a practical application or amount to significantly more than the abstract idea.
Claims 14:
Step 1: The claims 14, 24 and 25 is drawn to a system, method and non-transitory storage medium falling under one of the four statutory categories of invention.
Step 2A, Prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The limitations are bolded for abstract idea/judicial expception identification. Taking claim 14 as exemplary claim.
Claim 14
Mapping Under Step 2A Prong 1
14. (New) An information processing system comprising a processor, wherein the processor is configured to execute:
objective data acquiring processing of acquiring data of an object expressed as a mesh shape; and
inferring processing of using a first machine learning model
obtained by performing machine learning on a result of analysis of physical properties in units of relationships between two adjacent nodes in graph data corresponding to the mesh shape
to acquire a first inference result
inferring a result of analysis of physical properties for the object.
Abstract Idea/Mathematical Concept/Mental Process: The inferring step recites mental step which can be performed with pencil and paper as it is concerned with inferring a result of analysis (judgement/opinion/evaluation) of physical property between two adjacent nodes based on the objective data for the two nodes belonging to mesh shape (observation). (as in MPEP 2106.04(a)(2)(III)(A)).
This may use mathematical calculation to perform two tasks: (1) inferring a result of analysis of physical properties for the object – which is understood as conventional FEM (See specification Fig.8 Numerical simulation) simulation to generate an output (“a result of analysis of physical properties for the object) . (2) inferring a first inference result using the trained machine learning model. See mathematical calculations as in Fig.10 showing neural network (as in MPEP 2106.04(a)(2)(I)(C)).
The <> recites mental process (as in MPEP 2106.04(a)(2)(III)(A)).
Therefore performing mathematical calculations for (numerical simulation and machine learning simulation/model) to infer (compare) the outputs of the two mathematical calculation is an abstract idea.
Under its broadest reasonable interpretation, these covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (especially when the computation is limited to two nodes to determine the physical properties). That is, nothing in the claim element precludes the step from practically being performed in the mind or with the aid of pencil and paper but for the recitation of generic computer components (processor here). Also the mathematical concepts (generic machine learning and numerical simulation) disclosed may also be performed in the mind or with the aid of pencil and paper or computer/processor as tool.
Step 2A, Prong 2: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). As per (1) the additional elements are identified as bolded parts of the limitations in column 1 of the table below, and as per (2) the evaluation is shown in the mapping section of the table.
In accordance with this step, the judicial exception is not integrated into a practical application.
Claim 1
Mapping Under Step 2A Prong 2
14. (New) An information processing system comprising a processor, wherein the processor is configured to execute:
objective data acquiring processing of acquiring data of an object expressed as a mesh shape; and
inferring processing of using a first machine learning model obtained by performing machine learning on a result of analysis of physical properties in units of relationships between two adjacent nodes in graph data corresponding to the mesh shape to acquire a first inference result inferring a result of analysis of physical properties for the object.
Under MPEP 2106.05(g) & (b) and (f) use of generic processor to perform data gathering and mathematical computation does not integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B.
Under MPEP 2106.05(g) determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. In this case the objective data acquiring/processing is mere data gathering.
Under MPEP 2106.05(f)(1) the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". In this case the claim does appears to accomplish computing a first inference result (solution) without showing how the a result of analysis of physical properties in units of relationships between two adjacent nodes in graph data corresponding to the mesh shape is used to generate the solution. There is no mechanism claimed to generate the result and how is related to a result of analysis of physical properties (presumably from the numerical simulation). In other words, the claim does not claim how a first machine learning model is trained.
Further under MPEP 2106.05(a)/(h) is no specific technical field is claimed to be improved on or where the field of use of machine learning (& presumably numerical simulation) claimed. Citation of processor is generic and hence not considered as improvement in the functioning of the processor/ information processing system.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer/processor to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer/processing component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)). There is no application claimed in a technical field (e.g. as shown in Figs 11-12 of specification). The claims 14 is therefore considered to be patent ineligible.
Claim 24 and 25 recite similar limitations as claim 14 and are rejected with similar rationale.
Claims 15 recite similar limitations as claim 1, where the data is gathered based on generically claimed numerical analysis. Numerical analysis is not positively recited other than data of the object is from numerical analysis of physical properties. So this may be considered as extra-solution data gathering activity under MPEP 2106.05(g). Even if interpretation is made that numerical analysis of physical properties is positively recited, this generic recitation of numerical analysis may be considered as mathematical concept (under MPEP 2106.04(a)(2)(I)(C), e.g. determining weight when volume is known). The claims do not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 16 recites wherein, for each piece of data expressing a relationship between a node of interest and an adjacent node. This is considered a mental step to associate two nodes under step 2A Prong 1. This relationship aspect may be considered as mathematical concept under step 2A Prong 2. Claim 16 further recites the inferring processing inputs input data into an affine transformation matrix in the first machine learning model and consolidates the output of the first machine learning model for a plurality of nodes to acquire the first inference result inferring the result of analysis of physical properties. This aspect of affine transform and consolidation (e.g. averaging data) may be considered as mathematical concept under step 2A Prong 2. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 17 recites wherein the graph data corresponding to the mesh shape includes graph data composed of nodes and edges expressing the mesh shape and graph data in which data about physical properties is assigned to at least one of the nodes or edges. This is considered as mental step under step 2A Prong 1, which can be performed with pencil and paper or with computer as a tool. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 18 recites the data of the object expressed as the mesh shape is data of an object of a simulation…. The specification of data being simulation data does not change that is the data that is gathered. This step remains as extra-solution activity. Further the claim 18 recites , and the inferring processing acquires, as the result of analysis of physical properties, a result of a forward calculation in which a resulting state from an initial state in the object of the simulation is analyzed as a forward simulation problem. This can be interpreted as mental step based on forming an opinion/judgement/evaluation (resulting state … is analyzed as a forward simulation problem) based on the observation (initial state) without any details of how the forward simulation or resulting state are derived. This is therefore rejected under step 2A prong 1. This step may also be considered as mathematical concept as it generical recites forward calculation (result from initial state). Further use of resulting state in the inferring by machine step is simply data gathering step for use in inferring step under step 2A prong 2. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 19 recites the data of the object expressed as the mesh shape is data of an object of a simulation…. The specification of data being simulation data does not change that is the data that is gathered. This step remains as extra-solution activity. Further the claim 18 recites , and the inferring processing acquires, as the result of analysis of physical properties, a result of an inverse calculation in which a resulting state from an initial state in the object of the simulation is analyzed as a forward simulation problem. This can be interpreted as mental step based on forming an opinion/judgement/evaluation (resulting state … is analyzed as a inverse simulation problem) based on the observation (initial state) without any details of how the forward simulation or resulting state are derived. This step may also be considered as mathematical concept as it generical recites inverse calculation. This is therefore rejected under step 2A prong 1. The Further use of resulting state in the inferring by machine step is simply data gathering step for use in inferring step under step 2A prong 2. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 20 recites wherein the inferring processing acquires, as the result of analysis of physical properties, post- analysis graph data corresponding to the mesh shape expressing the object of analysis of physical properties. This is abstract idea related to mathematical concept (post analysis graph data) under step 2A Prong 1. Acquiring this data is as best considered as extra-solution activity under step 2A prong 2. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 21 recites wherein the inferring processing acquires, as the result of analysis of physical properties, vector data whose elements are post-analysis physical quantities of the overall object of analysis of physical properties. This is abstract idea related to mathematical concept (post analysis vector data calculation) under step 2A Prong 1. Acquiring this data is as best considered as extra-solution activity under step 2A prong 2. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 22 analysis is performed in table format for better mapping:
Claim limitation
Grounds and rationale for rejection
22. (New) The information processing system according to claim 14, wherein the processor is further configured to execute:
optimization parameter setting processing of setting parameters to be optimized in a range of performance that is achievable with respect to demanded specifications of a construction as the object to be designed;
evaluation value acquiring processing of acquiring an evaluation value expressed by an evaluation function of a designed construction with respect to the parameters to be optimized;
specific evaluation value acquiring processing of acquiring a specific evaluation value corresponding to at least one of a global maximum value or a local maximum value of the evaluation value; and
structure inferring processing of acquiring a second inference result obtained by using a second machine learning model to infer a structure of a construction according to the parameters to be optimized which correspond to the specific evaluation value,
the second machine learning model being obtained by performing machine learning on structures of a construction with respect to the parameters to be optimized,
wherein: data of the construction is expressed as a mesh shape, and physical properties of the construction treated as candidates of inference are acquired by using the first machine learning model obtained by performing machine learning on physical properties of constructions in units of relationships between two adjacent nodes in graph data corresponding to the mesh shape.
Under step 2A Prong 2, this is at best an extra solution activity of data gathering as shown in specification Fig.18 where the computer is used to specify ranges and identify the parameter.
Under step 2A Prong 1, this is mathematical concept under MPEP 2106.054(a)(2)(I)(C) where evaluation function (is calculation) generically defined and therefore an abstract idea.
Under step 2A Prong 2, this is considered as specifying the range allowed gathered as input. See MPEP 2106.05(g). This can also mental step under step 2A prong 1 for forming an opinion based on observation of evaluation value.
Under step 2A Prong 2, as per MPEP 2106.05(f)(1) this is an idea of solution that intends to achieve a solution (infer a structure of a construction) without any specific how the solution can be achieved (simply stating use the specific evaluation value cannot structure the inferring processing).
Under step 2A Prong 2: Further under MPEP 2106.05(f)(1) simply specifying the input to inferring/construction (of second machine learning) is mesh shape and physical properties does not show how the construction is done. There is no nexus between the two adjacent nodes and how the second machine learning model is created.
The generic use of processor does not add practical application and analysis between just two nodes can done with pencil and paper, and is therefore also not considered to add significantly more.
Claims 23 recite wherein the structure inferring processing applies the second machine learning model in which data about a construction to be designed and parameters expressing conditions of a design simulation are treated as input, and thereby acquires the second inference result inferring the structure of a construction that conforms to the parameters to be optimized which correspond to the specific evaluation value from among a plurality of structures obtained by varying structural elements of the construction to be designed. This step adds to the abstract idea by arbitrarily varying the structural elements for use in second machine learning model.. The claims do not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
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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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 14-18, 20-25 rejected under 35 U.S.C. 102(a)(1) as being anticipated by US PGPUB No. 20180341246 A1 by Coffman; Valerie R. et al.
Regarding Claims 14, 24 & 25
Coffman teaches (Claim 14) An information processing system (Coffman : Fig.2) comprising a processor (Coffman: Fig.2 element 207) , wherein the processor is configured to execute: /
(Claim 24) An information processing method (Coffman: Fig.3-4, 6-7) executed by an information processing system having a processor (Coffman: Fig.2 element 207), the information processing method comprising operating the processor to execute:/
(Claim 25. (New) A non-transitory storage medium (Coffman : [0038][0132]) encoded with a computer-readable program that controls a processor (Coffman: Fig.2 element 207) of an information processing system (Coffman: Fig.2) to execute processing comprising:
objective data acquiring processing of acquiring data of an object expressed as a mesh shape (Coffman: [0048] receiving the meshed/datapoint CAD file in "... a CAD file or other suitable electronic file can include data points corresponding to a mesh scheme of the digital model...."; [0040]-[0043] mesh analysis engine 211, point cloud analysis engine 213 and Symbolic function engine 215 calculates geometrical and physical properties of a physical object) ; and inferring processing of using a first machine learning model (Coffman : [0045] "... Predictive engine 217 includes a set of trained machine-learning models and other suitable computation models to infer axioms regarding a physical object represented in a digital model....") obtained by performing machine learning on a result of analysis of physical properties in units of relationships between two adjacent nodes in graph data corresponding to the mesh shape (Coffman : [0022] [0041] "... Some data structures to define point clouds include vectors of values with unique indexes corresponding to each point, octrees [as graph data], and other suitable data structures. Such a set of 3D coordinates defines a point cloud corresponding to the digital model...."; [0049]"... In some instances, a mesh scheme can include data structures containing information related to share vertices in a table of vertices. Such data structure encode simplicial elements as sets of n-tuples, for example, triangles encoded as triples of indices into the table of vertices. A mesh scheme can further include one or more processes for local and global traversal of a mesh including: 1) processes to access individual vertices, edges, and faces; 2) processes to determine a next edge in a face, also known as degree of a face and inverse operation for a previous half-edge; 3) processes to access faces attached to an edge; 4) processes to determine starting and/or ending vertices of an edge; and other suitable processes...."; [0042] "... [0042] Point cloud analysis engine 213 executes one or more processes to calculate implicit or volumetric properties from a point cloud. Such processes include but are not limited to processes based on functions to determine continuous algebraic surfaces, radial basis processes, functions to define discrete voxelizations of a digital model (i.e., discretization of a 3D digital model into discrete elements of volume that constitute a 3D space corresponding to a physical object), and other suitable implicit and volumetric functions...."; [0040] "... Mesh analysis engine 211 includes computer executable instructions to calculate geometric and physical properties associated with a digital model including but not limited to, Euler characteristics (i.e., vertices, edges, and faces), oriented bounding box, center of mass, curvature estimation, symmetry, and other suitable properties...." ; [0071] ) to acquire a first inference result inferring a result of analysis of physical properties for the object (Coffman: [0045] "... Predictive engine 217 [machine learning model] can take as input one or more attributes included in a digital model or attributes or parameters generated by the mesh analysis machine engine 211, point cloud analysis engine 213, and/or symbolic function 215. Thus, in some instances, predictive engine 217 can infer sufficient axioms to determine predictions by processing outputs generated by one or more of mesh analysis engine 211, point cloud engine 213, symbolic function 215, and/or data included in the electronic file with the digital object....") .
Regarding Claim 15
Coffman teaches the information processing system according to claim 14, wherein: the objective data acquiring processing acquires data of an object of numerical analysis expressed as the mesh shape as the data of the object (Coffman : [0040]-[0043] mesh analysis engine 211, point cloud analysis engine 213 and Symbolic function engine 215 as various numerical analysis methods. E.g. in [0043] "... symbolic function engine 215 can determine polynomial terms, cross terms, transcendental functions, and other suitable symbolic function elements to identify shapes of a digital model...", [0059]); and the inferring processing uses the first machine learning model (Coffman : [0045] "... Predictive engine 217 includes a set of trained machine-learning models and other suitable computation models to infer axioms regarding a physical object represented in a digital model....") obtained by performing machine learning on a result of numerical analysis of physical properties in units of relationships between two adjacent nodes in graph data corresponding to the mesh shape (Coffman : [0022], [0070], [0041] "... Some data structures to define point clouds include vectors of values with unique indexes corresponding to each point, octrees [as graph data], and other suitable data structures. Such a set of 3D coordinates defines a point cloud corresponding to the digital model...."; [0049]"... In some instances, a mesh scheme can include data structures containing information related to share vertices in a table of vertices. Such data structure encode simplicial elements as sets of n-tuples, for example, triangles encoded as triples of indices into the table of vertices. A mesh scheme can further include one or more processes for local and global traversal of a mesh including: 1) processes to access individual vertices, edges, and faces; 2) processes to determine a next edge in a face, also known as degree of a face and inverse operation for a previous half-edge; 3) processes to access faces attached to an edge; 4) processes to determine starting and/or ending vertices of an edge; and other suitable processes...."; [0042] "... [0042] Point cloud analysis engine 213 executes one or more processes to calculate implicit or volumetric properties from a point cloud. Such processes include but are not limited to processes based on functions to determine continuous algebraic surfaces, radial basis processes, functions to define discrete voxelizations of a digital model (i.e., discretization of a 3D digital model into discrete elements of volume that constitute a 3D space corresponding to a physical object), and other suitable implicit and volumetric functions...."; [0040] "... Mesh analysis engine 211 includes computer executable instructions to calculate geometric and physical properties associated with a digital model including but not limited to, Euler characteristics (i.e., vertices, edges, and faces), oriented bounding box, center of mass, curvature estimation, symmetry, and other suitable properties...." ; [0071] ) to acquire the first inference result inferring the result of numerical analysis for the object of numerical analysis (Coffman: [0045] "... Predictive engine 217 [machine learning model] can take as input one or more attributes included in a digital model or attributes or parameters generated by the mesh analysis machine engine 211, point cloud analysis engine 213, and/or symbolic function 215. Thus, in some instances, predictive engine 217 can infer sufficient axioms to determine predictions by processing outputs generated by one or more of mesh analysis engine 211, point cloud engine 213, symbolic function 215, and/or data included in the electronic file with the digital object....").
Regarding Claim 16
Coffman teaches the information processing system according to claim 14, wherein, for each piece of data expressing a relationship between a node of interest and an adjacent node (Coffman: [0076]-[0078], [0104]-[0106]) , the inferring processing inputs input data into an affine transformation matrix in the first machine learning model (Coffman : [0069] "... Thus, operations performed by independent variables preprocessor 511 include, for example, centering, scaling, parsing, performing imputations over datasets, performing descriptive or inferential statistical analysis over datasets, and/or other suitable operations used to shape datasets to be used as inputs for predictive machine learning models...", Further since the data is matrix form as shown in [0109]-[0110] where the affine transformation matrix is basically used to perform centering, scaling, parsing, performing imputations over datasets as detailed in [0069]) and consolidates the output of the first machine learning model for a plurality of nodes to acquire the first inference result inferring the result of analysis of physical properties (Coffman: See Fig.5 [0068]-[0072], [0076], nodes as independent variables leading to axioms (outputs) e.g. as in [0128] as computing cost of the whole product which was initially represented by mesh [0121]-[0128]) .
Regarding Claim 17
Coffman teaches the information processing system according to claim 14, wherein the graph data corresponding to the mesh shape includes graph data (Coffman : [0121]-[0125]; graph as tree) composed of nodes and edges expressing the mesh shape (Coffman : [0040], [0042] ) and graph data in which data about physical properties is assigned to at least one of the nodes or edges (Coffman: [0121] "... Examples of the physical and/or geometric parameters or attributed generated by mesh analysis engine 211 include first set of parameters includes any of a data value corresponding to a volume of the physical object,...") .
Regarding Claim 18
Coffman teaches the information processing system according to claim 14, wherein: the data of the object expressed as the mesh shape is data of an object of a simulation (Coffman: [0043] [0058]-[0062]) , and the inferring processing acquires, as the result of analysis of physical properties (Coffman: [0024] "... Such a set of axioms define characteristics of the physical object related to specific manufacture processes and/or techniques. A set of predicted outcomes associated with an overall manufacture process of the physical object is then determined based on the set of axioms....") , a result of a forward calculation in which a resulting state from an initial state in the object of the simulation is analyzed as a forward simulation problem (Coffman: Fig.3 shows the forward simulation flow) .
Regarding Claim 20
Coffman teaches the information processing system according to claim 14, wherein the inferring processing acquires, as the result of analysis of physical properties, post- analysis graph data corresponding to the mesh shape expressing the object of analysis of physical properties (Coffman: post-analysis graph is taught as [0082] "... The suggested fabrication materials can be expressed, for example, as an Edgeworth-Pareto solution based on user requirements and mechanical response properties of the physical object dependent on fabrication materials...."; Figs.5-6 & [0068]-[0090]) .
Regarding Claim 21
21. (New) The information processing system according to claim 14, wherein the inferring processing acquires, as the result of analysis of physical properties, vector data (Coffman : [0041], [0092], [0098]-[0111]) whose elements are post-analysis physical quantities of the overall object of analysis of physical properties (Coffman: post-analysis graph is taught as [0082] "... The suggested fabrication materials can be expressed, for example, as an Edgeworth-Pareto solution based on user requirements and mechanical response properties of the physical object dependent on fabrication materials...."; Figs.5-6 & [0068]-[0090]).
Regarding Claim 22
Coffman teaches the information processing system according to claim 14, wherein the processor (Coffman : Fig.2 element 207) is further configured to execute:
optimization parameter setting processing of setting parameters to be optimized in a range of performance that is achievable with respect to demanded specifications of a construction as the object to be designed (Coffman: [0080] between (0,1), [0088]-[0090]) ;
evaluation value acquiring processing of acquiring an evaluation value expressed by an evaluation function of a designed construction with respect to the parameters to be optimized (Coffman: Fig. 6 flow and [0090]) ;
specific evaluation value acquiring processing of acquiring a specific evaluation value corresponding to at least one of a global maximum value or a local maximum value of the evaluation value (Coffman: Fig. 6 flow and [0090] teaching specific evaluation value compared against desired accuracy level) ; and
structure inferring processing of acquiring a second inference result obtained by using a second machine learning model to infer a structure of a construction according to the parameters to be optimized which correspond to the specific evaluation value (Coffman: Fig. 6 is a iterative process, steps 605 & 607 perform optimization and validation of the model if the accuracy level in step 6015 is not reached; Also see Fig. 7 flow; there are plurality of machine learning models 501 as shown in Fig.5 & [0070]) , the second machine learning model being obtained by performing machine learning on structures of a construction with respect to the parameters to be optimized (Coffman: Fig.6-7, parameter to be optimized could be cost [0028], [0083], or mechanical properties of the physical object as in [0084]; feasibility of manufacturing given a material as in [0085]) , wherein: data of the construction is expressed as a mesh shape (Coffman: [0075], [0042]) , and physical properties of the construction treated as candidates of inference are acquired by using the first machine learning model obtained by performing machine learning on physical properties of constructions in units of relationships between two adjacent nodes in graph data corresponding to the mesh shape (Coffman: [0084]) .
Regarding Claim 23
Coffman teaches the information processing system according to claim 22, wherein the structure inferring processing applies the second machine learning model in which data about a construction to be designed and parameters expressing conditions of a design simulation are treated as input (Coffman: Fig.5, where there are plurality of machine learning models 501, and shown in Fig. 6 & 7 they are updated in iterative manner) , and thereby acquires the second inference result inferring the structure of a construction that conforms to the parameters to be optimized which correspond to the specific evaluation value from among a plurality of structures obtained by varying structural elements of the construction to be designed (Coffman: [0069]-[0077] showing optimization, where the specific evaluation can be cost [0028], [0083], or mechanical properties of the physical object as in [0084]; feasibility of manufacturing given a material as in [0085]).
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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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 19 is rejected under 35 U.S.C. 103 as being unpatentable over Claims 14-18, 20-25 rejected under 35 U.S.C. 102(a)(1) as being anticipated by US PGPUB No. 20180341246 A1 by Coffman; Valerie R. et al., in view of US PGPUB No. 20210357555 by LIU; Wing Kam et al.
Regarding Claim 19
Coffman teaches the information processing system according to claim 14, wherein: the data of the object expressed as the mesh shape is data of an object of a simulation (Coffman: [0043] [0058]-[0062]), and the inferring processing acquires, as the result of analysis of physical properties (Coffman: [0024] "... Such a set of axioms define characteristics of the physical object related to specific manufacture processes and/or techniques. A set of predicted outcomes associated with an overall manufacture process of the physical object is then determined based on the set of axioms...."), a result of an (Coffman: Fig.3 shows the forward simulation flow).
Coffman does not explicitly teach a result of an inverse calculation in which conditions for reaching a resulting state in the object of the simulation is analyzed as an inverse simulation problem.
Liu teaches a result of an inverse calculation in which conditions for reaching a resulting state in the object of the simulation is analyzed as an inverse simulation problem (Liu: [0716] "... One problem that emerges after modeling the interphase in the original filled rubber mesh is that the property of the interphase stays unknown. Due to a limitation of experimental techniques, the viscoelastic property of interphase, or its complex Young's or shear modulus cannot be given as a known input. Therefore, we propose an inverse modeling technique to compute the interphase property quantitatively, described in the next section, with the aim of producing a better prediction of tan(δ) of filled rubber....").
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Liu to Coffman to perform inverse modeling where the physical properties such as viscoelastic property cannot be easily be computed. The motivation to combine would have been that Liu and Coffman are analogous art to the instant claim in the same field of physical property determination based on use of machine learning to reduce computation cost (Liu: Abstract; [0716]; Coffman: Abstract & Fig.3 at least).
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Conclusion
All claims are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Examiner’s Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
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Communication
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Friday, October 31, 2025