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 .
DETAILED ACTION
This is a Non-Final Action of the instant application 18/379,506 (hereinafter the ‘506 application), filed 8/30/2023 and assigned to NEC.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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A. Claims 1-18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 of copending U.S. Patent Application 18/282,145, hereinafter the ‘145 APP (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 8 and 15 of ‘145 APP contain each and every limitation or an obvious variant thereof of independent claims 1, 7, and 13 of the instant application, except generating a recommendation based on the target. It would have been obvious to one of ordinary skill in the art at the time of the invention for the a detected node to correspond to the target and be provided as a recommendation output, because as stated earlier in claim 1 of the ‘145 APP that the goal is to find values that approach a target, then clearly what is being detected in a recommended material specification. Other claims overlap in a corresponding manner.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
B. Claims 1-18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-15 of copending U.S. Patent Application No. 18/853,818, hereinafter the ‘818 APP (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 6, and 11 of ‘818 APP contain each and every limitation or an obvious variant thereof of independent claims 1, 7, and 13 of the instant application, except vectorizing the value related to the material specification. It would have been obvious to one of ordinary skill in the art at the time of the invention for the a value to be represented as a vector, because it was notoriously well-known in the art at the time of the invention to use a vector to represent a value so that machine learning algorithms can understand and use them effectively. Other claims overlap in a corresponding manner.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al, WO 2020/056405, hereinafter Liu, Koishi, JP 2008-293315, and Katsuki, U.S. Publication No. 2020/0227143.
With regard to claims 1, 7 and 13, which teach “A recommendation data generation apparatus (method / medium) comprising: at least one memory that is configured to store instructions; and at least one processor that is configured to execute the instructions”, Liu teaches a system, method, medium for implementing a data driven design optimization and performance prediction tool for material based simulation (see pages 5, 31-34 and 54).
With regard to claims 1, 7 and 13, which teach “acquire, for each of a plurality of patterns of a material that can be used in a target process, material specification information representing a material specification of the material and physical property information indicating a physical property quantity for each of a plurality of physical properties of a product that can be generated in the target process by using the material; ” Liu teaches: "In one aspect of the invention, a method for design optimization and/or performance prediction of a material system includes generating a representation of the material system at a number of scales, wherein the representation at a scale comprises microstructure volume elements (MVE), that are building blocks of the material system at said scale: collecting data of response fields of the MVE computed from a material model of the material system over a predefined set of material properties and boundary conditions;” where "material properties and boundary conditions" encompass the physical property information including physical property quantities) (see page 32, lines 17-22; page 32, line 31-page 33, line 2).
With regard to claims 1, 7 and 13, which teach “generate, by using the physical property information, a self-organizing map on which each node is assigned a position in a map space and a physical property vector indicating a value related to a physical property quantity for each of a plurality of types of physical properties of the product;” Liu teaches "applying machine learning to the collected data of response fields to generate clusters that minimize a distance between points in a nominal response space within each cluster;" (see page 32, lines 22-24). "In one embodiment, the machine learning is performed with a self-organizing mapping (SOM) method, as to means clustering method, or the like.") (see page 33, lines 10-11).
With regard to claims 1, 7 and 13, which teach “assign a specification vector indicating a value related to a material specification to each of the nodes by using the material specification information;” “acquire target information indicating a desired physical property of the product and detecting, as a target node, the node to which the physical property vector matching a desired physical property indicated by the target information is assigned;” “and generate, by using the specification vector assigned to the target node, recommendation data representing the material specification of the material with which a product having the desired physical property can be generated”, Liu teaches to "provide optimized structure based on […] material behavior […]" where the "optimized structure" is recommendation data corresponding to the design of a product which is characterized by the choice of a particular material whose physical properties are simulated. (see figure 10 and page 10, lines 6-12).
On the basis of using a self-organizing map method for recommendation data generation disclosed in Liu, it is well known for those skilled in the art well to match an input vector with a node and its corresponding vector in a (Kohonen) self-organizing map (SOM). This is a conventional technical selection for those skilled in the art. This is a conventional manner of making predictions using these structures, which is known to those skilled in the art based on their common knowledge about neural networks. Liu repeatedly mentions simulating and storing various physical property values of materials, so as to predict the performance of products made from these materials and optimize their design. On the basis of constructing an SOM representing material physical properties and using it to generate clusters disclosed in Liu, those skilled in the art would readily conceive of specification assignment means for assigning a specification vector indicating a value related to a material specification to each of the nodes by using the material specification information; target node detection means for acquiring target information indicating a desired physical property of the product and detecting, as a target node, the node to which the physical property vector matching a desired physical property indicated by the target information is assigned; and generating recommendation data by using the specification vector assigned to the target node.
Koishi teaches a similar system for data analysis and multi scale simulation of a part (such as a tire) composed of a material (such as various rubber compounds / steel wire / etc.), where a self-organized map is utilized to help find an optimal composition of material for the part (see the last paragraph page 7 through the first paragraph page 8 of the translation), but further explicitly describes use of the target values within the self-organizing map for which to compare product properties to (see the last paragraph page 8 through the first paragraph page 9 of the translation), while further looking at the specific specification of the material that makes up the tire (filler dispersion / filler volume ration / etc.) (see the last paragraph page 7 through the first paragraph page 8 of the translation). It would be obvious to one of ordinary skill I the art to utilize the target values and material specification as used in Koishi to provide a more complete picture of what is known about a specific material being evaluated and to have a better idea of what characteristics meet a goal.
Katsuki teaches an analogous system for using neural networks and self-organizing maps to generate new material candidates based on a target (see abstract, paragraphs 3, and 18), but further teaches use of specific known material characteristics (hardness, melting point, ionic conductivity, glass transitions temp, molecular atomized energy, etc.) as well at material pattern / lattice in the evaluation (see paragraphs 33, 34, and 68). It would be obvious to one of ordinary skill I the art to utilize the detailed characteristics outlined in Katsuki to provide a more complete picture of what is known about a specific material being evaluated for a specific use.
With regard to claims 2, 8, and 14, which teaches “wherein the generation of the recommendation data further includes selecting at least one of the nodes from a sub-area in which the target node is included in the map space, and further generates, for each of the selected nodes, the recommendation data by using the specification vector assigned to that node”, in a recommendation system, it is a conventional technical selection in the art to expand from a target item to a similar item for diversified recommendations. A self-organizing map (SOM) has an inherent property that spatial proximity reflects feature similarity. Those skilled in the art. would readily conceive of using this property to extract more candidate solutions from the neighboring area of the target node. Liu already discloses the concepts of clustering and interaction tensors. Clustering essentially groups nodes with similar features into the same category. Those skilled in the art would readily conceive of using the cluster to which the target node belongs as the sub-area for recommendation. That is, those skilled in the art would readily conceive that the recommendation data generation means selects at least one of the nodes from a sub-area in which the target node is included in the map space, and further generates, for each of the selected nodes, the recommendation data by using the specification vector assigned to that node.
Katsuki further teaches generating recommendation by selecting candidates (nodes) with higher target property values as compared to other candidates (see paragraphs 55 and 68).
With regard to claims 3, 9, and 15, which teaches “wherein the generation of the recommendation data further includes using, as the sub-area, an area whose center is at the target node and in which a distance from the target node is equal to or shorter than a threshold”, in a recommendation system, it is a conventional technical selection in the art to expand from a target item to a similar item for diversified recommendations. A self-organizing map (SOM) has an inherent property that spatial proximity reflects feature similarity. Those skilled in the art would readily conceive of using this property to extract more candidate solutions from the neighboring area of the target node. DI already discloses the concepts of clustering and interaction tensors. Clustering essentially groups nodes with similar features into the same category. Those skilled in the art would readily conceive of using the cluster to which the target node belongs as the sub-area for recommendation. Those would readily conceive of defining an area with a point as the center and a distance threshold as the radius, i.e., using, as the sub-area, an area whose center is at the target node and in which a distance from the target node is equal to or shorter than a threshold.
With regard to claims 4, 10, and 16, which teaches “wherein the generation of the recommendation data further includes: dividing the map space into a plurality of clusters based on the specification vector assigned to each of the nodes; and using the cluster to which the target node belongs as the sub-area”, Liu further discloses the following features (see claims, and pages 5, 31-34 and 54 of the specification): applying machine learning to the collected data to generate clusters that minimize a distance between points in a nominal response space within each cluster; and compressing the microstructure volume element (MVE) into cluster representations through clustering, where material points within the same cluster have similar responses. That is, Liu already discloses clustering based on vectors and response fields. In order to more accurately filter material specifications with properties similar to the target node, those skilled in the art would readily conceive of clustering the map space based on the specification vectors. In order to expand the recommendation scope while maintaining consistency and reasonableness of the recommendation results, it is a conventional technical selection for those skilled in the art to select the cluster to which the target node belongs as the sub-area, and select a cluster as a neighboring area.
Katsuki further teaches using clusters in the data sets to select candidates (nodes) with higher desirability as compared to other candidates (see paragraph 32).
With regard to claims 5, 11, and 17, which teaches “wherein the generation of the recommendation data further includes generating the recommendation data for each of a predetermined number of nodes in an ascending order of the distance from the target node in the map space by using the specification vector assigned to the node”, in order to obtain recommendation results that better match the target performance, it is readily conceivable for those skilled in the art to select nodes in an ascending order of the distance from the target node. In the self-organizing map (SOM) and cluster analysis, closer distance implies more similar features, it is readily conceivable for those skilled in the art to output candidate results according to distance sorting and select a predetermined number of nodes in an ascending order of distance to generate recommendation data.
With regard to claims 6, 12, and 18, which teaches “wherein the at least one processor is configured to execute the instructions further to: cause a simulator to take the material specification represented by the recommendation data as an input and perform a simulation of generating the product with a material specified by the material specification taken as the input, the simulator being configured to generate prediction data of a physical property of the product; acquire the prediction data generated by the simulator; and output the prediction data and the recommendation data”; Liu further discloses the following features (see claims, pages 5, 31-34 and 54 of the specification): computing data of MVE response field through a material model; performing multi-scale numerical simulation based on preset material properties and boundary conditions; using clusters and interaction tensors to quickly solve governing equations; and obtaining response predictions of the material system for design optimization and performance prediction. Liu already discloses a simulation process for model and performance prediction with given material specifications. In order to verify the accuracy of the recommendation results, those skilled in the art would readily conceive of inputting the recommended material specifications into a simulator for simulation verification, providing a dedicated device to control the operation of the simulator, and outputting the recommendation data and simulation results. That is, those skilled in the art would readily conceive of simulator control means for causing a simulator to take the material specification represented by the recommendation data as an input and perform a simulation of generating the product with a material specified by the material specification taken as the input, the simulator being configured to generate prediction data of a physical property of the product; and simulation result output means for acquiring the prediction data generated by the simulator and outputting the prediction data and the recommendation data.
Katsuki further teaches generating recommendation by evaluating input characteristics of existing data on materials, generating new candidates via machine learning, and further selecting candidates (nodes) with higher target property values as compared to other candidates (see paragraphs 22 and 68).
Summary
Claims 1-18 are REJECTED.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Lecue et al., U.S. Publication No. 2019/0325265 and Siegl, U.S. Publication No. 2020/0167648.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS G BONSHOCK whose telephone number is (571)272-4047. The examiner can normally be reached M-F 7:15 - 4:45.
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/DENNIS G BONSHOCK/Primary Examiner, Art Unit 3992
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