DETAILED ACTION
The present office action is responsive to the applicant’s filling on 7/18/2024.
The application has claims 1-16 present. All present claims have been examined.
The Information Disclosure Statement (IDS) and cited references filed 7/18/2024 have
been reviewed by the examiner.
This action is made Non-Final.
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 .
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant 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. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Objections
Claim 16 is objected to because of the following informalities: It recites the term "and/or", which is selective language, the examiner suggests using either the "and" term or the "or" term, otherwise the claim should be worded in a clearer fashion to claim both terms. For the purpose of this examination the examiner is selecting the "or" term from this selective language and interpreting the language associated to the selected limitation.
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.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
In summary, Claim 15 recites a “non-transient medium” with instructions that perform various functions. In the Specification of the present application, there is no description for “non-transient medium” to expressly define and exclude transmission media. Thus, the broadest, reasonable interpretation of this “medium” encompasses nonstatutory subject matter (transmission media) that is unpatentable under 35 U.S.C. 101. The claim should state non-transitory medium instead as defined on the specification.
Accordingly, Claim 15 fails to recite statutory subject matter under 35 U.S.C. 101.
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, 5, 13, 14 and 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Subramaniyan et al. (US 20190056715).
In regards to claims 1 and 15, Subramaniyan discloses a method implemented by a processor for generating a structure of an object with a computer aided design system wherein the generated structure of the object fulfills one or more target physical parameter values (see abstract, FIG. 2, 17, 20 and at least para 3-5, 46, 48-49: CAD file, search platform, execute search and providing a model that meets the search. Para 46: “A search engine 1310 may receive a query from a designer device 1320 and use that query to select and appropriate generative model 1360 and/or a starting seed shape for a new project. For example, the search engine 1310 might select the generative model 1360 that most closely matches a designer's requirements. FIG. 14 is a method 1300 that may be associated with a generative model system in accordance with some embodiments. At 1410, the system may curate a repository storing a plurality of searchable generative models. At 1420, a designer can perform an automated search of the repository being providing the search requirements from a designer device. At 1420, the system may output generative model recommendations ranked by probabilities (e.g., with the most likely appropriate model and/or starting seed shape appearing at the top of a search results list)”), the method comprising: obtaining a set comprising at least one target physical parameter value for the structure of the object (see FIG. 4 element 140, and at least para 3-4, 52, 55: “The second step may be to get a seed idea from the designer (like a geometry outline and a query) and may be augmented continually by the predictions from a deep learning model that has codified designs from a multitude of sources—including images of real, manufactured parts and geometries. The deep learning model may predict potential designs once the designer starts adding boundaries and constraints. As the designer continues to add more constraints (or reject a predicted design), the system continually adjusts the design/predictions. The system may also suggest multiple designs for the designer to select one, or to at least narrow down the options, as each new condition or criteria is added.” para 52); obtaining a model trained using machine learning on a dataset comprising data corresponding to numerical descriptions of a plurality of structures of objects having known structural parameter values and physical parameter values, the model establishing relations between the structural parameter values of the structures of the objects and the physical parameter values of the structures of the objects (see at least FIG. 4 element 420 and at least para 3-5, 37, 46: “At 420, the deep learning model platform may access a design experience data store containing electronic records associated with prior industrial asset item designs. The deep learning model platform may also access a deep learning model associated with an additive manufacturing process (e.g., a model that implements a generative design process). At 430, the deep learning model platform may generatively create boundaries and geometries for the industrial asset item based on the prior industrial asset item designs and the received constraint and load information.” Para 37); and constructing, based on the set comprising at least one target parameter value and using the model, at least one generated structure of the object that fulfills the target physical parameter values (see at least FIG. 4 element 430 and at least para 3-5, 35, 37, 46: “The deep learning model 360 may use the information to generate appropriate boundaries and geometries of a final design at (B). According to some embodiments, the deep learning model platform 350 may transmit the appropriate boundaries and geometries to an additive manufacturing platforms”. On para 37: “At 430, the deep learning model platform may generatively create boundaries and geometries for the industrial asset item based on the prior industrial asset item designs and the received constraint and load information”).
In regards to claim 5, Subramaniyan discloses wherein obtaining the set further comprises:
- obtaining at least one target structural parameter value for the structure of the object (see at least FIG. 4 element 420 and at least para 3-5, 37-39, 46: “At 420, the deep learning model platform may access a design experience data store containing electronic records associated with prior industrial asset item designs. The deep learning model platform may also access a deep learning model associated with an additive manufacturing process (e.g., a model that implements a generative design process). At 430, the deep learning model platform may generatively create boundaries and geometries for the industrial asset item based on the prior industrial asset item designs and the received constraint and load information.” Para 37);
- constructing the at least one generated structure of the object based on the at least one target physical parameter value, the at least one target structural parameter value and using the model (see at least FIG. 4 element 430 and at least para 3-5, 35, 37, 46: “The deep learning model 360 may use the information to generate appropriate boundaries and geometries of a final design at (B). According to some embodiments, the deep learning model platform 350 may transmit the appropriate boundaries and geometries to an additive manufacturing platforms”. On para 37: “At 430, the deep learning model platform may generatively create boundaries and geometries for the industrial asset item based on the prior industrial asset item designs and the received constraint and load information”).
In regards to claim 13, Subramaniyan discloses wherein the structures the numerical description of which are part of the dataset differ by at least one among: size, shape, topology, volume, mass, material composition, thickness, number of basic structural elements, values of physical parameters (different geometries. See FIG. 16, at least para 49 and Claim 3 “illustrates a generative design process 1600 to optimize generative models in accordance with some embodiments. In this example, designer input, such as a text query and/or drawing/image/geometry may be provided to a set of generative models for global search 1630. The global search generates recommendations ranked by probabilities 1640 that are exposed to designer feedback, such as options 1660, additional boundary conditions 1670, a new geometry and/or image 168, etc. The designer feedback may provide a periodic update of the generative models 1630.” On para 52: “The second step may be to get a seed idea from the designer (like a geometry outline and a query) and may be augmented continually by the predictions from a deep learning model that has codified designs from a multitude of sources—including images of real, manufactured parts and geometries. The deep learning model may predict potential designs once the designer starts adding boundaries and constraints. As the designer continues to add more constraints (or reject a predicted design), the system continually adjusts the design/predictions. The system may also suggest multiple designs for the designer to select one, or to at least narrow down the options, as each new condition or criteria is added. For example, if the designer picks one option from many presented ones, the subsequent predictions may be tuned to the previous selections.”).
In regards to claim 14, Subramaniyan discloses further comprising:
- obtaining additional data corresponding to additional numerical descriptions of structures of objects having known structural parameter values and physical parameter values;
- updating the model using the additional data (see FIG. 16, at least para 49 and Claim 3 “illustrates a generative design process 1600 to optimize generative models in accordance with some embodiments. In this example, designer input, such as a text query and/or drawing/image/geometry may be provided to a set of generative models for global search 1630. The global search generates recommendations ranked by probabilities 1640 that are exposed to designer feedback, such as options 1660, additional boundary conditions 1670, a new geometry and/or image 168, etc. The designer feedback may provide a periodic update of the generative models 1630.” On para 52: “The second step may be to get a seed idea from the designer (like a geometry outline and a query) and may be augmented continually by the predictions from a deep learning model that has codified designs from a multitude of sources—including images of real, manufactured parts and geometries. The deep learning model may predict potential designs once the designer starts adding boundaries and constraints. As the designer continues to add more constraints (or reject a predicted design), the system continually adjusts the design/predictions. The system may also suggest multiple designs for the designer to select one, or to at least narrow down the options, as each new condition or criteria is added. For example, if the designer picks one option from many presented ones, the subsequent predictions may be tuned to the previous selections.”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 6, 10, 11 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramaniyan et al. (US 20190056715).
In regards to claim 6, Subramaniyan doesn’t specifically mention wherein the target physical parameter values comprise at least one among: maximum tolerated noise, maximum tolerated vibration, maximum tolerated harshness, tortional stiffness, bending stiffness, lateral stiffness, dynamic stiffness, free-free stiffness, maximum deformation during impact, resistance force during static impact, resistance force during dynamic impact, maximum tolerated temperature, mass of the object, Eigenfrequency of the object.
However, Subramaniyan teaches including load data in x,y and z axis and also stress data which is added for the objects design (see para 28-30, 37, 55: “The load information 120 might be associated with, for example, centrifugal loads, bolt loads, etc. along an x-axis, a y-axis, and a z-axis. Topology optimization 130 may then be performed to create an adjusted or refined design 140. The designer can then add stress information 150 (e.g., a maximum principal stress of a particular amount of kilo-pounds per square inch (“ksi”)). A shape optimization process 160 may then be performed, and a final design 170 may be provided to a three-dimensional printer 180.”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of in order to obtain and use different target physical parameter values which further refine model and object data, since it improves and enhances the system to optimize and improve the end resulting object that the user wants to manufacture.
In regards to claim 10, Subramaniyan doesn’t specifically teach further comprising:
- obtaining at least one fixed target structural parameter value for the structure of the object; and
- constructing, based on the set of target parameter values, the fixed target structure parameter value and using the model, at least one generated structure of the object that fulfills the at least one target physical parameter value.
However, Subramaniyan teaches receiving data from both the user and from database values that are used within the optimization process. These data values are used to generate structures to fulfill the request from the user. Data include geometry and images which can be considered fixed which in turn are used to further refine the generated object to be provided to the user (see at least para 28: “FIG. 1 is an overview of a design process 100. The process 100 begins with a baseline design 110 for which a designer can add load information 120. The load information 120 might be associated with, for example, centrifugal loads, bolt loads, etc. along an x-axis, a y-axis, and a z-axis. Topology optimization 130 may then be performed to create an adjusted or refined design 140. The designer can then add stress information 150 (e.g., a maximum principal stress of a particular amount of kilo-pounds per square inch (“ksi”)). A shape optimization process 160 may then be performed, and a final design 170 may be provided to a three-dimensional printer 180.” Also see para 46-46 : “FIG. 11 is a high-level diagram of a system 1100 for fine tuning a deep learning model according to some embodiments. According to this embodiment, a deep learning model creation platform 1110 can access a design experience data store 1130 (e.g., containing past item design). Moreover, the deep learning model creation platform receives feedback from a designer device 1120 that can be used to fine-tine the current dep learning model 1160 and/or future deep learning models. FIG. 12 is a method 1200 for fine tuning a deep learning model in accordance with some embodiments. At 1210, the system may continually provide results to a deep learning model creation platform. At 1220, the system may use those results to fine tune at least one deep learning model. [0046] In this way, over time, the generative models may become a repository of institutional knowledge that can be constantly mined for rapid design innovations. FIG. 13 is a high-level diagram of a generative model system 1300 according to some embodiments. In this case, a repository 1350 stores many different generative models 1360 (e.g., associated with different types of asset items). A search engine 1310 may receive a query from a designer device 1320 and use that query to select and appropriate generative model 1360 and/or a starting seed shape for a new project. For example, the search engine 1310 might select the generative model 1360 that most closely matches a designer's requirements. FIG. 14 is a method 1300 that may be associated with a generative model system in accordance with some embodiments. At 1410, the system may curate a repository storing a plurality of searchable generative models. At 1420, a designer can perform an automated search of the repository being providing the search requirements from a designer device. At 1420, the system may output generative model recommendations ranked by probabilities (e.g., with the most likely appropriate model and/or starting seed shape appearing at the top of a search results list)”. Also, para 49-50 “[0049] FIG. 16 illustrates a generative design process 1600 to optimize generative models in accordance with some embodiments. In this example, designer input, such as a text query and/or drawing/image/geometry may be provided to a set of generative models for global search 1630. The global search generates recommendations ranked by probabilities 1640 that are exposed to designer feedback, such as options 1660, additional boundary conditions 1670, a new geometry and/or image 168, etc. The designer feedback may provide a periodic update of the generative models 1630.
[0050] Thus, embodiments may provide a framework for designing structures through a combination of deep learning models and design expertise through generative techniques. Embodiments may provide an accelerated process for design using a search engine. Imagine, for example, a system that can quickly “search” or predict potential designs based on a few simple “questions” or boundaries the designer provides and then continually adjust results based on additional boundary conditions/feedback provided by the designer. Such a system may help a designer identify concepts that he or she was not be aware of and leverages the collective wisdom of all designs that have been codified in the system. Moreover, embodiments may provide the capability to generate new designs that are a combination of multiple prior, independent designs. This may open up totally new concepts that might not be possible with either the system alone or the designer alone)”.
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Subramaniyan in order to generated a structure of the object that fulfills the at least one target physical parameter value, since doing so it improves and enhances user experience by providing the users with results as similar or close as possible to the request.
In regards to claim 11, Subramaniyan doesn’t specifically teach wherein obtaining the model further comprises: - identifying, for each numerical description of the structure of an object, basic structural elements within the structure of the object; and - determining, using machine learning, a relation between structural parameter values of basic structures and the physical parameter values of the structures.
However, the Subramaniyan teaches that machine learning model and do structural analysis as it uses model data, geometry data and other past data to optimize the process of creating the objects requested by the user. The system includes a physics model for validating the generated objects (geometry and boundaries) and feeding back the data to further optimize and produce objects (see at least par 43-44: “FIG. 9 is a high-level diagram of a system 900 including a physics model according to some embodiments. As before, the system 900 includes a deep learning model platform 950 that can execute a deep learning model 960. In this case, the boundaries and geometries generated by the deep learning model platform may be provided to an optimizer 930 executing an optimizer process 940 and a physics model platform 960 executing a physics model 970. The results of the optimizer process 940 might be directly fed back to the deep learning model platform 950 (as illustrated by the dashed arrow in FIG. 9) or via a designer device 920 (e.g., after receiving one or more adjustments from a designer and/or an indication of the validity of the predicted design). Results from the physics model platform 960 may also be fed back to the deep learning model platform 950 to validate intermediate designs. [0044] FIG. 10 is a method 1000 that may be associated with a system having a physics model in accordance with some embodiments. At 1010, boundaries and geometries may be received at a physics model platform. At 1020, the physics model platform may execute a validation process on at least one intermediate industrial asset design (e.g., does the physics model indicate that the design is behaving as it is supposed to behave?). According to some embodiments, the validation process might be associated with a high-fidelity physics model, the Ansys®/LS_Dyna model, etc. At 1030, the results of the validation process may be fed back to continually re-train the deep learning model”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Subramaniyan in order to do structural analysis of the objects data, since doing so it improves and enhances user experience by fine tuning the models which in turn improve the generated objects presented to the users with the results/when requested.
In regards to claim 12, Subramaniyan doesn’t specifically teach wherein the model further comprises: - identifying, for each numerical description of the structure of an object, interactions between basic structural elements within the structure of the object; and - determining, using machine learning, a relation between an arrangement of basic structures and the physical parameter values of the structures.
However, the Subramaniyan teaches that machine learning model and do structural analysis as it uses model data, geometry data and other past data to optimize the process of creating the objects requested by the user. The system includes a physics model for validating the generated objects (geometry and boundaries) and feeding back the data to further optimize and produce objects (see at least par 43-44: “FIG. 9 is a high-level diagram of a system 900 including a physics model according to some embodiments. As before, the system 900 includes a deep learning model platform 950 that can execute a deep learning model 960. In this case, the boundaries and geometries generated by the deep learning model platform may be provided to an optimizer 930 executing an optimizer process 940 and a physics model platform 960 executing a physics model 970. The results of the optimizer process 940 might be directly fed back to the deep learning model platform 950 (as illustrated by the dashed arrow in FIG. 9) or via a designer device 920 (e.g., after receiving one or more adjustments from a designer and/or an indication of the validity of the predicted design). Results from the physics model platform 960 may also be fed back to the deep learning model platform 950 to validate intermediate designs. [0044] FIG. 10 is a method 1000 that may be associated with a system having a physics model in accordance with some embodiments. At 1010, boundaries and geometries may be received at a physics model platform. At 1020, the physics model platform may execute a validation process on at least one intermediate industrial asset design (e.g., does the physics model indicate that the design is behaving as it is supposed to behave?). According to some embodiments, the validation process might be associated with a high-fidelity physics model, the Ansys®/LS_Dyna model, etc. At 1030, the results of the validation process may be fed back to continually re-train the deep learning model”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Subramaniyan in order to do structural analysis of the objects data, since doing so it improves and enhances user experience by fine tuning the models which in turn improve the generated objects presented to the users with the results/when requested.
Claim(s) 2, 3, 4 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramaniyan et al. (US 20190056715) as applied to claims above, and further in view of Chiplonkar et al. (US 20170169112).
In regards to claim 2, Subramaniyan doesn’t specifically teach further comprising: obtaining the dataset comprising data corresponding to numerical descriptions of a plurality of structures of objects having known structural parameter values and physical parameter values, wherein the dataset is obtained by measuring or determining the physical parameter values of prototypes of at least one of the plurality of structures of objects.
Chiplonkar teaches further comprising: obtaining the dataset comprising data corresponding to numerical descriptions of a plurality of structures of objects having known structural parameter values and physical parameter values, wherein the dataset is obtained by measuring or determining the physical parameter values of prototypes of at least one of the plurality of structures of objects (see at least para 4, 20, 84: teaches searching process which obtains and uses parameter values from objects that are used to classify/group and provide search results when requested. “searching may comprise at least one processor configured to generate a graphical user interface (GUI) through a display device that enables a plurality of objects stored in a data store and classified by library nodes to be searchable via a selection of one or more library nodes and a selection of one or more filter values for a first plurality of filters corresponding to different object features of the objects. The at least one processor may be configured to be responsive to at least one input through an input device corresponding to a selection of a library node and/or a filter value: to determine a subset of the objects having object features corresponding to the selection; to cause at least some of the subset of objects to be displayed in the GUI; to determine a second plurality of filters that each have at least one filter value based on the determined subset of objects; and to cause at least some of the second plurality of filters to be displayed in the GUI in an order based on the filter values associated with the second plurality of filters”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Chiplonkar in combination with the teaching of Subramaniyan in order to accomplished obtaining data sets in a similar matter, since it improves and enhances user experience as it improves the process to provide results.
In regards to claim 3, Subramaniyan teaches further comprising: manufacturing an object based on the constructed at least one generated structure of the object, wherein the manufactured object comprises structural parameter values matching those of the at least one generated structure (see para 35: “In this way, the system 300 may efficiently and accurately facilitate creation of an industrial asset item. For example, at (A) the additive manufacturing platform 360 may receive design information (e.g., a selection of a starting seed shape, answers to questions, adjustments to various intermediate design proposals, etc.) to be used by the deep learning model 360. The deep learning model 360 may use the information to generate appropriate boundaries and geometries of a final design at (B). According to some embodiments, the deep learning model platform 350 may transmit the appropriate boundaries and geometries to an additive manufacturing platforms (e.g., by transmitting a definition file to the platform). The additive manufacturing platform can then communicate with the three-dimensional printer to initiate a printing process.”).
In regards to claim 4, Subramaniyan doesn’t specifically mention further comprising:
- measuring or determining physical parameter values on the manufactured object;
- determining a difference between the measured or determined physical parameter values on the manufactured object and the physical parameter values of the at least one generated structure of the object; - if the determined difference is above a predetermined threshold value, modifying the model using the determined difference.
However, Subramaniyan does teach within the process, means to optimize the model and the data, boundaries and geometries of the objects. The system obtains data from feedback, objects, and updates/optimizes the model data which is provided to the user (see para 41-50 provides process steps for optimizations. Para 42: “FIG. 8 is a design optimization method 800 in accordance with some embodiments. At 810, design adjustments may be received from a designer device. For example, the designer might alter a suggest shape, add or delete an element, indicate “approval” or “disapproval” of the intermediate design (is the design evolving in an appropriate direction), etc. At 820, an optimization process may be executed based on the received design adjustments. For example, a deep learning model computer processor may receive design adjustments from the designer device and, based on the received design adjustments, execute an optimization process. At 830, the received design adjustments may be fed back to continually re-train the deep learning model.” Para 49: …“a generative design process 1600 to optimize generative models in accordance with some embodiments. In this example, designer input, such as a text query and/or drawing/image/geometry may be provided to a set of generative models for global search 1630. The global search generates recommendations ranked by probabilities 1640 that are exposed to designer feedback, such as options 1660, additional boundary conditions 1670, a new geometry and/or image 168, etc. The designer feedback may provide a periodic update of the generative models 1630”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of in order to accomplished modifying the model using difference in data, since it improves and enhances the system to optimize and improve the model which provides results to the user when requested.
In regards to claim 7 Subramaniyan doesn’t specifically teach - comparing the at least one generated structure of the object with structures of objects in the dataset; - assigning the at least one generated structure a similarity index based on the comparison.
Chiplonka teaches - comparing the at least one generated structure of the object with structures of objects in the dataset; - assigning the at least one generated structure a similarity index based on the comparison (see para 54: teaches using threshold values to filter relative closeness to the 3D geometry. “Responsive to the input of the seed object and the threshold data, the application software component may be configured to carry out a search of objects in the database with a similar shape within the specified threshold based on shape indexes generated from CAD data for objects in the database. The resulting search results (with similarly shaped objects) maybe then be further refined using the GUI via the selection of library nodes and filter values as described previously.”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Chiplonkar in combination with the teaching of Subramaniyan in order to index and filter 3D objects based on closeness to the 3D geometries, since doing so it improves and enhances user experience by refining and providing the users with results as similar or close as possible to the request (see para 54).
Allowable Subject Matter
Claims 8-9 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 16 is allowed.
The following is an examiner’s statement of reasons for allowance of claim 16:
None of the references taken either alone or in combination with the prior art of record disclose:
“…the model establishing relations between the structural parameter values of the structures of the objects and the physical parameter values of the structures of the objects;
constructing, based on the set comprising at least one target parameter value and using the model, at least one generated structure of the object that fulfills the target physical parameter values;
manufacturing an object based on the constructed at least one generated structure of the object, wherein the manufactured object comprises structural parameter values matching those of the at least one generated structure;
subjecting the manufactured object to an evaluation test in which the response of the object to environmental conditions and/or stress conditions is assessed in the form of measured or determined physical parameter values;
comparing the assessed response to the target physical parameter values; and
upon determining that at least one measured or determined physical parameter value differs from a corresponding target physical parameter value by more than a predetermined amount:
selecting from among the constructed at least one generated structure another generated structure of the object; manufacturing a second object based on the selected generated structure; and subjecting the manufactured second object to the evaluation test.”
The closes prior art of record are:
Bhatt et al. (US 20250256460); teaches the predictive model is trained at least by identifying one or more manufacturing anomalies relating to the additive manufacturing of a test coupon, identifying one or more manufacturing defects within the resulting test coupon, and performing registration between the one or more manufacturing anomalies and the one or more manufacturing defects. The predictive model is used to monitor additive manufacturing processes and optionally inform the updating of process parameters. In some examples, one or more mechanical fatigue testing processes (AKA stress testing) may be applied to the test coupon after manufacturing. For example, the test coupon may be subjected to one or more compression tests, axial fatigue tests, torsion fatigue tests, etc.
Irissou et al (US 20180011959): teaches a design verification tool. When the received value of the physical parameter is outside the range of values of the physical parameter provided in the data sheet 2000, the layout design verification tool 190 determines that a layout design generated based on the schematic 1830 fails a test for the physical parameter. On the other hand, when the received value of the physical parameter is within the range of values of the physical parameter provided in the data sheet 2000, the layout design verification tool 190 determines that the layout design passes the test for the physical parameter.
Bains et al. (US 20170212903): teaches searching computer-aided design (CAD) data. One method includes receiving a selection of a type of CAD metadata, displaying a user interface including at least one input mechanism for receiving a search parameter associated with the type of CAD metadata, and receiving the search parameter through the user interface.
Qamhiyah et al. (US 7778995): teaches a method to provide information associated with a previously designed component having a geometry that is similar to a geometry of a source component.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIO M VELEZ-LOPEZ whose telephone number is (571)270-7971. The examiner can normally be reached on M-F 10:30am-5:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman, can be reached at telephone number 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARIO M VELEZ-LOPEZ/
Examiner, Art Unit 2118
/SCOTT T BADERMAN/Supervisory Patent Examiner, Art Unit 2118
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