Notice of Pre-AIA or AIA Status
Claims 1-20 are currently presented for Examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/19/2023 and 01/20/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to to an abstract idea without significantly more.
Step 1) Is the claims to a process, machine, manufacture, or composition of matter?
Claims: 1-10 is directed to method or process that falls on one of statutory category.
Claim 11-15 is directed to system or machine that falls on one of statutory category.
Claim 16-20 is directed to a computer program product comprising one or more computer readable storage media that falls on manufacture statutory category.
Step 2A Prong 1 (Whether a Claim is Directed to a Judicial Exception)
Claim 1, 11 and 16 recites
generating a digital twin of a new fixture using design information and usage characteristics of similar historical fixtures; (Generating a digital twin is a mental process that can be performed by the human mind either by paper and pencil, as by drawing a representation of the fixture, or by using a computer as a tool, such as by using a drawing program. This drawing could either be a pictorial representation of the form of the object or a diagram with datapoints, keywords, and attributes defining the representation. The claim is directed to an abstract idea, specifically a Mental process ([MPEP 2106.04(a)(2)(IIII)]))
simulating lifecycle usage of the new fixture using the digital twin; (Evaluating how a fixture wears down or performs over time is a process that can be performed in the human mind or through basic cognitive evaluation, so it recites Mental process of abstract idea. (See MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”)
identifying a simulated failure point in the new fixture based on the simulated lifecycle usage; (The act of "identifying" or evaluating a failure point based on usage data is something a person can do in their mind or with pen and paper. The claim is directed to an abstract idea, specifically a Mental process ([MPEP 2106.04(a)(2)(IIII)] Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”)
modifying the design information of the new fixture to mitigate the simulated failure point. (A person can review usage data, visualize where a fixture might break over time, and redesign the part using only pen, paper, and mental thought. The claim is directed to an abstract idea, specifically a Mental process ([MPEP 2106.04(a)(2)(IIII)] Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”)
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. In particular, the additional elements of an computer-implemented method and digital twin which are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The additional elements of a system comprising: one or more computer readable storage media storing program instructions; and one or more processors which, in response to executing the program instructions, are configured to perform a method in claim 11 which are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The additional elements of a computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising instructions configured to cause one or more processors to perform a method in claim 16 which are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In view of Step 2B, the claim as a whole does not amount 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. In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. In particular, the additional elements of an computer-implemented method and digital twin which are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The additional elements of a system comprising: one or more computer readable storage media storing program instructions; and one or more processors which, in response to executing the program instructions, are configured to perform a method in claim 11 which are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The additional elements of a computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising instructions configured to cause one or more processors to perform a method in claim 16 which are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); Thus, claim 1, 11 and 16 are not patent eligible.
Claim 2 further recites wherein the usage characteristics of the historical fixtures include loading characteristics, cycling characteristics, and environmental characteristics. A designer can review historical loading, cycling, and environmental data and mentally or manually calculate/predict how a new fixture will behave. Simply receiving design data, recalling historical operational parameters (how an old fixture loaded or cycled), and mentally or conceptually mapping them to a new design is considered an evaluation or observation process that humans can performed in their mind either by paper and pencil, Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 3, 12 and 17 further recites identifying the similar historical fixtures by: inputting the design information for the new fixture to a machine learning model, wherein the machine learning model is trained on a corpus of historical fixtures and corresponding usage characteristics; and receiving, as output from the machine learning model, the usage characteristics of the historical fixtures that are similar to the design information for the new fixture. The steps of analyzing design information, comparing it to historical data, identifying similarities, and determining historical usage characteristics can be performed in the human mind or by a person using pen and paper (comparing blueprints/drawings of fixtures to past records and noting similarities). Here, the generic machine learning model trained on data (historical fixtures) to receive input (design information) and yield an output (similar usage characteristics) which is treated as an abstract idea. Simply applying a generic computational tool/machine learning to a specific field of use (fixtures) does not turn an abstract concept into a patent eligible. The use of “machine learning” is no more than generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h). Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned in claim 1.
Claim 4 recites wherein simulating the lifecycle usage of the new fixture utilizes simulation software based on Finite Element Analysis (FEA). Finite Element Method (FEM) involves complex mathematical equations, algorithms, and calculations so it falls under the "mathematical concepts" grouping. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned in claim 1.
Claim 6, 13 and 18 further recites wherein modifying the design information of the new fixture includes changing a material composition of the new fixture in an area of the simulated failure point. An engineer can look at a failure point on a blueprint or simulation report and mentally decide to substitute a different material composition in that specific area. Under MPEP § 2106.04(a)(2)(III), processes that correspond to evaluation or judgment fall into the mental process. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 7, 14 and 19 further recites wherein modifying the design information of the new fixture includes changing a dimensional attribute of the new fixture in an area of the simulated failure point. It is further evaluating a "simulated failure point" and deciding to "change a dimensional attribute" is an act of reasoning, evaluating, and redesigning that an engineer can conceptually perform (and visualize/calculate) on scratch paper or mentally. The claim is directed to an abstract idea, specifically a Mental process ([MPEP 2106.04(a)(2) (III)]). Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned in claim 1.
Claim 8 further recites wherein modifying the design information of the new fixture includes adding a reinforcing ribbing to the new fixture in an area of the simulated failure point. Identifying a "simulated failure point" and deciding where a reinforcement is needed relies on evaluation, judgment, and determination—concepts that can theoretically be performed via analog analysis or in the human mind (or using pen and paper). Under MPEP § 2106.04(a)(2)(III), processes that correspond to evaluation or judgment fall into the mental process. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned in claim 1.
Claim 9 further recites wherein modifying the design information of the new fixture includes reducing a force concentration features of the new fixture in an area of the simulated failure point. Identifying a "simulated failure point" and deciding where a reinforcement is needed relies on evaluation, judgment, and determination—concepts that can theoretically be performed via analog analysis or in the human mind (or using pen and paper). It involves observing a simulation failure point, and deciding to change a shape or reduce dimensions—actions that a human engineer can perform in their mind or using pen and paper on a notepad—it falls under the mental processes grouping. Under MPEP § 2106.04(a)(2)(III), processes that correspond to evaluation or judgment fall into the mental process. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned in claim 1.
Claim 10 further recites fabricating the new fixture according to the modified design information using additive manufacturing. This recites insignificant post-solution activity (MPEP 2106.05(g). A claim element that amounts to merely insignificant post solution activity and is not indicative of integration into a practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP 2106.05)(g)Insignificant application) i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential); and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55. Furthermore, this is akin to a well-understood, routine, and conventional activity as shown by the following references:
Razzell et al. (US20200265122A1)
• [0001]: “This specification relates to computer aided design of physical structures, which can be manufactured using subtractive manufacturing systems and techniques in addition to additive manufacturing and/or other manufacturing systems and techniques.”
• [0054]: “In the first stage, a workpiece is built using known systems and techniques. These can include various additive manufacturing, casting and/or forging systems and techniques, such as described above.”
Nardi et al. (US20140277669 A1)
• [0002]: “Additive manufacturing (AM) has been investigated for the last two decades and currently has received considerable attention. Parts have been produced using various printing techniques (e.g., three-dimensional or 3D printing techniques).”
Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned in claim 1.
Claim 15 and 20 further recites wherein modifying the design information of the new fixture includes adding a reinforcing ribbing to the new fixture in an area of the simulated failure point. Identifying a "simulated failure point" and deciding where a reinforcement is needed relies on evaluation, judgment, and determination—concepts that can theoretically be performed via analog analysis or in the human mind (or using pen and paper). Claim also recites reducing a force concentration feature of the new fixture in an area of the simulated failure point. Identifying a "simulated failure point" and deciding where a reinforcement is needed relies on evaluation, judgment, and determination—concepts that can theoretically be performed via analog analysis or in the human mind (or using pen and paper). It involves observing a simulation failure point, and deciding to change a shape or reduce dimensions—actions that a human engineer can perform in their mind or using pen and paper on a notepad—it falls under the mental processes grouping. Under MPEP § 2106.04(a)(2)(III), processes that correspond to evaluation or judgment fall into the mental process. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned in claim 11 and 16.
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.
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.
5. Claim(s) 1-7, 9-14 and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Markov et al. (PUB NO: US20200055301A1) in view of Harris et al. (PUB NO: US20220237344A1).
Regarding claim 1
Markov teaches a computer-implemented method (see para 126- An alternative embodiment preferably implements the above methods in a computer-readable medium storing computer-readable instructions. The computer-executable component is preferably a processor but the instructions may alternatively or additionally be executed by any suitable dedicated hardware device.) comprising:
generating a digital twin of a new fixture using design information (see para 0052-56- Constructing a virtual support device S400 functions to create a virtual model of the support device (virtual support device), which can subsequently be used to manufacture a real support device to support the real part. The virtual support device is preferably constructed based on the features of the virtual part extending along the retention direction and proximal the virtual clamps (e.g., based on the geometry of the contact surface). Multiple virtual support devices for a given virtual part are preferably determined in parallel, wherein each virtual support device is determined for a different combination of the different retention directions, retention rotations, other part orientations, virtual support options, and/or other parameters (examples shown in FIG. 2 and FIG. 13).
and usage characteristics of similar historical fixtures (see para 53-Constructing the virtual support device can additionally include: clearing otherwise non-machinable features (e.g., including a tool aperture to enable a tool to access an otherwise corner enclosed by the support device, example shown in FIG. 12), including features to relieve part stress, rounding (e.g., filleting) virtual support device edges in virtual contact with or proximal the virtual part (e.g., to minimize visual defects), or otherwise post-processing the virtual support device geometry (example shown in FIG. 7).Non-machinable features can be determined: heuristically, deterministically, probabilistically (e.g., based on historic adjustments and the respective support device's performance), calculated, iteratively determined (e.g., by running one or more simulations; by generating a potential virtual support device with each adjustment and comparing the resultant virtual support devices, etc.), or otherwise determined. See para 0032- For example, the filtered set can include only pairs with a stock depth for the first piece that exceeds a threshold depth, wherein the threshold depth is required to fully support all features extending along a side of the virtual part. In a third embodiment, the filtered set can include only pairs previously used for similar parts (e.g., wherein the similarity can be based on part feature classifications, dimension, distribution, or other parameters; material; tolerances; etc., or otherwise determined). However, the set can be otherwise filtered.)
identifying failure point in the new fixture; (see claim 7- The method of claim 1, wherein the confidence score is determined based on one or more of: kinematic analysis of the virtual fixture, finite element analysis, and heuristics. See para 117- In a second variation, the support device can be modified to accommodate the toolpath. In this variation, the toolpath validation can include identifying the collision regions, wherein only parameters of the identified collision regions of the virtual support device are modified to form an adjusted virtual support device.) and
modifying the design information of the new fixture to mitigate the simulated failure point. (see para 53- see para 53- Constructing the virtual support device can additionally include: clearing otherwise non-machinable features (e.g., including a tool aperture to enable a tool to access an otherwise corner enclosed by the support device, example shown in FIG. 12), including features to relieve part stress, rounding (e.g., filleting) virtual support device edges in virtual contact with or proximal the virtual part (e.g., to minimize visual defects), or otherwise post-processing the virtual support device geometry (example shown in FIG. 7). see para 116-117-In one variation, a potential virtual support device can be rejected if the support device will interfere with a toolpath for the exposed part orientation. In a second variation, the support device can be modified to accommodate the toolpath. In this variation, the toolpath validation can include identifying the collision regions, wherein only parameters of the identified collision regions of the virtual support device are modified to form an adjusted virtual support device. Parameters that can be adjusted include adding features (e.g., adding a tool aperture, adding a tool channel around the collision region), removing features, adjusting the support device height (e.g., shortening the support device), or include any other suitable support device parameter. The amount of adjustment can be dependent on: the anticipated tool type to be used (e.g., specified by the toolpath), the parameters of the specific tool to be used (e.g., amount of estimated wear, etc.), the amount of interference, or based on any other suitable factor. Toolpath validation and virtual support device adjustment can be performed once, be repeated until the assembled unit includes less than a threshold number of collisions, or be performed any number of times.)
Markov does not teach simulating lifecycle usage of the new fixture using the digital twin and identifying a simulated failure point in the new fixture based on the simulated lifecycle usage.
In the related field of invention, Harris teaches simulating lifecycle usage of the new fixture using the digital twin; (see abstract- method includes obtaining a design space for a modeled object, one or more design criteria, one or more in-use load cases, and one or more specifications of material, wherein the design criteria comprise a required number of loading cycles for the modeled object; iteratively modifying a generatively designed three dimensional shape of the modeled object, comprising: performing numerical simulation of the modeled object. See para 20- Designs can be optimized according to a defined life of the designed object. See para 55- The numerical simulation performed by the CAD program(s) 116 can simulate one or more physical properties and can use one or more types of simulation to produce a numerical assessment of physical response (e.g., structural response) of the modelled object. For example, finite element analysis (FEA. Moreover, the CAD program(s) 116 can potentially implement hole and/or fixture generation techniques to support clamping during manufacturing and/or manufacturing control functions.)
identifying a simulated failure point in the new fixture based on the simulated lifecycle usage; (see para 311- A maximized stress or strain element is found 508 for each of the one or more in-use load cases for the physical structure, from the current numerical assessment of the physical response of the modeled object. The element can be a value at a point, location or region of the physical structure. See para 331- Returning to FIG. 5A, as described above with reference to the Treatment of SN-Curves and FIG. 5B, an expected number of loading cycles for each of the one or more in-use load cases for the physical structure is determined 510 using the maximized stress or strain element and the data relating fatigue strength to loading cycles.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for fixture generation in the manufacturing field as disclosed by Markov to include simulating lifecycle usage of the new fixture using the digital twin and identifying a simulated failure point in the new fixture based on the simulated lifecycle usage as taught by Harris in the system of Markov order to perform automatic generation of 3D geometry (generative design) for a part or one or more parts in a larger system of parts to be manufactured with damage prevention over loading cycles.(See [0008], Harris)
Regarding claim 11
Markov teaches a system comprising: one or more computer readable storage media storing program instructions; and one or more processors which, in response to executing the program instructions, are configured to perform a method comprising: (See para 126- An alternative embodiment preferably implements the above methods in a computer-readable medium storing computer-readable instructions. The instructions are preferably executed by computer-executable components preferably integrated with a support device generation system. The computer-readable medium may be stored on any suitable computer readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device. The computer-executable component is preferably a processor but the instructions may alternatively or additionally be executed by any suitable dedicated hardware device.)
The rest of claim 11 is rejected for the same reasons as Claim 1, as they share the same elements.
Regarding claim 16
Markov teaches a computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising instructions configured to cause one or more processors to perform a method comprising: (See para 126- An alternative embodiment preferably implements the above methods in a computer-readable medium storing computer-readable instructions. The instructions are preferably executed by computer-executable components preferably integrated with a support device generation system. The computer-readable medium may be stored on any suitable computer readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device. The computer-executable component is preferably a processor but the instructions may alternatively or additionally be executed by any suitable dedicated hardware device.)
The rest of claim 16 is rejected for the same reasons as Claim 1, as they share the same elements.
Regarding claim 2
The combination of Markov and Harris teaches the method of claim 1. Markov further teaches wherein the usage characteristics of the historical fixtures include loading characteristics, (see para 26- The support device can include one or more movable components (clamps) that function to engage the part along an engagement surface and to apply a compressive clamping force on the part.)
and environmental characteristics. (para 107- However, the method can include determining the tool speed (e.g., the tool speed can increase with increased work holding confidence score), machining type, tool type, specific tool, support devices or parts to be manufactured in the same manufacturing volume, on the same plate, or using the same toolpath as the physical analog of the virtual part (e.g., wherein those parts can have similar scores to the instantaneous assembled unit), coolant parameters, stepdown, or any other suitable manufacturing parameter based on the work holding confidence score in any other suitable manner.)
Markov does not teach cycling characteristics.
However, Harris further teaches cycling characteristics;(see para 0009- determining an expected number of loading cycles for each of the one or more in-use load cases for the physical structure using the maximized stress or strain element and the data relating fatigue strength to loading cycles, redefining a fatigue safety factor inequality constraint for the modeled object based on a damage fraction calculated from the required number of loading cycles for the modeled object and the expected number of loading cycles for each of the one or more in-use load cases for the physical structure,)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for fixture generation in the manufacturing field as disclosed by Markov to include cycling characteristics as taught by Harris in the system of Markov order to perform automatic generation of 3D geometry (generative design) for a part or one or more parts in a larger system of parts to be manufactured with damage prevention over loading cycles.(See [0008], Harris)
Regarding claim 3
The combination of Markov and Harris teaches the method of claim 1. Markov further teaches identifying the similar historical fixtures by: inputting the design information for the new fixture to a machine learning model, wherein the machine learning model is trained on a corpus of historical fixtures and corresponding usage characteristics; and receiving, as output from the machine learning model, the usage characteristics of the historical fixtures that are similar to the design information for the new fixture. (see para 91-95- The work holding confidence score can be calculated, heuristically determined, probabilistically determined, classified, determined using a neural network, determined using a genetic algorithm, or otherwise determined based on the assembled unit parameter values. The work holding confidence score determination methods can be static, dynamically updated (e.g., trained based on the quality control results of prior support devices, parts. In a second variation, the virtual support device can be selected to optimize manufacturing parameters (e.g., minimize manufacturing time, minimize cost, etc.), selected to maximize one or more manufacturing parameters, determined using a trained system (e.g., a convolutional neural network, genetic algorithm, etc.), using a combination of the above measures (e.g., a weighted sum of the support device manufacturability score and work holding confidence score), or otherwise selected. In one example, a sub-optimal virtual support device can be selected over a more optimal virtual support device when the sub-optimal virtual support device has manufacturing parameters closer to those of a secondary virtual part, such that an overall manufacturing parameter (e.g., manufacturing time) is optimized. See para 31- 32- For example, a physical standard support stock option for physical custom support device manufacture can be selected based on the virtual custom support device dimensions (e.g., wherein the standard physical stock size exceeds, substantially matches, or is otherwise related to the virtual custom support device dimensions). In a third embodiment, the filtered set can include only pairs previously used for similar parts (e.g., wherein the similarity can be based on part feature classifications, dimension, distribution, or other parameters; material; tolerances; etc., or otherwise determined). However, the set can be otherwise filtered. See para 119- The manufacturing analysis can be performed using: heuristics, pattern matching, classification, regression, or any other suitable method.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for fixture generation in the manufacturing field as disclosed by Markov to include cycling characteristics as taught by Harris in the system of Markov order to perform automatic generation of 3D geometry (generative design) for a part or one or more parts in a larger system of parts to be manufactured with damage prevention over loading cycles.(See [0008], Harris)
Regarding claim 12 and 17
Claims 12 and 17 are rejected for the same reasons as Claim 3, as they share the same elements.
Regarding claim 4
The combination of Markov and Harris teaches the method of claim 1.
Markov does not teach wherein the design information for the new fixture comprises a Computer Aided Design (CAD) model file of the new fixture.
However, Harris further teaches wherein the design information for the new fixture comprises a Computer Aided Design (CAD) model file of the new fixture. (see para 66- Once the user 160 is satisfied with a generatively designed 3D model, the 3D model can be stored as a 3D model document 130 and/or used to generate another representation of the model (e.g., an .STL file for additive manufacturing)).)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for fixture generation in the manufacturing field as disclosed by Markov to include wherein the design information for the new fixture comprises a Computer Aided Design (CAD) model file of the new fixture as taught by Harris in the system of Markov order to perform automatic generation of 3D geometry (generative design) for a part or one or more parts in a larger system of parts to be manufactured with damage prevention over loading cycles.(See [0008], Harris)
Regarding claim 5
The combination of Markov and Harris teaches the method of claim 1.
Markov does not teach wherein simulating the lifecycle usage of the new fixture utilizes simulation software based on Finite Element Analysis (FEA
However, Harris further teaches wherein simulating the lifecycle usage of the new fixture utilizes simulation software based on Finite Element Analysis (FEA).( See para 55- The numerical simulation performed by the CAD program(s) 116 can simulate one or more physical properties and can use one or more types of simulation to produce a numerical assessment of physical response (e.g., structural response) of the modelled object. For example, finite element analysis (FEA))
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for fixture generation in the manufacturing field as disclosed by Markov to include simulating the lifecycle usage of the new fixture utilizes simulation software based on Finite Element Analysis (FEA) as taught by Harris in the system of Markov order to perform automatic generation of 3D geometry (generative design) for a part or one or more parts in a larger system of parts to be manufactured with damage prevention over loading cycles.(See [0008], Harris)
Regarding claim 6
The combination of Markov and Harris teaches the method of claim 1. Markov further teaches wherein modifying the design information of the new fixture includes changing a material composition of the new fixture in an area of the simulated failure point. (see para 61- Constructing the virtual support device S400 can optionally include generating plugs for internal part voids. The plugs can be used to support the part from the part interior (e.g., to prevent part deformation) during part manufacture. see para 70-Generating plugs for internal part voids can optionally include selecting internal part voids for plug generation. This can function to exclude voids that have a low probability of contributing to part deformation during clamping or part manufacture, which can lead to increased manufacturing speed (e.g., by reducing the assembly time) and/or reduced manufacturing cost (e.g., by reducing the amount of material used to manufacture the plug.)
Regarding claim 13 and 18
Claims 13 and 18 are rejected for the same reasons as Claim 6, as they share the same elements.
Regarding claim 7
The combination of Markov and Harris teaches the method of claim 1. Markov further teaches wherein modifying the design information of the new fixture includes changing a dimensional attribute of the new fixture in an area of the simulated failure point. (see para 109- The support device stock can be standardized, custom, or have any other suitable dimension, material, or parameter. See para 117- Parameters that can be adjusted include adding features (e.g., adding a tool aperture, adding a tool channel around the collision region), removing features, adjusting the support device height (e.g., shortening the support device), or include any other suitable support device parameter.)
Regarding claim 14 and 19
Claims 14 and 19 are rejected for the same reasons as Claim 7, as they share the same elements.
Regarding claim 9
The combination of Markov and Harris teaches the method of claim 1. Markov further teaches wherein modifying the design information of the new fixture includes reducing a force concentration features of the new fixture in an area of the simulated failure point. (see para 53- Constructing the virtual support device can additionally include: clearing otherwise non-machinable features (e.g., including a tool aperture to enable a tool to access an otherwise corner enclosed by the support device, example shown in FIG. 12), including features to relieve part stress, rounding (e.g., filleting) virtual support device edges in virtual contact with or proximal the virtual part (e.g., to minimize visual defects), or otherwise post-processing the virtual support device geometry (example shown in FIG. 7). Non-machinable features cam include corners with radii below a threshold radius, corners having an internal angle less than a threshold angle, features with dimensions too small for manufacture or stability (e.g., based on the anticipated manufacturing forces, material strength, etc.), or any other suitable feature.)
Regarding claim 10
The combination of Markov and Harris teaches the method of claim 1. Markov further teaches further comprising: fabricating the new fixture according to the modified design information using additive manufacturing. (see para 109-the method can additionally include manufacturing a physical support device based on the manufacturing virtual support device, which functions to create a real support device to support and locate the real part. n a second example, the real support device can be formed through additive manufacturing (e.g., 3D printing).)
6. Claim(s) 8, 15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Markov et al. (PUB NO: US20200055301A1) in view of Harris et al. (PUB NO: US20220237344A1) and further in view of Xu et al. (PUB NO: CN103279614A)
Regarding claim 8
The combination of Markov and Harris teaches the method of claim 1. The combination does not teach wherein modifying the design information of the new fixture includes adding a reinforcing ribbing to the new fixture in an area of the simulated failure point.
In the related field of invention, Xu wherein modifying the design information of the new fixture includes adding a reinforcing ribbing to the new fixture in an area of the simulated failure point. (See 0024-The main load-bearing ribs are first arranged at the key positions and weak links, which ensures that the deformation of the important positions of the machine tool structure is small, and improves the reliability of the design results)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for fixture generation in the manufacturing field as disclosed by Markov to include modifying the design information of the new fixture includes adding a reinforcing ribbing to the new fixture in an area of the simulated failure point as taught by Harris in the system of Markov and Harris in order to improve the efficiency and quality of stiffener layout design, establishing a method for stiffener layout and design of large machine tool structural components is of great significance for the rapid design of machine tools.(See [0005], Xu)
Regarding claim 15
The combination of Markov and Harris teaches the system of claim 1. Markov further teaches wherein modifying the design information of the new fixture includes reducing a force concentration features of the new fixture in an area of the simulated failure point. (see para 53- Constructing the virtual support device can additionally include: clearing otherwise non-machinable features (e.g., including a tool aperture to enable a tool to access an otherwise corner enclosed by the support device, example shown in FIG. 12), including features to relieve part stress, rounding (e.g., filleting) virtual support device edges in virtual contact with or proximal the virtual part (e.g., to minimize visual defects), or otherwise post-processing the virtual support device geometry (example shown in FIG. 7). Non-machinable features cam include corners with radii below a threshold radius, corners having an internal angle less than a threshold angle, features with dimensions too small for manufacture or stability (e.g., based on the anticipated manufacturing forces, material strength, etc.), or any other suitable feature.)
The combination does not teach wherein modifying the design information of the new fixture includes adding a reinforcing ribbing to the new fixture in an area of the simulated failure point.
In the related field of invention, Xu wherein modifying the design information of the new fixture includes adding a reinforcing ribbing to the new fixture in an area of the simulated failure point. (See 0024-The main load-bearing ribs are first arranged at the key positions and weak links, which ensures that the deformation of the important positions of the machine tool structure is small, and improves the reliability of the design results)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for fixture generation in the manufacturing field as disclosed by Markov to include modifying the design information of the new fixture includes adding a reinforcing ribbing to the new fixture in an area of the simulated failure point as taught by Harris in the system of Markov and Harris in order to improve the efficiency and quality of stiffener layout design, establishing a method for stiffener layout and design of large machine tool structural components is of great significance for the rapid design of machine tools.(See [0005], Xu)
Regarding claim 20
Claim 20 is rejected for the same reasons as Claim 15, as they share the same elements.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kamath et al. US 20220397888 A1
Discussing the method for determining operational configuration of asset are provided. The method includes receiving a set of operating parameters associated with the asset, identifying data associated with the asset based on the received set of operating parameters, configuring a second digital twin of the asset based on the data identified from first knowledge database, simulating a behavior of the asset based on the configured second digital twin in a simulation environment, and determining an operational configuration associated with the variant of the asset based on results of the simulation.
b. Razzell et al. US20200265122A1
i. Discussing the method of obtaining a design space for a modeled object, load cases for physical simulation, and design criteria, wherein the modeled object includes specified geometry with which generatively designed geometry will connect, and wherein the load cases include at least one in-use load case for the physical structure and at least one subtractive-manufacturing load case associated with the specified geometry and with a subtractive manufacturing system; producing the generatively designed geometry in the design space for the modelled object in accordance with the load cases for physical simulation of the modelled object and the design criteria for the modeled object; and providing the modeled object with the generatively designed geometry for use in manufacturing the physical structure.
8. All claims 1-20 are rejected.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PURSOTTAM GIRI whose telephone number is (469)295-9101. The examiner can normally be reached 7:30-5:30 PM, Monday to Friday.
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/PURSOTTAM GIRI/
Examiner, Art Unit 2186
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