DETAILED ACTION
Claims 1-17 are presented for examination.
Claims 1, 10 and 12 have been amended.
Claims 7-9 have been canceled.
Claims 16-17 are new.
This office action is in response to the amendment submitted on 30-SEPT-2025.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/06/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed Application No. EP21163684.0, filed on 03/19/2021.
Response to Arguments – 35 USC 101
Applicant's arguments have been fully considered and they are not persuasive. However, new grounds of rejection have been introduced due to the amendments.
Rejection under 35 USC 101 is maintained.
Response to Arguments – 35 USC 103
Applicant’s arguments with respect to the 103 rejections have been considered, but are moot in view of the new ground(s) of rejection provided below.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Step 1: Statutory class – process.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes
“3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).” MPEP § 2106.04(a).
The claims are directed to an abstract idea of data processing and analysis. The claim recites:
to measure individual skill performance parameters and resultantly measure an overall performance of the design
The measure limitations are mental processes and mathematical calculations. By way of example, one can mentally calculate the time each skill takes and add up the various different skills to arrive at an overall performance for the design.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The additional elements are:
obtaining a process goal and one or more process constraints,
accessing a library of constructs, each construct comprising a digital representation of a component of the robotic cell or a geometric transformation of the robotic cell,
accessing a library of skills, each skill comprising a functional description for using a robot of the robotic cell to interact with a physical environment to perform a skill objective,
using a physics based simulation engine to simulate a multiplicity of designs, wherein each design is characterized by a combination of constructs and skills to achieve the process goal, wherein the physics-based simulation engine executes the skill code
obtaining a set of feasible designs that meet the one or more process constraints,
outputting recommended designs from the set of feasible designs.
Receiving and accessing are insignificant extra solution activity –mere data collection. MPEP § 2106.05(g).
Simulate using a simulation engine is mere instructions to apply an exception on a generic computer. MPEP § 2106.05(f).
Outputting is an insignificant extra solution activity –mere data output. MPEP § 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than judicial exception?
No, as discussed with respect to Step 2A, the additional limitation is mere data gathering/data output and a general purpose computer do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B. Further, in regards to step 2B and as cited above in step 2A, MPEP 2106.05(g) “Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir.2011)” is merely data gathering.
The additional elements have been considered both individually and as an ordered combination in the significantly more consideration.
This claim is ineligible.
Claim 2 recites using a machine-learning based generative model to generate the designs for simulation based on the one or more process constraints, which is mere instructions to apply an exception on a generic computer under Step 2A Prong Two and 2B. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 3 recites generating the designs for simulation by generating transformations of a baseline design based on identifying at least one bottleneck skill in the baseline design, which is a mental process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 4 recites outputting a recommendation based on execution of a skill code of the bottleneck skill, which is mere data output and a mental process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 5 recites the skill code of the bottleneck skill is based on a machine vision algorithm, and wherein the recommendation includes a change in design of a product being handled by the robotic cell, which is a mental process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 6 recites the skill code of the bottleneck skill is based on a machine-learning model, wherein the recommendation includes a re-training of the machine learning model, which is mere instructions to apply an exception on a generic computer under Step 2A Prong Two and 2B. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 10 recites limitations similar to claim 9 and is rejected under the same rationale. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 11 recites a non-transitory computer-readable storage medium: (statutory
category – machine)
including instructions that, when processed by a computer, configure the computer to perform the method, which is mere instructions to apply an exception on a generic computer under Step 2A Prong Two and 2B.
The remaining dependent claims correspond to claims 1-10 and are rejected under the same rationale.
Therefore, the claim is considered ineligible under 35 USC 101.
Claim 12-15 recite limitations similar to claims 1-4 respectively and are rejected under the same rationale. Therefore, the claims are considered ineligible under 35 USC 101.
Claim 16 recites comprising applying a clustering algorithm to cluster the designs, from the set of feasible designs, wherein outputting the recommended designs comprises outputting only one representative design for a given cluster, which is mere instructions to apply an exception on a generic computer under Step 2A Prong Two and 2B. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 17 recites limitations similar to Claim 16 and is rejected under the same rationale.
Claim Rejections - 35 USC § 103
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 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Rudnitsky et al. (US20200319630A1) in view of Mathews et al. (US20210149655A1).
Regarding Claim 1, Rudnitsky teaches obtaining a process goal and one or more process constraints for execution of a process by a robotic cell ([0069] “The designer defines the integration and assembling points and surfaces on the component parts to set geometrical connections between Constraints” and [0013] “The system can design a manufacturing process appropriate for the product's particular character, correspondingly to the technologies applicable in the factory. This method involves creating a suitable components creation, manipulation, refinement and assembly sequence, identifying subassemblies, integrating quality control, and designing each part so that its structure and design are compatible with the assembly method”).
accessing a library of constructs, each construct comprising a CAD model of a physical component of the robotic cell or a geometric transformation of the robotic cell (Fig. 21, [0036] “The Designer UI 223 might request some manufacturing technology process scenario part to be provided, the Constructive Function Solver 225 searches for corresponding library program for manufacturing technology process scenario part in the knowledge DB 243,” and [0014] “The innovation is an improved method for creating a product from drawing the design in CAD tools to assemble the product in hardware”).
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accessing a library of skills, each skill comprising a functional description for using a robot of the robotic cell to interact with a physical environment to perform a skill objective (Fig. 7 and [0015] “The Designer UI is coupled to databases which store information describing the robotic tooling and the robot tooling abilities that are available in a factory. The Designer UI determines a sequence of robot tooling abilities using the available robot tooling abilities to move, machine and assemble the parts and the assemblies to create the product that meets a set of product requirements”).
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using a physics-based simulation engine to simulate a multiplicity of designs, wherein each design is characterized by a combination of constructs and skills to achieve the process goal ([0123] “simulating the fabrication of a virtual product by virtual robot tooling with the control codes for the sequence of abilities and the options that were selected on a simulator”).
Wherein the physics-based simulation engine executes the skill code to measure individual skill performance parameters and resultantly measure an overall performance of the design ([0015] “ Based upon the product design, the product requirements and the options selected, the Designer UI creates tool control codes for producing the product from the sequence of abilities by the robot tooling. The tool control codes are used for simulating the fabrication of a virtual product by virtual robot tooling with the tool control codes for the sequence of abilities and the options that were selected on a Simulator.” Fig 21 illustrates the overall performance from simulation and the associated skill code. [0078] “the virtual robot tooling 477 includes the assembly time model of 2 minutes and 12 seconds, the assembly cost of 120.20 GBP and an estimated development time of 2 to 3 months.” The mates infrastructure specifies the values in its library [0070] “The mates/constrains for pieces define the technological process and robotic operations.” The mates schema in SolidWorks includes assembly costing, operating time, operation cost etc.).
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and therefrom obtaining a set of feasible designs that meet the one or more process constraints ([0041] “The PSC IDE 221 then determines a plurality of different product design choices and a plurality of different manufacturing methods and manufacturing options for creating different product designs which each meet the product requirements which can be displayed on a user interface UI of the Designer UI 223. During the analysis of the product design 203 the PSC IDE 221 can perform a product function analysis, so that all design decisions can be made with full knowledge of the advantages and disadvantages of each of the different possible product configurations. Rather than reviewing a single product design, a designer can compare multiple product designs and manufacturing processes and options as displayed on the UI of the Designer UI 223”).
outputting recommended designs from the set of feasible designs for implementation in a manufacturing environment ([0040] “The Factory Configurator tool 261 can analyze the manufacturing tooling availability and propose possible tooling options and dependencies. The Factory Configurator tool 261 can request additional tooling or tooling/technology modifications or providing human interaction with the product production.” Additionally, [0124] to [0142] the system presents different options for accomplishing the intended goal so that the cheapest, fastest, most desirable outcome can be chosen).
However, Rudnistky does no teach:
the physics-based simulation engine
Mathews teaches the physics-based simulation engine ([0083] “The ML in one embodiment may be trained with simulations of physics rather than using real world data. This is referred to as synthetic training data”).
Rudnitsky and Mathews are analogous art because they are from the same field of endeavor in autonomous robots and simulation in the setting of smart manufacturing. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to combine Rudnitsky and Mathews to arrive at integrating physics based simulation engines, machine vision and machine learning to eliminate bottlenecks and optimize inefficiencies in autonomous processes. “the following advantages … improves effectiveness of automation (cost, quality, speed, etc.) to manufacture a product via software that improves the design of the product” (Mathews: [0026]).
Regarding Claim 2, Rudnitsky in view of Mathews teaches the limitations of claim 1. Rudnitsky further teaches using a machine-learning based generative model to generate the designs for simulation based on the one or more process constraints ([0081] details the ML used for simulation, “generative adversarial networks (GANs) may be used to improve the quality of simulations and ML training” and [0082] “when using CAD/DFMA/optimization system A60 the system may take into account the manufacturing/assembly limitations of the robotic cells A10 in designing the end product”).
Regarding Claim 3, Rudnitsky in view of Mathews teaches the limitations of claim 1. Mathews further teaches generating the designs for simulation by generating transformations of a baseline design based on identifying at least one bottleneck skill in the baseline design ([0074] “FIG. 3B illustrates one embodiment of reconfigurations of the micro factory lines. The initial configuration includes four cells, F1, I1, Se1, and St1, each executing a particular function. If there is a failure at one of the cells (here cell I1), or the system determines that the process is lagging at cell I1 (that is the process at I1 slows the through-put of the system), this may be addressed in various ways.” and [0075] “By enabling fast reconfiguration of a line of one or more robotic cells when a piece of hardware fails, the remaining systems (and potentially a spare) could be reconfigured to adapt—much like how a data center does to a server failure. This might require tool changers in the robotic cells and/or re-configurable conveyors that can route the products to the right cells. Such routing might utilize a main conveyor and a secondary conveyor that can bypass working cells. This conveyance would be “scheduled” or “controlled” by software”).
Regarding Claim 4, Rudnitsky in view of Mathews teaches the limitations of claim 3. Rudnitsky further teaches outputting a recommendation based on execution of a skill code of the bottleneck skill ([0044] “The PSC IDE 221 can also optimize the existing technological processes for the product manufacturing based upon a desired production goal of reduced product cost, faster production time, etc.” and [0045] “If the product's manufacturing process specification demands for additional tooling to be added to a factory, the PSC IDE 221 can create a set of requirements for various possible factory upgrades, accordingly with the product designs and technological processes selected for the product manufacturing. The PSC IDE 221 can create different product designs that are adapted to the factory and possible improvements. The designer can estimate cost and time for the factory improvements on the UI of the Designer UI 223 and factor in the cost and time for the factory improvements when selecting a product design”).
Regarding Claim 5, Rudnitsky in view of Mathews teaches the limitations of claim 4. Mathews further teaches the skill code of the bottleneck skill is based on a machine vision algorithm, and wherein the recommendation includes a change in design of a product being handled by the robotic cell ([0123] “The computer vision 565 enables calibration and tracking of the robotic cell. Machine learning 570 enables the system to learn from use, for calibration as well as for efficiency and effectiveness” and [0134] “the adaptive robotic control 545 provides a form of automatic programming, or generative programming, derived from data extracted from a computer aided design (CAD) system describing the product, and/or a simulation system. For example, for a recipe that says “attach with screw,” the automatic programming may extract screw hole coordinates from CAD drawings or CAD models and automatically generate a manufacturing recipe that instructs a robot to acquire a screw from a screw presenter location, travel to the screw hole location (optionally based on computer vision)”).
Regarding Claim 6, Rudnitsky in view of Mathews teaches the limitations of claim 4. Mathews further teaches the skill code of the bottleneck skill is based on a machine-learning model ([0137] “the feedback from inspection and tracking may be used by configuration management system 540 to validate or revert changes, and by the machine learning system 570”).
the recommendation includes a re-training of the machine learning model ([0123] “Machine learning 570 enables the system to learn from use, for calibration as well as for efficiency and effectiveness” and [0081] “The machine learning might be based on one or more of: learning from prior projects and data, or simulations. The system may use feedback from the sensor data from robotic cells to further improve the DFMA and/or generative programming system. In one embodiment, the system may be trained with simulations of physics, e.g., synthetic training data. In one embodiment, generative adversarial networks (GANs) may be used to improve the quality of simulations and ML training. In one embodiment, a convolutional neural network (CNN) may be use for simulation and ML training”).
Regarding Claim 10, Rudnitsky in view of Mathews teaches the limitations of claim 1. Rudnitsky further teaches the one or more skill performance parameters are selected from the group consisting of: execution time, error-rate and cost ([0001] “the manufacturing processes should also be optimized based upon a criteria such as: cost, time to market, parallel assembly, equipment load, energy savings, production strategy advantages, etc.”).
Regarding Claim 11, Rudnitsky in view of Mathews teaches the limitations of claim 1-10. Rudnitsky further teaches a non-transitory computer-readable storage medium including instructions that, when processed by a computer, configure the computer to perform the method ([0100] “The computer program product may also contain instructions that, when executed, perform one or more methods, such as those described above. The information carrier may be a non-transitory computer- or machine-readable storage medium, such as the memory 904, the storage device 906, or memory on processor 902”).
Claims 12-15 recites limitations similar to claim 1-4 and are rejected under the same rationale.
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Rudnitsky et al. (US20200319630A1) in view of Mathews et al. (US20210149655A1) and further in view of Ahn (A Similarity-Based Hierarchical Clustering Method for Manufacturing Process Models).
Regarding Claim 16, Rudnitsky in view of Mathews teaches the limitations of claim 1. Ahn teaches applying a clustering algorithm to cluster the designs, from the set of feasible designs, wherein outputting the recommended designs comprises outputting only one representative design for a given cluster (Pg. 1, Introduction, “we propose a similarity-based hierarchical clustering method for manufacturing process models to facilitate managerial activities of manufacturing processes at a group level (e.g., the design and engineering tasks).” Table 4 shows the clustering results, where M5 has the highest score hence being the one chosen for M1).
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Rudnitsky, Mathews, and Ahn are analogous art because they are from the same field of endeavor in automation in the setting of smart manufacturing. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to combine Rudnitsky, Mathews, and Ahn to arrive at integrating clustering algorithm to simplify deciding which design process closely achieves the desired outcome. “We expect that our method enables manufacturing companies to design and manage a vast amount of manufacturing processes at a coarser level, and it also can be applied to various process (re)engineering problems” (Ahn, Abstract).
Claims 17 recites limitations similar to claim 16 and is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20200030988A1: Discloses generative design techniques for robot behavior.
US-11986961-B2: Discloses ML model operation for robot behavior.
US-10262074-B2: Discloses generative design techniques for robot behavior.
US-9811074-B1: Discloses optimizing robot training in a physics based simulation environment.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.E.D./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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