DETAILED ACTION
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 .
Prior art of Record
The prior art made of record in this office action shall be referred to as follows;
U.S. 2022/0197246 Cella et al. (‘Cella hereafter), App 17/683162, Filed 02/28/2022
U.S. 2019/0243338 Michael W. Golway (‘Golway hereafter), App 16/264833,
U.S. 2023/0191543 Field et al. (‘Field hereafter), App 17/814485,
The above references will be referred to hereafter by the names or numbers indicated above.
Claim status:
Claims 1 - 20 are currently being examined.
Claims 9 - 20 have been withdrawn.
Claim 6 has been canceled.
No Claims are allowed or objected to for allowable subject matter.
Information Disclosure Statement
The following IDS statements, was noted in previous office action mailed on 03/23/2026:
An interview was conducted with Applicant on 03/25/2026 regarding the submission of 206 documents in 5 ids documents. Examiner requested for a list of relevant art of the 206 documents and applicant was unable to provide. On 04/29/2026 Examiner included the following list of deficiencies of said 206 documents, and applicant has not responded to either the deficiencies listed below, nor the request for relevant art.
The information disclosure statements (4 IDS’) filed 06/16/2025 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered.
The IDS documents have been signed and labeled at the top of the documents IDS Numbers 1 – 5.
The following documents had no date in the document or the date was after the filing date:
IDS No. 1 Item 4, Item 40, Item 41
IDS No. 2 Item 2, Item 4, Item 5, Item 7, Item 8, Item 10, Item 11, Item 16, Item 39, Item 49,
IDS No. 3 Item 14, Item 27, Item 32,
IDS No. 4 Item 6, Item 11, Item 12, Item 25, Item 36.
The following documents had a different date in the document:
IDS No. 1 Item 9, Item 10, Item 16, Item 31
IDS No. 2 Item 19, item 44, Item 48
IDS No. 4 Item 4, Item 7, Item 8, Item 22, Item 30, Item 46.
The following documents were not found:
IDS No. 1 Item 39,
IDS No. 2 Item 6,
IDS No. 3 Item 33,
IDS No. 4 Item 17.
The documents were not found either because Applicant had failed to supply the document, or the labels for the documents listed in the IDS were not apparent to the Examiner and were labeled different than Examiner’s interpretation of the title of the documents.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 – 5, 7 & 8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
There are numerous antecedent basis issues such as so and so that needs to be corrected.
Claim 1 recites the limitation "a component” in ln 2, should be “the component.”
Claim 1 recites the limitation "available tools” in ln 5, should be "the available tools.”
Claim 1 recites the limitation "available tools” in ln 8, should be "the available tools.” There is insufficient antecedent basis for this limitation in the claim. Applicant should amend all claims to satisfy sufficient antecedent basis.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claims 1 – 5, 7 & 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2022/0197246 Cella et al. (‘Cella hereafter), and in view of U.S. 2019/0243338 Michael W. Golway (‘Golway hereafter).
Regarding Claim[s] 1, ‘Cella discloses all the claim limitations including: A method for manufacturing a component (‘Cella, Abst, “An autonomous additive manufacturing platform includes sensors positioned in, on, and/or near a part and configured to collect sensor data related to the part. An adaptive intelligence system is configured to receive the sensor data from the sensors. The adaptive intelligence system includes a machine learning system configured to input the sensor data as training data into one or more machine learning models. The machine learning models are configured to transform the sensor data into simulation data. A digital twin system is configured to create a part twin based on the simulation data. The part twin provides for representation of the part and simulation of a possible future state of the part via the simulation data. An artificial intelligence system is configured to execute simulations on the digital twin system. The machine learning models are utilized to make classifications, predictions, and other decisions relating to the part.”) comprising:
receiving for a component to be manufactured by a computing device (‘Cella, Abst, Para 0007, “Additive manufacturing, encompassing technologies like 3D printing, vapor deposition, polymer (or other material) coating, epitaxial and/or crystalline growth approaches, and others, alone or in combination with other technologies, such as subtractive or assembly technologies, enables manufacturing of a three-dimensional product from a design via a process of forming successive layers of the product, with optional interim or subsequent steps to arrive at a finished component or system. The design may be in the form of a data source like an electronic 3D model created with a computer aided design package or via 3D scanner. The 3D printing or other additive process then involves forming a first material-layer and then adding successive material layers wherein each new material-layer is added on a pre-formed material-layer, until the entire designed three dimensional product is completed. References to 3D printing or other particular additive manufacturing technologies throughout this disclosure should be understood to encompass alternative embodiments involving other additive manufacturing technologies, except where context specifically indicates otherwise.”);
determining available tools (‘Cella, Para 1221, “In embodiments, the AI reporting tool 8360 may be configured to receive a request to report a state from a client device 8050. In embodiments, the AI-reporting tool 8360 may identify the appropriate recipients of the reported state based on the type of request, the role of the user that issued the request and the organizational structure of the entity. In some embodiments, the AI-reporting tool may determine the role of the user and the recipients of the report from the organizational digital twin of the enterprise. In some embodiments, the AI-reporting tool 8360 may determine whether the intended recipients of a notification have access rights to the data being shared from the executive digital twin.”) and available materials at a location by the computing device (‘Cella, Para 0044, “and an artificial intelligence system configured to execute simulations on the digital twin system; wherein the one or more models are utilized by the artificial intelligence system to make classifications, predictions, recommendations, and/or to generate or facilitate decisions or instructions relating to the product and the part, such as decisions or instructions governing design, configuration, material selection, shape selection, manufacturing type, job scheduling and many others.”);
determining one or more designs for the component based on the design constraints (‘Cella, Para 1746, “Conformance may, in embodiments, be based on a scan of a body part or anatomical feature, such as a laser or other structured light scan, a MRI, EEG, computed tomography, ultrasound or other imaging scan, or the like. A 3D topology for the anatomical feature may be used as an input source for generation by a CAD system or other design system (which may be linked to or integrated into an additive manufacturing platform) of a design for additive manufacturing.”),
determined available tools (120) (‘Cella, Para 1221), and determined available materials by the computing device (‘Cella, Para 0044, “Aspects provided herein include an autonomous additive manufacturing platform comprising: a plurality of sensors positioned in, on, and/or near a product or a part and configured to collect sensor data related to the product or the part, the sensor data being substantially real-time sensor data; an adaptive intelligence system connected to the plurality of sensors and configured to receive the sensor data from the plurality of sensors, the adaptive intelligence system including: a machine learning system configured to input the sensor data into one or more machine learning models, the sensor data being used as training data for the machine learning models, the machine learning models being configured to transform the sensor data into simulation data; and a digital twin system configured to create a product twin or a part twin based on the simulation data, the product twin or the part twin providing for substantially real-time representation of the product or the part and providing for simulation of a possible future state of the product or the part via the simulation data; and an artificial intelligence system configured to execute simulations on the digital twin system; wherein the one or more models are utilized by the artificial intelligence system to make classifications, predictions, recommendations, and/or to generate or facilitate decisions or instructions relating to the product and the part, such as decisions or instructions governing design, configuration, material selection, shape selection, manufacturing type, job scheduling and many others.”);
Except ‘Cella is silent regarding: determining one or more manufacturing processes for the component based on the determined design and determined available tools by the computing device
However, ‘Golway teaches: determining one or more manufacturing processes for the component based on the determined design and determined available tools by the computing device (‘Golway, Para 0004, “The modeling component may include a user interface, at least one suite of tools for performing an object operation selected from creating, editing, modeling, transforming, image property modulating, sketching, print supporting, simulating, material testing and combinations thereof, a material database, and software comprising the one or more instructions stored in the memory and executable by the hardware processor to facilitate a method for designing a volumetric model of a construct at the user interface, the modeling component being operationally linked to the robotic assembly workstation component.” Para 0005, “In accordance with yet another embodiment of the present disclosure, a method is described for designing a volumetric model of a construct at a user interface through use of a 3-D design, fabrication and assembly system comprising a modeling component, a robotic assembly workstation component comprising a six-axis robot, a workflow configuration module, and a hardware processor coupled to a memory, the modeling component comprising the user interface, at least one suite of tools for performing an object operation selected from creating, editing, modeling, transforming, image property modulating, sketching, print supporting, simulating, material testing and combinations thereof, a material database, and software stored in the memory and executable by the hardware processor to facilitate the method for designing the volumetric model of the construct at the user interface, the modeling component being operationally linked to the robotic assembly workstation component.”);
Hence, it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to provide ‘Cella with a process for determining processes based on design and available tools as taught by ‘Golway in order to further configure and provide the plurality of motion paths for the robot arm associated with the assembly order based on a configuration of an associated production run either automatically or by a user (‘Golway, Para 0020).
predicting attributes of the determined one or more designs and the determined one or more manufacturing processes using one or more models by the computing device (‘Cella, 0715, “In embodiments, training may be done based on feedback received by the system, which is also referred to as “reinforcement learning.” In embodiments, the artificial intelligence system 1160 may receive a set of circumstances that led to a prediction (e.g., attributes of a machine, attributes of a model, and the like) and an outcome related to the machine and may update the model according to the feedback.”);
choosing one or more combinations of the determined designs and manufacturing processes to create the component based on the predicted attributes by the computing device (‘Cella, 1758, “The digital twin allows for simulation of the one or more distributed manufacturing network entities during both design and operation phases of the one or more distributed manufacturing network entities, as well as simulation of hypothetical operation conditions and configurations of the one or more distributed manufacturing network entities. The digital twin allows for analysis and simulation of the one or more distributed manufacturing network entities, by facilitating observation and measurement of nearly any type of metric, including temperature, pressure, wear, light, humidity, deformation, expansion, contraction, deflection, bending, stress, strain, load-bearing, shrinkage, in, on, and around each of the one or more distributed manufacturing network entities. The insights gained from analysis and simulation using digital twins may be passed onto the design or manufacturing processes for improvement of these processes.”);
executing the chosen manufacturing processes to generate the component according to the chosen design by the computing device (‘Cella, Para 0067, “Aspects provided herein include a computer-implemented method for facilitating the manufacture and delivery of a 3D printed product to a customer using one or more manufacturing nodes of a distributed manufacturing network, comprising receiving one or more product requirements from the customer; tokenizing and storing the product requirements in a distributed ledger system; determining one or more manufacturing nodes, printers, processes and materials based on the product requirements; generating a quote including pricing and delivery timelines; and upon acceptance of the quote by the customer, manufacturing and delivering the 3D printed product to the customer. In embodiments, the quote is automatically generated and configured into a smart contract for additive manufacturing.”);
receiving data generated during the chosen manufacturing processes about the generated component by the computing device (‘Cella, Para 1016, “In embodiments, the control tower may include or interface with an enterprise management platform (or “EMP”). In embodiments, an EMP may be configured to generate, integrate with, support, and/or or operate on one or more digital twins. In general, digital twins merge data from multiple data sources into a model and representation of the salient characteristics of things, assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, such as, without limitation: machines and their components (e.g., delivery vehicles, forklifts, conveyors, loading machines, cranes, lifts, haulers, trucks, loading machines, unloading machines, packing machines, picking machines, and many others, including robotic systems (e.g., physical robots, collaborative robots, “cobots”), drones, autonomous vehicles, software bots and many others); value chain processes, such as shipping processes, hauling processes, maritime processes, inspection processes, hauling processes, loading/unloading processes, packing/unpacking processes, configuration processes, assembly processes, installation processes, quality control processes, environmental control processes (e.g., temperature control, humidity control, pressure control, vibration control, and others), border control processes, port-related processes, software processes (including applications, programs, services, and others), packing and loading processes, financial processes (e.g., insurance processes, reporting processes, transactional processes, and many others), testing and diagnostic processes, security processes, safety processes, reporting processes, asset tracking processes, and many others; wearable and portable devices, such as mobile phones, tablets, dedicated portable devices for value chain applications and processes, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, wearables, head-worn devices, clothing-integrated devices, bands, bracelets, neck-worn devices, AR/VR devices, headphones, and many others; workers, such as delivery workers, shipping workers, barge workers, port workers, dock workers, train workers, ship workers, distribution of fulfillment center workers, warehouse workers, vehicle drivers, business managers, engineers, floor managers, demand managers, marketing managers, inventory managers, supply chain managers, cargo handling workers, inspectors, delivery personnel, environmental control managers, financial asset managers, process supervisors and workers (for any of the processes mentioned herein), security personnel, safety personnel and many others); suppliers, such as suppliers of goods and related services of all types, component suppliers, ingredient suppliers, materials suppliers, manufacturers, and many others; customers, including consumers, licensees, businesses, enterprises, value added and other resellers, retailers, end users, distributors, and others who may purchase, license, or otherwise use a category of goods and/or related services; a wide range of operating facilities, such as loading and unloading docks, storage and warehousing facilities, vaults, distribution facilities and fulfillment centers, air travel facilities, including aircraft, airports, hangars, runways, refueling depots, and the like, maritime facilities, such as port infrastructure facilities, such as docks, yards, cranes, roll-on/roll-off facilities, ramps, containers, container handling systems, waterways, locks, and many others), shipyard facilities, floating assets, such as ships, barges, boats and others), facilities and other items at points of origin and/or points of destination, hauling facilities, such as container ships, barges, and other floating assets, as well as land-based vehicles and other delivery systems used for conveying goods, such as trucks, trains, and the like; items or elements factoring in demand (i.e., demand factors), including market factors, events, and many others; items or elements factoring in supply (i.e., supply factors), including market factors, weather, availability of components and materials, and many others; logistics factors, such as availability of travel routes, weather, fuel prices, regulatory factors, availability of space, such as on a vehicle, in a container, in a package, in a warehouse, in a fulfillment center, on a shelf, or the like, and many others; retailers, including online retailers and others; pathways for conveyance, such as waterways, roadways, air travel routes, railways and the like; robotic systems, including mobile robots, cobots, robotic systems for assisting human workers, robotic delivery systems, and others; drones, including for package delivery, site mapping, monitoring or inspection, and the like; autonomous vehicles, such as for package delivery; software platforms, such as enterprise resource planning platforms, customer relationship management platforms, sales and marketing platforms, asset management platforms, Internet of Things platforms, supply chain management platforms, platform-as-a-service platforms, infrastructure-as-a-service platforms, software-based data storage platforms, analytic platforms, artificial intelligence platforms, and others; and many others.”);
determining actual attributes of the generated component and the chosen manufacturing processes from the received data by the computing device (‘Cella, Para 0490,” In embodiments, another set of solutions, which may be deployed alone or in connection with other elements of the platform, including the artificial intelligence store 3504, may include a set of functional imaging capabilities 3502, which may comprise monitoring systems 640 and in some cases physical process observation systems 1510 and/or software interaction observation systems 1500, such as for monitoring various value chain entities 652. Functional imaging systems 3502 may, in embodiments, provide considerable insight into the types of artificial intelligence that are likely to be most effective in solving particular types of problems most effectively. As noted elsewhere in this disclosure and in the documents incorporated by reference herein, computational and networking systems, as they grow in scale, complexity and interconnections, manifest problems of information overload, noise, network congestion, energy waste, and many others. As the Internet of Things grows to hundreds of billions of devices, and virtually countless potential interconnections, optimization becomes exceedingly difficult. One source for insight is the human brain, which faces similar challenges and has evolved, over millennia, reasonable solutions to a wide range of very difficult optimization problems. The human brain operates with a massive neural network organized into interconnected modular systems, each of which has a degree of adaptation to solve particular problems, from regulation of biological systems and maintenance of homeostasis, to detection of a wide range of static and dynamic patterns, to recognition of threats and opportunities, among many others. Functional imaging 3502, such as functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), computed tomography (CT) and other brain imaging systems have improved to the point that patterns of brain activity can be recognized in real time and temporally associated with other information, such behaviors, stimulus information, environmental condition data, gestures, eye movements, and other information, such that via functional imaging 3502, either alone or in combination with other information collected by monitoring systems 808, the platform may determine and classify what brain modules, operations, systems, and/or functions are employed during the undertaking of a set of tasks or activities, such as ones involving software interaction 1500, physical process observations 1510, or a combination thereof. This classification may assist in selection and/or configuration of a set of artificial intelligence solutions, such as from an artificial intelligence store 3504, that includes a similar set of capabilities and/or functions to the set of modules and functions of the human brain when undertaking an activity, such as for the initial configuration of a robotic process automation (RPA) system 1442 that automates a task performed by an expert human. Thus, the platform may include a system that takes input from a functional imaging system FRMP 102 to configure, optionally automatically based on matching of attributes between one or more biological systems, such as brain systems, and one or more artificial intelligence systems, a set of artificial intelligence capabilities for a robotic process automation system. Selection and configuration may further comprise selection of inputs to robotic process automation and/or artificial intelligence that are configured at least in part based on functional imaging of the brain while workers undertake tasks, such as selection of visual inputs (such as images from cameras) where vision systems of the brain are highly activated, selection of acoustic inputs where auditory systems of the brain are highly activated, selection of chemical inputs (such as chemical sensors) where olfactory systems of the brain are highly activated, or the like. Thus, a biologically aware robotic process automation system may be improved by having initial configuration, or iterative improvement, be guided, either automatically or under developer control, by imaging-derived information collected as workers perform expert tasks that may benefit from automation.”);
wherein the actual attributes include a state of material of general component;
updating the one or more models based on the predicted attributes and the actual attributes by the computing device (‘Cella, Para 0715,” In embodiments, training may be done based on feedback received by the system, which is also referred to as “reinforcement learning.” In embodiments, the artificial intelligence system 1160 may receive a set of circumstances that led to a prediction (e.g., attributes of a machine, attributes of a model, and the like) and an outcome related to the machine and may update the model according to the feedback.”);
determining model corrections based on the updated one or more models (‘Cella, Para 0572, “For example, in some embodiments, the artificial intelligence system 2010 may apply rules-based logic to determine an adjustment to make to the process to reduce or resolve the waste condition. Additionally, or alternatively, the artificial intelligence may leverage a model that recommends an adjustment to make to the process to reduce or resolve the waste condition.”)
and updating the state of the material of the generated component; simulating the chosen manufacturing processes using the updated state of the material (‘Cella, Para 1727, “In embodiments, the material handling systems 10108 may include or integrate with, optionally in the same housing, unit or system, a material capture and processing system 10127 for capturing material (such as recapturing unused material from jobs and/or capturing available material from a work site, such as from used, broken, or defective items) and rendering the material suitable to use as a source material, such as by: (a) automatically analyzing an item to determine its compatibility for use as source material (e.g., by identifying it as a given type of metal, alloy, polymer or plastic, such as by machine vision, chemical testing, image-based testing, weighing the item, or the like); (b) cleaning, filtering, disassembling, or otherwise pre-processing the item or material, such as to remove non-conforming material; (c) rendering a solid item or material into a thermoplastic state, such as by controlled heating, such as according to a material-specific heating profile; (d) filtering or otherwise treating the material, such as to remove defects; (e) storing the item in an appropriate vessel or form factor for later use, with appropriate reporting of capacity and availability, such as to a broader system for managing jobs, including cooling and/or otherwise processing the material into a wire, powder, mesh, rod, filament or the like until the need for a job arises; (f) delivering the item for additive manufacturing operation; and/or (g) reporting on measures of recapture and savings, including material cost savings, savings on recycling costs, and/or time savings. For example, in embodiments a broken part may be melted down onsite and reprinted. For example, in embodiments a material that would otherwise be disposed of or recycled may be rendered useful on site, without the need for reverse logistics. In embodiments a common heating source is used, with alternate points of heating at different temperatures, to render recaptured material into a thermoplastic state and for preparing material for additive manufacturing operations.” Para 1732, “In embodiments, the additive manufacturing unit 10102 may be configured to execute Fused Deposition Modeling (FDM)™ process (also known as, for example, Fused Filament Fabrication™). The process of FDM may involve a software process which may process an input file, such as an STL (stereolithography) file. An object may be produced by extruding small beads of, for example, thermoplastic material to form layers as the material hardens immediately after extrusion from a nozzle. Extrusion is the 3D printing technique where the material, such as a polymer, metal (including alloys), or the like, is pushed in fluid form through a tube and into a moving nozzle which extrudes the material to a target location where the material subsequently hardens in place. By accurately moving the extruder either continuously or starting and stopping at extremely fast speeds the design is built layer by layer. The source material is typically supplied and stored in solid form, such as in a filament or wire that is wound in a coil and then unwound to supply material to a heating element to render the material into a thermoplastic state and an extrusion nozzle which can control the flow of the material between an “off” state and a maximal flow state. A worm-drive, or any other suitable drive system, may be provided to push the filament into the nozzle at a controlled rate. The nozzle is heated to melt the material. The thermoplastic materials are heated past their state transition temperature (from solid to fluid) and are then deposited by an extrusion head. The nozzle can be moved in both horizontal and vertical directions, such as by a numerically controlled mechanism. In embodiments, the nozzle may follow a tool-path that is controlled by a computer-aided manufacturing (CAM) software package, and the object is fabricated layer-by-layer, such as from the bottom up.” Para 1737, ” In embodiments, the additive manufacturing unit 10102 may be configured to execute a selective heat sintering process. The process may involve a thermal printhead to apply heat to layers of powdered source material to render it to a thermoplastic state. When a layer is finished, the powder bed of source material moves down, and an automated roller adds a new layer of material, which is sintered to form the next cross-section of the object. Power bed printing may refer to a technique where one or more powders, typically a metal powder, are connected via various methods such as lasers or heat in order to rapidly produce the end product. Typically, it is done by either having an area filled with powder and only connecting the design areas of the powder while layer by layer removing the rest, or by adding powder layer-by-layer while simultaneously connecting it. Similar to light polymerization, powder bed printing is significantly faster than other types of 3D printing. In embodiments, the additive manufacturing unit 10102 may employ multiple powder bed/roller subsystems, thereby enabling simultaneous work on different target points of work and/or multi-material powder bed applications that allow switching between materials.”)
and the corrected one or more models; and repeating execution of the chosen manufacturing processes based on the simulated chosen manufacturing processes until it is determined that a target geometry and performance are met by the generated component (‘Cella,Para 0007, “Additive manufacturing, encompassing technologies like 3D printing, vapor deposition, polymer (or other material) coating, epitaxial and/or crystalline growth approaches, and others, alone or in combination with other technologies, such as subtractive or assembly technologies, enables manufacturing of a three-dimensional product from a design via a process of forming successive layers of the product, with optional interim or subsequent steps to arrive at a finished component or system. The design may be in the form of a data source like an electronic 3D model created with a computer aided design package or via 3D scanner. The 3D printing or other additive process then involves forming a first material-layer and then adding successive material layers wherein each new material-layer is added on a pre-formed material-layer, until the entire designed three-dimensional product is completed. References to 3D printing or other particular additive manufacturing technologies throughout this disclosure should be understood to encompass alternative embodiments involving other additive manufacturing technologies, except where context specifically indicates otherwise.” Para 0050, “In embodiments, the models trained by the machine learning system are utilized by the artificial intelligence system to execute simulations on the part twin for predicting deformations or failure in a 3D printed part. In embodiments, the models may also determine a set or sequence of process control parameter adjustments that will implement a corrective action, e.g., to adjust a layer dimension or thickness, so as to correct a defect. In embodiments, the system may send a warning or error signal to an operator or a user, or automatically abort the printing process.” Para 1020, “In embodiments, the EMP may be configured to perform simulations using and/or with respect to one or more enterprise digital twins. In embodiments, digital twins (including enterprise digital twins) may be configured to behave in accordance with a set of constraints, such as laws of nature, laws of physics, mechanical properties, material properties, economic principals, chemical properties, and the like. In this way, the EMP may vary one or more parameters of an enterprise digital twin and may execute a simulation within the digital twin that conforms with real-word conditions and behaviors. For example, in executing a simulation of a logistics process that simulates outcomes associated with different packaging materials, the EMP may simulate variation of the packaging materials of one or more products. During the simulation, the products may be “exposed” to different conditions (e.g., different temperatures, humidity, motions, and the like) by varying one or more parameters of an environment digital twin of an environment of the products, a product digital twin of the product, and/or the logistics digital twin. The simulation may be executed to determine the fraction of products that are likely to be damaged using the different packaging materials, which may affect the profitability of shipments vis-à-vis the cost of the different packaging materials and cost of replacing damaged products. In this way, the simulation may be run to help select the most cost-effective packaging material, such that estimated product loss is taken into account. Furthermore, in some embodiments, digital twins may be leveraged to perform simulations to predict future states of the thing or group of things and/or modeling behaviors in order to extrapolate states of the thing or group of things; to represent results of such simulations (including states, event and flows); and to offer opportunities to control things that are represented in the digital twins based on the simulations. For example, the EMP may receive sensor readings from temperature sensors, humidity sensors, and fan speed sensors deployed throughout an environment. The EMP may apply one or more thermodynamics equations to the received sensor readings and the dimensions of the environment to model the thermodynamic behavior of the environment to determine, to represent in the digital twin the temperatures in areas that do not have temperature sensors and to offer opportunities to adjust one or more systems, such as HVAC systems, or components thereof, to induce a change in the environment.” Para 1073, “In embodiments, the data structuring system 8106 is configured to process and structure data into a format that can be consumed by an enterprise digital twin. In embodiments, processing by the data structuring system 8106 may include compression, computation, filtering, aggregation, multiplexing, selective switching, batching, packetization, streaming, summarization, fusion, fragmentation, encoding, decoding, transcoding, encryption, decryption, duplication, deduplication, normalization, cleansing, identification, copying, storage, decompression, syndication, augmentation (e.g., by metadata), content inspection, classification, extraction, transformation, loading, formatting, error correction, data structuring, and/or many other processing actions. In embodiments, the data structuring system 8106 may leverage ETL (extract, transform, load) tools, data streaming, and other data integration tooling to structure the various types of digital twin data. In embodiments, the data structuring system 8106 structures the data according to a digital twin data model that may be defined by the digital twin configuration system 8102 and/or a user. In embodiments, a digital twin data model may refer to an abstract model that organizes elements of enterprise-related data and standardizes the manner by which those elements relate to one another and to the properties of digital twin entities. For instance, a digital twin data model of an environment that includes vehicles (e.g., a vehicle assembly facility or an environment where vehicles operate) may specify that the data element representing a vehicle be composed of a number of other elements which represent sub-elements or attributes of the vehicle (the color of the vehicle, the dimensions of the vehicle, the engine of the vehicle, the engine parts of the vehicle, the owner of the vehicle, the performance specifications of the vehicle, and the like). In this example, the digital twin model components may define how the physical attributes are tied to respective physical locations on the vehicle. In embodiments, digital twin data models may define a formalization of the objects and relationships found in a particular application domain. For example, a digital twin data model may represent the customers, products, and orders found in a manufacturing enterprise and how they relate to each other within the various digital twins. In another example, a digital twin data model may define a set of concepts (e.g., entities, attributes, relations, tables, and/or the like) used in defining such formalizations of data or metadata within the environment. For example, a digital twin data model used in connection with a banking application may be defined using the entity-relationship data model and how the entity-relationship data model is then related to the various executive digital twin views.” Para 1077, “In embodiments, the digital twin perspective builder 8110 leverages metadata, artificial intelligence, heuristic methods, 3D rendering algorithms and/or other data processing techniques to produce a definition of information required for generation of the digital twin in the digital twin generation system 8108. In some embodiments, different relevant datasets are hooked to a digital twin (e.g., an executive digital twin, an environment digital twin, or the like) at the appropriate level of granularity, thereby allowing for the structural aspects of the data (e.g., system of accounts, sensor readings, sales data, or the like) to be a part of the data analytics process. One aspect of making a perspective function is that the user can change the structural view or the granularity of data while potentially forecasting future events or changes to the structure to guide control of the area of the business at question. In embodiments, the term “grain of data” may refer to the base unit of a type of data, such as a single line of data, a single aggregated line of data, a single byte of data, a single file, a single instance, or the like. Examples of “grains of data” may include a detailed record on a single sale, a single block in a blockchain in a distributed ledger, a single event in an event log, a single vibration reading from a vibration sensor, or similar singular or atomic data units, and the like. Grain or atomicity may impose a constraint in how the data can be combined or processed to form different outputs. For example if some element of data is captured only at the level of once-per-day, then it can only be broken down to single days (or aggregation of days) and cannot be broken down to hours or minutes, unless derived from the day representation (e.g., using inference techniques and/or statistical models). Similarly, if data is provided only at the aggregate business unit level, it can be broken down to the level of an individual employee only by, for example, averaging, modeling, or inductive functions. Generally, role-based and other enterprise digital twins may often benefit from finer levels of data, as aggregations and other processing steps may produce outputs that are dynamic in nature and/or that relate to dynamic processes and/or real-time decision-making. It is noted that different types of digital twins may have different “sized” grains of data. For example, the grains of data that feed a CEO digital twin may be at a higher granularity level than the grains of data that feed a COO digital twin. In some embodiments, however, a CEO may drill down into a state of the CEO digital twin and the granularity for the selected state may be increased.” Para 1724,” Accordingly, the term “additive manufacturing platform” used herein encompasses a platform that prints, builds, or otherwise produces 3D parts and/or products at least in part using an additive manufacturing technique. The additive manufacturing platform may encompass technologies like 3D printing, vapor deposition, polymer (or other material) coating, epitaxial and/or crystalline growth approaches, and others, alone or in combination with other technologies, such as subtractive or assembly technologies, enables manufacturing of a three-dimensional product from a design via a process of forming successive layers of the product, with optional interim or subsequent steps to arrive at a finished component or system. The design may be in the form of a data source like an electronic 3D model created with a computer aided design package or via 3D scanner. The 3D printing or other additive process then involves forming a first material-layer and then adding successive material layers wherein each new material-layer is added on a pre-formed material-layer, until the entire designed three-dimensional product is completed. The additive manufacturing platform may be a stand-alone unit, a sub-unit of a larger system or production line, and/or may include other non-additive manufacturing features, such as subtractive-manufacturing features, pick-and-place features, coating features, finishing features (such as etching, lithography, painting, polishing and the like), two-dimensional printing features, and the like. Further, the platform may include three-dimensional additive manufacturing machines configured for rapid prototyping, three-dimensional printing, two-dimensional printing, freeform fabrication, solid freeform fabrication, and stereolithography; subtractive manufacturing machines including computer numerical controlled fabrication machines; injection molding machines and the like.” Para 1878, “A part design comprising model information and product requirements is presented to the design and simulation 10116 where it is evaluated for manufacturing compatibility with at least one type of the additive manufacturing unit 10102 in the manufacturing node 10100. The design and simulation 10116 may be assisted by the artificial intelligence 10212, the simulation management 10514, the printer twin 10506 (which in embodiments may be a twin of any type of additive manufacturing unit) and the process and material selection twin 10702 for performing the optimization. An example analysis includes the use of the printer twin 10506 in the digital twin system 10214 to simulate and compare part design dimensions and accuracy with available 3D printer working envelopes and specifications.” Para 2031, “In embodiments, the dynamic vision system 11200 may utilize saccades to characterize objects by context and build a rich model of the object in its environment by capturing contextual intelligence through associations. This mirrors how saccades capture information about an object in its environment. Saccade denotes a quick, simultaneous movement of both eyes between two or more areas of focus. While viewing a scene, human eyes make sporadic saccadic movements stopping several times while locating key parts of the scene, moving quickly between each stop and building up a mental three-dimensional map corresponding to the scene. The dynamic vision system 11200 and methods described herein may use saccades to characterize objects by context and allow control of an optical system to more quickly identify and characterize a field of view. Saccades integrate varying physical/optical properties, along with object-oriented learning, to rapidly improve understanding and search in the visual sphere.” Para 2034, “The conformable liquid lens 11308 of the optical assembly 11304 may frequently adjust in real-time based, in part, on change in one or more optical parameters by the control system 11314 creating real-time data streams at the sensor 11310 which are then provided to the processing system 11306 to generate a situational awareness or computerized understanding of the world that the dynamic vision system 11300 is operating in. This understanding may include rich contextual intelligence about the object and its environment and may be represented as an object concept. The object concept may be used by the processing system for object recognition, predicting object motion, location and orientation, creating a 3D model of the object, monitoring the object for any defects and other applications. For example, the adaptive intelligence system 11318 may process the object concept to build a three-dimensional representation of the object. The machine learning system 11322 in the adaptive intelligence system 11318 may input the object concept into one or more machine learning models, the object concept being used as training data for the machine learning models. Further, the artificial intelligence system 11326 may be configured to make classifications, predictions, and other decisions relating to the object including determining the position, orientation and motion of the object.” Para 2065, ” In embodiments, the machine learning models 11520 may process the data received from sensors, including the event data and the state data to define simulation data for use by the digital twin system 11320. The machine learning models 11520 may, for example, receive state data and event data related to a particular component of the dynamic vision system 11300 and perform a series of operations on the state data and the event data to format the state data and the event data into a format suitable for use by the digital twin system 11320. For example, machine learning models 11520 may collect data from one or more sensors positioned on, near, in, and/or around the liquid lens to process the sensor data into simulation data and output the simulation data to the digital twin system 11320. The digital twin system 11320 may then use the simulation data to create the liquid lens twin 11506, the simulation including for example metrics including shape, material, focal length, specularity, environment, lighting, color, temperature, pressure, wear and vibration. The simulation may be a substantially real-time simulation, allowing for a user of the dynamic vision system 11300 to view the simulation of the liquid lens, metrics related thereto, and metrics related to parts thereof, in substantially real time. The simulation may be a predictive or hypothetical situation, allowing for a user of the dynamic vision system 11300 to view a predictive or hypothetical simulation of the liquid lens, metrics related thereto, and metrics related to components thereof.” Para 2336, “In embodiments, the modular AI-on-a-chip packages may be trained with models to execute and govern robotic process automation, such as recognizing situations (bottlenecks in warehouse, congestion/lines in store, thin/sparse customer mix in part of an environment), classifying and recognizing objects/faces/products/emotions, setting demand-side parameters (price, promotion, advertising location); managing supply-side interactions including governing onboard chatbot interactions, managing recommendation engine for recommending a basket of complementary products and the like. In embodiments, the modular AI-on-a-chip packages may be trained with models to analyze physiological, neurological, emotional, cognitive state of a user and tailor the response of the MPR 12100 based on such state. For example, the package may analyze facial expressions, speech, tone, body movements of a user to determine the state, analyze the state information to derive information on customer interest, response, preference etc. and then feed such information to edge devices for content delivery, product recommendations, advertising, and the like. In embodiments, the modular AI-on-a-chip packages may be trained with models to analyze security threat vectors and other vulnerabilities to the MPR 12100 or the robotic fleet. For example, the package may use biometric analysis, behavioral modeling, facial and voice recognition, for enabling authentication; learning models for recognizing and preventing attacks by malware, spyware, ransomware, viruses, worms, trojans and the like; classification, clustering or regression models for threat intelligence, anomaly detection, network and end-point security etc. In embodiments, the modular AI-on-a-chip packages may be trained with models to analyze weather conditions, light, temperature, water usage or soil conditions collected from farms in agricultural planning by determining seed and crop choices and optimizing utilization of farming resources including land, water and nutrition. The MPR 12100 may for example, use the information to follow a planting and nutrition routine, perform phenotyping for selective breeding provide optimized wavelengths of light for crops using AI-controlled LED lights. In embodiments, the modular AI-on-a-chip packages may be trained with models to detect diseases, pests, weed, nutritional deficiencies in soil or crops on agricultural farms. For example, the MPR 12100 may utilize propeller or miniaturized jet engine of transport system to fly over the farm, capture images of the farm using cameras of the vision and sensing system and then use the modular AI-on-a-chip package to identify problem areas and potential improvements. For example, the images may show the presence of unwanted plants or weeds. The MPR 12100 may then make decisions about treatment with herbicides or may select one or more end-effectors for eliminating the weeds. In embodiments, the modular AI-on-a-chip packages may be trained with models to monitor and harvest crops, plants, fruits and vegetables of various shapes and sizes. For example, the package may utilize machine vision and other sensors for identifying the crops ready to be harvested. The package may also include trained policies for navigating the farm, estimating the position and orientation of crops relative to the MPR 12100, grasping fruits and vegetables of different shapes and sizes, select suitable end effectors for selective harvesting, and finally storing or packaging the harvested fruits and vegetables. In embodiments, the modular AI-on-a-chip packages may be trained with models to manage a controlled closed loop environment for an aquaponics system based on needs of plants and fish. For example, an example module AI-on-a-chip package may receive sensed oxygen levels in an aquatic environment and may determine whether the water is sufficiently oxygenated, under-oxygenated, or over-oxygenated. In embodiments, the modular AI-on-a-chip packages may be trained with models for optimizing 3D printing parameters.”).
Regarding Claim[s] 2, ‘Cella & ‘Golway disclose all the claim limitations including: certifying a component or manufacturing process using the updated one or more models (‘Cella, Para 0715, “In embodiments, training may be done based on feedback received by the system, which is also referred to as “reinforcement learning.” In embodiments, the artificial intelligence system 1160 may receive a set of circumstances that led to a prediction (e.g., attributes of a machine, attributes of a model, and the like) and an outcome related to the machine and may update the model according to the feedback.”).
Regarding Claim[s] 3, ‘Cella & ‘Golway disclose all the claim limitations including: wherein the manufacturing process is an automated manufacturing process and includes one or more of deformation, casting, machining, additive manufacturing and welding (‘Cella, Para 0013, “Furthermore, automation is revolutionizing value chains for almost all categories of items, and robotics is at the heart of the revolution. While physical robots have played an ever-expanding role in manufacturing for years, typical implementations have historically focused on fixed location robots completing prescribed tasks in pre-defined arrangements, such as painting, welding, and so forth in an assembly line. These limited roles produced and continue to produce significant improvements in quality, cost, and productivity, but do not take full advantage of emerging technologies in engineering, materials science, software process automation, artificial intelligence, additive manufacturing, data-driven analytics, digital twins, blockchains, smart contracts, and the like. These technologies can be integrated with developments in robotics (including hardware and software robotics) to produce an innovative array of highly functional autonomous robots with interactive capabilities. Emerging and future robot classes and capabilities provide opportunity for ever-expanding robot use cases and management platforms that can automatically configure, organize, deploy, and control robots and robot fleets to securely deliver reliable services, including contracted services that access robotic fleet capabilities in “robotics-as-a-service” platforms, among others.” And ‘Golway, Para 0021, “ The workflow configuration module 24 may operate in a single configuration, such as shown in system 22, or in a workstation series configuration, such as shown in system 22'. The workflow configuration module 24 may access an available number of robotic assembly workstation components 1 and provide this information to a user such that 3D printing and/or assembly tasks may be distributed across the available robotic assembly workstation components 1. An assembly order, as described above, may include
such information and be translated in a production run to achieve a desired workflow as developed by a user, for example. Referring to the system 22' including four robotic
assembly workstation components 1, a user and/or the workflow configuration module 24 may opt to 3D print, quality measure, assemble, and the like across available robotic workstation components 1. The workflow configuration module 24 may minimize one or more assembly constraints to maximize throughput across the available robotic workstation component(s) 1 of systems 22, 22'. The workflow configuration module 24 is configured to provide cycle time information and insight including decision making loops and parallel processing path may be integrated.”) .
Regarding Claim[s] 4, ‘Cella & ‘Golway disclose all the claim limitations including: manufacturing process is performed by an Auto-Fab system, and the location is the location of the Auto-Fab system (Applicant’s Spec, Para 0004, defines “Auto-Fab” as “A component manufacturing device called an autonomous factory artisan box ("Auto-Fab") is disclosed.”) (‘Cella, Abst, “An autonomous additive manufacturing platform includes sensors positioned in, on, and/or near a part and configured to collect sensor data related to the part. An adaptive intelligence system is configured to receive the sensor data from the sensors. The adaptive intelligence system includes a machine learning system configured to input the sensor data as training data into one or more machine learning models. The machine learning models are configured to transform the sensor data into simulation data. A digital twin system is configured to create a part twin based on the simulation data. The part twin provides for representation of the part and simulation of a possible future state of the part via the simulation data. An artificial intelligence system is configured to execute simulations on the digital twin system. The machine learning models are utilized to make classifications, predictions, and other decisions relating to the part.”).
Regarding Claim[s] 5, ‘Cella & ‘Golway disclose all the claim limitations including: manufacturing process is performed by a virtual Auto-Fab system, and
the component may be shipped from one location to another during manufacturing (‘Cella, Abst, and Para 0270, “These value chain entities 652 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, such as, without limitation: machines 724 and their components (e.g., delivery vehicles, forklifts, conveyors, loading machines, cranes, lifts, haulers, trucks, loading machines, unloading machines, packing machines, picking machines, and many others, including robotic systems, e.g., physical robots, collaborative robots (e.g., “cobots”), drones, autonomous vehicles, software bots and many others); products 650 (which may be any category of products, such as a finished goods, software products, hardware products, component products, material, items of equipment, items of consumer packaged goods, consumer products, food products, beverage products, home products, business supply products, consumable products, pharmaceutical products, medical device products, technology products, entertainment products, or any other type of products and/or set of related services); value chain processes 722 (such as shipping processes, hauling processes, maritime processes, inspection processes, hauling processes, loading/unloading processes, packing/unpacking processes, configuration processes, assembly processes, installation processes, quality control processes, environmental control processes (e.g., temperature control, humidity control, pressure control, vibration control, and others), border control processes, port-related processes, software processes (including applications, programs, services, and others), packing and loading processes, financial processes (e.g., insurance processes, reporting processes, transactional processes, and many others), testing and diagnostic processes, security processes, safety processes, reporting processes, asset tracking processes, and many others); wearable and portable devices 720 (such as mobile phones, tablets, dedicated portable devices for value chain applications and processes, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, hearables, head-worn devices, clothing-integrated devices, arm bands, bracelets, neck-worn devices, AR/VR devices, headphones, and many others); workers 718 (such as delivery workers, shipping workers, barge workers, port workers, dock workers, train workers, ship workers, distribution of fulfillment center workers, warehouse workers, vehicle drivers, business managers, engineers, floor managers, demand managers, marketing managers, inventory managers, supply chain managers, cargo handling workers, inspectors, delivery personnel, environmental control managers, financial asset managers, process supervisors and workers (for any of the processes mentioned herein), security personnel, safety personnel and many others); suppliers 642 (such as suppliers of goods and related services of all types, component suppliers, ingredient suppliers, materials suppliers, manufacturers, and many others); customers 662 (including consumers, licensees, businesses, enterprises, value added and other resellers, retailers, end users, distributors, and others who may purchase, license, or otherwise use a category of goods and/or related services); a wide range of operating facilities 712 (such as loading and unloading docks, storage and warehousing facilities 654, vaults, distribution facilities 658 and fulfillment centers 628, air travel facilities 740 (including aircraft, airports, hangars, runways, refueling depots, and the like), maritime facilities 622 (such as port infrastructure facilities 622 (such as docks, yards, cranes, roll-on/roll-off facilities, ramps, containers, container handling systems, waterways 732, locks, and many others), shipyard facilities 638, floating assets 620 (such as ships, barges, boats and others), facilities and other items at points of origin 610 and/or points of destination 628, hauling facilities 710 (such as container ships, barges, and other floating assets 620, as well as land-based vehicles and other delivery systems 632 used for conveying goods, such as trucks, trains, and the like); items or elements factoring in demand (i.e., demand factors 644) (including market factors, events, and many others); items or elements factoring in supply (i.e., supply factors 648)(including market factors, weather, availability of components and materials, and many others); logistics factors 750 (such as availability of travel routes, weather, fuel prices, regulatory factors, availability of space (such as on a vehicle, in a container, in a package, in a warehouse, in a fulfillment center, on a shelf, or the like), and many others); retailers 664 (including online retailers 730 and others such as in the form of eCommerce sites 730); pathways for conveyance (such as waterways 732, roadways 734, air travel routes, railways 738 and the like); robotic systems 744 (including mobile robots, cobots, robotic systems for assisting human workers, robotic delivery systems, and others); drones 748 (including for package delivery, site mapping, monitoring or inspection, and the like); autonomous vehicles 742 (such as for package delivery); software platforms 752 (such as enterprise resource planning platforms, customer relationship management platforms, sales and marketing platforms, asset management platforms, Internet of Things platforms, supply chain management platforms, platform as a service platforms, infrastructure as a service platforms, software-based data storage platforms, analytic platforms, artificial intelligence platforms, and others); and many others. In some example embodiments, the product 650 may be encompassed as an intelligent product 650 or the VCNP 604 may include the intelligent product 650. The intelligent product 650 may be enabled with a set of capabilities such as, without limitation data processing, networking, sensing, autonomous operation, intelligent agent, natural language processing, speech recognition, voice recognition, touch interfaces, remote control, self-organization, self-healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, sound processing systems, data storage, data integration, and/or various Internet of Things capabilities, among others. The intelligent product 650 may include a form of information technology. The intelligent product 650 may have a processor, computer random access memory, and a communication module. The intelligent product 650 may be a passive intelligent product that is similar to a RFID type of data structure where the intelligent product may be pinged or read. The product 650 may be considered a value chain network entity (e.g., under control of platform) and may be rendered intelligent by surrounding infrastructure and adding an RFID such that data may be read from the intelligent product 650. The intelligent product 650 may fit in a value chain network in a connected way such that connectivity was built around the intelligent product 650 through a sensor, an IoT device, a tag, or another component.”).
Regarding Claim[s] 7, ‘Cella & ‘Golway disclose all the claim limitations including:
component is a medical device tailored to conform to a patient's anatomy (‘Cella, Para 0115, “In embodiments, the distributed manufacturing network information technology system is configured to provide 3D printed products that conform to a body part or anatomy of a user wherein the 3D printed product is a wearable selected from a group consisting of eyewear, footwear, earwear and headgear.” Para 1746, “In embodiments, the platform 10110 may provide 3D printed products that conform to a body part/anatomy of the user including wearables like eyewear, footwear, earwear and headgear. Conformance may, in embodiments, be based on a scan of a body part or anatomical feature, such as a laser or other structured light scan, a MRI, EEG, computed tomography, ultrasound or other imaging scan, or the like. A 3D topology for the anatomical feature may be used as an input source for generation by a CAD system or other design system (which may be linked to or integrated into an additive manufacturing platform) of a design for additive manufacturing. The design may be configured to produce an anatomy-compatible item that conforms well to anatomy (such as a hearable unit that fits the inner ear, headgear that fits the head, a brace that fits a joint, or the like) and/or an item that is intended to replace a part of the anatomy, such as a prosthetic.”).
Regarding Claim[s] 8, ‘Cella & ‘Golway disclose all the claim limitations including:
design is topologically optimized within manufacturing constraints to meet structural constraints such as strength, stiffness, fracture resistance, fatigue resistance, corrosion resistance, and corrosion fatigue resistance (‘Cella, Para 0042, “Aspects provided herein include a distributed manufacturing network comprising: an additive manufacturing management platform with an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from a set of distributed manufacturing network entities for optimizing manufacturing, supply chain, demand management, service, maintenance and other processes and workflows; and a distributed ledger integrated with digital threads of the distributed manufacturing network entities.” Para 0052, “,In embodiments, the models trained by the machine learning system are utilized by the artificial intelligence system to execute simulations on the part twin for optimizing the build process to minimize the occurrence of deformations.” Para 0554, “.In embodiments, the digital representation for a digital twin may include a set of data structures (e.g., classes of objects) that collectively define a set of properties, attributes, and/or parameters of a represented physical asset, device, or environment, possible behaviors or activities thereof and/or possible states or conditions thereof, among other things. For example, a set of properties of a physical asset may include a type of the physical asset, the shape and/or dimensions of the asset, the mass of the asset, the density of the asset, the material(s) of the asset, the physical properties of the material(s), the chemical properties of the asset, the expected lifetime of the asset, the surface of the physical asset, a price of the physical asset, the status of the physical asset, a location of the physical asset, and/or other properties, as well as identifiers of other digital twins contained within or linked to the digital twin and/or other relevant data sources that may be used to populate the digital twin (such as data sources within the management platform described herein or external data sources, such as environmental data sources that may impact properties represented in the digital twin (e.g., where ambient air pressure or temperature affects the physical dimensions of an asset that inflates or deflates). Examples of a behavior of a physical asset may include a state of matter of the physical asset (e.g., a solid, liquid, plasma or gas), a melting point of the physical asset, a density of the physical asset when in a liquid state, a viscosity of the physical asset when in a liquid state, a freezing point of the physical asset, a density of the physical asset when in a solid state, a hardness of the physical asset when in a solid state, the malleability of the physical asset, the buoyancy of the physical asset, the conductivity of the physical asset, electromagnetic properties of the physical asset, radiation properties, optical properties (e.g., reflectivity, transparency, opacity, albedo, and the like), wave interaction properties (e.g., transparency or opacity to radio waves, reflection properties, shielding properties, or the like), a burning point of the physical asset, the manner by which humidity affects the physical asset, the manner by which water or other liquids affect the physical asset, and the like. In another example, the set of properties of a device may include a type of the device, the dimensions of the device, the mass of the device, the density of the density of the device, the material(s) of the device, the physical properties of the material(s), the surface of the device, the output of the device, the status of the device, a location of the device, a trajectory of the device, identifiers of other digital twins that the device is connected to and/or contains, and the like. Examples of the behaviors of a device may include a maximum acceleration of a device, a maximum speed of a device, possible motions of a device, possible configurations of the device, operating modes of the device, a heating profile of a device, a cooling profile of a device, processes that are performed by the device, operations that are performed by the device, and the like. Example properties of an environment may include the dimensions of the environment, environmental air pressure, the temperature of the environment, the humidity of the environment, the airflow of the environment, the physical objects in the environment, currents of the environment (if a body of water), and the like. Examples of behaviors of an environment may include scientific laws that govern the environment, processes that are performed in the environment, rules or regulations that must be adhered to in the environment, and the like.”).
Claims 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2022/0197246 Cella et al. (‘Cella hereafter), and in view of U.S. 2019/0243338 Michael W. Golway (‘Golway hereafter), and in further view of U.S. 2023/0191543 Field et al. (‘Field hereafter).
Regarding Claim[s] 21, ‘Cella & ‘Golway disclose all the claim limitations including:
determining the actual attributes including the state of the material comprises assessing the state of the material using x-ray diffraction (‘Cella, Para 0128, In embodiments, the optical parameters adjusted by the control system include focal length, liquid materials, specularity, color, environment, lens shape, or some other type of parameter which in turn impacts spherical aberration, field curvature, coma, chromatic aberration, distortion, vignetting, ghosting, flaring, diffraction, and/or some other characteristic.“ X-ray are a form of light, Para 1762, “In embodiments, a distributed manufacturing network entity, such as the additive manufacturing unit 10102 or the platform 10110, may, optionally automatically, generate a set of digital twins of a set of manufactured items, such as products, components, parts, or the like. In embodiment, the digital twin of a manufactured item generated by the additive manufacturing unit 10102 or the platform 10110 may include, link to, be enriched by, and/or integrate with, among other things: (a) an instruction set according to which an item was additively manufactured, such as including shape information, material layering information, functional information, operational parameter information (such as described elsewhere herein), and the like; (b) a training data set based upon which an artificial intelligence system was trained in connection with the design or manufacturing of the item; (c) a sensor data set, such as containing time series sensor data (such as imaging data from various imaging systems) indicating exact conditions of manufacturing of the item, such as linking a series of images of layers of the item as it was generated with data indicating, in case with respect to the item, the environment in which it was manufactured, the equipment or tools used, the materials used, and/or the like; temperatures, pressures, fluid flow rates, heat flux data, volume data, topological data, radiation data (e.g., intensity of lasers, visible light, infrared light, UV, x-rays, magnetic fields, electrical fields and the like), chemical information (e.g., presence of reactants, catalysts, and the like), biological data (e.g., presence and states of biomaterials, pathogens, and other factors), and others; (d) a testing data set, such as indicating outcomes of testing before, during or after manufacturing, such as equipment testing, material testing, stress testing, visual inspection (including by machine vision), strain testing, torsion testing, load testing, impact testing, operational testing, and the like; (e) manufacturing information relating to similar items, such as outcomes of manufacturing, usage, or the like; and others. In embodiments, the additive manufacturing unit 10102 may automatically create the digital twin upon receiving an instruction to manufacturing an item and subsequently enrich and/or modify the digital twin during manufacturing and/or after manufacturing. In embodiments, the additive manufacturing unit 10102 may automatically embed the above-referenced data for the digital twin of the item in or on the item (such as by writing to a data structure that is embedded in or disposed on the item, such as chip), on a tag for the item, on a container or package, or the like.”) , and
wherein repeating the execution of the chosen manufacturing processes comprises manipulating a temperature and a strain path of the generated component (‘Cella, Paras, 0007, 1020, 1073, 1077, 1724, 2031, 2336)
including an amount of effective strain and principal directions of maximum extension, and utilizing the determined available tools to perform local deformation to correct porosity or coarse phases in the generated component (‘Cella, Para, 0050, “In embodiments, the models trained by the machine learning system are utilized by the artificial intelligence system to execute simulations on the part twin for predicting deformations or failure in a 3D printed part. In embodiments, the models may also determine a set or sequence of process control parameter adjustments that will implement a corrective action, e.g., to adjust a layer dimension or thickness, so as to correct a defect. In embodiments, the system may send a warning or error signal to an operator or a user, or automatically abort the printing process.” Para 1762, “In embodiments, a distributed manufacturing network entity, such as the additive manufacturing unit 10102 or the platform 10110, may, optionally automatically, generate a set of digital twins of a set of manufactured items, such as products, components, parts, or the like. In embodiment, the digital twin of a manufactured item generated by the additive manufacturing unit 10102 or the platform 10110 may include, link to, be enriched by, and/or integrate with, among other things: (a) an instruction set according to which an item was additively manufactured, such as including shape information, material layering information, functional information, operational parameter information (such as described elsewhere herein), and the like; (b) a training data set based upon which an artificial intelligence system was trained in connection with the design or manufacturing of the item; (c) a sensor data set, such as containing time series sensor data (such as imaging data from various imaging systems) indicating exact conditions of manufacturing of the item, such as linking a series of images of layers of the item as it was generated with data indicating, in case with respect to the item, the environment in which it was manufactured, the equipment or tools used, the materials used, and/or the like; temperatures, pressures, fluid flow rates, heat flux data, volume data, topological data, radiation data (e.g., intensity of lasers, visible light, infrared light, UV, x-rays, magnetic fields, electrical fields and the like), chemical information (e.g., presence of reactants, catalysts, and the like), biological data (e.g., presence and states of biomaterials, pathogens, and other factors), and others; (d) a testing data set, such as indicating outcomes of testing before, during or after manufacturing, such as equipment testing, material testing, stress testing, visual inspection (including by machine vision), strain testing, torsion testing, load testing, impact testing, operational testing, and the like; (e) manufacturing information relating to similar items, such as outcomes of manufacturing, usage, or the like; and others. In embodiments, the additive manufacturing unit 10102 may automatically create the digital twin upon receiving an instruction to manufacturing an item and subsequently enrich and/or modify the digital twin during manufacturing and/or after manufacturing. In embodiments, the additive manufacturing unit 10102 may automatically embed the above-referenced data for the digital twin of the item in or on the item (such as by writing to a data structure that is embedded in or disposed on the item, such as chip), on a tag for the item, on a container or package, or the like.” Para 2023, “FIG. 123 is a schematic illustrating an example implementation of a dynamic vision system 11200 for dynamically learning an object concept about an object 11202 of interest according to an embodiment of the present disclosure. The dynamic vision system 11200 may replace and/or augment the lens 11104 of a conventional vision system 11100 with a variable focus liquid lens 11204. The variable focus liquid lens 11204 may be an electrically controlled cell containing optical-grade liquid, that is deformed through electric current, changing the shape of the lens. The dynamic vision system 11200 leverages this flexibility of liquid lens 11204 by constantly adjusting lens parameters to dynamically change various optical characteristics of light that pass through the lens including focal length, spherical aberration, field curvature, coma, chromatics aberrations, distortion, vignetting, ghosting and flaring, and diffraction of light. A fully variable liquid lens thus allows for more dynamic input for a sensor 11206 enabling it to capture visual information and metadata that is otherwise lost in the conventional computer vision system 11100.”)
Except ‘Cella is silent regarding: wherein the determined available tools used for the local deformation include a high velocity impulse device utilizing a vaporizing foil or a micro explosive.
However, ‘Field does teach: wherein the determined available tools used for the local deformation include a high velocity impulse device utilizing a vaporizing foil or a micro explosive (‘Field, Abst, “A spatially coherent machine for manufacturing comprises, in one example, a workpiece holder configured to secure a workpiece, a toolholder with at least one axis of motion control configured to perform a subtractive machining operation on the workpiece using a machining tool, a heating element configured to perform a heating operation on the workpiece, and a forming element configured to perform a forming operation in which force is applied to the workpiece in an amount that causes plastic deformation of the workpiece material. The workpiece holder secures the workpiece during the heating, forming, and subtractive operations such that the forming and subtractive operations are performed in a spatially coherent manner.” Paras 0003 – 0011, “[0003] Many additive, subtractive, deformative, and transformative techniques are known in the field of parts manufacturing. However, until the present time it has not been possible to perform certain operations together in the same machine. For example, parts that require forged elements as well as machined elements have required that forging operations be performed in forging machines, and that machining operations be performed in a lathe, mill, or other machining center. Parts that required cast elements as well as machined elements have required that casting operations be performed in a foundry or casting machine and then removed to turning, milling, or turn-mill equipment to be machined. [0004] Similarly, parts that required forged elements as well as 3D printed elements have required that 3D printing operations be performed in additive manufacturing machines and forging operations be performed in forging machines. Parts that required transformative operations such as heat-treating as well as forming operations such as forging and subtractive operations such as machining have required that forging be performed in forging machines, subtractive operations be performed in a machining center, and heat treatments be performed in separate ovens dedicated to the purpose.[0005] Whenever two operations must be performed on two different machines, additional labor and equipment costs are incurred because of the need to remove parts from one machine, transport them to another machine, load them, locate and align them, and perform the secondary operation. Labor costs may increase significantly if additional machine operators are required. [0006] There is also a large cost associated with the difficulty of establishing adequate spatial alignment of workpiece and tooling in a secondary machine to precisely match the alignment in the primary machine. Each additional operation that requires part repositioning reduces the achievable part tolerances because of small errors in locating and aligning a part after repositioning. Another source of error arises because the various motion axes of one machine are not in perfect alignment with the various motion axes of another machine. Every time a part must be handled the likelihood of error rises, and the value lost to waste and failed quality metrics rises with it. Certain parts may require the addition and sometimes subsequent removal of special fiducial features to permit relocating, realigning, and workholding in a second machine. Other parts simply cannot be made by moving them from machine to machine in this way. Even if spatial coherence (maintenance of three-dimensional alignment and registration of a part within specified tolerances across multiple operations) can be established, re-establishing and maintaining the correct alignment and registration to within necessary tolerances may require exotic techniques that add cost and difficulty. In a practical sense it is impossible to achieve perfect coaxiality between two operations performed in two different machines. [0007] When thermal energy is involved in manufacturing, moving parts from one machine to another may require allowing them to cool, which can result in changes in dimension and alignment that must be accounted for before a second operation can be performed, adding complexity, cost and waste. The requirement that parts be allowed to cool before moving to another machine can lead to other problems. For example, in certain materials the cooling process may result in hardening that can make subsequent machining difficult and costly to the point of being prohibitive. [0008] When errors due to loss or degradation of spatial coherence occur, they may manifest as a part that appears to be correct but fails to pass subsequent close inspection, meaning the part must be remanufactured, consuming twice as much time, labor, and materials as planned. This problem is compounded when working with high-value alloys that are very difficult to machine due to high toughness and a high tendency to work hardening. If the failure is detected only after a job run is complete, it may require an entirely new machine setup, interrupting other scheduled jobs and causing a ripple effect that can have significant economic impact. [0009] Machines used for forging typically require a significant amount of floor space to accommodate dedicated equipment such as forges and presses, and the movement of hot metal parts between those machines often requires special safety measures and standoff distances. Adding such traditional equipment and measures to a machining workflow or a 3D printing workflow thus entails additional cost and disruption to the existing workflow. [0010] All of these difficulties are compounded when multiple steps must be performed requiring alternation between two different types of operations, or when more than two types of operations are to be performed, as when a part having elements that result from additive operations also requires forging and machining. In many scenarios these difficulties make it prohibitively expensive or even impossible to manufacture a desired part as a single component. [0011] The need to perform multiple operations of fundamentally different types on a single workpiece is an important factor that raises costs, risks, and complexity and can prevent the manufacture of a desired product.” Para 0029, “An operation of a SCOFAST machine may comprise any of the methods and techniques appearing in any part of this specification or in any document incorporated by reference, together with additional methods and techniques known to those having skill in the relevant arts and such additional methods and techniques as may be discovered or invented in the future.” Para 0299, “Explosive forming is a metalworking technique in which an explosive charge is used to produce the forming force.”).
Hence, it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to provide ‘Cella with a method of combining two types of operations on a single device as taught by ‘Field in order to provide to not compound difficulties and cost when combining operations (‘Field, Paras 0009 – 0010).
Conclusion
Examiner encourages Applicant to fill out and submit form PTO-SB-439 to allow internet communications in accordance with 37 CFR 1.33 (MPEP 02.03). Should the need arise to perfect applicant-proposed or examiner’s amendments, authorization for e-mail correspondence would have already been authorized and would save time.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/LAWRENCE AVERICK/ Primary Examiner, Art Unit 3799
09/03/2026
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