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 .
Claims 1-20 are pending, of which claims 1, 9, and 17 are independent claims.
Priority
Applicant’s claim for the priority benefit of US provisional application No. 63/596,486 filed on November 6, 2023 is acknowledged.
Information Disclosure Statement
The references cited in the information disclosure statements (IDS) submitted on October 22, 2024 and April 24, 2025 have been considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Independent claim 1 recites, “... process first data from a set of first builds to learn behavior from the set of first builds; classify each build of the set of first builds as a standard build or a non-standard build; model the learned behavior to form a standard reference behavior and a non-standard reference behavior, the standard reference behavior including first features and the non-standard reference behavior including second features... process the second data in comparison to the standard reference behavior and the non-standard reference behavior; classify the second build as a standard build or a non-standard build…”
Under its broadest reasonable interpretation, if a claim limitation covers performance that can be executed in the human mind, but for the recitation of generic electronic devices or generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Under their broadest reasonable interpretation and based on the description provided in the Specification, such as paragraphs [0139] and [0151]-[0157], for instance, the processing and classifying limitations are mental processes that can be performed through observation, evaluation and judgement.
Under their broadest reasonable interpretation and based on the description provided in the published Specification, such as paragraphs [0122], [0129], [0139], [0141], [0142], and [0155], for instance, the limitations of the modeling to form a standard reference behavior and non-standard reference behavior, as claimed, is a process that entails purely mathematical relationships, mathematical formulas or equations, and mathematical calculations. In the alternative, the modeling is a mental process that can be performed through observation, evaluation and judgement.
Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements of, “learner circuitry to: … output the standard reference behavior and the non-standard reference behavior to classify additional builds; and evaluator circuitry to: ingest second data for a second build; … and when the second build is classified as a non-standard build, output a corrective action to address at least one second feature of the non-standard build behavior associated with the second build”.
The features including “learner circuitry” and “evaluator circuitry”, as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used as recited in independent claim 1, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic or computer components. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not indicative of integration into a practical application.
The ingest limitation is an insignificant extra-solution activity under MPEP 2106.05(g), without imposing meaningful limits. The limitation amounts to necessary data gathering. (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. In accord with MPEP 2105(g), “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
The outputting of the standard reference behavior and the non-standard reference behavior and the outputting of a corrective action to address at least one second feature of the non-standard build behavior associated with the second build are post-solution activities that do not integrate the abstract idea into a practical application and it is an insignificant extra-solution activity to the judicial exception MPEP 2106.05(g).
In view of the foregoing, the additional limitations, individually or combined, are not sufficient to demonstrate integration of a judicial exception into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The features including “learner circuitry” and “evaluator circuitry”, as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used for the functions recited in claim 1, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. See MPEP 2106.05(f) Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more. See Elec. Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (“Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information.”)
The ingest limitation represents a function that is recognized as well-understood, routine, and conventional. For instance, US Patent Publication No. 2021/0178697 A1 to Nilakantan et al. describes in paragraph [0058] that “New part transactions 412 are broadcast to the machine-learning model 418. The proof-of-work historical record of the user experience database 410 compiles new part transaction data into a block. The proof-of-work historical record becomes more resilient, richer with knowledge, and iteratively stronger with each block addition and feeds the machine-learning model 418, which is iteratively re-trained either with each block addition or periodically. The machine-learning model 418 provides real-time feedback on the next part transaction based on history. The machine-learning model 418 utilizes the notion of similarity and creates a mapping between, on the one hand, voxel data, orientation data, geometry data, technical language data, and fabrication device variables and parameters, and, on the other hand, the part to be 3D printed. These mappings can, for example, be represented within a structured probability distribution. The proof-of-work historical record 410, together with the machine-learning model 418, learns probable model orientation, support structure, toolpaths, and boundary curves based on input. The machine-learning model 418 utilizes neural networks and pattern recognition to make suggestions, predictions, and warnings. The machine-learning model 418 provides outputs 422, 424 that optimize user/object interaction.” US Patent Publication No. 2020/0242496 A1 to Salasoo et al. describes in paragraph [0017] “The method further includes applying a first algorithm to at least the received sensor data to generate a quality score. The first algorithm is trained by receiving a reference derived from physical measurements performed on at least one reference part built using a reference set of build parameters. The method further includes outputting the quality score via the communication interface of the device.” US Patent Publication No. 2021/0283850 A1 to Zeng et la. describes in Paragraph [0031] “The apparatus 200 may further include an input/output interface 214 through which the processor 210 may communicate with an external device(s) (not shown), for instance, to receive and store the information pertaining to the parts to be printed. The input/output interface 214 may include hardware and/or machine-readable instructions to enable the processor 210 to communicate with the external device(s). The input/output interface 214 may enable a wired or wireless connection to the output device(s).”
The output the standard reference behavior and the non-standard reference behavior to classify additional builds and the output of the corrective action to address at least one second feature of the non-standard build behavior associated with the second build represent functions that are recognized as well-understood, routine, and conventional. For instance, Nilakantan describes in Paragraph [0056] “Thus, the user experience database 410 may store information in the above categories collected from a number of prior print production jobs. The machine-learning model 418 can be trained on this user experience database 410 using, for example, deep neural networks 420. An input vector 416 parametrizing any or all of the above information for a new part transaction 412 may be fed to a trained machine-learning model 418 for processing to determine part optimization outputs 422 and/or command initiation outputs 424, in either case modifying, updating, or filling the gaps in input vector 416. Commands generated thereby can be sent to an additive manufacturing device 426 such as a 3D printer to program the printer for a print job. In some cases, such outputs 422, 424 can be provided as suggestions to a human operator for confirmation prior to carrying out steps that would preprocess a model file or program a printer for a print job.” Paragraph [0057] describes “Machine-learning model 108, 214, 306, or 418 can thus record information generally relating to part programming, on the one hand, and specific machine variables and/or specific software variables on the other hand. In this context, part programming can consist of (1) configuring the 3D printer; (2) orienting the STL model (CAD converted to a stereolithography format); (3) “slicing” the STL model by intersecting the STL model with a series of horizontal planes to create slice curves; (4) creating support curves, defining where temporary supports will be built in the part, which supports will ultimately be disposed of; (5) creating toolpath fill for model and support curves; (6) saving a toolpath file; and (7) downloading the toolpath file to the printer for part building... In turn, the machine-learning model 410 can produce the part optimization and command initiation outputs 422, 424 based on training from the above parameters as stored in the user experience database 410 (which can take the form of a blockchain) and the input vector 416. In other words, the machine-learning model can be capable of performing the part programming and the parameter setting in an automated way for a new part job based on the past part job data.” US Patent Publication No. 2021/0400077 A1 to Sites et al. describes in Paragraph [0076] “Deep learning comprises an artificial neural network that is composed of many hidden layers between the inputs and outputs. The system moves from layer to layer to compile enough information to formulate the correct output for a given input. In artificial intelligence models for natural language processing, words can be represented (also described as embedded) as vectors. Vector space models (VSMs) represent or embed words in a continuous vector space where semantically similar words are mapped to nearby points (are embedded nearby each other).” US Patent Publication No. 2021/0362402 A1 to Kothari et al. describes in Paragraph [0035] “he method may include, in response to a determination that the sensor detects a change in a process parameter, taking a remedial action to correct the part drag. The remedial action comprises, with an ablation laser, removing protrusions from the part along an x,y plane of the build region, tagging the part as a confirmed draggable part, abandoning the build of a layer of the part, abandoning the build of the part, initiating a new build of the part, adjusting a layer thickness of a deposited layer, adjusting a printing parameter of an agent deposited on the build region, adjusting torque output by the build material spreader roller, or combinations thereof. Detecting a change in a process parameter associated with the operation of the layer deposition device comprises observing violations of an upper control limit (UCL) and a lower control limit (LCL).”
The Office respectfully notes that Paragraph [0144] of the published Specification of the present application describes that, in certain examples, corrective instructions are logged and transmitted to an operator for adjustment. In other examples, corrective instructions are executed by the additive machine 100, 602, the additive machine controller 120, etc., to adjust the machine to correct the build. Thus, the Office recommends amending the claim to clarify that how the corrective action is used by the machine in response to the second build being classified as a non-standard build.
Therefore, the additional claimed features, individually or combined, do not amount to significantly more and the claim is not patent eligible.
Regarding claim 2, this claim recites “memory circuitry to store the standard reference behavior and the non-standard reference behavior”. This limitation does not integrate the abstract idea into a practical application and is an insignificant extra-solution activity to the judicial exceptions, which are merely nominal or tangential addition to the claim. See MPEP 2106.05(g). Thus, this limitation is not sufficient to demonstrate integration of a judicial exception into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 2 recitation is an example of an activity that the courts have found to be well-understood, routine, and conventional activities when claimed in a generic manner. See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (storing and retrieving information in memory).
Therefore, the additional claimed features do not amount to significantly more and the claim is not patent eligible.
Regarding claims 3-8, these claims are also directed to further defining the abstract idea as recited in independent claim 1. There are no additional limitations in the claim to apply, rely on, or use the judicial exception in a manner that would impose a meaningful limitation on the judicial exception. The claims are not more than a drafting effort designed to monopolize the exception. The claims also do not include additional elements that integrate the judicial exception into a practical application and that would be sufficient to amount to significantly more than the judicial exception. Thus, claims 3-8 are not patent eligible.
The functions of independent claim 9 are implemented by similar functions as those of the apparatus of independent 1 with substantially the same limitations. Therefore, the rejection applied to independent claim 1 above also applies to independent claim 9. Independent claim 9 is not deemed patent eligible.
The functions of claims 10-16 are implemented by similar functions as those of the apparatus of claims 2-8, respectively, with substantially the same limitations. Therefore, the rejections applied to claims 2-10 above also apply to claims 10-16, respectively. Claims 10-16 are not deemed patent eligible.
The functions of independent claim 17 are implemented by similar functions as those of the apparatus of independent claim 1 with substantially the same limitations. Therefore, the rejection applied to independent claim 1 above also applies to independent claim 17. Independent claim 17 is not deemed patent eligible.
The functions of claims 18-20 are implemented by similar functions as those of the apparatus of claims 3, 4, and 8, respectively, with substantially the same limitations. Therefore, the rejections applied to claims 3, 4, and 8 above also apply to claims 18-20, respectively. Claims 18-20 are not deemed patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15, 17, and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nilakantan (US Patent Publication No. 2021/0178697 A1) (“Nilakantan”).
Regarding independent claim 1, Nilakantan teaches:
An apparatus comprising: Nilakantan: Abstract (“Systems and methods for machine-learning-based additive manufacturing use a machine-learning model to process an input vector describing a new part transaction, thereby providing part optimization outputs and command initiation outputs to configure additive manufacturing of a new part.”)
learner circuitry to: Nilakantan: Paragraph [0028] (“In the illustrated implementation, an additive manufacturing knowledge base 202 provides additive manufacturing data regarding one or more past print jobs or print runs to a data analysis component implemented as a general-purpose processor 204 operatively connected to a non-transitory computer-readable medium 206 storing machine-executable instructions.”) [The general-purpose processor reads on “learner circuitry”.]
process first data from a set of first builds to learn behavior from the set of first builds; Nilakantan: Paragraph [0028] (“In the illustrated implementation, an additive manufacturing knowledge base 202 provides additive manufacturing data regarding one or more past print jobs or print runs to a data analysis component implemented as a general-purpose processor 204 operatively connected to a non-transitory computer-readable medium 206 storing machine-executable instructions. In the illustrated system 200, the additive manufacturing data can include not only part programming information but also specific machine or software variables.”) Nilakantan: Paragraph [0040] (“FIG. 3 illustrates a basic flow diagram of an example training of a machine-learning model for additive manufacturing. For each proof-of-work 302, information is populated in an additive manufacturing knowledge base, e.g. a distributed ledger, as parametric block data 304. A proof-of-work is an additively manufactured part that has been processed, completed, and successfully used or delivered to a customer. Any given completion constitutes a part transaction, which can be recorded in the knowledge base. A proof-of-work chain is a blockchain ledger comprising a plurality of part transactions for parts produced by an enterprise or organization, each part transaction being a timestamped block in the chain. The proof-of-work chain thereby collects information on how a part was “transacted”—how it was created, how it was built, what was the resource burden/expense involved in making the part, and what type of difficulties were encountered in successfully making the part. In practice, the proof-of-work chain can constantly collect part transaction information, just as a blockchain used in a cryptocurrency constantly collects currency ownership transfer transactions.”)
classify each build of the set of first builds as a standard build or a non-standard build; Nilakantan: Paragraph [0052] (“Information stored under the categories of “lessons learned, successes & failures” are mistakes made by operators in past production runs which led to print failures or, conversely, felicitous choices that resulted in an exceptional job completion (an “epic”). Such information can include, for example, file conversion steps or settings that resulted in inaccurate file conversion, printer settings that resulted in a print failure including inadequate or overkill choice of resolution, incorrect orientation(s), inadequate support structure design(s), poor choice of material(s), etc. Other “lessons learned” can include an orientation found to be best, a Z height with respect to base area, contour, loft, curvature, material behavior information, post-processing steps, and ultrasonic tank effects.”)
model the learned behavior to form a standard reference behavior and a non-standard reference behavior, the standard reference behavior including first features and the non-standard reference behavior including second features; and Nilakantan: Paragraphs [0040] and [0052] [As described above.] Nilakantan: Paragraph [0056] (“Thus, the user experience database 410 may store information in the above categories collected from a number of prior print production jobs. The machine-learning model 418 can be trained on this user experience database 410 using, for example, deep neural networks 420. An input vector 416 parametrizing any or all of the above information for a new part transaction 412 may be fed to a trained machine-learning model 418 for processing to determine part optimization outputs 422 and/or command initiation outputs 424, in either case modifying, updating, or filling the gaps in input vector 416. Commands generated thereby can be sent to an additive manufacturing device 426 such as a 3D printer to program the printer for a print job. In some cases, such outputs 422, 424 can be provided as suggestions to a human operator for confirmation prior to carrying out steps that would preprocess a model file or program a printer for a print job.”) Nilakantan: Paragraph [0067] (“FIG. 12 illustrates a method 1200 for machine-learning based additive manufacturing. A user experience database can be populated 1202 with additive manufacturing user experience data from a plurality of users. For example, the user experience database can consist of a plurality of entries, each entry in the user experience database including at least data defining requirements for an additively manufactured part, specifications describing an additive manufacturing fabrication device, a selection of a raw material type fed to the fabrication device for fabrication of the part, a fabrication spatial orientation of the part within the fabrication device, a fabrication slicing resolution of the part, and toolpaths taken by the device in fabricating the part. For example, each entry in the user experience database is a timestamped block in a blockchain. A machine-learning model (ML model) can be trained 1204 based on the user experience database. Then, the machine-learning model is used to process 1206 an input vector describing a new part transaction, to provide at least one part optimization output and at least one command initiation output to configure additive manufacturing of a new part. As examples, the at least one part optimization output can include a fabrication spatial orientation of the new part, a fabrication slicing resolution of the new part, a set of support curves describing support structures additively manufactured along with the new part, and/or a selection of material type from which the new part is additively manufactured; the at least one command initiation output can include, for example, a command sent to a 3D printer to initiate printing along a toolpath generated by the machine-learning model based on the input vector. An additive manufacturing fabrication device, such as a 3D printer, can then be controlled 1208 based on the command initiation output, e.g., to fabricate the new part on the basis of and in accordance with the ML model outputs.”) [The information or felicitous choices in the successes of past production runs reads on “a standard reference behavior… including first features”. The information in the failures of past production runs reads on “a non-standard reference behavior… including second features”.]
output the standard reference behavior and the non-standard reference behavior to classify additional builds; and Nilakantan: Paragraphs [0040], [0052], and [0056] [As described above.] Nilakantan: Paragraph [0057] (“Machine-learning model 108, 214, 306, or 418 can thus record information generally relating to part programming, on the one hand, and specific machine variables and/or specific software variables on the other hand. In this context, part programming can consist of (1) configuring the 3D printer; (2) orienting the STL model (CAD converted to a stereolithography format); (3) “slicing” the STL model by intersecting the STL model with a series of horizontal planes to create slice curves; (4) creating support curves, defining where temporary supports will be built in the part, which supports will ultimately be disposed of; (5) creating toolpath fill for model and support curves; (6) saving a toolpath file; and (7) downloading the toolpath file to the printer for part building... In turn, the machine-learning model 410 can produce the part optimization and command initiation outputs 422, 424 based on training from the above parameters as stored in the user experience database 410 (which can take the form of a blockchain) and the input vector 416. In other words, the machine-learning model can be capable of performing the part programming and the parameter setting in an automated way for a new part job based on the past part job data.”)
evaluator circuitry to: Nilakantan: Paragraph [0028] [As described above.] [The general-purpose processor reads on “evaluator circuitry”.]
ingest second data for a second build; Nilakantan: Paragraph [0027] (“A machine learning model 108 determines at least one prefabrication model adjustment parameter and/or print setting parameter for a new print job and/or at least one print job outcome estimate from the metric. As examples, not meant to be an exhaustive list, the prefabrication model adjustment parameter can represent a print orientation of a component or assembly to be printed in a print job, a number of layers to be used or a layer thickness or thicknesses, a geometry parameter descriptive of how a three-dimensional model is divided for printing during a prefabrication model processing phase, or information controlling the placement or geometry of support structures added to the model during a prefabrication model processing phase. As examples, not meant to be an exhaustive list, the print setting parameter can represent a material to be used for the job, a particular printer or model of printer to be used, a nozzle aperture thickness or temperature, or any of a number of other parameters that may be accessible for modification on a 3D printer or other additive manufacturing fabrication device. As examples, not meant to be an exhaustive list, the outcome estimate can be an estimate of print time for the job, an estimate of raw material usage for the print job, or an estimate of the likelihood of success of a print job. The prefabrication model adjustment parameter or print setting parameter provide or contribute to part optimization or command initiation, respectively.”) Nilakantan: Paragraph [0058] (“New part transactions 412 are broadcast to the machine-learning model 418. The proof-of-work historical record of the user experience database 410 compiles new part transaction data into a block. The proof-of-work historical record becomes more resilient, richer with knowledge, and iteratively stronger with each block addition and feeds the machine-learning model 418, which is iteratively re-trained either with each block addition or periodically. The machine-learning model 418 provides real-time feedback on the next part transaction based on history. The machine-learning model 418 utilizes the notion of similarity and creates a mapping between, on the one hand, voxel data, orientation data, geometry data, technical language data, and fabrication device variables and parameters, and, on the other hand, the part to be 3D printed. These mappings can, for example, be represented within a structured probability distribution. The proof-of-work historical record 410, together with the machine-learning model 418, learns probable model orientation, support structure, toolpaths, and boundary curves based on input. The machine-learning model 418 utilizes neural networks and pattern recognition to make suggestions, predictions, and warnings. The machine-learning model 418 provides outputs 422, 424 that optimize user/object interaction.”) [The compiling of new part transaction data reads on “ingest second data”. The new print job reads on “a second build”.]
process the second data in comparison to the standard reference behavior and the non-standard reference behavior; Nilakantan: Paragraphs [0056]-[0058] and [0067] [As described above.] [Using the machine-learning model to process an input vector describing a new part transaction, to provide at least one part optimization output and at least one command initiation output to configure additive manufacturing of a new part based on successes and failures reads on “process the second data in comparison to the standard reference behavior and the non-standard reference behavior”.]
classify the second build as a standard build or a non-standard build; and Nilakantan: Paragraphs [0040], [0052], and [0056]-[0058] [As described above.] [The determination of whether optimization outputs and/or command outputs are to be produced based on the input vector reads on “classify the second build as a standard build or a non-standard build”.]
when the second build is classified as a non-standard build, output a corrective action to address at least one second feature of the non-standard build behavior associated with the second build. Nilakantan: Paragraphs [0040], [0052], and [0056]-[0058] [As described above.] [The adjustment parameter(s) or outputs based on real-time feedback of the new print job or part to be 3D printed using trained machine-learning model based on history reads on “output a corrective action to address at least one second feature of the non-standard build behavior associated with the second build”.]
Regarding claim 2, Nilakantan teaches all the claimed features of claim 1, from which claim 2 depends. Nilakantan further teaches:
The apparatus of claim 1, further including memory circuitry to store the standard reference behavior and the non-standard reference behavior. Nilakantan: Paragraph [0028], [0052], and [0056] [As described in claim 1.] [The database reads on “memory circuitry”.]
Regarding claim 3, Nilakantan teaches all the claimed features of claim 1, from which claim 3 depends. Nilakantan further teaches:
The apparatus of claim 1, wherein the standard reference behavior and the non-standard reference behavior form a composite model, the composite model deployed for use by the evaluator circuitry to classify the second build. Nilakantan: Paragraphs [0056]-[0058] and [0067] [As described in claim 1.] [The configuration of the additive manufacturing of the new part with adjustments based on the successes and failures of previous builds reads on “the standard reference behavior and the non-standard reference behavior form a composite model, the composite model deployed for use by the evaluator circuitry to classify the second build”.]
Regarding claim 5, Nilakantan teaches all the claimed features of claim 1, from which claim 5 depends. Nilakantan further teaches:
The apparatus of claim 1, wherein the set of first builds are from one or more additive manufacturing machines. Nilakantan: Paragraphs [0052] and [0056] [As described in claim 1.] Nilakantan: Paragraph [0042] (“FIG. 4 illustrates an example of a machine-learning-based additive manufacturing process 400. Over the course of a plurality of additive manufacturing jobs, a number of users 402, 404, 406, . . . 408 contribute to a user experience database 410 by inputting fabrication parameters and outcome information. User experience database 410 can correspond, for example, to knowledge base 102 of FIG. 1 or knowledge base 202 of FIG. 2. An example listing of the types of information that can be parametrized and stored in the database 410 is provided in box 414. The user experience database 410 can also be updated in an automated fashion from part optimization information 422 or command initiation information 424 provided by the machine-learning-based additive manufacturing process 400, e.g., by instructions used to preprocess a three-dimensional model to prepare it for 3D printing, or commands to be issued to a printer or print controller as part of a machine-learning-based additive manufacturing system.”) [The 3D printer that printed a job based on prior print production jobs reads on “the set of first builds are from one or more additive manufacturing machines”.]
Regarding claim 6, Nilakantan teaches all the claimed features of claim 1, from which claim 6 depends. Nilakantan further teaches:
The apparatus of claim 1, wherein the second build is an ongoing build on an additive manufacturing machine. Nilakantan: Paragraph [0039] (“Regardless of the specific model employed, the prefabrication model adjustment parameter, print setting parameter, and/or outcome estimate generated at the machine learning model 214 can be provided to a user at the display 220 via a user interface 216, stored on the non-transitory computer readable medium 206, for example, in a file or database or database entry associated with the print job, or output to a printer 222 to configure, schedule, or execute a print job or run of multiple print jobs.”)
Regarding claim 7, Nilakantan teaches all the claimed features of claim 1, from which claim 7 depends. Nilakantan further teaches:
The apparatus of claim 1, wherein the first features and the second features include build-level features and layer-level features. Nilakantan: Paragraph [0027] (“As examples, not meant to be an exhaustive list, the prefabrication model adjustment parameter can represent a print orientation of a component or assembly to be printed in a print job, a number of layers to be used or a layer thickness or thicknesses, a geometry parameter descriptive of how a three-dimensional model is divided for printing during a prefabrication model processing phase, or information controlling the placement or geometry of support structures added to the model during a prefabrication model processing phase. As examples, not meant to be an exhaustive list, the print setting parameter can represent a material to be used for the job, a particular printer or model of printer to be used, a nozzle aperture thickness or temperature, or any of a number of other parameters that may be accessible for modification on a 3D printer or other additive manufacturing fabrication device. As examples, not meant to be an exhaustive list, the outcome estimate can be an estimate of print time for the job, an estimate of raw material usage for the print job, or an estimate of the likelihood of success of a print job. The prefabrication model adjustment parameter or print setting parameter provide or contribute to part optimization or command initiation, respectively. The prefabrication model adjustment parameter, print setting parameter, and/or outcome estimate provided by the machine learning model 108 can be stored on a non-transitory computer-readable medium associated with the system 100 and/or provided to a user at a display via a user interface (not shown in FIG. 1, but see FIG. 2).”) [The layer thickness or thicknesses reads on “layer-level features” and the geometry parameter, the raw material, and/or the placement or geometry of support structures read on “build-level features”.]
Regarding independent claim 9, this claim recites similar limitations as corresponding independent claim 1 and is rejected using the same teachings and rationale.
Regarding claim 10, this claim recites similar limitations as corresponding claim 2 and is rejected using the same teachings and rationale.
Regarding claim 11, this claim recites similar limitations as corresponding claim 3 and is rejected using the same teachings and rationale.
Regarding claim 13, this claim recites similar limitations as corresponding claim 5 and is rejected using the same teachings and rationale.
Regarding claim 14, this claim recites similar limitations as corresponding claim 6 and is rejected using the same teachings and rationale.
Regarding claim 15, this claim recites similar limitations as corresponding claim 7 and is rejected using the same teachings and rationale.
Regarding independent claim 17, this claim recites similar limitations as corresponding independent claim 1 and is rejected using the same teachings and rationale.
Regarding claim 18, this claim recites similar limitations as corresponding claim 3 and is rejected using the same teachings and rationale.
It is noted that any citations to specific paragraphs or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
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 4, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Nilakantan in view of Sites et al. (US Patent Publication No. 2021/0400077 A1) [Submitted in IDS filed on April 24, 2025] (“Sites”).
Regarding claim 4, Nilakantan teaches all the claimed features of claim 1, from which claim 4 depends. Nilakantan does not expressly teach the features of claim 4. However, Sites describes a security awareness server or an artificial intelligence machine learning system that establishes a job score for a user based on the user's job title. Sites teaches:
The apparatus of claim 1, wherein the learner circuitry is to build and process at least one univariate model and at least one multivariate model to form the standard reference behavior and the non-standard reference behavior. Sites: Paragraph [0072] (“The structure of AI models may be bidirectional or unidirectional. AI models may use, for example, regression predictive modeling or classification predictive modeling. The term ‘feature’ is used to refer to data that are fed into an AI model as inputs into the model or for training. Features may be fed into one or more AI models separately or in combination. AI models may be optimized for certain kinds of data inputs, for example a univariate model will be fed one feature at a time, whereas a multivariate model will be fed multiple features at one. One or more models or combinations of models may be used.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Nilakantan and Sites before them, to build and process at least one univariate model and at least one multivariate model to form the standard reference behavior and the non-standard reference behavior because the references are in the same field of endeavor as the claimed invention.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because similar to Nilakantan, Sites implements various machine learning models, in addition, to including those that are optimized for certain kinds of data inputs including univariate and multivariate models. Sites Paragraph [0072]
Regarding claim 12, this claim recites similar limitations as corresponding claim 4 and is rejected using the same teachings and rationale.
Regarding claim 19, this claim recites similar limitations as corresponding claim 4 and is rejected using the same teachings and rationale.
Claims 8, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nilakantan in view of Salasoo et al. (US Patent Publication No. 2020/0242496 A1) (“Salasoo”).
Regarding claim 8, Nilakantan teaches all the claimed features of claim 1, from which claim 8 depends. Nilakantan does not expressly teach the features of claim 8. However, Salasoo describes determining a quality score for a part manufactured by an additive manufacturing machine. Salasoo teaches:
The apparatus of claim 1, wherein the learner circuitry is to compute a non-compliance severity metric to classify a build as a standard build or a non-standard build, the non-compliance severity metric based on scores associated with the first features and the second features, the non-compliance severity metric enabling identification of one or more of the second features contributing to the classification as a non-standard build. Salasoo: Paragraph [0035] (“FIG. 2 is a block diagram of a system 100 for correcting build parameters for producing a part, in an additive manufacturing process, using a quality score for the part derived from build parameters and sensor data, as described above. A set of nominal build parameters is input to an additive manufacturing machine (AMM) 110, such as, for example, a direct metal laser melting (DMLM) printer (i.e., machine). The nominal build file 120 may be in the form of a common layer interface (CLI) file, which may include a set of scan parameters, i.e., build parameters. As the DMLM printer 110 performs the build, based on the nominal build file 120, sensor data 130 is collected from various sensors associated with the printer. The sensor data 130 is input to a quality score generator 140, which calculates a score indicative of the quality of the built part without physical testing of the part.”) Salasoo: Paragraph
[0037] (“The sensor data 130, the quality score calculated by the quality score generator 140, and the output of the thermal model are input to an iterative learning control (ILC) 160. As described in further detail below, the ILC 160 uses machine learning algorithms to produce an updated build file 170 based on these inputs. The ILC 160 thus creates a mapping between the scan parameters of a build file and the resulting quality score of a part produced using the build file, which allows a build file to be optimized using an iterative machine learning process. This process results in a built part having higher quality without performing multiple rounds of experimental testing, as in conventional approaches.”) Salasoo: Paragraph [0039] (“In disclosed embodiments, given various inputs, e.g., sensor inputs and process parameters, a model can predict quality score which, in turn, can be used to determine whether the built part will be acceptable. If predicted part quality is not acceptable, then various actions can be taken to improve the manufacturing processes. In other words, given the model, given the response map with sensors, given the build data and the scan file (e.g., CLI build file), the quality score generator can be used to predict whether a build was acceptable or not. If the quality score indicates that the build will not be acceptable then the ILC tries to understand what is not acceptable (e.g., via machine learning algorithms) and make corrections to the scan file of the part being built to make future builds more acceptable.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Nilakantan and Salasoo before them, to compute a non-compliance severity metric to classify a build as a standard build or a non-standard build, the non-compliance severity metric based on scores associated with the first features and the second features, the non-compliance severity metric enabling identification of one or more of the second features contributing to the classification as a non-standard build because the references are in the same field of endeavor as the claimed invention.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow to make corrections to the scan file of the part being built to make future builds more acceptable. Salasoo Paragraph [0039]
Regarding claim 16, this claim recites similar limitations as corresponding claim 8 and is rejected using the same teachings and rationale.
Regarding claim 20, this claim recites similar limitations as corresponding claim 8 and is rejected using the same teachings and rationale.
It is noted that any citations to specific paragraphs or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Publication No. 2021/0283850 A1 to Zeng et al. describes a cumulative difference score for a build volume 700 may be determined. For instance, a difference between the z height 710 of the bounding box 704 and the z height 708 of the center of mass 706 may be determined for each part in the build volume 700. The cumulative difference score may then be computed by summating the z-height differences for each of the parts. A maximum z-height difference may also be determined as the z-height difference with the highest value. A minimum z-height difference may further be determined as the z-height difference with the lowest value. Other methods for determining the cumulative difference score may be used.
US Patent Publication No. 2016/0361878 A1 to Gain et al. describes a build density of the part 100 extracted by a controller 202 from the data file 204. This data file 204 may include any Computer Aided Design (CAD) file such as an AutoCAD, NX, or CREO file, or .stl fixing software such as NETFABB and MAGICS which contains the part 100 drawn by the user. The controller 202 may assign the weightage and/or the intermediate score to the build density. In one embodiment, the controller 202 compares the build density of the part 100 with a predetermined threshold or range and then assigns the intermediate score based on the comparison. For example, based on the build density that is received by the controller 202, the controller 202 assigns the intermediate score.
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/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117
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