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 presented for examination
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters "364" and "366" have both been used to designate one or more sensors. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “702” has been used to designate both security components and power source. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 1708. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure does not commence on a separate sheet in accordance with 37 CFR 1.52(b)(4) and 1.72(b). A new abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to software per se.
Claims 1-18 are directed to “A system comprising, a machine learning model training framework… a metadata configurer… a deployment manager…” and the Applicant’s specification does not explicitly preclude these claim elements from being interpreted as pure software elements, the broadest reasonable interpretation of these claims encompasses an embodiment that is entirely implemented in a software system. Software per se is not patentable. See MPEP 2106.03. Thus, claims 1-18 are rejected under 35 U.S.C. 101 as it is not directed to eligible subject matter.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to signals per se.
Claim 20 is directed to “computer-readable media” and the Applicant’s specification does not explicitly preclude this claim from being interpreted as signals per se. Per MPEP 2106.03(II) “A claim whose BRI covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter”. Additionally, per MPEP 2106.03(I) “Even when a product has a physical or tangible form, it may not fall within a statutory category. For instance, a transitory signal, while physical and real, does not possess concrete structure that would qualify as a device or part under the definition of a machine, it not a tangible article or commodity under the definition of a manufacture (even though it is man-made and physical in that it exists in the real world and has tangible causes and effects), and is not composed of matter such that it would qualify as a composition of matter”. Thus, claim 20 is rejected under 35 U.S.C. 101 as it is not directed to patent eligible subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hou et al. (US 20210325861 A1, filed 06/25/2021), hereinafter Hou.
Regarding claim 1, Hou teaches: A system comprising: (Hou; [0006], Briefly described, an edge computing system):
a machine learning model training framework that generates trained machine learning
models: (Hou; [0085], Briefly described, outputting or generating candidate model combinations based on a model update process or framework using artificial intelligence or machine learning model data; [0100], Briefly described, machine learning models for the model update process):
a metadata configurer that generates metadata for trained machine learning model
implementation: (Hou; [0050], Briefly described, machine learning models using data generated by factories or other data sources or configurers like industrial data sources and commercial data sources, where environmental variations are part of the data; [0056], Briefly described, metadata being described environmental to be used for updating the model; [0071], Briefly described, metadata indicating environmental conditions in a factory being used as data to be coupled with a machine learning model in a model repository):
a deployment manager that deploys trained machine learning models, metadata or
trained machine learning models and metadata to remote devices according to one or more implementation strategies: (Hou; [0050] Figure 1, Briefly described, deployment of machine learning models through an intelligent deployment circuitry or a deployment manager to a factory environment through edge infrastructure to communicate with the end point remote devices; [0071], Briefly described, metadata deployed with a machine learning model, with implementation strategies for the deployment of the model using a similarity score based on the model performance in the past; [0094], Briefly described, another implementation strategy for model deployment based on a performance score of the model).
Regarding claim 2, Hou teaches the apparatus of claim 1 wherein the remote devices comprise field devices: (Hou; [0049], Briefly described, remote devices to communicate through client endpoints consisting of field devices like mobile devices, computers, autonomous vehicles, business computing equipment, and industrial processing equipment; [0050] Figure 1, Briefly described, field devices in which the machine learning model communicating with the end points is used).
Regarding claim 3, Hou teaches the apparatus of claim 2 wherein the field devices comprise one or more of a wellhead field device, a surface network field device, a hydraulic fracturing field device, a seismic sensing field device, a flare monitoring field device, a drilling fluid field device, a drilling rig field device, a downhole field device, and a drone field device: (Hou, [0049], Briefly described, the devices listed fall under mobile devices, computers, autonomous vehicles, business computing equipment, or industrial processing equipment to be used as field devices).
Regarding claim 4, Hou teaches the apparatus of claim 2 wherein the field devices comprise a gateway field device: (Hou; [0045], Briefly described, coupled with the field device deployments, gateway nodes, edge aggregation nodes, and one or more core data centers).
Regarding claim 5, Hou teaches the apparatus of claim 4 wherein the gateway field device is operatively coupled to at least one other field device: (Hou; [0045], Briefly described, distributed computing devices consisting of an edge computing system field device coupled with other distributed computing devices though a telecommunication service provider).
Regarding claim 6, Hou teaches the apparatus of claim 4 wherein the gateway field device controls implementation of one or more deployed trained machine learning models: (Hou; [0049], Briefly described, a telecommunication service provider gateway controlling the implementation of a trained machine learning model through deploying computation and storage resources like aggregation nodes to provide content at their request; [0038] Figure 1, Briefly described, edge computing using an edge cloud consisting of gateways to provide to field devices like autonomous vehicles or video capture devices, controlling the implementation of the machine learning model deployed on the edge infrastructure communicating with the devices through computation in real-time case and low latency cases; [0050], Briefly described, machine learning models deployed in edge infrastructure to receive control and communication from the gateway device).
Regarding claim 7, Hou teaches the apparatus of claim 1 wherein the deployment manager deploys implementation instructions to at least one of the remote devices: (Hou; [0100], Briefly described, the example intelligent deployment circuitry or deployment manager works to automate the development and/or deployment of machine learning models in the remote devices; [0078], Briefly described, machine readable instructions to be deployed to one or more hardware devices consisting of the machine learning models).
Regarding claim 8, Hou teaches the apparatus of claim 7 wherein the implementation instructions are executable by a gateway field device: (Hou; [0100], Briefly described, the deployed machine learning models with implemented instructions factory environment or other subjected environmental variations; [0078], Briefly described, machine readable instructions, described as one or more executable programs, which may be distributed across multiple hardware devices and executed by the hardware devices like an endpoint client hardware device or an intermediate client hardware device like a gateway field device).
Regarding claim 9, Hou teaches the apparatus of claim 7 wherein the implementation instructions comprise implementation rules and/or logic: (Hou; [0100], Briefly described, the deployed machine learning models with implemented instructions factory environment or other subjected environmental variations; [0155], Briefly described, machine readable instructions utilizing logic gates involving logic).
Regarding claim 10, Hou teaches the apparatus of claim 1 wherein the one or more implementation strategies comprise a variant strategy that deploys variants of a trained machine learning model: (Hou; [0071], Briefly described, variants of model candidates in the form of a plurality of models in an example artificial intelligence model repository as an implementation strategy using a similarity score to narrow down the variants to one that can be deployed).
Regarding claim 11, Hou teaches the apparatus of claim 1 wherein the one or more implementation strategies comprise a multiple model contest strategy that deploys a trained machine learning model as an entry to a multiple model contest: (Hou; [0071], Briefly described, a contest strategy through first deploying a selected model from the example model repository to go through a similarity score contest among other variants in the multiple model contest).
Regarding claim 12, Hou teaches the apparatus of claim 1 wherein the one or more implementation strategies comprise a proof of work strategy: (Hou; [0070], Briefly described, a proof of work strategy where models are considered to have a second or third type or instance used if there are anomalies in the data of the deployed model from ever-changing configurations and environmental settings indicating the model is performing unacceptably; [0071], Briefly described, an implementation strategy of selecting model candidates based off the similarity scores based off model performance, using the metadata of environmental conditions to select the deployed model that works; [0034], Briefly described, feedback being captured from the output of the deployed model to determine the accuracy).
Reading claim 13, Hou teaches the apparatus of claim 12 wherein the proof of work strategy calls for running trained machine learning models in a background mode until a consensus of proof of work metric is met: (Hou; [0070], Briefly described, a proof of work strategy where models are considered to have a second or third instance used if there are anomalies in the data of the deployed model from ever-changing configurations and environmental settings indicating the model is performing unacceptably; [0071], Briefly described, an implementation strategy of selecting model candidates based off the similarity scores based off model performance, using the metadata of environmental conditions to select the deployed model that works, each model having been deployed previously in an artificial intelligence model repository running in the background; [0034], Briefly described, feedback being captured from the output of the deployed model to determine the accuracy).
Regarding claim 14, Hou teaches the apparatus of claim 1 wherein the metadata comprise input binding metadata: (Hou; [0071], Briefly described, forms of input binding metadata like the production line the model was deployed on and the equipment/process configuration of that particular production line).
Regarding claim 15, Hou teaches the apparatus of claim 1 wherein the metadata comprise preprocessing metadata: (Hou; [0071], Briefly described, forms of preprocessing metadata like the model performance and the environmental condition of the factory the model can perform preprocessing operations on).
Regarding claim 16, Hou teaches the apparatus of claim 1 wherein the remote devices comprise remote devices with runtime engines configurable to run the deployed trained machine learning models: (Hou; [0064], Briefly described, machine learning models running on other identical production lines as a part of remote devices consisting of runtime engines).
Regarding claim 17, Hou teaches the apparatus of claim 16 wherein the metadata configure the runtime engines: (Hou; [0071], Briefly described, the metadata of a model configuring the runtime of each machine learning model as previously deployed and keeping track of when each model was running in the repository).
Regarding claim 18, Hou teaches the apparatus of claim 1 wherein the deployment manager comprises a graphical user interface for selection of one or more of the one or more implementation strategies: (Hou; [0108] Figure 9, Briefly described, an example management console like a visual display, monitoring station instrument panel, dashboard, etc. outputting different configurations or implementation strategies of the model to be displayed to the human; [0102] Figure 9, Briefly described, monitoring through a graphical user interface the intelligent deployment circuitry or deployment manager selecting acceptable model candidates using an implementation strategy of performing a screw driving task and calculating the faulty rate of whether a robot using certain model candidates were acceptable or not; [0107], Briefly described, more weight given to the model candidate that matched better with the desired average faulty weight to complete the selected implementation strategy).
Regarding claim 19, it is a method claim that corresponds to apparatus claim 1. Therefore, it is rejected for the same reason as claim 1 above.
Regarding claim 20, it is a computer-readable media claim that corresponds to apparatus claim 1. Therefore, it is rejected for the same reason as claim 1 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Garg et al. (WO 2018111270 A1) teaches in paragraph [0133] a generated metadata based on the differing information to be associated with a machine learning model.
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/ROLANDO PATRICK VIRREIRA/Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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