Detailed Action
This Non-Final Rejection is responsive to the claims filed on 10/28/2022. Claims 1-20 are pending. Claims 1, 11 and 16 are independent claims.
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 .
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 claims are directed towards an abstract idea without significantly more.
Regarding claim 1, Step 1 – The claim recites a statutory system.
Step 2A, prong 1—The following limitations are abstract idea elements, which can be performed mentally—“receive, from a first distributed computing component, first component metadata associated with one or more operating parameters of the first distributed computing component; receive, from a second distributed computing component, second component metadata associated with one or more operating parameters of the second distributed computing component; determine, a centralized governance dataset based upon the first component metadata and the second component metadata; and generate a representation of the centralized governance dataset”.
Step 2A, prong 2—The following limitations are considered additional elements—“ A system for centralized data governance within distributed component computing environments, the system comprising: at least one non-transitory storage device; and at least one processor coupled to the at least one non-transitory storage device, wherein the at least one processor is configured to:, via a machine learning (ML) subsystem, wherein the representation is accessible by one or more of the first distributed computing component or the second distributed computing component.”. However, these elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)).
Step 2B-- These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)). The limitations, taken either alone or in combination, fail to provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 2, Step 1 – The claim recites a statutory system.
Step 2A, prong 1—The following limitations are abstract idea elements, which can be performed mentally—“ wherein, in determining the centralized governance dataset”.
Step 2A, prong 2—The following limitations are considered additional elements—“ the processor is further configured to deploy, via the ML subsystem, a trained ML model on the first component metadata and the second component metadata.”. However, these elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)).
Step 2B-- These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)). The limitations, taken either alone or in combination, fail to provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 3, Step 1 – The claim recites a statutory system.
Step 2A, prong 1—The following limitations are abstract idea elements, which can be performed mentally—“ generate a feature set using the first component metadata and the second component metadata”.
Step 2A, prong 2—The following limitations are considered additional elements—“ wherein, prior to deployment of the trained ML model, the processor is further configured to:; and train, using the ML subsystem, the trained ML model using the feature set to generate the trained ML model.”. However, these elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)).
Step 2B-- These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)). The limitations, taken either alone or in combination, fail to provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 4, Step 1 – The claim recites a statutory system.
Step 2A, prong 1—The following limitations are abstract idea elements, which can be performed mentally—“ wherein, in determining the centralized governance dataset,”.
Step 2A, prong 2—The following limitations are considered additional elements—“ the processor is further configured to: deploy, via the ML subsystem, a first trained ML model on the first component metadata; and deploy, via the ML subsystem, a second trained ML model on the second component metadata.”. However, these elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)).
Step 2B-- These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)). The limitations, taken either alone or in combination, fail to provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 5, Step 1 – The claim recites a statutory system.
Step 2A, prong 1—The following limitations are abstract idea elements, which can be performed mentally—“ generate a first feature set using the first component metadata”.
Step 2A, prong 2—The following limitations are considered additional elements—“ wherein, prior to deployment of the first trained ML model, the processor is further configured to:; and train, using the ML subsystem, the first trained ML model using the first feature set to generate the first trained ML model.”. However, these elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)).
Step 2B-- These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)). The limitations, taken either alone or in combination, fail to provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 6, Step 1 – The claim recites a statutory system.
Step 2A, prong 1—The following limitations are abstract idea elements, which can be performed mentally—“ wherein: the first component metadata defines a first component format; and the second component metadata defines a second component format”.
Step 2A, prong 2—The following limitations are considered additional elements—“ wherein, prior to deployment of the second trained ML model, the processor is further configured to:; and train, using the ML subsystem, the second trained ML model using the second feature set to generate the second trained ML model”. However, these elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)).
Step 2B-- These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)). The limitations, taken either alone or in combination, fail to provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 7, Step 1 – The claim recites a statutory system.
Step 2A, prong 1—The following limitations are abstract idea elements, which can be performed mentally and/or pen and paper—“ wherein: the first component metadata defines a first component format; and the second component metadata defines a second component format”.
Step 2A, prong 2—There’s no additional element in this claim.
Regarding claim 8, Step 1 – The claim recites a statutory system.
Step 2A, prong 1—The following limitations are abstract idea elements, which can be performed mentally—“ wherein, in determining the centralized governance dataset, the processor is further configured to reconcile the first component format and the second component format to a centralized governance format.”.
Step 2A, prong 2—There’s no additional element in this claim.
Regarding claim 9, Step 1 – The claim recites a statutory system.
Step 2A, prong 1—The following limitations are abstract idea elements, which can be performed mentally and/or pen and paper—“modify one or more of the first component format or the second component format responsive to the centralized governance dataset”.
Step 2A, prong 2—The following limitations are considered additional elements—“ wherein the processor is further configured to”. However, this element is recited at such a high level of generality that it fails to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)).
Step 2B-- this element is recited at such a high level of generality that it fails to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)). The limitations, taken either alone or in combination, fail to provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 10, Step 1 – The claim recites a statutory system.
Step 2A, prong 1—The following limitations are abstract idea elements, which can be performed mentally and/or pen and paper—“ modify one of more operations of the first distributed computing component and/or one or more operations the second distributed computing component responsive to the centralized governance dataset.”.
Step 2A, prong 2—The following limitations are considered additional elements—“ wherein the processor is further configured to”. However, this element is recited at such a high level of generality that it fails to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)).
Step 2B-- this element is recited at such a high level of generality that it fails to integrate the abstract idea into a practical application, since it only amounts to “apply it” using generic computer components (MPEP 2106.05(f)). The limitations, taken either alone or in combination, fail to provide an inventive concept. Thus, the claim is not patent eligible.
Claims 11-15 recite a computer program product with similar limitations to claim 1-2, 4, and 8-9, and are likewise deficient.
Claims 16-20 recite a method with similar limitations to claim 1-2, 4, and 8-9, and are likewise deficient.
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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lee US 20200133964 A1.
Regarding claim 1, Lee teaches a networked or distributed computer system (104-105)--A system for centralized data governance within distributed component computing environments, the system comprising: at least one non-transitory storage device; and at least one processor coupled to the at least one non-transitory storage device, wherein the at least one processor is configured to:
In addition, Lee teaches converting documents and extracting document data and metadata from those documents. The documents include various types of documents including clause regulation compliance, contracts, etc (44, 9, fig.2) —receive, from a first distributed computing component, first component metadata associated with one or more operating parameters of the first distributed computing component; receive, from a second distributed computing component, second component metadata associated with one or more operating parameters of the second distributed computing component.
Further, Lee teaches using machine learning framework to change or label information or dataset previously generated and passed to it (94, 96, 44, fig.13, 16)— determine, via a machine learning (ML) subsystem, a centralized governance dataset based upon the first component metadata and the second component metadata;
Furthermore, Lee teaches IDE manager to enable users to edit, or interact with the data (48-49, fig.2-3) — and generate a representation of the centralized governance dataset, wherein the representation is accessible by one or more of the first distributed computing component or the second distributed computing component.
Regarding claim 2, Lee teaches using machine learning components to convert underlying LUME representations into machine-readable vectors version of the data (47, 44, fig.2) —wherein, in determining the centralized governance dataset, the processor is further configured to deploy, via the ML subsystem, a trained ML model on the first component metadata and the second component metadata.
Regarding claim 3, Lee teaches using machine learning components to convert underlying LUME representations into machine-readable vectors version of the data for fast processing (47, fig.2)--prior to deployment of the trained ML model, the processor is further configured to: generate a feature set using the first component metadata and the second component metadata; and train, using the ML subsystem, the trained ML model using the feature set to generate the trained ML model.
Regarding claim 4, Lee teaches using machine learning components to convert underlying LUME representations into machine-readable vectors version of the data (47, 44, fig.2) —wherein, in determining the centralized governance dataset, the processor is further configured to: deploy, via the ML subsystem, a first trained ML model on the first component metadata; and deploy, via the ML subsystem, a second trained ML model on the second component metadata.
Regarding claim 5, Lee teaches using machine learning components to convert underlying LUME representations into machine-readable vectors version of the data for fast processing (47, fig.2)-- prior to deployment of the first trained ML model, the processor is further configured to: generate a first feature set using the first component metadata; and train, using the ML subsystem, the first trained ML model using the first feature set to generate the first trained ML model.
Regarding claim 6, Lee teaches using a deep machine learning component to learn categorical or numeric outputs based on the LUME representations (47, fig.2)-- wherein, prior to deployment of the second trained ML model, the processor is further configured to: generate a second feature set using the second component metadata; and train, using the ML subsystem, the second trained ML model using the second feature set to generate the second trained ML model.
Regarding claim 7, Lee teaches converting input documents into a common LUME format, wherein the documents are input in different format types (43, 44) — wherein: the first component metadata defines a first component format; and the second component metadata defines a second component format.
Regarding claim 8, Lee teaches the extraction of document information or data in the context of policies, rules, etc, answer questions, produce insights, identify patterns and anomalies in the extracted information (38-39) —wherein, in determining the centralized governance dataset, the processor is further configured to reconcile the first component format and the second component format to a centralized governance format.
Regarding claim 9, Lee teaches the extraction of document information or data, and answer questions, produce insights, identify patterns and anomalies in the extracted information (38-39, 41) —wherein the processor is further configured to modify one or more of the first component format or the second component format responsive to the centralized governance dataset.
Regarding claim 10, Lee teaches the extraction of document information or data, and answer questions, produce insights, identify patterns and anomalies in the extracted information (38-39, 41) —wherein the processor is further configured to modify one of more operations of the first distributed computing component and/or one or more operations the second distributed computing component responsive to the centralized governance dataset.
Claims 11-15 are directed towards a computer program product similar to the system found in claims 1-2, 4, 8-9, and are likewise rejected.
Claims 16-20 are directed towards a method similar to the system found in claims 1-2, 4, 8-9, and are likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Owen et al. US 20200111379 A1, teaches a common description language can be used to store data from these various data inputs in a structured and consistent way.
Gwozdz US 20220019624 A1, shows an utility for extracting document data and metadata and storing it in a format used to perform natural language processing.
Cui US 20200111379 A1, discloses metadata creation to understand the relevance, quality, and structure of the information/data contained within information at testing sites.
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/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
