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 1-19 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on September 13th, 2024, and October 19th, 2024, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The use of the terms KUBERNETES, OPENSTACK, NVIDIA, GITHUB, KERAS, GPT, and KUBESEC, which are trade names or marks used in commerce, have been noted in this application. The terms should be accompanied by the generic terminology; furthermore, the terms should be capitalized wherever they appear or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) is permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
The disclosure is objected to because of the following informalities:
Page 2 Lines 1 and 9: The phrase "extracting a dataset of one of more features" should read "one or more features."
Page 11 Line 21: The sentence "After using t-SNE to project he clustered vectors in a 2D space" should read "After using t-SNE to project the clustered vectors in a 2D space."
Page 1 Line 26: "such that they can benefit of established best practices" should read "benefit from established best practices."
Page 8 Line 23: "Maatenand Hinton, 2008" should read "Maaten and Hinton, 2008."
Appropriate correction is required.
Claim Objections
Claims 1-19 are objected to because of the following informalities:
Claims 1, 7, 10, 16, and 19 recite "a dataset of one of more features" and "the dataset of one of more features," which should read "one or more features."
Claims 5 and 14 recite “wherein at least a third fourth feature,” which should read “wherein at least a fourth feature”
Claim 10 recites "An method comprising," which should read "A method comprising."
Claim 19 recites "A non-transitory computer-readable storage storing instructions," which should read "A non-transitory computer-readable storage medium storing instructions."
Appropriate correction is required.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
Claim 1
Step 1: The claim recites an apparatus; therefore, it is directed to the statutory category of a machine.
Step2A Prong 1: The claim recites, inter alia:
extracting a dataset of one of more features from a set of manifest files…: This limitation recites a mental process because it involves observing the contents of a set of files and selecting out the features from the files, which can be performed in the human mind or by pen and paper.
clustering the dataset of one of more features extracted from the set of manifest files: This limitation recites a mental process because it involves grouping extracted features from the files into clusters, which can be performed mentally or by pen and paper.
labelling the clustered dataset of the one or more features extracted from the manifest files: This limitation recites a mental process because it involves the observation/evaluation/judgement of assigning a label to each group of data, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
[a]n apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause at least: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
…from a set of manifest files for deploying at least one resource in a cloud network environment: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
and using the labeled dataset to train a large language machine learning model: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
[a]n apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause at least: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
…from a set of manifest files for deploying at least one resource in a cloud network environment: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
and using the labeled dataset to train a large language machine learning model: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
The elements in combination as an ordered whole still do not amount to significantly more than the judicial exception (i.e., the mental processes for feature extraction, clustering of data, and labelling of data). The claim merely describes a process of applying known data analysis and organizational techniques (using the labeled clustered data to train a model) to analyze and organize data. The recitation of at least one processor, at least one memory storing instructions, a large language machine learning model, and manifest files for deploying at least one resource in a cloud network environment merely indicates a technological environment in which the abstract ideas are applied, without improving the functioning of a computer or the machine learning model itself.
Therefore, the claim as a whole remains focused on the abstract idea and fails Step 2B of the eligibility analysis.
Claim 2
Step 1: A machine, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on
claim 1, which recites an abstract idea.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
at least a first feature of the one or more features classifies a quality of at least one manifest file in the set of manifest files: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
at least a first feature of the one or more features classifies a quality of at least one manifest file in the set of manifest files: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 3
Step 1: A machine, as above.
Step2A Prong 1: The claim recites, inter alia:
at least a second feature of the one or more features indicates which features extracted from at least one manifest file in the set of manifest files contribute to an outcome of a classification…: This limitation recites a mental process because it involves the observation/ evaluation/judgement of evaluating which of the extracted features contribute to a classification outcome, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
…performed by the large language machine learning model: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
…performed by the large language machine learning model: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 4
Step 1: A machine, as above.
Step2A Prong 1: The claim recites, inter alia:
at least a third feature of the one or more features extracted from at least one manifest file in the set of manifest files indicates a design problem and/or a recommended suitable fix for the design problem associated with the at least one manifest file: This limitation recites a mental process because it involves evaluating a feature of a file to identify a design problem and/or forming an opinion or recommendation as to a suitable fix for that problem, which can be performed in the human mind or by pen and paper.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 5
Step 1: A machine, as above.
Step2A Prong 1: The claim recites, inter alia:
at least a third fourth feature of the one or more features extracted from at least one manifest file indicates one or more relations among components…: This limitation recites a mental process because it involves evaluating the relationships among components, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
…of the at least one resource deployed in the cloud network environment: The limitation merely describes the type of data being processed and thus amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
…of the at least one resource deployed in the cloud network environment: The limitation merely describes the type of data being processed and thus amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 6
Step 1: A machine, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on
claim 1, which recites an abstract idea.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
at least a first manifest file from the set of manifest files is used to deploy the at least one resource comprising an application or a service in the cloud network environment: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
at least a first manifest file from the set of manifest files is used to deploy the at least one resource comprising an application or a service in the cloud network environment: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 7
Step 1: A machine, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on
claim 1, which recites an abstract idea.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
the clustering of the dataset of one of more features uses unsupervised clustering: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
the clustering of the dataset of one of more features uses unsupervised clustering: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 8
Step 1: A machine, as above.
Step2A Prong 1: The claim recites, inter alia:
the labelling further comprises… an initial set of one or more annotations to label at least a portion of the clustered dataset: This limitation recites a mental process because it involves applying annotations to label a portion of the clustered data, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
receiving an initial set of one or more annotations…: Mere data gathering recited at a high level of generality, and thus is an insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
receiving an initial set of one or more annotations…: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 9
Step 1: A machine, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on
claim 1, which recites an abstract idea.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
the labeled dataset uses at least in part supervised learning to train the large language machine learning model: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
the labeled dataset uses at least in part supervised learning to train the large language machine learning model: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 10
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a process.
Step2A Prong 1: The claim recites, inter alia:
extracting a dataset of one of more features from a set of manifest files…: This limitation recites a mental process because it involves observing the contents of a set of files and selecting out the features from the files, which can be performed in the human mind or by pen and paper.
clustering the dataset of one of more features extracted from the set of manifest files: This limitation recites a mental process because it involves grouping extracted features from the files into clusters, which can be performed mentally or by pen and paper.
labelling the clustered dataset of the one or more features extracted from the manifest files: This limitation recites a mental process because it involves the observation/evaluation/judgement of assigning a label to each group of data, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
…from a set of manifest files for deploying at least one resource in a cloud network environment: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
and using the labeled dataset to train a large language machine learning model: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
…from a set of manifest files for deploying at least one resource in a cloud network environment: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
and using the labeled dataset to train a large language machine learning model: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 11 recites similar limitations to claim 2. Therefore, claim 11 is rejected using the same rationale as claim 2.
Claim 12 recites similar limitations to claim 3. Therefore, claim 12 is rejected using the same rationale as claim 3.
Claim 13 recites similar limitations to claim 4. Therefore, claim 13 is rejected using the same rationale as claim 4.
Claim 14 recites similar limitations to claim 5. Therefore, claim 14 is rejected using the same rationale as claim 5.
Claim 15 recites similar limitations to claim 6. Therefore, claim 15 is rejected using the same rationale as claim 6.
Claim 16 recites similar limitations to claim 7. Therefore, claim 16 is rejected using the same rationale as claim 7.
Claim 17 recites similar limitations to claim 8. Therefore, claim 17 is rejected using the same rationale as claim 8.
Claim 18 recites similar limitations to claim 9. Therefore, claim 18 is rejected using the same rationale as claim 9.
Claim 19
Step 1: The claim recites a non-transitory computer-readable storage; therefore, it is directed to the statutory category of an article of manufacture.
Step2A Prong 1: The claim recites, inter alia:
extracting a dataset of one of more features from a set of manifest files…: This limitation recites a mental process because it involves observing the contents of a set of files and selecting out the features from the files, which can be performed in the human mind or by pen and paper.
clustering the dataset of one of more features extracted from the set of manifest files: This limitation recites a mental process because it involves grouping extracted features from the files into clusters, which can be performed mentally or by pen and paper.
labelling the clustered dataset of the one or more features extracted from the manifest files: This limitation recites a mental process because it involves the observation/evaluation/judgement of assigning a label to each group of data, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
[a] non-transitory computer-readable storage storing instructions that, when executed by at least one processor, cause at least: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
…from a set of manifest files for deploying at least one resource in a cloud network environment: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
and using the labeled dataset to train a large language machine learning model: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
[a] non-transitory computer-readable storage storing instructions that, when executed by at least one processor, cause at least: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
…from a set of manifest files for deploying at least one resource in a cloud network environment: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
and using the labeled dataset to train a large language machine learning model: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
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 (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 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 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 1-7, 9-16, and 18-19 is rejected under 35 U.S.C. 103 as being unpatentable over Blaise ("Stay at the Helm: secure Kubernetes deployments via graph generation and attack reconstruction", 2022) in view of Ben-Arie (US 11281998 B2) and in further view of Thapa ("Transformer-Based Language Models for Software Vulnerability Detection", 2022).
Regarding claim 1,
Blaise teaches extracting a dataset of one of more features from a set of manifest files for deploying at least one resource in a cloud network environment (Page 1 Introduction, “Deployments can be performed either manually via a series of command-line inputs or, in automation-heavy environments, via deployment files such as Helm Charts. Helm is the package manager of choice for K8s, and Charts consist of deployment-ready collections of YAML manifest files that describe the model for deploying the microservice application containers to K8s [9]… A basic topological graph is extracted by parsing, analysing, and correlating various components and policies extracted from the Helm Chart. Step 2. Extracted data is aggregated into six main features…”, Page 3 Section IV, “Enrich the topological graph through six main features in line with the CIS K8s best practices, namely: 1) vulnerabilities of containers, 2) pods accessibility, 3) the security policies in places among pods, 4) access control (RBAC) rules, 5) firewall rules, and 6) affinities among deployed components.”
Blaise parses a Helm Chart consisting of YAML manifest files that describe the model for deploying microservice application containers to a Kubernetes cluster, and extracts from those manifest files a dataset of six enumerated features characterizing the deployment. The Helm Chart's YAML manifest files correspond to the claimed set of manifest files, the deployment of the microservice application containers to the Kubernetes cluster corresponds to the deploying of a resource in a cloud network environment, and the six extracted features aggregated from those files correspond to the extracted features from the set of manifest files.)
Blaise does not teach an apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause at least… clustering the dataset of one of more features extracted from the set of manifest files… labelling the clustered dataset of the one or more features extracted from the manifest files and using the labeled dataset to train a large language machine learning model.
Ben-Arie, in the same field of endeavor, teaches an apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause at least (Col. 7 Lines 20-27, “the third-party service provider computer 612 may each include one or more processors, memories, and other appropriate components for executing computer-executable instructions such as program code and/or data. The computer-executable instructions may be stored on one or more computer readable mediums or computer readable devices to implement the various applications, data, and steps described herein.”, Col. 5 Lines 17-20, “executable code stored on a non-transitory, tangible, machine readable media that, when run on one or more hardware processors, may cause a system to perform one or more of the operations 502-516.”)
clustering the dataset of one of more features extracted from the [datasets] (Col. 3 Lines 52-56 of Ben-Arie, "The data set can be input into an unsupervised machine learning—clustering model 204, which is designed to group the data sets into clusters. The clusters 206 may therefore correspond to grouping of the data set 202 based on one or more similarities.", Col. 14 Claim 1, "retrieve clusters and features associated with the clustered data set, wherein the features were inputs to the unsupervised machine learning model"
Ben-Arie inputs a dataset of features into an unsupervised clustering model that groups the data into clusters, where the features were the inputs to that clustering model.)
labelling the clustered dataset of the one or more features extracted from the [datasets] (Col. 3 Lines 46-55 and Lines 60-63 of Ben-Arie, "the labeling enhancement is introduced after the data set 202 has been clustered 206… auto-labeling model 208 is introduced… a model design to run an algorithm capable of adding labels to clusters 206 output from the clustering model 204", Col. 14 Claim 1, "output cluster labels for each of the clusters retrieved, wherein the cluster labels include the features that meet the threshold ratio or the threshold coverage criteria."
Ben-Arie applies an auto-labeling model to the clustered dataset after clustering, outputting cluster labels for each cluster.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Blaise's extraction of a dataset of features from a set of manifest files for deploying a resource in a cloud network environment with Ben-Arie's unsupervised clustering of a feature dataset and labelling of the clustered dataset in order to classify the information into groups such that the data within the group is more similar to each other than to data in other groups and to obtain a more comprehensive view of the features and details covered within a cluster (Col. 3 Lines 52-56 and claim 1 of Ben-Arie).
Blaise in view of Ben-Arie does not teach using the labeled dataset to train a large language machine learning model.
Thapa, in the same field of endeavor, teaches using the labeled dataset to train a large language machine learning model (Page 2 Introduction of Thapa, " Transformer-based language models for software vulnerability detection: By considering (i) transformer-based (large) models of various architectures and sizes, including BERT… this work contributes the following: The frame work details the translation of the source codes to vectorized inputs for the models, description of the models, models’ preparation, and inference… and multi-classification tasks are carried out to evaluate the models with a vulnerability dataset" models, Page 4 Section 2.3.2, " Now they are specialized in the software vulnerability detection task, which is a classification task, through fine-tuning. Usually, this is performed with a small data set than the pre-training data set and carried under supervised learning that requires the knowledge of the data labels… For all the transformer-based models in this paper, we allow the entire model architecture to update during the fine-tuning step…").
Thapa trains large transformer-based language models such as BERT, GPT-2, and GPT-J on labeled source-code data under supervised learning that requires knowledge of the data labels.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Blaise in view of Ben-Arie's labeled clustered dataset with Thapa's training of a large language machine learning model in order to obtain enhanced performance of the language models over other neural models (Introduction of Thapa).
Regarding claim 2,
Blaise teaches at least a first feature of the one or more features classifies a quality of at least one manifest file in the set of manifest files (Page 3 Section IV of Blaise, “Step 3: Score the level of danger associated to the topological graph for each attacker tactic from the K8s threat matrix;”, Page 5 Section C Single-weight scoring system, “This aims to model the overall risk level associated with each component, aggregating all the security assessments into a single value.”, Page 2 Section II, “Recent works focus on K8s manifests and Helm Charts to point out misconfigurations affecting security. [24] examines Helm Charts to assess the quality of declarative chart artefacts, e.g., detecting Charts with no maintainer or with a name collision… However, the methodologies developed in both articles require human intervention, and analyze only the commit logs and not the actual content of K8s manifests.”, Page 8 Section D, “The global danger score gives a good overview of the security of a pod, but heterogeneous reasons may hide behind high danger scores.”
Blaise scores the level of danger associated with each component of the deployment described by the manifest files, modeling the overall risk level associated with each component by aggregating the security assessments into a single value, and further produces a global danger score for the Chart as a whole. Assigning a security-risk score that assesses the quality of the manifest-described components and the Chart corresponds to the first feature that classifies a quality of at least one manifest file in the set of manifest files.)
Regarding claim 3,
Blaise teaches at least a second feature of the one or more features indicates which features extracted from at least one manifest file in the set of manifest files (Page 1 Introduction, “Deployments can be performed either manually via a series of command-line inputs or, in automation-heavy environments, via deployment files such as Helm Charts. Helm is the package manager of choice for K8s, and Charts consist of deployment-ready collections of YAML manifest files that describe the model for deploying the microservice application containers to K8s [9]… A basic topological graph is extracted by parsing, analysing, and correlating various components and policies extracted from the Helm Chart. Step 2. Extracted data is aggregated into six main features…”, Page 3 Section IV, “Enrich the topological graph through six main features in line with the CIS K8s best practices, namely: 1) vulnerabilities of containers, 2) pods accessibility, 3) the security policies in places among pods, 4) access control (RBAC) rules, 5) firewall rules, and 6) affinities among deployed components.”
Blaise parses a Helm Chart consisting of YAML manifest files that describe the model for deploying microservice application containers to a Kubernetes cluster, and extracts from those manifest files a dataset of six enumerated features characterizing the deployment. The Helm Chart's YAML manifest files correspond to the claimed set of manifest files, the deployment of the microservice application containers to the Kubernetes cluster corresponds to the deploying of a resource in a cloud network environment, and the six extracted features aggregated from those files correspond to the extracted features from the set of manifest files)
Blaise in view of Ben-Arie does not teach at least one manifest file in the set of manifest files contribute to an outcome of a classification performed by the large language machine learning model.
Thapa, in the same field of endeavor, teaches at least one … file in the set of … files contribute to an outcome of a classification performed by the large language machine learning model (Page 4 Section 2.3, “In this paper, we consider the pre-trained model approach of transfer learning, where we first pick a pre-trained transformer-based model, then a classification head is attached at the top of the final layer of the model, and the resulting model is fine-tuned through the software vulnerability dataset consisting of C/C++ source codes.”, Page 2 Section 2.1, “Code Gadgets and its extraction. Code gadgets in software vulnerability are first proposed by Li et al. [23]. It is generated as follows: Load all C/C++ files for analysis of relations between classes.”, Page 2 Introduction, “Comparative performances of the models on software vulnerability detection in C/C++ source code databases… we provide the fine-tuning time to present an overall time cost of the models. Secondly, we further extend the performance analysis on multiple C/C++ vulnerabilities corresponding to the library function call, pointer usage, array usage, and arithmetic expression...”, See Figure 2,
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Thapa trains large transformer-based language models such as BERT, GPT-2, and GPT-J on labeled source-code data under supervised learning.)
Regarding claim 4,
Blaise teaches wherein at least a third feature of the one or more features extracted from at least one manifest file in the set of manifest files indicates a design problem and/or a recommended suitable fix for the design problem associated with the at least one manifest file (Page 2 Section C of Blaise, “Additionally, our methodology extracts the most significant risks in the deployment and identifies the riskiest attack paths. In this way, it also acts as a useful tool for the decision-making process, as opposed to other solutions that do not provide any prioritization of the actions to be taken. Furthermore, our methodology embeds natively the K8s threat matrix to model the security of the deployment and detect potential issues with accurate mapping to real-world attacks.”, Page 3 Section IV, “Step 1: Parse the descriptors files and correlate various resources to extract a basic topological graph composed of nodes and edges; Step 2: Enrich the topological graph through six main features in line with the CIS K8s best practices, namely: 1) vulnerabilities of containers, 2) pods accessibility, 3) the security policies in places among pods, 4) access control (RBAC) rules, 5) firewall rules, and 6) affinities among deployed components… Step 4: Identify the riskiest attack paths according to each step of the attack scenarios defined at the previous step.”, Page 5 Section B, “The number of high or critical severity vulnerabilities, i.e., those whose Common Vulnerability Scoring System (CVSS) score is greater than 7, associated with each container is recorded.”
Blaise extracts from the manifest files of the Helm Chart a feature recording the high or critical severity vulnerabilities associated with each container and uses the extracted features to diagnose misconfigurations and to identify the riskiest attack paths in the deployment. Recording the container vulnerabilities and identifying the misconfigurations and riskiest attack paths in the deployment corresponds to the third feature that indicates a design problem associated with the manifest file.)
Regarding claim 5,
Blaise teaches at least a fourth feature of the one or more features extracted from at least one manifest file indicates one or more relations among components of the at least one resource deployed in the cloud network environment (Page 3 Section IV, “Step 1: Parse the descriptors files and correlate various resources to extract a basic topological graph composed of nodes and edges; Step 2: Enrich the topological graph through six main features in line with the CIS K8s best practices, namely: 1) vulnerabilities of containers… 6) affinities among deployed components…”, Page 4 Section A, “Chart to feed a data structure holding the overall deployment system resources and to build topological information about how resources of a deployment are correlated together.”, Page 6 Section A Dataset, “Helm… is the equivalent of a package manager for K8s and employs a packaging format called Charts. A Helm Chart packs a set of (deployment-related) K8s resources into a single YAML configuration file. Thus, a single Chart might be used to deploy … a Memcached pod, or even a full web app stack with multiple server replicas, databases, caches, load balancers...”
Blaise parses the manifest descriptor files and correlates the various resources to extract a topological graph composed of nodes and connecting edges and enriches that graph with a feature representing the affinities among the deployed components. This indicates how the resources of the deployment are correlated together. The extracted feature representing the affinities among the deployed components corresponds to the claimed fourth feature that indicates one or more relations among components of the at least one resource deployed in the cloud network environment.)
Regarding claim 6,
Blaise teaches wherein at least a first manifest file from the set of manifest files is used to deploy the at least one resource comprising an application or a service in the cloud network environment (Page 1 Introduction, “Today, containers are widely employed from hyperscalers to private clouds to deploy applications and services, practically replacing traditional Virtual Machines (VMs) as the de facto DevOps standard due to their inherent scalability and portability advantages [1]… Helm is the package manager of choice for K8s, and Charts consist of deployment-ready collections of YAML manifest files that describe the model for deploying the microservice application containers to K8s [9].”, Page 2 Section A, “The first category of tools retrieves configuration files and settings and executes a set of checks based on CIS bench marks, related both to Docker [11] and K8s [12]. The second category executes custom checks for best practices against provided configuration files and settings.”, Page 2 Section C, “With respect to related works, our methodology can be assimilated to auditing microservice deployment descriptor files through custom compliance checks… our methodology embeds natively the K8s threat matrix to model the security of the deployment and detect potential issues with accurate mapping to real-world attacks.”, Pages 2-3 Section 3, “K8s automates the management of containers and microservices. However, complexity shifts from properly running ap plications, to properly configuring their deployment model via descriptor files.”
Blaise discloses that the YAML manifest files of the Helm Chart describe the model for deploying the microservice application containers to a Kubernetes cluster and that the containers are deployed to private clouds to deploy applications and services.)
Regarding claim 7,
Blaise does not teach the clustering of the dataset of one of more features uses unsupervised clustering.
Ben-Arie, in the same field of endeavor, teaches the clustering of the dataset of one of more features uses unsupervised clustering (Col. 14 Claim 1 of Ben-Arie, "determine that a clustered data set outputted by an unsupervised machine learning model is available for processing", Col. 3 Lines 52-55, "The data set can be input into an unsupervised machine learning—clustering model 204, which is designed to group the data sets into clusters"
Ben-Arie's clustering is performed by an unsupervised machine learning clustering model.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Blaise’s extraction of a dataset of features from a set of manifest files for deploying a resource in a cloud network environment with Ben-Arie’s unsupervised clustering of a feature dataset in order to classify the information into groups such that the data within the group is more similar to each other than to data in other groups (Col. 3 Lines 52-56 of Ben-Arie).
Regarding claim 9,
Blaise in view of Ben-Arie does not teach the labeled dataset uses at least in part supervised learning to train the large language machine learning model.
Thapa, in the same field of endeavor, teaches the labeled dataset uses at least in part supervised learning to train the large language machine learning model (Page 4 Section 2.3.2 of Thapa, “Initially, all of our models, except CodeBERT, are pre-trained on the natural language data. Now they are specialized in the software vulnerability detection task, which is a classification task, through fine-tuning. Usually, this is performed with a small dataset than the pre-training dataset and carried under supervised learning that requires the knowledge of the data labels.”, Page 4 Section 2.3, “we first pick a pre-trained transformer-based model, then a classification head is attached at the top of the final layer of the model, and the resulting model is fine-tuned through the software vulnerability dataset consisting of C/C++ source codes.”
Thapa fine-tunes the large transformer-based language models under supervised learning that requires knowledge of the data labels.)
Regarding claim 10,
Blaise teaches [a] method comprising: extracting a dataset of one of more features from a set of manifest files for deploying at least one resource in a cloud network environment (Page 1 Introduction, “Deployments can be performed either manually via a series of command-line inputs or, in automation-heavy environments, via deployment files such as Helm Charts. Helm is the package manager of choice for K8s, and Charts consist of deployment-ready collections of YAML manifest files that describe the model for deploying the microservice application containers to K8s [9]… A basic topological graph is extracted by parsing, analysing, and correlating various components and policies extracted from the Helm Chart. Step 2. Extracted data is aggregated into six main features…”, Page 3 Section IV, “Enrich the topological graph through six main features in line with the CIS K8s best practices, namely: 1) vulnerabilities of containers, 2) pods accessibility, 3) the security policies in places among pods, 4) access control (RBAC) rules, 5) firewall rules, and 6) affinities among deployed components.”
Blaise parses a Helm Chart consisting of YAML manifest files that describe the model for deploying microservice application containers to a Kubernetes cluster, and extracts from those manifest files a dataset of six enumerated features characterizing the deployment. The Helm Chart's YAML manifest files correspond to the claimed set of manifest files, the deployment of the microservice application containers to the Kubernetes cluster corresponds to the deploying of a resource in a cloud network environment, and the six extracted features aggregated from those files correspond to the extracted features from the set of manifest files.)
Blaise does not teach clustering the dataset of one of more features extracted from the set of manifest files… labelling the clustered dataset of the one or more features extracted from the manifest files and using the labeled dataset to train a large language machine learning model.
Ben-Arie, in the same field of endeavor, teaches clustering the dataset of one of more features extracted from the [datasets] (Col. 3 Lines 52-56 of Ben-Arie, "The data set can be input into an unsupervised machine learning—clustering model 204, which is designed to group the data sets into clusters. The clusters 206 may therefore correspond to grouping of the data set 202 based on one or more similarities.", Col. 14 Claim 1, "retrieve clusters and features associated with the clustered data set, wherein the features were inputs to the unsupervised machine learning model"
Ben-Arie inputs a dataset of features into an unsupervised clustering model that groups the data into clusters, where the features were the inputs to that clustering model.)
labelling the clustered dataset of the one or more features extracted from the [datasets] (Col. 3 Lines 46-55 and Lines 60-63 of Ben-Arie, "the labeling enhancement is introduced after the data set 202 has been clustered 206… auto-labeling model 208 is introduced… a model design to run an algorithm capable of adding labels to clusters 206 output from the clustering model 204", Col. 14 Claim 1, "output cluster labels for each of the clusters retrieved, wherein the cluster labels include the features that meet the threshold ratio or the threshold coverage criteria."
Ben-Arie applies an auto-labeling model to the clustered dataset after clustering, outputting cluster labels for each cluster.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Blaise's extraction of a dataset of features from a set of manifest files for deploying a resource in a cloud network environment with Ben-Arie's unsupervised clustering of a feature dataset and labelling of the clustered dataset in order to classify the information into groups such that the data within the group is more similar to each other than to data in other groups and to obtain a more comprehensive view of the features and details covered within a cluster (Col. 3 Lines 52-56 and claim 1 of Ben-Arie).
Blaise in view of Ben-Arie does not teach using the labeled dataset to train a large language machine learning model.
Thapa, in the same field of endeavor, teaches using the labeled dataset to train a large language machine learning model (Page 2 Introduction of Thapa, " Transformer-based language models for software vulnerability detection: By considering (i) transformer-based (large) models of various architectures and sizes, including BERT… this work contributes the following: The frame work details the translation of the source codes to vectorized inputs for the models, description of the models, models’ preparation, and inference… and multi-classification tasks are carried out to evaluate the models with a vulnerability dataset" models, Page 4 Section 2.3.2, " Now they are specialized in the software vulnerability detection task, which is a classification task, through fine-tuning. Usually, this is performed with a small data set than the pre-training data set and carried under supervised learning that requires the knowledge of the data labels… For all the transformer-based models in this paper, we allow the entire model architecture to update during the fine-tuning step…").
Thapa trains large transformer-based language models such as BERT, GPT-2, and GPT-J on labeled source-code data under supervised learning that requires knowledge of the data labels.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Blaise in view of Ben-Arie's labeled clustered dataset with Thapa's training of a large language machine learning model in order to obtain enhanced performance of the language models over other neural models (Introduction of Thapa).
Claim 11 recites similar limitations to claim 2. Therefore, claim 11 is rejected using the same rationale as claim 2.
Claim 12 recites similar limitations to claim 3. Therefore, claim 12 is rejected using the same rationale as claim 3.
Claim 13 recites similar limitations to claim 4. Therefore, claim 13 is rejected using the same rationale as claim 4.
Claim 14 recites similar limitations to claim 5. Therefore, claim 14 is rejected using the same rationale as claim 5.
Claim 15 recites similar limitations to claim 6. Therefore, claim 15 is rejected using the same rationale as claim 6.
Claim 16 recites similar limitations to claim 7. Therefore, claim 16 is rejected using the same rationale as claim 7.
Claim 18 recites similar limitations to claim 9. Therefore, claim 18 is rejected using the same rationale as claim 9.
Regarding claim 19,
Blaise teaches extracting a dataset of one of more features from a set of manifest files for deploying at least one resource in a cloud network environment (Page 1 Introduction, “Deployments can be performed either manually via a series of command-line inputs or, in automation-heavy environments, via deployment files such as Helm Charts. Helm is the package manager of choice for K8s, and Charts consist of deployment-ready collections of YAML manifest files that describe the model for deploying the microservice application containers to K8s [9]… A basic topological graph is extracted by parsing, analysing, and correlating various components and policies extracted from the Helm Chart. Step 2. Extracted data is aggregated into six main features…”, Page 3 Section IV, “Enrich the topological graph through six main features in line with the CIS K8s best practices, namely: 1) vulnerabilities of containers, 2) pods accessibility, 3) the security policies in places among pods, 4) access control (RBAC) rules, 5) firewall rules, and 6) affinities among deployed components.”
Blaise parses a Helm Chart consisting of YAML manifest files that describe the model for deploying microservice application containers to a Kubernetes cluster, and extracts from those manifest files a dataset of six enumerated features characterizing the deployment. The Helm Chart's YAML manifest files correspond to the claimed set of manifest files, the deployment of the microservice application containers to the Kubernetes cluster corresponds to the deploying of a resource in a cloud network environment, and the six extracted features aggregated from those files correspond to the extracted features from the set of manifest files.)
Blaise does not teach [a] non-transitory computer-readable storage storing instructions that, when executed by at least one processor, cause at least: clustering the dataset of one of more features extracted from the set of manifest files… labelling the clustered dataset of the one or more features extracted from the manifest files and using the labeled dataset to train a large language machine learning model.
Ben-Arie, in the same field of endeavor, teaches [a] non-transitory computer-readable storage storing instructions that, when executed by at least one processor, cause at least (Col. 7 Lines 20-27, “the third-party service provider computer 612 may each include one or more processors, memories, and other appropriate components for executing computer-executable instructions such as program code and/or data. The computer-executable instructions may be stored on one or more computer readable mediums or computer readable devices to implement the various applications, data, and steps described herein.”, Col. 5 Lines 17-20, “executable code stored on a non-transitory, tangible, machine readable media that, when run on one or more hardware processors, may cause a system to perform one or more of the operations 502-516.”)
clustering the dataset of one of more features extracted from the [datasets] (Col. 3 Lines 52-56 of Ben-Arie, "The data set can be input into an unsupervised machine learning—clustering model 204, which is designed to group the data sets into clusters. The clusters 206 may therefore correspond to grouping of the data set 202 based on one or more similarities.", Col. 14 Claim 1, "retrieve clusters and features associated with the clustered data set, wherein the features were inputs to the unsupervised machine learning model"
Ben-Arie inputs a dataset of features into an unsupervised clustering model that groups the data into clusters, where the features were the inputs to that clustering model.)
labelling the clustered dataset of the one or more features extracted from the [datasets] (Col. 3 Lines 46-55 and Lines 60-63 of Ben-Arie, "the labeling enhancement is introduced after the data set 202 has been clustered 206… auto-labeling model 208 is introduced… a model design to run an algorithm capable of adding labels to clusters 206 output from the clustering model 204", Col. 14 Claim 1, "output cluster labels for each of the clusters retrieved, wherein the cluster labels include the features that meet the threshold ratio or the threshold coverage criteria."
Ben-Arie applies an auto-labeling model to the clustered dataset after clustering, outputting cluster labels for each cluster.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Blaise's extraction of a dataset of features from a set of manifest files for deploying a resource in a cloud network environment with Ben-Arie's unsupervised clustering of a feature dataset and labelling of the clustered dataset in order to classify the information into groups such that the data within the group is more similar to each other than to data in other groups and to obtain a more comprehensive view of the features and details covered within a cluster (Col. 3 Lines 52-56 and claim 1 of Ben-Arie).
Blaise in view of Ben-Arie does not teach using the labeled dataset to train a large language machine learning model.
Thapa, in the same field of endeavor, teaches using the labeled dataset to train a large language machine learning model (Page 2 Introduction of Thapa, " Transformer-based language models for software vulnerability detection: By considering (i) transformer-based (large) models of various architectures and sizes, including BERT… this work contributes the following: The frame work details the translation of the source codes to vectorized inputs for the models, description of the models, models’ preparation, and inference… and multi-classification tasks are carried out to evaluate the models with a vulnerability dataset" models, Page 4 Section 2.3.2, " Now they are specialized in the software vulnerability detection task, which is a classification task, through fine-tuning. Usually, this is performed with a small data set than the pre-training data set and carried under supervised learning that requires the knowledge of the data labels… For all the transformer-based models in this paper, we allow the entire model architecture to update during the fine-tuning step…").
Thapa trains large transformer-based language models such as BERT, GPT-2, and GPT-J on labeled source-code data under supervised learning that requires knowledge of the data labels.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Blaise in view of Ben-Arie's labeled clustered dataset with Thapa's training of a large language machine learning model in order to obtain enhanced performance of the language models over other neural models (Introduction of Thapa).
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Blaise ("Stay at the Helm: secure Kubernetes deployments via graph generation and attack reconstruction", 2022), in view of Ben-Arie (US 11281998 B2), in view of Thapa ("Transformer-Based Language Models for Software Vulnerability Detection", 2022) and in further view of Damerau (US 6697998 B1).
Regarding claim 8,
Blaise in view of Ben-Arie and in further view of Thapa does not teach wherein the labelling further comprises receiving an initial set of one or more annotations to label at least a portion of the clustered dataset.
Damerau, in the same field of endeavor, teaches the labelling further comprises receiving an initial set of one or more annotations to label at least a portion of the clustered dataset (Col. 1 Lines 38-45 of Damerau, "A document collection is established as a reference answer set. A label, e.g., the URL of a Web page, is attached to each document… Unlabeled text data are categorized relative to the centroids by a nearest neighbor algorithm", Col. 1 Lines 61-64, "the document collection established as the reference answer set might be manually augmented and/or edited with additional information useful to the categorization process…"
Damerau establishes a reference answer set of documents, each having an attached label that uses an initial labeled reference set to categorize and label the clustered unlabeled data.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Blaise in view of Ben-Arie and in further view of Thapa's labelling of the clustered dataset with Damerau's receiving of an initial set of annotations to label the clustered data in order to build a categorization system from a reference answer set (Col. 1 Lines 38-55 of Damerau).
Claim 17 recites similar limitations to claim 8. Therefore, claim 17 is rejected using the same rationale as claim 8.
Conclusion
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/M.M.H./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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