DETAILED ACTION
The office action is in response to Claims filed 6/12/2023.
Claims 1-19 are pending.
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 Objections
Claims 3, 7, 12, and 16 are objected to because of the following informalities:
Claims 3 and 12 contain a minor typo. The claim currently recites “the neural network of claim 1 wherein, wherein” (for claim 3) and “the neural network of claim 10 wherein, wherein” (for claim 12). For the purposes of compact prosecution, applicant is recommended to amend as follows: “the neural network of claim 1, wherein” for claim 3 and “the neural network of claim 10, wherein” for claim 12.
Claims 3 and 12 contains a minor deficiency. The claim currently recites “wherein the cloud migration is determined to have more than a threshold cloud migration compliance score, rerun the implementation of the cloud migration.” However, claims 2 and 11 defines “a cloud migration compliance score” which is the only structural limitation that can be compared to “a threshold cloud migration compliance score”. Applicant is recommended to amend as follows: The neural network of claims 2 or 11 (respectively based on changed claim dependencies) “wherein the cloud migration compliance score is determined to have more than a threshold cloud migration compliance score, rerun the implementation of the cloud migration.”
Claims 7 and 16 contain a minor deficiency. The claim currently recites “in which the application is written”. For the purposes of consistency and clarity, Applicant is recommended to amend as follows “in which the predetermined application is written”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “a single node selected from the plurality of CICD nodes, a single node selected from the plurality of cloud configuration nodes, a single node selected from the plurality of SSO nodes, and a single node selected from the plurality of application nodes.” in lines 11-14. It is unclear if “a single node” refers to the previously mentioned “single node” (line 11) or to something else entirely. Thus, the claim is rendered vague and indefinite. For the purposes of compact prosecution, Examiner interprets the claim as follows: “a single CICD node selected from the plurality of CICD nodes, a single cloud configuration node selected from the plurality of cloud configuration nodes, a single SSO node selected from the plurality of SSO nodes, and a single application node selected from the plurality of application nodes.”
Claims 2 and 11 recite the limitation "the pipeline" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claims 2 recite the limitation "the migration" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claims 7 recite the limitation "the code" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites the limitation “selected from a plurality of cloud configuration nodes” in lines 9-10. It is unclear if “selected from a plurality of cloud configuration nodes” refers to the aforementioned “plurality of cloud configuration nodes” or to something else entirely. Thus, the claim is rendered vague and indefinite. For the purposes of compact prosecution, Examiner interprets the claim as follows “selected from the plurality of cloud configuration nodes”
Claim 10 further recites the limitation “selecting a single node” in line 11. It is unclear if “a single node” refers to the aforementioned “single node” in line 9 or something else entirely. Therefore, the claim is rendered vague and indefinite. For the purposes of compact prosecution, Examiner interprets the claim as follows: “selecting a single SSO node”
Claim 19 recites the limitation “for initiating cloud migration” in lines 12-13. It is unclear if “cloud migration” refers to aforementioned “cloud migration” or to something else entirely. Therefore, the claim is rendered vague and indefinite. For the purposes of compact prosecution, Examiner interprets the claim as follows: “the cloud migration”.
Claims 2-9 are rejected in light of their chain of dependency upon claim 1.
Claims 11-18 are rejected in light of their chain of dependency upon claim 10.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to “a neural network” and methods “for using a neural network to implement a cloud migration”. However, the recited components of the system appear to lack the necessary physical components (hardware) to constitute a machine or manufacture under 101. The recited components of the system can be construed to cover software under the broadest reasonable interpretation (often referred to as “software per se”). Therefore, the claimed system is ineligible subject matter under 101.
Claims 2-9 depend on claim 1 and does not cure the deficiency of claim 1. Therefore, claims 2-9 are rejected for the same reasons set forth in the rejection of claim 1.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
Claim Interpretation: Under the broadest reasonable interpretation (BRI), the limitations of Claim 1 are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP § 2111.
Step 1: Claim 1 is directed to a neural network. However, the recited components of the neural network can be construed to cover software (often referred to as “software per se”) and thus, the claim does not fall within one of the statutory categories of invention. However, the claim can be amended to fall within one of the statutory categories of invention.
Step 2A, Prong One: Claim 1 recites the limitations:
(1) “wherein a single cloud configuration is formed from a single node selected from the plurality of CICD nodes, a single node selected from the plurality of cloud configuration nodes, a single node selected from the plurality of SSO nodes, and a single node selected from the plurality of application nodes”
These recited steps, under the broadest reasonable interpretation (BRI), cover performance of the steps in the human mind alone or with the aid of pen and paper. That is, other than reciting:
(2) “A neural network for use with implementing a cloud migration in response to a receiving a request to provide the cloud migration for a predetermined application, the neural network comprising: a plurality of continuous integration continuation deployment (CICD) nodes; a plurality of cloud configuration nodes, each of the plurality of cloud configuration nodes couples to each of the CICD nodes; a plurality of single sign on nodes (SSO), each of the SSO nodes coupled to the cloud configuration nodes; and a plurality of application nodes coupled to each of the plurality of SSO nodes;”
Nothing in the claim precludes the steps from practically being performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper. For example, the limitation (1) in the context of the claim encompasses a human selecting a node from a plurality of nodes can be performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper to perform the selection. See MPEP § 2106.04(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind alone or with the aid of pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements:
(1) “a plurality of continuous integration continuation deployment (CICD) nodes;”
(2) “a plurality of cloud configuration nodes, each of the plurality of cloud configuration nodes couples to each of the CICD nodes; a plurality of single sign on nodes (SSO), each of the SSO nodes coupled to the cloud configuration nodes; “
(3) “and a plurality of application nodes coupled to each of the plurality of SSO nodes;”
(4) “A neural network for use with implementing a cloud migration”
The additional elements (1) to (4) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the judicial exception using generic computer components. The plurality of cloud CICD nodes, cloud configuration nodes, and application nodes, and the neural network are used as a tool to perform the selecting steps of the claim 1. See MPEP § 2106.05(f).
Also, the claim recites the additional element:
(5) “in response to receiving a request to provide the cloud migration for a predetermined application”
The additional element (5) is mere data gathering/transmitting/outputting recited at a high level of generality and thus, are insignificant extra-solution activities. See MPEP § 2106.05(g). Furthermore, all uses of the recited judicial exception require such data gathering/transmitting/outputting, and, as such, the additional elements do not impose any meaningful limits on the claim. The additional elements amount to necessary data gathering/transmitting/outputting. See MPEP § 2106.05(g).
Accordingly, even when viewed in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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 when considered both individually and as a combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional elements:
(1) “a plurality of continuous integration continuation deployment (CICD) nodes;”
(2) “a plurality of cloud configuration nodes, each of the plurality of cloud configuration nodes couples to each of the CICD nodes; a plurality of single sign on nodes (SSO), each of the SSO nodes coupled to the cloud configuration nodes; “
(3) “and a plurality of application nodes coupled to each of the plurality of SSO nodes;”
(4) “A neural network for use with implementing a cloud migration”
The additional elements (1) to (4) amount to no more than mere instructions to apply the judicial exception using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. The use of a computer or other machinery in its ordinary capacity does not integrate a judicial exception into a practical application or provide significantly more.
Also, the claim recites the additional element:
(5) “in response to receiving a request to provide the cloud migration for a predetermined application”
The additional element (5) simply appends well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept. MPEP § 2106.05(d)(II) expressly states that the courts have recognized the computer function of receiving or transmitting data over a network, e.g., using the Internet to gather data as a well‐understood, routine, and conventional computer function when it is claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activities. Thus, a person of ordinary skill in the art would readily comprehend that it is well-understood, routine, and conventional in the computing art to receive a request to provide a cloud migration for a predetermined application. Therefore, the limitations remain insignificant extra-solution activities even upon reconsideration and do not amount to significantly more.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as a combination adds nothing that is not already present when looking at the additional elements taken individually. Even when considered in combination, the additional elements represent mere instructions to apply a judicial exception using generic computer components, only the idea of a solution or outcome, insignificant extra-solution activities, and a field of use or technological environment, and therefore do not provide an inventive concept. The claim is not patent eligible.
Step 1: Claim 10 is directed to a method, which is a process (a series of steps or acts), and falls within one of the statutory categories of invention.
Step 2A, Prong One: Claim 10 recites the limitations:
(1) “selecting a single cloud continuous integration continuation deployment (CICD) node from a plurality of continuous integration continuation deployment (CICD) nodes;”
(2) “selecting a single cloud configuration node from a plurality of cloud configuration nodes; selecting a single node selected from a plurality of cloud configuration nodes;”
(3) “and selecting a single node selected from a plurality of single sign on nodes (SSO); and selecting a single application node from a plurality of application nodes.”
These recited steps, under the broadest reasonable interpretation (BRI), cover performance of the steps in the human mind alone or with the aid of pen and paper. That is, other than reciting:
(4) “A method for using a neural network to implement a cloud migration in response to receiving a request to provide the cloud migration for a predetermined application”
Nothing in the claim precludes the steps from practically being performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper. For example, the limitations (1) to (3) in the context of the claim encompasses a human selecting a node from a plurality of nodes can be performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper to perform the selection. See MPEP § 2106.04(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind alone or with the aid of pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements:
(1) “A method for using a neural network to implement a cloud migration”
The additional element (1) is recited at a high-level of generality such that they amount to no more than mere instructions to apply the judicial exception using generic computer components. The neural network is a tool to perform the cloud migration steps of the claim 10. See MPEP § 2106.05(f).
Also the claim recites the additional element:
(2) “in response to receiving a request to provide the cloud migration for a predetermined application”
The additional element (2) is mere data gathering/transmitting/outputting recited at a high level of generality and thus, are insignificant extra-solution activities. See MPEP § 2106.05(g). Furthermore, all uses of the recited judicial exception require such data gathering/transmitting/outputting, and, as such, the additional elements do not impose any meaningful limits on the claim. The additional elements amount to necessary data gathering/transmitting/outputting. See 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 when considered both individually and as a combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional elements:
(1) “A method for using a neural network to implement a cloud migration”
The additional element (1) amounts to no more than mere instructions to apply the judicial exception using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. The use of a computer or other machinery in its ordinary capacity does not integrate a judicial exception into a practical application or provide significantly more.
Also the claim recites the additional element:
(2) “in response to receiving a request to provide the cloud migration for a predetermined application”
The additional element (2) simply appends well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept. MPEP § 2106.05(d)(II) expressly states that the courts have recognized the computer function of receiving or transmitting data over a network, e.g., using the Internet to gather data as a well‐understood, routine, and conventional computer function when it is claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activities. Thus, a person of ordinary skill in the art would readily comprehend that it is well-understood, routine, and conventional in the computing art to receive a request to provide a cloud migration for a predetermined application. Therefore, the limitations remain insignificant extra-solution activities even upon reconsideration and do not amount to significantly more.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as a combination adds nothing that is not already present when looking at the additional elements taken individually. Even when considered in combination, the additional elements represent mere instructions to apply a judicial exception using generic computer components, only the idea of a solution or outcome, insignificant extra-solution activities, and a field of use or technological environment, and therefore do not provide an inventive concept. The claim is not patent eligible.
Step 1: Claim 19 is directed to a method, which is a process (a series of steps or acts), and falls within one of the statutory categories of invention.
Step 2A, Prong One: Claim 19 recites the limitations:
(1) “selecting a single cloud continuous integration continuation deployment (CICD) node from a plurality of continuous integration continuation deployment (CICD) nodes;”
(2) “selecting a single cloud configuration node from a plurality of cloud configuration nodes; selecting a single node selected from a plurality of cloud configuration nodes;”
(3) “and selecting a single node selected from a plurality of single sign on nodes (SSO); and selecting a single application node from a plurality of application nodes.”
These recited steps, under the broadest reasonable interpretation (BRI), cover performance of the steps in the human mind alone or with the aid of pen and paper. That is, other than reciting:
(4) “A method for using a neural network to implement a cloud migration in response to receiving a request to provide the cloud migration for a predetermined application… wherein said selected CICD node, said selected cloud configuration node, said selected SSO node, and said selected single application node form the neural network for initiating cloud migration for the predetermined application.”
Nothing in the claim precludes the steps from practically being performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper. For example, the limitation (1) to (3) in the context of the claim encompasses a human selecting a node from a plurality of nodes can be performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper to perform the selection. See MPEP § 2106.04(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind alone or with the aid of pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements:
(1) “A method for using a neural network to implement a cloud migration”
(2) “wherein said selected CICD node,
(3) “said selected cloud configuration node, said selected SSO node,”
(4) “and said selected single application node form the neural network”
The additional elements (1) to (4) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the judicial exception using generic computer components. The neural network, CICD node, cloud configuration node, SSO node, and application node are tools to perform the cloud migration steps of the claim 19. See MPEP § 2106.05(f).
Also, the claim recites the additional element:
(5) “for initiating cloud migration for the predetermined application”
The additional element (5) merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element (5) limits the identified judicial exceptions (1) to (3), however, this type of limitation merely confines the use of the abstract idea to a particular field of use or technological environment and thus, fails to add an inventive concept to the claims. See MPEP § 2106.05(h).
Also the claim recites the additional element:
(6) “in response to receiving a request to provide the cloud migration for a predetermined application”
The additional element (6) is mere data gathering/transmitting/outputting recited at a high level of generality and thus, are insignificant extra-solution activities. See MPEP § 2106.05(g). Furthermore, all uses of the recited judicial exception require such data gathering/transmitting/outputting, and, as such, the additional elements do not impose any meaningful limits on the claim. The additional elements amount to necessary data gathering/transmitting/outputting. See MPEP § 2106.05(g).
Accordingly, even when viewed in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea
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 when considered both individually and as a combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional elements:
(1) “A method for using a neural network to implement a cloud migration”
(2) “wherein said selected CICD node,
(3) “said selected cloud configuration node, said selected SSO node,”
(4) “and said selected single application node form the neural network”
The additional elements (1) to (4) amount to no more than mere instructions to apply the judicial exception using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. The use of a computer or other machinery in its ordinary capacity does not integrate a judicial exception into a practical application or provide significantly more.
Also, the claim recites the additional element:
(5) “for initiating cloud migration for the predetermined application”
The additional element (5) merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element (5) limits the identified judicial exceptions (1) to (3) however, this type of limitation merely confines the use of the abstract idea to a particular field of use or technological environment and thus, fails to add an inventive concept to the claims. See MPEP § 2106.05(h).
Also the claim recites the additional element:
(6) “in response to receiving a request to provide the cloud migration for a predetermined application”
The additional element (6) simply appends well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept. MPEP § 2106.05(d)(II) expressly states that the courts have recognized the computer function of receiving or transmitting data over a network, e.g., using the Internet to gather data as a well‐understood, routine, and conventional computer function when it is claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activities. Thus, a person of ordinary skill in the art would readily comprehend that it is well-understood, routine, and conventional in the computing art to receive a request to provide a cloud migration for a predetermined application. Therefore, the limitations remain insignificant extra-solution activities even upon reconsideration and do not amount to significantly more.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as a combination adds nothing that is not already present when looking at the additional elements taken individually. Even when considered in combination, the additional elements represent mere instructions to apply a judicial exception using generic computer components, only the idea of a solution or outcome, insignificant extra-solution activities, and a field of use or technological environment, and therefore do not provide an inventive concept. The claim is not patent eligible.
Claims 2 and 11 recites the additional limitation “to test the migration … and return a cloud migration compliance score based on the testing.” which are processes that can be practically performed by the human mind through observation, evaluation, judgement, and/or opinion with the aid of pen and paper. Thus, the limitations fall under the “Mental Processes” group of abstract ideas.
The claims recite additional elements that do not integrate the abstract idea into a practical application. The claims recite the additional elements “in the pipeline … using a CICD pipeline integrator” which is recited at a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits upon practicing the abstract idea.
The claims recite additional elements that do not amount to significantly more than the abstract idea. The claims recite the additional elements “in the pipeline … using a CICD pipeline integrator” which is recited at a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional elements recited in the claims cannot provide an inventive concept nor amount to significantly more. Thus, the claims are not patent eligible.
Claims 3 and 12 recite the additional limitation “wherein the cloud migration is determined to have more than a threshold cloud migration compliance score, rerun the implementation of the cloud migration.” which are processes that can be practically performed by the human mind through observation, evaluation, judgement, and/or opinion with the aid of pen and paper. Thus, the limitations fall under the “Mental Processes” group of abstract ideas.
Claims 4 and 13 recite an additional element that does not integrate the abstract idea into a practical application. The claim recites the additional element “further comprising using a neural network interface to interface between the request and the neural network” which is recited at a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits upon practicing the abstract idea.
The claims recite additional elements that do not amount to significantly more than the abstract idea. The claims recite the additional element “further comprising using a neural network interface to interface between the request and the neural network” which is recited at a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional elements recited in the claims cannot provide an inventive concept nor amount to significantly more. Thus, the claims are not patent eligible.
Claims 5 and 14 recite an additional element that does not integrate the abstract idea into a practical application. The claim recites the additional element “further comprising using a cloud plugin integrator to provide an integration point between a plurality of application requirements of the application and the cloud migration as defined in the neural network interface” which is recited at a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits upon practicing the abstract idea.
The claims recite additional elements that do not amount to significantly more than the abstract idea. The claims recite the additional element “further comprising using a cloud plugin integrator to provide an integration point between a plurality of application requirements of the application and the cloud migration as defined in the neural network interface” which is recited at a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional elements recited in the claims cannot provide an inventive concept nor amount to significantly more. Thus, the claims are not patent eligible.
Claims 6 and 15 recite the additional limitation “to determine an identity of the application being migrated.” which is a process that can be practically performed by the human mind through observation, evaluation, judgement, and/or opinion with the aid of pen and paper. Thus, the limitation falls under the “Mental Processes” group of abstract ideas.
The judicial exception is not integrated into a practical application. The claims recite the additional element “further comprising using a platform interpreter” which is recited a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits upon practicing the abstract idea.
The claims recite additional elements that do not amount to significantly more than the abstract idea. The claims recite the additional element “further comprising using a platform interpreter” which is recited a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional elements recited in the claims cannot provide an inventive concept nor amount to significantly more. Thus, the claims are not patent eligible.
Claim 7 recites the additional limitation “for rewriting the code in which the application is written” which is a process that can be practically performed by the human mind through observation, evaluation, judgement, and/or opinion with the aid of pen and paper. Thus, the limitation falls under the “Mental Processes” group of abstract ideas.
The additional elements are not integrated into a practical application. The claims recite the additional element “further comprising a code repository, said code repository coupled to the neural network, said code repository” which is recited at a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). The claim further recites the additional element “and integrating the code for use with the cloud migration” that does no more than generally link a judicial exception to a particular technological environment, which is merely indicating a field of use or technological environment to apply a judicial exception (See MPEP 2106.05(h)).Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits upon practicing the abstract idea.
The additional elements do not amount to significantly more than the abstract idea. The claims recite the additional element “further comprising a code repository, said code repository coupled to the neural network, said code repository” which is recited at a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). The claim further recites the additional element “and integrating the code for use with the cloud migration” that does no more than generally link a judicial exception to a particular technological environment, which is merely indicating a field of use or technological environment to apply a judicial exception (See MPEP 2106.05(h)). Accordingly, the additional elements recited in the claims cannot provide an inventive concept nor amount to significantly more. Thus, the claim is not patent eligible.
Claim 16 recites the additional limitation “to rewrite the code in which the application is written” which is a process that can be practically performed by the human mind through observation, evaluation, judgement, and/or opinion with the aid of pen and paper. Thus, the limitation falls under the “Mental Processes” group of abstract ideas.
The additional element does not integrate the abstract idea into a practical application. The claim recites the additional element “coupling a code repository to the neural network, and using said code repository” which is recited at a high level of generality such that it amounts to no more than mere generic computer/computing components to apply the abstract idea (See MPEP 2106.05(f)). The claims further recite the additional element “and to integrate the code for use with the cloud migration” which is a process, under its broadest reasonable interpretation, that is directed to the insignificant extra-solution activity of mere data outputting (See MPEP 2106.05(g)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits upon practicing the abstract idea.
The additional elements do not amount to significantly more than the abstract idea. The claim recites the additional element “coupling a code repository to the neural network, and using said code repository” which is recited at a high level of generality such that it amounts to no more than mere generic computer/computing components to apply the abstract idea (See MPEP 2106.05(f)). The claims further recite the additional element “and to integrate the code for use with the cloud migration” which has been determined to be a well-known, routine, and/or conventional activity of receiving or transmitting data over a network (See MPEP 2106.05(d)(II)). Accordingly, the additional elements recited in the claims cannot provide an inventive concept nor amount to significantly more. Thus, the claim is not patent eligible.
Claims 8 and 17 recite the additional element “using an application dynamics interface” which is recited at a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). The claims further recite the additional element “to provide a plurality of user app specific metrics post cloud migration.” which is a process, under its broadest reasonable interpretation, that is directed to the insignificant extra solution activity of data gathering or outputting (See MPEP 2106.05(g)). Accordingly, the additional elements cannot integrate the abstract idea into a practical application because they do not impose any meaningful limits upon practicing the abstract idea.
The additional elements do not amount to significantly more than the abstract idea. The claims recite the additional element “using an application dynamics interface” which is recited at a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). The claim further recites the additional element “to provide a plurality of user app specific metrics post cloud migration” which has been determined to be a well-known, routine, and/or conventional activity of transmitting or receiving data over a network (See MPEP 2106.05(d)(II)). Accordingly, the additional elements recited in the claims cannot provide an inventive concept nor amount to significantly more. Thus, the claims are not patent eligible.
Claims 9 and 18 recite the additional element “further comprising using a single sign on (SSO) platform interface to provide an SSO library for use with the neural network” which is recited at a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional element cannot integrate the abstract idea into a practical application because it does not impose any meaningful limits upon practicing the abstract idea.
The additional element does not amount to significantly more than the abstract idea. The claims recite the additional element “further comprising using a single sign on (SSO) platform interface to provide an SSO library for use with the neural network” which is recited at a high level of generality such that it amounts to no more than a mere generic computer/computing component to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional elements recited in the claims cannot provide an inventive concept nor amount to significantly more. Thus, the claims are not patent eligible.
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.
Claims 1, 4-6, 9, 10, 13-15, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20240070435 A1 hereinafter “Shinde” in view of US 20220327124 A1 hereinafter “Sweeney” and further in view of US 20210360083 A1 hereinafter “Duggal”.
With regards to claim 1, Shinde teaches
A neural network for use with implementing a cloud migration in response to a receiving a request to provide the cloud migration for a predetermined application, the neural network comprising: (Shinde [0020-22], “Cloud migration can move databases, applications, services, workloads, or other digital content of an on-premises system from a local data center to a cloud computing environment [for use with implementing a cloud migration]. The on-premises system can include a set of databases, applications, services, workloads, or other digital content that executes on hardware located at a location that is physically accessible to a corresponding entity, such as an organization or enterprise. However, due to the large number of parameters of the on-premises system and different cloud providers, it can be challenging and burdensome to identify a target cloud architecture/system that is optimally suited for a given on-premises system. The systems and methods described in this specification can assess interdependencies of various technical components, underlying infrastructure fabric, application specific non-functional requirements (NFRs) of the on-premises system; map them against each cloud providers' service releases, specifications, features currently being offered, and features being updated over future; and provide guidance on selection cloud vendors and cloud infrastructure options. Furthermore, the systems and methods described in this specification can train a neural network model for the recommendation of target cloud architectures [A neural network]. The systems and the methods described in this specification can train the neural network model in an efficient way. Specifically, the neural network model can be trained by applying various tools such as convolutional filters, ReLu activation functions, and max pooling … The on-premises system can include a set of databases, applications, services, workloads, or other digital content that executes on hardware located at a location that is physically accessible to the corresponding entity. The entity can request to migrate at least part of the on-premises system to a cloud architecture for various reasons, such as increased flexibility, increasing resource demands, reduction in costs, etc. [receiving a request to provide the cloud migration for a predetermined application].”)
a plurality of cloud configuration nodes, each of the plurality of cloud configuration nodes couples [to each of the CICD nodes;] (Shinde [0042], “An initial weight can be assigned to each node of the neural network. The values of the set of input parameters corresponding to the metadata or the features of the on-premises system are provided into the input layer. The one or more hidden layers can process the outputs from the previous layer [each of the plurality of … nodes couples]. The output layer can provide one or more classification or prediction results. In other words, the input layer collects input patterns. The output layer has classification or out signals to which input patterns may map. The hidden layers can extrapolate salient features in the input data that have predictive power regarding the outputs.”) and (Shinde [0061-63]” Extracting the set of input parameters can include executing extraction script at the on-premises system to extract metadata of the on-premises system, and generating the set of input parameters based on analyzing the metadata of the on-premises system. By executing the script, the server can extract the metadata of the on-premises system and better understand the features and execution environment of the on-premises system. Based on analyzing the metadata, the server can generate a set of input parameters that can be used to determine the features of the cloud architecture… At step 306, the server can execute the trained neural network model using the set of input parameters to obtain a set of output parameters associated with a target cloud architecture [cloud configuration].”) [Examiner’s Note: input parameters define the target cloud migration which in turn defines the metadata into the neural network. The metadata will map to individual classified layers comprising cloud nodes which can then couple to other layers.]
[a plurality of single sign on nodes (SSO), each of the SSO nodes] coupled to the cloud configuration nodes; (Shinde [0041-42], “Afterwards, the output is passed through an activation function, which determines the output. If that output exceeds a given threshold, it activates the node, passing data to the next layer in the network. This result in the output of the node becomes the input of the next node in the next layer [coupled to the…nodes]. This process of passing data from one layer to the next layer defines the neural network as a feedforward network. An initial weight can be assigned to each node of the neural network. The values of the set of input parameters corresponding to the metadata or the features of the on-premises system are provided into the input layer. The one or more hidden layers can process the outputs from the previous layer. The output layer can provide one or more classification or prediction results. In other words, the input layer collects input patterns. The output layer has classification or out signals to which input patterns may map. The hidden layers can extrapolate salient features in the input data that have predictive power regarding the outputs.”) and (Shinde [0061-63]” Extracting the set of input parameters can include executing extraction script at the on-premises system to extract metadata of the on-premises system, and generating the set of input parameters based on analyzing the metadata of the on-premises system. By executing the script, the server can extract the metadata of the on-premises system and better understand the features and execution environment of the on-premises system. Based on analyzing the metadata, the server can generate a set of input parameters that can be used to determine the features of the cloud architecture… At step 306, the server can execute the trained neural network model using the set of input parameters to obtain a set of output parameters associated with a target cloud architecture. [cloud configuration]”) [Examiner’s Note: By activating the node and traversing the layers of the network, the neural network is capable of making associations or coupling between layers of classified nodes (one layer being classifying said cloud configurations)]
wherein a single cloud configuration is formed (Shinde [0071], “In the solution design phase 420, the server can determine cloud strategy. In step 4 422, the server can process source system details and target system details, along with external parameters impacting the cloud migration journey. The server can produce solution blueprint for the cloud migration, such as the recommended features of the target cloud architecture. The recommendation features of the target cloud architecture can include identifier of the target cloud architecture, versions of the components of the target cloud architecture, methods for cloud migration, cloud target shapes/sizes, licensing impact, estimated time, etc. For example, the server can execute a trained neural network using the set of input parameters corresponding to the source system details. The server can obtain a set of output parameters from the neural network model.”) [from a single node selected from the plurality of CICD nodes] a single node selected from the plurality of cloud configuration nodes, [a single node selected from the plurality of SSO nodes, and a single node selected from the plurality of application nodes.] (Shinde [0039-40], “At step 208, the server can extract a set of output parameters of the target cloud architecture. The set of output parameters can include the metadata or the features of the target cloud architecture. For example, the set of output parameters can include target cloud versions, migration methods, target cloud shapes and sizes, licensing impact, etc. [cloud configuration]. The neural network can include layers of interconnected nodes [from the plurality of … nodes]. The neural network can include an input layer, one or more hidden layers, and an output layer. Each layer can include a set of nodes or neurons. Each node connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated [a single node selected], sending data to the next layer of the neural network. Otherwise, no data is passed along to the next layer of the neural network.”) [Examiner’s Note: A layer consists of multiple neural network nodes of a particular type. Using weights and biases the neural network will traverse the layer by picking the node using its activation.]
Shinde teaches a single cloud configuration is formed from a single node selected from the plurality of cloud configuration nodes but does not teach: a plurality of continuous integration continuation deployment (CICD) nodes;
[wherein a single cloud configuration is formed from] a single node selected from the plurality of CICD nodes [a single node selected from the plurality of cloud configuration nodes,] a single node selected from the plurality of SSO nodes, and a single node selected from the plurality of application nodes.
However, in an analogous art Sweeney teaches a plurality of continuous integration continuation deployment (CICD) nodes; (Sweeney [0028], “However, the new project may be inferred based on interactions between people (e.g., determined from schedule/calendar information in the knowledge graph), reporting structures (e.g., nodes/edges that represent who has applied to work at what positions at the organization, who receives what benefits (e.g., insurance, salary, etc.) at the organization, or any other relationship indicated by human resource information), software development patterns (e.g., nodes/edges that indicate changes to version control systems or other software management information), and/or information technology (IT) relationships (e.g., indicated by nodes/edges that represent incidents/errors with computer systems) indicated by the knowledge graph. For example, the new project may be inferred via a machine learning model as discussed in more detail below.”) [Examiner’s Note: A knowledge graph will contain a plurality of nodes associated with different entities. Sweeney describes nodes that are associated with software development patterns thereby teaching a CICD node.]
[…] a single node selected from the plurality of CICD nodes […] (Sweeney [0028-29], “software development patterns (e.g., nodes/edges that indicate changes to version control systems or other software management information), … The graph subsystem 114 may determine a set of the nodes stored in the database 106 (e.g., a subset of the nodes shown in FIG. 2, or the set may include all the nodes shown in FIG. 2) that should be used in a machine learning model to determine an answer to a query. The graph subsystem 114 may identify a first node in the knowledge graph corresponding to the query. The graph subsystem 114 may determine nodes connected to the first node that may be helpful in answering the query. For example, if the query indicates a request for information on how to use a product associated with node 220, the graph subsystem 114 may identify node 220 as a starting point in the knowledge graph (e.g., the product itself). The graph subsystem 114 may determine a plurality of edges connecting the first node (in this example, the first node may be node 220) with other nodes in the knowledge graph.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Sweeney into the teachings of Shinde This combination of teachings would have resulted in a method configured to determine the target cloud migration infrastructure through neural networks, as in Shinde, using continuous integration and deployment node traversal, as in Sweeney. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of connecting edge nodes to indicate a project and/or project in a knowledge graph for software development data (Sweeney [0026-27]).
The combination of Shinde and Sweeney teaches coupled to the cloud configuration nodes…wherein a single cloud configuration is formed from a single node selected from the plurality of CICD nodes, a single node selected from the plurality of cloud configuration nodes, but does not teach: a plurality of single sign on nodes (SSO), each of the SSO nodes [coupled to the cloud configuration nodes;]
and a plurality of application nodes coupled to each of the plurality of SSO nodes;
[wherein a single cloud configuration is formed from a single node selected from the plurality of CICD nodes, a single node selected from the plurality of cloud configuration nodes,] a single node selected from the plurality of SSO nodes, and a single node selected from the plurality of application nodes.
However, in an analogous art Duggal teaches a plurality of single sign on nodes (SSO), each of the SSO nodes […] (Duggal [0124-125], “The object 302 includes types 304, concepts 306, and policies 308. The types 304, concepts 306, and/or policies 308 may comprise references to corresponding types, concepts, and/or policies of an ontology and/or domain model (e.g., of one or more domain models such as a domain model 212) … The design environment may enable technical staff to map properties, behaviors, constraints and dependencies to domain concepts and types. The graph topology represents mappings as a set of conditional, policy-based relationships, which allow one-or-more implementations of an object based on prototypal, non-hierarchical, inheritance.”) (Duggal [0148], “The platform services 416 may include middleware capabilities inherited through the type system and automatically linked to the object graph. The middleware capabilities may include portal services (e.g., browser-based JSON portal (cross-platform)), dynamic applications, work-lists, forms, enterprise search, UI-Integration (e.g., templates, content, mashups), security services (e.g., identity, role-based access control, single sign-on authentication/authorization protocols (such as Kerberos, OAuth, SAML, XACML, or the like), certification and credential management, encryption in-flight/at-rest)”) [Examiner’s Note: The neural network is a graph of nodes/objects. The object types are linked to middleware capabilities including security services such as Single Sign On.]
and a plurality of application nodes (Duggal [0148], “The platform services 416 may include middleware capabilities inherited through the type system and automatically linked to the object graph… Modeling/onboarding endpoints (service, API, system, database, device)), protocol translation, data type transformations, entity mapping, proxy services, fault management, controller services (e.g., automate configuration and control), network services (e.g., network integration (virtual functions, orchestrators, target hosts, network resources)), M2M/IoT services (e.g., Machine/Device Integration (sensors, actuators, gateways)), entity management services (e.g., Lifecycle management of all system objects (models and instances, apps and data, nodes and machines/devices)), application services (e.g., application integration, data services (e.g., data integration (structured, semi-structured and un-structured)), process services (e.g., service choreography and orchestration, system and human workflows, collaboration), policy services (e.g., enforcement and execution of declarative policies), decision services (e.g., decision tables, decision trees)).”) coupled to each of the plurality of SSO nodes; (Duggal [0126-127], “With GOAL, objects 302 may have a logical model that references a conceptual model of a domain (e.g., an ontology), which also describes its physical model (e.g., implementations); eliminating or reducing system overhead and supporting greater expressivity and adaptability of the system. Objects are loosely-coupled to their underlying elements, to each other and to the domain model for a complete separation of concerns and a completely metadata configurable environment. By mapping all connected elements to a common domain model, the platform may accommodate heterogeneous interfaces with diverse formats, data protocols and support interoperability with third party components (middleware, tools, runtimes, etc.), databases, network resources and devices/machines where specified which may relax constraints to promote interoperability and allow the system boundaries to be extended in a natural manner.”) [Examiner’s Note: Duggal teaches objects that are linked or classified with application/middleware capabilities. These objects can be linked/coupled to each other and reconfigurable through metadata meaning an application object can be linked to an SSO object according to user intentions.]
[…] a single node selected from the plurality of SSO nodes, and a single node selected from the plurality of application nodes. (Duggal [0171], “As distinct from Map-Reduce algorithms, which divide a workload across multiple workers and then aggregate results, in this example, diverse workloads may be distributed to agents and coordinated such that overall processing of a complex event may be modeled in a single language with a unified execution engine with all agents leveraging shared domain semantics and object data store so metadata and state is exchanged efficiently. Agents may run in the same compute node or distributed nodes, which may represent different deployment technologies (e.g., servers, virtual machines, containers) and the placement of agent workloads may itself be an automated, domain-driven, policy-controlled decision based on real-time metadata and state.”) [Examiner’s Note: Policies can define the selection of nodes or objects of the knowledge graph. These nodes are linked to applications and SSO as described in paragraph [0148]]
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Duggal into the teachings of Shinde in view of Sweeney. This combination of teachings would have resulted in a method configured to determine the target cloud migration infrastructure through neural networks, as in Shinde, using continuous integration and deployment node traversal, as in Sweeney, while determining the associated single sign on as well as application nodes associated with neural network requirements, as in Duggal. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of composing an application that can coordinate from many sources with associations with many events and policies that are secure and scalable (Duggal [0067]).
With regards to claim 4, the rejection of claim 1 is incorporated.
Shinde further teaches comprising a neural network interface for interfacing between the request and the neural network. (Shinde [0023-24], “The server 102 can receive the request from a user device (not shown) associated with the entity over the network 104, such as Internet. The request can include a plurality of parameters associated with the on-premises system. For example, the plurality of parameters can include an identifier of the on-premises system, identifiers of components of the on-premises system, migration requirements, and the like. The server 102 can use a neural network model to predict the features of a target cloud architecture. Specifically, the server 102 can extract, from the plurality of parameters, a set of input parameters substantially affecting the migration of the on-premises system. The server 102 can use the set of input parameters to execute a trained neural network model to obtain a set of output parameters.”) [Examiner’s Note: A user device is an interface that allows for a user to send requests and interact/interface with the neural network.]
With regards to claim 5, the rejection of claim 4 is incorporated.
Shinde further teaches comprising a cloud plugin integrator for providing an integration point (Shinde [0036-37], “For example, the server can identify one or more target cloud architectures that satisfy the requirements of the on-premises system, the server can further determine the migration methods or the migration paths to migrate the on-premises system to the target cloud architectures. For instance, the migration paths can include infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). The server can choose one of the migration paths based on the database version, data editions, workload type, dedicated/shared infrastructure, features, RAC option, and other features available in the on-premises system and the respective mapped features in the cloud architecture. In some implementations, the server can consider the cost and performance requirements for migrating the on-premises system to the cloud architecture. The target cloud architecture can be selected such that the selected cloud architecture satisfies one or more threshold conditions associated with the on-premises system. For example, the target cloud architecture can provide services that satisfy the performance thresholds required by the entity.”) between a plurality of application requirements of the application and the cloud migration as defined in the neural network interface. (Shinde [0064], “The trained neural network model can use a set of input parameters associated with the on-premises system to generate a set of output parameters associated with the target cloud architecture that is compliant with the set of input parameters and satisfies the threshold conditions associated with the migration, such as the performance thresholds and/or cost thresholds. The server can execute the trained neural network model to obtain the set of output parameters. The set of output parameters can indicate the metadata or features required for the target cloud architecture and can be used to identify the target cloud architecture.”)
With regards to claim 6, the rejection of claim 1 is incorporated.
Shinde further teaches comprising a platform interpreter for determining an identity of the application being migrated. (Shinde [0033], “Extracting the set of input parameters can include executing an extraction script at the on-premises system to extract metadata of the on-premises system, and generating the set of input parameters based on analyzing the metadata of the on-premises system. In some implementations, by executing the script, the server can extract the metadata of the on-premises system and identify the features and execution environment of the on-premises system. Based on analyzing the metadata, the server can generate a set of input parameters that can be used to determine the features of the cloud architecture.”)
With regards to claim 9, the rejection of claim 1 is incorporated.
The combination of Shinde and Sweeney does not teach: further comprising an SSI single sign on (SSO) platform interface, said SSO platform interface for providing an SSO library for use with the cloud migration.
However, in an analogous art Duggal teaches further comprising an SSI single sign on (SSO) platform interface, said SSO platform interface (Duggal [0148], “The platform services 416 may include middleware capabilities inherited through the type system and automatically linked to the object graph. The middleware capabilities may include portal services (e.g., browser-based JSON portal (cross-platform)), dynamic applications, work-lists, forms, enterprise search, UI-Integration (e.g., templates, content, mashups), security services (e.g., identity, role-based access control, single sign-on authentication/authorization protocols (such as Kerberos, OAuth, SAML, XACML, or the like), certification and credential management, encryption in-flight/at-rest), gateway services (e.g., Modeling/onboarding endpoints (service, API, system, database, device))”) for providing an SSO library for use with the cloud migration. (Duggal [0571], “In some embodiments, at run-time, the platform's execution environment references the library to dynamically construct the tool-chain it may need in order to realize the network service. It may assemble just the right tools for the job just-in-time, in a single middleware backplane allowing extremely efficient, compute and Input/Output intensive, graph transaction processing.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Duggal into the teachings of Shinde in view of Sweeney. This combination of teachings would have resulted in a method configured to determine the target cloud migration infrastructure through neural networks, as in Shinde, using continuous integration and deployment node traversal, as in Sweeney, while determining the associated single sign on as well as application nodes associated with neural network requirements, as in Duggal. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of composing an application that can coordinate from many sources with associations with many events and policies that are secure and scalable (Duggal [0067]).
With regards to claim 10, Shinde teaches
A method for using a neural network to implement a cloud migration in response to receiving a request to provide the cloud migration for a predetermined application, the method comprising: (Shinde [0020-22], “Cloud migration can move databases, applications, services, workloads, or other digital content of an on-premises system from a local data center to a cloud computing environment [for use with implementing a cloud migration]. The on-premises system can include a set of databases, applications, services, workloads, or other digital content that executes on hardware located at a location that is physically accessible to a corresponding entity, such as an organization or enterprise. However, due to the large number of parameters of the on-premises system and different cloud providers, it can be challenging and burdensome to identify a target cloud architecture/system that is optimally suited for a given on-premises system. The systems and methods described in this specification can assess interdependencies of various technical components, underlying infrastructure fabric, application specific non-functional requirements (NFRs) of the on-premises system; map them against each cloud providers' service releases, specifications, features currently being offered, and features being updated over future; and provide guidance on selection cloud vendors and cloud infrastructure options. Furthermore, the systems and methods described in this specification can train a neural network model for the recommendation of target cloud architectures [A neural network]. The systems and the methods described in this specification can train the neural network model in an efficient way. Specifically, the neural network model can be trained by applying various tools such as convolutional filters, ReLu activation functions, and max pooling … The on-premises system can include a set of databases, applications, services, workloads, or other digital content that executes on hardware located at a location that is physically accessible to the corresponding entity. The entity can request to migrate at least part of the on-premises system to a cloud architecture for various reasons, such as increased flexibility, increasing resource demands, reduction in costs, etc. [receiving a request to provide the cloud migration for a predetermined application].”)
selecting a single cloud configuration node from a plurality of cloud configuration nodes; (Shinde [0039-40], “At step 208, the server can extract a set of output parameters of the target cloud architecture. The set of output parameters can include the metadata or the features of the target cloud architecture. For example, the set of output parameters can include target cloud versions, migration methods, target cloud shapes and sizes, licensing impact, etc. [cloud configuration]. The neural network can include layers of interconnected nodes [from the plurality of … nodes]. The neural network can include an input layer, one or more hidden layers, and an output layer. Each layer can include a set of nodes or neurons. Each node connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated [a single node selected], sending data to the next layer of the neural network. Otherwise, no data is passed along to the next layer of the neural network.”)
selecting a single node selected from a plurality of cloud configuration nodes; (Shinde [0039-40], “At step 208, the server can extract a set of output parameters of the target cloud architecture. The set of output parameters can include the metadata or the features of the target cloud architecture. For example, the set of output parameters can include target cloud versions, migration methods, target cloud shapes and sizes, licensing impact, etc. The neural network can include layers of interconnected nodes. The neural network can include an input layer, one or more hidden layers, and an output layer. Each layer can include a set of nodes or neurons. Each node connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the neural network. Otherwise, no data is passed along to the next layer of the neural network.”)
Shinde does not teach: selecting a single cloud continuous integration continuation deployment (CICD) node from a plurality of continuous integration continuation deployment (CICD) nodes;
However in an analogous art Sweeney teaches selecting a single cloud continuous integration continuation deployment (CICD) node from a plurality of continuous integration continuation deployment (CICD) nodes; (Sweeney [0028-29], “software development patterns (e.g., nodes/edges that indicate changes to version control systems or other software management information), [cloud continuous integration continuation deployment (CICD)] … The graph subsystem 114 may determine a set of the nodes stored in the database 106 (e.g., a subset of the nodes shown in FIG. 2, or the set may include all the nodes shown in FIG. 2) that should be used in a machine learning model to determine an answer to a query [selecting a single … node]. The graph subsystem 114 may identify a first node in the knowledge graph corresponding to the query. The graph subsystem 114 may determine nodes connected to the first node that may be helpful in answering the query. For example, if the query indicates a request for information on how to use a product associated with node 220, the graph subsystem 114 may identify node 220 as a starting point in the knowledge graph (e.g., the product itself). The graph subsystem 114 may determine a plurality of edges connecting the first node (in this example, the first node may be node 220) with other nodes in the knowledge graph [from a plurality of continuous integration continuation deployment (CICD) nodes;].”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Sweeney into the teachings of Shinde This combination of teachings would have resulted in a method configured to determine the target cloud migration infrastructure through neural networks, as in Shinde, using continuous integration and deployment node traversal, as in Sweeney. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of connecting edge nodes to indicate a project and/or project in a knowledge graph for software development data (Sweeney [0026-27]).
The combination of Shinde and Sweeney does not teach: selecting a single node selected from a plurality of single sign on nodes (SSO); and
selecting a single application node from a plurality of application nodes.
However, in an analogous art Duggal teaches selecting a single node selected from a plurality of single sign on nodes (SSO); and
selecting a single application node from a plurality of application nodes. (Duggal [0148], “The platform services 416 may include middleware capabilities inherited through the type system and automatically linked to the object graph. The middleware capabilities may include portal services (e.g., browser-based JSON portal (cross-platform)), dynamic applications, work-lists, forms, enterprise search, UI-Integration (e.g., templates, content, mashups), security services (e.g., identity, role-based access control, single sign-on authentication/authorization protocols (such as Kerberos, OAuth, SAML, XACML, or the like) [from a plurality of single sign on nodes (SSO)], certification and credential management, encryption in-flight/at-rest), gateway services (e.g., Modeling/onboarding endpoints (service, API, system, database, device)), protocol translation, data type transformations, entity mapping, proxy services, fault management, controller services (e.g., automate configuration and control), network services (e.g., network integration (virtual functions, orchestrators, target hosts, network resources)), M2M/IoT services (e.g., Machine/Device Integration (sensors, actuators, gateways)), entity management services (e.g., Lifecycle management of all system objects (models and instances, apps and data, nodes and machines/devices)), application services (e.g., application integration [from a plurality of application nodes], data services (e.g., data integration (structured, semi-structured and un-structured)), process services (e.g., service choreography and orchestration, system and human workflows, collaboration), policy services (e.g., enforcement and execution of declarative policies), decision services (e.g., decision tables, decision trees)).”) and (Duggal [0171], “As distinct from Map-Reduce algorithms, which divide a workload across multiple workers and then aggregate results, in this example, diverse workloads may be distributed to agents and coordinated such that overall processing of a complex event may be modeled in a single language with a unified execution engine with all agents leveraging shared domain semantics and object data store so metadata and state is exchanged efficiently. Agents may run in the same compute node or distributed nodes, which may represent different deployment technologies (e.g., servers, virtual machines, containers) and the placement of agent workloads may itself be an automated, domain-driven, policy-controlled decision based on real-time metadata and state [selecting a single node].”) [Examiner’s Note: Policies/decision tables/decision trees can define the selection of nodes or objects of the knowledge graph. These nodes are linked to applications and SSO as described in paragraph [0148]]
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Duggal into the teachings of Shinde in view of Sweeney. This combination of teachings would have resulted in a method configured to determine the target cloud migration infrastructure through neural networks, as in Shinde, using continuous integration and deployment node traversal, as in Sweeney, while determining the associated single sign on as well as application nodes associated with neural network requirements, as in Duggal. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of composing an application that can coordinate from many sources with associations with many events and policies that are secure and scalable (Duggal [0067]).
With regards to claim 19,
A method for using a neural network to implement a cloud migration in response to receiving a request to provide the cloud migration for a predetermined application, the method comprising: (Shinde [0020-22], “Cloud migration can move databases, applications, services, workloads, or other digital content of an on-premises system from a local data center to a cloud computing environment [for use with implementing a cloud migration]. The on-premises system can include a set of databases, applications, services, workloads, or other digital content that executes on hardware located at a location that is physically accessible to a corresponding entity, such as an organization or enterprise. However, due to the large number of parameters of the on-premises system and different cloud providers, it can be challenging and burdensome to identify a target cloud architecture/system that is optimally suited for a given on-premises system. The systems and methods described in this specification can assess interdependencies of various technical components, underlying infrastructure fabric, application specific non-functional requirements (NFRs) of the on-premises system; map them against each cloud providers' service releases, specifications, features currently being offered, and features being updated over future; and provide guidance on selection cloud vendors and cloud infrastructure options. Furthermore, the systems and methods described in this specification can train a neural network model for the recommendation of target cloud architectures [A neural network]. The systems and the methods described in this specification can train the neural network model in an efficient way. Specifically, the neural network model can be trained by applying various tools such as convolutional filters, ReLu activation functions, and max pooling … The on-premises system can include a set of databases, applications, services, workloads, or other digital content that executes on hardware located at a location that is physically accessible to the corresponding entity. The entity can request to migrate at least part of the on-premises system to a cloud architecture for various reasons, such as increased flexibility, increasing resource demands, reduction in costs, etc. [receiving a request to provide the cloud migration for a predetermined application].”)
selecting a single cloud configuration node from a plurality of cloud configuration nodes; (Shinde [0039-40], “At step 208, the server can extract a set of output parameters of the target cloud architecture. The set of output parameters can include the metadata or the features of the target cloud architecture. For example, the set of output parameters can include target cloud versions, migration methods, target cloud shapes and sizes, licensing impact, etc. The neural network can include layers of interconnected nodes. The neural network can include an input layer, one or more hidden layers, and an output layer. Each layer can include a set of nodes or neurons. Each node connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the neural network. Otherwise, no data is passed along to the next layer of the neural network.”)
[wherein said selected CICD node,] said selected cloud configuration node, [said selected SSO node, and said selected single application node] form the neural network for initiating cloud migration for the predetermined application. (Shinde [0039-40], “At step 208, the server can extract a set of output parameters of the target cloud architecture. The set of output parameters can include the metadata or the features of the target cloud architecture. For example, the set of output parameters can include target cloud versions, migration methods, target cloud shapes and sizes, licensing impact, etc. [for initiating cloud migration for the predetermined application] … The neural network can include layers of interconnected nodes [form the neural network for]. The neural network can include an input layer, one or more hidden layers, and an output layer. Each layer can include a set of nodes or neurons. Each node connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated [a single node selected], sending data to the next layer of the neural network. Otherwise, no data is passed along to the next layer of the neural network.”) [Examiner’s Note: A layer consists of multiple neural network nodes of a particular type. Using weights and biases the neural network will traverse the layer by picking the node using its activation.]
Shinde teaches cloud configuration node… form the neural network for initiating cloud migration for the predetermined application does not teach: selecting a single cloud continuous integration continuation deployment (CICD) node from a plurality of CICD nodes;
wherein said selected CICD node, [said selected cloud configuration node, said selected SSO node, and said selected single application node form the neural network for initiating cloud migration for the predetermined application.]
However, in an analogous art Sweeney teaches selecting a single cloud continuous integration continuation deployment (CICD) node from a plurality of CICD nodes; (Sweeney [0028-29], “software development patterns (e.g., nodes/edges that indicate changes to version control systems or other software management information), [cloud continuous integration continuation deployment (CICD)] … The graph subsystem 114 may determine a set of the nodes stored in the database 106 (e.g., a subset of the nodes shown in FIG. 2, or the set may include all the nodes shown in FIG. 2) that should be used in a machine learning model to determine an answer to a query [selecting a single … node]. The graph subsystem 114 may identify a first node in the knowledge graph corresponding to the query. The graph subsystem 114 may determine nodes connected to the first node that may be helpful in answering the query. For example, if the query indicates a request for information on how to use a product associated with node 220, the graph subsystem 114 may identify node 220 as a starting point in the knowledge graph (e.g., the product itself). The graph subsystem 114 may determine a plurality of edges connecting the first node (in this example, the first node may be node 220) with other nodes in the knowledge graph [from a plurality of continuous integration continuation deployment (CICD) nodes;].”)
wherein said selected CICD node, […] (Sweeney [0028-29], “software development patterns (e.g., nodes/edges that indicate changes to version control systems or other software management information), [cloud continuous integration continuation deployment (CICD)] … The graph subsystem 114 may determine a set of the nodes stored in the database 106 (e.g., a subset of the nodes shown in FIG. 2, or the set may include all the nodes shown in FIG. 2) that should be used in a machine learning model to determine an answer to a query [selecting a single … node]. The graph subsystem 114 may identify a first node in the knowledge graph corresponding to the query. The graph subsystem 114 may determine nodes connected to the first node that may be helpful in answering the query. For example, if the query indicates a request for information on how to use a product associated with node 220, the graph subsystem 114 may identify node 220 as a starting point in the knowledge graph (e.g., the product itself). The graph subsystem 114 may determine a plurality of edges connecting the first node (in this example, the first node may be node 220) with other nodes in the knowledge graph [from a plurality of continuous integration continuation deployment (CICD) nodes;].”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Sweeney into the teachings of Shinde This combination of teachings would have resulted in a method configured to determine the target cloud migration infrastructure through neural networks, as in Shinde, using continuous integration and deployment node traversal, as in Sweeney. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of connecting edge nodes to indicate a project and/or project in a knowledge graph for software development data (Sweeney [0026-27]).
The combination of Shinde and Sweeney teaches wherein said selected CICD node, and selected cloud configuration node form the neural network for initiating cloud migration for the predetermined application does not teach: selecting a single sign on (SSO) node selected from a plurality of SSO nodes; and
selecting a single application node from a plurality of application nodes,
[wherein said selected CICD node, said selected cloud configuration node,] said selected SSO node, and said selected single application node [form the neural network for initiating cloud migration for the predetermined application.]
However, in an analogous art Duggal teaches selecting a single sign on (SSO) node selected from a plurality of SSO nodes; and
selecting a single application node from a plurality of application nodes, (Duggal [0148], “The platform services 416 may include middleware capabilities inherited through the type system and automatically linked to the object graph. The middleware capabilities may include portal services (e.g., browser-based JSON portal (cross-platform)), dynamic applications, work-lists, forms, enterprise search, UI-Integration (e.g., templates, content, mashups), security services (e.g., identity, role-based access control, single sign-on authentication/authorization protocols (such as Kerberos, OAuth, SAML, XACML, or the like) [from a plurality of single sign on nodes (SSO)], certification and credential management, encryption in-flight/at-rest), gateway services (e.g., Modeling/onboarding endpoints (service, API, system, database, device)), protocol translation, data type transformations, entity mapping, proxy services, fault management, controller services (e.g., automate configuration and control), network services (e.g., network integration (virtual functions, orchestrators, target hosts, network resources)), M2M/IoT services (e.g., Machine/Device Integration (sensors, actuators, gateways)), entity management services (e.g., Lifecycle management of all system objects (models and instances, apps and data, nodes and machines/devices)), application services (e.g., application integration [from a plurality of application nodes], data services (e.g., data integration (structured, semi-structured and un-structured)), process services (e.g., service choreography and orchestration, system and human workflows, collaboration), policy services (e.g., enforcement and execution of declarative policies), decision services (e.g., decision tables, decision trees)).”) and (Duggal [0171], “As distinct from Map-Reduce algorithms, which divide a workload across multiple workers and then aggregate results, in this example, diverse workloads may be distributed to agents and coordinated such that overall processing of a complex event may be modeled in a single language with a unified execution engine with all agents leveraging shared domain semantics and object data store so metadata and state is exchanged efficiently. Agents may run in the same compute node or distributed nodes, which may represent different deployment technologies (e.g., servers, virtual machines, containers) and the placement of agent workloads may itself be an automated, domain-driven, policy-controlled decision based on real-time metadata and state [selecting a single node].”) [Examiner’s Note: Policies can define the selection of nodes or objects of the knowledge graph. These nodes are linked to applications and SSO as described in paragraph [0148]] […] said selected SSO node, and said selected single application node […] (Duggal [0148], “The platform services 416 may include middleware capabilities inherited through the type system and automatically linked to the object graph. The middleware capabilities may include portal services (e.g., browser-based JSON portal (cross-platform)), dynamic applications, work-lists, forms, enterprise search, UI-Integration (e.g., templates, content, mashups), security services (e.g., identity, role-based access control, single sign-on authentication/authorization protocols (such as Kerberos, OAuth, SAML, XACML, or the like) [from a plurality of single sign on nodes (SSO)], certification and credential management, encryption in-flight/at-rest), gateway services (e.g., Modeling/onboarding endpoints (service, API, system, database, device)), protocol translation, data type transformations, entity mapping, proxy services, fault management, controller services (e.g., automate configuration and control), network services (e.g., network integration (virtual functions, orchestrators, target hosts, network resources)), M2M/IoT services (e.g., Machine/Device Integration (sensors, actuators, gateways)), entity management services (e.g., Lifecycle management of all system objects (models and instances, apps and data, nodes and machines/devices)), application services (e.g., application integration [from a plurality of application nodes], data services (e.g., data integration (structured, semi-structured and un-structured)), process services (e.g., service choreography and orchestration, system and human workflows, collaboration), policy services (e.g., enforcement and execution of declarative policies), decision services (e.g., decision tables, decision trees)).”) and (Duggal [0171], “As distinct from Map-Reduce algorithms, which divide a workload across multiple workers and then aggregate results, in this example, diverse workloads may be distributed to agents and coordinated such that overall processing of a complex event may be modeled in a single language with a unified execution engine with all agents leveraging shared domain semantics and object data store so metadata and state is exchanged efficiently. Agents may run in the same compute node or distributed nodes, which may represent different deployment technologies (e.g., servers, virtual machines, containers) and the placement of agent workloads may itself be an automated, domain-driven, policy-controlled decision based on real-time metadata and state [selecting a single node].”) [Examiner’s Note: Policies/decision tables/decision trees can define the selection of nodes or objects of the knowledge graph. These nodes are linked to applications and SSO as described in paragraph [0148]]
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Duggal into the teachings of Shinde in view of Sweeney. This combination of teachings would have resulted in a method configured to determine the target cloud migration infrastructure through neural networks, as in Shinde, using continuous integration and deployment node traversal, as in Sweeney, while determining the associated single sign on as well as application nodes associated with neural network requirements, as in Duggal. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of composing an application that can coordinate from many sources with associations with many events and policies that are secure and scalable (Duggal [0067]).
Claims 13-15 and 18 are directed to a method corresponding to the neural network as disclosed in claims 4-6 and 9 respectively. Thus, claims 13-15 and 18 are rejected for the same reasons set forth in claims 4-6 and 9.
Claims 2-3 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Shinde in view of Sweeney in view of Duggal as applied to claims 1 and 10 above, and further in view of US 20240152343 A1 hereinafter “Prasad”.
With regards to claim 2, the rejection of claim 1 is incorporated.
The combination of Shinde, Sweeney, and Duggal does not teach: further comprising a CICD pipeline integrator, said CICD pipeline integrator that tests the migration in the pipeline and returns a cloud migration compliance score based on the testing.
However, in an analogous art Prasad teaches further comprising a CICD pipeline integrator, said CICD pipeline integrator that tests the migration in the pipeline (Prasad [0059-60], “In some embodiments, the one or more enterprise change management systems, integrated change control gateway systems, change control modules, cognitive change evaluation and assistance systems, secure tokenization modules, deployment authentication modules, implementation modules, and/or production modules and/or systems may perform one or more of the steps described herein with respect to the process flows described herein with respect to FIGS. 2-4 [further comprising a CICD pipeline integrator]. FIG. 2 illustrates a process flow 200 for evaluating, validating, and implementing system environment production deployment tools using cognitive learning input, in accordance with an embodiment of the invention. In some embodiments, a release management module is configured to interface with one or more enterprise change management systems, integrated change control gateway systems, change control modules, cognitive change evaluation and assistance systems, secure tokenization modules, deployment authentication modules, implementation modules, production modules and/or systems, and/or the like (e.g., similar to one or more of the systems described herein with respect to FIG. 1) may perform one or more of the steps of process flow 200. It is understood that the present invention is primarily focused on effective assessment of software releases [said CICD pipeline integrator that tests the migration in the pipeline].”) and returns a cloud migration compliance score based on the testing. (Prasad [0083-84], “In some embodiments, this may include extracting and assigned a CI level confidence score. As shown in FIG. 3, the process flow 300 may include assigning the quantitative failure chance score to the each operation or configuration item based on the cognitive intelligence built from previous modules to produce a package level assessment, which is then compared to a project threshold to determine if the production package passes or not. In further embodiments, the smart decision system module calculates the confidence score of the proposed change based in the failure chance and criticality of a particular configuration item.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Prasad into the teachings of Shinde in view of Sweeney in view of Duggal. This combination of teachings would have resulted in a method configured to determine the target cloud migration infrastructure through neural networks, as in Shinde, using continuous integration and deployment node traversal, as in Sweeney, while determining the associated single sign on as well as application nodes associated with neural network requirements, as in Duggal, with a pipeline integrator to test the migration, as in Prasad. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of providing a system for evaluating, validating, and implementing software release change requests into a system environment while obtaining meaningful conclusions regarding potential negative impacts (Prasad [0023]).
With regards to claim 3, the rejection of claim 1 is incorporated.
The combination of Shinde, Sweeney, and Duggal does not teach: wherein the cloud migration is determined to have more than a threshold cloud migration compliance score, rerun the implementation of the cloud migration.
However, in an analogous art Prasad teaches wherein the cloud migration is determined to have more than a threshold cloud migration compliance score, (Prasad [0071], “The process flow 200 may include determining whether the confidence score is greater than the threshold limit, where higher confidence scores are associated with higher likelihoods of software release change request not negatively impacting the system environment. Some embodiments are described herein in connection with thresholds. As used herein, satisfying a threshold may, depending on the context, refer to a value being greater than the threshold, more than the threshold, higher than the threshold”) rerun the implementation of the cloud migration. (Prasad [0087-88], “In this way, the post implementation data is analyzed to identify any post-implementation issues. As shown in FIG. 3, the process flow 300 may include applying a cognitive learning AI/ML model, as indicated in module 4 of FIG. 2. In this way the post-implementation analysis module may access threshold limits either predefined or defined by other modules such as the production certificate module or smart decision system module. The post implementation analysis module may update inferences and adjust thresholds as necessitated by insights from post implementation data. The post implementation analysis module analyzes the implementation defects, incident, problem, and change data, and applies cognitive learning to refine the inferences and thresholds. These inferences drawn on this module can be used as a “lessons learned document” for future releases. As indicated in FIG. 3, all of the modules shown collectively are used to build the cognitive input using AI/ML, artificial network, and cognitive learning models, in order to proceed to the steps shown in FIG. 4, as indicated in the process flow continuation marker “A”. FIG. 4 illustrates a process flow 400 for evaluating, validating, and implementing system environment production deployment tools using cognitive learning input, in accordance with an embodiment of the invention. In some embodiments, a release management module is configured to interface with one or more enterprise change management systems, integrated change control gateway systems, change control modules, cognitive change evaluation and assistance systems, secure tokenization modules, deployment authentication modules, implementation modules, production modules and/or systems, and/or the like”) [Examiner’s Note: FIG 3. Describes a post assessment flow that triggers the steps of FIG. 4 which is to run the cloud migration once more]
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Prasad into the teachings of Shinde in view of Sweeney in view of Duggal. This combination of teachings would have resulted in a method configured to determine the target cloud migration infrastructure through neural networks, as in Shinde, using continuous integration and deployment node traversal, as in Sweeney, while determining the associated single sign on as well as application nodes associated with neural network requirements, as in Duggal, with a pipeline integrator to test the migration, as in Prasad. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of providing a system for evaluating, validating, and implementing software release change requests into a system environment while obtaining meaningful conclusions regarding potential negative impacts (Prasad [0023]).
Claims11-12 are directed to a method corresponding to the neural network as disclosed in claims 2-3 respectively. Thus, claims 11-12 are rejected for the same reasons set forth in claims 2-3.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Shinde in view of Sweeney in view of Duggal as applied to claims 1 and 10 above, and further in view of US 20240345832 A1 hereinafter “Iravati”.
With regards to claim 7, the rejection of claim 1 is incorporated.
The combination of Shinde, Sweeney, and Duggal does not teach: comprising a code repository, said code repository coupled to the neural network, said code repository for rewriting the code in which the application is written and integrating the code for use with the cloud migration.
However, in an analogous art Iravati teaches comprising a code repository, said code repository coupled to the neural network, (Iravati [0017], “The build agent server may collect possible information on every build from a Mainframe Environment for every object, and may prepare a knowledge graph using support vector machine classification and bi-directional long-short term memory (BI-LSTM) deep learning algorithms to identify errors, object dependencies, and source code patterns, and simulate the build on non-mainframe servers. The build agent may be introduced in a continuous integration chain. Whenever any module or object is changed in source repository, it may trigger a build on a non-mainframe environment to identify risks or errors prior to executing the build for mainframe source code components.”) said code repository for rewriting the code in which the application is written and integrating the code for use with the cloud migration. (Iravati [0024-27], “Source repository device 104 may be or include one or more devices (e.g., servers, server blades, or the like), which may, e.g., be configured to store source code (e.g., which has not yet been built, compiled, run, or the like). For example, the source repository device 104 may communicate with the enterprise user device 103 and/or other devices to initially obtain the mainframe source code [said code repository for rewriting the code in which the application is written] … The mainframe build and deployment engine 106 may be or include one or more devices (e.g., servers, server blades, or the like) configured to execute builds of mainframe source code. For example, once the mainframe source code has been analyzed by the build agent server 102, it may be passed to the mainframe build and deployment engine 106 for execution of the build process. The mainframe build and deployment engine 106 may be configured to send information of the build (e.g., build confirmation, error notifications, or the like) to the build agent server 102 and/or the enterprise user device 103. Computing environment 100 also may include one or more networks, which may interconnect build agent server 102, enterprise user device 103, source repository device 104, dependency data storage system 105, and mainframe build and deployment engine 106.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Iravati into the teachings of Shinde in view of Sweeney in view of Duggal. This combination of teachings would have resulted in a method configured to determine the target cloud migration infrastructure through neural networks, as in Shinde, using continuous integration and deployment node traversal, as in Sweeney, while determining the associated single sign on as well as application nodes associated with neural network requirements, as in Duggal, and integrating a code repository coupled to the neural network in order to manage migration accordingly, as in Iravati. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of enabling user commits to a source repository with automated processes to check for errors and suggest remediation accordingly with knowledge graphs (Iravati [0020]).
Claim 16 is directed to a method corresponding to the neural network as disclosed in claim 7. Thus, claim 16 is rejected for the same reasons set forth in claim 7.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Shinde in view of Sweeney in view of Duggal as applied to claims 1 and 10 above, and further in view of US 20210117425 A1 hereinafter “Rao”.
With regards to claim 8, the rejection of claim 1 is incorporated.
The combination of Shinde, Sweeney, and Duggal does not teach: comprising an application dynamics interface, the application dynamics interface that posts a plurality of user app specific metrics post cloud migration.
However, in an analogous art Rao teaches comprising an application dynamics interface, the application dynamics interface that posts a plurality of user app specific metrics post cloud migration. (Rao [0383-385], “he data intake and query platform provides various features that simplify the developers' task to create various applications. One such application is a virtual machine monitoring application, such as SPLUNK® APP FOR VMWARE® that provides operational visibility into granular performance metrics, logs, tasks and events, and topology from hosts, virtual machines and virtual centers [comprising an application dynamics interface, the application dynamics interface]. It empowers administrators with an accurate real-time picture of the health of the environment, proactively identifying performance and capacity bottlenecks … In contrast, the virtual machine monitoring application stores large volumes of minimally processed machine data, such as performance information and log data, at ingestion time for later retrieval and analysis at search time when a live performance issue is being investigated [post cloud migration]. In addition to data obtained from various log files, this performance-related information can include values for performance metrics obtained through an application programming interface (API) provided as part of the vSphere Hypervisor™ system [that posts a plurality of user app specific metrics]”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Rao into the teachings of Shinde in view of Sweeney in view of Duggal. This combination of teachings would have resulted in a method configured to determine the target cloud migration infrastructure through neural networks, as in Shinde, using continuous integration and deployment node traversal, as in Sweeney, while determining the associated single sign on as well as application nodes associated with neural network requirements, as in Duggal, with an interface to monitor and display post migration performance, as in Rao. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of using computing environments to generate machine data that can be monitored and analyzed to derive insights (Rao [0128]).
Claim 17 is directed to a method corresponding to the neural network as disclosed in claim 8. Thus, claim 17 is rejected for the same reasons set forth in claim 8.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRAVIS VIET TRAN whose telephone number is (571)272-3720. The examiner can normally be reached Monday-Friday 8:30AM-5PM.
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/T.V.T./ Examiner, Art Unit 2191
/WEI Y MUI/ Supervisory Patent Examiner, Art Unit 2191
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