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 .
DETAILED ACTION
This office action is in response to the application filed on 12/11/2004. Claims 1-20 are pending in this application. Claims 1,7,15 are independent claims.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. IN202441072537, filed on September 25, 2024.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 - 20 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception, an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Regarding claims 1, 7 the limitations “determine a compatible target service pack for the computer system” as drafted, are functions that, under their broadest reasonable interpretation, recite the abstract idea of a mental process. These limitations encompass a human mind carrying out these functions through observation, evaluation judgment and /or opinion, or even with the aid of pen and paper. Thus, this limitation recites and falls within the “Mental Processes” grouping of abstract ideas under Prong 1.
Under Prong 2, the judicial exception is not integrated into a practical application. The additional elements “one or more processors”, “one or more non-transitory computer-readable storage media…”, “trained neural network model” , “input the obtained version…” and “initiate an upgrade process to install the compatible target service pack on the computer system” merely recite instructions to implement an abstract idea on a generic computer or merely uses a generic computer or computer components as a tool to perform the abstract idea, thus is not a practical application under Prong 2. The additional elements “obtain version information for a plurality of software and firmware.”, “receive from the trained neural network model” do nothing more than add insignificant extra solution activity to the judicial exception of merely gathering data. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application and the claim is therefore directed to the judicial exception. See MPEP 2106.05(f) and (g), respectively.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above in prong 2, the additional elements “one or more processors”, “one or more non-transitory computer-readable storage media…”, and “trained neural network model “, “input the obtained version…” and “initiate an upgrade process to install the compatible target service pack on the computer system” merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea, and the additional elements “obtained version information”; “received a prediction of a service pack version” are merely gathering data which the courts have identified as well-understood, routine conventional activity. Therefore, the additional elements do not amount to significantly more, thus, cannot provide an inventive concept. Accordingly, the claims are not patent eligible under 35 USC 101.
Regarding claim 2 and 12, the additional element “Convolutional Neural Network (CNN) model” recites at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or generic computer components. See MPEP 2106.05(f); Therefore, the additional elements as recited do not integrate the judicial exception into a practical application.
Regarding claim 4 the limitation “receive a probability score” is merely gathering data which the courts have identified as well-understood, routine conventional activity. Therefore, the additional elements do not amount to significantly more, thus, cannot provide an inventive concept. Accordingly, the claims are not patent eligible under 35 USC 101.
Regarding claim 5 and 10, the limitation “convert the obtained version into delineated string”; “converting the delineated string into tokens” recites additional mental process under Prong 1.
Regarding claim 6, 8 The limitation “Application Programming Interface” is the additional element that recites at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or generic computer components. See MPEP 2106.05(f); Therefore, the additional elements as recited do not integrate the judicial exception into a practical application.
Regarding claim 14, the limitation “determining an appropriate upgrade path” recites additional mental process under Prong 1.
Regarding claim 15, the limitation “predicting a base service pack version installed on the server node”, “determining a target service pack version and a supported upgrade path to reach the target service pack version based on the predicted base service pack version” recite the functions that can be mentally done in human mind under Prong I step 2A. The other limitations “ disaggregated hyper-converged infrastructure (dHCI) system “, “trained three-channel CNN model” is the additional element that recites at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or generic computer components. See MPEP 2106.05(f); Therefore, the additional elements as recited do not integrate the judicial exception into a practical application.
Regarding claim 16, the additional element “randomizing sub-component versions” recites at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or generic computer components. See MPEP 2106.05(f); Therefore, the additional elements as recited do not integrate the judicial exception into a practical application.
Regarding claim 17, the limitation “converting the augmented dataset into tokens using a predefined data map” recites additional mental process under Prong 1.
Regarding claim 19, the limitation “concatenating outputs into a single concatenated output” recites the function that can be mentally done in human mind under Prong I step 2A. The other limitations “three-channel CNN model” is the additional element that recites at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or generic computer components. See MPEP 2106.05(f); Therefore, the additional elements as recited do not integrate the judicial exception into a practical application.
Regarding claim 20, the limitation “determining whether the probability score meets or exceeds a predetermined threshold” recites additional mental process under Prong 1.
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.
Claim 1, 6,7, 8, 9, 10,14 are rejected under 35 U.S.C. 103 as being unpatentable over Lazar et al. (US 20260044332 A1) in view of Nagarajan et al. (US 20230418582 A1).
Regarding claim 1, Lazar teaches A system, comprising: one or more processors (Fig. 11 -1104 - Processing Unit); and one or more non-transitory computer-readable storage media (Fig. 11, 1112 - RAM) storing programming (Fig. 11, 1132 – Applications) for execution by the one or more processors, the programming comprising instructions to:
Lazar teaches obtain version information for a plurality of software and firmware sub-components of a service pack installed on a computer system (Fig 4 , 408 - [Identifying …]; Pg. 4, [0077]: ”Operation 408 depicts identifying software operating on a computer system, firmware components of the computer system that are to be upgraded, and a type of the computer system. That is, a current system to upgrade can be identified, including its software, server type, and what firmware it has.” - Examiner notes those with ordinary skill in the art would recognize the step of identifying software/firmware to be updated implicitly include obtaining versions currently installed on a computer system; Pg. 3 [0056]: “At inference time, the engine can be provided with information about SW and FW compatibility, priority of security patches and the type and characteristics of the server to upgrade, and produce a FW lineup, which can be considered a FW lineup that is considered to be most likely successful FW lineup. “; Examiner further notes software/firmware update under its broadest reasonable interpretation includes software/firmware patch which is a targeted fix for specific identified problem.);
Lazar teaches input the obtained version information into a trained neural network model (Fig. 4, 410 – [Inputting the software…]; Pg. 4, [0080], lines 1-3:” Operation 410 depicts inputting the software, the firmware components, and the type to a trained machine learning model”. Examiner notes machine learning is a broad field within Artificial Learning while neural networks are a subset of machine learning) ;
Lazar teaches determine, based on the predicted service pack version, a compatible target service pack for the computer system (Pg. 4, [0081]:” In some examples, the one or more firmware types are determined to be compatible with the software. In some examples, the one or more firmware types are determined to be compatible with the type of the computer system. That is, input data can be processed to produce an output that comprises a lineup of firmware components that are compatible with existing software applications that are executed on a given server type.”);
Lazar teaches initiate an upgrade process to install the compatible target service pack on the computer system (Fig. 4 [412] :”Upgrading One or More Firmware Components of The Computer System With the One or More Firmware Types Based on The Output]; Pg. 4, [0085]:” Operation 412 depicts upgrading the one or more firmware components of the computer system with the one or more firmware types based on the output. That is, the upgrade identified in operation 410 can be carried out.”; Fig. 1 Computer 102 initiates upgrade to the computer system 106).
Lazar does not teach receive, from the trained neural network model, a prediction of a service pack version corresponding to the plurality of software and firmware sub-components.
Nagarajan teaches receive, from the trained neural network model, a prediction of a service pack version corresponding to the plurality of software and firmware sub-components ( Nagarajan, Pg. 3, [0028] lines 10-13:” In addition, a need exists to automatically determine the current state of the servers and the OSs, and applications, to determine which servers require a pending update/patch.”; Pg. 6 [0046], lines 1-7 continue to Pg. 7, lines 1-3:” As part of the analysis, the update analysis engine may intelligently extract information, such as by using a trained predictive model, to identify one or more similar applications that may require coordination of updates and/or to identify each server of a plurality of servers that may require the update or patch to be installed. For example, the enterprise organization may have a multiple servers running different versions of a same operating system, such as in cases where one or more applications running on the server are not yet supported by an updated version.”; Pg. 7, lines 6-10:” By utilizing the predictive model the update analysis engine may properly identify versions of applications, operating systems, drivers, firmware, and/or the like, that require a particular patch or update.“ Examiner notes Nagarajan teaches automatically determine the current OS application using a trained predictive model. Examiner further notes identify versions of applications, operating systems, drivers, firmware, and/or the like that require patch or update inherently encompasses identifying service patch version using predictive model).
Lazar and Nagarajan are considered analogous to the claimed invention because they are both directed to system and method to identify versions and upgrade software/firmware applications using AI train model. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to utilize Nagarajan’s teaching of “receive, from the trained neural network model, a prediction of a service pack version corresponding to the plurality of software and firmware sub-components “ to Lazar’s invention in order to identify versions of applications, operating systems, drivers, firmware, and/or the like, that require a particular patch or update.
Regarding claim 6, Lazer in view Nagarajan teach wherein obtaining the version information for the plurality of software and firmware sub-components of the service pack installed on the computer system comprises sending a request, via an Application Programming Interface (API), to the computer system to retrieve the version information for the plurality of software and firmware sub-components (Lazar, Fig. 11, [Interface] 1124, [Interface] 1126, [Interface] 1128, [Input device interface] 1144; Pg. 9, [0169]:” As another example, an interface can include input/output (I/O) components as well as associated processor, application, and/or application programming interface (API) components.).
Claim 7, 8 correspond to computer-implemented claim of the system claims 1, 6; therefore, they are rejected under the same rationale of the computer-implemented method claim 1, 6 as shown above.
Regarding Claim 9, Lazar in view of Nagarajan teach wherein the plurality of software and firmware sub-components of the service pack comprise at least 20 sub-components (Nagarajan, Pg. 1 [0012]:” In many large enterprises, computing devices (e.g., workstations, servers and the like) or other networked computing devices are distributed globally across diverse computing network infrastructure. Often, various networked computing devices implement many different operating systems and facilitate execution of different software packages, applications, tools and the like. In some cases, a single server may host many different software packages, applications or the like.”. Examiner notes operating systems, software packages, application tools include many sub-components in the range of hundreds).
Regarding claim 10, Lazar in view of Nagarajan teach performing a first data preparation process on the obtained version information to remove invalid entries in the obtained version information (Nagarajan, Pg. 3, [0039]:” Computer system to upgrade 106 can be communicatively coupled to computer 102 via communications network 104. Computer 102 can receive an indication to select and validate firmware for an upgrade of computer system to upgrade 106, where at least some of firmware 112 is to be upgraded.” Examiner notes select and validation encompass selecting valid entries i.e. removing invalid ones.);
Regarding claim 14, Lazar in view of Nagarajan teach determining, based on the predicted service pack version, an appropriate upgrade path to reach the compatible target service pack for the computer system (Lazar, Pg. 2, lines 3-11:” [0031] In some examples, a component can detect the firmware updates for the given server in a cluster and find the list of firmware to be updated based on priority. For instance, firmware with security vulnerability fixes can be given precedence over normal firmware update. A latest firmware compatibility matrix can be maintained per workload vendor based on validation/dry run results or vendor's recommendation.”; Pg. 3, [0039]:”System architecture 200 comprises CI/CD pipeline 202 (software applications, firmware comps), firmware lineup selection and validation engine 204, anonymized upgraded data from current telecommunications deployments 206, determine firmware upgrade duration in telecommunications deployment 208, and outcome 210 (1. firmware lineup, 2. upgrade procedures for individual servers, 3. firmware upgrade duration, 4. final upgrade decision).”. Examiner notes firmware lineup, procedures , duration and decision are considered upgrade path. )
Claim 2,3, 5 12, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Lazar et al. (US 20260044332 A1) in view of Nagarajan (US 20230418582 A1) and in further view of Klainman et al. (US 20210049443 A1)
Regarding claim 2, Lazar in view of Nagarajan teach input the obtained version information into a trained neural network model (claim1). However, Lazar in view of Nagarajan do not teach the trained neural network model is a Convolutional Neural Network (CNN) model.
Klainman teaches the trained neural network model is a Convolutional Neural Network (CNN) model (Klainman, Pg. 1, [0044]:”In some variations, one or more features disclosed herein including the following features may optionally be included in any feasible combination. Each of the plurality of convolution layers may operate on the input to the dense convolutional neural network concatenated with outputs from all preceding convolution layers in the first dense convolution block “).
Lazar in view of Nagarajan and Klainman are considered analogous to the claimed invention because they are all directed to system and method to classify text (e.g. software/firmware/service pack versions) using AI trained neural network. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to utilize Klainman teaching of “trained neural network model is a Convolutional Neural Network (CNN) model“ to Lazar in view of Nagarajan’s invention in order to expand the claimed invention to different neural network model include Convolutional Neural Network (CNN) model which offers better solution to that the claimed invention try to address.
Regarding claim 3, Lazar in view of Nagarajan in view of Klainman teach the CNN model comprises three parallel networks, each of the three parallel networks having a different filter size from the other parallel networks (Klainman, Fig. 2, three parallel networks: 210a, 210bg, 210c and three different n-gram(filter size) 215a, 215b, 215c ; Pg. 4, [0035] :”As shown in FIG. 2, each of the first dense convolution block 210a, the second dense convolution block 210b, and the third dense convolution block 210c may operate in parallel on different sized n-grams to generate corresponding feature maps.” ).
Regarding claim 5, Lazar in view of Nagarajan in view of Klainman teach performing a data preparation process to convert the obtained version information into a delineated string (Klainman, Pg. 4, [0032], lines 1-8:” in some example embodiments, the classification engine 110 may be configured to preprocess the text 155 prior to applying the dense convolutional neural network 115. The classification engine 110 may preprocess the text 155 by at least tokenizing the text 155 to form a plurality of tokens, each of which corresponding to one or more characters that form a partial word or a full word from the text 155.” Examiner notes those of ordinary skill in the art would recognize tokenization is the process of splitting text into smaller units (tokens) by configurable delimiters (i.e. blank spaces, periods, commas; semi-colons etc. );
and converting the delineated string into tokens (As shown above :“The classification engine 110 may preprocess the text 155 by at least tokenizing the text 155 to form a plurality of tokens”) using a predefined data map (Klainman , Pg. 3, [0030], lines 1-5:”The dense convolutional neural network 115 may be trained based on training data. Each training sample in the training data may include text associated with at least one ground-truth label corresponding to a correct label for the text.” Examiner notes one ground-truth label corresponding to a correct label is predefined data map.).
Claim 10 corresponds to computer-implemented claim of the system claims 5; therefore, they are rejected under the same rationale of the system claims 2, 3 respectively as shown above.
Regarding claim 11, Lazar in view of Nagarajan in view of Klainman teach inputting the obtained version information into the trained neural network model further comprises inputting the tokens into the trained neural network model (Lazar, Fig. 4, 410 – [Inputting the software…]; Pg. 4, [0080], lines 1-3:” Operation 410 depicts inputting the software, the firmware components, and the type to a trained machine learning model”. Examiner notes machine learning is a broad field within Artificial Learning while neural networks are a subset of machine learning. Also as noted in claim 10 above, Lazar in view of Nagarajan in view of Klainman teach converting the obtained version information into a delineated string and converting the delineated string into tokens using a predefined data map therefore the input to the trained neural network model are the tokens from the output of claim 10).
Regarding claim 12, Lazar in view of Nagarajan in view of Klainman teach the trained neural network model is a Convolutional Neural Network (CNN) model (Klainman, Pg. 1, [0044]:” In some variations, one or more features disclosed herein including the following features may optionally be included in any feasible combination. Each of the plurality of convolution layers may operate on the input to the dense convolutional neural network concatenated with outputs from all preceding convolution layers in the first dense convolution block “).
Regarding claim 13, Lazar in view of Nagarajan in view of Klainman teach the CNN model comprises three parallel networks, each of the three parallel networks having a different filter size from the other parallel networks (Klainman, Fig. 2, three parallel networks: 210a, 210bg, 210c and three different n-gram(filter size) 215a, 215b, 215c ; Pg. 4, [0035] :”As shown in FIG. 2, each of the first dense convolution block 210a, the second dense convolution block 210b, and the third dense convolution block 210c may operate in parallel on different sized n-grams to generate corresponding feature maps.” ).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Lazar et al. (US 20260044332 A1) in view of Nagarajan (US 20230418582 A1) in view of Hatten (NPL Ediva Hatten - What is Confidence Score in Machine Learning. (Year: 2023))
Regarding claim 4, Lazar in view of Nagarajan do not teach The system of claim 1, wherein the programming comprises further instructions to: receive, from the trained neural network model, a probability score that indicates a confidence of the trained neural network model in the prediction of the service pack version.
Hatten teaches receive, from the trained neural network model, a probability score that indicates a confidence of the trained neural network model in the prediction of the service pack version (Hatten, Pg. 2, Definition of Confidence Score: “A confidence score is a numerical value assigned to a prediction made by a machine learning model. It represents the level of certainty or confidence that the model has in its prediction. In other words, it quantifies how reliable the machine learning algorithm believes its output to be.” Examiner notes level of certainty or confidence is probability score that indicates a confidence of the trained neural network model).
Lazar in view of Nagarajan and Hatten are considered analogous to the claimed invention because they are both directed to trained machine learning prediction. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to utilize Hatten’s teaching of “a probability score that indicates a confidence of the trained neural network model in the prediction of the service pack version “ to Lazar in view of Nagarajan’s invention in order to calculate respective risks and aiding enterprises in making their decisions on whether to upgrade.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Lazar et al. (US 20260044332 A1) in view of Schneider (NPL AAron Schneider - HPE dHCI for the Win! (Year: 2022) ) in view of Nagarajan (US 20230418582 A1) and in further view of Klainman et al. (US 20210049443 A1)
Regarding claim 15, Lazar teaches A computer-implemented method, comprising:
Lazar teaches fetching a list of discrete sub-component versions from a server node (Lazar, Fig 4 , 408 - [Identifying …]; Pg. 4, [0077]: ”Operation 408 depicts identifying software operating on a computer system, firmware components of the computer system that are to be upgraded, and a type of the computer system. That is, a current system to upgrade can be identified, including its software, server type, and what firmware it has.” - Examiner notes those with ordinary skill in the art would recognize the step of identifying software/firmware to be updated implicitly include obtaining versions currently installed on a computer system; Pg. 3 [0056]: “At inference time, the engine can be provided with information about SW and FW compatibility, priority of security patches and the type and characteristics of the server to upgrade, and produce a FW lineup, which can be considered a FW lineup that is considered to be most likely successful FW lineup. “; Examiner further notes software/firmware update under its broadest reasonable interpretation includes software/firmware patch which is a targeted fix for specific identified problem.)
using an Application Programming Interface (API) (Lazar, Fig. 11, [Interface] 1124, [Interface] 1126, [Interface] 1128, [Input device interface] 1144; Pg. 9, [0169]:” As another example, an interface can include input/output (I/O) components as well as associated processor, application, and/or application programming interface (API) components.);
Lazar does not teach the server node being part of a disaggregated hyper-converged infrastructure (dHCI) system;
Lazar teaches inputting the fetched sub-component versions into a trained neural network model (Fig. 4, 410 – [Inputting the software…]; Pg. 4, [0080], lines 1-3:” Operation 410 depicts inputting the software, the firmware components, and the type to a trained machine learning model”. Examiner notes machine learning is a broad field within Artificial Learning while neural networks are a subset of machine learning);
Lazar does not teach the trained neural networks is three-channel convolutional neural network (CNN) model;
Lazar does not teach predicting a base service pack version installed on the server node;
Lazar teaches determining a target service pack version (Pg. 4, [0081]:” In some examples, the one or more firmware types are determined to be compatible with the software. In some examples, the one or more firmware types are determined to be compatible with the type of the computer system. That is, input data can be processed to produce an output that comprises a lineup of firmware components that are compatible with existing software applications that are executed on a given server type.”)
and a supported upgrade path to reach the target service pack version based on the predicted base service pack version (Lazar, Pg. 2, lines 3-11:” [0031] In some examples, a component can detect the firmware updates for the given server in a cluster and find the list of firmware to be updated based on priority. For instance, firmware with security vulnerability fixes can be given precedence over normal firmware update. A latest firmware compatibility matrix can be maintained per workload vendor based on validation/dry run results or vendor's recommendation.”; Pg. 3, [0039]:”System architecture 200 comprises CI/CD pipeline 202 (software applications, firmware comps), firmware lineup selection and validation engine 204, anonymized upgraded data from current telecommunications deployments 206, determine firmware upgrade duration in telecommunications deployment 208, and outcome 210 (1. firmware lineup, 2. upgrade procedures for individual servers, 3. firmware upgrade duration, 4. final upgrade decision).”. Examiner notes firmware lineup, procedures , duration and decision are considered upgrade path. );
Lazar teaches and updating the server node by installing the target service pack version on the server node (Fig. 4 [412] :”Upgrading One or More Firmware Components of The Computer System With the One or More Firmware Types Based on The Output]; Pg. 4, [0085]:” Operation 412 depicts upgrading the one or more firmware components of the computer system with the one or more firmware types based on the output. That is, the upgrade identified in operation 410 can be carried out.”; Fig. 1 Computer 102 initiates upgrade to the computer system 106).
Lazar does not expressly teach the server node being part of a disaggregated hyper-converged infrastructure (dHCI) system;
Schneider teaches the server node being part of a disaggregated hyper-converged infrastructure (dHCI) system (NPL HPE dHCI for the Win! – Examiner notes the technical paper describes HPE solution for updating the firmware in a dHCI architecture);
Lazar and Schneider are considered analogous to the claimed invention because they are all directed to updating software/firmware to network clusters. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to utilize Schneider teaching of “server node being part of a disaggregated hyper-converged infrastructure (dHCI) system “ to Lazar’s invention in order to broaden the claimed invention to different server architecture including dHCI.
Lazar in view of Schneider do not teach the trained neural networks is three-channel convolutional neural network (CNN) model.
Klainman teaches the trained neural network model is three-channel convolutional neural network (CNN) model (Klainman, Fig. 2, three parallel networks: 210a, 210bg, 210c and three different n-gram(filter size) 215a, 215b, 215c ; Pg. 4, [0035] :”As shown in FIG. 2, each of the first dense convolution block 210a, the second dense convolution block 210b, and the third dense convolution block 210c may operate in parallel on different sized n-grams to generate corresponding feature maps.” ).
Lazar in view of Schneider and Klainman are considered analogous to the claimed invention because they are all directed to system and method to classify text (e.g. software/firmware/service pack versions) using AI trained neural network. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to utilize Klainman teaching of “trained neural network model is a Convolutional Neural Network (CNN) model“ to Lazar in view of Schneider’s invention in order to expand the claimed invention to different neural network model include Convolutional Neural Network (CNN) model which offers better solution to that the claimed invention try to address.
Lazar in view of Schneider and in view of Klainman do not teach predicting a base service pack version installed on the server node;
Nagarajan teaches predicting a base service pack version installed on the server node;( Nagarajan, Pg. 3, [0028] lines 10-13:” In addition, a need exists to automatically determine the current state of the servers and the OSs, and applications, to determine which servers require a pending update/patch.”; Pg. 6 [0046], lines 1-7 continue to Pg. 7, lines 1-3:” As part of the analysis, the update analysis engine may intelligently extract information, such as by using a trained predictive model, to identify one or more similar applications that may require coordination of updates and/or to identify each server of a plurality of servers that may require the update or patch to be installed. For example, the enterprise organization may have a multiple servers running different versions of a same operating system, such as in cases where one or more applications running on the server are not yet supported by an updated version.”; Pg. 7, lines 6-10:” By utilizing the predictive model the update analysis engine may properly identify versions of applications, operating systems, drivers, firmware, and/or the like, that require a particular patch or update.“ Examiner notes Nagarajan teaches automatically determine the current OS application using a trained predictive model. Examiner further notes identify versions of applications, operating systems, drivers, firmware, and/or the like that require patch or update inherently encompasses identifying service patch version using predictive model).;
Lazar in view of Schneider in view of Klainman and Nagarajan are considered analogous to the claimed invention because they are both directed to system and method to identify versions and upgrade software/firmware applications using AI train model. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to utilize Nagarajan’s teaching of “receive, from the trained neural network model, a prediction of a service pack version corresponding to the plurality of software and firmware sub-components “ to Lazar in view of Schneider in view of Klainman’s invention in order to identify versions of applications, operating systems, drivers, firmware, and/or the like, that require a particular patch or update.
Regarding claim 18, Lazar in view of Schneider in view of Klainman in view of Naragajan teach The computer-implemented method of claim 15, wherein each channel of the three-channel CNN model has a different filter size (Klainman, Fig. 2, three parallel networks: 210a, 210bg, 210c and three different n-gram(filter size) 215a, 215b, 215c ; Pg. 4, [0035] :”As shown in FIG. 2, each of the first dense convolution block 210a, the second dense convolution block 210b, and the third dense convolution block 210c may operate in parallel on different sized n-grams to generate corresponding feature maps.” Examiner notes n-gram size is filter size).
Regarding claim 19, Lazar in view of Schneider in view of Klainman in view of Naragajan teach The computer-implemented method of claim 15, wherein predicting the base service pack version installed on the server node using the trained three-channel CNN model comprises: concatenating outputs from all the channels of the trained three-channel CNN model into a single concatenated output; and passing the single concatenated output through a dense layer for classification (Klainman, Pg. 1, [0044]:”In some variations, one or more features disclosed herein including the following features may optionally be included in any feasible combination. Each of the plurality of convolution layers may operate on the input to the dense convolutional neural network concatenated with outputs from all preceding convolution layers in the first dense convolution block “).
Regarding claim 20, Lazar in view of Schneider in view of Klainman in view of Naragajan teach The computer-implemented method of claim 15, further comprising: generating, using the trained three-channel CNN model, a probability score that indicates a confidence level of the prediction of the base service pack version () ; and determining whether the probability score meets or exceeds a predetermined threshold value before proceeding with determining the target service pack version and the supported upgrade path (Klain, Pg. 4, [0033] lines 9-13: “In order to apply byte-pair-encoding, the classification engine 110 may generate, based on training data, a byte-pair-encoding vocabulary including partial words and/or full words that appear in the training data at an above-threshold frequency.” Examiner notes the step outlined above belongs to converting the determined service into tokens thus the step is before proceeding with determining the target service pack version and the supported upgrade path ).
Claim 16, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lazar (US 20260044332 A1) in view of Schneider (NPL AAron Schneider - HPE dHCI for the Win! (Year: 2022) ) in view of Klainman (US 20210049443 A1) in view of Naragajan (US 20230418582 A1) and in further view of De Souza (WO 2024254415 A1)
Regarding claim 16, Lazar in view of Schneider in view of Klainman in view of Naragajan do not teach The computer-implemented method of claim 15, further comprising training the three-channel CNN model, the training comprising randomizing sub-component versions across supported base service pack versions to generate an augmented dataset.
De Souza teaches The computer-implemented method of claim 15, further comprising training the three-channel CNN model, the training comprising randomizing sub-component versions across supported base service pack versions to generate an augmented dataset (De Souza, Pg. 8, [0025] lines 9-11: “After suitable TVAR parameters and functional forms are determined, the system 100 can plug them into the TVAR process formula in Equation (1) to create randomized augmented data for a given signal class).
Lazar in view of Schneider in view of Klainman in view of Naragajan and De Souza are considered analogous to the claimed invention because they are all directed to AI model training. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to utilize De Souza’s teaching of “training comprising randomizing sub-component versions across supported base service pack versions to generate an augmented dataset “ to Lazar in view of Schneider in view of Klainman in view of Naragajan ’s invention in order to create an randomized augmented data set to start the training of neural networks.
Regarding claim 17, Lazar in view of Schneider in view of Klainman in view of Naragajan in view of De Souza (WO 2024254415 A1) teaches The computer-implemented method of claim 16, wherein training the three-channel CNN model further comprises converting the augmented dataset into tokens using a predefined data map (Klainman , Pg. 3, [0030], lines 1-5:”The dense convolutional neural network 115 may be trained based on training data. Each training sample in the training data may include text associated with at least one ground-truth label corresponding to a correct label for the text.” Examiner notes one ground-truth label corresponding to a correct label is predefined data map; Pg. 4, [0032], lines 1-8:” in some example embodiments, the classification engine 110 may be configured to preprocess the text 155 prior to applying the dense convolutional neural network 115. The classification engine 110 may preprocess the text 155 by at least tokenizing the text 155 to form a plurality of tokens, each of which corresponding to one or more characters that form a partial word or a full word from the text 155);
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
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/Chat C Do/ Supervisory Patent Examiner, Art Unit 2193
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