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
1. This Office Action is in response to the amendment filed on 11/11/2025. Claims 1-20 are pending in this application. Claims 1, 8 and 14 are independent claims. This Office Action is made Final.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1, 8 and 14 are corresponding to one of four statutory categories including method, system, and method respectively under step 1. The claims 1, 8 and 14 similarly recite “a system for intelligent automatic resolution of inactive computer code in a multi cloud environment, the system comprising: a processing device; a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: accessing cloud data within a cloud computing environment, the cloud data comprising log files; processing the cloud data using an artificial intelligence ("Al") analyzer, wherein processing the cloud data comprises identifying one or more instances of inactive code, by parsing the cloud data and perform feature extraction to the cloud data to identify numerical features; computing a confidence level for each of the one or more instances of inactive code; determining whether the confidence level exceeds a defined threshold; and based on determining whether the confidence level exceeds the defined threshold, executing one or more remediation processes for each of the one or more instances of inactive code where the one or more remediation processes further comprises: generating, using the AI analyzer, replacement code, wherein the replacement code removes the one or more instances of inactive code from the cloud computing environment: testing the replacement code within a testing environment: and based on testing the replacement code within the testing environment, deploying the replacement code into the cloud computing environment”.
The limitation of the claims 1, 8 and 14 of “wherein processing the cloud data comprises identifying one or more instances of inactive code by parsing the cloud data and perform feature extraction to the cloud data to identify numerical features;” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “identifying”. For example, a human may identify one or more instances of inactive code by parsing the cloud data and perform feature extraction to the cloud data to identify numerical features with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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 under Step 2A Prong I.
The limitation of the claims 1, 8 and 14 of “computing a confidence level for each of the one or more instances of inactive code;” as a drafted is a mathematical operation that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “computing”. For example, a human may compute a confidence level for each of the one or more instances of inactive code with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mathematical Operations” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong I.
The limitation of the claims 1, 8 and 14 of “determining whether the confidence level exceeds a defined threshold;” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “determining”. For example, a human may determine whether the confidence level exceeds a defined threshold with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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 under Step 2A Prong I.
The limitation of the claims 1, 8 and 14 of “based on determining whether the confidence level exceeds the defined threshold, executing one or more remediation processes for each of the one or more instances of inactive code” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “executing remediation processes (i.e. suggesting/scheduling a remedy)”. For example, a human may suggest based on determining whether the confidence level exceeds the defined threshold, one or more remediation processes for each of the one or more instances of inactive code with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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 under Step 2A Prong I.
The limitation of the claims 1, 8 and 14 of “where the one or more remediation processes further comprises: generating, using the AI analyzer, replacement code, wherein the replacement code removes the one or more instances of inactive code from the cloud computing environment:” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “generating replacement code or removing inactive codes [changing code]”. For example, a human may generate replacement code, wherein the replacement code removes the one or more instances of inactive code from the cloud computing environment with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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 under Step 2A Prong I.
This judicial exception is not integrated into a practical application. In particular, the claims 1, 8 and 14 recite additional elements such as “accessing cloud data within a cloud computing environment, the cloud data comprising log files;”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data gathering under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 1, 8 and 14 recite additional elements such as “processing the cloud data using an artificial intelligence ("Al") analyzer,”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data gathering under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional elements such as “the system comprising: a processing device; a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps” and the claim 8 recites additional elements such as “the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to perform the steps”.
Examiner would like to point out that with the broad reasonable interpretation, this element
amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which
does not impose any meaningful limits on practicing the mental process. Accordingly, this additional
element does not integrate the abstract idea into a practical application because it does not impose any
meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 1, 8 and 14 recite additional elements such as “testing the replacement code within a testing environment: and based on testing the replacement code within the testing environment, deploying the replacement code into the cloud computing environment”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to apply it under MPEP § 2106.05(f): Mere Instructions to Apply an Exception, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
The limitation of the claims 2, 9 and 15 of “detecting that the confidence level exceeds the defined threshold; and executing an automated resolution process on the one or more instances of inactive code” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “detecting” and “executing resolution process (i.e. suggesting or scheduling a resolution)”. For example, a human may detect that the confidence level exceeds the defined threshold; and suggest an automated resolution process on the one or more instances of inactive code with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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 under Step 2A Prong I.
The limitation of the claims 3, 10 and 16 of “generating, using the Al analyzer, replacement code, wherein the replacement code removes the one or more instances of inactive code from the cloud computing environment;” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “generating (writing)”. For example, a human may write, using the Al analyzer, replacement code, wherein the replacement code removes the one or more instances of inactive code from the cloud computing environment with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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 under Step 2A Prong I.
This judicial exception is not integrated into a practical application. In particular, the claims 3, 10 and 16 recite additional elements such as “testing the replacement code within a testing environment; and based on testing the replacement code within the testing environment, deploying the replacement code into the cloud computing environment”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to apply it under MPEP § 2106.05(f): Mere Instructions to Apply an Exception, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
The limitation of the claims 4, 11 and 17 of “detecting that the confidence level falls below the defined threshold” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “detecting”. For example, a human may detect that the confidence level falls below the defined threshold with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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 under Step 2A Prong I.
This judicial exception is not integrated into a practical application. In particular, the claims 4, 11 and 17 recite additional elements such as “transmitting a notification to a user computing device associated with the one or more instances of inactive code, wherein the notification comprises a report generated by the AI analyzer regarding the one or more instances of inactive code”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data gathering under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 5, 12 and 18 recite additional elements such as “the report comprises the confidence level”.
Examiner would like to point out that with the broad reasonable interpretation, this element
amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which
does not impose any meaningful limits on practicing the mental process. Accordingly, this additional
element does not integrate the abstract idea into a practical application because it does not impose any
meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
The limitation of the claims 6, 13 and 19 of “parsing and performing feature extraction of the log files to generate numerical features from the log files; and using one or more deep learning based processes to analyze the log files based on the numerical features” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “parsing” and “analyzing”. For example, a human may parse or extract feature of the log files to generate numerical features from the log files; and using one or more deep-learning-based processes, analyze the log files based on the numerical features with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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 under Step 2A Prong I.
This judicial exception is not integrated into a practical application. In particular, the claims 7 and 20 recite additional elements such as “the one or more deep learning based processes are based on a deep convolutional neural network”.
Examiner would like to point out that with the broad reasonable interpretation, this element
amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which
does not impose any meaningful limits on practicing the mental process. Accordingly, this additional
element does not integrate the abstract idea into a practical application because it does not impose any
meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
Dependent claims 2-7, 9-13 and 15-20 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-7, 9-13 and 15-20 are also rejected for incorporating the deficiency of their independent claims 1, 8 and 14 respectively.
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. Claims 1, 2, 4, 6-9, 11, 13-15, 17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Seck (US PGPub 20250061089), in view of Beckwith (US PGPub 20090138847) and further in view of Hicks (US PGPub 20220188098).
As per Claim 1, Seck teaches of a system for intelligent automatic resolution of inactive computer code in a multi cloud environment, the system comprising: a processing device; a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: accessing cloud data within a cloud computing environment, the cloud data comprising log files; (Par 35, Other removal conditions may include the log file being associated with a disabled monitoring program (e.g., disabled as described above or disabled in the past by a user) and/or the log file being associated with a cloud-based application or an API endpoint that is deprecated or otherwise discontinued, among other examples. Par 41, The log storage may include a cloud storage associated with the log file. The command may trigger the log storage to delete the log file, which in turn reduces memory space consumed at the log storage.)
processing the cloud data using an artificial intelligence ("Al") analyzer, wherein processing the cloud data comprises identifying one or more instances of inactive code; (Par 23-24, As by reference number 125, the redundancy system may apply a machine learning model to the log files to detect redundancies (e.g., one or more redundancies). The machine learning model may be trained and used as described in connection with FIGS. 2A-2B. For example, the machine learning model may determine which files, out of the plurality of log files, are redundant as compared with remaining files out of the plurality of log files. Par 62, As another example, if the machine learning system were to predict a value of “Log 2” for the target variable of an identified redundancy, then the machine learning system may provide a different recommendation (e.g., recommending a different monitoring program to disable) and/or may perform or cause performance of a different automated action (e.g., disabling a monitoring program associated with Log 2). Par 65, In this way, the machine learning system may apply a rigorous and automated process to identifying redundancies in log files. The machine learning system may enable recognition and/or identification of tens, hundreds, thousands, or millions of features and/or feature values for tens, hundreds, thousands, or millions of observations, thereby increasing accuracy and consistency and reducing delay associated with disabling monitoring programs relative to requiring computing resources to be allocated for tens, hundreds, or thousands of operators to disable the monitoring programs based on the log files.)
… by parsing the cloud data and perform feature extraction to the cloud data to identify numerical features (Par 48, In some implementations, the machine learning system may determine variables for a set of observations and/or variable values for a specific observation based on input received from the redundancy system. For example, the machine learning system may identify a feature set (e.g., one or more features and/or corresponding feature values) from structured data input to the machine learning system, such as by extracting data [cloud data] from a particular column of a table, extracting data from a particular field of a form and/or a message, and/or extracting data received in a structured data format. Additionally, or alternatively, the machine learning system may receive input from an operator to determine features and/or feature values. In some implementations, the machine learning system may perform natural language processing and/or another feature identification technique to extract features (e.g., variables [numerical features]) and/or feature values (e.g., variable values) from text (e.g., unstructured data) input to the machine learning system, such as by identifying keywords and/or values associated with those keywords from the text.)
Seck does not specifically teach, however Beckwith teaches of computing a confidence level for each of the one or more instances of inactive code; (Par 38, If not, all references to that virtual function are deleted from the dispatch tables. Otherwise, the invention finds the highest inheritance level [confidence level for “the inactive or the unused”] associated with a virtual function call. All references to virtual functions above the found highest inheritance level are designated as "unused" and those below the highest inheritance level are designated as "used." The invention then deletes all references to the "unused" virtual functions. Par 29-35, The first code reduction optimization for this example occurs if the program does not ever call any of the foo( ) virtual functions. In this case all references to A::foo( ), B::foo( ), C::foo( ) and D::foo( ) are eliminated from the dispatch tables thus allowing a program-build-tool-chain to eliminate the unused foo( ) virtual functions.)
determining whether the confidence level exceeds a defined threshold; and (Par 48, All references to object oriented methods that are not called by the application are also designated as unused, block 120. The highest inheritance level associated with an object oriented method is found, block 130. All references to object oriented methods above the highest inherence level are designated as unused, block 140. All references to object oriented methods below the highest inherence level are designate as used, block 150. All references to object oriented methods that are designated as unused are deleted from the dispatch table, block 160.)
based on determining whether the confidence level exceeds the defined threshold, executing one or more remediation processes for each of the one or more instances of inactive code. (Par 19, The highest inheritance level associated with an object oriented method is found. All references to object oriented methods above the highest inherence level are designated as unused. All references to object oriented methods below the highest inherence level are designate as used. All references to object oriented methods that are designated as unused are deleted from the dispatch table. Par 20, all references to object oriented methods that are not called by the application are also designated as unused and deleted [remediation process] from the dispatch table. Par 38, Otherwise, the invention finds the highest inheritance level associated with a virtual function call. All references to virtual functions above the found highest inheritance level are designated as "unused" and those below the highest inheritance level are designated as "used." The invention then deletes all references to the "unused" virtual functions.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add computing a confidence level for each of the one or more instances of inactive code; determining whether the confidence level exceeds a defined threshold; and based on determining whether the confidence level exceeds the defined threshold, executing one or more remediation processes for each of the one or more instances of inactive code, as conceptually seen from the teaching of Beckwith, into that of Seck because this modification can help measure the reliability of the application potentially containing unused codes by prioritizing unused code removal based on confidence or critical level.
Neither Seck nor Beckwith specifically teaches however Hicks teaches of where the one or more remediation processes further comprises: generating, using the Al analyzer, replacement code, wherein the replacement code removes the one or more instances of inactive code from the cloud computing environment; (Par 4, recommending, by the one or more processors, an alternative software source code for use in the software source code module to replace the deprecated software source code to the first one or more software developers. And Par 18-19, The predictions can include potential solutions, i.e., source code to replace the deprecated source code, based on source code replacing the same or similar source code in other parts of the same application or different applications.)
testing the replacement code within a testing environment; and based on testing the replacement code within the testing environment, deploying the replacement code into the cloud computing environment. (Par 19, The embodiments can provide predictions on the amount of testing required for the source code replacing the deprecated source code. The amount of testing is based on factors such as, but not limited to, the number of locations the replacement source code is employed, the length of time the replacement source code has been deployed in similar functional areas, the volume of calls to the replacement source code, etc. Par 65, Deprecated source code replacement component 404 can provide predictions of the level of testing, both unit testing and functional testing, suggested for source code replacement of deprecated source code.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add generating, using the Al analyzer, replacement code, wherein the replacement code removes the one or more instances of inactive code from the cloud computing environment; testing the replacement code within a testing environment; and based on testing the replacement code within the testing environment, deploying the replacement code into the cloud computing environment, as conceptually seen from the teaching of Hicks, into that of Seck and Beckwith because this modification can help eliminate unused codes by reducing the overall size and memory utilization while replacing them with alternative codes for better and accurate performance.
As per Claim 2, Seck further teaches of the system of claim 1, wherein determining whether the confidence level exceeds the defined threshold comprises: detecting that the confidence level exceeds the defined threshold; and executing an automated resolution process on the one or more instances of inactive code. (Par 62, the trained machine learning model 245 may predict a value of “Log 1” for the target variable of an identified redundancy for the new observation, as shown by reference number 255. Based on this prediction (e.g., based on the value having a particular label or classification or based on the value satisfying or failing to satisfy a threshold), the machine learning system may provide a recommendation and/or output for determination of a recommendation, such as recommending a monitoring program to disable. Additionally, or alternatively, the machine learning system may perform an automated action and/or may cause an automated action to be performed (e.g., by instructing another device to perform the automated action), such as disabling a monitoring program associated with Log 1. As another example, if the machine learning system were to predict a value of “Log 2” for the target variable of an identified redundancy, then the machine learning system may provide a different recommendation (e.g., recommending a different monitoring program to disable) and/or may perform or cause performance of a different automated action (e.g., disabling a monitoring program associated with Log 2). In some implementations, the recommendation and/or the automated action may be based on the target variable value having a particular label (e.g., classification or categorization) and/or may be based on whether the target variable value satisfies one or more threshold (e.g., whether the target variable value is greater than a threshold, is less than a threshold, is equal to a threshold, or falls within a range of threshold values).)
As per Claim 4, neither Seck nor Beckwith specifically teaches however Hicks teaches of the system of claim 1, wherein determining whether the confidence level exceeds the defined threshold comprises: detecting that the confidence level falls below the defined threshold; and transmitting a notification to a user computing device associated with the one or more instances of inactive code, wherein the notification comprises a report generated by the AI analyzer regarding the one or more instances of inactive code. (Par 63, In another aspect deprecated source code replacement component 404 can alert the identified software developers based on automation tools, e.g., a tool comprising a continuous integration and continuous deployment pipeline. Par 66, At step 504, the embodiment can alert, via deprecated source code replacement component 404, software developers to the use of deprecated software source code. At step 506, the embodiment can recommend, via deprecated source code replacement component 404, alternative software source code to the developers. Claim 1, alerting, by the one or more processors, a first one or more software developers responsible for maintaining a software source code module using the deprecated software source code. Par 20, responsive to identifying deprecated software source code, alerting, by the one or more processors, one or more software developers responsible for maintaining a software source code module containing the deprecated software source code. It’s obvious to only alert the developer of the application containing potential unused codes if the unused code is not critical or minimal or not found.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add detecting that the confidence level falls below the defined threshold; and transmitting a notification to a user computing device associated with the one or more instances of inactive code, wherein the notification comprises a report generated by the AI analyzer regarding the one or more instances of inactive code, as conceptually seen from the teaching of Hicks, into that of Seck and Beckwith because this modification can help eliminate unused codes by reducing the overall size and memory utilization while replacing them with alternative codes for better and accurate performance.
As per Claim 6, Seck further teaches of the system of claim 1, wherein processing the cloud data using the AI analyzer comprises: using one or more deep learning based processes to analyze the log files based on the numerical features. (Par 48, In some implementations, the machine learning system may determine variables for a set of observations and/or variable values for a specific observation based on input received from the redundancy system. For example, the machine learning system may identify a feature set (e.g., one or more features and/or corresponding feature values) from structured data input to the machine learning system, such as by extracting data from a particular column of a table, extracting data from a particular field of a form and/or a message, and/or extracting data received in a structured data format. Additionally, or alternatively, the machine learning system may receive input from an operator to determine features and/or feature values. In some implementations, the machine learning system may perform natural language processing and/or another feature identification technique to extract features (e.g., variables) and/or feature values (e.g., variable values) from text (e.g., unstructured data) input to the machine learning system, such as by identifying keywords and/or values associated with those keywords from the text.)
As per Claim 7, Seck further teaches of the system of claim 1, wherein the one or more deep learning based processes are based on a deep convolutional neural network. (Par 66, For example, the machine learning model may be trained using a different process than what is described in connection with FIG. 2A. Additionally, or alternatively, the machine learning model may employ a different machine learning algorithm than what is described in connection with FIGS. 2A-2B, such as a Bayesian estimation algorithm, a k-nearest neighbor algorithm, an a priori algorithm, a k-means algorithm, a support vector machine algorithm, a neural network algorithm (e.g., a convolutional neural network algorithm), and/or a deep learning algorithm.)
Re Claim 8, it is the product claim, having similar limitations of claim 1. Thus, claim 8 is also rejected
under the similar rationale as cited in the rejection of claim 1.
Re Claim 9, it is the product claim, having similar limitations of claim 2. Thus, claim 9 is also rejected
under the similar rationale as cited in the rejection of claim 2.
Re Claim 11, it is the product claim, having similar limitations of claim 4. Thus, claim 11 is also rejected
under the similar rationale as cited in the rejection of claim 4.
Re Claim 13, it is the product claim, having similar limitations of claim 6. Thus, claim 13 is also rejected
under the similar rationale as cited in the rejection of claim 6.
Re Claim 14, it is the method claim, having similar limitations of claim 1. Thus, claim 14 is also rejected
under the similar rationale as cited in the rejection of claim 1.
Re Claim 15, it is the method claim, having similar limitations of claim 2. Thus, claim 15 is also rejected
under the similar rationale as cited in the rejection of claim 2.
Re Claim 17, it is the method claim, having similar limitations of claim 4. Thus, claim 17 is also rejected
under the similar rationale as cited in the rejection of claim 4.
Re Claim 19, it is the method claim, having similar limitations of claim 6. Thus, claim 19 is also rejected
under the similar rationale as cited in the rejection of claim 6.
Re Claim 20, it is the method claim, having similar limitations of claim 7. Thus, claim 20 is also rejected
under the similar rationale as cited in the rejection of claim 7.
8. Claims 5, 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Seck (US PGPub 20250061089), in view of Beckwith (US PGPub 20090138847), and in view of Hicks (US PGPub 20220188098), and further in view of Jonna (US PGPub 20220019419).
As per Claim 5, none of Seck, Beckwith and Hicks specifically teaches however Jonna teaches of the system of claim 4, wherein the report comprises the confidence level. (Par 33, Regardless of whether the deployment is a success or a failure, the deployment evaluation system may use the results of the deployment to determine what aspects of the proposed upgrade caused the success or failure. The deployment evaluation system may also examine proposed upgrades with low trust scores to determine what caused the low trust score. This data may all be fed back into the processes of performing the natural language synthesis, generating the trust score, identifying the inactive code, and the like, in order to more accurately predict the likelihood of successfully deploying future upgrades and to generate better and more complete deployment reports. Par 62, For example, the evaluation data 422 may include deployment reports on proposed upgrades generated by the natural language synthesis application 410, trust scores generated by the trust score application 412, identifications of active versus inactive code on the platform(s) to be upgraded that are determined by the active code segregator application 414, and the experiences documented by the experiences interpreter application 416, as discussed in further detail below.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add the report comprises the confidence level, as conceptually seen from the teaching of Jonna, into that of Seck, Beckwith and Hicks because this modification can help measure the reliability of the application potentially containing unused codes by prioritizing unused code removal based on confidence or critical level.
Re Claim 12, it is the product claim, having similar limitations of claim 5. Thus, claim 12 is also rejected
under the similar rationale as cited in the rejection of claim 5.
Re Claim 18, it is the method claim, having similar limitations of claim 5. Thus, claim 18 is also rejected
under the similar rationale as cited in the rejection of claim 5.
Response to Arguments
Applicant's arguments with respect to claims 1, 8 and 14 have been fully considered but they are not persuasive.
Regarding the first argument of the remark on pages 9-12 with respect to 101 Abstract Idea rejection that the amendment would integrate a judicial exception into a practical application, the examiner would like to point out that in order to determine if additional element is integrating the abstract idea into a practical application, 1) The specification should describe the claimed improvement to achieve the desired goal and 2) The claimed improvement should be reflected at least in the additional elements by specifying how the claimed improvement performs the additional element to improve functioning of a computer or existing technical field.
2106.05(a) Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field [R-07.2022]
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art.
I. RELEVANT CONSIDERATIONS FOR EVALUATING WHETHER ADDITIONAL ELEMENTS INTEGRATE A JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION
The Supreme Court and Federal Circuit have identified a number of considerations as relevant to the evaluation of whether the claimed additional elements demonstrate that a claim is directed to patent-eligible subject matter. The list of considerations here is not intended to be exclusive or limiting. Additional elements can often be analyzed based on more than one type of consideration and the type of consideration is of no import to the eligibility analysis. Additional discussion of these considerations, and how they were applied in particular judicial decisions, is provided in MPEP § 2106.05(a) through (c) and MPEP § 2106.05(e) through (h).
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Regarding the second argument of the remark on pages 13-14 that the prior art does not teach of automatic resolution of inactive computer code by identifying instances of inactive code by performing feature extraction to identify numerical features, the examiner would like to point out that Seck teaches in Par 23-24, As by reference number 125, the redundancy system may apply a machine learning model to the log files to detect redundancies (e.g., one or more redundancies). The machine learning model may be trained and used as described in connection with FIGS. 2A-2B. For example, the machine learning model may determine which files, out of the plurality of log files, are redundant as compared with remaining files out of the plurality of log files. Par 62, As another example, if the machine learning system were to predict a value of “Log 2” for the target variable of an identified redundancy, then the machine learning system may provide a different recommendation (e.g., recommending a different monitoring program to disable) and/or may perform or cause performance of a different automated action (e.g., disabling a monitoring program associated with Log 2). Par 65, In this way, the machine learning system may apply a rigorous and automated process to identifying redundancies in log files. The machine learning system may enable recognition and/or identification of tens, hundreds, thousands, or millions of features and/or feature values for tens, hundreds, thousands, or millions of observations, thereby increasing accuracy and consistency and reducing delay associated with disabling monitoring programs relative to requiring computing resources to be allocated for tens, hundreds, or thousands of operators to disable the monitoring programs based on the log files. Par 48, In some implementations, the machine learning system may determine variables for a set of observations and/or variable values for a specific observation based on input received from the redundancy system. For example, the machine learning system may identify a feature set (e.g., one or more features and/or corresponding feature values) from structured data input to the machine learning system, such as by extracting data [cloud data] from a particular column of a table, extracting data from a particular field of a form and/or a message, and/or extracting data received in a structured data format. Additionally, or alternatively, the machine learning system may receive input from an operator to determine features and/or feature values. In some implementations, the machine learning system may perform natural language processing and/or another feature identification technique to extract features (e.g., variables [numerical features]) and/or feature values (e.g., variable values) from text (e.g., unstructured data) input to the machine learning system, such as by identifying keywords and/or values associated with those keywords from the text.
And also Hicks teaches of removing the inactive or deprecated code and testing/deploying in par 19, The embodiments can provide predictions on the amount of testing required for the source code replacing the deprecated source code. The amount of testing is based on factors such as, but not limited to, the number of locations the replacement source code is employed, the length of time the replacement source code has been deployed in similar functional areas, the volume of calls to the replacement source code, etc. Par 65, Deprecated source code replacement component 404 can provide predictions of the level of testing, both unit testing and functional testing, suggested for source code replacement of deprecated source code.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAE UK JEON whose telephone number is (571)270-3649. The examiner can normally be reached 9am-6pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chat Do can be reached at 571-272-3721. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JAE U JEON/Primary Examiner, Art Unit 2193