Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending in this office action.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claims
Place holders
Functions
1
one or more computing devices
Identify, cause to host, present, cause to initiate
2
one or more computing devices
obtain
3
one or more computing devices
obtain
4
one or more computing devices
Cause to host
5
one or more computing devices
initiate
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Manohar et al US12223456B1 in view of Darji et al US20250044991A1.
With respect to claim 1, Manohar discloses a system, comprising: one or more computing devices, wherein the one or more computing devices include instructions that upon execution on or across the one or more computing devices cause the one or more computing devices to:
Col 2 lines 60-65” The computer-implemented includes obtaining, by one or more hardware processors, one or more items of data from at least one of: one or more electronic devices associated with one or more users and one or more databases”;
identify, at an optimization automation service of a cloud computing environment:
This element is interpreted under 35 U.S.C. 112(f) as the device if fig. 9 with a processor 9010 executing steps of 0019 to carry step a and b below.
Manohar discloses a computer system 102 with hardware processor 112 to identify insight into optimization of services using steps of col 24 lines 63-65.
Examiner interpretation:
col 24 lines 63-65” The one or more insights generated from the computer-implemented system 102 may contribute to more informed automated decision-making. The computer-implemented system 102 may utilize the one or more insights to proactively address issues, optimize performance, and generate strategic decisions related to service levels”.
(a) a plurality of user engagement optimization task categories of a target service:
Col18 lines 27-30 “Upon obtaining the one or more data from at least one of: one or more electronic devices 106 associated with the one or more users and the one or more databases 104”.
and (b) a plurality of dynamically updated data sources associated with the target service, including at least a first data source comprising documentation in natural language:
col 24 lines 50-55 “The determination of the semantics and structure of the natural language texts, the real-time data and the utilization of the pre-defined rules, by the large language model, may contribute to the development of an efficient computer-implemented system 102 (i.e., an automated SLA and SLO system) to generate corrective actions if needed. “;
cause, corresponding to individual ones of the plurality of user engagement optimization task categories, (a) respective task proposer agents and (b) respective task implementer agents to be hosted at the cloud computing environment:
This element is interpreted under 35 U.S.C. 112(f) as the device if fig. 9 with a processor 9010 executing steps of 0020 to carry step a and b below.
Manohar discloses a computer system 102 with hardware processor 112 to implement agents for identification and implementations of tasks using steps col 6 lines 46-56.
Examiner interpretation:
Col 6 lines46-56” (b) generating, by the one or more hardware processors, one or more plan trajectories to accomplish in meeting the one or more service level objectives (SLOs) based on the initial state of the one or more service level objectives (SLOs) and associated metrics, by querying the first artificial intelligence (AI) model from the Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent; (c) generating, by the one or more hardware processors, the one or more actions, to be applied to the one or more environments,”
wherein individual ones of the respective task proposer agents utilize a respective language model, and wherein individual ones of the respective task implementer agents utilize a respective language model:
Col 6 lines 55-65” by querying the first artificial intelligence (AI) model from the Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent; (c) generating, by the one or more hardware processors, the one or more actions, to be applied to the one or more environments, based on at least one of: current state of the one or more service level objectives (SLOs) and probabilities of meeting the one or more service level objectives (SLOs), by at least one of: learning, planning procedures, and resembling actor-critic updates for Partial Observable Markov Decision Processes (POMDPs), wherein the Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent with the Partial Observable Markov Decision Processes (POMDPs) is configured to monitor the one or more services for adjusting one or more governance principles in the one or more environments;
automatically present, in response to detection of a first triggering condition, a first prompt to a first language model of a first task proposer agent of the respective task proposer agents, without receiving a request from an end user of the optimization automation service to invoke the first task proposer agent:
This element is interpreted under 35 U.S.C. 112(f) as the device if fig. 9 with a processor 9010 executing steps of 0021 to input a prompt to LLM.
Manohar discloses a computer system 102 with hardware processor 112 to allow the LLM to receive real-time data and SLA without user intervention using steps of Col42 lines 51-65.
Examiner interpretation:
col 42 lines 51-65” At step 2308, the one or more first real-time data including the one or more actual performance levels, and the one or more service level indictors (SLIs), of the one or more services associated with the one or more applications, are obtained from the one or more monitoring platforms. At step 2310, the first artificial intelligence (AI) model (e.g., the large language model (LLM)) determines whether the one or more actual performance levels of the one or more services associated with the one or more applications, are compliant with the one or more expected performance levels by comparing the one or more actual performance levels with one or more pre-defined key performance indicators (KPIs) and the one or more pre-defined goals of the one or more services defined in at least one of: the service level agreements (SLAs) and the service level objectives (SLOs).
wherein the first prompt instructs the first language model to, based at least in part on analysis of content of the first data source, identify a first candidate optimization task of a first user engagement optimization task category of the plurality of user engagement optimization task categories:
col43 lines 13-20 “At step 2314, the one or more insights associated with one or more actions, to be applied to one or more corresponding services, to be performed to automatically manage the one or more applications, are generated, based on the automatic updates on the one or more service level objectives (SLOs) and associated metrics corresponding to the one or more services specified in the one or more service level agreements (SLAs), using the first artificial intelligence (AI) model”;
present, via one or more programmatic interfaces to a first end user of the optimization automation service, in response to detection of a second triggering condition, (a) a natural language representation of the first candidate optimization task:
This element is interpreted under 35 U.S.C. 112(f) as the device if fig. 9 with a processor 9010 executing steps of 0022 to present/display the action to be implemented using steps of [0022]
Manohar discloses a computer system 102 with hardware processor 112 to allow environments 116 to receive the actions that will be implemented using steps of col 43 lines 22-25
Examiner interpretation:
Col 43 lines 22-25 “At step 2316, the information associated with the one or more actions, to be applied to the one or more corresponding services to automatically manage the one or more applications, are provided to the one or more environments 116.”.
But not explicitly:
and (b) one or more reasons for presentation of the first candidate optimization task to the first end user.
in response to obtaining approval, via the one or more programmatic interfaces of the first candidate optimization task from the first end user, cause a first task implementer agent of the respective task implementer agents to initiate, using at least a first language model of the first task implementer agent, the first candidate optimization task;
and present, via the one or more programmatic interfaces, an indication of a change, subsequent to implementation of the first candidate optimization task, in a metric of the target service:
Darji discloses:
and (b) one or more reasons for presentation of the first candidate optimization task to the first end user.
[0266] “In some embodiments, the input data to the LLM may include a prompt indicating that the LLM should generate code that, when applied to the code of the application that experienced the error, fixes or remediates the error. In some embodiments, the prompt may include an indication of the particular error to be fixed. For example, the prompt may indicate a particular error code, a line of source code where the error was detected, and the like. In some embodiments, these indications of the error may be included in the data describing the error input to the LLM”.
in response to obtaining approval, via the one or more programmatic interfaces of the first candidate optimization task from the first end user, cause a first task implementer agent of the respective task implementer agents to initiate, using at least a first language model of the first task implementer agent, the first candidate optimization task;
This element is interpreted under 35 U.S.C. 112(f) as the device if fig. 9 with a processor 9010 executing steps of 0022 after approval to start the task optimization implementation.
Darji discloses a computer system 102 with hardware processor 112 to allow implementations of the optimized task after receiving user approval, using steps of [0289].
Examiner interpretation:
[0289] The method of FIG. 9 differs from FIG. 4 in that updating 410 the software deployment based on the verified code update includes requesting 902 an approval of the verified code update. In some embodiments, updating 410 the software deployment based on the verified code update may require manual (e.g., user) approval. Accordingly, in some embodiments, an approval of the verified code update may be request 902 in response to verifying 406 the code update. For example, in some embodiments, a message or notification may be sent to a particular user (e.g., a predesignated user, a user of a predesignated role, and the like) or potentially multiple users requesting approval of the verified code update. As another example, a pull request to merge the code update into a code base may be generated, with the pull request requiring user approval.
and present, via the one or more programmatic interfaces, an indication of a change, subsequent to implementation of the first candidate optimization task, in a metric of the target service:
This element is interpreted under 35 U.S.C. 112(f) as the device if fig. 9 with a processor 9010 executing steps of 0022 to present implementation optimization to user based on performance [0022].
Darji discloses a computer system 102 with hardware processor 112 to allow implementations of the optimized task after receiving user approval, present changes implemented using steps of [0290].
Examiner interpretation:
[0290]For example, in some embodiments, the request for approval may indicate the particular code changes performed by the code update, including a location in the code that was updated, a state of the code both before and after applying the code update, a marked up or notated version of the code indicating the particular changes performed by the code update, and the like….. As a further example, in some embodiments, the request for approval may indicate the output of executing test cases before and/or after updating 408 the cloned software deployment based on the code update”.
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Darji into teachings of Manohar to efficiently communicate within a distributed system. With zones aligned to the internal physical properties of the device, several inefficiencies in the placement of data can be eliminated. In such embodiments, each zone may be mapped, for example, to a separate application such that functions like wear levelling and garbage collection could be performed on a per-zone or per-application basis rather than across the entire device. [Darji 0194].
As per claim 2, the rejection of claim 1 is incorporated and furthermore Manohar discloses:
obtain, from an administrator of the target service, via one or more additional programmatic interfaces, an indication of one or more of (a) the first user engagement optimization task category or (b) the first data source:
This element is interpreted under 35 U.S.C. 112(f) as the device if fig. 9 with a processor 9010 executing steps of 0023 to receive data information.
Manohar discloses a computer system 102 with hardware processor 112 to obtain real-time data using steps of col 20lines 16-20.
Examiner interpretation:
col 20 lines 16-20 “For extracting the one or more second real-time data from the one or more first real-time data, the data extraction subsystem 220 is configured to obtain the one or more first real-time data from the one or more monitoring platforms using a data ingestion layer. “;
As per claim 3, the rejection of claim 1 is incorporated and furthermore Manohar discloses:
obtain, from an administrator of the target service, via one or more additional programmatic interfaces, an indication of one or more of: (a) the first triggering condition or (b) the second triggering condition.
This element is interpreted under 35 U.S.C. 112(f) as the device if fig. 9 with a processor 9010 executing steps of [0034] to receive/detect triggering condition.
Manohar discloses a computer system 102 with hardware processor 112 to detect condition triggering optimization and adapt to it using steps of col 22 lines 41-47.
Examiner interpretation:
col 22 lines 41-47 “The decision-making subsystem 216 is further configured to optimize the generated one or more actions to adapt with evolving conditions within the one or more environments 116 by updating the learning, planning procedures. The decision-making subsystem 216 is further configured to provide the optimized one or more actions to the one or more environments 116 to accomplish in meeting the one or more service level objectives (SLOs)”;
As per claim 4, the rejection of claim 1 is incorporated and furthermore Manohar discloses:
cause, based at least in part on input received via one or more additional programmatic interfaces, another task proposer agent to be hosted at the cloud computing environment, wherein the other task proposer agent is configured to identify candidate optimization tasks of another user engagement optimization task category:
This element is interpreted under 35 U.S.C. 112(f) as the device if fig. 9 with a processor 9010 executing steps of [0047 to spawn more tasks.
Manohar discloses a computer system 102 with hardware processor 112 to initiate task for other optimization tasks using steps of col 27 lines 37-43.
Examiner interpretation:
col 27 lines 37-43 “ This means that the web application should ideally respond to user requests within this timeframe to meet the defined performance standard. In the semantic analysis, a context is within the realm of web development and performance optimization. The SLOs are common metrics used to set goals and to measure the performance of the web applications. The context further implies that achieving a response rate of less than 30 ms is considered desirable for optimal user experience and efficient operation of the web application.”.
As per claim 5, the rejection of claim 1 is incorporated and furthermore Manohar discloses:
cause a second task implementer agent of the respective task implementer agents to initiate, using at least a second language model of the second task implementer agent, a second candidate optimization task identified by a second task proposer agent of the respective task proposer agents:
This element is interpreted under 35 U.S.C. 112(f) as the device if fig. 9 with a processor 9010 executing steps of [0047 to spawn more tasks and use LLMs.
Manohar discloses a computer system 102 with hardware processor 112 to use fine-tuned LLM to optimize the applications based on different SLA and SLO, col 35 lines 20-26.
Examiner interpretation:
col35 lines 20-26 “(139) FIG. 19 illustrates a block diagram 1900 representing generation of one or more plan trajectories to accomplish in meeting the one or more service level objectives (SLOs) based on an initial state of the one or more service level objectives (SLOs) and associated metrics, using the fine-tuned large language model, in accordance with an embodiment of the present disclosure”.
wherein the second candidate optimization task is initiated without presenting a natural language representation of the second candidate optimization task to an end user.
Col 35 lines 26-32 “(140) FIG. 20 illustrates a block diagram 2000 representing execution of an agent (Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent) to generate the one or more actions to be applied to the one or more environments 116 using the fine-tuned large language model, in accordance with an embodiment of the present disclosure”.
Examiner interpretation: no end-user interaction, only the fined tuned model receives the state and recommend actions (optimization task to implement). See also Darji [0294].
With respect to Claim 6, Manohar discloses a method comprising:
configuring, corresponding to individual ones of a plurality of optimization task categories of a target service, respective task proposer agents:
Col 6 lines46-56” (b) generating, by the one or more hardware processors, one or more plan trajectories to accomplish in meeting the one or more service level objectives (SLOs) based on the initial state of the one or more service level objectives (SLOs) and associated metrics, by querying the first artificial intelligence (AI) model from the Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent; (c) generating, by the one or more hardware processors, the one or more actions, to be applied to the one or more environments,”
wherein individual ones of the respective task proposer agents comprise a respective generative artificial intelligence model (GAIM).
Col 6 lines 55-65” by querying the first artificial intelligence (AI) model from the Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent; (c) generating, by the one or more hardware processors, the one or more actions, to be applied to the one or more environments, based on at least one of: current state of the one or more service level objectives (SLOs) and probabilities of meeting the one or more service level objectives (SLOs), by at least one of: learning, planning procedures, and resembling actor-critic updates for Partial Observable Markov Decision Processes (POMDPs), wherein the Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent with the Partial Observable Markov Decision Processes (POMDPs) is configured to monitor the one or more services for adjusting one or more governance principles in the one or more environments;
presenting a first prompt to a first GAIM of a first task proposer agent of the respective task proposer agents, wherein the first prompt instructs the first GAIM to, based at least in part on analysis of content of one or more natural language data sources, identify a first candidate optimization task of a first optimization task category of the plurality of optimization task categories.
col 42 lines 51-65” At step 2308, the one or more first real-time data including the one or more actual performance levels, and the one or more service level indictors (SLIs), of the one or more services associated with the one or more applications, are obtained from the one or more monitoring platforms. At step 2310, the first artificial intelligence (AI) model (e.g., the large language model (LLM)) determines whether the one or more actual performance levels of the one or more services associated with the one or more applications, are compliant with the one or more expected performance levels by comparing the one or more actual performance levels with one or more pre-defined key performance indicators (KPIs) and the one or more pre-defined goals of the one or more services defined in at least one of: the service level agreements (SLAs) and the service level objectives (SLOs).
But not explicitly:
and in response to obtaining approval, via one or more programmatic interfaces of the first candidate optimization task, causing the first candidate optimization task to be initiated.
Darji discloses:
and in response to obtaining approval, via one or more programmatic interfaces of the first candidate optimization task, causing the first candidate optimization task to be initiated.
[0289] The method of FIG. 9 differs from FIG. 4 in that updating 410 the software deployment based on the verified code update includes requesting 902 an approval of the verified code update. In some embodiments, updating 410 the software deployment based on the verified code update may require manual (e.g., user) approval. Accordingly, in some embodiments, an approval of the verified code update may be request 902 in response to verifying 406 the code update. For example, in some embodiments, a message or notification may be sent to a particular user (e.g., a predesignated user, a user of a predesignated role, and the like) or potentially multiple users requesting approval of the verified code update. As another example, a pull request to merge the code update into a code base may be generated, with the pull request requiring user approval.
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Darji into teachings of Manohar to efficiently communicate within a distributed system. With zones aligned to the internal physical properties of the device, several inefficiencies in the placement of data can be eliminated. In such embodiments, each zone may be mapped, for example, to a separate application such that functions like wear levelling and garbage collection could be performed on a per-zone or per-application basis rather than across the entire device. [Darji 0194].
As per claim 7, the rejection of claim 6 is incorporated and furthermore, Manohar discloses:
configuring, corresponding to individual ones of the plurality of optimization task categories of a target service, respective task implementer agents, wherein individual ones of the respective task implementer agents comprise a respective GAIM, and wherein the first candidate optimization task is initiated by a particular GAIM of a first task implementer agent of the respective task implementer agents.
Col 6 lines 55-65” by querying the first artificial intelligence (AI) model from the Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent; (c) generating, by the one or more hardware processors, the one or more actions, to be applied to the one or more environments, based on at least one of: current state of the one or more service level objectives (SLOs) and probabilities of meeting the one or more service level objectives (SLOs), by at least one of: learning, planning procedures, and resembling actor-critic updates for Partial Observable Markov Decision Processes (POMDPs), wherein the Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent with the Partial Observable Markov Decision Processes (POMDPs) is configured to monitor the one or more services for adjusting one or more governance principles in the one or more environments;
As per claim 8, the rejection of claim 6 is incorporated and furthermore, Manohar discloses:
presenting, via the one or more programmatic interfaces, a natural language explanation for identification of the first candidate optimization task.
Col 24 lines 60-65 “The one or more insights generated from the computer-implemented system 102 may contribute to more informed automated decision-making. The computer-implemented system 102 may utilize the one or more insights to proactively address issues, optimize performance, and generate strategic decisions related to service levels.”;
As per claim 9, the rejection of claim 6 is incorporated and furthermore, Manohar discloses:
wherein the first prompt is presented to the first GAIM based at least in part on detecting that a first triggering condition has been satisfied, wherein said detecting comprises determining that a first data source of the one or more natural language data sources has been updated since a previous execution of the first task proposer agent:
Col 15 lines 50-55 “The plurality of subsystems further comprises a service level objectives updating subsystem configured to automatically update the one or more service level objectives (SLOs) and associated metrics corresponding to the one or more services specified in the one or more service level agreements (SLAs), based on deviations of the one or more actual performance levels of the one or more services from the one or more expected performance levels, using the first artificial intelligence (AI) model.”;
As per claim 10, the rejection of claim 6 is incorporated and furthermore, Manohar discloses:
obtaining, via the one or more programmatic interfaces, an indication of a triggering condition for presenting the first prompt to the first GAIM, wherein the first prompt is presented to the first GAIM based at least in part on detecting that the triggering condition has been satisfied.
Col 8 lines 25-32 “ The plurality of subsystems further comprises a decision-making subsystem configured to generate one or more insights associated with one or more actions, to be applied to one or more corresponding services, to be performed to automatically manage the one or more applications based on the automatic updates on the one or more service level objectives (SLOs) and associated metrics corresponding to the one or more services specified in the one or more service level agreements (SLAs), using the first artificial intelligence (AI) model”;
As per claim 11, the rejection of claim 6 is incorporated and furthermore, Manohar does not explicitly disclose:
presenting, via the one or more programmatic interfaces, prior to said obtaining approval, an indication of the first candidate optimization task in response to detecting that a triggering condition has been satisfied:
Darji discloses:
presenting, via the one or more programmatic interfaces, prior to said obtaining approval, an indication of the first candidate optimization task in response to detecting that a triggering condition has been satisfied:
[0291] In some embodiments, an interface for requesting 902 approval of the verified code update may allow a user to edit code including the verified code update. In such embodiments, any received approval may be applied to the modified verified code update such that the software deployment will be updated using the modified verified code update.
[0290] In some embodiments, the request for approval may include various data, or references to such data, that may be used when approving the verified code update. For example, in some embodiments, the request for approval may indicate the particular code changes performed by the code update, including a location in the code that was updated, a state of the code both before and after applying the code update, a marked up or notated version of the code indicating the particular changes performed by the code update, and the like. As another example, in some embodiments, the request for approval may indicate the particular error to be fixed by the code update”;
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Darji into teachings of Manohar to efficiently communicate within a distributed system. With zones aligned to the internal physical properties of the device, several inefficiencies in the placement of data can be eliminated. In such embodiments, each zone may be mapped, for example, to a separate application such that functions like wear levelling and garbage collection could be performed on a per-zone or per-application basis rather than across the entire device. [Darji 0194].
As per claim 12, the rejection of claim 11 is incorporated and furthermore, Manohar discloses:
wherein detecting that the triggering condition has been satisfied comprises one or more of: (a) detecting that a first end user has logged in to a system or (b) detecting that an amount of time that has elapsed since another candidate optimization action was presented to a first end user satisfies a criterion:
Col 24 lines55-60 “The computer-implemented system 102 may continuously evaluate performance metrics, compare the performance metrics against the pre-defined goals, and generate the one or more insights or alerts based on the comparison and analysis.”
As per claim 13, the rejection of claim 6 is incorporated and furthermore, Manohar discloses:
receiving, via the one or more programmatic interfaces, an indication of the plurality of optimization task categories:
col 23 lines 10-17 “…examine the key performance indicators of individual processes and consolidate the key performance indicators of all individual processes into a unified system that may provide the one or more insights enabling the one or more actions to be taken to either correct the process or to make the process more efficient. The reported data may not always be real-time, and the reported data may be days or even months old, depending on the reporting frequency.
As per claim 14, the rejection of claim 6 is incorporated and furthermore, Manohar discloses:
receiving, via the one or more programmatic interfaces, an indication of one or more data sources to be utilized for identifying candidate optimization tasks, including at least a first natural language data source of the one or more natural language data sources:
Col 8 lines 50-52” wherein the one or more data comprise information associated with one or more service level agreements (SLAs) corresponding to the one or more applications in the one or more environments, and wherein the one or more service level agreements (SLAs) associated with the one or more applications comprise one or more natural language texts; (b) determining one or more semantics and structure of the one or more natural language texts associated with the one or more service level agreements (SLAs) based on analysis of the one or more natural language texts associated with the one or more service level agreements (SLAs), using a first artificial intelligence (AI) model”;
As per claim 15, the rejection of claim 6 is incorporated and furthermore, Manohar discloses:
wherein the first candidate optimization task comprises one or more of: (a) causing an additional end user segment of the target service to be defined, (b) causing content to be presented to an end user segment of the target service, (c) changing a rate at which content is presented to an end user segment of the target service, or (d) changing an allocation of resources associated with presentation of content to end users of the target service:
col 35 lines 62-67 “The above said states may serve as representations of the application's performance, emphasizing the goal of optimizing its functionality to consistently meet the SLA. However, achieving this objective must be balanced with resource considerations, as resources come with associated costs. Striking an optimal balance involves ensuring the application meets the SLA within defined resource constraints, considering factors including at least one of: cost, compliance, governance, and other infrastructure limitations. In essence, the challenge lies in optimizing performance while responsibly managing and allocating resources to achieve the desired operational state.”
Col 39 lines 55-63 “The scale_up action may suggest increasing capacity or resources allocated to the system. The scale_up action is commonly employed to handle increased demand or workload. Scaling up may involve adding more servers, increasing computing power, or expanding resources to improve system performance. The scale_down action may involve reducing the allocated resources or capacity of the system. Scaling down is often used when the demand or workload decreases, allowing for cost savings using fewer resources. “;
Claims 16, 18 are the one or more non-transitory computer-accessible storage media claims corresponding to method claims 6, 8 and rejected under the same rational set forth in connection with the rejection of claims 6, 8 above.
As per claim 17, the rejection of claim 16 is incorporated and furthermore, Manohar discloses:
wherein contents of the one or more data sources comprise one or more of: a knowledge base entry of the target service, documentation of the target service, one or more records indicating a metric trend of the target service, one or more records indicating an optimization task of the target service which was approved earlier, or one or more records indicating an optimization task of the target service which was rejected earlier.
Col 7 lines 42-50 “The plurality of subsystems further comprises a natural language processing (NLP) subsystem configured to determine one or more semantics and structure of the one or more natural language texts associated with the one or more service level agreements (SLAs) based on analysis of the one or more natural language texts associated with the one or more service level agreements (SLAs), using a first artificial intelligence (AI) model.”;
As per claim 19, the rejection of claim 16 is incorporated and furthermore, Manohar does not explicitly disclose:
present, via one or more programmatic interfaces, a summary of the first prompt.
Darji discloses:
present, via one or more programmatic interfaces, a summary of the first prompt.
[0290]“For example, in some embodiments, the request for approval may indicate the particular code changes performed by the code update, including a location in the code that was updated, a state of the code both before and after applying the code update, a marked up or notated version of the code indicating the particular changes performed by the code update, and the like. As another example, in some embodiments, the request for approval may indicate the particular error to be fixed by the code update”;
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Darji into teachings of Manohar to efficiently communicate within a distributed system. With zones aligned to the internal physical properties of the device, several inefficiencies in the placement of data can be eliminated. In such embodiments, each zone may be mapped, for example, to a separate application such that functions like wear levelling and garbage collection could be performed on a per-zone or per-application basis rather than across the entire device. [Darji 0194].
As per claim 20, the rejection of claim 16 is incorporated and furthermore, Manohar discloses:
Wherein individual ones of the respective GAIMs are configured to run at respective computing resources of a virtualized computing service of a cloud provider network.
Col 12 lines 52-57 “The computer-implemented system 102 may be at least one of, but not limited to, a central server, a cloud server, a remote server, an electronic device, a portable device, and the like. Further, the computer-implemented system 102 may be communicatively coupled to the database 104, via the communication network 108”.
See also Darji fig. 3A-3C.
Pertinent arts:
US 20250045148 A1:
The error context is used to identify the location of the software code file that comprises the software code. The error context and the software code file are used to identify a prompt. The prompt, the error context, and/or the identified software code are provided as input to a language model. The language model provides an output that is responsive to the user request and may perform actions to further evaluate or repair the detected issue.
Conclusion
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/BRAHIM BOURZIK/Examiner, Art Unit 2191
/WEI Y MUI/Supervisory Patent Examiner, Art Unit 2191
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