DETAILED ACTION
This Office Action is in response to the amendments filed on 03/17/2026.
Claims 1, 12, and 17 are currently amended.
Claims 1-2, 4-7, 9-13, 16-18 and 21-26 are currently pending in this application and have been examined.
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 .
Response to Arguments
In reference to Applicant’s arguments on page(s) 10-12 regarding rejections made under 35 U.S.C. 101:
With regard to the rejection of claims 1-2, 4-7, 9-13, 16-18 and 21-26 under §101, Applicant traverses on the ground that the claims are not directed to an abstract idea. Notwithstanding the foregoing traversal, Applicant has amended the claims without prejudice and solely in order to expedite prosecution.
Page 12 of the Office Action alleges that "The judicial exception is not integrated into practical application." However, Applicant respectfully traverses and submits that as stated in Ex Parte Desjardins et al., No. 2024-000567 (PTAB Appeals Review Panel, September 26, 2025), "[c]ategorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology," and improvements to how a machine learning model itself operates, and moreover, adjustments made to the performance of entire datacenters, independent of a need for additional hardware cost and a need for additional run time, resulting from improvements to a machine learning model used therein, represent improvements to computer functionality. Accordingly, even if one assumes for purposes of argument only that previously-presented independent claims 1, 12 and 17 could somehow be construed as reciting an abstract idea, these claims clearly integrate any such abstract idea into practical applications that provide improvements in computer technology.
Accordingly, Applicant requests withdrawal of the rejection of claims 1-2, 4-7, 9-13, 16- 18 and 21-26 under §101.
Examiner’s response
Applicant’s arguments have been fully considered but are found to be not persuasive.
Applicant argues that the instant invention recites similarities to that of Ex parte Desjardin, Examiner disagrees. The deciding factor in Desjardin was that it provided a method for the machine learning model to learn new data while maintaining information on previously learned data, therefore providing an improvement to machine learning models and specifically the problem of “catastrophic forgetting”. The instant application does not set out to solve any specific problem relating to the training of a machine learning model but instead uses a computer implemented method to determine the most optimal parameters for a data center. The instant application is devoid of any mention of training a model and therefore is not similar to that of Ex parte Desjardin. By Applicant’s own admission via the presented claim amendments, there is not necessarily an improvement being made on the data center, but there is simply an “adjustment” being made to the performance of said data center.
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 101 are maintained and updated below.
In reference to Applicant’s arguments on page(s) 12-14 regarding rejections made under 35 U.S.C. 103:
With regard to the rejection of claims 1-2, 4-7, 9-13, 16-18 and 21-26 under §103 as allegedly being unpatentable over Modukuri, Jenatton, Nijim and Bakshi, Applicant notes that a proper prima facie case of obviousness requires that the cited references, when combined, must "teach or suggest all the claim limitations," and that there be some suggestion or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to combine the references or to modify the reference teachings. See MPEP §706.02(j).
Applicant respectfully traverses the §103 rejection of claims 1-2, 4-7, 9-13, 16-18 and 21- 26 over Modukuri, Jenatton, Nijim and Bakshi on the ground that Modukuri, Jenatton, Nijim and Bakshi fails to teach or suggest each and every limitation of claims 1-2, 4-7, 9-13, 16-18 and 21- 26 as originally presented, and on the further ground that there is no suggestion or motivation to modify the collective teachings of Modukuri, Jenatton, Nijim and Bakshi in a manner that would reach these particular recitations.
For example, Applicant submits that the collective teachings of Modukuri, Jenatton, Nijim and Bakshi fail to teach or suggest the limitations of determining, upon determining the status indicating that the at least a portion of the detected topological information is not part of previous topological information, one or more hyperparameter values for the one or more selected hyperparameters of the at least one deep learning model in connection with analyzing a set of one or more latency-related rules, arranged as recited in the independent claims.
Notwithstanding the foregoing traversal, Applicant has amended the claims without prejudice and solely in order to expedite prosecution. The claim amendments herein are not made for reasons relating to patentability over the Modukuri, Jenatton, Nijim and Bakshi references, or any other prior art references of record, as the claims as originally presented recite patentable subject matter over these references.
More particularly, independent claims 1, 12 and 17 have been amended to clarify that with respect to detecting topological information associated with at least a portion of the systems by processing at least a portion of the input information, the topological information comprises identification of at least one number of each of multiple designated types of processors in at least a portion of the plurality of systems, and identification of multiple connection configurations among at least a portion of the multiple designated types of processors in the at least a portion of the plurality of systems.
Applicant respectfully submits that neither Modukuri, nor Jenatton, nor Nijim, nor Bakshi, alone or in combination, teach the active and specific step of detecting identification of at least one number of each of multiple designated types of processors in at least a portion of the plurality of systems, and identification of multiple connection configurations among at least a portion of the multiple designated types of processors in the at least a portion of the plurality of systems, arranged as required by the amended independent claims.
Accordingly, Applicant asserts that the cited references do not teach or suggest at least the above-noted limitations as explicitly recited in independent claims 1, 12 and 17, and for at least this reason, the §103 rejection of claims 1-2, 4-7, 9-13, 16-18 and 21-26 over Modukuri, Jenatton, Nijim and Bakshi should be withdrawn.
Examiner’s response:
Applicant’s arguments have been fully considered but are found to be not persuasive.
Applicant argues that the previously applied prior art references do not teach the newly amended claim limitations. Examiner agrees. A search of previously applied art as well as new art was conducted for the newly added limitations in order for proper examination to be completed.
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 103 are withdrawn and new grounds for rejection is presented below.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-2, 4-7, 9-13, 16-18 and 21-26 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1 analysis:
Independent Claim 1 recites, in part, a computer-implemented method, therefore falling into the statutory category of process. Independent Claim 12 recites, in part, a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, therefore falling into the statutory category of machine. Independent Claim 17 recites, in part, an apparatus comprising: at least one processing device comprising a processor coupled to a memory, therefore falling into the statutory category of manufacture.
Regarding Claim 1:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“detecting topological information associated with at least a portion of the systems by processing at least a portion of the input information, wherein the topological information comprises identification of at least one number of each of multiple designated types of processors in at least a portion of the plurality of systems, and identification of multiple connection configurations among at least a portion of the multiple designated types of processors in the at least a portion of the plurality of systems”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses identifying a number of different hardware configurations.
“automatically selecting one or more of multiple hyperparameters of at least one deep learning model based at least in part on the detected topological information”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses selecting a parameter of a neural network.
“determining a status of at least a portion of the detected topological information by processing, during an inference phase of the at least one deep learning model, the detected topological information and data from at least one systems-related database”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses identifying the status of hardware components.
“wherein determining a status comprises determining a status indicating that the at least a portion of the detected topological information is not part of previous topological information associated with the at least a portion of the systems”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses identifying that a portion of the topological information is new topological information.
“performing, in connection with at least a portion of the one or more selected hyperparameters of the at least one deep learning model, one or more automated actions based at least in part on the determining”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses doing something with a chosen parameter.
“wherein performing one or more automated actions comprises determining, upon determining the status indicating that the at least a portion of the detected topological information is not part of previous topological information, one or more hyperparameter values for the one or more selected hyperparameters of the at least one deep learning model in connection with analyzing a set of one or more latency-related rules”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses determining parameter values in connection with a set of rules.
“adjusting performance of the datacenter, independent of a need for additional hardware cost and a need for additional run time, by automatically implementing the one or more determined hyperparameter values in the at least one deep learning model”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses setting parameters of a deep learning model prior to the model training.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim
recites the additional elements of:
“deploying at least one load balancer device within a datacenter”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (load balancer) (See MPEP 2106.05(f)).
“distributing workloads, using the at least one load balancer device, from one or more edge devices outside the datacenter to a plurality of systems within the datacenter”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (load balancer) (See MPEP 2106.05(f)).
“obtaining input information associated with a plurality of systems associated within a datacenter”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
“wherein obtaining the input information comprises deploying at least one load balancer device within the datacenter to distribute workloads from one or more edge devices outside the datacenter to the systems within the datacenter”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (load balancer) (See MPEP 2106.05(f)).
“and obtaining the input information at least in part from the at least one load balancer device”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (load balancer) (See MPEP 2106.05(f)).
“wherein the method is performed by at least one processing device comprising a processor coupled to a memory”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (processor and memory) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “obtaining input information from one or more systems associated with a datacenter” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As discussed above, the additional element(s) of “deploying at least one load balancer device within a datacenter”, “distributing workloads, using the at least one load balancer device, from one or more edge devices outside the datacenter to a plurality of systems within the datacenter” ,“wherein obtaining the input information comprises deploying at least one load balancer device within the datacenter to distribute workloads from one or more edge devices outside the datacenter to the systems within the datacenter”, “and obtaining the input information at least in part from the at least one load balancer device”, and “wherein the method is performed by at least one processing device comprising a processor coupled to a memory” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 2:
Step 2A: Prong 1 analysis:Claim 2 recites in part:
“wherein determining a status comprises determining a status indicating that at least an additional portion of the detected topological information is part of previous topological information associated with the at least a portion of the systems”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses identifying that certain computer hardware has been seen before.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim
recites the additional elements of:
“and wherein performing one or more automated actions comprises automatically retrieving one or more values from the at least one systems-related database upon determining the status indicating that the at least an additional portion of the detected topological information is part of previous topological information”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “and wherein performing one or more automated actions comprises automatically retrieving one or more values from the at least one systems-related database upon determining the first status” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 4:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim
recites the additional elements of:
“outputting the one or more determined hyperparameter values to one or more production systems associated with the datacenter”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “outputting the one or more determined hyperparameter values to one or more production systems associated with the datacenter” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 5:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim
recites the additional elements of:
“automatically generating data pertaining to the one or more determined hyperparameter values in JavaScript object notation format”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “automatically generating data pertaining to the one or more determined hyperparameter values in JavaScript object notation format” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 6:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim
recites the additional elements of:
“wherein performing one or more automated actions comprises translating results of the determining and outputting at least a portion of the translated results via at least one user interface”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. post-solution activity of outputting/displaying data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein performing one or more automated actions comprises translating results of the determining and outputting at least a portion of the translated results via at least one user interface” is/are recited at a high level of generality and amount(s) to extra solution activity because it/they is/are a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying/outputting a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 7:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim
recites the additional elements of:
“wherein outputting at least a portion of the translated results via at least one user interface comprises outputting the at least a portion of the translated results via at least one web graphical user interface”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. post-solution activity of outputting/displaying data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein outputting at least a portion of the translated results via at least one user interface comprises outputting the at least a portion of the translated results via at least one web graphical user interface” is/are recited at a high level of generality and amount(s) to extra solution activity because it/they is/are a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying/outputting a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 9:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim
recites the additional elements of:
“wherein the plurality of systems comprise multiple systems with multiple different layouts and multiple different configurations”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (computer systems) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “wherein the one or more systems comprise multiple systems with multiple different layouts and multiple different configurations” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 10:
Step 2A: Prong 2 analysis:The judicial exception is not integrated into practical application. In particular, the claim
recites the additional elements of:
“wherein the at least one deep learning model comprises one or more of at least one binary search model, at least one genetic algorithm, at least one Bayesian model, at least one MetaRecentering model, at least one covariance matrix adaption (CMA) model, at least one Nelder-Mead model, and at least one differential evolution model”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (deep learning models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the at least one deep learning model comprises one or more of at least one binary search model, at least one genetic algorithm, at least one Bayesian model, at least one MetaRecentering model, at least one covariance matrix adaption (CMA) model, at least one Nelder-Mead model, and at least one differential evolution model” is/are directed to a particular field of use (deep learning models) (MPEP 2106.05(h)) and therefore do(es) not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 11:
Step 2A: Prong 1 analysis:
Claim 11 recites in part:
“wherein automatically selecting one or more of multiple hyperparameters of at least one deep learning model comprises automatically selecting one or more of multiple hyperparameters of the at least one deep learning model based at least in part on the detected topological information and one or more performance variables”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses selecting hyperparameters of a deep learning model based on hardware information and a performance variable.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 12:
Due to claim language similar to that of Claim 1, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of one limitation covered below.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim
recites the additional elements of:
“A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (processor readable storage) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 13:
Due to claim language similar to that of Claim 2, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 16:
Due to claim language similar to that of Claim 6, Claim 16 is rejected for the same reasons as presented above in the rejection of Claim 6.
Regarding Claim 17:
Due to claim language similar to that of claims 1 and 12, Claim 17 is rejected for the same reasons as presented above in the rejections of claims 1 and 12.
Regarding Claim 18:
Due to claim language similar to that of claims 2 and 13, Claim 18 is rejected for the same reasons as presented above in the rejections of claims 2 and 13.
Regarding Claim 21:
Due to claim language similar to that of claims 6 and 16, Claim 21 is rejected for the same reasons as presented above in the rejections of claims 6 and 16.
Regarding Claim 22:
Due to claim language similar to that of Claim 10, Claim 22 is rejected for the same reasons as presented above in the rejections of Claim 10.
Regarding Claim 23:
Due to claim language similar to that of Claim 11, Claim 23 is rejected for the same reasons as presented above in the rejections of Claim 11.
Regarding Claim 24:
Due to claim language similar to that of claims 11 and 23, Claim 24 is rejected for the same reasons as presented above in the rejections of claims 11 and 23.
Regarding Claim 25:
Due to claim language similar to that of claim 9, Claim 24 is rejected for the same reasons as presented above in the rejections of claim 9.
Regarding Claim 26:
Due to claim language similar to that of claims 9 and 25, Claim 26 is rejected for the same reasons as presented above in the rejections of claims 9 and 25.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 4-7, 9-12, 16, 17, 21-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Modukuri et al (US 20210286752 A1, hereinafter Modukuri) in view of Jenatton et al (US 11593704 B1, hereinafter Jenatton), in view of Nijim et al (US 12061934 B1, hereinafter Nijim), and in further view of Bakshi et al (US 6574663 B1, hereinafter Bakshi).
Regarding Claim 1:
Modukuri teaches
obtaining input information associated with the plurality of systems within the datacenter (Modukuri [0054]: “a computer system running an API determines data transfer path based, at least in part, on a representation of a hardware topology that includes first hardware component and second hardware component. In at least one embodiment, representation of hardware topology is a device hierarchy tree, and one or more components of computer system 100”);
detecting topological information associated with at least a portion of the one or more systems by processing at least a portion of the input information (Modukuri [0054]: “a computer system running an API determines data transfer path based, at least in part, on a representation of a hardware topology that includes first hardware component and second hardware component. In at least one embodiment, representation of hardware topology is a device hierarchy tree, and one or more components of computer system 100”);
determining a status of at least a portion of the detected topological information by processing, during an inference phase of the at least one deep learning model, the detected topological information and data from at least one systems-related database (Modukuri [0054]: “one or more components of computer system 100 determine a plurality of values corresponding to a plurality of dynamic component conditions, and determine data transfer path based, at least in part, on one or more of determined plurality of values”; (EN): “dynamic component conditions” is analogous to a “status” of a portion of the detected topological information);
wherein the method is performed by at least one processing device comprising a processor coupled to a memory (Modukuri [0123]: “FIG. 9 illustrates a processing system 900, in accordance with at least one embodiment. In at least one embodiment, processing system 900 includes one or more processors”; (EN): Fig. 9 also depicts a memory device coupled to the processor(s).).
Modukuri does not distinctly disclose
automatically selecting one or more of multiple hyperparameters of at least one deep learning model based at least in part on the detected topological information;
performing, in connection with at least a portion of the one or more selected hyperparameters of the at least one deep learning model, one or more automated actions based at least in part on the determining, wherein performing one or more automated actions comprises: determining […] one or more hyperparameter values for the one or more selected hyperparameters of the at least one deep learning model
automatically implementing the one or more determined hyperparameter values in the at least one deep learning model;
However Jenatton teaches
automatically selecting one or more of multiple hyperparameters of at least one deep learning model based at least in part on the detected topological information (Jenatton [Col 6, lines 1-3]: “Once the optimal search spaces are found the HPO 114 and/or optimal search space selector 116 may randomly select hyperparameter values”);
performing, in connection with at least a portion of the one or more selected hyperparameters of the at least one deep learning model, one or more automated actions based at least in part on the determining, wherein performing one or more automated actions comprises: determining […] one or more hyperparameter values for the one or more selected hyperparameters of the at least one deep learning model (Jenatton [Col 6, lines 10-16]: “Accordingly, the optimal search space selector 116 may provide at least a first set of an optimal search spaces of hyperparameters for use to the HPO 114, which may then issue a command to a training engine platform 122 at circle (4) to begin one or multiple training jobs as part of the tuning utilizing the set(s) of hyperparameters.”; (EN): applicant’s claim of “performing one or more automated actions” is analogous to simply doing something with the determined hyperparameters).
automatically implementing the one or more determined hyperparameter values in the at least one deep learning model (Jenatton [Col 4 lines 38-42]: "Thus, the model training system 120 may select different sets of hyperparameters and run different trainings via a training engine platform 122 and then choose the hyperparameter values that result in a model that performs the best, as measured by a metric that you choose.");
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Modukuri and Jenatton before him or her, to modify the systems and techniques to route data transfers between hardware devices of Modukuri to include the techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters as shown in Jenatton. The motivation for doing so would have been to use the system of Jenatton to determine a search space for hyperparameters for a neural network model, and further determining optimal hyperparameters within that search space (Jenatton [Abstract]: “determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets.”).
Modukuri + Jenatton does not distinctly disclose
deploying at least one load balancer device within a datacenter;
distributing workloads, using the at least one load balancer device, from one or more edge devices outside the datacenter to a plurality of systems within the datacenter;
wherein obtaining the input information comprises obtaining information, pertaining to at least a portion of the workloads, from the at least one load balancer device;
in connection with analyzing a set of one or more latency-related rules;
However, Nijim teaches
deploying at least one load balancer device within a datacenter (Nijim [Col 8 lines 44-56]: "Load balancer 216 includes executable code or processes that operate to efficiently distribute application processing and/or other operations/processes across available edge locations 104 (e.g., via one or more servers at one or more edge locations 104). In some cases, system 200 receives concurrent application processing requests from hundreds of clients in order to return application data whether resulting from edge-only processing, hybrid edge-cloud processing, or cloud-only processing. Based in part on output of the machine learning engine 110, load balancer 216 can distribute application processing operations to provide reliable, fast, and efficient processing using edge and/or cloud resources");
distributing workloads, using the at least one load balancer device, from one or more edge devices outside the datacenter to a plurality of systems within the datacenter (Nijim [Col 8 lines 44-56]: "Load balancer 216 includes executable code or processes that operate to efficiently distribute application processing and/or other operations/processes across available edge locations 104 (e.g., via one or more servers at one or more edge locations 104). In some cases, system 200 receives concurrent application processing requests from hundreds of clients in order to return application data whether resulting from edge-only processing, hybrid edge-cloud processing, or cloud-only processing. Based in part on output of the machine learning engine 110, load balancer 216 can distribute application processing operations to provide reliable, fast, and efficient processing using edge and/or cloud resources");
wherein obtaining the input information comprises obtaining information, pertaining to at least a portion of the workloads, from the at least one load balancer device (Nijim [Col 8 lines 44-52]: "Load balancer 216 includes executable code or processes that operate to efficiently distribute application processing and/or other operations/processes across available edge locations 104 (e.g., via one or more servers at one or more edge locations 104). In some cases, system 200 receives concurrent application processing requests from hundreds of clients in order to return application data whether resulting from edge-only processing, hybrid edge-cloud processing, or cloud-only processing.");
in connection with analyzing a set of one or more latency-related rules (Nijim [Col 12 lines 41-46]: "Also, once it is determined to use edge-only resources for processing operations, the edge only resources will continue to be used to thereby prevent any latency contribution by cloud resources until a business rule is invoked or machine learning triggers a change in a processing recommendation");
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Modukuri + Jenatton and Nijim before him or her, to modify the systems and techniques to route data transfers between hardware devices of Modukuri + Jenatton to include the techniques for adaptively allocating processing operations according to an edge-only processing mode, a hybrid edge-cloud processing mode, and/or cloud-only processing mode as shown in Nijim. The motivation for doing so would have been to use the load balancer of Nijim in order to minimize latency and optimize any given machine learning parameter (Nijim [Col 4 lines 55-58]: “critical components, containers, or modules can be placed and/or executed at one or more edge locations as part of minimizing an amount of latency as much as possible as well as optimizing any other machine learning parameter”).
Modukuri + Jenatton + Nijim does not distinctly disclose
wherein the topological information comprises identification of at least one number of each of multiple designated types of processors in at least a portion of the plurality of systems, and identification of multiple connection configurations among at least a portion of the multiple designated types of processors in the at least a portion of the plurality of systems;
wherein determining a status comprises determining a status indicating that the at least a portion of the detected topological information is not part of previous topological information associated with the at least a portion of the systems;
upon determining the status indicating that the at least a portion of the detected topological information is not part of previous topological information
adjusting performance of the datacenter, independent of a need for additional hardware cost and a need for additional run time
However, Bakshi teaches
wherein the topological information comprises identification of at least one number of each of multiple designated types of processors in at least a portion of the plurality of systems, and identification of multiple connection configurations among at least a portion of the multiple designated types of processors in the at least a portion of the plurality of systems (Bakshi [Col 4 lines 41-49]: "The active topology server 120 also maintains an active topology map to indicate current connectivity among all active devices in the network 110 and the software and hardware resources or configurations in each active device. The information about the hardware may include the type of the processor, available RAM and space in the hard drive, the speed of communication ports, and so on. The information on the connectivity part of this active topology map may be derived from the general topology map");
wherein determining a status comprises determining a status indicating that the at least a portion of the detected topological information is not part of previous topological information associated with the at least a portion of the systems (Bakshi [Col 5 lines 28-31]: "At steps 220 and 230, the active topology server 120 receives the presence packet and determines whether the active device is in the existing list of active devices stored in the active topology server 120.");
upon determining the status indicating that the at least a portion of the detected topological information is not part of previous topological information (Bakshi [Col 5 lines 28-31]: "At steps 220 and 230, the active topology server 120 receives the presence packet and determines whether the active device is in the existing list of active devices stored in the active topology server 120.")
adjusting performance of the datacenter, independent of a need for additional hardware cost and a need for additional run time (Bakshi [Col 7 lines 8-15]: "The active topology mechanism in the active topology server monitors and captures the changes in the network. This mechanism continuously optimizes the performance of the network by selecting proper active devices for desired services. Hence, a particular network service may be performed from different active devices as the network evolves and a new service may be strategically deployed at a selected location to achieve an optimized performance")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Modukuri + Jenatton + Nijim and Bakshi before him or her, to modify the systems and techniques to route data transfers between hardware devices of Modukuri + Jenatton + Nijim to include the methods for operating a network by using two different databases relating to new and available topologies as shown in Bakshi. The motivation for doing so would have been to use the databases of Bakshi in order to verify whether or not the present topology is already a part of the system of if it is a new topology (Bakshi [Col 2 lines 1-7]: “The active topology server can be configured to maintain a first database on topology of all of the passive and active devices and a second database on topology, software and hardware configurations of only the active devices. The first and second databases are combined to select a desired active device based on its network location, software, and hardware to perform or install a function”).
Regarding Claim 4:
Modukuri does not distinctly disclose
The computer-implemented method of claim 1, further comprising outputting the one or more determined hyperparameter values to one or more production systems associated with the datacenter.
However, Jenatton teaches
The computer-implemented method of claim 1, further comprising outputting the one or more determined hyperparameter values to one or more production systems associated with the datacenter (Jenatton [Col 9 lines 31-33]: "The search space is sampled at 417 to determine and output optimal HPs. For example, a Bayesian search or (uniform) random sampling is performed.").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Modukuri and Jenatton before him or her, to modify the systems and techniques to route data transfers between hardware devices of Modukuri to include the techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters as shown in Jenatton. The motivation for doing so would have been to use the system of Jenatton to determine a search space for hyperparameters for a neural network model, and further determining optimal hyperparameters within that search space (Jenatton [Abstract]: “determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets.”).
Regarding Claim 5:
Modukuri does not distinctly disclose
The computer-implemented method of claim 3, further comprising: automatically generating data pertaining to the one or more determined hyperparameter values in JavaScript object notation format.
However, Jenatton teaches
The computer-implemented method of claim 1, further comprising: automatically generating data pertaining to the one or more determined hyperparameter values in JavaScript object notation format (Jenatton [Fig. 9]: Fig. 9 is a representation of hyperparameter configuration settings in the JSON format)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Modukuri and Jenatton before him or her, to modify the systems and techniques to route data transfers between hardware devices of Modukuri to include the techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters as shown in Jenatton. The motivation for doing so would have been to use the system of Jenatton to determine a search space for hyperparameters for a neural network model, and further determining optimal hyperparameters within that search space (Jenatton [Abstract]: “determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets.”).
Regarding Claim 6:
Modukuri does not distinctly disclose
The computer-implemented method of claim 1, wherein performing one or more automated actions comprises translating results of the determining and outputting at least a portion of the translated results via at least one user interface.
However, Jenatton teaches
The computer-implemented method of claim 1, wherein performing one or more automated actions comprises translating results of the determining and outputting at least a portion of the translated results via at least one user interface (Jenatton [Col 5, lines 1-6]: “a user 110 may utilize or configure an application (e.g., client 109, such as a web browser executing a web application, a standalone application) executed by a computing device 108 to send one or more requests, at circle (1), to a machine learning service 112 to configure and execute a model tuning job. The one or more requests may be sent responsive to the user 110 having utilized a graphical user interface (GUI), console, or the like to configure an ML model tuning”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Modukuri and Jenatton before him or her, to modify the systems and techniques to route data transfers between hardware devices of Modukuri to include the techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters as shown in Jenatton. The motivation for doing so would have been to use the system of Jenatton to determine a search space for hyperparameters for a neural network model, and further determining optimal hyperparameters within that search space (Jenatton [Abstract]: “determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets.”).
Regarding Claim 7:
Modukuri does not distinctly disclose
The computer-implemented method of claim 6, wherein outputting at least a portion of the translated results via at least one user interface comprises outputting the at least a portion of the translated results via at least one web graphical user interface.
However, Jenatton teaches
The computer-implemented method of claim 6, wherein outputting at least a portion of the translated results via at least one user interface comprises outputting the at least a portion of the translated results via at least one web graphical user interface (Jenatton [Col 5, lines 1-6]: “a user 110 may utilize or configure an application (e.g., client 109, such as a web browser executing a web application, a standalone application) executed by a computing device 108 to send one or more requests, at circle (1), to a machine learning service 112 to configure and execute a model tuning job. The one or more requests may be sent responsive to the user 110 having utilized a graphical user interface (GUI), console, or the like to configure an ML model tuning”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Modukuri and Jenatton before him or her, to modify the systems and techniques to route data transfers between hardware devices of Modukuri to include the techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters as shown in Jenatton. The motivation for doing so would have been to use the system of Jenatton to determine a search space for hyperparameters for a neural network model, and further determining optimal hyperparameters within that search space (Jenatton [Abstract]: “determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets.”).
Regarding Claim 9:
Modukuri teaches
The computer-implemented method of claim 1, wherein the plurality of systems comprise multiple systems with multiple different layouts and multiple different configurations (Modukuri [0064]: “It should be understood that the specific configuration and components of computer system 200 are presented for purposes of illustration, and that any suitable computer system configuration and/or hardware components can implement a dynamic data routing and/or data transfer path determination capability of various embodiments”).
Regarding Claim 10:
Modukuri does not distinctly disclose
The computer-implemented method of claim 1, wherein the at least one deep learning model comprises one or more of at least one binary search model, at least one genetic algorithm, at least one Bayesian model, at least one MetaRecentering model, at least one covariance matrix adaption (CMA) model, at least one Nelder-Mead model, and at least one differential evolution model.
However, Jenatton teaches
The computer-implemented method of claim 1, wherein the at least one deep learning model comprises one or more of at least one binary search model, at least one genetic algorithm, at least one Bayesian model, at least one MetaRecentering model, at least one covariance matrix adaption (CMA) model, at least one Nelder-Mead model, and at least one differential evolution model (Jenatton [Col 2, lines 22-27]: “Bayesian optimization (BO) provides a principled approach to this problem: an acquisition function, which takes as input a cheap probabilistic surrogate model of the target black-box function, repeatedly scores promising HP configurations by performing an explore-exploit trade-off”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Modukuri and Jenatton before him or her, to modify the systems and techniques to route data transfers between hardware devices of Modukuri to include the techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters as shown in Jenatton. The motivation for doing so would have been to use the system of Jenatton to determine a search space for hyperparameters for a neural network model, and further determining optimal hyperparameters within that search space (Jenatton [Abstract]: “determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets.”).
Regarding Claim 11:
Modukuri does not distinctly disclose
The computer-implemented method of claim 1, wherein automatically selecting one or more of multiple hyperparameters of at least one deep learning model comprises automatically selecting one or more of multiple hyperparameters of the at least one deep learning model based at least in part on the detected topological information and one or more performance variables.
However, Jenatton teaches
The computer-implemented method of claim 1, wherein automatically selecting one or more of multiple hyperparameters of at least one deep learning model comprises automatically selecting one or more of multiple hyperparameters of the at least one deep learning model based at least in part on the detected topological information and one or more performance variables (Jenatton [Col 6, lines 1-3]: “Once the optimal search spaces are found the HPO 114 and/or optimal search space selector 116 may randomly select hyperparameter values”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Modukuri and Jenatton before him or her, to modify the systems and techniques to route data transfers between hardware devices of Modukuri to include the techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters as shown in Jenatton. The motivation for doing so would have been to use the system of Jenatton to determine a search space for hyperparameters for a neural network model, and further determining optimal hyperparameters within that search space (Jenatton [Abstract]: “determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets.”).
Regarding Claim 12:
Due to claim language similar to that of Claim 1, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of one limitation covered below.
Modukuri teaches
A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device (Modukuri [0393]: “In at least one embodiment, code (e.g., executable code or source code) is stored on a set of one or more non-transitory computer-readable storage media having stored thereon executable instructions (or other memory to store executable instructions) that, when executed (i.e., as a result of being executed) by one or more processors of a computer system, cause computer system to perform operations described herein”)
Regarding Claim 16:
Due to claim language similar to that of Claim 6, Claim 16 is rejected for the same reasons as presented above in the rejection of Claim 6.
Regarding Claim 17:
Due to claim language similar to that of claims 1 and 12, Claim 17 is rejected for the same reasons as presented above in the rejections of claims 1 and 12.
Regarding Claim 21:
Due to claim language similar to that of claims 6 and 16, Claim 21 is rejected for the same reasons as presented above in the rejections of claims 6 and 16.
Regarding Claim 22:
Due to claim language similar to that of Claim 10, Claim 22 is rejected for the same reasons as presented above in the rejections of Claim 10.
Regarding Claim 23:
Due to claim language similar to that of Claim 11, Claim 23 is rejected for the same reasons as presented above in the rejections of Claim 11.
Regarding Claim 24:
Due to claim language similar to that of claims 11 and 23, Claim 24 is rejected for the same reasons as presented above in the rejections of claims 11 and 23.
Regarding Claim 25:
Due to claim language similar to that of claim 9, Claim 24 is rejected for the same reasons as presented above in the rejections of claim 9.
Regarding Claim 26:
Due to claim language similar to that of claims 9 and 25, Claim 26 is rejected for the same reasons as presented above in the rejections of claims 9 and 25.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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