DETAILED ACTION
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 .
Examiner’s Note
In regards to the claim objections on claim(s) 2, 10 and 17, have been withdrawn in light of the instant amendments to the claims.
Response to Argument
Applicant's arguments filed 10/14/2025 ("Arguments/Remarks") have been fully considered but they are not persuasive.
35 U.S.C. § 101 argument on (page: 13 – 14), Applicant contends: “Each of claims 1-20 stand rejected under 35 U.S.C. § 101. However, as discussed during the Interview, the claims are patent eligible under 35 U.S.C. § 101 at least because the claims integrate any allegedly-recited abstract ideas into a practical application by improving the functioning of a computer.”
Regarding the above argument, the Examiner respectfully disagrees with Applicant assertion that the claims integrate any allegedly-recited abstract ideas into a practical application by improving the functioning of a computer. Applicant assertion does not identify a specific technological improvement or explain how the purported improvement is achieved. The cited description merely states a desired result, determining whether low performance is entity specific or common across a cohort, without providing sufficient technical detail regarding the mechanism or operation that implement this solution. The specification must include a technical explanation of the asserted improvement, and the claim must recite a particular way of achieving that improvement. Although the amended limitation recites switching between running a local version of the application or a cloud version of the application, still lacks the required detail explaining how the improvement is achieved or how entities that meet the performance requirements are used to perform any task in a manner that would demonstrate a practical application. To determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement., MPEP 2106.04(d)(1).
35 U.S.C. § 103 argument on (page: 14 – 15), Applicant contends: “In particular, the cited references, even in combination, would at least fail to disclose “determining, by the one or more processors, that the performance metric for at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than a threshold percentage,” or “in response to determining that the performance metric for at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage, instructing, by the one or more processors, the at least one entity that there is a possible performance issue with respect to the computing environment associated with the at least one entity,” as recited by claim 1. Claims 9 and 16 recite similar features.”
Regarding the above argument, the Examiner notes that the claim limitation as claimed in light of the teaching of the prior art Dang (Dang, (col. 2, lines [30 - 55]), discloses monitoring performance of multiple entities and generating alerts based on deviations from thresholds. Although the alerts are initially triggered by threshold rather than a cohort-based metric, the subsequent analysis of a first group of issued alerts establishes relationship between alerts of different entities. By analyzing these relationships and predicting future alerts, the system effectively identifies entities whose performance deviates relative to other entities in the group, which can be interpreted as a functional equivalent to detecting that an entity’s performance deviates from a group-based metric by more than a threshold percentage. Accordingly, Dang cover the claimed limitation of identifying entities whose performance deviates from a cohort metric and issuing an alert.
Claim Rejections - 35 USC § 112: New Matter
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 – 2, 4 – 10, 12 – 17, and 19 – 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AlA), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AlA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Specifically, claim 1 recites “… switching, by the one or more processors and based on the possible performance issue with respect to the computing environment associated with the at least one entity, between: (i) running a local version of the medical image streaming application, or (ii) running the cloud- based version of the medical image streaming application, on the computing environment associated with the at least one entity.” that is considered new matter because the original disclosure does not appear to support based on the performance, switching, running a local version of the medical image streaming application or running the cloud-based version of the medical image streaming application. The noted limitations are therefore considered new matter.
Regarding claim(s) 9 and 16, the limitations recite similar limitations to claim 1, and are thus rejected under the same rationale.
Regarding the dependent claim(s) 2, 4 – 8, 10, 12 – 15, 17 and 19 – 20 do not resolve the noted deficiencies and thus are appropriately rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1 – 2, 4 – 10, 12 – 17 and 19 – 20 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
In step 1, of the 101-analysis set forth in the MPEP 2106, the examiner has determined
that the following limitations recite a process that, under the broadest reasonable interpretation, falls within one or more statutory categories (processes).
In step 2A prong 1, of the 101-analysis set forth in MPEP 2106, the examiner has determined
that the following limitations recite a process that, under broadest reasonable interpretation, covers
a mental process but for the recitation of generic computer components:
Regarding claim 1,
identifying a plurality of entities
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves recognizing or categorizing of data elements (entities) based on observing patterns or features. See (MPEP 2106.04)).
for each entity of the plurality of entities, determining [ ] a performance metric associated with the computing environment
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves collecting and analyzing data (performance metric) related to each entity’s computing environment, which constitutes evaluation or measuring information to make a determination, See (MPEP 2106.04)).
for each entity of the plurality of entities, determining [ ] a plurality of characteristics associated with the computing environment
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing and identifying attributes or parameters related to each entity’s computing environment such as configuration, usage pattern, or system property, See (MPEP 2106.04)).
based on the performance metrics associated with each entity of the plurality of entities, assigning [ ] each entity of the plurality of entities to a cohort of a plurality of cohorts;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating data (performance metrics) and classifying or categorizing entities into groups based on the evaluation, See (MPEP 2106.04)).
for each cohort of the plurality of cohorts, determining [ ] a plurality of characteristics associated with the cohort based on the plurality of characteristics of the computing environments associated with each entity assigned to the cohort;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves aggregating and analyzing known characteristics of individual entities to infer or derive group-level characteristics, See (MPEP 2106.04)).
for each cohort of the plurality of cohorts, determining [ ] a performance metric for the cohort based on the performance metrics determined for each entity assigned to the cohort
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves determining an aggregate or summary performance measure for a group by analyzing the individual performance metrics of its members, See (MPEP 2106.04)).
determining [ ] that the performance metric for at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than a threshold percentage
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves comparing a numerical value (an entity’s performance metric) to another numerical value and making a determination based on whether the difference exceeds a threshold value, See (MPEP 2106.04)).
In response to determining that the performance metric for the at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage, instructing, [ ], the at least one entity that there is a possible performance issue with respect to the computing environment associated with the at least one entity
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing performance metrics, comparing them to a threshold, identify deviations and providing a notification based on that comparison., See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101-analysis, set forth in MPEP 2106, the examiner has determined that
the following additional elements do not integrate this judicial exception into a practical application:
As evaluated below:
• The preamble is deemed insufficient to transform the judicial exception to a patentable
invention to a patentable invention because the preamble generally links the use of a
judicial exception to a particular technological environment or field of use, see MPEP
2106.05(h).
… by a computing device, by one or more processors
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
wherein the performance metric is a performance metric associated with running a cloud-based version of a medical image streaming application, and wherein the performance metric includes one or more of: response time, bandwidth, throughput, latency, availability, or reliability;
Deemed insufficient to transform the judicial exception to a patentable invention because, the claim recites limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
wherein each device is associated with a computing environment;
Deemed insufficient to transform the judicial exception to a patentable invention because, the claim recites limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
switching, by the one or more processors and based on the possible performance issue with respect to the computing environment associated with the at least one entity, between: (i) running a local version of the medical image streaming application, or (ii) running the cloud- based version of the medical image streaming application, on the computing environment associated with the at least one entity.
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the
claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (I and IV), recites mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP
2106.05(f).
Regarding limitation (II and III), deemed insufficient to transform the judicial exception to a patentable invention because they generally link the judicial exception to the technology environment, see MPEP 2106.05(h).
As analyzed above, the additional elements, analyzed above, do not integrate the noted judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Regarding claim 9,
identifying a plurality of entities
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves recognizing or categorizing of data elements (entities) based on observing patterns or features. See (MPEP 2106.04)).
for each entity of the plurality of entities, determining a performance metric associated with the computing environment
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves collecting and analyzing data (performance metric) related to each entity’s computing environment, which constitutes evaluation or measuring information to make a determination, See (MPEP 2106.04)).
for each entity of the plurality of entities, determining a plurality of characteristics associated with the computing environment
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing and identifying attributes or parameters related to each entity’s computing environment such as configuration, usage pattern, or system property, See (MPEP 2106.04)).
based on the performance metric associated with each entity of the plurality of entities, assigning each entity to a cohort of a plurality of cohorts
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating data (performance metrics) and classifying or categorizing entities into groups based on the evaluation, See (MPEP 2106.04)).
for each cohort of the plurality of cohorts, determining a plurality of characteristics associated with the cohort based on the plurality of characteristics of the computing environments associated with each entity assigned to the cohort;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves aggregating and analyzing known characteristics of individual entities to infer or derive group-level characteristics, See (MPEP 2106.04)).
for each cohort of the plurality of cohorts, determining a performance metric for the cohort based on the performance metrics determined for each entity assigned to the cohort
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves determining an aggregate or summary performance measure for a group by analyzing the individual performance metrics of its members, See (MPEP 2106.04)).
determining that the performance metric for at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than a threshold percentage
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves comparing a numerical value (an entity’s performance metric) to another numerical value and making a determination based on whether the difference exceeds a threshold value, See (MPEP 2106.04)).
In response to determining that the performance metric for the at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage, instructing, [ ], the at least one entity that there is a possible performance issue with respect to the computing environment associated with the at least one entity
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing performance metrics, comparing them to a threshold, identify deviations and providing a notification based on that comparison., See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101-analysis, set forth in MPEP 2106, the examiner has determined that
the following additional elements do not integrate this judicial exception into a practical application:
As evaluated below:
• The preamble is deemed insufficient to transform the judicial exception to a patentable
invention to a patentable invention because the preamble generally links the use of a
judicial exception to a particular technological environment or field of use, see MPEP
2106.05(h).
one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
wherein each entity, of the plurality of entities is associated with a computing environment;
Deemed insufficient to transform the judicial exception to a patentable invention because, the claim recites limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
wherein the performance metric is a performance metric associated with running a cloud-based version of a medical image streaming application, and wherein the performance metric includes one or more of: response time, bandwidth, throughput, latency, availability, or reliability:
Deemed insufficient to transform the judicial exception to a patentable invention because, the claim recites limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
switching, based on the possible performance issue with respect to the computing environment associated with the at least one entity, between: (i) running a local version of the medical image streaming application, or (ii) running the cloud-based version of the medical image streaming application, on the computing environment associated with the at least one entity.
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the
claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (I and IV), recites mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Regarding limitation (II and III), deemed insufficient to transform the judicial exception to a patentable invention because they generally link the judicial exception to the technology environment, see MPEP 2106.05(h).
As analyzed above, the additional elements, analyzed above, do not integrate the noted judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Regarding claim 16,
identifying a plurality of entities
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves recognizing or categorizing of data elements (entities) based on observing patterns or features. See (MPEP 2106.04)).
for each entity of the plurality of entities, determining a performance metric associated with the computing environment
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves collecting and analyzing data (performance metric) related to each entity’s computing environment, which constitutes evaluation or measuring information to make a determination, See (MPEP 2106.04)).
for each entity of the plurality of entities, determining a plurality of characteristics associated with the computing environment
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing and identifying attributes or parameters related to each entity’s computing environment such as configuration, usage pattern, or system property, See (MPEP 2106.04)).
based on the performance metric associated with each entity of the plurality of entities, assigning each entity to a cohort of a plurality of cohorts
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating data (performance metrics) and classifying or categorizing entities into groups based on the evaluation, See (MPEP 2106.04)).
for each cohort of the plurality of cohorts, determining a plurality of characteristics associated with the cohort based on the plurality of characteristics of the computing environments associated with each entity assigned to the cohort;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves aggregating and analyzing known characteristics of individual entities to infer or derive group-level characteristics, See (MPEP 2106.04)).
for each cohort of the plurality of cohorts, determining a performance metric for the cohort based on the performance metrics determined for each entity assigned to the cohort
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves determining an aggregate or summary performance measure for a group by analyzing the individual performance metrics of its members, See (MPEP 2106.04)).
determining that the performance metric for at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than a threshold percentage
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves comparing a numerical value (an entity’s performance metric) to another numerical value and making a determination based on whether the difference exceeds a threshold value, See (MPEP 2106.04)).
In response to determining that the performance metric for the at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage, instructing, [ ], the at least one entity that there is a possible performance issue with respect to the computing environment associated with the at least one entity
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing performance metrics, comparing them to a threshold, identify deviations and providing a notification based on that comparison., See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101-analysis, set forth in MPEP 2106, the examiner has determined that
the following additional elements do not integrate this judicial exception into a practical application:
As evaluated below:
• The preamble is deemed insufficient to transform the judicial exception to a patentable
invention to a patentable invention because the preamble generally links the use of a
judicial exception to a particular technological environment or field of use, see MPEP
2106.05(h).
One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
wherein each entity, of the plurality of entities is associated with a computing environment;
Deemed insufficient to transform the judicial exception to a patentable invention because, the claim recites limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
wherein the performance metric is a performance metric associated with running a cloud-based version of a medical image streaming application, and wherein the performance metric includes one or more of: response time, bandwidth, throughput, latency, availability, or reliability:
Deemed insufficient to transform the judicial exception to a patentable invention because, the claim recites limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
switching, based on the possible performance issue with respect to the computing environment associated with the at least one entity, between: (i) running a local version of the medical image streaming application, or (ii) running the cloud-based version of the medical image streaming application, on the computing environment associated with the at least one entity.
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the
claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (I and IV), recites mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Regarding limitation (II and III), deemed insufficient to transform the judicial exception to a patentable invention because they generally link the judicial exception to the technology environment, see MPEP 2106.05(h).
As analyzed above, the additional elements, analyzed above, do not integrate the noted judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Regarding claim 2, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the plurality of characteristics associated with the computing environment comprise, network characteristics, location characteristics, workload characteristics, and computing resource characteristics.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim(s) 10 and 17 recite similar subject matter as claim 2, so are rejected under the same rationale.
Regarding claim 4, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
determining [ ] an average performance metric based on the determined performance metrics for some or all of the entities assigned to the cohort; and
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: a mathematical concept: It involves performing a calculation (an average) on collected performance data from a group of entities, See (MPEP 2106.04)).
determining [ ] the performance metric for the cohort using the average performance metric.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves assigning group’s performance metric based on previously calculated average performance, See (MPEP 2106.04)).
by the one or more processors
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim(s) 12 and 19 recite similar subject matter as claim 4, so are rejected under the same rationale.
Regarding claim 5, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
by the one or more processors
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
receiving [ ] an identifier of a new entity by the computing device, wherein the new entity is not part of the plurality of entities;
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
Determining [ ] one or more characteristics of a computing environment associated with the new entity by the computing device;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing or collecting information about a computing environment (entity) to identify is characteristics, See (MPEP 2106.04)).
based on the determined one or more characteristics of the computing environment associated with the new entity, assigning [ ] the new entity to a cohort of the plurality of cohorts
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves classifying a new entity into a group based on observed characteristics, See (MPEP 2106.04)).
returning [ ] the performance metric for the cohort assigned to the new entity as an expected performance metric for the new entity.
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
Claim(s) 13 and 20 recite similar subject matter as claim 5, so are rejected under the same rationale.
Regarding claim 6, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
in response to determining that the performance metric for the at least one entity assigned to the at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage, reassigning, [ ] the at least one entity to a different cohort of the plurality of cohorts.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating current grouping criteria and change the classification of an entity based on the evaluation, See (MPEP 2106.04)).
by the one or more processors
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 14 recites similar subject matter as claim 6, so is rejected under the same rationale.
Regarding claim 7, dependent upon claim 6, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
Associating, [ ] some or all of the characteristics of the computing environment associated with the at least one entity to the different cohort.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves relating or associating data attributes from one classification group to another, See (MPEP 2106.04)).
by the one or more processors
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 15 recites similar subject matter as claim 7, so is rejected under the same rationale.
Regarding claim 8, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein determining the one or more characteristics of the computing environment associated with the new entity comprises providing [ ] a questionnaire to the new entity and determining [ ] the one or more characteristics of the computing environment associated with the new entity based on a response to the questionnaire from the new entity.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
by the one or more processors
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 – 2, 9 – 10 and 16 – 17 are rejected under 35 U.S.C. 103 as being unpatentable over Makaya, et al., Pub. No.: US20230168925A1, in view of Ferdowsi et al., Pub. No.: US20220255833A1, Strenski et al., Pub. No.: US20220326993A1, Dang et al., Pub. No.: US11438214B2, Salunke et al., Pub. No.: US20170329660A1 and Cao et al., Pub. No.: US10361930B2.
Regarding claim 1, Makaya teaches: A computer-implemented method comprising: identifying by one or more processors, a plurality of entities, wherein each entity, of the plurality of entities, is associated with a computing environment;
(Makaya, “[0014] Federated Learning (FL) is an approach to enable distributed machine learning on computing devices while preserving privacy of data. An FL system is a network of computing devices (also referred to as FL workers) and a scheduler device (also referred to as an aggregation node or FL server). In some examples, computing devices [identifying by one or more processors, a plurality of entities, wherein each entity, of the plurality of entities, is associated with a computing environment] used as FL workers may be diverse and heterogeneous, with different connectivity link capabilities.”)
for each entity of the plurality of entities, determining, by one or more processors, a performance metric associated with the computing environment.
(Makaya, “[0022] Upon receiving the computing task 109, a computing device 104 [for each entity of the plurality of entities] may determine [determining, by one or more processors] an intrusiveness metric 112 that indicates an impact of the computing task 109 on the performance of the computing device 104 [a performance metric associated with the computing environment by the computing device]. The intrusiveness metric 112 may be determined with an intrusiveness machine learning model 110.”)
for each entity of the plurality of entities, determining by one or more processors, a plurality of characteristics associated with the computing environment;
(Makaya, “[0015] In an example, machine learning jobs (e.g., training) may be run on computing devices while preserving data privacy, improving utilization, and job completion time, without loss or degradation of machine learning performance and accuracy. To achieve such a goal, the machine learning jobs may be scheduled efficiently. Instead of using predictive analytics based on time-series, federated learning and execution trace (e.g., instructions issued/retired) may be utilized for a deeper understanding of the computing devices [for each entity of the plurality of entities] to determine how computational resources (e.g., processors) [determining, by one or more processors, a plurality of characteristics associated with the computing environment] perform when jobs have been deployed. By using FL, the level of intrusiveness of a computing task (also referred to as a job) may be determined. This intrusiveness metric may be reported back to the scheduler device that is retrained to optimally schedule and allocate computing tasks on the computing devices during future iterations.”)
Makaya does not teach:
based on the performance metrics associated with each entity of the plurality of entities, assigning, by one or more processors, each entity, of the plurality of entities to a cohort of a plurality of cohorts;
for each cohort of the plurality of cohorts, determining, by one or more processors, a performance metric for the cohort based on the performance metrics determined for each entity assigned to the cohort;
for each cohort of the plurality of cohorts, determining, by one or more processors, a plurality of characteristics associated with the cohort based on the plurality of characteristics of the computing environments associated with each entity assigned to the cohort;
determining, by one or more processors, that the performance metric for at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than a threshold percentage and
instructing, by the one or more processors, the at least one entity that there is a possible performance issue with respect to the computing environment associated with the at least one entity
wherein the performance metric is a performance metric associated with running a cloud-based version of a medical image streaming application, and wherein the performance metric includes one or more of: response time, bandwidth, throughput, latency, availability, or reliability;
in response to determining that the performance metric for the at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage,
switching, by the one or more processors and based on the possible performance issue with respect to the computing environment associated with the at least one entity, between: (i) running a local version of the medical image streaming application, or (ii) running the cloud- based version of the medical image streaming application, on the computing environment associated with the at least one entity.
Ferdowsi teaches:
based on the performance metrics associated with each entity of the plurality of entities, assigning, by one or more processors, each entity, of the plurality of entities to a cohort of a plurality of cohorts;
(Ferdowsi, “[0017] One aspect of the present disclosure describes a system for landscaping various client devices by clustering client devices into clusters [assigning, by one or more processors, each entity, of the plurality of entities to a cohort of a plurality of cohorts] based on similar performance metrics [based on the performance metrics associated with each entity of the plurality of entities]. In an example, the cluster can range from 0 to 5, where a cluster 0 includes the least performant devices and cluster 5 includes flagship devices. The clustering is an off-line process that is performed using actual client devices during workloads when a user engages an application running on a client device. The performance metrics are functional in that they relate to a user using and engaging the application running on the client device, and the performance metrics do not depend on a network. The performance metrics are stored and dynamically updated in a server system based on aggregated performance data. Performance data of the application is aggregated over time for each of the client devices, and lists ranking the various client devices are maintained at a server system. Charts are also maintained that include the performance metrics of client devices. New client devices that come available are combined with a clustering list based on how close their performance metrics are to the centroid of clusters. The top client devices are based on WAU and WNU, and some are based on expert opinion.”)
for each cohort of the plurality of cohorts, determining, by one or more processors, a performance metric for the cohort based on the performance metrics determined for each entity assigned to the cohort by the computing device;
(Ferdowsi, “[0036] FIG. 2 is a block diagram illustrating the clustering system 124 operable within server system 108. Clustering system 124 is seen to comprise data structures including a device list 202 associating various client devices 110 to a cluster, and a chart 204 illustrating the performance metrics of each client device 110. The device list 202 and chart 204 are stored in memory 904 (FIG. 9). Devices are scored by applying Principal Component Analysis model to the performance metrics. First component of the model is used to score and rank the devices. Cluster boundaries are formed by first dividing the set into 5 same size group [for each cohort of the plurality of cohorts] and then adjust based on expert's knowledge on the phone performance. Then cluster centroids are defined based on median values of performance metrics for devices in the same cluster [determining, by one or more processors, a performance metric for the cohort based on the performance metrics determined for each entity assigned to the cohort by the computing device] and as new phones come to the market, they get assigned to the cluster that they are most closed to. When a new generation of flagship phones come into the market, which has a lot higher performance than cluster 5 centroids (high performant phones), a new set of clusters are defined to accommodate the new generation of phones.”)
Ferdowsi and Makaya are related to the same field of endeavor (i.e.: distributed computing). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teachings of Ferdowsi with teachings of Makaya to enhance a distributed cluster by dynamically grouping client devices into performance-based clusters using real world application engagement metric, (Ferdowsi, ¶[0016] – [0017]).
Makaya in view of Ferdowsi do not teach:
for each cohort of the plurality of cohorts, determining, by one or more processors, a plurality of characteristics associated with the cohort based on the plurality of characteristics of the computing environments associated with each entity assigned to the cohort;
determining, by one or more processors, that the performance metric for at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than a threshold percentage and
instructing, by the one or more processors, the at least one entity that there is a possible performance issue with respect to the computing environment associated with the at least one entity
wherein the performance metric is a performance metric associated with running a cloud-based version of a medical image streaming application, and wherein the performance metric includes one or more of: response time, bandwidth, throughput, latency, availability, or reliability;
in response to determining that the performance metric for the at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage,
switching, by the one or more processors and based on the possible performance issue with respect to the computing environment associated with the at least one entity, between: (i) running a local version of the medical image streaming application, or (ii) running the cloud- based version of the medical image streaming application, on the computing environment associated with the at least one entity.
Strenski teaches:
for each cohort of the plurality of cohorts, determining, by one or more processors, a plurality of characteristics associated with the cohort based on the plurality of characteristics of the computing environments associated with each entity assigned to the cohort;
(Strenski, “[0026] ... For example, a High-Performance Unpack code or SPECfp benchmark code may be used as test-computing jobs. In some other examples, the existing code may be modified, or a new code may be developed as test-computing jobs. One or more performance metrics are used to determine the current/actual performance of each node in the cluster of nodes [for each cohort of the plurality of cohorts]. The one or more performance metrics may include, but not limited to, an actual processing speed, a storage capacity, actual memory availability and read/write speed, a networking speed [determining, by one or more processors, a plurality of characteristics associated with the cohort], etc. The one or more test-computing jobs may be set to a low priority. The one or more test-computing jobs are executed on each node when each node becomes available. For example, if a particular node is executing certain high-priority jobs, then the test-computing jobs may be put in a queue. In one example, the test-computing job is run periodically on each node to determine the instantaneous performance of the cluster of nodes [based on the plurality of characteristics of the computing environments associated with each entity assigned to the cohort].”)
Strenski, Makaya and Ferdowsi are related to the same field of endeavor (i.e.: distributed computing). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teachings of Strenski with teachings of Makaya and Ferdowsi to add performance aware node selection to a distributed architecture by periodically benchmarking nodes with test jobs and storing their metrics in a database, (Strenski, Abstract).
Makaya in view of Ferdowsi and Strenski do not teach:
determining, by one or more processors, that the performance metric for at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than a threshold percentage and
instructing, by the one or more processors, the at least one entity that there is a possible performance issue with respect to the computing environment associated with the at least one entity
wherein the performance metric is a performance metric associated with running a cloud-based version of a medical image streaming application, and wherein the performance metric includes one or more of: response time, bandwidth, throughput, latency, availability, or reliability;
in response to determining that the performance metric for the at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage,
switching, by the one or more processors and based on the possible performance issue with respect to the computing environment associated with the at least one entity, between: (i) running a local version of the medical image streaming application, or (ii) running the cloud- based version of the medical image streaming application, on the computing environment associated with the at least one entity.
Dang teaches:
determining, by the one or more processors, that the performance metric for at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than a threshold percentage by the computing device; and
(Dang, (col. 2, lines [30 - 55]), “Accordingly, a first example embodiment may involve a computing system disposed within a remote network management platform and configured to support a managed network, the computing system comprising: one or more processors; memory; and program instructions, stored in the memory, that upon execution by the one or more processors cause the computing system to perform operations including: monitoring respective performance of each network entity of a plurality of network entities of the managed network [determining that the performance metric for at least one entity assigned to at least one cohort of the plurality of cohorts], each network entity being at least one of a service of the managed network or a computing device of the managed network, wherein each service of the managed network executes on at least one computing device of the managed network; for each of the plurality of network entities, issuing an alert in response to determining that the monitored respective performance is below a respective threshold performance level; [deviates from the performance metric for the at least one cohort by more than a threshold percentage by the computing device] based on analysis of a first group of one or more issued alerts, determining a statistical likelihood that a different alert will be issued for the monitored respective performance of a particular network entity of the plurality for which no respective alert has yet been issued; and issuing a score notification for the different alert in response to the determined statistical likelihood exceeding a score threshold, wherein the score notification includes the determined statistical likelihood and an identity of the particular network entity.”)
instructing, by the one or more processors, the at least one entity that there is a possible performance issue with respect to the computing environment associated with the at least one entity
(Dang, (col. 2, lines [30 - 55]), “Accordingly, a first example embodiment may involve a computing system disposed within a remote network management platform and configured to support a managed network, the computing system comprising: one or more processors; memory; and program instructions, stored in the memory, that upon execution by the one or more processors cause the computing system to perform operations including: monitoring respective performance of each network entity of a plurality of network entities of the managed network [the at least one entity that there is a possible performance issue with respect to the computing environment associated with the at least one entity], each network entity being at least one of a service of the managed network or a computing device of the managed network, wherein each service of the managed network executes on at least one computing device of the managed network; for each of the plurality of network entities, issuing an alert [instructing, by the one or more processors,] that the monitored respective performance is below a respective threshold performance level; based on analysis of a first group of one or more issued alerts in response to determining, determining a statistical likelihood that a different alert will be issued for the monitored respective performance of a particular network entity of the plurality for which no respective alert has yet been issued; and issuing a score notification for the different alert in response to the determined statistical likelihood exceeding a score threshold, wherein the score notification includes the determined statistical likelihood and an identity of the particular network entity.”)
Dang, Makaya, Ferdowsi and Strenski are related to the same field of endeavor (i.e.: distributed computing). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teachings of Dang with teachings of Makaya, Ferdowsi and Strenski to enhance proactive maintenance and incident prevention by issuing early warning predictions with confidence scores, enabling faster response and reduced downtime, (Dang, Abstract).
Makaya in view of Ferdowsi, Strenski and Dang do not teach:
wherein the performance metric is a performance metric associated with running a cloud-based version of a medical image streaming application, and wherein the performance metric includes one or more of: response time, bandwidth, throughput, latency, availability, or reliability;
in response to determining that the performance metric for the at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage,
switching, by the one or more processors and based on the possible performance issue with respect to the computing environment associated with the at least one entity, between: (i) running a local version of the medical image streaming application, or (ii) running the cloud- based version of the medical image streaming application, on the computing environment associated with the at least one entity.
Salunke teaches:
wherein the performance metric is a performance metric associated with running a cloud-based version of a medical image streaming application, and wherein the performance metric includes one or more of: response time, bandwidth, throughput, latency, availability, or reliability;
(Salunke, “[0059] A metric may refer to any data that may be measured periodically and represented as a time series. Examples of metrics may include, without limitation, response times that indicate how long a target resource takes to respond to a request [wherein the performance metric is a performance metric associated with running a cloud-based version of a medical image streaming application, and wherein the performance metric includes one or more of: response time, bandwidth, throughput, latency, availability, or reliability], wait times that indicate how long a target resource is waiting on a request, request counts that indicate how many requests have been received and/or sent to/from a target resource, session counts that indicate how many sessions are active within a database or other software resource, logon counts that track the number of logons, error message counts that track the number of logged error messages, memory bandwidth metrics that indicate how much memory is free, and/or other utilization metrics that are indicative of the utilization rate of a respective target resource.”)
in response to determining that the performance metric for the at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage,
(Salunke, “[0120] At 920, evaluation logic 112 determines the nearest cluster center for observed changes within a target time period that is being evaluated. A cluster center in this context may be computed by averaging the change values for all changes that belong to the cluster. The nearest cluster may be determined by comparing a distance (e.g., Euclidean, Jaccardian, etc.) between the value of the change and the values of each of the cluster centers [in response to determining that the performance metric for the at least one entity assigned to at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage].”)
Salunke, Makaya, Ferdowsi, Strenski and Dang are related to the same field of endeavor (i.e.: distributed computing). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teachings of Dang with teachings of Makaya, Ferdowsi, Strenski and Dang to add evaluation accounts for relationship between multiple metrics overtime and identify deviations from expected baseline, enabling detection of impactful conditions, (Salunke, Abstract).
Makaya in view of Ferdowsi, Strenski, Dang and Salunke do not teach:
switching, by the one or more processors and based on the possible performance issue with respect to the computing environment associated with the at least one entity, between: (i) running a local version of the medical image streaming application, or (ii) running the cloud- based version of the medical image streaming application, on the computing environment associated with the at least one entity.
Cao teaches:
switching, by the one or more processors and based on the possible performance issue with respect to the computing environment associated with the at least one entity, between: (i) running a local version of the medical image streaming application, or (ii) running the cloud- based version of the medical image streaming application, on the computing environment associated with the at least one entity.
(Cao, (col.8 Line [6 – 29]), “FIG. 4 shows one suitable example of the streams manager 350 shown in FIG. 3. The streams manager 350 is software that manages one or more streaming applications, including managing operators and data flow connections between operators in a flow graph that represents a streaming application. The streams manager 350 includes a performance monitor 410 with one or more performance thresholds 420. Performance thresholds 420 can include static thresholds, such as percentage used of current capacity or tuple rate, and can also include any suitable heuristic for measuring performance of a streaming application as a whole or for measuring performance of one or more operators in a streaming application. Performance thresholds 420 may include different thresholds and metrics at the operator level, at the level of a group of operators, and/or at the level of the overall performance of the streaming application. The stream performance monitor 410 monitors performance of a streaming application, and when current performance compared to the one or more performance thresholds 420 indicates current performance needs to be improved [based on the possible performance issue with respect to the computing environment associated with the at least one entity,], the stream performance monitor 410 communicates with the streams manager 350 to attempt to improve performance by rerouting streams to another operator [switching, by the one or more processors, [] between: (i) running a local version of the medical image streaming application, or (ii) running the cloud- based version of the medical image streaming application, on the computing environment associated with the at least one entity] as described further below.”)
Cao, Makaya, Ferdowsi, Strenski, Dang and Salunke are related to the same field of endeavor (i.e.: distributed computing). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teachings of Cao with teachings of Makaya, Ferdowsi, Strenski, Dang and Salunke to add monitoring performance conditions at multiple levels of granularity to trigger adaptive reallocation of work then performance levels are dropped from a defined thresholds. and , (Cao, Abstract).
Claim 9 recites analogous limitations as claim 1, so is rejected under similar rationale.
Regarding claim 2, Makaya in view of Ferdowsi, Strenski, Dang, Salunke and Cao teach the method of claim 1.
Strenski further teaches: wherein the plurality of characteristics associated with the computing environment comprise: network characteristics, location characteristics, workload characteristics, and computing resource characteristics.
(Strenski, “[0026] …The sub-applications may perform operations to replicate real-time simulations to determine performance metrics. For example, a sub-application of the test-computing job may be developed to determine a CPU performance. In some examples, a time taken for execution of the test-computing job may be measured to determine CPU performance/speed (performance metric). For example, a High-Performance Unpack code or SPECfp benchmark code may be used as test-computing jobs. In some other examples, the existing code may be modified, or a new code may be developed as test-computing jobs. One or more performance metrics are used to determine the current/actual performance of each node in the cluster of nodes. The one or more performance metrics may include, but not limited to, an actual processing speed, a storage capacity, actual memory availability and read/write speed [computing resource characteristics], a networking speed [network characteristics], etc. The one or more test-computing jobs may be set to a low priority. The one or more test-computing jobs are executed on each node when each node becomes available. For example, if a particular node is executing certain high-priority jobs, then the test-computing jobs may be put in a queue. In one example, the test-computing job is run periodically on each node to determine the instantaneous performance of the cluster of nodes.”)
workload characteristics
(Strenski, “[0025] The processing element 205 may fetch, decode, and execute the instructions 222 to gather information about a cluster of nodes in the high-performance computing system. The high-performance computing system may be in a production state/condition with one or more computational workloads or computational jobs [workload characteristics] getting executed on it. In some examples, the processing element 205 of the scheduler node 200 may gather information about the available nodes and creates a list of the nodes. The ‘available nodes’ may include operational nodes. In other examples, the ‘available nodes’ may be based on the health information of the nodes, for example—the health information may include, but not limited to, operating temperature, speed, age of node, etc.”)
Makaya further teaches: location characteristics
(Makaya, “[0013] Efficient scheduling and orchestration of computing resources may be performed in a distributed machine learning context. For example, efficient management of machine learning and artificial intelligence resources may be used to provide high performance of applications run on computing devices. Furthermore, data privacy is a concern in the case of edge devices located at different sites or on-premises data centers [location characteristics], where the edge devices may collect data that cannot be shared across or uploaded to a central storage (e.g., cloud). As used herein, “edge devices” include mobile devices, workstations, notebooks, laptops, edge gateway, IoT devices, etc.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Makaya with teachings of Ferdowsi, Strenski, Dang, Salunke and Cao for the same reasons disclosed for claim 1.
Claim(s) 10 and 17 recite analogous limitations as claim 2, so are rejected under similar rationale.
Regarding claim 16, Makaya teaches: One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
(Makaya, “[0047] The computer-readable medium 220 may be any electronic, magnetic, optical, or other physical storage device that contains or stores electronic information (e.g., instructions and/or data) [when executed by one or more processors, cause the one or more processors to perform operations comprising]. Thus, the computer-readable medium 220 may be, for example, Random Access Memory (RAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), a storage device, an optical disc, and the like. In some implementations, the computer-readable medium 220 may be a non-transitory tangible computer-readable medium [One or more non-transitory computer-readable media storing processor-executable instructions], where the term “non-transitory” does not encompass transitory propagating signals.”)
The rest of the limitation are analogous to claim 1, so is rejected under similar rationale.
Claim(s) 4, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Makaya in view of Ferdowsi, Strenski, Dang, Salunke, Cao and in further view of Ramalingam et al., Pub. No.: US11375255B1, (hereafter Ramalingam).
Regarding claim 4, Makaya in view of Ferdowsi, Strenski, Dang, Salunke and Cao teach the method of claim 1.
Ferdowsi further teaches: wherein determining, a performance metric for the cohort based on the performance metrics determined for each entity assigned to the cohort comprises: determining, by the one or more processors, an average performance metric based on the determined performance metrics for some or all of the entities assigned to the cohort; and
(Ferdowsi, “[0040] FIG. 5 illustrates chart 204 showing average performance metrics [determining, by the one or more processors, an average performance metric] for client devices 110 of each cluster 0 through 5 [based on the determined performance metrics for some or all of the entities assigned to the cohort]. Chart 204 illustrates the number of weekly active users (WAU) and the number of weekly new users (WNU). This chart 204 also illustrates client devices 110 that are considered unclassified.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Ferdowsi with teachings of Makaya, Strenski, Dang, Salunke and Cao for the same reasons disclosed for claim 1.
Makaya in view of Ferdowsi, Strenski Dang, Salunke and Cao do not teach:
Determining, by the one or more processors, the performance metric for the cohort using the average performance metric.
Ramalingam teaches:
Determining, by the one or more processors, the performance metric for the cohort using the average performance metric.
(Ramalingam, (col. 15 lines [17 - 33]), “As shown in FIG. 7, the dynamic system 710 may include feature extraction module 711, classification module 713, classification module 714, and playback optimization service 715. The feature extraction module 711 may determine and/or calculate features from the network performance data that is not specific to the computing device and/or performance metrics data specific to the computing device [determining, by the one or more processors, the performance metric]. For example, the feature extraction module 711 may extract features 712 such as average [using the average performance metric], min, max, standard deviation, and/or variance of throughput and latency. The extracted features may be specific to a device, location, network, or user profile. The extracted features may be sent to and/or communicated to the cluster module 713, which may be the as cluster module 112 of FIG. 1. The cluster module 713 may include and/or be in communication with one or more of the module or components described above with respect to cluster system 105 and/or cluster system 310 [for the cohort].”)
Ramalingam, Makaya, Ferdowsi, Strenski, Dang, Salunke and Cao are related to the same field of endeavor (i.e.: distributed computing). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teachings of Ramalingam with teachings of Makaya, Ferdowsi, Strenski, Dang, Salunke and Cao to add continuous, session-aware performance tuning, (Ramalingam, Abstract).
Claim(s) 12 and 19 recite analogous limitations as claim 4, so are rejected under similar rationale.
Claim(s) 5, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Makaya in view of Ferdowsi, Strenski, Dang, Salunke, Cao and in further view of O'Sullivan et al., Pub. No.: US9495651B2, (hereafter O'Sullivan).
Regarding claim 5, Makaya in view of Ferdowsi, Strenski, Dang, Salunke and Cao teach the method of claim 1.
Ferdowsi further teaches: receiving, by the one or more processors, an identifier of a new entity, wherein the new entity is not part of the plurality of entities;
(Ferdowsi, “[0017] One aspect of the present disclosure describes a system for landscaping various client devices by clustering client devices into clusters based on similar performance metrics. In an example, the cluster can range from 0 to 5, where a cluster 0 includes the least performant devices and cluster 5 includes flagship devices. The clustering is an off-line process that is performed using actual client devices during workloads when a user engages an application running on a client device. The performance metrics are functional in that they relate to a user using and engaging the application running on the client device, and the performance metrics do not depend on a network. The performance metrics are stored and dynamically updated in a server system based on aggregated performance data. Performance data of the application is aggregated over time for each of the client devices, and lists ranking the various client devices are maintained at a server system. Charts are also maintained that include the performance metrics of client devices. New client devices that come available [receiving, by the one or more processors, an identifier of a new entity, wherein the new entity is not part of the plurality of entities] are combined with a clustering list based on how close their performance metrics are to the centroid of clusters. The top client devices are based on WAU and WNU, and some are based on expert opinion.”)
Returning, by the one or more processors, the performance metric for the cohort assigned to the new entity as an expected performance metric for the new entity.
(Ferdowsi, “[0017] One aspect of the present disclosure describes a system for landscaping various client devices by clustering client devices into clusters based on similar performance metrics. In an example, the cluster can range from 0 to 5, where a cluster 0 includes the least performant devices and cluster 5 includes flagship devices. The clustering is an off-line process that is performed using actual client devices during workloads when a user engages an application running on a client device. The performance metrics are functional in that they relate to a user using and engaging the application running on the client device, and the performance metrics do not depend on a network. The performance metrics are stored and dynamically updated in a server system based on aggregated performance data. Performance data of the application is aggregated over time for each of the client devices, and lists ranking the various client devices are maintained at a server system. Charts are also maintained that include the performance metrics of client devices. New client devices that come available [assigned to the new entity] are combined with a clustering list [returning, by the one or more processors, the performance metric for the cohort] based on how close their performance metrics are to the centroid of clusters [as an expected performance metric for the new entity]. The top client devices are based on WAU and WNU, and some are based on expert opinion.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Ferdowsi with teachings of Makaya, Strenski, Dang, Salunke and Cao for the same reasons disclosed for claim 1.
Makaya in view of Ferdowsi, Strenski, Dang, Salunke and Cao do not teach:
Determining, by the one or more processors, one or more characteristics of a computing environment associated with the new entity;
based on the determined one or more characteristics of the computing environment associated with the new entity, assigning, by the one or more processors, the new entity to a cohort of the plurality of cohorts
O'Sullivan teaches:
determining, by the one or more processors, one or more characteristics of a computing environment associated with the new entity;
(O'Sullivan, (col. 28 line [61 – 67] and col. 29 line [1 – 16]), “The cohort management services 324 may store the profiles 600 generated by the system administrator or other authorized user so that they may be applied to the computing resource configuration information in the CMDB 332 and the CEEC terms and conditions specified in the CEECs stored in the CEEC database 334. Initially, when the system initializes, when a new computing resource is added to the system [associated with the new entity by the computing device], or when a new CEEC is created, there will not be any utilization information in the database system 330 to use as a basis for determining membership in cohorts defined by the various profiles 600. Thus, membership in cohorts may be initially determined based on computing resource configuration information [determining, by the one or more processors, one or more characteristics of a computing environment] and CEEC terms/conditions specified in the database system 330. Thereafter, as the computing resources are utilized under various CEECs, utilization information may be collected/generated and used as a basis to dynamically adjust the membership of computing resources and CEECs in the various cohorts defined by the profiles in accordance with the adjustment criteria specified in these profiles 600. Thus, the cohort management services 324 may continuously or periodically monitor and apply the profiles to the information stored in the database system 330 to determine adjustments to cohort membership.”)
based on the determined one or more characteristics of the computing environment associated with the new entity, assigning, by the one or more processors, the new entity to a cohort of the plurality of cohorts
(O'Sullivan, (col. 28 line [61 – 67] and col. 29 line [1 – 16]), “The cohort management services 324 may store the profiles 600 generated by the system administrator or other authorized user so that they may be applied to the computing resource configuration information in the CMDB 332 and the CEEC terms and conditions specified in the CEECs stored in the CEEC database 334. Initially, when the system initializes, when a new computing resource is added to the system, or when a new CEEC is created [associated with the new entity], there will not be any utilization information in the database system 330 to use as a basis for determining membership in cohorts defined by the various profiles 600. Thus, membership in cohorts may be initially determined based on computing resource configuration information [based on the determined one or more characteristics of the computing environment] and CEEC terms/conditions specified in the database system 330. Thereafter, as the computing resources are utilized under various CEECs, utilization information may be collected/generated and used as a basis to dynamically adjust the membership of computing resources and CEECs in the various cohorts [assigning, by the one or more processors, the new entity to a cohort of the plurality of cohorts] defined by the profiles in accordance with the adjustment criteria specified in these profiles 600. Thus, the cohort management services 324 may continuously or periodically monitor and apply the profiles to the information stored in the database system 330 to determine adjustments to cohort membership.”)
O'Sullivan, Makaya, Ferdowsi, Strenski, Dang, Salunke and Cao are related to the same field of endeavor (i.e.: distributed computing). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teachings of O'Sullivan with teachings of Makaya, Ferdowsi, Strenski, Dang, Salunke and Cao to enhance usability and decision making by allowing targeted actions on specific cohorts based on visual inspection and user input, (O'Sullivan, Abstract).
Claim(s) 13 and 20 recite analogous limitations as claim 5, so are rejected under similar rationale.
Claim(s) 6 – 7 and 14 – 15 are rejected under 35 U.S.C. 103 as being unpatentable over Makaya in view of Ferdowsi, Strenski, Dang, Salunke, Cao and in further view of Soundararajan et al., Pub. No.: US8898289B1, (hereafter Soundararajan).
Regarding claim 6, Makaya in view of Ferdowsi, Strenski, Dang, Salunke and Cao teach the method of claim 1.
Makaya in view of Ferdowsi, Strenski, Dang, Salunke and Cao do not teach:
in response to determining that the performance metric for the at least one entity assigned to the at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage, reassigning, by the one or more processors the at least one entity to a different cohort of the plurality of cohorts.
Soundararajan teaches:
in response to determining that the performance metric for the at least one entity assigned to the at least one cohort of the plurality of cohorts deviates from the performance metric for the at least one cohort by more than the threshold percentage, reassigning, by the one or more processors the at least one entity to a different cohort of the plurality of cohorts.
(Soundararajan, (col. 9 line[20 - 35]), “The entity routing module 412 maintains each of the groups in a data structure 109, such as a log or a routing table that maps each entity to one or more entity managers within the environment 100. A designated (“centralized”) entity manager can maintain a single mapping for the entire environment. Alternatively, the mapping 109 can be distributed among the entity managers of the environment 100 so that each entity manager maintains a portion of the mapping. In either scenario, the entity manager uses its mapping to determine which entity or entity manager to route the notification. Reassigning an entity from one group to another group [reassigning, by the one or more processors the at least one entity to a different cohort of the plurality of cohorts], creating a new group, or removing an entity from a group can cause the mapping to be automatically updated, as described above, to reflect an association between the entity, its group, its managing entity, and potentially other information, such as the network address of the entity manager.”)
Soundararajan, Makaya, Ferdowsi, Strenski, Dang, Salunke and Cao are related to the same field of endeavor (i.e.: distributed computing). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teachings of Soundararajan with teachings of Makaya, Ferdowsi, Strenski, Dang, Salunke and Cao to enable automatic distribution of event alerts from one entity to other affected entity within a group, which improves coordination and responsiveness across distributed system, (Soundararajan, Abstract).
Claim 14 recites analogous limitations as claim 6, so is rejected under similar rationale.
Regarding claim 7, Makaya in view of Ferdowsi, Strenski, Dang, Salunke, Cao and Soundararajan teach the method of claim 6.
Soundararajan further teaches: further comprising associating, by the one or more processors some or all of the characteristics of the computing environment associated with the at least one entity to the different cohort.
(Soundararajan, (col. 10 line [19 - 31]), “Groups can be created manually, randomly, and/or dynamically at the entity manager. For example, by creating an administrative policy at the entity manager an administrator (or other user) can manually specify that entities having similar characteristics (e.g., entities monitoring volumes, or entities located in a particular geographic location) [associating, by the one or more processors some or all of the characteristics of the computing environment associated with the at least one entity] be assigned to the same group. Alternatively, entities can be randomly assigned by an entity manager into one or more groups. Regardless of whether an entity was manually or randomly assigned to a particular group, the entity manager can dynamically reassign an entity to another group to optimize performance [to the different cohort], as further explained below.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Soundararajan with teachings of Makaya, Ferdowsi, Strenski, Dang, Salunke, Cao for the same reasons disclosed for claim 6.
Claim 15 recites analogous limitations as claim 7, so is rejected under similar rationale.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Makaya in view of Ferdowsi, Strenski, Dang, Salunke, Cao and in further view of Goh et al., Pub. No.: US20210201412A1, (hereafter Goh).
Makaya in view of Ferdowsi, Strenski, Dang, Salunke, Cao and O'Sullivan teach the method of claim 5.
Makaya in view of Ferdowsi, Strenski, Dang, Salunke, Cao and O'Sullivan do not teach;
wherein determining the one or more characteristics of the computing environment associated with the new entity comprises providing, by the one or more processors, a questionnaire to the new entity and determining, by the one or more processors the one or more characteristics of the computing environment associated with the new entity based on a response to the questionnaire from the new entity.
Goh teaches:
wherein determining the one or more characteristics of the computing environment associated with the new entity comprises providing, by the one or more processors, a questionnaire to the new entity and
(Goh, “[0091] The processing unit 110 executes the processing instruction to cause the processing unit 110 to acquire response from the user for sequentially provided plurality of questionnaire sets. Throughout the present disclosure, the term questionnaire sets refer to a set of questions with a choice of answers, devised for the purposes of a survey or statistical study of various operating condition of the entity. Therefore, the plurality of questionnaire sets [providing, by the one or more processors, a questionnaire] refers the plurality of sets of questions for survey or statistical study of various operating condition of the entity [to the new entity]. Furthermore, each questionnaire set comprises plurality of queries associated with operation of the entity. In an example, the sets of questions may include audit strategy of the entity that may set out the scope, timing and direction of the audit which will guide the development of the audit plan.”)
determining, by the one or more processors the one or more characteristics of the computing environment associated with the new entity based on a response to the questionnaire from the new entity.
(Goh, “[0115] The first user interface displays the first machine-coded questionnaires for understanding, defining, calculating or suggesting various conditions of the entity. The questionnaires comprise plurality of risk assessment parameters [determining, by the one or more processors, the one or more characteristics of the computing environment]. Specifically, questionnaires may include questions/queries related to a set of procedures elements/components in the entity [associated with the new entity] that may be used to identify assurance risk. The questionnaires may be logic supported to determine one or more condition in the entity. In an example, the questions/queries may be related to business environment of the entity. In such instance, if a response to the questions/queries related to business environment of the entity refers to dynamic business environment [based on a response to the questionnaire from the new entity] (i.e.: response to questions is analyzed to infer conditions/characteristics of the entity (e.g., dynamic business environment -> high risk)) ; it would be considered that the business entity involves high risk. It will be appreciated that risk assessment parameters may be include the questions related to the risk consideration, risk assessment of the entity.”)
Goh, Makaya, Ferdowsi, Strenski, Dang, Salunke, Cao and O'Sullivan are related to the same field of endeavor (i.e.: distributed computing). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teachings of Goh with teachings of Makaya, Ferdowsi, Strenski, Dang, Salunke, Cao and O'Sullivan to enhance the system with automated generation of assurance planning process and audit documentation by leveraging input data from the entity, (Goh, Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sandeep Akinapelli, Pub. No.: US20220147390A1, (2020-11-10), G06F9/5027
Akinapelli teaches adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster.
Robert R. Friedlander, Pub. No: US20130254202A1 (2012-07-31), G16B50/00
Friedlander teaches parallelization of updating synthetic events with genetic surprisal data comprising dividing the synthetic event into cohort parts and assigning the cohort parts to one of a plurality of computer processing elements. reassigning each match of data to the new centroid of each cluster based on the shortest calculated Euclidean distance to the new centroid for each cluster;
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/M.T.M./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148
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