The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Notice to Applicant
In response to the communication received on 10/27/2025, the following is a Final Office Action for Application No. 18658995.
Status of Claims
Claims 1-20 are pending.
Response to Amendments
Applicant’s amendments have been fully considered.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are not persuasive, and the rejection reflects the amendments. Applicant argues the following to which Examiner respectfully disagrees and provides updated reference according to amended claims which is reflected in the Office action:
determine a plurality of analytic models that are enabled corresponding to the at least one asset based on the received asset data, wherein the plurality of analytic models comprises one or more rules defining threshold values for one or more parameters associated with the plurality of assets and one or more calculation models configured to calculate efficiency of the plurality of assets;execute the one or more calculation models to calculate the efficiency of the at least one asset to determine whether a maintenance is to be scheduled for the at least one asset (¶0031 FIG. 1A illustrates an example system 100 in which a machine learning agent 102 extracts a set of states 104 as well as an aging model 106 from a control system 108. As mentioned briefly above, the aging model 106 may be associated one or several components 110 of the control system 108 and can define predicted operating conditions for a future point in time. The predicted operating conditions may differ from the current operating conditions 112. For example, a future efficiency of a component predicted by the aging model 106 may be lower than a current efficiency due to age related degradation. In various examples, the aging model 106 can be a statistical model such as a power law, a Weibull distribution, or an exponential decay model. However, the aging model 106 can be any suitable representation of aging or drift. ¶0032 By analyzing the aging model in addition to the set of states 104, the machine learning agent 102 can determine an action set 114 that is applied to the control system 108. The action set 114 can define one or more modifications to characteristics of the control system 108 or components 110 … ¶0048 In addition, the machine learning agent 102 can be configured to receive a predetermined performance level 302 from an administrative entity. The administrative entity can be a system administrator, an engineer, or other entity that can direct the machine learning agent 102. In various examples, the predetermined performance level 302 can define a specific state 104 or operating condition 112 of interest. For example, in the example system 200 described above, the predetermined performance level 302 can be a specific value of kW/ton efficiency 208 of the climate control system 202 that the administrative entity desires. ¶0053 In various examples, the maintenance schedule 402 can define a period of time during which the control system 108 is taken offline for inspection and maintenance (e.g., once annually). Alternatively, the maintenance schedule 402 can be configured to monitor the control system 108 through the operating conditions 112, the measure performance level 306, and the like. If one or more operating conditions 112, measure performance level 306, or other metrics reach a threshold value, the maintenance schedule 402 can be enabled to generate a maintenance alert 404. In this way, the control system 108 can enable predictive maintenance to preempt potential issues far in advance.); and
render visualization of execution status of each of the plurality of analytic models corresponding to the at least one asset on a display device, wherein the execution status indicates whether the each of the plurality of analytic models is enabled or disabled for each asset of the plurality of assets (¶0036-0038 As mentioned above, the optimization term 120 can be the primary goal of the machine learning agent 102 and is thus what the machine learning agent 102 seeks to maximize through various iterations of the action set 114 (e.g., performance). Moreover, the aging term 122 can margin the calculation of the optimality score 118 by quantifying the impact of aging on the components 110 of the control system 108. While a specific example of an optimality function 116 is given above, it should be understood that the optimality function can be any mechanism for measuring the success of an action set 114. In addition, while the optimality score 118 can be a numerical score, any measure of optimality or figure of merit can be used to express the viability of an action set 114. In one example, a non-aging aware solution may simply maximize the optimization term 120 extracted by a machine learning agent 102 and deem the approach optimal as the resultant optimality score 118 reaches a maximum. However, by margining the calculation of the optimality score 118, the disclosed system may discover that naively maximizing the optimization term 120 may lead to reduced optimality as the control system 108 and its components 110 degrade. Thus, the machine learning agent 102 that is equipped with aging awareness may instead take a more conservative approach to the optimization term 120 to prolong the lifespan of the control system 108 and the components 110. Turning now to FIG. 1B, a first phase of operation of the example system 100 is shown and described. As mentioned above, the machine learning agent 102 can extract a set of states 104 and an aging model 106 from the components 110 of the control system 108. Based on the aging model 106, the machine learning agent 102 can derive an aging term 122 to quantify the future operating conditions 112 predicted by the aging model 106. For the sake of discussion, it is helpful to consider the set of states as meters that collect and display readings pertaining to the control system. For example, an individual state 104 may relate to an individual associated operating condition 112 of a component 110 such as a fan speed or a temperature of a chiller, a flow rate of raw material in a manufacturing line, and so forth. ¶0046 As mentioned above, while both aging aware and non-aging aware solutions can realize gains in performance, efficiency, or other factors, the rate at which optimality is extracted from the control system 108 must also be augmented with consideration for component 110 degradation. For a climate control system 202, a non-aging aware solution may greedily extract the full kW/ton efficiency 208 from the chillers 204 at initial deployment (e.g., when a time t=0). However, while this can lead to highly efficient operation for a time, the non-aging aware approach gives no consideration to the degradation of the chillers 204 over time. Thus, the kW/ton efficiency of the chillers 204 may worsen over time naturally. In contrast, by taking into account the aging related degradation of the chillers 204 and adjusting the setpoint accordingly, the machine learning agent 102 can maintain excellent efficiency for an extended period of time.).
Although not explicitly taught by Maitra, Jayathirtha teaches in the analogous art of dashboard visualization for a portfolio of assets:
determine a plurality of analytic models that are enabled corresponding to the at least one asset based on the received asset data (¶0064 The modeling phase includes an onboarding process for syncing the models between the edge 115 and the cloud 105. For example, in one or more embodiments, the onboarding process includes a simple onboarding process, a complex onboarding process, and/or a standardized rollout process. The simple onboarding process includes the knowledge graph 251 receiving raw model data from the edge 115 and running context discovery algorithms to generate the model. …the knowledge graph 251 receives “TMP” during the modeling phase and determine that “TMP” relates to “temperature.” The generated models are then published. The complex onboarding process includes the knowledge graph 251 receiving the raw model data, receiving point history data, and receiving site survey data. According to various embodiments, the knowledge graph 251 then uses these inputs to run the context discovery algorithms. According to various embodiments, the generated models are edited and then the models are published. The standardized rollout process includes manually defining standard models in the cloud 105 and pushing the models to the edge 115.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the dashboard visualization for a portfolio of assets of Jayathirtha with the system for aging aware reward construct for machine teaching of Maitra for the reasons stated in the Office action.
As per the 101 rejection, Applicant argues that the claims are in favor of eligibility per Prong One of Step 2A, however Examiner respectfully disagrees. Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Since the recitation of the claims falls into at least one of the above Groupings, there is a basis for providing further analysis with regard to Prong Two of Step 2A to determine whether the recitation of an abstract idea is deduced to being directed to an abstract idea. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Prong Two of Step 2A, however Examiner respectfully disagrees. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor and/or memory medium is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic processor server limitation is no more than mere instructions to apply the exception using a generic computer component. Further, processor and/or memory medium to inter alia perform the function of rendering visualization of execution status of each of the plurality of analytic models corresponding to the at least one asset is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In other words, the present claims use a generic processing device and memory medium to inter alia perform the function of rendering visualization of execution status of each of the plurality of analytic models corresponding to the at least one asset which is a concept that can be performed in the human mind. The processor is merely used to perform the function(s), and the processor does not integrate the abstract idea into a practical application since there are no meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Step 2B, however Examiner respectfully disagrees. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: processor and/or memory medium. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, processor and/or memory medium to inter alia perform the function of rendering visualization of execution status of each of the plurality of analytic models corresponding to the at least one asset is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include the non-limiting or non-exclusive examples of MPEP § 2106.05. Thus, the rejection is maintained.
In an effort to further expedite prosecution, see: Appendix 1 to the October 2019 Update: Subject Matter Eligibility, Life Sciences & Data Processing Examples, October 2019 30, Example 46. Livestock Management. Per claim 1 of Example 46, the memory, display and processor are recited so generically (no details whatsoever are provided other than that they are a memory, display and processor) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. As an exemplary direction for similar claim limitations to be eligible, see claims 2-4 of Example 46.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims fall within statutory class of process or machine or manufacture; hence, the claims fall under statutory category of Step 1.
Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
a memory; and a processor coupled to the memory, the processor configured to: receive asset data corresponding to at least one asset of a plurality of assets; determine a plurality of analytic models that are enabled corresponding to the at least one asset based on the received asset data, wherein the plurality of analytic models comprises one or more rules defining threshold values for one or more parameters associated with the plurality of assets and one or more calculation models configured to calculate efficiency of the plurality of assets;execute the one or more calculation models to calculate the efficiency of the at least one asset to determine whether a maintenance is to be scheduled for the at least one asset;determine that the maintenance is scheduled for the at least one asset;disable at least one analytic model from the plurality of analytic models during the schedule for the maintenance of the at least one asset;re-enable the at least one analytic model after completion of the schedule for the maintenance of the at least one asset; andrender visualization of execution status of each of the plurality of analytic models corresponding to the at least one asset on a display device, wherein the execution status indicates whether the each of the plurality of analytic models is enabled or disabled for each asset of the plurality of assets
[or]
receiving asset data corresponding to at least one asset of a plurality of assets;determining a plurality of analytic models that are enabled corresponding to the at least one asset based on the received asset data, wherein the plurality of analytic models comprises one or more rules defining threshold values for one or more parameters associated with the plurality of assets and one or more calculation models configured to calculate efficiency of the plurality of assets;executing the one or more calculation models to calculate the efficiency of the at least one asset to determine whether a maintenance is to be scheduled for the at least one asset;determining that the maintenance is scheduled for the at least one asset;disabling at least one analytic model from the plurality of analytic models during the schedule for the maintenance of the at least one asset;re-enabling the at least one analytic model after completion of the schedule for the maintenance of the at least one asset; andrendering visualization of execution status of each of the plurality of analytic models corresponding to the at least one asset on a display device, wherein the execution status indicates whether the each of the plurality of analytic models is enabled or disabled for each asset of the plurality of assets
[or]
at least one non-transitory computer-readable medium having computer-readable program instructions stored therein, the computer-readable program instructions comprising instructions, which when performed by at least one processor, configure the at least one processor to: receive asset data corresponding to at least one asset of a plurality of assets; determine a plurality of analytic models that are enabled corresponding to the at least one asset based on the received asset data, wherein the plurality of analytic models comprises one or more rules defining threshold values for one or more parameters associated with the plurality of assets and one or more calculation models configured to calculate efficiency of the plurality of assets;executing the one or more calculation models to calculate efficiency of the at least one asset to determine whether a maintenance is to be scheduled for the at least one asset;determine that the maintenance is scheduled for the at least one asset;disable at least one analytic model from the plurality of analytic models based on the determination;re-enable the at least one analytic model after completion of the schedule for the maintenance of the at least one asset; andrender visualization of execution status of each of the plurality of analytic models corresponding to the at least one asset on a display device, wherein the execution status indicates whether the each of the plurality of analytic models is enabled or disabled for each asset of the plurality of assets.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). “[In a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Limitations are considered together as a single abstract idea for further analysis.
Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor and/or memory medium is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic processor and/or memory medium limitation is no more than mere instructions to apply the exception using a generic computer component. Further, rendering visualization of execution status of each of the plurality of analytic models corresponding to the at least one asset by a processor and/or memory medium is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B.
Per Step 2B, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: processor and memory medium. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, rendering visualization of execution status of each of the plurality of analytic models corresponding to the at least one asset by a processor and/or memory medium is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶0089 wherein “the processor 610 is a single core processor, a multi-core processor, multiple processors internal to the service case management system 600, a remote processor (e.g., a processor implemented on a server), and/or a virtual machine.” Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims).
The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. For example, claims 2 and 6-10 and similar dependent claims include a further narrowing of the configuration of the processor without integration into a practical application or significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101.
Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Maitra et al. (US 20230288882 A1) hereinafter referred to as Maitra in view of Jayathirtha et al. (US 20220398665 A1) hereinafter referred to as Jayathirtha.
Maitra teaches:
Claim 1. A system, comprising:
a memory; and a processor coupled to the memory, the processor configured to (¶0060 Although the following illustration refers to the components of the figures, it should be appreciated that the operations of the routine 500 may be also implemented in many other ways. For example, the routine 500 may be implemented, at least in part, by a processor of another remote computer or a local circuit. In addition, one or more of the operations of the routine 500 may alternatively or additionally be implemented, at least in part, by a chipset working alone or in conjunction with other software modules. In the example described below, one or more modules of a computing system can receive and/or process the data disclosed herein. Any service, circuit or application suitable for providing the techniques disclosed herein can be used in operations described herein. With reference to FIG. 5, routine 500 begins at operation 502 where a machine learning agent 102 extracts an aging model 106 from a control system 108 at a current timeframe ¶0078 In various examples, the computing environment 702 includes servers 708, data storage 610, and one or more network interfaces 712. The servers 708 can host various services, virtual machines, portals, and/or other resources. In the illustrated configuration, the servers 708 host virtual machines 714, Web portals 716, mailbox services 718, storage services 720, and/or, social networking services 722. As shown in FIG. 7 the servers 708 also can host other services, applications, portals, and/or other resources (“other resources”) 724.):
receive asset data corresponding to at least one asset of a plurality of assets (¶0031 the aging model 106 may be associated one or several components 110 of the control system 108 and can define predicted operating conditions for a future point in time ¶0032 The action set 114 can define one or more modifications to characteristics of the control system 108 or components 110. In one example, an action defined by the action set 114 may change the temperature setting of a chiller in a climate control system. It should be understood that the action set 114 can comprise any number of actions that change one or more associated characteristics of the control system 108 and its constituent components 110. ¶0036 the aging term 122 can margin the calculation of the optimality score 118 by quantifying the impact of aging on the components 110 of the control system 108. While a specific example of an optimality function 116 is given above, it should be understood that the optimality function can be any mechanism for measuring the success of an action set 114.);
determine a plurality of analytic models that are enabled corresponding to the at least one asset based on the received asset data, wherein the plurality of analytic models comprises one or more rules defining threshold values for one or more parameters associated with the plurality of assets and one or more calculation models configured to calculate efficiency of the plurality of assets;execute the one or more calculation models to calculate the efficiency of the at least one asset to determine whether a maintenance is to be scheduled for the at least one asset (¶0031 FIG. 1A illustrates an example system 100 in which a machine learning agent 102 extracts a set of states 104 as well as an aging model 106 from a control system 108. As mentioned briefly above, the aging model 106 may be associated one or several components 110 of the control system 108 and can define predicted operating conditions for a future point in time. The predicted operating conditions may differ from the current operating conditions 112. For example, a future efficiency of a component predicted by the aging model 106 may be lower than a current efficiency due to age related degradation. In various examples, the aging model 106 can be a statistical model such as a power law, a Weibull distribution, or an exponential decay model. However, the aging model 106 can be any suitable representation of aging or drift. ¶0032 By analyzing the aging model in addition to the set of states 104, the machine learning agent 102 can determine an action set 114 that is applied to the control system 108. The action set 114 can define one or more modifications to characteristics of the control system 108 or components 110 … ¶0048 In addition, the machine learning agent 102 can be configured to receive a predetermined performance level 302 from an administrative entity. The administrative entity can be a system administrator, an engineer, or other entity that can direct the machine learning agent 102. In various examples, the predetermined performance level 302 can define a specific state 104 or operating condition 112 of interest. For example, in the example system 200 described above, the predetermined performance level 302 can be a specific value of kW/ton efficiency 208 of the climate control system 202 that the administrative entity desires. ¶0053 In various examples, the maintenance schedule 402 can define a period of time during which the control system 108 is taken offline for inspection and maintenance (e.g., once annually). Alternatively, the maintenance schedule 402 can be configured to monitor the control system 108 through the operating conditions 112, the measure performance level 306, and the like. If one or more operating conditions 112, measure performance level 306, or other metrics reach a threshold value, the maintenance schedule 402 can be enabled to generate a maintenance alert 404. In this way, the control system 108 can enable predictive maintenance to preempt potential issues far in advance.);
determine that the maintenance is scheduled for the at least one asset (¶0054 the machine learning agent 102 can be configured to modify the maintenance schedule 402 based on various factors such as the states 104, the measured performance level 306, the operating conditions 112, and so forth. For example, the machine learning agent 102 may receive a predetermined performance level 302 that results in reduced strain on the components 110. In this example, the maintenance schedule 402 may specify maintenance is to occur once every six months. However, due to the updated operating conditions 112, the machine learning agent 102 may determine that maintenance may not be required until one year from the present timeframe. Accordingly, the maintenance schedule 402 can be modified to reflect this updated assessment of component 110 degradation.);
disable at least one analytic model from the plurality of analytic models during the schedule for the maintenance of the at least one asset (¶0089 generating a retraining schedule of the machine learning agent that synchronizes with the maintenance schedule defining a period of time during which the machine learning agent is disabled. ¶0096 in response to receiving the maintenance schedule, generate a retraining schedule of the machine learning agent that synchronizes with the maintenance schedule defining a period of time during which the machine learning agent is disabled. ¶0102);
re-enable the at least one analytic model after completion of the schedule for the maintenance of the at least one asset (¶0055 Turning now to FIG. 5, aspects of a routine 500 for enabling aging aware training of machine learning agents for control systems are shown and described. For ease of understanding, the processes discussed in this disclosure are delineated as separate operations represented as independent blocks. ¶0054 As mentioned above, the machine learning agent 102 can be configured to receive the maintenance schedule 402 to synchronize the retraining schedule 310 with the maintenance schedule 402. In various examples, the maintenance schedule 402 can be extracted alongside the aging model 106 to enable the machine learning agent 102 to predict future behaviors of the control system 108. In addition, the machine learning agent 102 can be configured to modify the maintenance schedule 402 based on various factors such as the states 104, the measured performance level 306, the operating conditions 112, and so forth. For example, the machine learning agent 102 may receive a predetermined performance level 302 that results in reduced strain on the components 110. In this example, the maintenance schedule 402 may specify maintenance is to occur once every six months. However, due to the updated operating conditions 112, the machine learning agent 102 may determine that maintenance may not be required until one year from the present timeframe. Accordingly, the maintenance schedule 402 can be modified to reflect this updated assessment of component 110 degradation.); and
render visualization of execution status of each of the plurality of analytic models corresponding to the at least one asset on a display device, wherein the execution status indicates whether the each of the plurality of analytic models is enabled or disabled for each asset of the plurality of assets (¶0036-0038 As mentioned above, the optimization term 120 can be the primary goal of the machine learning agent 102 and is thus what the machine learning agent 102 seeks to maximize through various iterations of the action set 114 (e.g., performance). Moreover, the aging term 122 can margin the calculation of the optimality score 118 by quantifying the impact of aging on the components 110 of the control system 108. While a specific example of an optimality function 116 is given above, it should be understood that the optimality function can be any mechanism for measuring the success of an action set 114. In addition, while the optimality score 118 can be a numerical score, any measure of optimality or figure of merit can be used to express the viability of an action set 114. In one example, a non-aging aware solution may simply maximize the optimization term 120 extracted by a machine learning agent 102 and deem the approach optimal as the resultant optimality score 118 reaches a maximum. However, by margining the calculation of the optimality score 118, the disclosed system may discover that naively maximizing the optimization term 120 may lead to reduced optimality as the control system 108 and its components 110 degrade. Thus, the machine learning agent 102 that is equipped with aging awareness may instead take a more conservative approach to the optimization term 120 to prolong the lifespan of the control system 108 and the components 110. Turning now to FIG. 1B, a first phase of operation of the example system 100 is shown and described. As mentioned above, the machine learning agent 102 can extract a set of states 104 and an aging model 106 from the components 110 of the control system 108. Based on the aging model 106, the machine learning agent 102 can derive an aging term 122 to quantify the future operating conditions 112 predicted by the aging model 106. For the sake of discussion, it is helpful to consider the set of states as meters that collect and display readings pertaining to the control system. For example, an individual state 104 may relate to an individual associated operating condition 112 of a component 110 such as a fan speed or a temperature of a chiller, a flow rate of raw material in a manufacturing line, and so forth. ¶0046 As mentioned above, while both aging aware and non-aging aware solutions can realize gains in performance, efficiency, or other factors, the rate at which optimality is extracted from the control system 108 must also be augmented with consideration for component 110 degradation. For a climate control system 202, a non-aging aware solution may greedily extract the full kW/ton efficiency 208 from the chillers 204 at initial deployment (e.g., when a time t=0). However, while this can lead to highly efficient operation for a time, the non-aging aware approach gives no consideration to the degradation of the chillers 204 over time. Thus, the kW/ton efficiency of the chillers 204 may worsen over time naturally. In contrast, by taking into account the aging related degradation of the chillers 204 and adjusting the setpoint accordingly, the machine learning agent 102 can maintain excellent efficiency for an extended period of time.).
Although not explicitly taught by Maitra, Jayathirtha teaches in the analogous art of dashboard visualization for a portfolio of assets:
determine a plurality of analytic models that are enabled corresponding to the at least one asset based on the received asset data (¶0064 The modeling phase includes an onboarding process for syncing the models between the edge 115 and the cloud 105. For example, in one or more embodiments, the onboarding process includes a simple onboarding process, a complex onboarding process, and/or a standardized rollout process. The simple onboarding process includes the knowledge graph 251 receiving raw model data from the edge 115 and running context discovery algorithms to generate the model. …the knowledge graph 251 receives “TMP” during the modeling phase and determine that “TMP” relates to “temperature.” The generated models are then published. The complex onboarding process includes the knowledge graph 251 receiving the raw model data, receiving point history data, and receiving site survey data. According to various embodiments, the knowledge graph 251 then uses these inputs to run the context discovery algorithms. According to various embodiments, the generated models are edited and then the models are published. The standardized rollout process includes manually defining standard models in the cloud 105 and pushing the models to the edge 115.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the dashboard visualization for a portfolio of assets of Jayathirtha with the system for aging aware reward construct for machine teaching of Maitra for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Maitra ¶0004 teaches that there is a need for machine learning approaches for autonomous control systems that minimizes the risk of overcompensation while maintaining control system performance;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Maitra Abstract teaches techniques disclosed enable systems to integrate aging awareness into machine learning agents for management of control systems, and Jayathirtha Abstract teaches providing a dashboard visualization for a portfolio of assets; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Maitra at least the above cited paragraphs, and Jayathirtha at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the dashboard visualization for a portfolio of assets of Jayathirtha with the system for aging aware reward construct for machine teaching of Maitra. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Maitra teaches:
Claim 2. The system of claim 1, wherein the processor is further configured to receive knowledge graph data of a facility (¶0050 In response, the machine learning agent 102 can parse the goal statement 306 for specific terms such as “operating efficiency” and “minimal impact on equipment aging” to derive various terms and construct the optimality function 116. In this example, the machine learning agent 102 may determine “operating efficiency” as the optimization term 120 with “minimal impact” as the aging term 122. In other examples, the goal statement 306 can be provided in other formats such as through speech, text, a graphical user interface, or other low code methods that enable an administrative entity that lacks technical expertise to configure the machine learning agent 102. ¶0045 In the context of a climate control system 202, the machine learning agent 102 may be configured to optimize the chillers 204 for efficiency which can typically be measured as kilowatts per ton of cooling. Accordingly, the optimality function 116 can be constructed with a term relating to kW/ton efficiency 208 of the chillers 204 and the aging term 122. As discussed above, the optimization term 120 can be constructed from various operating conditions 112 of the control system 108. For the climate control system 202, the kW/ton efficiency 208 can be determined based on a first operating condition 112, the energy expenditure of the chillers 204 and a second operating condition 112, the amount of cooling the chillers 204 provide.).
Maitra teaches:
Claim 3. The system of claim 1, wherein the plurality of analytic models comprises at least one of a rule-based model, a machine learning model, and a human-coded workflow (¶0028 The techniques described herein provide systems for enhancing the autonomous control systems through the introduction of aging awareness in machine learning agents. As mentioned above, the machine learning agent can be configured to extract information from a control system such as a set of states and an aging model the various constituent components. Based on the states and the aging term that is derived from the aging mode, the machine learning agent can construct a set of actions to apply to the control system.).
Maitra teaches:
Claim 4. The system of claim 1, wherein the schedule for maintenance of the at least one asset is determined based on at least one of a maintenance record or a user input (¶0070 A basic input/output system containing the basic routines that help to transfer information between elements within the computer architecture 600, such as during startup, is stored in the ROM 608. The computer architecture 600 further includes a mass storage device 612 for storing an operating system 614, application(s) 616, modules 618, and other data described herein. ¶0053 In various examples, the maintenance schedule 402 can define a period of time during which the control system 108 is taken offline for inspection and maintenance (e.g., once annually). Alternatively, the maintenance schedule 402 can be configured to monitor the control system 108 through the operating conditions 112, the measure performance level 306, and the like. If one or more operating conditions 112, measure performance level 306, or other metrics reach a threshold value, the maintenance schedule 402 can be enabled to generate a maintenance alert 404. In this way, the control system 108 can enable predictive maintenance to preempt potential issues far in advance. This dynamic maintenance schedule 402 can be achieved via various computing techniques or the machine learning agent 102. In addition, the maintenance schedule 402 can be generated automatically based on the aging model 106 and the operating conditions 112. Alternatively, the maintenance schedule 402 can be manually set by an administrative entity such as a system engineer, technician, and the like.).
Maitra teaches:
Claim 5. The system of claim 1, wherein the asset data comprises at least one of an asset type, operational data, and telemetry data (¶0038 Turning now to FIG. 1B, a first phase of operation of the example system 100 is shown and described. As mentioned above, the machine learning agent 102 can extract a set of states 104 and an aging model 106 from the components 110 of the control system 108. Based on the aging model 106, the machine learning agent 102 can derive an aging term 122 to quantify the future operating conditions 112 predicted by the aging model 106. For the sake of discussion, it is helpful to consider the set of states as meters that collect and display readings pertaining to the control system. For example, an individual state 104 may relate to an individual associated operating condition 112 of a component 110 such as a fan speed or a temperature of a chiller, a flow rate of raw material in a manufacturing line, and so forth. ¶0052 Turning now to FIG. 4, aspects of the control system 108 are shown and described. As described above, the control system 108 can comprise various components 110 that are controlled by the machine learning agent 102 via an action set 114. The action set 114 can directly or indirectly modify the operating conditions 112 of the components 110.).
Maitra teaches:
Claim 6. The system of claim 1, wherein the processor is further configured to render visualization of the plurality of analytic models corresponding to the at least one asset in a single view on the display device (¶0038 Turning now to FIG. 1B, a first phase of operation of the example system 100 is shown and described. As mentioned above, the machine learning agent 102 can extract a set of states 104 and an aging model 106 from the components 110 of the control system 108. Based on the aging model 106, the machine learning agent 102 can derive an aging term 122 to quantify the future operating conditions 112 predicted by the aging model 106. For the sake of discussion, it is helpful to consider the set of states as meters that collect and display readings pertaining to the control system. For example, an individual state 104 may relate to an individual associated operating condition 112 of a component 110 such as a fan speed or a temperature of a chiller, a flow rate of raw material in a manufacturing line, and so forth).
Maitra teaches:
Claim 7. The system of claim 2, wherein the processor is further configured to: receive metadata corresponding to a plurality of analytic models that are not enabled corresponding to the at least one asset; and generate, based on the metadata and the knowledge graph data, a recommendation indicating applicability of at least one analytic model of the plurality of analytic models that are not enabled corresponding to the at least one asset (¶0079 The datastores 726 are configured to host data used or created by the servers 808 and/or other data. That is, the datastores 726 also can host or store web page documents, word documents, presentation documents, data structures, algorithms for execution by a recommendation engine, and/or other data utilized by any application program. Aspects of the datastores 726 may be associated with a service for storing files.).
Although not explicitly taught by Maitra, Jayathirtha teaches in the analogous art of dashboard visualization for a portfolio of assets:
generate, based on the metadata and the knowledge graph data, a recommendation indicating applicability of at least one analytic model of the plurality of analytic models that are not enabled corresponding to the at least one asset (¶0064 The modeling phase includes an onboarding process for syncing the models between the edge 115 and the cloud 105. For example, in one or more embodiments, the onboarding process includes a simple onboarding process, a complex onboarding process, and/or a standardized rollout process. The simple onboarding process includes the knowledge graph 251 receiving raw model data from the edge 115 and running context discovery algorithms to generate the model. …the knowledge graph 251 receives “TMP” during the modeling phase and determine that “TMP” relates to “temperature.” The generated models are then published. The complex onboarding process includes the knowledge graph 251 receiving the raw model data, receiving point history data, and receiving site survey data. According to various embodiments, the knowledge graph 251 then uses these inputs to run the context discovery algorithms. According to various embodiments, the generated models are edited and then the models are published. The standardized rollout process includes manually defining standard models in the cloud 105 and pushing the models to the edge 115 ¶0071 According to various embodiments, when a fault or opportunity is identified, the IoT platform 125 provides recommendations about an optimal corrective action to take. Initially, the recommendations are based on expert knowledge that has been pre-programmed into the system by process and equipment experts. A recommendation services module presents this information in a consistent way regardless of source, and supports workflows to track, close out, and document the recommendation follow-up. According to various embodiments, the recommendation follow-up is employed to improve the overall knowledge of the system over time as existing recommendations are validated (or not) or new cause and effect relationships are learned by users and/or analytics).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the dashboard visualization for a portfolio of assets of Jayathirtha with the system for aging aware reward construct for machine teaching of Maitra for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Maitra ¶0004 teaches that there is a need for machine learning approaches for autonomous control systems that minimizes the risk of overcompensation while maintaining control system performance;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Maitra Abstract teaches techniques disclosed enable systems to integrate aging awareness into machine learning agents for management of control systems, and Jayathirtha Abstract teaches providing a dashboard visualization for a portfolio of assets; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Maitra at least the above cited paragraphs, and Jayathirtha at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the dashboard visualization for a portfolio of assets of Jayathirtha with the system for aging aware reward construct for machine teaching of Maitra. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Maitra teaches:
Claim 8. The system of claim 7, wherein the processor is further configured to control display of the generated recommendation on the display device (¶0079 The datastores 726 are configured to host data used or created by the servers 808 and/or other data. That is, the datastores 726 also can host or store web page documents, word documents, presentation documents, data structures, algorithms for execution by a recommendation engine, and/or other data utilized by any application program. Aspects of the datastores 726 may be associated with a service for storing files).
Although not explicitly taught by Maitra, Jayathirtha teaches in the analogous art of dashboard visualization for a portfolio of assets:
control display of the generated recommendation on the display device (¶0064 The modeling phase includes an onboarding process for syncing the models between the edge 115 and the cloud 105. For example, in one or more embodiments, the onboarding process includes a simple onboarding process, a complex onboarding process, and/or a standardized rollout process. The simple onboarding process includes the knowledge graph 251 receiving raw model data from the edge 115 and running context discovery algorithms to generate the model. …the knowledge graph 251 receives “TMP” during the modeling phase and determine that “TMP” relates to “temperature.” The generated models are then published. The complex onboarding process includes the knowledge graph 251 receiving the raw model data, receiving point history data, and receiving site survey data. According to various embodiments, the knowledge graph 251 then uses these inputs to run the context discovery algorithms. According to various embodiments, the generated models are edited and then the models are published. The standardized rollout process includes manually defining standard models in the cloud 105 and pushing the models to the edge 115 ¶0071 According to various embodiments, when a fault or opportunity is identified, the IoT platform 125 provides recommendations about an optimal corrective action to take. Initially, the recommendations are based on expert knowledge that has been pre-programmed into the system by process and equipment experts. A recommendation services module presents this information in a consistent way regardless of source, and supports workflows to track, close out, and document the recommendation follow-up. According to various embodiments, the recommendation follow-up is employed to improve the overall knowledge of the system over time as existing recommendations are validated (or not) or new cause and effect relationships are learned by users and/or analytics.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the dashboard visualization for a portfolio of assets of Jayathirtha with the system for aging aware reward construct for machine teaching of Maitra for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Maitra ¶0004 teaches that there is a need for machine learning approaches for autonomous control systems that minimizes the risk of overcompensation while maintaining control system performance;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Maitra Abstract teaches techniques disclosed enable systems to integrate aging awareness into machine learning agents for management of control systems, and Jayathirtha Abstract teaches providing a dashboard visualization for a portfolio of assets; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Maitra at least the above cited paragraphs, and Jayathirtha at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the dashboard visualization for a portfolio of assets of Jayathirtha with the system for aging aware reward construct for machine teaching of Maitra. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Although not explicitly taught by Maitra, Jayathirtha teaches in the analogous art of dashboard visualization for a portfolio of assets:
Claim 9. The system of claim 1, wherein the processor is further configured to determine at least one third party analytic model from among the plurality of analytic models, wherein the at least one third party analytic model is not disabled during the schedule for the maintenance of the at least one asset (¶0061 As used herein, EOM 250 includes a collection of application programming interfaces (APIs) that enables seeded semantic object models to be extended. For example, the EOM 250 of the present disclosure enables a customer's knowledge graph 251 to be built subject to constraints expressed in the customer's semantic object model. Thus, the knowledge graphs 251 are generated by customers (e.g., enterprises or organizations) to create models of the edge devices 161a-161n of an enterprise 160a-160n, and the knowledge graphs 251 are input into the EOM 250 for visualizing the models (e.g., the nodes and links). ¶0066 The enterprise integration layer 210 includes one or more components for events/messaging, file upload, and/or REST/OData. The components of the enterprise integration layer 210 enable the IoT platform 125 to communicate with third party cloud applications 211, such as any application(s) operated by an enterprise in relation to its edge devices. For example, the enterprise integration layer 210 connects with enterprise databases, such as guest databases, customer databases, financial databases, patient databases, etc. The enterprise integration layer 210 provides a standard application programming interface (API) to third parties for accessing the IoT platform 125. The enterprise integration layer 210 also enables the IoT platform 125 to communicate with the OT systems 163a-163n and IT applications 164a-164n of the enterprise 160a-160n. Thus, the enterprise integration layer 210 enables the IoT platform 125 to receive data from the third-party applications 211 rather than, or in combination with, receiving the data from the edge devices 161a-161n directly.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the dashboard visualization for a portfolio of assets of Jayathirtha with the system for aging aware reward construct for machine teaching of Maitra for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Maitra ¶0004 teaches that there is a need for machine learning approaches for autonomous control systems that minimizes the risk of overcompensation while maintaining control system performance;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Maitra Abstract teaches techniques disclosed enable systems to integrate aging awareness into machine learning agents for management of control systems, and Jayathirtha Abstract teaches providing a dashboard visualization for a portfolio of assets; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Maitra at least the above cited paragraphs, and Jayathirtha at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the dashboard visualization for a portfolio of assets of Jayathirtha with the system for aging aware reward construct for machine teaching of Maitra. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Although not explicitly taught by Maitra, Jayathirtha teaches in the analogous art of dashboard visualization for a portfolio of assets:
Claim 10. The system of claim 9, wherein the processor is further configured to filter a plurality of service cases generated corresponding to the at least one third party analytic model during the schedule for the maintenance of the at least one asset (¶0061 As used herein, EOM 250 includes a collection of application programming interfaces (APIs) that enables seeded semantic object models to be extended. For example, the EOM 250 of the present disclosure enables a customer's knowledge graph 251 to be built subject to constraints expressed in the customer's semantic object model. Thus, the knowledge graphs 251 are generated by customers (e.g., enterprises or organizations) to create models of the edge devices 161a-161n of an enterprise 160a-160n, and the knowledge graphs 251 are input into the EOM 250 for visualizing the models (e.g., the nodes and links). ¶0066 The enterprise integration layer 210 includes one or more components for events/messaging, file upload, and/or REST/OData. The components of the enterprise integration layer 210 enable the IoT platform 125 to communicate with third party cloud applications 211, such as any application(s) operated by an enterprise in relation to its edge devices. For example, the enterprise integration layer 210 connects with enterprise databases, such as guest databases, customer databases, financial databases, patient databases, etc. The enterprise integration layer 210 provides a standard application programming interface (API) to third parties for accessing the IoT platform 125. The enterprise integration layer 210 also enables the IoT platform 125 to communicate with the OT systems 163a-163n and IT applications 164a-164n of the enterprise 160a-160n. Thus, the enterprise integration layer 210 enables the IoT platform 125 to receive data from the third-party applications 211 rather than, or in combination with, receiving the data from the edge devices 161a-161n directly. ¶0122 the listing of notifications associated with the notification data 1602 is ranked based on prioritized actions for the portfolio of assets, actionable insights associated with the portfolio of assets, changes associated with the portfolio of assets, and/or criticality of issues associated with the portfolio of assets. In certain embodiments, the electronic interface 1600 provides for filtering of the notification data 1602 based on location, notification type, asset type, date, and/or other criteria associated with the portfolio of assets. In certain embodiments, the electronic interface 1600 allows a user to change criticality of an asset issue, group assignment of assets issues, update a status related to asset issues, add a note related to an asset issue, collaborate with one or more other users with respect to an asset issue, and/or close an asset issue. In certain embodiments, the electronic interface 1600 allows a user to perform root cause analysis with respect to an asset issue and/or to provide input with respect to addressing an asset issue.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the dashboard visualization for a portfolio of assets of Jayathirtha with the system for aging aware reward construct for machine teaching of Maitra for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Maitra ¶0004 teaches that there is a need for machine learning approaches for autonomous control systems that minimizes the risk of overcompensation while maintaining control system performance;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Maitra Abstract teaches techniques disclosed enable systems to integrate aging awareness into machine learning agents for management of control systems, and Jayathirtha Abstract teaches providing a dashboard visualization for a portfolio of assets; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Maitra at least the above cited paragraphs, and Jayathirtha at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the dashboard visualization for a portfolio of assets of Jayathirtha with the system for aging aware reward construct for machine teaching of Maitra. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
As per claims 12-19 and 20, the method and computer program product tracks the system of claims 1,2,6-11 and 1, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1,2,6-11 and 1 are applied to claims 12-19 and 20, respectively. Maitra discloses that the embodiment may be found as a method and computer program product (Fig. 7 and ¶0057).
Conclusion
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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/KURTIS GILLS/Primary Examiner, Art Unit 3624
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