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
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/23/2026 has been entered.
Notice to Applicant
In response to the communication received on 04/23/2026, the following is a Non-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. Applicant’s amendments to the claims overcome the 35 U.S.C 101 rejection, and hence the 35 U.S.C. 101 rejection has been withdrawn.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in light of the new grounds of rejection, as necessitated by amendment.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 in further view of Phan et al. (US 20220058590 A1) hereinafter referred to as Phan.
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, and wherein the execution status of each of the plurality of analytic models is updated in real-time on the display device to reflect a current state of each of the plurality of analytic models corresponding to the at least one asset (¶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).
Although not explicitly taught by Maitra in view of Jayathirtha, Phan teaches in the analogous art of equipment maintenance in geo-distributed equipment:
generate one or more service cases corresponding to the at least one asset based on the plurality of analytic models enabled corresponding to the at least one asset…wherein disabling the at least one analytic model during the schedule for the maintenance prevents generation of one or more service cases corresponding to the at least one analytic model during the schedule for the maintenance (¶0045 In the example shown in FIG. 4, the historical data for E is used to model a degradation curve, at block 402. The degradation curve is a graph of the equipment E's condition or remaining service potential plotted over time. Further, using the sensor data from the sensors 214 of E, an asset health index (AHI) is determined, at block 404. The AHI is an asset score that outlines the condition and likely performance of the equipment E based on parameters such as condition risk, using predictive analytics. The index facilitates comparing health of E with a predetermined baseline for the equipment E. The comparison of the AHI and the predetermined baseline provides an effective age of E, at block 406. The effective age is subsequently compared with the degradation curve to determine a probability of failure of E. ¶0046 A criticality and impact of the failure of E is determined, at block 410, based on the network topology. The criticality and impact of the failure can indicate other equipment 212 that can be rendered unusable because of the failure in E. ¶0048 In this manner, the risk index for each equipment 212 in the entire geo-distributed network 210 is computed. Referring back to the flowchart in FIG. 3, the method 300 further includes creating a list of tasks for each equipment 212, at block 304. The list of tasks for an equipment 212 includes a series of needs for action (NDAs), which are generated through a set of rules. Each equipment 212 can have a separate set of rules that determines what series of NDAs is to be performed for improving the state of that equipment 212. These NDAs represent actions that can be performed on the equipment 212 that, in general, can have (positive) effect on the risk index of that equipment 212. ¶0049 The maintenance schedule 204 can be generated for performing the list of tasks, at block 306. The maintenance scheduler 202 can generate the maintenance schedule 204, at this time, according to a prescribed procedure associated with the list of tasks. ¶0051 FIG. 5 depicts a block diagram representing the optimization of the maintenance schedule according to one or more embodiments of the present invention. The optimization of the maintenance schedule 204 (at block 308) is based on a set of inputs 502. The inputs 502 include the list of tasks, i.e., the NDAs for the equipment 212. In one or more embodiments of the present invention, the inputs 502 can further include a prioritization of order in which tasks are to be performed. For example, regulatory compliance related tasks may be given highest priority, whereas tasks that are to be performed for supporting the infrastructure in the geo-distributed network 210 from a long-term perspective may be given a lower priority. It is understood that the prioritization example shown in FIG. 5 is one possible set of priorities, and that the prioritization can vary in other embodiments. ¶0052 Further, the optimization (308) results in outputs 504 that include a grouping of the tasks in a job order, which is sent to the maintenance system 206. The outputs can also include an operation-plan for a predetermined duration in the future, for example, a one-year plan, for the equipment 212. See also ¶0055-0056.).
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 equipment maintenance in geo-distributed equipment of Phan with the system for aging aware reward construct for machine teaching of Maitra in view of Jayathirtha 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 Phan Astract teaches a computer-implemented method for maintaining equipment in a geo-distributed system includes receiving, by a processor, a selection of quantities to optimize when adjusting a maintenance schedule of the geo-distributed system that includes multiple pieces of equipment that are spread over a geographical region, and wherein the maintenance schedule identifies when a set of maintenance tasks are executed at a first equipment from the geo-distributed system over a predetermined duration; 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 in view of Jayathirtha at least the above cited paragraphs, and Phan 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 equipment maintenance in geo-distributed equipment of Phan with the system for aging aware reward construct for machine teaching of Maitra in view of Jayathirtha. 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
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/KURTIS GILLS/Primary Examiner, Art Unit 3624
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