DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/23/2025 is in compliance with the provisions of 37 CFR 1.97 and have been entered into the record. Accordingly, the information disclosure statements are being considered by the examiner.
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 non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 8-14), computer program product (claims 15-20), and system (claims 1-7) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which falls into the “Certain methods of organizing human activity” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of organizing human activity or the mental processes grouping. Thus, the claim recites a mental process for performing certain methods of organizing human activity.
The limitations reciting the abstract idea(s) (Mental process and Certain methods of organizing human activity), as set forth in exemplary claim 8, are: generating… a maintenance schedule for assets based on objectives for an industrial plant; predicting…that the maintenance schedule, which is deployed, will not meet at least one of the objectives; generating…optimized maintenance schedules for the assets, based on the objectives; outputting… the optimized maintenance schedules for the assets, with explanations how each optimized maintenance schedule would meet the objectives following deployment; and enabling…a selection and deployment of any one of the optimized maintenance schedules for the assets, thereby changing a scheduled time when an asset maintenance action is performed. Independent claims 1 and 15 recite the CRM and system for performing the method of independent claim 8 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to: by a hybrid artificial intelligence-driven decision support system using real-time sensor data … via a graphical user interface…; A system for a hybrid artificial intelligence-driven decision support system for real-time predictive industrial plant asset optimization, the system comprising: one or more processors; and a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors to… A computer program product, comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein to be executed by one or more processors, the program code including instructions to…; (as recited in claims 1, 8, and 15). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: by a hybrid artificial intelligence-driven decision support system using real-time sensor data … via a graphical user interface…; A system for a hybrid artificial intelligence-driven decision support system for real-time predictive industrial plant asset optimization, the system comprising: one or more processors; and a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors to… A computer program product, comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein to be executed by one or more processors, the program code including instructions to…; (as recited in claims 1, 8, and 15) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g).
In addition, Applicant’s Specification (paragraph [0030]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (2-7, 9-14, and 16-20) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 9-14 “wherein the objectives comprise a combination of a performance, a cost, a sustainability, or an equipment risk for an industrial plant; wherein the objectives comprise a combination of a performance, a cost, a sustainability, or an equipment risk for an industrial plant; further comprises comparisons of each optimized maintenance schedule to the deployed maintenance schedule; wherein the comparisons of each optimized maintenance schedule to the deployed maintenance schedule are based on depicting data values corresponding to at least three objectives for each maintenance schedule on a graph comprising at least three dimensions corresponding to the at least three objectives; wherein the computer-implemented method further comprises assigning, by the hybrid artificial intelligence-driven decision support system, at least one weight that corresponds to at least one of the objectives, in response to a selection to deploy an optimized maintenance schedule other than an optimized maintenance schedule that is ranked as more optimal than the other optimized maintenance schedules, thereby changing subsequent rankings of at least some of the optimized maintenance schedules; wherein the computer-implemented method further comprises responding to deployment of the optimized maintenance schedule by the hybrid artificial intelligence-driven decision support system occasionally predicting whether the deployed optimized maintenance schedule will meet the objectives”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (2-7and 16-20) recite the CRM and system for performing the method of claims 9-14. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
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, 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 1-4, 7-11, 14-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20240103959 (hereinafter “Murali”) et al., in view of U.S. PGPub 20220319240 to (hereinafter “Ito”) et al., in further view of U.S. PGPub 20220067851 (hereinafter “Sinha”) et al.
As per claim 8, Murali teaches a computer-implemented method for a hybrid artificial intelligence-driven decision support system for real-time predictive industrial plant asset optimization, the computer-implemented method comprising:
generating, by a hybrid artificial intelligence-driven decision support system using real-time sensor data, a maintenance schedule for assets based on objectives for an industrial plant; 0017: “ the optimization employing sophisticated mathematical calculations taking into account a rich variety of factors and a large basis of data, which may include both historical data, and incoming realtime data from distributed sensors and data sources comprised in and/or in association with distributed assets of the infrastructure…0047-0055: infrastructure 220 may comprise a plurality of electrical power generation plants of any of various energy sources and technologies, electrical substations, a plurality of power cables and towers and electricity distribution lines and nodes, a plurality of monitoring facilities, computing facilities, storage facilities, office buildings, warehouses, garages, vehicles, and so forth, and various other assets of any type that may be necessary and/or conducive to successful operation and delivery of electrical power for an operating region or entity of any arbitrary scale…Dynamic asset fleet maintenance scheduling system 200 may formulate a dynamic optimization model as a mixed-integer linear program that determines an optimal maintenance schedule for the rest of a planning time horizon. On the other hand, dynamic asset fleet maintenance scheduling system 200 may only implement a maintenance schedule for a short-term upcoming time period at a time. This optimization model may enable operators to control power usage in real-time using demand-side management strategies. Dynamic asset fleet maintenance scheduling system 200 may dynamically optimize among a multi-objective function to aim to minimize the sum of three terms for each time period (e.g., each upcoming discrete maintenance scheduling period): (1) cost due to loss of power in the network, (2) cost due to corrective maintenance (CM) upon asset failure, and (3) cost due to preventive maintenance (PM).”
predicting, by the hybrid artificial intelligence-driven decision support system using real-time sensor data, that the maintenance schedule, which is deployed, will not meet at least one of the objectives; 0017-0054: “incoming realtime data from distributed sensors and data sources comprised in and/or in association with distributed assets of the infrastructure. Such methods and techniques are thus far beyond what would be possible to perform merely mentally, and have a number of characteristics that are uniquely susceptible to implementation by one or more computing devices and/or a computing system. Such methods, techniques, devices, and systems of this disclosure may also enable unique inventive advantages, illustratively such as optimizing performance and consistent up-time of a sophisticated infrastructure, with optimized efficiency and costs, in ways beyond what would be possible with prior art systems…may determine to reschedule a pre-planned maintenance of a particular asset, dependent on the time at which the particular asset transitions to a critical state with respect to the predicted or expected time at which it was expected to transition to that state, and the time at which maintenance for the particular asset had been pre-scheduled. The operating mode of the system may be defined by that of each individual asset: operational, failed, or under preventive maintenance (PM)/in a maintenance state.”
Murali may not explicitly teach the following. However, Ito teaches:
generating, by the hybrid artificial intelligence-driven decision support system using real-time sensor data, optimized maintenance schedules for the assets, based on the objectives; 0105: “FIG. 9 is a schematic diagram illustrating another example of the maintenance schedule display screen by the management server 10. The management server 10 may display the maintenance schedule display screen illustrated in FIG. 9 on the display unit 14 instead of the maintenance schedule display screen illustrated in FIG. 8. A title 106a of “Maintenance schedule” indicating that this screen is the maintenance schedule display screen is displayed in the uppermost portion of the maintenance schedule display screen illustrated in FIG. 9. Tabs 106b for switching and displaying a plurality of maintenance schedule candidates are provided below the title 106a. In this example, it is assumed that the schedule determination model 12b of the management server 10 can output a plurality of candidates for the maintenance schedules of the analysis devices A1 to A3. The management server 10 extracts three candidates from the plurality of maintenance schedules output by the schedule determination model 12b according to, for example, the magnitude of an evaluation value and displays the maintenance schedules in association with the tabs “Candidate 1”, “Candidate 2”, and “Candidate 3”. The administrator can select one of the tabs 106b to check the corresponding maintenance schedule…0120: the reinforcement learning that gives a high reward in a case in which the operating rate of the analysis devices A1 to A3 is high is performed to generate the schedule determination model 12b for determining the maintenance schedule. Therefore, the maintenance schedule is determined such that the operating rate of the analysis devices A1 to A3 is high, and it is possible to achieve efficient maintenance.”
outputting, via a graphical user interface, the optimized maintenance schedules for the assets …and enabling, by the graphical user interface, a selection and deployment of any one of the optimized maintenance schedules for the assets, thereby changing a scheduled time when an asset maintenance action is performed;0104-0105: “ Labels “Date”, “Device”, and “Component” are displayed so as to be arranged in the horizontal direction in an upper portion of the maintenance schedule display region 105b. A list of the maintenance schedules of the analysis devices A1 to A3 is displayed below the three labels. In addition, the maintenance schedules displayed on this screen are maintenance schedules recommended by the management server 10, and the administrator determines whether or not to perform maintenance according to the schedules. In this example, the maintenance of the filters F11, F12, and F13 of the analysis device A1 is recommended on Mar. 25, 2019, and the maintenance of the pumps P21, P22, and P23 of the analysis device A2 is recommended on the same date. In addition, the maintenance of the filters F31, F32, and F33 of the analysis device A3 is recommended on Apr. 8, 2019. A region 105c in which icons for performing setting operations related to the maintenance schedule display screen, a switching operation from this screen to another screen, or the like are arranged is provided in the lowest portion of the maintenance schedule display screen. The administrator who uses the management server 10 can examine the maintenance schedule of each of the analysis devices A1 to A3 on the basis of the maintenance schedule display screen. FIG. 9 is a schematic diagram illustrating another example of the maintenance schedule display screen by the management server 10. The management server 10 may display the maintenance schedule display screen illustrated in FIG. 9 on the display unit 14 instead of the maintenance schedule display screen illustrated in FIG. 8. A title 106a of “Maintenance schedule” indicating that this screen is the maintenance schedule display screen is displayed in the uppermost portion of the maintenance schedule display screen illustrated in FIG. 9. Tabs 106b for switching and displaying a plurality of maintenance schedule candidates are provided below the title 106a. In this example, it is assumed that the schedule determination model 12b of the management server 10 can output a plurality of candidates for the maintenance schedules of the analysis devices A1 to A3. The management server 10 extracts three candidates from the plurality of maintenance schedules output by the schedule determination model 12b according to, for example, the magnitude of an evaluation value and displays the maintenance schedules in association with the tabs “Candidate 1”, “Candidate 2”, and “Candidate 3”. The administrator can select one of the tabs 106b to check the corresponding maintenance schedule.”
Murali and Ito are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Murali with the aforementioned teachings from Ito with a reasonable expectation of success, by adding steps that allow the software to generate and update data with the motivation to more efficiently and accurately organize and analyze information [Ito 0105].
Murali and Ito may not explicitly teach the following. However, Sinha teaches:
…with explanations how each optimized maintenance schedule would meet the objectives following deployment; 0273: “each element 1664-1668 indicate predicted updates to the infectious disease risk score and monthly energy cost (e.g., increase or decreases) that will result from the settings of each recommendation. An accept element is included within each of the elements 1664-1668 allowing a user to interact with the interface 1660 and select one of the recommendations. Responsive to selecting one of the recommendations, e.g., the recommendation of element 1664, a user interface displaying operational adjustments 1670, e.g., the user interface 1680 can be displayed…0335: Referring now to FIG. 38D, the user interface 3806 is shown including a comment 3814 and a recommendation explanation 3815, according to an exemplary embodiment. If a user hovers over one of the light bulb icons in the elements 3808-3811, the comment 3814 and/or the explanation 3815 can be provided in the user interface 3806. The comment 3814 may be a comment entered by a user as shown in FIG. 38C. This can provide a user with an indication as to why the user, or another user, accepted or declined a recommendation. Furthermore, the explanation 3815 can provide an indication as to why the recommendation engine 3707 generated a recommendation. The explanation 3815 can include a static estimate and/or dynamic info (e.g., energy saving info) based on analysis of the building systems 3709. The explanation 3815 can provide an indication of a potential benefit of adopting a particular explanation.”
Murali, Ito, and Sinha are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Murali and Ito with the aforementioned teachings from Sinha with a reasonable expectation of success, by adding steps that allow the software to meet limits with the motivation to more efficiently and accurately organize and analyze information [Sinha 0273].
Claims 1 and 15 are the system and CRM for performing the method of claim 8 above. Because the art teaches the system and CRM, the same art and rationale are applied.
As per claim 9, Murali, Ito, and Sinha teach all the limitations of claim 8.
In addition, Murali teaches:
wherein the hybrid artificial intelligence-driven decision support system comprises at least one of a predictive maintenance model, a physics-based process simulation model, or a probabilistic risk model for the assets of the industrial plant; 0049: “as described with respect to FIG. 2, dynamic asset fleet maintenance scheduling system 200 (e.g., model generating module 211) generates a parametric model that expresses condition states for each of a plurality of assets, and the probability of the assets transitioning between the condition states (310). The parametric model may be based at least in part on historical asset health data, and the condition states may comprise at least a new state, an operational state, a soon-to-fail state, a failed state, and an under-maintenance state. Dynamic asset fleet maintenance scheduling system 200 (e.g., prediction generating module 213) generates stochastic degradation predictions of a group of the assets based at least in part on the condition states for at least some of the assets (320). Dynamic asset fleet maintenance scheduling system 200 (e.g., schedule generating module 215) generates a maintenance schedule based at least in part on: the stochastic degradation predictions of the group of the assets, costs of corrective maintenance for assets in the failed state, and costs of scheduled maintenance for the assets (330). Various aspects are further discussed below. Dynamic asset fleet maintenance scheduling system 200 may perform further steps and functions in various embodiments.”
Claims 2 and 16 are the system and CRM for performing the method of claim 9 above. Because the art teaches the system and CRM, the same art and rationale are applied.
As per claim 10, Murali, Ito, and Sinha teach all the limitations of claim 8.
In addition, Murali teaches:
wherein the objectives comprise a combination of a performance, a cost, a sustainability, or an equipment risk for an industrial plant; 0055: “Dynamic asset fleet maintenance scheduling system 200 may formulate a dynamic optimization model as a mixed-integer linear program that determines an optimal maintenance schedule for the rest of a planning time horizon. On the other hand, dynamic asset fleet maintenance scheduling system 200 may only implement a maintenance schedule for a short-term upcoming time period at a time. This optimization model may enable operators to control power usage in real-time using demand-side management strategies. Dynamic asset fleet maintenance scheduling system 200 may dynamically optimize among a multi-objective function to aim to minimize the sum of three terms for each time period (e.g., each upcoming discrete maintenance scheduling period): (1) cost due to loss of power in the network, (2) cost due to corrective maintenance (CM) upon asset failure, and (3) cost due to preventive maintenance (PM).”
Claims 3 and 17 are the system and CRM for performing the method of claim 10 above. Because the art teaches the system and CRM, the same art and rationale are applied.
As per claim 11, Murali, Ito, and Sinha teach all the limitations of claim 8.
In addition, Sinha teaches:
wherein the output, via the graphical user interface, further comprises comparisons of each optimized maintenance schedule to the deployed maintenance schedule; 0273: “each element 1664-1668 indicate predicted updates to the infectious disease risk score and monthly energy cost (e.g., increase or decreases) that will result from the settings of each recommendation. An accept element is included within each of the elements 1664-1668 allowing a user to interact with the interface 1660 and select one of the recommendations. Responsive to selecting one of the recommendations, e.g., the recommendation of element 1664, a user interface displaying operational adjustments 1670, e.g., the user interface 1680 can be displayed.”
Murali, Ito, and Sinha are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Murali and Ito with the aforementioned teachings from Sinha with a reasonable expectation of success, by adding steps that allow the software to meet limits with the motivation to more efficiently and accurately organize and analyze information [Sinha 0273].
Claim 4 is the system for performing the method of claim 11above. Because the art teaches the system and CRM, the same art and rationale are applied.
As per claim 14, Murali, Ito, and Sinha teach all the limitations of claim 8.
In addition, Chan teaches:
wherein the computer-implemented method further comprises responding to deployment of the optimized maintenance schedule by the hybrid artificial intelligence-driven decision support system occasionally predicting whether the deployed optimized maintenance schedule will meet the objectives; 0054-0055: “Dynamic asset fleet maintenance scheduling system 200 may determine to reschedule a pre-planned maintenance of a particular asset, dependent on the time at which the particular asset transitions to a critical state with respect to the predicted or expected time at which it was expected to transition to that state, and the time at which maintenance for the particular asset had been pre-scheduled. The operating mode of the system may be defined by that of each individual asset: operational, failed, or under preventive maintenance (PM)/in a maintenance state. Dynamic asset fleet maintenance scheduling system 200 may formulate a dynamic optimization model as a mixed-integer linear program that determines an optimal maintenance schedule for the rest of a planning time horizon. On the other hand, dynamic asset fleet maintenance scheduling system 200 may only implement a maintenance schedule for a short-term upcoming time period at a time. This optimization model may enable operators to control power usage in real-time using demand-side management strategies. Dynamic asset fleet maintenance scheduling system 200 may dynamically optimize among a multi-objective function to aim to minimize the sum of three terms for each time period (e.g., each upcoming discrete maintenance scheduling period): (1) cost due to loss of power in the network, (2) cost due to corrective maintenance (CM) upon asset failure, and (3) cost due to preventive maintenance (PM).
Claims 7 and 20 are the system and CRM for performing the method of claim 14 above. Because the art teaches the system and CRM, the same art and rationale are applied.
Claims 5, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20240103959 (hereinafter “Murali”) et al., in view of U.S. PGPub 20220319240 to (hereinafter “Ito”) et al., in further view of U.S. PGPub 20220067851 (hereinafter “Sinha”) et al., and in further view of U.S. PGPub 20220284519 (hereinafter “Pancholi”) et al.
As per claim 12, Murali, Ito, and Sinha teach all the limitations of claim 11.
Murali, Ito, and Sinha may not explicitly teach the following. However, Pancholi teaches:
wherein the comparisons of each optimized maintenance schedule to the deployed maintenance schedule are based on depicting data values corresponding to at least three objectives for each maintenance schedule on a graph comprising at least three dimensions corresponding to the at least three objectives; 0239-0240: “ GUI 1500 is shown as a radar chart (e.g., a spider chart, a web chart, etc.) that illustrates the values of several performance metrics (shown as “Carbon Emissions,” “Productivity,” “Financial Cost,” “Indoor Air Quality,” “Infection Risk,” and “Water Usage”) for a first simulation result (“Simulation A”) represented by line 1504 and a second simulation result (“Simulation B”) represented by line 1502. The values of the performance metrics can be represented by the positions of the vertices of lines 1502-1504 along the axes that correspond to the various performance metrics. In some embodiments, the performance metric axes may be labeled with numerical values or a number scale for each performance metric shown in GUI 1500. In some embodiments, planning tool 600 generates GUI 1500 or modifies GUI 1400 to become a radar chart if the user selects more than three performance metrics to plot graphically. Although only two simulation results and six performance metrics are shown in FIG. 15, it is contemplated that any number of simulation results and/or performance metrics could be plotted graphically in GUI 1500. In some embodiments, GUI 1500 includes one or more performance metric selection elements and/or simulation selection elements (e.g., dropdown boxes) which allow a user to select the particular performance metrics and simulation results to plot in the radar graph. In some embodiments, planning tool 600 may dynamically expand or modify GUI 1500 to include additional, fewer, or different performance metrics and/or simulation results based on the user selections made via the selection elements… the simulations performed by planning tool 600 may include multi-objective optimization or Pareto optimization.”
Murali, Ito, Sinha, and Pancholi are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Murali, Ito, and Sinha with the aforementioned teachings from Pancholi with a reasonable expectation of success, by adding steps that allow the software to meet limits with the motivation to more efficiently and accurately organize and analyze information [Pancholi 0239].
Claims 5 and 18 are the system and CRM for performing the method of claim 12 above. Because the art teaches the system and CRM, the same art and rationale are applied.
Claims 6, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20240103959 (hereinafter “Murali”) et al., in view of U.S. PGPub 20220319240 to (hereinafter “Ito”) et al., in further view of U.S. PGPub 20220067851 (hereinafter “Sinha”) et al., and in further view of U.S. Patent 7836057 (hereinafter “Micaelian”) et al.
As per claim 13, Murali, Ito, and Sinha teach all the limitations of claim 8.
Murali, Ito, and Sinha may not explicitly teach the following. However, Micaelian teaches:
wherein the computer-implemented method further comprises assigning, by the hybrid artificial intelligence-driven decision support system, at least one weight that corresponds to at least one of the objectives, in response to a selection to deploy an optimized maintenance schedule other than an optimized maintenance schedule that is ranked as more optimal than the other optimized maintenance schedules, thereby changing subsequent rankings of at least some of the optimized maintenance schedules; 032-033: “Once the user reorders the list, the weight inference engine calculates the weights based on this reordering. The calculated new weights are then displayed to the user so the user is aware of the effect of his choices on the relative importance of the selection criteria. The process may go on indefinitely as long as the user keeps reordering the list. After each new ordering new weights will be calculated, and a newly ranked list displayed… ranking a Ford Mustang as the user's number one choice may result in the weight inference engine 43 inferring that performance is more important than price for the user. If a Ford Escort is input as the user's top choice, the weight inference engine would calculate and display that cost is heavily weighted while performance is not. FIG. 11 shows this type of inputted list. The algorithm for weight inference is shown in FIGS. 18-23.”
Murali, Ito, Sinha, and Micaelian are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Murali, Ito, and Sinha with the aforementioned teachings from Micaelian with a reasonable expectation of success, by adding steps that allow the software to meet limits with the motivation to more efficiently and accurately organize and analyze information [Micaelian 032].
Claims 6 and 19 are the system and CRM for performing the method of claim 13 above. Because the art teaches the system and CRM, the same art and rationale are applied.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Griffith; Douglas J.. PREDICTING HARDWARE FAILURES IN A SERVER, .U.S. PGPub 20150281015 The present invention relates generally to the field of preventive maintenance, and more particularly to predicting hardware failures in a computing system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”)./Arif Ullah/Primary Examiner, Art Unit 3625
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