DETAILED ACTION
Status of Claims
This action is in reply to the communication filed on 24 March, 2026.
Claims 1, 11 and 19 have been amended.
Claims 1 – 20 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 24 March, 2026 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept – i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
Claim 1 is representative. Claim 1 recites:
A method implemented by an autonomous maintenance management (AMM) system of an industrial facility, the method comprising:
monitoring, by the AMM system, at least one of operational data values and an event associated with an equipment of the industrial facility to determine key performance indicators and a trend pattern corresponding to the equipment,
wherein the event is characterized by deviations in the operational data values from a pre-determined threshold value, and
wherein the trend pattern is indicative at least of operational history of the equipment,
wherein the operational data values are obtained from one or more sensors coupled to the equipment;
predicting a behavior of the equipment, by the AMM system using a machine learning model, based on at least one of the key performance indicators and the trend pattern corresponding to the equipment;
wherein the machine learning module correlates and analyses one or more historical operational data values and one or more failures instances associated with one or more equipment types;
generating, by the AMM system, a dynamic maintenance schedule for the equipment in correspondence to at least the predicted behavior of the equipment,
wherein the dynamic maintenance schedule includes a task to be executed for maintenance of the equipment; and
wherein the dynamic maintenance schedule is generated based at least on optimizing a performance loss associated with the equipment, wherein the performance loss is determined using one or more simulation models;
obtaining, by the AMM system, personnel information of the industrial facility to identify a personnel to perform the task to be executed;
issuing, by the AMM system, a maintenance work order in correspondence to the dynamic maintenance schedule to the personnel of the industrial facility for maintenance of the equipment;
updating, by the AMM system, the trend pattern and the dynamic maintenance schedule based on feedback data associated with completion of the maintenance work order; and
automatically refining the predicted behavior of the equipment and subsequent maintenance schedules based on the updated trend pattern and the updated dynamic maintenance schedule.
Claim 19 recites medium with instructions executed by a processor, and Claim 11 recites a system that executes the steps of the method recited in Claim 1.
STEP 1
The claims are directed to a system, a method and non-transitory computer readable medium which are included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea within the “mental processes” grouping – concepts performed in the human mind including observation, evaluation, judgment and opinion.
The claims recite a multi-step process for automating maintenance management of a facility including process steps for monitoring equipment to determine key performance indicators (KPI) and a trend pattern; predicting equipment behavior based on the KPI and trend patterns; and generating a dynamic maintenance schedule, including a task to be executed, corresponding to the predicted behavior. Additional process steps include obtaining personnel information; identifying a personnel to perform the maintenance task; and issuing a work order to the personnel; as well as updating the predicted behavior and maintenance schedule based on feedback related to completing the work order. Each of these processes including collecting and analyzing information to obtain a result.
Collecting information, including when limited to particular content, is within the realm of abstract ideas, and analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, are mental processes within the abstract idea category (Electric Power Group v. Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016).
The monitoring and predicting process includes: collecting data (i.e. monitoring at least one of operational data values and an event associated with an equipment of the industrial facility); and analyzing the data to obtain a result (i.e. determine key performance indicators and a trend pattern, wherein the event is characterized by deviations in the operational data values from a pre-determined threshold value, and wherein the trend pattern is indicative at least of operational history of the equipment; predicting a behavior of the equipment based on at least one of the key performance indicators and the trend pattern corresponding to the equipment; generating a dynamic maintenance schedule for the equipment in correspondence to at least the predicted behavior of the equipment, wherein the dynamic maintenance schedule includes a task to be executed for maintenance of the equipment; and wherein the dynamic maintenance schedule is generated based at least on optimizing a performance loss associated with the equipment). The claim further recite collecting data (i.e. obtaining personnel information of the industrial facility); and analyzing the data to obtain a result (i.e. identify a personnel to perform the task to be executed). The claim further recite collecting data (i.e. updating the trend pattern and the dynamic maintenance schedule based on feedback data associated with completion of the maintenance work order); and analyzing the data to obtain a result (i.e. refining the predicted behavior of the equipment and subsequent maintenance schedules based on the updated trend pattern and the updated dynamic maintenance schedule).
The specification discloses that managing and monitoring equipment in a facility is currently performed to ensure that all equipment work in an efficient and reliable manner, by monitoring changes to understand when and how maintenance is to be carried out for the equipment (@002). Further, the specification teaches that typical facilities have a team of personnel dedicated to scheduling and conducting maintenance of equipment, and rely on manual analysis of equipment (@ 0019 – 0020).
Obtaining and analyzing equipment operational data and an event to determine KPI’s and trends, predict equipment behavior, and generate a maintenance schedule to optimize performance loss; and updating equipment data to generate an updated maintenance schedule, using well-known techniques as disclosed in the specification, is a process that, except for generic computer implementation steps, can be performed in the human mind. Similarly, analyzing personnel data to identify the right person for a task is a process that, except for generic computer implementation steps, can be performed in the human mind. For example, matching a personnel to a particular task based on comparing skills, cost, working shifts, etc. of the personnel, to those required by the task, is a process that can be performed mentally.
In particular, the claims recite determining key performance indicators (KPI’s) and a trend pattern corresponding to the equipment using the operational data values and an event. A KPI is defined as “a quantifiable measure used to evaluate equipment performance” such as: parts per a given time period, energy used per unit of production, cycle time, etc. (@ 0025) Similarly, “a trend pattern” includes observed deviations in the operational data values. Both KPI’s and trends are data elements that can be generated mentally using observation and judgement.
The claims further recite predicting a behavior of the equipment. The specification discloses that predicting behavior include judgements based on current operational data values and trends. For example, the specification discloses that “if the current vibration levels continue to increase, it could lead to premature bearing failure . . .” (@00101) These are judgements made based on historical instances of failures (@ 00100).
The claims further recite updating the trend pattern and dynamic maintenance schedule based on feedback; refining the predicted behavior and subsequent maintenance schedules. The specification discloses that “personnel of the facility may also provide a possible maintenance schedule . . . based on the feedback.” (@ 0093) Such updating and refining recite a mental process. As noted above, the specification teaches that teams of personnel typically analyze equipment data and schedule appropriate maintenance for equipment in facilities on an on-going basis.
The claims further recite generating a dynamic maintenance schedule that includes a task, identify a personnel to perform the task, and issue a work order. The specification discloses that scheduling maintenance is conventionally, a mental process. As noted above, the specification teaches that teams of personnel typically analyze equipment data and schedule appropriate maintenance for equipment in facilities. As such, the claims recite an abstract idea within the mental process grouping.
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea within the “certain methods of organizing human activity” grouping –
managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
The claims recite a method that merely automates a known manual process for the management of equipment maintenance for a facility. The claims monitor equipment operating values, including deviations/events, and determines KPI’s and trend patterns used to predict equipment behavior. The predicted behavior leads to generating a maintenance schedule that includes a task, and the selection of the right person to perform the task. This type of activity, i.e. monitoring equipment and managing maintenance tasks is a conventional activity in industrial facilities. As such, the claims recite an abstract idea within the certain methods of organizing human activity grouping.
The claims, as illustrated by Claim 1, also recite limitations that encompass an abstract idea within the mathematical formula or relationship grouping.
The claims determine KPI’s or trends. Determining KPI’s is disclosed as a mathematical formula or relationship. Further, the claims recite: wherein the performance loss is determined using one or more simulation models. The specification describes simulation models as being known in the art such as the UniSim Design performance models (@ 0077) or using a method proposed by Williams (@ 0081), both of which are mathematical relationships. As such, the claims recite a mathematical formula or relationship.
STEP 2A PRONG TWO
The claims recite limitations that include additional elements beyond those that encompass the abstract idea above including:
an AMM system;
issuing a maintenance work order in correspondence to the dynamic maintenance schedule to the personnel of the industrial facility for maintenance of the equipment;
wherein the operational data values are obtained from one or more sensors coupled to the equipment;
predicting a behavior of the equipment, by the AMM system using a machine learning module; wherein the machine learning module correlates and analyses one or more historical operational data values and one or more failures instances associated with one or more equipment types;
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with the MPEP. (see MPEP 2106.05)
The AMM system is recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using a generic computer component. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment. The specification discloses that the process is executed by a generic computer. (@ 0034 – 0036)
Issuing work orders for scheduled maintenance that results from the abstract monitoring/predicting/generating process does not improve the computer itself, or any other technology, and is ancillary to the abstract process itself.
The equipment sensors are also recited at a high level of generality that generally link the abstract idea to a particular technological environment. The specification at least implies that the sensors are known; and that they gather operational data by operating in their normal capacity. For example, the specification teaches that a sensor may read “pressure values, flow rate, electrical current, rotational speed, and the like” (@ 0047) Equipment may have specified “OEM performance data and pre-determined thresholds” for the operational data and the sensors are configured to measure these values.
The claims recite “using a machine learning module” to predict equipment behavior. In particular, the claims replace the knowledge and experience of a “team of personnel” at the industrial facility by applying established methods of machine learning to an abstract equipment behavior prediction process in a new data environment – i.e. applying a machine learning module to the equipment operational and event information. The specification teaches that the learning model correlates historical operational data values with failures; using an unspecified machine learning model. (@ 0060). Machine learning limitations reciting broad, functionally described, well-known techniques executed by generic and conventional computing devices, as the claims do here, does not provide a practical application of the abstract diagnostic process. “Today we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under §101.” (Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025)).
Nothing in the claim recites specific limitations directed to an improved technology or technological process. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception by use of conventional computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a generic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claim do not integrate the abstract equipment maintenance management process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract equipment maintenance management process. Issuing work orders for scheduled maintenances is a conventional technique, a fact for which Examiner takes Official Notice; and is an ancillary part of the abstract process itself as in Electric Power Group.
The additional structural elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generic computer structure (i.e. AMM system, a machine learning module, sensors). Each of the above components are disclosed in the specification as being purely conventional and/or known in the industry. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting well-understood, routine and conventional computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently well-known that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination the limitations recited in the claims add nothing that is not already present when the steps are considered individually. As such, the additional elements recited in the claim do not provide significantly more than the abstract equipment maintenance management process, or an inventive concept.
The dependent claims add additional features including:
those that merely serve to further narrow the abstract idea above such as:
further limiting the schedule generating to pre-defined intervals (Claim 2, 12);
further limiting the type of KPI (Claim 3);
those that recite additional abstract ideas such as:
assigning a priority index to tasks (Claim 4, 13);
identifying an anomaly with equipment (Claim 5, 14);
analyze past schedules to determine an optimal maintenance interval (Claim 7, 18);
determine a training program for the task (Claim 8, 17, 20);
determine KPI using various analysis techniques (Claim 10);
those that recite well-understood, routine and conventional activity or computer functions such as:
generating in a CMMS format of the facility (Claim 9, 16);
those that recite insignificant extra-solution activities such as:
issue a corrective work order (Claim 5, 14);
or those that are an ancillary part of the abstract idea such as;
receiving and storing feedback (Claim 6, 15).
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. These elements merely narrow the abstract idea, recite additional abstract ideas, or append conventional activity to the abstract process. As such, the additional element do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention.
The apparatus claims are no different from the method claims in substance. “The equivalence of the method, system and media claims is readily apparent.” “The only difference between the claims is the form in which they were drafted.” (Bancorp). The method claims recite the abstract idea implemented on a generic computer, while the apparatus claims recite generic computer components configured to implement the same idea. Specifically, Claims 11 – 20 merely add the generic hardware noted above that nearly every computer will include. The apparatus claim’s requirement that the same method be performed with a programmed computer does not alter the method’s patentability under U.S.C. 101 (In re Grams). Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3 – 6, 8, 10, 11, 13 – 15, 17, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lepine et al.: US PGPUB 2017/0117064 A1.
CLAIMS 1, 11 and 19
Lepine discloses a system and method for managing equipment maintenance in a facility that includes the following limitations:
A method implemented by an autonomous maintenance management (AMM) system of an industrial facility, (Lepine 0004); the method comprising:
monitoring, by the AMM system, at least one of operational data values and an event associated with an equipment of the industrial facility to determine key performance indicators and a trend pattern corresponding to the equipment, wherein the event is characterized by deviations in the operational data values from a pre-determined threshold value, and wherein the trend pattern is indicative at least of operational history of the equipment; wherein the operational data values a obtained from one or more sensors coupled to the equipment; (Lepine 0003 - 0005, 0014 – 0017, 0019, 0022, 0031, 0033, 0053 – 0056, 0072, 0076 – 0078, 0080);
predicting a behavior of the equipment, by the AMM system using a machine learning module, based on at least one of the key performance indicators and the trend pattern corresponding to the equipment, wherein the machine learning module correlates and analyses one or more historical operational data values and one or more failures instances associated with one or more equipment types; (Lepine 0022, 0080 - 0094);
Lepine discloses a system and method for managing equipment maintenance in a facility that includes monitoring operational data values for equipment in an nuclear plant – (i.e. an industrial facility). The operational data is analyzed by an analytical platform, performing equipment performance analytics to determine current performance metrics (i.e. key performance indicators) for the equipment, and whether the equipment is degrading – i.e. a trend pattern. Lepine discloses using machine learning techniques in the analytical platform including generating a performance based model based on historical operational information and historical state (i.e. failure instances) associated with a particular equipment type. Lepine also discloses the following limitations:
generating, by the AMM system, a dynamic maintenance schedule for the equipment in correspondence to at least the predicted behavior of the equipment, wherein the dynamic maintenance schedule includes a task to be executed for maintenance of the equipment; (Lepine 0022 – 0024, 0027, 0104);
obtaining, by the AMM system, personnel information of the industrial facility to identify a personnel to perform the task to be executed; and issuing, by the AMM system, a maintenance work order in correspondence to the dynamic maintenance schedule to the personnel of the industrial facility for maintenance of the equipment; (Lepine 0018, 0024 – 0027, 0060 – 0062, 0115, 0121).
Lepine discloses that the system generates a schedule of tasks to be performed for maintenance of the equipment based on the analysis, obtains worker information, and assigns the task to an appropriate, identified worker (i.e. identify a personnel to perform the task to be executed), by issuing a work package (i.e. a maintenance work order). Lepine also discloses the following limitations:
wherein the dynamic maintenance schedule is generated based on optimizing a performance loss associated with the equipment; (Lepine 0011, 0013, 0014, 0022, 0027, 0057, 0083, 0099, 0104, 0106
wherein the performance loss is determined using one or more simulation models; (Lepine 0096
Lepine assigns maintenance tasks dynamically in order to “reduce operation and/or maintenance costs”, while maintaining or improving “a level of performance”, such as may be measured by a “performance metric”. Lepine performs a task valuation analysis, by generating a maintenance value model, (i.e. one or more simulation models), based on historical performance information associated with equipment, for which a particular task was performed (or not performed), and correlated with an outcome including an impact on equipment performance. The analysis includes providing a schedule for performing the tasks such that tasks having a higher value are performed first. Improving a performance metric is equivalent to optimizing a performance loss. Lepine also discloses the following limitations:
updating, by the AMM system, the trend pattern and the dynamic maintenance schedule based on feedback data associated with completion of the maintenance work order; automatically refining the predicted behavior of the equipment and subsequent maintenance schedules based on the updated trend pattern and the updated dynamic maintenance schedule; (Lepine 0014, 0016, 0017, 0022, 0027, 0055 – 0057, 0082 – 0092, 0111).
Lepine discloses a system and method for managing equipment maintenance for a facility that includes real-time updating, including automatically updating analysis results when additional information is received. The system receives operational information including maintenance information describing a maintenance activity performed on the equipment, the progress of the task, corrective action information, tasks associated with ongoing observations of the equipment, etc. (i.e. feedback). The system also receives an operational state of the equipment from the worker (i.e. updating the trend pattern). The system analyzes the data to determine a predicted future performance (i.e. predicted behavior), and generates a dynamic maintenance schedule to be performed based on the feedback. For example, if a worker indicates that immediate corrective action is required, the system updates the maintenance schedule accordingly
CLAIMS 3 – 6, 8, 10, 13 – 15, 17 and 20
Lepine discloses the limitations above relative to Claims 1, 11 and 19. Additionally Lepine discloses the following limitations:
wherein the key performance indicators correspond to at least one of performance, failure, availability, reliability, and life of the equipment; (Lepine 0022, 0055, 0056, 0080 – 0083); - disclosing performance metrics for a level of performance;
wherein the dynamic maintenance schedule comprises one or more tasks, wherein each task amongst the one or more tasks is assigned with a priority index; (Lepine 0023, 0024, 0099); - disclosing a schedule of prioritized tasks;
identifying an anomaly associated with the equipment; and issuing a corrective work order to address the anomaly; (Lepine 0017, 0057, 0082, 0087 - 0092); - disclosing corrective action work orders in response to a reported anomaly;
receiving a feedback from the personnel corresponding to the maintenance work order; and updating a work order execution database with the feedback received; (Lepine 0014, 0017, 0055, 0061, 0073, 0074); - disclosing real-time information updates including for maintenance activities performed, measured performance, progress on a task, or other information relevant to an assigned task;
determining, by the AMM system, a training program to train the personnel in a manner in which the task is to be executed; (Lepine 0025, 0062, 0107, 0113, 0125); - disclosing determining training programs for a task;
wherein the key performance indicators are determined by performing at least one of a parameter analysis of the operational data values, a fault analysis of the equipment, and a reliability analysis of the equipment; (Lepine 0022, 0055, 0080); - disclosing performance metrics based on data analysis.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lepine et al.: US PGPUB 2017/0117064 A1 in view of Applicant’s admission as to the state of the prior art.
CLAIMS 7 and 18
Lepine discloses the limitations above relative to Claims 1 and 11. With respect to the following limitations:
analyzing, by the AMM system, a plurality of past dynamic maintenance schedules for the equipment to determine an optimal maintenance time interval, the optimal maintenance time interval being a time interval within which if maintenance is carried out, the equipment would have maximum life.
The present specification discloses that “the method used to find optimal maintenance interval may be based on the one proposed by Williams as would be understood to a person skilled in the art.” (@ 0081) As such, Applicant admits the recited features are known. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the maintenance management system of Lepine so as to have included determining an optimal maintenance interval, in accordance with the Applicant’s Admission, in order to reduce production downtime.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Lepine et al.: US PGPUB 2017/0117064 A1 in view of Nakazato: (US PGPUB 2017/0185970 A1.
CLAIMS 2 and 12
Lepine discloses the limitations above relative to Claims 1 and 11. With respect to the following limitations:
wherein the AMM system is configurable to generate the dynamic maintenance schedule at predefined time intervals; (Nakazato Abstract, 0013, 0014, 0028, 0043).
Lepine does not disclose a pre-determined time interval for generating a maintenance schedule; however, Nakazato does:. Nakazato discloses a system and method for managing maintenance of machines such as machine tools or production machinery. Nakazato monitors machine data and generates a maintenance schedule at pre-determined intervals. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the maintenance management system of Lepine so as to have included generating schedules at predetermined intervals, in accordance with the teaching of Nakazato, in order to reduce production downtime.
Claims 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lepine et al.: US PGPUB 2017/0117064 A1 in view of Nesler et al.: (US PGPUB 2010/0324962 A1.
CLAIMS 9 and 16
Lepine discloses the limitations above relative to Claims 1 and 11. With respect to the following limitations:
wherein the issuing comprises generating the maintenance work order in a format compatible with a Computerized Maintenance Management System (CMMS) of the industrial facility; (Nesler 0005, 0029, 0030, 0038, 0039, 0041).
Lepine does not disclose work orders in a CMMS compatible format; however, Nesler does. Nesler discloses a system and method for managing maintenance of equipment in a building. Nakazato monitors equipment data and generates workorders in a format particular to the building via a CMMS application. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the maintenance management system of Lepine so as to have included generating CMMS compatible work orders, in accordance with the teaching of Nesler, in order to improve building efficiency. Examiner further asserts that CMMS applications are old and well-known in the art.
Response to Arguments
Applicant's arguments filed 24 March, 2026, with respect to the U.S.C. §101 Rejection, have been fully considered but they are not persuasive.
The U.S.C. §101 Rejection
Applicant asserts that the claims are “not directed to an abstract idea” because “one or more features . . . cannot be performed or executed in the human mind.” In particular, Applicant asserts that the claims recite subject matter “inextricably rooted in specialized computing” that includes “detecting deviations and tracking performance with precision unattainable by human observation”. Applicant argues that the claims rely on “advanced machine learning models” to predict equipment behavior. Examiner disagrees.
Initially, Examiner asserts that the machine learning module is not in any way “advanced”. The specification describes the machine learning module only in one section (@ 0060), and in functionally generic terms. There is not even a disclosure relative to the particular functionality of the machine learning module such as being a neural network of any type, or other machine learning algorithm, reinforcing the generic nature of this feature.
Applicant asserts that “one or more features . . . cannot be performed or executed in the human mind.” Using this interpretation, a claim that recites an abstract idea may be patent-eligible if it also recites at least one feature that cannot be performed mentally. For example, a claim to a mathematical calculation would be patent eligible if the claim also recites displaying, storing or transmitting the result – features that cannot be performed mentally. This is clearly inconsistent with the guidance provided in the MPEP. Rather, a claim may be directed to a mental process if one or more features recited in the claims CAN be performed mentally. Here, predicting equipment behavior, generating a maintenance schedule, assigning tasks to personnel and updating based on results are expressly disclosed in the specification as processes that are currently and typically performed by a team of personnel.
Anecdotally, Examiner offers a personal example – a pool pump and filter equipment. Sensors monitor water pressure, flow rate and temperature. When pressure exceeds a threshold or trends upward, and the flow rate decreases below a specific limit; maintenance must be performed such as cleaning the filter, otherwise one may reliably predict that the pump and filter will no longer provide an adequate flow rate or effectively filter the water. Similarly, leaks, vibration or abnormal noise levels may be observed and used to predict pump failure. The only difference between this example and the claims is the involvement of a generic computer and machine learning module.
The present specification further refutes the notion that monitoring equipment, predicting behavior, generating and assigning maintenance tasks; and then updating based on results, cannot be performed in the human mind. The specification is replete with descriptions of how facilities have a “team of personnel” to perform these functions.
There is no specialized computing infrastructure described anywhere in the specification. Rather, the AMM, relied on by the Applicant, is implemented in “any computing system, such as . . . a server, a desktop or a laptop, a computing device . . .”, etc.
Applicant does not address the Examiner’s rejection in that the claims are directed to a method of organizing human activity, or to the rejection that the claims are directed to a mathematical relationship.
Applicant further asserts that “the alleged abstract idea is integrated into a practical application.” In particular that the claims “recites a practical application” – an autonomous system that provides predictive maintenance optimization. Nonetheless, “reciting a practical application” is not the same as being “integrated into a practical application”. Whether a judicial exception is integrated is based on an analysis of additional elements. Here, the additional elements are limited to a generic computer, generic machine learning, and issuing work orders. The features relied on by Applicant to show an improvement are limited to an improved abstract idea, and not any technical improvement – i.e. predict behavior with improved accuracy.
Applicant asserts an inventive concept – i.e. “effective equipment maintenance and operational decision making”. However, as above, improving an abstract idea does not provide an inventive concept.
The U.S.C. §102/103 Rejection
Applicant asserts that Lupine does not disclose “wherein the dynamic maintenance schedule is generated based at least on optimizing a performance loss associated with the equipment, wherein the performance loss is determined using one or more simulation models”. Examiner disagrees.
Initially, Examiner asserts that dynamically generating the maintenance schedule based at least on “optimizing a performance loss associated with the equipment”, as claimed, and dynamically generating the maintenance schedule based at least on “improving a performance metric”, as in Lepine; have the same meaning, where “optimizing” means “reducing”.
The specification discloses that simulation models for determining performance loss may include well-known models, for example, models such as “UniSim Design performance models”. UniSim is a registered trademark of Honeywell. Nonetheless, the claims are not so limited – the broadest reasonable interpretation includes any simulation model. Performance loss may be simulated as in Lepine, using a performance model that is generated based on historical operation information, historical state information and historical corrective action (i.e. maintenance tasks) information associated with equipment. The model predicts (i.e. simulates) the level of performance for the equipment if the particular task is performed (or not performed).
Here, Examiner notes that Applicant expressly discloses that simulation models are known in the art. Indeed, the UniSim™ model is known for monitoring plant performance and equipment and predicting the impact on performance. As such, even if Lepine’s disclosure is inadequate, Applicant’s admission may readily be combined with Lepine in a U.S.C. § 103 rejection.
Applicant does not separately argue the dependent claims.
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Honeywell UniSim Design | Process Simulation Software website discloses the UniSim™ Design Suite.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to John A. Pauls whose telephone number is (571) 270-5557. The Examiner can normally be reached on Mon. - Fri. 8:00 - 5:00 Eastern. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773.
Official replies to this Office action may now be submitted electronically by registered users of the EFS-Web system. Information on EFS-Web tools is available on the Internet at: http://www.uspto.gov/patents/process/file/efs/guidance/index.jsp. An EFS-Web Quick-Start Guide is available at: http://www.uspto.gov/ebc/portal/efs/quick-start.pdf.
Alternatively, official replies to this Office action may still be submitted by any one of fax, mail, or hand delivery. Faxed replies should be directed to the central fax at (571) 273-8300. Mailed replies should be addressed to “Commissioner for Patents, PO Box 1450, Alexandria, VA 22313-1450.” Hand delivered replies should be delivered to the “Customer Service Window, Randolph Building, 401 Dulany Street, Alexandria, VA 22314.”
/JOHN A PAULS/Primary Examiner, Art Unit 3683
Date: 4 June, 2026
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