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 . 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 (i.e., changing from AIA to pre-AIA ) 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.
DETAILED ACTION
The following NON-FINAL Office Action is in response to Request for Continued Examination filed on 6/11/2026.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/11/2026 has been entered.
Status of Claims
Claims 1-2, 4-14, 16-21, 23 are currently pending.
Claims 1, 6-7, 10, 13, 17-19 are currently amended.
Claims 3, 15, 22 are cancelled.
Claim 23 is newly added.
Claims 1-2, 4-14, 16-21, 23 are currently under examination and have been rejected as follows.
IDS
The information disclosure statements filed on 12/24/2025, 1/23/2026, 3/5/2026, 8/25/2026 comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
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Response to Amendment
The previously pending rejections under 35 USC 101 will be maintained. The 101 rejection is updated in view of the amendments.
New grounds for rejection 35 USC 103 are applied as necessitated by the amendments.
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Response to Arguments
Regarding Applicant’s remarks pertaining to 35 USC 101:
Step 2A Prong 1:
Applicant argues on page 10 of remarks 5/20/2026:
“At least the acts of formulating and submitting a prompt, directed to a generative artificial intelligence (Al) model, designed to obtain a response from the generative Al model that is used by an analysis component to formulate one or more first maintenance tasks or the one or more second maintenance tasks do not represent mere mental activities implemented by a processor that could otherwise be performed by the human mind or by a human using pencil and paper. Instead, formulation and submission of such prompts and processing of a subsequent response from the generative Al model are computer-centric functions that cannot be untethered from the computerized hardware and associated software that performs these functions.”
Examiner respectfully disagrees. See Examiner's response to arguments on p. 3 of office action 3/25/2026. Claims can recite a mental process even if they are claimed as being performed on a computer or in a computer environment (see MPEP 2106.04(a)(2) Ill C). Monitoring industrial asset operational conditions and functioning, assessing probable risk thereof, formulating mitigation measures, and prescribing maintenance can still be performed in the human mind, albeit faster, more accurately, and more efficiently on a computer. If Examiner did not interpret the claims as amended this way, in arguendo, Examiner submits the claims still fall within mitigating risk at is pertains to fundamental economic principles, and following rules or instructions as it pertains to managing personal behavior or relationships or interactions between people, each under the larger abstract grouping of Certain Methods of Organizing Human Activity
(MPEP 2106.04(a)(2) II).
Step 2A Prong 2:
Applicant argues on page 11 of remarks 5/20/2026:
“These remarks appear to apply an overly reductive interpretation of independent claim 1, alleging that the step of formulating and submitting a prompt, directed to a generative artificial intelligence (Al) model, designed to obtain a response from the generative Al model that is used by the analysis component to formulate the one or more first maintenance tasks or the one or more second maintenance tasks amounts to nothing more than part of a process of 'monitoring industrial asset operational conditions and functioning, assessing probably risk thereof, formulating mitigation measures, and prescribing maintenance,' ....”
Examiner respectfully disagrees. Applicant specification ¶ [0003] states: " .... a system is provided ... to monitor industrial asset data ... wherein the industrial asset data comprises operational and status information ... based on analysis of the industrial asset data, that a subset of the industrial asset data satisfies a condition indicative of a current or predicted risk to a first industrial asset of the industrial assets, formulate one or more first maintenance tasks predicted to mitigate the current or predicted risk ... and formulate one or more second maintenance tasks predicted to mitigate the performance issue; and a work order generation component configured to ... generate a work order prescribing the one or more first maintenance tasks and the one or more second maintenance tasks." Examiner's paraphrasing of the claims as whole being directed to "monitoring industrial asset operational conditions and functioning, assessing probably risk thereof, formulating mitigation measures, and prescribing maintenance" appear consistent with Applicant specification.
Applicant argues on page 12 of remarks 5/20/2026:
“It is respectfully submitted that independent claim 1 as amended herein - which recites that industrial asset data generated by industrial assets is monitored, that an analysis of this asset data determines that subset of the industrial asset data satisfies a condition indicative of a current or predicted risk to a first industrial asset of the industrial assets, that first and second maintenance tasks are formulated to mitigate the risk and other related performance issues, and that the system then generates a work order prescribing these tasks and transmits a notification of the work order to a client device associated with a technician assigned to the work order - clearly grounds the alleged method of organizing human activity into a practical application, thereby satisfying the second prong of step 2A of the eligibility analysis. See, for example, Example 42 .... Similarly, amended independent claim 1 grounds the alleged method of organizing human activity into a practical application by providing real time notifications of work orders and
associated maintenance tasks when a risk to an asset is discovered in real-time.”
Examiner respectfully disagrees and submits eligibility analysis is performed on case-by-case basis. However, arguendo, Example 42 achieves eligibility not only by converting updated information that was input by a user in a non-standardized form to a standardized format, automatically generating a message whenever updated information is stored, and transmitting the message to all of the users; but also the additional elements recite a specific improvement over prior art systems in their field by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user. The additional computer-based elements in the independent claims as amended include "user interface", "memory", "processor", "monitoring component", "first industrial asset", "second industrial asset", "analysis component", "work order generation component", "generative Al model", and "non-transitory computer-readable medium", ''work order management system", which are all well known, routine and conventional additional elements. Generative Al models are described as routine and conventional Applicant specification ¶ [0037]: " ... the work order management system can leverage generative artificial intelligence (Al) or other types of Al in connection with determining when and how to schedule a maintenance task intended to mitigate asset risk"; ¶ [0050]: " ... the system 202 can learn to recognize conditions of the asset data indicative of an elevated risk to an asset using machine learning, Al , generative Al, or other analytic techniques"; and ¶ [0056]: " ... the analysis component 212 can implement prompt engineering functionality using associated trained models 312 trained with various types of training data, and can use these prompt engineering features to interface with a generative Al model 308 (e.g., an LLM or another type of model) and associated neural networks." Industrial assets are noted as routine and conventional additional elements in an industrial facility (specification ¶ [0004] among others). The remaining additional elements, which are computer-based, are described as routine and conventional in specification ¶ [0122] - [0145]. Insufficient technological explanation of how the functions are performed is clear to distinguish the claims as improvement over existing technology.
Accordingly, the previously pending rejections under 35 USC 101 will be maintained. The 101 rejection is updated in view of the amendments.
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Regarding Applicant’s remarks pertaining to 35 USC 103:
Applicant argues on page 17 of remarks 5/20/2026:
“More specifically, Cella et al. does not indicate that a determination that a second
industrial asset has a likelihood of experiencing a performance issue due to a current or predicted risk to a first industrial asset is made based on this functional interdependency, where this functional interdependency specifies that the second industrial asset receives parts or material from the first industrial asset, as also recited in independent claim 1 as amended…. Cella et al. nowhere indicates that this relational database 3500 defines a functional interdependency between a first industrial asset and a second industrial asset, where this functional interdependency specifies that the second industrial asset receives parts or material from the first industrial asset.”
Examiner respectfully finds the argument moot on new grounds. Examiner submits the further limited features of modeled interdependency among industrial assets as described by the amended claims are taught by reference Juaquet et al. US 20160282854 A1, hereinafter Jauquet, inter alia at ¶ [0016], [0018], [0019], and [0066]. Additional details and citations are included in the 103 rejection section below.
Examiner further notes that the amended claim limitation “a user interface component configured to transmit a notification of the work order to a client device associated with a technician assigned to the work order” is taught by previously presented reference Travalini with additional support at ¶ [0045], [0100]. Additional details and citations are included in the 103 rejection section below.
Accordingly, new grounds for rejection 35 USC 103 are applied as necessitated by the amendments.
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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 1-2, 4-14, 16-21, 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-2, 4-12, 21 are directed to a system or machine which is a statutory category.
Claims 13-14, 16-18 are directed to a method or process which is a statutory category.
Claims 19-20, 23 are directed to a non-transitory computer-readable medium or article of manufacture, which is a statutory category.
Step 2A Prong One: The claims recite, describe, or set forth a judicial exception of an abstract idea (see MPEP 2106.04(a)). Specifically, the claims recite, describe or set forth concepts performed in the human mind (including observation, evaluation, judgement, or opinion) as well as mitigating risk and following rules or instructions, including: “monitor industrial asset data… wherein the industrial asset data comprises operational and status information”, “based on… a condition indicative of a current or predicted risk… formulate one or more first maintenance tasks predicted to mitigate the current or predicted risk”, “determine, based on a functional or locational interdependency between the first industrial asset and a second industrial asset of the industrial assets as
defined in a plant model, that the second industrial asset has a likelihood of experiencing a performance issue due to the current or predicted risk to the first industrial asset, wherein the functional interdependency defined in the plant model specifies that the second industrial asset receives parts or material from the first industrial asset”, “formulate… tasks predicted to mitigate the performance issue”, “generate a work order prescribing the one or more first maintenance tasks and the one or more second maintenance tasks”, and “…transmit a notification of the work order… associated with a technician assigned to the work order”. Monitoring industrial asset operational conditions and functioning, assessing probable risk thereof, formulating mitigation measures, and prescribing maintenance falls within concepts performed in the human mind (including observation, evaluation, judgement, or opinion) under the abstract grouping Mental Processes (MPEP 2106.04(a)(2) III); as well as within mitigating risk at is pertains to fundamental economic principles, and following rules or instructions as it pertains to managing personal behavior or relationships or interactions between people, each under the larger abstract grouping of Certain Methods of Organizing Human Activity (MPEP 2106.04(a)(2) II)1. Examiner also points to MPEP2106.04(a)(2) III C finding that computer aided processes such as: 1. Performing a mental process on a generic computer, 2. Performing a mental process in a computer environment, 3. Using a computer as a tool to perform a mental process can still be considered to recite a mental process.
Step 2A Prong Two: Independent claims 1, 13, 19 recite the following additional elements: “memory”, “processor”, “monitoring component”, “first industrial asset”, “second industrial asset”, “analysis component”, “work order generation component”, “generative AI model”, “non-transitory computer-readable medium”, “work order management system” and “user interface”. The functions of these additional elements include examples such as generating and monitoring industrial asset data, analyzing industrial asset data for conditions indicative of a current or predicted risk to the assets, formulating maintenance tasks based on the analysis to mitigate the risk, identifying interdependencies between assets, formulating maintenance tasks for other assets potentially affected, and transmitting work orders to specified technicians. The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of generating, collecting, evaluating, communicating, and presenting data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Therefore, these functions can be viewed as not meaningfully different than a business method or mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i). The claims are directed to an abstract idea and the judicial exception does not integrate the abstract idea into a practical application.
Step 2B: According to MPEP 2106.05(f)(1), considering whether the claim recites only the idea of a solution or outcome i.e., the claims fail to recite the technological details of how the actual technological solution to the actual technological problem is accomplished. The recitation of claim limitations that attempt to cover an entrepreneurial and thus abstract solution to an entrepreneurial problem with no technological details on how the technological result is accomplished and no description of the mechanism for accomplishing the result do not provide significantly more than the judicial exception.
Dependent claims 4, 16, 20 recite the additional element “a model trained with training data”. Dependent claims 10-11 recite the additional element “user interface”. Dependent claim 11 recites the additional elements “chat interface”. Dependent claim 12 recites the additional elements “user interface component” and “personal device”. The functions of these additional elements include examples such as determining condition satisfaction, generating responses to prompts, rendering work order content, rendering a chat interface, sending notifications, and receiving notifications. The additional elements are also recited at a high level of generality (i.e. as a generic computer performing functions of generating, evaluating, communicating, and presenting data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Additionally, the additional element “generative artificial intelligence (AI) model” and “a model trained with training data” language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “generative artificial intelligence (AI) model” and “a model trained with training data” alone is insufficient to show a practical application of the recited abstract idea.
Further, dependent claims 2, 5-9, 14, 17-18, 21, 23 merely incorporate the additional elements recited in claims 1, 13, 19 along with further narrowing of the abstract idea of claims 1, 13, 19 and their execution of the abstract idea. Specifically, the dependent claims narrow the “memory”, “processor”, “monitoring component”, “industrial assets”, “analysis component”, “work order generation component”, and “non-transitory computer-readable medium”, “work order management system” to capabilities such as select, generate, define, learn, monitor, designate, reference, and determine various forms of data such as technicians, tasks, work orders, designations, KPIs, levels of training, levels of experience, criteria, certifications, current or predicted risk conditions, trends, statuses, finances, etc. which, when evaluated per MPEP 2106.05(f)(2) represent mere invocation of computers to perform existing processes. Therefore, the additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application (Step 2A prong two) and for the same reasons they also fail to provide significantly more (Step 2B). Thus, claims 1-2, 4-14, 16-21, 23 are reasoned to be patent ineligible.
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Rejections Based on Prior Art
Examiner Note: Some rejections will contain bracketed comments preceded by an “EN” that will denote an examiner note. This will be placed to further explain a rejection.
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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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 1-2, 4-14, 16-21, 23 are rejected under 35 U.S.C. 103 as being unpatentable over:
Cella et al. US 20200133257 A1, hereinafter Cella, in view of
Travalini et al. US 20230376847 A1, hereinafter Travalini, and
Juaquet et al. US 2016/0282854 A1, hereinafter Jauquet. As per,
Regarding claim 1: Cella teaches:
A system, comprising:
a memory that stores executable components (Cella ¶ [0050]); and
a processor, operatively coupled to the memory, that executes the executable components (Cella ¶ [0050]), the executable components comprising:
a monitoring component configured to monitor industrial asset data generated by industrial assets in service within an industrial facility, wherein the industrial asset data comprises operational and status information for the industrial assets (Cella ¶ [0010]: In embodiments, an industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network);
an analysis component configured to, in response to a determination, based on analysis of the industrial asset data, that a subset of the industrial asset data satisfies a condition indicative of a current or predicted risk to a first industrial asset of the industrial assets (Cella ¶ [0010]: The system may further include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection [EN: risk condition] and classification algorithms thereto. ¶ [0011]: In embodiments, a method of predicting a service event from vibration data may include a set of operational steps including capturing vibration data from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine. The captured vibration data may be processed to determine at least one of a frequency, amplitude, and gravitational force of the captured vibration. Next, a segment of a multi-segment vibration frequency spectra that bounds the captured vibration may be determined, based on, for example the determined frequency. Thus, calculating a vibration severity unit [EN: risk condition] for the captured vibration may be based on the determined segment and at least one of the peak amplitudes and the gravitational force derived from the vibration data),
formulate one or more first maintenance tasks predicted to mitigate the current or predicted risk (Cella ¶ [0011]: Additionally, the method may include generating
a signal in a predictive maintenance circuit for executing a maintenance action on the portion of the industrial machine based on the severity unit),
determine, based on a functional interdependency between the first industrial asset, and a second industrial asset of the industrial assets as defined in a plant model, that the second industrial asset has a likelihood of experiencing a performance issue due to the current or predicted risk to the first industrial asset, [..], and formulate one or more second maintenance tasks predicted to mitigate the performance issue (See Cella Fig. 13 showing Plant 3200 model with Machine One 3202 and Machine Two 3204, Linking Tables, Relational Database 3500 [EN: functional or relational interdependency] and related text at ¶ [0377]. End-¶ [0022]: In embodiments, at least one of the hierarchical templates [EN: functional relationship] is associated with similar elements associated with at least the first machine and a second machine. In embodiments, at least one of the hierarchical templates is associated with at least the first machine being proximate in location to a second machine. ¶ [4284]: … FIG. 317 depicts a preventive maintenance schedule 30108 for a set of bearings in a group of industrial machines 30102 that use the bearings [EN: therefore interdependent]. As presented, preventive maintenance events A, B, C, and D for the bearings are scheduled to occur at intervals over time for each of the machines. Data collected and analyzed by a predictive maintenance system using the methods and systems for predictive maintenance of industrial machines as described herein may indicate that a different schedule of bearing maintenance is needed to prevent failures. In the example of FIG. 317, failures 30104 of machines 4 and 3 [EN: first assets] occur after preventive maintenance activity B. In response there to, and when taking into consideration other factors, such as operating cycle rate of the industrial machines, a new bearing maintenance schedule may be established for the machines. Since machines 1 and 2 [EN: second assets] have not yet failed, a predictive maintenance event may be setup for machine 1 30110 and for machine 2 30112. In embodiments, an operational rate of machine 2 may be substantive less than machine 1; therefore, while both machines use the bearings that have failed in machines 3 and 4, a predictive maintenance event schedule may be prepared individually for each machine. The predictive maintenance event for machine 1 30110 may be set to occur earlier than planned (event C) in the preventive maintenance schedule 30108. An additional maintenance event for the machine 2 30112 may be set to occur soon after the upcoming scheduled preventive maintenance event (again event C) based on, for example timing of failure of machines 3 and 4 after preventive maintenance event B. By setting a shorter interval between preventive maintenance event C and predictive maintenance event 2 (30112), a risk of a bearing-related failure may be reduced);
a work order generation component configured to, in response to the determination by the analysis component that the subset of the industrial data satisfies the condition, generate a work order prescribing the one or more first maintenance tasks for the first industrial asset and the one or more second maintenance tasks for the second industrial asset (Cella ¶ [0010]: The system may further include a computerized maintenance management system (CMMS) that produces at least one of [EN: work] orders and requests for service and parts responsive to receiving the industrial machine service recommendations. ¶ [4284]: Since machines 1 and 2] have not yet failed, a predictive maintenance event may be setup for machine 1 30110 and for machine 2 30112. In embodiments, an operational rate of machine 2 may be substantive less than machine 1; therefore, while both machines use the bearings that have failed in machines 3 and 4, a predictive maintenance event [EN: work order] schedule may be prepared individually for each machine); and
[..]
wherein the analysis component is configured to, as part of the analysis, formulate
and submit a prompt [..] that is used by the analysis component to formulate the one or more first maintenance tasks or the one or more second maintenance tasks (Cella ¶ [0010]: The system may further include a computerized maintenance management system (CMMS) that produces at least one of orders and requests for service and parts [EN: maintenance tasks] responsive to receiving the industrial machine service recommendations [EN: prompts]).
Although Cella teaches generating work orders / maintenance tasks as a result of predictive maintenance analysis, Cella does not specifically teach a UI component configured to transmit a work order notification to a technician’s device, using generative AI to gather maintenance details and generate enriched work orders and maintenance tasks, or interdependent assets exchanging parts or materials in the plant model.
However, Travalini in analogous art of automated industrial maintenance systems teaches or suggests:
a user interface component configured to transmit a notification of the work order
to a client device associated with a technician assigned to the work order (Travalini mid-¶ [0045]: In this regard, the chatbot [EN: user interface component] may be able to perform a work order intake using the information provided by the user during the conversation. Additionally or alternatively, the chatbot may generate an enriched work order, which may be provided to the technician (e.g. via transmission to a technician device associated with the technician, wherein the technician device comprises at least one of a mobile device, a cellular phone, a laptop computer, a tablet, or the like),
[..] formulate and submit a prompt, directed to a generative artificial intelligence (AI) model, designed to obtain a response from the generative AI model [..] to formulate the one or more first maintenance tasks or the one or more second maintenance tasks (Travalini ¶ [0042]: The present disclosure describes an AI-driven, natural language processing (NLP), onmichannel chatbot [EN: generative AI]. Mid-¶ [0043]: In response to the first communication, the user may respond with a description of the problem he/she is having. In some embodiments, the user response may comprise a maintenance repair request. ¶ [0045]: When the chatbot determines that there is sufficient information to begin diagnosing the problem… the chatbot may offer to schedule a technician visit for the user. In this regard, the chatbot may be able to perform a work order intake using the information provided by the user during the conversation. Additionally or alternatively, the chatbot may generate an enriched work order, which may be provided to the technician.… This may allow the technician to obtain the correct parts (e.g., corresponding SKUs), tools, and/or personnel to resolve the problem quicker and/or reduce the number of visits to the unit. ¶ [0161]: (CP15) The AI computing platform as described in (CP14), wherein the first regression model uses large language models (LLMs) to generate a vectorization structure, wherein the vectorization structure comprises a node graph).
Travalini and Cella are found as analogous art of automated industrial maintenance systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Cella’s industrial machine operating condition detection system and method to have included Travalini’s teachings around a UI component configured to transmit a work order notification to a technician’s device and using generative AI to gather maintenance details and generate enriched work orders and maintenance tasks. The benefit of these additional features would have allowed intake and work order generation with a higher degree of accuracy in completing advanced diagnostics before technicians/vendors are sent out (Travalini ¶ [0003]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Cella in view of Travalini (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of automated industrial maintenance systems. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Cella in view of Travalini above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Furthermore, Juaquet in analogous art of automated industrial maintenance systems teaches or suggests:
wherein the functional interdependency defined in the plant model specifies that the second industrial asset receives parts or material from the first industrial asset (Juaquet ¶ [0016]: …industrial automation systems are often used to perform a process in facilities, such as plants or factories. For example, in some embodiments, the process
may include a manufacturing process… a batch process, a chemical process, a material handling process…. ¶ [0018]: Since the industrial automation system may include various types of industrial automation equipment, operation of the industrial automation equipment may be interdependent with each other and with various types of control and monitoring equipment. ¶ [0066]: …the processor 36 may detect overlapping shut downs of industrial automation equipment 16 to determine a root cause for placing a particular piece of industrial automation equipment 16 offline… a matrix of the facility [EN: plant model] may be maintained that represents the connections between industrial automation equipment 16 in the facility and reasons for placing each industrial automation equipment 16 offline... the upstream or downstream equipment [EN: interdependent assets exchanging materials] may be experiencing one or more issues (e.g., excess temperature, vibrations, or the like) that triggers placing the upstream or downstream equipment offline. However, to properly place the upstream or downstream equipment offline, the particular piece of industrial automation equipment 16 may be placed offline in a defined order (e.g., first), which may suggest a correlation to fault).
Juaquet, Travalini and Cella are found as analogous art of automated industrial maintenance systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Cella’s industrial machine operating condition detection system and method to have included Juaquet’s teachings around interdependent assets exchanging parts or materials in the plant model. The benefit of these additional features would have improved service automation of interdependent industrial assets (Juaquet ¶ [0018]-[0019]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Cella in view of Travalini and Juaquet (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of automated industrial maintenance systems. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Cella in view of Travalini and Juaquet above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claim 13: Cella teaches:
A method, comprising:
monitoring, by a system comprising a processor, industrial asset data generated by industrial assets that are in service within an industrial facility, wherein the industrial asset data comprises operational and status information for the industrial assets (Cella ¶ [0010]: In embodiments, an industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network); and
in response to determining, based on analysis of the industrial asset data, that a subset of the industrial asset data satisfies a condition indicative of a current or predicted risk to a first industrial asset of the industrial assets (Cella ¶ [0010]: The system may further include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection [EN: risk condition] and classification algorithms thereto. ¶ [0011]: In embodiments, a method of predicting a service event from vibration data may include a set of operational steps including capturing vibration data from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine. The captured vibration data may be processed to determine at least one of a frequency, amplitude, and gravitational force of the captured vibration. Next, a segment of a multi-segment vibration frequency spectra that bounds the captured vibration may be determined, based on, for example the determined frequency. Thus, calculating a vibration severity unit [EN: risk condition] for the captured vibration may be based on the determined segment and at least one of the peak amplitudes and the gravitational force derived from the vibration data):
determining, by the system, one or more first maintenance tasks predicted to mitigate the current or predicted risk (Cella ¶ [0011]: Additionally, the method may include generating a signal in a predictive maintenance circuit for executing a maintenance action on the portion of the industrial machine based on the severity unit);
determining, by the system based on a functional interdependency between the first industrial asset, and a second industrial asset of the industrial assets as defined in a plant model, that the second industrial asset has a likelihood of experiencing a performance issue due to the current or predicted risk to the first industrial asset, [..] (See Cella Fig. 13 showing Plant 3200 model with Machine One 3202 and Machine Two 3204, Linking Tables, Relational Database 3500 [EN: functional or relational interdependency] and related text at ¶ [0377]. End-¶ [0022]: In embodiments, at least one of the hierarchical templates [EN: functional relationship] is associated with similar elements associated with at least the first machine and a second machine. In embodiments, at least one of the hierarchical templates is associated with at least the first machine being proximate in location to a second machine. ¶ [4284]: … FIG. 317 depicts a preventive maintenance schedule 30108 for a set of bearings in a group of industrial machines 30102 that use the bearings [EN: therefore interdependent]. As presented, preventive maintenance events A, B, C, and D for the bearings are scheduled to occur at intervals over time for each of the machines. Data collected and analyzed by a predictive maintenance system using the methods and systems for predictive maintenance of industrial machines as described herein may indicate that a different schedule of bearing maintenance is needed to prevent failures. In the example of FIG. 317, failures 30104 of machines 4 and 3 [EN: first assets] occur after preventive maintenance activity B. In response there to, and when taking into consideration other factors, such as operating cycle rate of the industrial machines, a new bearing maintenance schedule may be established for the machines. Since machines 1 and 2 [EN: second assets] have not yet failed, a predictive maintenance event may be setup for machine 1 30110 and for machine 2 30112. In embodiments, an operational rate of machine 2 may be substantive less than machine 1; therefore, while both machines use the bearings that have failed in machines 3 and 4, a predictive maintenance event schedule may be prepared individually for each machine. The predictive maintenance event for machine 1 30110 may be set to occur earlier than planned (event C) in the preventive maintenance schedule 30108. An additional maintenance event for the machine 2 30112 may be set to occur soon after the upcoming scheduled preventive maintenance event (again event C) based on, for example timing of failure of machines 3 and 4 after preventive maintenance event B. By setting a shorter interval between preventive maintenance event C and predictive maintenance event 2 (30112), a risk of a bearing-related failure may be reduced);
determining, by the system, one or more second maintenance tasks predicted to mitigate the performance issue; and generating, by the system, a work order prescribing the one or more first maintenance tasks for the first industrial asset and the one or more second maintenance tasks for the second industrial asset (Cella ¶ [0010]: The system may further include a computerized maintenance management system (CMMS) that produces at least one of [EN: work] orders and requests for service and parts responsive to receiving the industrial machine service recommendations. ¶ [4284]: Since machines 1 and 2] have not yet failed, a predictive maintenance event may be setup for machine 1 30110 and for machine 2 30112. In embodiments, an operational rate of machine 2 may be substantive less than machine 1; therefore, while both machines use the bearings that have failed in machines 3 and 4, a predictive maintenance event [EN: work order] schedule may be prepared individually for each machine); and
[..]
wherein the determining of the one or more first maintenance tasks or the determining of the one or more second maintenance tasks comprises formulating and submitting a prompt [..] that is used to formulate the one or more first maintenance tasks or the one or more second maintenance tasks (Cella ¶ [0010]: The system may further include a computerized maintenance management system (CMMS) that produces at least one of orders and requests for service and parts [EN: maintenance tasks] responsive to receiving the industrial machine service recommendations [EN: prompts]).
Although Cella teaches generating work orders / maintenance tasks as a result of predictive maintenance analysis, Cella does not specifically teach a UI component configured to transmit a work order notification to a technician’s device, using generative AI to gather maintenance details and generate enriched work orders and maintenance tasks, or interdependent assets exchanging parts or materials in the plant model. However, Travalini in analogous art of automated industrial maintenance systems teaches or suggests:
transmitting, by the system, a notification of the work order to a client device
associated with a technician assigned to the work order (Travalini mid-¶ [0045]: In this regard, the chatbot [EN: user interface component] may be able to perform a work order intake using the information provided by the user during the conversation. Additionally or alternatively, the chatbot may generate an enriched work order, which may be provided to the technician (e.g. via transmission to a technician device associated with the technician, wherein the technician device comprises at least one of a mobile device, a cellular phone, a laptop computer, a tablet, or the like);
[..] formulating and submitting a prompt, directed to a generative artificial intelligence (AI) model, designed to obtain a response from the generative AI model that is used to formulate the one or more first maintenance tasks or the one or more second maintenance tasks (Travalini ¶ [0042]: The present disclosure describes an AI-driven, natural language processing (NLP), onmichannel chatbot [EN: generative AI]. Mid-¶ [0043]: In response to the first communication, the user may respond with a description of the problem he/she is having. In some embodiments, the user response may comprise a maintenance repair request. ¶ [0045]: When the chatbot determines that there is sufficient information to begin diagnosing the problem… the chatbot may offer to schedule a technician visit for the user. In this regard, the chatbot may be able to perform a work order intake using the information provided by the user during the conversation. Additionally or alternatively, the chatbot may generate an enriched work order, which may be provided to the technician.… This may allow the technician to obtain the correct parts (e.g., corresponding SKUs), tools, and/or personnel to resolve the problem quicker and/or reduce the number of visits to the unit. ¶ [0161]: (CP15) The AI computing platform as described in (CP14), wherein the first regression model uses large language models (LLMs) to generate a vectorization structure, wherein the vectorization structure comprises a node graph).
Travalini and Cella are found as analogous art of automated industrial maintenance systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Cella’s industrial machine operating condition detection system and method to have included Travalini’s teachings around a UI component configured to transmit a work order notification to a technician’s device and using generative AI to gather maintenance details and generate enriched work orders and maintenance tasks. The benefit of these additional features would have allowed intake and work order generation with a higher degree of accuracy in completing advanced diagnostics before technicians/vendors are sent out (Travalini ¶ [0003]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Cella in view of Travalini (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of automated industrial maintenance systems. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Cella in view of Travalini above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Furthermore, Juaquet in analogous art of automated industrial maintenance systems teaches or suggests:
wherein the functional interdependency defined in the plant model specifies that the second industrial asset receives parts or material from the first industrial asset (Juaquet ¶ [0016]: …industrial automation systems are often used to perform a process in facilities, such as plants or factories. For example, in some embodiments, the process
may include a manufacturing process… a batch process, a chemical process, a material handling process…. ¶ [0018]: Since the industrial automation system may include various types of industrial automation equipment, operation of the industrial automation equipment may be interdependent with each other and with various types of control and monitoring equipment. ¶ [0066]: …the processor 36 may detect overlapping shut downs of industrial automation equipment 16 to determine a root cause for placing a particular piece of industrial automation equipment 16 offline… a matrix of the facility [EN: plant model] may be maintained that represents the connections between industrial automation equipment 16 in the facility and reasons for placing each industrial automation equipment 16 offline... the upstream or downstream equipment [EN: interdependent assets exchanging materials] may be experiencing one or more issues (e.g., excess temperature, vibrations, or the like) that triggers placing the upstream or downstream equipment offline. However, to properly place the upstream or downstream equipment offline, the particular piece of industrial automation equipment 16 may be placed offline in a defined order (e.g., first), which may suggest a correlation to fault).
Juaquet, Travalini and Cella are found as analogous art of automated industrial maintenance systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Cella’s industrial machine operating condition detection system and method to have included Juaquet’s teachings around interdependent assets exchanging parts or materials in the plant model. The benefit of these additional features would have improved service automation of interdependent industrial assets (Juaquet ¶ [0018]-[0019]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Cella in view of Travalini and Juaquet (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of automated industrial maintenance systems. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Cella in view of Travalini and Juaquet above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claim 19: Cella teaches:
A non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a work order management system comprising a processor to perform operations, the operations comprising:
monitoring industrial asset data generated by industrial assets that are in service within an industrial facility, wherein the industrial asset data comprises operational and status information for the industrial assets (Cella ¶ [0010]: In embodiments, an industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network);
in response to determining, based on analysis of the industrial asset data, that a subset of the industrial asset data satisfies a condition indicative of a current or predicted risk to an industrial asset of the industrial assets: (Cella ¶ [0010]: The system may further include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection [EN: risk condition] and classification algorithms thereto. ¶ [0011]: In embodiments, a method of predicting a service event from vibration data may include a set of operational steps including capturing vibration data from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine. The captured vibration data may be processed to determine at least one of a frequency, amplitude, and gravitational force of the captured vibration. Next, a segment of a multi-segment vibration frequency spectra that bounds the captured vibration may be determined, based on, for example the determined frequency. Thus, calculating a vibration severity unit [EN: risk condition] for the captured vibration may be based on the determined segment and at least one of the peak amplitudes and the gravitational force derived from the vibration data)
formulating one or more first maintenance tasks predicted to mitigate the current or predicted risk (Cella ¶ [0011]: Additionally, the method may include generating
a signal in a predictive maintenance circuit for executing a maintenance action on the portion of the industrial machine based on the severity unit);
identifying, based on a functional interdependency between the first industrial asset and a second industrial asset of the industrial assets as defined by a plant model, that the second industrial asset has a likelihood of experiencing a performance issue due to the current or predicted risk to the first industrial asset, [..]; formulating one or more second maintenance tasks predicted to mitigate the performance issue (See Cella Fig. 13 showing Plant 3200 model with Machine One 3202 and Machine Two 3204, Linking Tables, Relational Database 3500 [EN: functional or relational interdependency] and related text at ¶ [0377]. End-¶ [0022]: In embodiments, at least one of the hierarchical templates [EN: functional relationship] is associated with similar elements associated with at least the first machine and a second machine. In embodiments, at least one of the hierarchical templates is associated with at least the first machine being proximate in location to a second machine. ¶ [4284]: … FIG. 317 depicts a preventive maintenance schedule 30108 for a set of bearings in a group of industrial machines 30102 that use the bearings [EN: therefore interdependent]. As presented, preventive maintenance events A, B, C, and D for the bearings are scheduled to occur at intervals over time for each of the machines. Data collected and analyzed by a predictive maintenance system using the methods and systems for predictive maintenance of industrial machines as described herein may indicate that a different schedule of bearing maintenance is needed to prevent failures. In the example of FIG. 317, failures 30104 of machines 4 and 3 [EN: first assets] occur after preventive maintenance activity B. In response there to, and when taking into consideration other factors, such as operating cycle rate of the industrial machines, a new bearing maintenance schedule may be established for the machines. Since machines 1 and 2 [EN: second assets] have not yet failed, a predictive maintenance event may be setup for machine 1 30110 and for machine 2 30112. In embodiments, an operational rate of machine 2 may be substantive less than machine 1; therefore, while both machines use the bearings that have failed in machines 3 and 4, a predictive maintenance event schedule may be prepared individually for each machine. The predictive maintenance event for machine 1 30110 may be set to occur earlier than planned (event C) in the preventive maintenance schedule 30108. An additional maintenance event for the machine 2 30112 may be set to occur soon after the upcoming scheduled preventive maintenance event (again event C) based on, for example timing of failure of machines 3 and 4 after preventive maintenance event B. By setting a shorter interval between preventive maintenance event C and predictive maintenance event 2 (30112), a risk of a bearing-related failure may be reduced); and
generating a work order prescribing the one or more first maintenance tasks and the one or more second maintenance tasks (Cella ¶ [0010]: The system may further include a computerized maintenance management system (CMMS) that produces at least one of [EN: work] orders and requests for service and parts responsive to receiving the industrial machine service recommendations. ¶ [4284]: Since machines 1 and 2] have not yet failed, a predictive maintenance event may be setup for machine 1 30110 and for machine 2 30112. In embodiments, an operational rate of machine 2 may be substantive less than machine 1; therefore, while both machines use the bearings that have failed in machines 3 and 4, a predictive maintenance event [EN: work order] schedule may be prepared individually for each machine); and
[..]
wherein the formulating of the one or more first maintenance tasks or the formulating of the one or more second maintenance tasks comprises formulating and submitting a prompt [..] that is used to formulate the one or more first maintenance tasks or the one or more second maintenance tasks (Cella ¶ [0010]: The system may further include a computerized maintenance management system (CMMS) that produces at least one of orders and requests for service and parts [EN: maintenance tasks] responsive to receiving the industrial machine service recommendations [EN: prompts].
Although Cella teaches generating work orders / maintenance tasks as a result of predictive maintenance analysis, Cella does not specifically teach a UI component configured to transmit a work order notification to a technician’s device, using generative AI to gather maintenance details and generate enriched work orders and maintenance tasks, or interdependent assets exchanging parts or materials in the plant model. However, Travalini in analogous art of automated industrial maintenance systems teaches or suggests:
transmitting a notification of the work order to a client device associated with a technician assigned to the work order (Travalini mid-¶ [0045]: In this regard, the chatbot [EN: user interface component] may be able to perform a work order intake using the information provided by the user during the conversation. Additionally or alternatively, the chatbot may generate an enriched work order, which may be provided to the technician (e.g. via transmission to a technician device associated with the technician, wherein the technician device comprises at least one of a mobile device, a cellular phone, a laptop computer, a tablet, or the like),
[..] formulating and submitting a prompt, directed to a generative artificial intelligence (AI) model, designed to obtain a response from the generative AI model that is used to formulate the one or more first maintenance tasks or the one or more second maintenance tasks (Travalini ¶ [0042]: The present disclosure describes an AI-driven, natural language processing (NLP), omnichannel chatbot [EN: generative AI]. Mid-¶ [0043]: In response to the first communication, the user may respond with a description of the problem he/she is having. In some embodiments, the user response may comprise a maintenance repair request. ¶ [0045]: When the chatbot determines that there is sufficient information to begin diagnosing the problem… the chatbot may offer to schedule a technician visit for the user. In this regard, the chatbot may be able to perform a work order intake using the information provided by the user during the conversation. Additionally or alternatively, the chatbot may generate an enriched work order, which may be provided to the technician.… This may allow the technician to obtain the correct parts (e.g., corresponding SKUs), tools, and/or personnel to resolve the problem quicker and/or reduce the number of visits to the unit. ¶ [0161]: (CP15) The AI computing platform as described in (CP14), wherein the first regression model uses large language models (LLMs) to generate a vectorization structure, wherein the vectorization structure comprises a node graph).
Travalini and Cella are found as analogous art of automated industrial maintenance systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Cella’s industrial machine operating condition detection system and method to have included Travalini’s teachings around a UI component configured to transmit a work order notification to a technician’s device and using generative AI to gather maintenance details and generate enriched work orders and maintenance tasks. The benefit of these additional features would have allowed intake and work order generation with a higher degree of accuracy in completing advanced diagnostics before technicians/vendors are sent out (Travalini ¶ [0003]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Cella in view of Travalini (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of automated industrial maintenance systems. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Cella in view of Travalini above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Furthermore, Juaquet in analogous art of automated industrial maintenance systems teaches or suggests:
wherein the functional interdependency defined in the plant model specifies that the second industrial asset receives parts or material from the first industrial asset (Juaquet ¶ [0016]: …industrial automation systems are often used to perform a process in facilities, such as plants or factories. For example, in some embodiments, the process
may include a manufacturing process… a batch process, a chemical process, a material handling process…. ¶ [0018]: Since the industrial automation system may include various types of industrial automation equipment, operation of the industrial automation equipment may be interdependent with each other and with various types of control and monitoring equipment. ¶ [0066]: …the processor 36 may detect overlapping shut downs of industrial automation equipment 16 to determine a root cause for placing a particular piece of industrial automation equipment 16 offline… a matrix of the facility [EN: plant model] may be maintained that represents the connections between industrial automation equipment 16 in the facility and reasons for placing each industrial automation equipment 16 offline... the upstream or downstream equipment [EN: interdependent assets exchanging materials] may be experiencing one or more issues (e.g., excess temperature, vibrations, or the like) that triggers placing the upstream or downstream equipment offline. However, to properly place the upstream or downstream equipment offline, the particular piece of industrial automation equipment 16 may be placed offline in a defined order (e.g., first), which may suggest a correlation to fault).
Juaquet, Travalini and Cella are found as analogous art of automated industrial maintenance systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Cella’s industrial machine operating condition detection system and method to have included Juaquet’s teachings around interdependent assets exchanging parts or materials in the plant model. The benefit of these additional features would have improved service automation of interdependent industrial assets (Juaquet ¶ [0018]-[0019]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Cella in view of Travalini and Juaquet (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of automated industrial maintenance systems. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Cella in view of Travalini and Juaquet above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claims 2, 14: Cella / Travalini / Juaquet teaches all the limitations of claims 1, 13 above.
Although Cella teaches generating work orders / maintenance tasks as a result of predictive maintenance analysis and a first series of prompt / response / maintenance task generation, Cella does not specifically teach using generative AI to prompt for a second, more detailed response satisfying a condition.
However, Travalini in analogous art of automated industrial maintenance systems teaches or suggests:
the prompt is a first prompt, the response is a first response, and the analysis component is configured to, as part of the analysis, generate a prompt, directed to the generative AI model, designed to obtain a second response from the generative AI model that is used by the analysis component to determine whether the subset of the industrial asset data satisfies the condition (Travalini mid-[0043]: … the chatbot may make a determination whether the user's communication contains a threshold level of information
to begin diagnosing the problem. ¶ [0044]: For example, if the chatbot determines that there is not enough information to begin diagnosing the problem, the chatbot may determine what information is missing or cannot be inferred and select an appropriate response to the user. As shown in FIG. 2A, the chatbot recognizes that the user has not identify a symptom (e.g., problem) with the toilet. The chatbot may select a response designed to elicit additional [EN: second] information from the user. Accordingly, the chat bot may respond to the user with: "I understand you're having trouble with your toilet. Can you tell me, is it leaking, running, clogged, damaged, or something else?" The user may then provide additional information that will allow the chatbot to identify the problem and/or offer solutions to the problem).
Travalini and Cella are found as analogous art of automated industrial maintenance systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Cella’s industrial machine operating condition detection system and method to have included Travalini’s teachings around using generative AI to prompt for a second, more detailed response satisfying a condition. The benefit of these additional features would have allowed intake and work order generation with a higher degree of accuracy in completing advanced diagnostics before technicians/vendors are sent out (Travalini ¶ [0003]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Cella in view of Travalini (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of automated industrial maintenance systems. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Cella in view of Travalini above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claims 4, 16, 20: Cella / Travalini / Juaquet teaches all the limitations of claims 1, 13, 19 above.
Cella further teaches:
wherein the analysis component is further configured to determine (claim 4) / further comprising determining (claims 16, 20) whether the subset of the industrial asset data satisfies the condition based on a model trained with training data comprising at least one of technical specification data for the industrial assets, information from past work orders that were generated for the industrial assets, historical operational or status data for the industrial assets, information about technicians employed by the plant facility, or financial data for the plant facility (Cella ¶ [1007]: In many embodiments, an expert system or neural network may be trained, such as by a human operator or supervisor, or based on a data set, model, or the like. Training may include presenting the neural network with one or more training data sets that represent values, such as sensor data, event data, parameter data, and other types of data (including the many types described throughout this disclosure), as well as one or more indicators of an outcome, such as an outcome of a process, an outcome of a calculation, an outcome of an event, an outcome of an activity, or the like. Mid-¶ [4353]: Additionally, information that may be available about machines, sub-assemblies, individual components, accessories, rotating integrated parts, and the like may include design parameters, test specifications, operating specifications, revisions to the products, and the like…. Machine information available may cover aspects of the equipment that relate to one or more rotating components, such as a count of gear teeth of one or more gears ( e.g., a gear box such as a helical gearbox, worm reduction gearbox, planetary gearbox and the like, a power transfer gear set, and the like), a count of motor rotor bars (e.g., rotor bars in a squirrel-cage rotor and winding, such as a synchronous motor, and the like), RPM rate for rotating components and the like).
Regarding claim 5: Cella / Travalini / Juaquet teaches all the limitations of claim 1 above.
Cella further teaches:
wherein the analysis component is further configured to formulate the one or more first maintenance tasks or the one or more second maintenance tasks based on content of the plant model, and the plant model defines the industrial assets in service within the plant facility, functional relationships between the industrial assets, and geographical relationships between the industrial assets (See Cella Fig. 13 showing Plant 3200 model with Machine One 3202 and Machine Two 3204, Linking Tables, Relational Database 3500 [EN: functional or relational interdependency] and related text at ¶ [0377]. Cella end-¶ [0022]: In embodiments, at least one of the hierarchical templates [EN: functional relationship] is associated with similar elements associated with at least the first machine and a second machine. In embodiments, at least one of the hierarchical templates is associated with at least the first machine being proximate in location [EN: geographic relationship] to a second machine).
Regarding claims 6, 17: Cella / Travalini / Juaquet teaches all the limitations of claims 1, 13 above.
Cella further teaches:
wherein the analysis component is further configured to select (claim 6) / further comprising selecting, by the system (claim 17) the technician, from a set of technicians registered as being employed by the plant facility, to perform the one or more maintenance tasks (Cella ¶ [4217]: In embodiments, methods and systems for finding a set of workers having relevant know-how and expertise about maintenance, service and repair of a specific machine may employ machine learning algorithms with worker selection algorithms to ensure timely, quality workers are selected and deployed for industrial machine servicing, such as for predictive maintenance and the like described herein), and
the work order generation component is configured to generate the work order to define a designation of (claim 6) / wherein the generating of the work order comprises designating (claim 17) the one or more first maintenance tasks or the one or more second maintenance tasks to the technician (Cella mid-¶ [1162]: The state may be one of a normal operation, a maintenance required, a failure, or an imminent failure. The monitoring system 10800 may trigger an alert, shut down equipment/ component/line, initiate maintenance/lubrication/alignment based on the predicted outcome or state, deploy a field technician based on the predicted outcome or state, recommend a vibration absorption/dampening device based on the predicted outcome or state, modify a process to utilize backup equipment/component based on the predicted outcome or state, and the like.).
Regarding claims 7, 18: Cella / Travalini / Juaquet teaches all the limitations of claims 6, 17 above.
Cella further teaches:
wherein the analysis component is configured to (claim 7) / wherein (claim 18) the selecting comprises:
reference technician key performance indicator (KPI) data that defines, for respective technicians of the set of technicians, the technicians’ levels of training or experience in different types of maintenance activities (Cella mid-[4217]: The worker finding facility 28702 may further access, such as by retrieving information about workers from a worker database 28722, information that facilitates characterizing one or more workers, including procedures for which the worker has experience, training, certification and the like. Mid-¶ [4292]: The service technician may be rated [EN: KPI] along a range of criteria, including without limitation, ease of scheduling, degree of expertise/training with a specific machine and/or service activity…. ¶ [4296]: In embodiments, a procedure to perform a maintenance task may be clear to a service technician who is familiar with the particular machine, yet it may not be sufficiently clear to service personnel with less experience. Therefore, information about the service technician completing the procedure rating task may be applied to better weight the ratings. Additionally, a service procedure may be rated on an experience scale that may facilitate identifying when a less experienced person could be used to perform a service task and when an experienced provider is preferred), and
select the technician based on a determination that the technician KPI data indicates that the technician has a level of training or experience in performing the one or more first maintenance tasks or the one or more second maintenance tasks that satisfies a defined criterion (Cella end-¶ [4217]: One or more workers who have experience and the like with the procedure may be selected for further refinement, which may include matching a worker location to a machine location, a worker availability and/or schedule to a machine service schedule, worker rates/fees to machine owner service budgets and the like. One or more workers on a resulting list of refined workers may be contacted about a service to be performed on the machine. Mid-¶ [4296]: …Such information may be useful to an industrial machine predictive maintenance system for facilitating selection of a service entity suitable for performing a required service task and the like. In embodiments, an industrial machine predictive maintenance system may gather information that may be descriptive of various aspects of a service/maintenance procedure, such as the experience scale rating [EN: defined criterion] when facilitating access to vetted service personnel).
Regarding claim 8: Cella / Travalini / Juaquet teaches all the limitations of claim 7 above.
Cella further teaches:
wherein the analysis component is further configured to generate the technician KPI data based on at least one of information regarding the respective technicians’ level of training on types of maintenance activities or industrial assets, information regarding the respective technicians’ certifications, or analysis of closed work orders for maintenance activities performed by the respective technicians (Cella mid-[4217]: The worker finding facility 28702 may further access, such as by retrieving information about workers from a worker database 28722, information that facilitates characterizing one or more workers, including procedures for which the worker has experience, training, certification and the like. One or more workers who have experience and the like with the procedure may be selected for further refinement, which may include matching a worker location to a machine location, a worker availability and/or schedule to a machine service schedule, worker rates/fees to machine owner service budgets and the like. One or more workers on a resulting list of refined workers may be contacted about a service to be performed on the machine).
Regarding claim 9: Cella / Travalini / Juaquet teaches all the limitations of claim 1 above.
Cella further teaches:
wherein the analysis component is configured to learn the condition indicative of the current or predicted risk based on analysis of trends in the industrial asset data over time (Cella end-[0597]: An example of data that may indicate a need for some action may include changes that may be detectable through trends present in the data from the set of sensors. Another example is trends of analysis values derived from the set of sensors. ¶ [0598]: In embodiments, the set of collection band parameters may include values received from a sensor that is configured to sense a condition of the industrial machine (e.g., bearing vibration). However, a set of collection band parameters may instead be a trend of data received from the sensor (e.g., a trend of bearing vibration across a plurality of vibration measurements by a bearing vibration sensor). In embodiments, a set of collection band parameters may be a composite of data and/or trends of data from a plurality of sensors (e.g., a trend of data from on-axis and off-axis vibration sensors). In embodiments, when a data value derived from one or more sensors as described herein is sufficiently close to a value of data in the set of collection band parameters, the data collection activity from the set of sensors may be triggered).
Regarding claim 10: Cella / Travalini / Juaquet teaches all the limitations of claim 1 above.
Cella further teaches:
wherein the user interface is further configured to render content of the work order generated by the work order generation component, and the content comprises at least one of a description of the current or predicted risk, descriptions of the one or more first maintenance tasks and the one or more second maintenance tasks, an identity of the technician assigned to the work order, a status of the work order, a priority of the work order, or an identity of the industrial asset (Cella mid-[4292]: In embodiments, the service technician may use a user interface of a computing device, such as a tablet, portable phone, industrial portable computer and the like via which the technician accesses the service procedure [EN: work order content and maintenance tasks]).
Regarding claim 11: Cella / Travalini / Juaquet teaches all the limitations of claim 10 above.
Cella further teaches:
wherein the user interface is further configured to render a chat interface configured to receive a natural language request or query directed to the work order, wherein the natural language request or query comprises at least one of a question about the work order, a request to append or edit the work order, a request to change an assignment of technicians to the work order, or a request to change a due date for the work order (Cella mid-[0410]: The consumer interface may include an interface to a data market search system 4118, which may include features that enable a user to indicate what types of data a user wishes to obtain [EN: request], such as by entering keywords in a natural language search interface [EN: chat interface] that characterize data or metadata. The search interface can use various search and filtering techniques, including keyword matching…. End-¶ [0126]: In embodiments, a convolutional neural network may be used for processing inputs, such as for natural language processing of instructions provided by one or more parties involved in a workflow [EN: work order] in an environment. Mid-¶ [4210]: …a part inquiry may be placed with one or more parts providers in anticipation of the service being conducted by the qualified service indication from the preventive maintenance subsystem 28616 with one or more service recommendations), and
the analysis component is configured to generate a natural language response to the request or query or to implement the request or query using the generative AI model (Cella mid-[0397]: Genetic algorithms and machine learning techniques and systems may be used in computational intelligence systems, computer vision, Natural Language Processing (NLP), recommender systems, reinforcement learning, building graphical models, and the like. By way of this example, the machine learning systems may be used to perform intelligent computing based control and be responsive to tasks in a wide variety of systems (such as interactive websites and portals)….).
Regarding claim 12: Cella / Travalini / Juaquet teaches all the limitations of claim 1 above.
Cella further teaches:
wherein the monitoring component is further configured to monitor a location of a user having a technician role, and the executable components further comprise a user interface component configured to, in response to a determination by the monitoring component that the location is within a defined distance from an industrial asset, of the industrial assets, for which an open work order is pending, send a notification of the open work order to a personal device associated with the user (Cella end-¶ [4217]: One or more workers who have experience and the like with the procedure may be selected for further refinement, which may include matching a worker location to a machine location, a worker availability and/or schedule to a machine service schedule, worker rates/fees to machine owner service budgets and the like. One or more workers on a resulting list of refined workers may be contacted [EN: notified] about a service to be performed on the machine. Mid ¶ [4292]: In embodiments, the service technician may use a user interface of a computing device, such as a tablet, portable phone, industrial portable computer and the like via which the technician accesses the service procedure. End-¶ [1143]: Based on this predicted state of required maintenance [EN: pending], the expert system may deploy a field technician to perform the maintenance).
Regarding claim 21: Cella / Travalini / Juaquet teaches all the limitations of claim 1 above.
Cella further teaches:
wherein the functional or locational interdependency defined by the plant model is an indication that the second industrial asset operates downstream from the first industrial asset (Cella mid-¶ [1105]: For example, the model may be an operating model for an industrial environment, machine, or workflow. In another example, the model may be for anticipating states, for predicting fault and optimizing maintenance. ¶ [1106]: The iteration of the expert system may result in any number of downstream actions based on analysis of data from the smart band…. a specified/threshold output production rate… an avoidance of a fault condition, an avoidance of a dangerous condition or catastrophic failure, and the like…. the relationship of the equipment to other features of the environment (including any other systems that provide input to or take input from the equipment)…. The expert system may be seeded with a model for operation of the pipeline in a manner that results in a specified profit goal, such as indicating a given flow rate of material through the pipeline based on the current market sale price for the material and the cost of getting the material into the pipeline),
Regarding claim 22: Cella / Travalini / Juaquet teaches all the limitations of claim 1 above.
Cella further teaches:
wherein the functional or locational interdependency defined by the plant model is an indication that the first industrial asset provides materials or parts to the second industrial asset (Cella mid-¶ [1105]: For example, the model may be an operating model for an industrial environment, machine, or workflow. In another example, the model may be for anticipating states, for predicting fault and optimizing maintenance. ¶ [1106]: The iteration of the expert system may result in any number of downstream actions based on analysis of data from the smart band…. a specified/threshold output production rate… an avoidance of a fault condition, an avoidance of a dangerous condition or catastrophic failure, and the like…. the relationship of the equipment to other features of the environment (including any other systems that provide input to or take input from the equipment)…. The expert system may be seeded with a model for operation of the pipeline in a manner that results in a specified profit goal, such as indicating a given flow rate of material through the pipeline based on the current market sale price for the material and the cost of getting the material into the pipeline).
Regarding claim 23: Cella / Travalini / Juaquet teaches all the limitations of claim 19 above.
Although Cella teaches deploying plant technicians to service industrial assets, Cella does not specifically teach selecting a technician from an available list and generating work orders with specified technicians.
However, Travalini in analogous art of automated industrial maintenance systems teaches or suggests:
wherein the operations further comprise selecting the technician, from a set of technicians registered as being employed by the plant facility, to perform the one or more first maintenance tasks or the one or more second maintenance tasks (Travalini end-¶ [0064]: The technician skill set module may maintain a table (or database) of available the skill set associated with each available technician (e.g., a generalist, a fixture specialist, a water heater specialist, etc.). The technician rating module may store ratings and/or reviews associated with each available technician based on previous jobs completed by each technician. The pricing module, the availability module, the skill set module, and the rating module may be used by the routing and assignment engine to assign a technician to a worker order. End-¶ [0100]: For example, the home profile data store may indicate that the water heater associated with the unit is situated in a location that is difficult to reach and, as such, requires a specific set of tools. In such instances, the intelligent routing and assignment engine may determine that a technician with extensive water heater experience and with the requisite tools should be selected to complete the maintenance repair job),
wherein the generating of the work order comprises designating the one or more first maintenance tasks or the one or more second maintenance tasks to the technician (Travalini end-¶ [0064]: As illustrated in FIG. SA by way of introduction, once user input is received via the input component 901 and a work order (or enriched work order) is generated by the processing component 902 of the computing system employing the artificial intelligence-driven chatbot, the work order (or enriched work order) is transmitted to the fulfillment component 903 of the computing system. FIG. 9 illustrates the engines and components that the fulfillment component uses to identify a technician (or plurality of technicians) who is capable of performing the maintenance repair job and to assign the maintenance repair job to the technician. End-¶ [0100]: For example, the home profile data store may indicate that the water heater associated with the unit is situated in a location that is difficult to reach and, as such, requires a specific set of tools. In such instances, the intelligent routing and assignment engine may determine that a technician with extensive water heater experience and with the requisite tools should be selected to complete the maintenance repair job).
Travalini and Cella are found as analogous art of automated industrial maintenance systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Cella’s industrial machine operating condition detection system and method to have included Travalini’s teachings around selecting a technician from an available list and generating work orders with specified technicians. The benefit of these additional features would have allowed intake and work order generation with a higher degree of accuracy in completing advanced diagnostics before technicians/vendors are sent out (Travalini ¶ [0003]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Cella in view of Travalini (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of automated industrial maintenance systems. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Cella in view of Travalini above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
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Conclusion
The following art is made of record and considered pertinent to Applicant’s disclosure:
Jadhav; Sneha R. et al. US 20230090297 A1, Industrial machine monitoring path for computerized maintenance management system.
Stenning; Braden et al. US 20190108747 A1, Augmented industrial management.
Dillon; Steven R. et al. US 20080288321 A1, Automatic maintenance estimation in a plant environment.
Vedantam; Somasekhar et al. US 20220230125 A1, System and method for optimizing management of machine asset maintenance and production operations.
Honey; Kaitlyn Jo et al. US 20240378534 A1, Event learning and operational risk assessment for asset performance management system.
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Smiley et al. US 20140365264 A1, Criticality profile for industrial asset.
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/REED M. BOND/Examiner, Art Unit 3624
September 16, 2026
/HAMZEH OBAID/Primary Examiner, Art Unit 3624 September 21, 2026
1 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
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