DETAILED ACTION
This Non-Final Office Action is in response to Applicant's amendments and arguments and request for continued examination filed on May 21, 2026. Applicant has amended claims 1, 11 and 19. Currently, claims 1-20 are pending. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/21/26 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendments
The 35 U.S.C. 101 rejections of claims 1-20 are maintained in light of applicant’s amendments to claims 1, 11 and 19.
The 35 U.S.C. 103 rejections of claims 1-20 are maintained in light of applicant’s amendments to claims 1, 11 and 19.
Response to Arguments
Applicant’s arguments submitted on 5/7/26 have been considered but are not persuasive. Applicant argues on p. 9 of the remarks that the 101 rejection is improper. Examiner disagrees. Applicant argues that the formulating and submitting of an AI prompt cannot be performed in the human mind. Examiner notes this is considered an additional element to the abstract idea. Applicant argues on p. 10 of the remarks that the this is a key technical element. Examiner disagrees and notes this is a tool for implementing the abstract idea itself. Applicant makes comparisons to example 42. Examiner notes the example 42 has different additional elements and limitations then applicant’s claims. Therefore, the 101 rejections are maintained. Applicant argues on p. 12 of the remarks that the 103 rejection is improper. Examiner disagrees. Applicant argues on p. 15-16 of the remarks that the work order analysis is only performed on closed work orders, not open work orders. Esmalifalak shows at para [0050] that "The system 202 stores discrete sets of submitted work order data 304 as work orders 222 (e.g., on memory 224). Each work order 222 is classified as either an open work order representing a pending maintenance job to be performed on one or more industrial assets (e.g., machines, production lines, industrial devices, etc.) or a closed work order representing a maintenance job that has been completed." It would be obvious to one or ordinary skill in the art that the open work orders data can be analyzed in a similar way to closed work order data especially because Esmalifalak shows analysis of both types of work orders. Examiner notes this is an obviousness rejection. Applicant argues on p. 16-17 that Esmalifalak shows different types of analysis on closed and open work orders and quotes para [0092]-[0093]. However, para [0092] explicitly shows applying machine learning analysis to the new work order within the context of similar past work orders. The past closed work orders are thus used to generate the results and in fact even part of the analysis of the open work order itself and not only new work order data. Applicant further argues on p. 18 of the remarks that Esmalifalak does not show the aggregated optimization analysis. Examiner disagrees and notes that Esmalifalak explicitly shows aggregating work order data at [0034] and [0045] and para [00039] shows anomalies and changes in configuration which can be considered a change in the operating context and para [0053] shows the work order data can include an analysis from different facilities. Therefore, it would be obvious to one of ordinary skill in the art that such an aggregated optimization analysis would be obvious. Therefore, the 103 rejections are maintained.
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 clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (system, method and non-transitory computer readable medium). Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 11 and 19 recites the abstract idea of maintaining open work orders for performing maintenance tasks on industrial assets within an industrial facility and monitoring industrial asset data generated by the industrial assets, wherein the industrial asset data comprises operational and status information for the industrial assets and in response to detecting, based on analysis of the industrial asset data, a change to an operating context of an industrial asset of the industrial assets and performing an aggregated optimization analysis on the open work orders, information about the change in the operating context, and location information indicating locations of the industrial assets within the industrial facility and formulating, based on a result of the aggregated optimization analysis, a modification to an open work order, of the open work orders, that causes the open work orders to implement a holistic maintenance system that satisfies a defined maintenance optimization criteria based on the change to the operating context wherein the defined maintenance optimization criteria comprise at least a minimization of a total time to execute the open work orders and implementing, the modification to the open work order to yield a modified work order and transmitting a notification of the modified work order, wherein the modification is at least one of a change to a priority level of the open work order, a change to a completion date of the open work order, a change to a scheduled time to perform maintenance tasks defined by the open work order, a change to an assignment of technicians to the open work order, or a change to a resource allocated for execution of the open work order. The claims are directed to a type of modification of work orders based on analysis of data. Under prong 1 of Step 2A, these claims are considered abstract because the claims are certain method of organizing human activity including commercial interactions including business relations. Applicant’s claims are organizing human activity including commercial interactions by showing work orders (which can be considered both business and human activity) and those work orders are organized by modifications based on data analysis. Under prong 2 of Step 2A, the judicial exception is not integrated into a practical application because the claims (the judicial exception and any additional elements individually or in combination such as a system comprising a system a memory that stores executable components and a processor, a processor, operatively coupled to the memory, that executes the executable components, a non-transitory computer-readable medium having stored thereon instructions that, in response to execution to perform steps and generating and submitting, based on a change to the operating context, a prompt to a generative artificial intelligence model designed to obtain a response from the generative AI model used to formulate the modification to the open work order and transmitting, by the system, a notification of the modified work order to a client device associated with a technician assigned to the modified work order) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception. These limitations at best are merely implementing an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as a system comprising a system a memory that stores executable components and a processor, a processor, operatively coupled to the memory, that executes the executable components, a non-transitory computer-readable medium having stored thereon instructions that, in response to execution to perform steps and generating and submitting, based on a change to the operating context, a prompt to a generative artificial intelligence model designed to obtain a response from the generative AI model used to formulate the modification to the open work order and transmitting, by the system, a notification of the modified work order to a client device associated with a technician assigned to the modified work order (as evidenced by para [0024], [0030]-[0032], [0040]-[0042], [0057], [00108]-[00131] of applicant’s own specification) are well understood, routine and conventional in the field. Dependent claims 2-6, 9-10, 12-16, 20 also do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements either individually or in combination are merely an extension of the abstract idea itself by further showing wherein the change to the operating context is at least one of a performance issue experienced by the industrial asset, a change in an operating mode of the industrial asset, or a change in a production recipe being executed by the industrial asset and wherein the defined maintenance optimization criterion is at least one of maximization of overall maintenance efficiency, minimization of labor or material costs associated with execution of the open work orders, minimization of a number of technicians or autonomous vehicles required to execute the open work orders, or minimization of a number of steps taken by the technicians to complete the open work orders and the monitoring component is further configured to monitor schedule data that records current work schedules for technicians employed by the plant facility, and the analysis component is further configured to, in response to detecting, based on analysis of the schedule data, a change in a scheduled availability of one or more of the technicians, determine another modification to an open work order, of the open work orders, that causes the holistic maintenance strategy to satisfy the defined maintenance optimization criteria based on the change in the scheduled availability and the monitoring component is further configured to monitor inventory data that records current inventory levels of parts or materials used to perform maintenance tasks, and the analysis component is further configured to, in response to detecting, based on analysis of the inventory data, a change in the current inventory levels, determine another modification to an open work order, of the open work orders, that causes the holistic maintenance strategy to satisfy the defined maintenance optimization criteria based on the change in the current inventory levels and a subset of the industrial asset data identifies a new industrial asset that is not registered with the system, and the analysis component is further configured to register the new industrial asset in the system in response to receipt of the subset of the industrial asset data and wherein the analysis component is configured to determine the modification to the open work order 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 and the 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 an industrial asset of the industrial assets, formulate one or more maintenance tasks predicted to mitigate the current or predicted risk, and the work order generation component is configured to generate a work order prescribing the one or more maintenance tasks. Dependent claims 7-8, 17-18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as wherein the subset of the industrial asset data that identifies the new industrial asset is collected by and received from a mobile industrial robot and wherein the analysis component is configured to determine the modification to the open work order based on part on a response prompted from a generative artificial intelligence (AI) model (as evidenced by para [0024], [0030]-[0032], [0040]-[0042], [0057], [00108]-[00131] of applicant’s own specification) are well understood, routine and conventional in the field.
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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1-3, 5-6, 8-13, 15-16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dillon et al. (US 2020/0401124 A1) (hereinafter Dillon) in view of Esmalifalak et al. (US 20230027594 A1) (hereinafter Esmalifalak) in view of Travalini et al. (US 20230259821 A1) (hereinafter Travalini).
Claims 1, 11 and 19:
Dillon, as shown, discloses the following limitations:
A system (and corresponding method and non-transitory computer readable medium – see para [0024] and Fig 1, showing equivalent computing functionality and structure), comprising: a memory that stores executable components (see para [0024] and Fig 1, showing equivalent computing functionality and structure) and work order data defining open work orders for performing maintenance tasks on industrial assets within an industrial facility (see para [0002], " The present invention relates generally to process plants and, more particularly, to a distributed maintenance and service system that provides enhanced maintenance planning and scheduling features in a process plant." and see para [0015], " A process plant maintenance system includes a work order initiation and tracking system (e.g., implemented as an application) coupled between various plant asset diagnostic systems (which may be remote from the plant or within the plant) and a work or business management system within the plant. The work order initiation and tracking system may include a user interface that enables a user, such as maintenance personnel, to review diagnostic data, recommendations, and other information from one or more of the remote or in-plant diagnostic systems regarding potential or recommended changes to plant assets, and to initiate the generation of one or more work orders to implement recommendations or corrections based on information from the diagnostic systems. The work order initiation and tracking system may assist the user in generating work orders by tracking and viewing the created work orders associated with various assets in the plant based on previous diagnostic information or recommendations, by enabling the user to copy data or information from the diagnostic applications into the work order generation system (thereby reducing work and increasing accuracy in the generation of work orders), by translating or associating data, such as asset name and tag data, from the diagnostic systems to the work order generation system to assure that the correct assets are identified in the work orders, and by tracking the progress of the work orders sent to and generated by the business and work management system to provide the user with information pertaining to previously ordered work within the plant. This last benefit helps to reduce the generation of multiple work orders for the same problem."); and
a processor, operatively coupled to the memory, that executes the executable components, the executable components (see para [0024] and Fig 1, showing equivalent computing functionality and structure) comprising: a monitoring component configured to monitor industrial asset data generated by the industrial assets, wherein the industrial asset data comprises operational and status information for the industrial assets (see para [0015]-[0017], where tracking work orders generated for a specific plant asset shows monitoring component and see para [0030], "As illustrated in FIG. 1, the process control system 5 also includes one or more operator and/or maintenance workstations 71 that are communicatively connected to the data highway 10. Using the operator and maintenance workstations 71, operators or maintenance personnel may view and monitor real-time operations of the process plant 5, as well as take any diagnostic, corrective, maintenance, and/or other actions that may be required. Generally speaking, operators take actions to change the ongoing operation of the plant during run-time to thereby cause the plant control system to work better. On the other hand, maintenance personnel generally view data pertaining to the operational states or statuses of various devices in the plant and take actions to maintain, repair, calibrate, etc. the devices to assure that the devices are operating in a manner that enables the control system to perform satisfactorily. At least some of the operator and maintenance workstations 71 may be located at various, protected areas in or near the plant 5, and in some situations, at least some of the operator and maintenance workstations 71 may be remotely located, but nonetheless be in communicative connection with the plant 5. The operator and maintenance workstations 71 may be wired or wireless computing devices.");
an analysis component configured to, in response to detecting, based on analysis of the industrial asset data, a change to an operating context of an industrial asset of the industrial assets (see para [0039], "These systems may operate in any known manner to make recommendations or suggestions for changes to be made in the plant 5. Moreover, data analytic systems 210, 212 and 214 may be part of and/or may incorporate aspects of other known systems, such as maintenance systems (e.g., the AMS system 180), control systems (such as information within the workstations 71 and the databases 72 and 73), the configuration system (such as the configuration system 72), etc. Generally speaking, the data analytic systems 210, 212 and 214 may run or execute various programs or routines on data from the plant (e.g., process data, alerts and alarm data, etc.) to detect poorly operating equipment in the plant 5 and notify a user of problems, or potential problems in the plant 5, and may make recommendations for changes. The data analytic systems 210, 212 and 214 may then generate and send condition or recommendation messages to users (e.g., maintenance personnel) indicating the detected existence of a significant condition in the plant and/or a recommendation for modifying one or more assets in the plant 5 to make the plant 5 run better, or to alleviate or reduce a problem in the plant 5.")
Dillon, however, does not specifically disclose a work order generation component configured to implement the modification to the open work order, wherein the modification is at least one of a change to a priority level of the open work order, a change to a completion date of the open work order, a change to a scheduled time to perform maintenance tasks defined by the open work order, a change to an assignment of technicians to the open work order, or a change to a resource allocation for the open work order. In analogous art, Esmalifalak discloses the following limitations:
perform an aggregated optimization analysis of the open work orders, information about the change in the operating context, and location information indicating locations of the industrial assets within the industrial facility (see para [0034], "Some industrial environments may also include other systems or devices relating to specific aspects of the controlled industrial systems. These may include, for example, one or more data historians 110 that aggregate and store production information collected from the industrial controllers 118 and other industrial devices." and see para [0039], "Open or newly initiated work orders are also analyzed to identify work order features that were improperly entered or chosen during the submission process. The system continually reevaluates work orders to discover new anomalies so that if a work order becomes more risky over its lifespan—e.g., due to a change of its configuration or the amount of time the work order has been open—the work order is reclassified to reflect its new level of risk." and see para [0045], "Risk score component 212 can be configured to generate, for each work order, a risk score indicating the work order's determined level of risk. The risk score for a given work order can be generated based on an aggregation of the work order's z-score(s), as generated by the z-scoring component 208, and an assessment of the work order relative to all other work orders as determined by the holistic anomaly detection component 210." and see para [0053], "Embodiments of work order analysis system 202 that are implemented on a cloud platform or public network can accept work order data 304 submitted from multiple facilities of an industrial enterprise for collective analysis and generation of site-specific summaries FIG. 4 is a diagram illustrating a generalized architecture in which a cloud-based work order analysis system 202 generates work order reports 306 based on geographically diverse industrial facilities. In this example, an industrial enterprise comprises N industrial facilities 402.sub.1-402.sub.N at respective different geographic locations. Users at each of the facilities 402 submit work order data 304 to the cloud-based system 202 for maintenance tracking, anomaly detection, and risk assessment, as described above. System 202 can cluster the work orders 222 received from the multiple facilities 402 according to types of maintenance, such that at least some clusters include work orders 222 from more than one facility 402. This allows work orders 222 for a particular type of maintenance operation performed at multiple different facilities to be analyzed collectively to identify anomalies or high-risk work orders 222." and see para [0076]-[0077]), and
determine, based on a result of the aggregated optimization analysis, a modification to an open work order, of the open work orders, that causes the open work orders to implement a holistic maintenance strategy that satisfies a defined maintenance optimization criteria based on the change to the operating context, wherein the defined maintenance optimization criteria comprises at least a minimization of a total time to execute the open work orders (see para [0037], "To address these and other issues, one or more embodiments described herein provide a work order analysis system that applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process." and see para [0049], "The user interface component 204 can allow client devices 308a to communicatively interface with the work order analysis system 202 and submit work order data 304. This work order data 304 can represent either a newly initiated work order for a maintenance task to be performed, or updated information for an open work order that was previously submitted to the system 202. Substantially any work order format can be supported by various embodiments of work order analysis system 202. In this regard, user interface component 204 can generate and deliver, to the client device 308a, user interface displays that render editable data fields representing features of the maintenance job represented by the work order. Items of work order data 304 that can be submitted to the system 202 in this manner can include, but are not limited to, a type of maintenance to be performed, a description of the maintenance, the number of personnel required to perform the maintenance, an estimated number of hours to perform the maintenance, an actual number of hours spent on the job, identities and numbers of industrial assets that are subject to the maintenance, identities of industrial sites or facilities in which the maintenance takes place, materials to be used to perform the job, an expected cost to perform the job (e.g., costs of replacement parts), or other such information." and see para [0052], "To this end, system 202 supports a set of work order analysis tools 302 that group work orders 222 representing similar types of maintenance operations into work order clusters and performs various types of statistical and machine learning analysis to the individual clusters as well as to the totality of the work orders 222 in a holistic manner Based on results of these analyses, system 202 identifies anomalous work orders 222 and generates insights into potential maintenance inefficiencies that, if corrected, may improve asset performance, increase machine uptime, reduce maintenance costs, reduce the amount of re-work currently being performed, improve maintenance efficiency, or mitigate equipment failures. User interface component 204 can render results of these analytics as work order reports 306 delivered to client devices 308b having appropriate authorization credentials to access the reports. In various embodiments, these reports 306 can classify work orders 222 based on their risk levels (e.g., high, medium, and low risk), identify the types of risk associated with respective work orders (e.g., abnormal delay, abnormal configuration, etc.), quantify costs associated with high-risk work orders 222 (e.g., amount of excess duration to complete a maintenance task, number of excess failures, etc.), render site-specific summaries that facilitate comparison of maintenance performance across multiple facilities of an industrial enterprise, or provide other such information." where it is obvious to one or ordinary skill in the art that identifying unnecessarily costly maintenance procedures and analytics that show reducing delays for work orders and excess duration to complete maintenance tasks can be considered to show minimization of a total time to execute the open work orders);
a work order generation component configured to implement the modification to the open work order to yield a modified work order (see para [0092]-[0093], "When a new work order 222b is submitted to the system 202, a validation component 214 applies predictive analysis to the work order 222b in view of past work orders 222a to determine whether any user-defined features of the work order 222b are likely to be underestimated or overestimated. For example, if a submitted work order 222b includes a data field indicating an estimate of the number of hours that will be required to complete the maintenance operation, the validation component 214 can apply statistical and/or machine learning analysis to the new work order 222b within the context of similar past work orders 222a to determine whether similar maintenance operations performed on the same industrial asset (or same type of industrial asset) have required more or fewer hours to complete. In some embodiments, the analysis performed by the validation component 214 can be similar to one or more of the anomaly detection analyses performed on closed work orders 222a described above. In this regard, the validation component 214 can be trained, using the historical closed work orders 222a, to discover anomalies or inaccurate entries in new work orders 222b during the work order submission process. For example, validation component 214 may apply statistical analysis to a subset of the available closed work orders 222a determined to correspond to a similar type of maintenance task as that of the new work order 222b (e.g., the same or similar job description performed on the same or similar industrial assets) to determine an expected range of time typically spent performing the task. If the expected amount of time to be spent on the new work order 222b, as entered by the user submitting the new work order 222b, is within this expected range, the validation component 214 can generate a confirmation that the expected time duration entered by the user is within expectations and that the maintenance operation is not expected to be delayed. Alternatively, if the expected time duration entered by the user is less than the minimum expected time duration as learned by the statistical analysis, the validation component 214 can generate an indication that the maintenance task is expected to be delayed, or to take longer than the user expects. If the expected time duration entered by the user is greater than the maximum amount of time spent on the task, the validation component 214 can generate a warning that the user may be overestimating the amount of time required to complete the maintenance operation. These various types of feedback can be displayed by the user interface component 204 during the work order submission process, affording the user an opportunity to revise features of the work order to bring those features within expectations if appropriate. In an example embodiment, the validation component 214 may render a list of statuses 1002 for each open work order 222b indicating whether that work order is expected to be delayed or not delayed relative to the expected number of hours entered by the user."),
a user interface component configured to transmit a notification of the modified work order to a client device associated with a technician assigned to the modified work order (see para [0035], ". To manage the large number of maintenance operations carried out at a given industrial enterprise, work order management systems can be used to initiate work orders for new maintenance operations to be performed and to track the statuses of these work orders. Maintenance technicians or managers fill out and submit work orders for respective maintenance operations or tasks to the system. A work order remains open as its corresponding maintenance task is performed, and is then closed once the task is completed." and see para [0049], "The user interface component 204 can allow client devices 308a to communicatively interface with the work order analysis system 202 and submit work order data 304. This work order data 304 can represent either a newly initiated work order for a maintenance task to be performed, or updated information for an open work order that was previously submitted to the system 202. Substantially any work order format can be supported by various embodiments of work order analysis system 202. In this regard, user interface component 204 can generate and deliver, to the client device 308a, user interface displays that render editable data fields representing features of the maintenance job represented by the work order. Items of work order data 304 that can be submitted to the system 202 in this manner can include, but are not limited to, a type of maintenance to be performed, a description of the maintenance, the number of personnel required to perform the maintenance, an estimated number of hours to perform the maintenance, an actual number of hours spent on the job, identities and numbers of industrial assets that are subject to the maintenance, identities of industrial sites or facilities in which the maintenance takes place, materials to be used to perform the job, an expected cost to perform the job (e.g., costs of replacement parts), or other such information." and Fig. 4); and
wherein the modification is at least one of a change to a priority level of the open work order, a change to a completion date of the open work order, a change to a scheduled time to perform maintenance tasks defined by the open work order, a change to an assignment of technicians to the open work order, or a change to a resource allocation for the open work order (see para [0092]-[0093], "When a new work order 222b is submitted to the system 202, a validation component 214 applies predictive analysis to the work order 222b in view of past work orders 222a to determine whether any user-defined features of the work order 222b are likely to be underestimated or overestimated. For example, if a submitted work order 222b includes a data field indicating an estimate of the number of hours that will be required to complete the maintenance operation, the validation component 214 can apply statistical and/or machine learning analysis to the new work order 222b within the context of similar past work orders 222a to determine whether similar maintenance operations performed on the same industrial asset (or same type of industrial asset) have required more or fewer hours to complete. In some embodiments, the analysis performed by the validation component 214 can be similar to one or more of the anomaly detection analyses performed on closed work orders 222a described above. In this regard, the validation component 214 can be trained, using the historical closed work orders 222a, to discover anomalies or inaccurate entries in new work orders 222b during the work order submission process. For example, validation component 214 may apply statistical analysis to a subset of the available closed work orders 222a determined to correspond to a similar type of maintenance task as that of the new work order 222b (e.g., the same or similar job description performed on the same or similar industrial assets) to determine an expected range of time typically spent performing the task. If the expected amount of time to be spent on the new work order 222b, as entered by the user submitting the new work order 222b, is within this expected range, the validation component 214 can generate a confirmation that the expected time duration entered by the user is within expectations and that the maintenance operation is not expected to be delayed. Alternatively, if the expected time duration entered by the user is less than the minimum expected time duration as learned by the statistical analysis, the validation component 214 can generate an indication that the maintenance task is expected to be delayed, or to take longer than the user expects. If the expected time duration entered by the user is greater than the maximum amount of time spent on the task, the validation component 214 can generate a warning that the user may be overestimating the amount of time required to complete the maintenance operation. These various types of feedback can be displayed by the user interface component 204 during the work order submission process, affording the user an opportunity to revise features of the work order to bring those features within expectations if appropriate. In an example embodiment, the validation component 214 may render a list of statuses 1002 for each open work order 222b indicating whether that work order is expected to be delayed or not delayed relative to the expected number of hours entered by the user.").
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Esmalifalak with Dillon because including such modifications enables more optimized work orders (see Esmalifalak, para [0002]-[0006]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for anomaly detection for maintenance work orders as taught by Esmalifalak in the enhanced work order generation and tracking system of Dillon, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Dillon and Esmalifalak do not specifically disclose the analysis component is further configured to formulate and submit, based on the change to the operating context, a prompt to a generative artificial intelligence (AI) model designed to obtain a response from the generative AI model used to determine the modification to the open work order. In analogous art, Travalini discloses the following limitations:
the analysis component is further configured to formulate and submit, based on the change to the operating context, a prompt to a generative artificial intelligence (AI) model designed to obtain a response from the generative AI model used to determine the modification to the open work order (see para [0021], "In accordance with various aspects of the disclosure, methods, computer-readable media, and apparatuses are disclosed involving an artificial intelligence (AI)-driven chatbot configured to diagnose and troubleshoot problems using conversational prompts. The AI-driven chatbot may complete advanced diagnostics, generate work orders with a higher degree of accuracy, advise tenants and/or homeowners on do-it-yourself (DIY) solutions, and/or build intelligent home profiles using the conversational prompts." and see para [0103], "The intelligent routing and assignment engine may continuously train the regression model as the intelligent routing and assignment engine receives updated data (e.g., updated time-to-fix data, updated price book data, new/updated work orders (or enriched work orders), or the like). The intelligent routing and assignment engine may continuously determine the accuracy of the regression model using test data 1120. As discussed above, test data 1120 may consist of the data that was parsed from the work order (or enriched work order) by the intelligent routing and assignment engine. The intelligent routing and assignment engine may modify the accuracy of the regression model using feedback from the technician, as discussed below. The intelligent routing and assignment engine may use the feedback from the technician to determine whether a technician should be assigned particular work orders (or enriched work orders). The intelligent routing and assignment engine may continuously update test data 1120 as the intelligent routing and assignment engine receives new work orders (or enriched work orders). The intelligent routing and assignment engine may run the regression model to predict at least one technician who can complete the maintenance repair job indicated on the work order (or enriched work order).")
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Travalini with Dillon and Esmalifalak because formulating and submitting a prompt improves the efficiency for stakeholders involved in performing and needing the maintenance (see Travalini, para [0001]-[0003]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for troubleshooting maintenance repair requests using an artificial intelligence driven chatbot as taught by Travalini in the Dillon and Esmalifalak combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 2 and 12:
Further, Dillon discloses the following limitations:
wherein the change to the operating context is at least one of a performance issue experienced by the industrial asset, a change in an operating mode of the industrial asset, or a change in a production recipe being executed by the industrial asset (see para [0039], where detecting poorly operating equipment can be considered performance issue of industrial asset)
Claims 3, 13 and 20:
Dillon does not specifically disclose wherein the defined maintenance optimization criterion is at least one of maximization of overall maintenance efficiency, minimization of a total time to execute the open work orders, minimization of labor or material costs associated with execution of the open work orders, minimization of a number of technicians or autonomous vehicles required to execute the open work orders, or minimization of a number of steps taken by the technicians to complete the open work orders. In analogous art, Esmalifalak discloses the following limitations:
wherein the defined maintenance optimization criteria further comprises at least one of maximization of overall maintenance efficiency, minimization of labor or material costs associated with execution of the open work orders, minimization of a number of technicians or autonomous vehicles required to execute the open work orders, or minimization of a number of steps taken by the technicians to complete the open work orders (see para [0052], "To this end, system 202 supports a set of work order analysis tools 302 that group work orders 222 representing similar types of maintenance operations into work order clusters and performs various types of statistical and machine learning analysis to the individual clusters as well as to the totality of the work orders 222 in a holistic manner Based on results of these analyses, system 202 identifies anomalous work orders 222 and generates insights into potential maintenance inefficiencies that, if corrected, may improve asset performance, increase machine uptime, reduce maintenance costs, reduce the amount of re-work currently being performed, improve maintenance efficiency, or mitigate equipment failures. User interface component 204 can render results of these analytics as work order reports 306 delivered to client devices 308b having appropriate authorization credentials to access the reports. In various embodiments, these reports 306 can classify work orders 222 based on their risk levels (e.g., high, medium, and low risk), identify the types of risk associated with respective work orders (e.g., abnormal delay, abnormal configuration, etc.), quantify costs associated with high-risk work orders 222 (e.g., amount of excess duration to complete a maintenance task, number of excess failures, etc.), render site-specific summaries that facilitate comparison of maintenance performance across multiple facilities of an industrial enterprise, or provide other such information.")
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for anomaly detection for maintenance work orders as taught by Esmalifalak in the enhanced work order generation and tracking system of Dillon, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 5 and 15:
Dillon does not specifically disclose wherein the monitoring component is further configured to monitor inventory data that records current inventory levels of parts or materials used to perform maintenance tasks, and the analysis component is further configured to, in response to detecting, based on analysis of the inventory data, a change in the current inventory levels, determine another modification to an open work order, of the open work orders, that causes the open work orders to satisfy the defined maintenance optimization criterion based on the change in the current inventory levels. In analogous art, Esmalifalak discloses the following limitations:
wherein the monitoring component is further configured to monitor inventory data that records current inventory levels of parts or materials used to perform maintenance tasks (see para [0038], "Statistical analysis is then applied to work orders within a given cluster to identify any work orders that are anomalous in one or more respects; e.g., number of hours spent on the work, materials used, number of maintenance personnel who performed the work, etc. A risk type is then applied to any anomalous work orders discovered within the cluster based on the nature of the discovered anomaly. For example, if a work order is found to have been delayed longer than other work orders within its cluster, the system indicates that the work order represents an abnormal delay. The system then applies a risk score to each work order. The risk score is a metric of how much the work order differs from the others in its cluster and the impact that this deviation may have on operations." and see para [0043], " Work order features that are assessed in this manner can include, but are not limited to, the estimated or actual number of hours required to complete the job, the number of people assigned to the job, materials used to complete the job, expenses associated with the job, a number of steps to be taken to complete the job, or other such factors."), and the analysis component is further configured to, in response to detecting, based on analysis of the inventory data, a change in the current inventory levels, determine another modification to an open work order, of the open work orders, that causes the holistic maintenance strategy to satisfy the defined maintenance optimization criteria based on the change in the current inventory levels (see para [0049], " updated information for an open work order that was previously submitted to the system 202. Substantially any work order format can be supported by various embodiments of work order analysis system 202. In this regard, user interface component 204 can generate and deliver, to the client device 308a, user interface displays that render editable data fields representing features of the maintenance job represented by the work order. Items of work order data 304 that can be submitted to the system 202 in this manner can include, but are not limited to, a type of maintenance to be performed, a description of the maintenance, the number of personnel required to perform the maintenance, an estimated number of hours to perform the maintenance, an actual number of hours spent on the job, identities and numbers of industrial assets that are subject to the maintenance, identities of industrial sites or facilities in which the maintenance takes place, materials to be used to perform the job, an expected cost to perform the job (e.g., costs of replacement parts), or other such information." showing updated work order may be for materials used).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for anomaly detection for maintenance work orders as taught by Esmalifalak in the enhanced work order generation and tracking system of Dillon, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 6 and 16:
Dillon does not specifically disclose wherein a subset of the industrial asset data identifies a new industrial asset that is not registered with the system, and the analysis component is further configured to register the new industrial asset in the system in response to receipt of the subset of the industrial asset data. In analogous art, Esmalifalak discloses the following limitations:
wherein a subset of the industrial asset data identifies a new industrial asset that is not registered with the system, and the analysis component is further configured to register the new industrial asset in the system in response to receipt of the subset of the industrial asset data (see para [0090], "In addition to gleaning insights into an enterprise's maintenance processes and approaches by analyzing closed work orders 222a for past maintenance tasks, embodiments of the work order analysis system 202 can also examine newly initiated or open work orders for pending maintenance tasks to identify missing or incorrectly entered work order information, as well as to provide customized feedback regarding expected delays in performing open maintenance tasks based on past maintenance history. In general, after a work order for a planned or unplanned maintenance operation is created in the system 202, there may be unknown risks associated with the maintenance operation that prevent on-time closure of the work order. These risks can grow over time and during pendency of the maintenance operation, leading to losses in terms of machine runtime (e.g., due to delay in restoring a production line after a shutdown for maintenance) or additional time spent on maintenance" where a work order created for unplanned maintenance operation can be considered to show a new asset that is registered in the system is obvious given broadest reasonable interpretation and see para [0067], showing creating subgroups of assets to be analyzed by the system).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for anomaly detection for maintenance work orders as taught by Esmalifalak in the enhanced work order generation and tracking system of Dillon, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 8-10 and 18:
Dillon does not specifically disclose wherein the analysis component is configured to determine the modification to the open work order based on part on a response prompted from a generative artificial intelligence (AI) model. In analogous art, Esmalifalak discloses the following limitations:
wherein the analysis component is configured to determine the modification to the open work order based on part on the response prompted from the generative AI model (see para [0037], "To address these and other issues, one or more embodiments described herein provide a work order analysis system that applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations.")
wherein the analysis component is configured to determine the modification to the open work order 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 (see para [0059], "Once the maintenance operation corresponding to an open work order 222 has been completed, an authorized user (e.g., a member of the maintenance staff or a maintenance manager) can change the state of the work order can from open to closed. The work order analysis tools 302 can apply a variety of machine learning and statistical analytics to these closed work orders 222a to identify anomalies and inefficiencies in an enterprise's maintenance processes, to quantify the costs of these inefficiencies, and to recommend changes to the maintenance processes that are likely to recover these costs. These analysis tools analyze historical work orders 222a to estimate ranges of expected or typical feature values for work orders corresponding to a specific type of maintenance operation. If observed values for one or more features of a work order 222a are not sufficiently similar to corresponding estimated values (e.g., within learned ranges of typical values), the system 202 flags the work order 222a as an anomaly for further investigation. Work order features that can be analyzed for deviations can include, for example, the time between creating and completing a work order, the site identifier, corresponding assets, the number of assets used, technicians involved in completing the work order, the recorded descriptions, or other features." and see para [0091], "o address these issues, one or more embodiments of the work order analysis system 202 can apply classification-based algorithms that are trained using historical labeled data from past work orders to calculate the probability of future delays associated with an open work order. FIG. 10 is a data flow diagram illustrating analyses that can applied to open or pending work orders 222b by some embodiments of the work order analysis system 202. Open work orders 222b represent newly initiated or pending work orders for maintenance tasks that have yet to be completed. As described above, users can enter a new work order 222b for a task to be completed via interaction with suitable work order entry interface displays rendered by user interface component 204. These interactive displays allow the user to submit information about the maintenance operation to be performed by entering values for respective data fields of the work orders (e.g., description of the problem to be addressed by the maintenance, a type of maintenance, the number of personnel to be assigned to the task, the estimated number of hours to complete the task, identification of the industrial assets on which the task is to be performed, the industrial site or facility in which the task is to be performed, etc.).")
wherein the 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 an industrial asset of the industrial assets, formulate one or more maintenance tasks predicted to mitigate the current or predicted risk (see para [0003]-[0005], [0042], showing generating risk and outputting risk levels for work orders and see para [0052], showing mitigating equipment failures and inefficiencies and see para [0109], showing mitigating delays ), and
the work order generation component is configured to generate a work order prescribing the one or more maintenance tasks (see para [0058]-[0059], showing creation of work orders and see para [0110], showing a new work order based on a delayed work orders and see para [0101]).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for anomaly detection for maintenance work orders as taught by Esmalifalak in the enhanced work order generation and tracking system of Dillon, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Dillon, Esmalifalak and Travalini, as applied above, and further in view of Gordenker et al. (US 20210019690 A1) (hereinafter Gordenker)
Claims 4 and 14:
Dillon, Esmalifalak and Travalini do not specifically disclose wherein the monitoring component is further configured to monitor schedule data that records current work schedules for technicians employed by the plant facility, and the analysis component is further configured to, in response to detecting, based on analysis of the schedule data, a change in a scheduled availability of one or more of the technicians, determine another modification to an open work order, of the open work orders, that causes the open work orders to satisfy the defined maintenance optimization criterion based on the change in the scheduled availability. In analogous art, Gordenker discloses the following limitations:
wherein the monitoring component is further configured to monitor schedule data that records current work schedules for technicians employed by the plant facility, and the analysis component is further configured to, in response to detecting, based on analysis of the schedule data, a change in a scheduled availability of one or more of the technicians, determine another modification to an open work order, of the open work orders, that causes the holistic maintenance strategy to satisfy the defined maintenance optimization criteria based on the change in the scheduled availability (see para [0016], "In one example, a dispatching computer system may be configured to run a scheduling program on a regular basis (e.g., every five minutes, 10 minutes, hourly, daily, etc.) to update dispatching schedules for changes in technician availability (e.g., technicians newly labeled as available at various time intervals or calling in sick, etc.), job requests (e.g., receipt of urgent job requests, etc.), local conditions (e.g., weather, traffic, etc.), and/or various other factors. Upon running, the dispatching system may retrieve an appropriate model for a respectively scheduled tenant (e.g., company customer, etc.) to generate a dispatching schedule based on particularities specific to the respective tenant (e.g., technician rate information, business unit information, service locations, etc.)." and see para [0018], " In one example, generating a dispatching schedule includes defining an interval of time over which to generate the dispatching schedule, retrieving information on available time for technicians associated with the respective tenant, retrieving information for jobs to be assigned to technicians and for jobs which have been manually assigned to technicians, and retrieving non-job event (e.g., time off, medical leave, parental leave, etc.) information regarding the technicians. Job information may include, for example and without imputing limitation, job status (e.g., scheduled, dispatched, or working), estimated cost, timing information (e.g., estimated start and/or end time, etc.), location information, and/or assignment information pairing a technician to the respective job. In some examples, non-job events may be represented as manually assigned jobs and thus similarly be locked to a scheduling window for the respective technician (e.g., the dispatching system may refrain from rescheduling the event, etc.)." and see para [0064], "Once a technician completes a job, scheduling information may be updated automatically. For example, if a technician completes a job before the end of a two hour period or after the end of a two hour period, information in the GUI 500 could be updated to identify an actual amount of time that the technician spent performing the job. GUI 500 may also be updated to reflect changes in values, this may occur when a technician identifies and performs additional work for a particular customer. As such, methods consistent with the present disclosure may schedule future events and also track and update work related details as that work is performed. Furthermore, schedules could be re-generated each time a technician provides information to a scheduling computer. For example, when a first technician is expectantly delayed, a second technician may be assigned and dispatched to a job location dynamically on-the-fly.").
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Gordenker with Dillon, Esmalifalak and Travalini because monitoring the technician schedule enables more efficient solution to the task management by optimizing the dispatching of technicians to perform the job (see Gordenker, para [0003]-[0004]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the technician dispatching method as taught by Gordenker in the Dillon, Esmalifalak and Travalini combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dillon, Esmalifalak and Travalini, as applied above, and further in view of Janey et al. (US 20230259893 A1) (hereinafter Janey).
Claims 7 and 17:
Dillon, Esmalifalak and Travalini do not specifically disclose wherein the subset of the industrial asset data that identifies the new industrial asset is collected by and received from a mobile industrial robot. In analogous art, Janey discloses the following limitations:
wherein the subset of the industrial asset data that identifies the new industrial asset is collected by and received from a mobile industrial robot (see para [0085], "In some examples, the data related to a topography or terrain type is collected from a combination of the satellite or drone captured images and surveillance of the site by an autonomous vehicle. As the autonomous vehicle 112 surveys the site, it can collect data for slow tilt and other similar minor topographical or terrain changes that would not be detectable via satellite or drone image. In other aspects, a predefined threshold of complexity (e.g., 80% complexity) for one or more areas, or portions thereof, can trigger collection of additional information for the area beyond what the satellite and/or drone imagery provides. For example, a human surveyor will use their existing knowledge about the area or go out to physically survey the area, and/or an autonomous vehicle 112 is driven around the site with a human operator O1 to determine more information about the areas to facilitate determinations regarding complexity and sensitivity of the regions.")
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Janey with Dillon, Esmalifalak and Travalini because integration with robots enables more efficient ways of performing maintenance tasks (see Janey, para [0002]-[0003]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the maintenance system as taught by Janey in the Dillon, Esmalifalak and Travalini combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Piety et al. (US 2014/0222378 A1), a method to analyze vibration measurements from one or more machines including a sensor to detect vibration signals from a machine-under-test according to a predetermined test, a data acquisition unit to receive the vibration signals and to process the vibration signals to generate vibration data, an input unit to selectively receive field notes information from an operator of the data acquisition unit, a communication unit to transmit the vibration data and the field notes information to a remote analyst over a communication network, and to receive feedback from the remote analyst via the communication network regarding health of the machine-under test based on characteristics of the transmitted vibration data and field notes information, and a control unit to control operations of the sensor, the data acquisition unit, the input unit, and the communication unit
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624
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