DETAILED ACTION
This Non-Final Office Action is in response to Applicant's amendments and arguments and request for continued examination filed on February 03, 2026. Applicant has amended claim 14. Currently, claims 14-26 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/3/26 has been entered.
Response to Amendments
The 35 U.S.C. 101 rejections of claims 14-26 are withdrawn in light of applicant’s amendments and arguments.
The 35 U.S.C. 103 rejections of claims 14-26 are withdrawn in light of applicant’s amendments and arguments. Applicant’s amendments necessitated the new grounds for rejection in this office action.
Response to Arguments
Applicant’s remarks submitted on 2/3/26 have been considered and are persuasive in regards to the 101 rejection. Applicant’s arguments against the 103 rejection are moot in light of the newly cited Mathews reference.
Eligible Subject Matter
The claims are eligible subject matter because the abstract idea is integrated into practical implementation for similar reasons to Ex Parte Desjardins and applicant’s 2/3/26 remarks.
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 14-16, 18-21, 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over Billi-Duran et al. (US 2018/0088564 A1) (hereinafter Billi-Duran) in view of Hedge et al. (EP 3009900 A1) (hereinafter Hedge) in view of Mathews et al. (US 2021/0149655 A1) (hereinafter Mathews).
Claim 14:
Billi-Duran, as shown, discloses the following limitations of claim 14:
A method of generating user specific engineering programs in a multi-user engineering environment (see para [0004], " In one or more embodiments, a system for learning industrial workflows is provided, comprising a discovery component configured to collect industrial data items from multiple data sources of an industrial facility, and to collect behavioral data from one or more client devices within the industrial facility, the behavioral data representing behaviors of one or more users associated with the one or more client devices; and a workflow generation component configured to generate workflow data based on a correlation of the industrial data items with the behavioral data, wherein the workflow data defines a sequence of user actions for performing a task relating to an industrial system." and see para [0031], "Industrial controllers and their associated I/O devices are central to the operation of modern automation systems. These controllers interact with field devices on the plant floor to control automated processes relating to such objectives as product manufacture, material handling, batch processing, supervisory control, and other such applications. Industrial controllers store and execute user-defined control programs to effect decision-making in connection with the controlled process. Such programs can include, but are not limited to, ladder logic, sequential function charts, function block diagrams, structured text, or other such platforms."), the method comprising: generating, by a processing unit , a plurality of user profiles for a plurality of users based on an analysis of a plurality of user actions performed by the plurality of users in the multi-user engineering environment, wherein a user action is an engineering action performed by a user to perform development, maintenance, and testing of an engineering project, and wherein a plurality of patterns is identified by the processing unit in the plurality of user actions (see para [0090], "In addition to indexing available plant data as described above, workflow tracking and identification system 302 can also monitor and record operator behaviors during plant operation, learn correlations between sequences of operator behaviors and production or operational outcomes, and generate workflow data that records these behavior sequences in association with the outcomes. FIG. 11 is a diagram illustrating collection of user data 1108—including behavior data—together with plant operational and status data 1106 according to one or more embodiments. As described above, indexing system 506 can collect and index operational and status data 1106 from industrial devices and systems 1102 throughout the industrial enterprise. Simultaneously, the system 302 can collect user data 1108 for various users—typically plant employees—during plant operation. This information can be collected by device interface component 316 from a client device or wearable computer associated with the user 1104, from biometric equipment worn by the user, from multimedia devices that record the user's behaviors, or from other devices that facilitate collection of data relating to the user's location, state, and/or activities. For example, a client device carried by the user, or a wearable computer worn by the user, can establish a communicative link to the workflow tracking and identification system 302 via device interface component 316, which can confirm authorization of the user to access the system. As the user traverses the industrial environment, the device interface component 316 collects user data 1108 from the user's client device." and see para [0096], "Workflow generation component 318 may also incorporate specific user identities into the learned workflows. For example, workflow generation component 318 may learn, based on observation of the plant data and user behavior data, that one or more specific employees congregate at a particular location relative to an automation system when a certain alarm or downtime condition occurs. Accordingly, the workflow generation component 318 may associate the identities of these employees with the workflow for addressing that particular issue. In another scenario, the system 302 may learn, based on analysis of the plant data and user behavior data, that recovery of the automation system occurs more quickly when the issue is addressed by certain identified employees. Accordingly, the system 302 may link the workflow for the particular issue to those preferred user identities, such that the system 302 will subsequently direct notifications to those employees when the downtime condition is detected, or will inform other maintenance personnel that these identified users may be able to provide guidance in connection with addressing the detected issue. and see para [0107], " In some embodiments, identification of the suitable recipients can be learned by the system 302 as part of the workflow learning routine. For example, if the system 302 observes over multiple instances of a particular machine downtime condition that certain specific employees typically congregate to address the issue, the workflow generation component 318 may link these identified employees with the learned workflow associated with this downtime event, and modify the notification rules 512 to reflect this association. In some embodiments, if various personnel are observed to address different occurrences of the downtime condition, the workflow generation component 318 may further determine which of the personnel typically recover the machine in the least amount of time relative to other technicians. In accordance with this determination, the workflow generation component 318 may prioritize delivery of subsequent downtime notifications and corresponding workflows to this user.");
detecting, by the processing unit, at least one user action performed by the user of the plurality of users in the multi-user engineering environment (see para [0090], "In addition to indexing available plant data as described above, workflow tracking and identification system 302 can also monitor and record operator behaviors during plant operation, learn correlations between sequences of operator behaviors and production or operational outcomes, and generate workflow data that records these behavior sequences in association with the outcomes. FIG. 11 is a diagram illustrating collection of user data 1108—including behavior data—together with plant operational and status data 1106 according to one or more embodiments. As described above, indexing system 506 can collect and index operational and status data 1106 from industrial devices and systems 1102 throughout the industrial enterprise. Simultaneously, the system 302 can collect user data 1108 for various users—typically plant employees—during plant operation. This information can be collected by device interface component 316 from a client device or wearable computer associated with the user 1104, from biometric equipment worn by the user, from multimedia devices that record the user's behaviors, or from other devices that facilitate collection of data relating to the user's location, state, and/or activities. For example, a client device carried by the user, or a wearable computer worn by the user, can establish a communicative link to the workflow tracking and identification system 302 via device interface component 316, which can confirm authorization of the user to access the system. As the user traverses the industrial environment, the device interface component 316 collects user data 1108 from the user's client device.");
determining, by the processing unit, a user profile associated with the user from the generated plurality of user profiles based on the detected at least one user action (see para [0096], where identifies can be considered equivalent to profiles given broadest reasonable interpretation);
generating, by the processing unit, a workflow which is specific to the determined user profile and an application of a trained artificial intelligence model on the detected at least one user action (especially see para [0096], "Workflow generation component 318 may also incorporate specific user identities into the learned workflows. For example, workflow generation component 318 may learn, based on observation of the plant data and user behavior data, that one or more specific employees congregate at a particular location relative to an automation system when a certain alarm or downtime condition occurs. Accordingly, the workflow generation component 318 may associate the identities of these employees with the workflow for addressing that particular issue. In another scenario, the system 302 may learn, based on analysis of the plant data and user behavior data, that recovery of the automation system occurs more quickly when the issue is addressed by certain identified employees. Accordingly, the system 302 may link the workflow for the particular issue to those preferred user identities, such that the system 302 will subsequently direct notifications to those employees when the downtime condition is detected, or will inform other maintenance personnel that these identified users may be able to provide guidance in connection with addressing the detected issue." and see para [0090]-[0095], [0097], [0105] and Fig 11); and
executing, by the processing unit, the generated workflow in the multi-user engineering environment (see para [0110], “Execution of the workflow data 1302 on the client device causes the device to render the workflow for reference by the user, thereby informing the user of the proper sequence of operations to be performed in order to best address the detected condition. In some embodiments, while the detected condition is being addressed by one or more recipients of the workflow, the system 302 can continuously compare actual operator actions with the optimal workflow represented by the workflow data, and provide feedback to the user if the user's actions deviate from the optimal workflow. FIG. 14 is a diagram illustrating real-time workflow feedback being delivered to a user 1402 as the user is correcting a downtime issue associated with automation system 1102. During diagnosis and correction of the downtime issue, the device interface component 316 continues monitoring operational and status data for the automation system 1102 as the user 1402 carries out steps to correct the issue and recover the system.”); and
receiving, by the processing unit, a user action which is executed by the user in the multi-user engineering environment (see para [0095], “The system 302 may detect variations of this workflow carried out over multiple instances of the detected alarm condition. For example, some operators may change the order of some steps, or may use a different set of steps for addressing the alarm condition, where the different set of steps may share some common steps with the originally detected workflow. Over time, the workflow generation component can refine the recorded workflow based on these variations, as well as inferences of which workflows provide best results when addressing the alarm conditions (e.g., which operations cause the automation system to recover more quickly, or result in less accumulated downtime for the machine). For example, if multiple different sequences of operations are detected as resulting in recovery of a machine from a particular alarm condition, the workflow generation component may perform comparisons of the total times required for each of the sequences to recover the machine, and select as the preferred workflow that which consistently recovers the machine with the least amount of total downtime.” and see para [0097]);
determining, by the processing unit, whether an execution of the user action is successful based on an analysis of one or more key performance indicators in the multi-user engineering environment (see para [0097], “In addition to generating workflows for addressing alarm or downtime issues, the system 302 can also learn workflows for carrying out operational tasks that result in optimal automation system performance or yield preferred production results. For example, the workflow generation component 318 may learn over time that a greatest product output of a particular automation system, or a greatest duration of total system runtime per shift, is achieved when a particular operator methodology is practiced. For example, it may be learned that machine downtime is minimized when the operator sets the system speed to be within a certain range, or when certain operating setpoints are set to be within determined optimal ranges. In another example, the system 302 may determine that a particular operator action—e.g., a manual acknowledgement, removal of a part from a station of the automation system, and adjustment of a setpoint, etc. —when performed at an identified stage of an operating system's cycle, repeatedly results in greater product throughput or lower cycle times relative to performing this operation during another stage of the cycle, or not performing the operation at al. Accordingly, the workflow generation component 318 can generate a workflow for operating the system that incorporates this step at the appropriate stage of the machine's cycle.” where it is obvious to one of ordinary skill in the art that attributes such as runtime per shift or product output are type of kpis); and
generating, by the processing unit, another workflow for the determined user profile of the user using the artificial intelligence model, wherein the artificial intelligence model is trained based on a determination that the execution of the user action is successful, and the generated another workflow comprises user action (see para [0096], “Workflow generation component 318 may also incorporate specific user identities into the learned workflows. For example, workflow generation component 318 may learn, based on observation of the plant data and user behavior data, that one or more specific employees congregate at a particular location relative to an automation system when a certain alarm or downtime condition occurs. Accordingly, the workflow generation component 318 may associate the identities of these employees with the workflow for addressing that particular issue. In another scenario, the system 302 may learn, based on analysis of the plant data and user behavior data, that recovery of the automation system occurs more quickly when the issue is addressed by certain identified employees. Accordingly, the system 302 may link the workflow for the particular issue to those preferred user identities, such that the system 302 will subsequently direct notifications to those employees when the downtime condition is detected, or will inform other maintenance personnel that these identified users may be able to provide guidance in connection with addressing the detected issue.” and see para [0095], [0097], [0105]).
Billi-Duran, however, does not specifically disclose dynamically generating an engineering program for use in an automation system based on the executing of the generated workflow and based on an analysis of the plurality of user actions performed by the plurality of users in the multi-user engineering environment and the detected at least one user action of the user. In analogous art, Hedge discloses the following limitations:
dynamically generating an engineering program for use in an automation system based on the executing of the generated workflow and based on an analysis of the plurality of user actions performed by the plurality of users in the multi-user engineering environment and the detected at least one user action of the user (see para [0008], “Therefore, it is an object of the present invention to provide a method and apparatus for dynamically recommending one or more elements from a plurality of elements which are suitable for use in the engineering configuration.” and see para [0031], “ The element library module 116, causes the processor 102, to dynamically determine an element suitable for use in the engineering configuration being created in response to the detected user action. Thereafter, the element library module 116, causes the processor 102, to obtain graphical objects representing different types of the determined element from the element database 118 and display the graphical objects in the element library window. For example, according to the identified pattern of user activity, the user selects a central processing unit (CPU) from the element library window after opening the network view in the editor window. Accordingly, the processor 102 displays graphical objects representing different types of CPUs in the element library window so that the user can use one of the different types of CPUs displayed in the element library window to the engineering configuration.” and see para [0064], “Following this, the engineering station 100 determines user action performed after selecting the graphical object 604 representing the control element using the pattern of user activity. Consider that the pattern of user activity indicates that a runtime element is selected after selecting the control element. Thus, the engineering station 100 determines different types of runtime element from the pattern of user activity. Also, the engineering station 100 determines rank associated with the different types of runtime element and obtains graphical objects 608 representing the different types of runtime element. Then, the engineering station 100 displays the graphical objects 608 representing the different types of runtime element in the element library window 504 as shown in FIG 6B. In this manner, the engineering station 100 dynamically recommends graphical objects representing elements to the user in the element library window 504 for use in HMI screens based on currently performed user action.”)
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Hedge with Billi-Duran because including a dynamic configuration based on executed workflows enables more customized and specific workflows to be generated in an efficient way (see Hedge, para [0001]-[0008]).
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 dynamic recommendation for engineering configuration as taught by Hedge in the industrial workflow tracking and identification system of Billi-Duran, 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.
Billi-Duran and Hedge, however, do not specifically disclose deploying, by the processing unit, the engineering program in one or more physical engineering objects for managing the one or more physical engineering objects in a technical installation. In analogous art, Mathews discloses the following limitations:
deploying, by the processing unit, the engineering program in one or more physical engineering objects for managing the one or more physical engineering objects in a technical installation (see para [0043]-[0056], "The present system integrates several technologies and methods into a software controlled manufacturing system that automates the process of engineering (designing, deploying, configuring, calibrating, programming, debugging, and improving) and operating automated manufacturing systems (aka “automating automation”). In one embodiment, the techniques enabled by the “automating automation” system may include: Design for Automation/Design for Manufacturing and Assembly/Generative Programming; Feedback system to DFA/DFMA/Generative system; The ability to Import and define computer aided design (CAD) designs for objects to be manufactured and/or assembled; Process design and configuration of modules that function Online and Offline; Process programming that can be created, tested, and deployed Offline and Online; Process programming that provides declarative, high level programming, which is abstracted from the underlying hardware that it is implemented on, and is virtualized; Virtualized robotic cells and micro-factories which mirror physical devices; Configuration management, Approvals management, and Workflow management; Cloud-based deployment, configuration, and reconfiguration; Integrated debugger systems; IoT/Monitoring/remote management Automated quality inspection of the product and product manufacturing process steps; and Continuous improvement of process parameters based on sensor data and machine learning." where it is obvious to one of ordinary skill in the art that such a such a manufacturing system with automation could integrate the engineering program shown in Billi-Duran and Hedge)
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Mathews with Billi-Duran and Hedge because deploying such solutions can make the automation solutions less expensive and time consuming (see Mathews, para [0004]-[0008]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the software defined manufacturing system as taught by Mathews in the Billi-Duran and Hedge 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.
Claim 15:
Further, Billi-Duran discloses the following limitations:
determining, by the processing unit, a plurality of patterns in the plurality of user actions based on an application of an unsupervised learning algorithm on the plurality of user actions (see para [0090]-[0097], where the learning can be considered show a learning algorithm and where a learning algorithm that is used to identify correlations can be considered to show an unsupervised learning algorithm is obvious to one of ordinary skill in the art);
determining, by the processing unit, whether each user action of a set of user actions in the plurality of user actions are of an identical pattern in the plurality of patterns (see para [0005], “ Also, one or more embodiments provide a method for creating and delivering industrial workflows, where the method comprises collecting, by a system comprising at least one processor, industrial data from an automation system of a plant facility; collecting, by the system, behavioral data from one or more client devices within the plant facility, the behavioral data representing behaviors of one or more users associated with the one or more client devices; correlating, by the system, the behavioral data with at least a subset of the industrial data; and generating, by the system, workflow data based on the correlating, wherein the workflow data defines a sequence of user actions for performing a task relating to the automation system.” and see para [0090]-[0097], where it would be obvious to one of ordinary skill in the art that correlating could include identical matches);
determining, by the processing unit, that the determined set of user actions is executed by two or more users of the plurality of users (see para [0093], “System 302 can learn correlations between subsets of the monitored or inferred user behaviors and their corresponding impacts on performance of the industrial processes, systems, and devices within the plant. FIG. 12 is an example overview of an area within a plant. In this example, a number of users 1208a are monitored by workflow tracking and identification system 302 as the users operate and/or perform maintenance on various automation systems within the plant facility. Based on the users' monitored locations as well as known locations of the various industrial systems within the plant (as indexed in federated data model 202), the system 302 can determine when the users are positioned near the various automation systems, and, in some examples, when the users are near certain control panels or stations of the automation systems. The system 302 can also link inferred operator behaviors with these locations as part of a learned workflow.”); and
Billi-Duran does not specifically disclose grouping, by the processing unit, the two or more users into another user profile of the plurality of user profiles based on the determination that the set of user actions which are of the identical pattern is executed by the two or more users. In analogous art, Hedge discloses the following limitations:
grouping, by the processing unit, the two or more users into another user profile of the plurality of user profiles based on the determination that the set of user actions which are of the identical pattern is executed by the two or more users (see para [0036], “The storage unit 106 may be a non-transitory storage medium configured for storing databases. For example, the storage unit 106 contains the elements database 118 which stores graphical objects representing various elements useful for creating engineering configuration. The storage unit 106 contains the pattern database 120 which stores various patterns of user activity associated with the user or other users. In one embodiment, the pattern of user activity is based on actions performed by a particular user during creation of previous engineering configurations. In another embodiment, the pattern of user activity is based on standard actions that any user is likely to perform while creating a specific engineering configuration. In yet another embodiment, the pattern of activity is based on actions performed by other users during creation of same type of engineering configuration.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for dynamic recommendation for engineering configuration as taught by Hedge in the industrial workflow tracking and identification system of Billi-Duran, 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.
Claim 16:
Further, Billi-Duran discloses the following limitations:
training an artificial intelligence model to generate workflows specific to each user profile of the plurality of user profiles, wherein the artificial intelligence model is trained based on the analysis of the plurality of user actions of the plurality of users in the multi-user engineering environment (see para [0090]-[0097], showing training the system that determines correlations and infers user behavior (AI system) based on detected user actions).
Claim 18:
Billi-Duran do not specifically disclose wherein the plurality of patterns in the plurality of user actions comprises a pattern of interrelationships between each user action of the plurality of user actions, and an engineering project associated with the multi-user engineering environment. In analogous art, Hedge discloses the following limitations:
wherein the plurality of patterns in the plurality of user actions comprises a pattern of interrelationships between each user action of the plurality of user actions, and an engineering project associated with the multi-user engineering environment (see para [0010], “The method further comprises dynamically determining at least one element suitable for use in the engineering configuration from a plurality of elements in response to the user action based on a pattern of user activity associated with the engineering configuration. For example, a pattern database comprises a plurality of patterns of user activity. Each pattern of user activity comprises a sequence of user actions typically performed during creation of similar type of engineering configurations. Each user action in the sequence of user actions indicates an element used by the user in the similar type of engineering configurations. According to the present invention, the pattern of user activity is identified from the pattern database in response to the detected user action based on pre-defined criteria. For example, the pattern of user activity is identified based on type of engineering configuration being created. Alternatively, the pattern of user activity is identified based on a unique identifier associated with the user. In an exemplary implementation, the detected user action is identified among the sequence of user actions in the identified pattern of user activity. A user action following the detected user action is determined using the pattern of user activity. Furthermore, an element likely to be used for the engineering configuration is determined from the user action. In certain cases, the user may have used different types of the element in the determined user action. In such a case, the determined user action may indicate different types of element used in the previous engineering configurations. Then, the method comprises determining different types of the element that were used following the detected user action using the pattern of user activity. In this manner, the element suitable for use in the engineering configuration is dynamically determined using the pattern of user activity associated with the engineering configuration. Accordingly, the method comprises displaying a graphical object representing the element on the graphical user interface screen for use in the engineering configuration. In an exemplary implementation, the graphical object representing the element is obtained from an elements database and displayed in the element library window. Thus, the element suitable for use in the engineering configuration being created is recommended to the user. Hence, the user can use the graphical object representing the element in the engineering configuration being created. Advantageously, the need for manually searching the graphical object representing the element in the element database is eliminated. As a result, time and effort required to create the engineering configuration is saved.” and see para [0015], “Therein, the method comprises capturing a series of user actions performed by the user during creation of engineering configurations. The series of user actions represents the pattern of user activity. Each of the series of user actions is indicative of different types of the element used by the user during creation of the engineering configurations. Thus, the captured pattern of user activity is stored and used for recommending elements suitable for use in the engineering configuration being created. Advantageously, the time and effort required to manually search for elements for use in the engineering configuration in the elements database is saved. Therein, the method comprises updating the pattern of user activity associated with the engineering configuration. For example, the user action performed by the user may deviate from the series of user actions associated with the pattern of user activity during creation of the engineering configuration. In such case, the pattern of user activity is updated based on the deviating user action.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for dynamic recommendation for engineering configuration as taught by Hedge in the industrial workflow tracking and identification system of Billi-Duran, 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.
Claim 19:
Further, Billi-Duran discloses the following limitations:
extracting, by the processing unit, a user action from the generated workflow (see para [0072], “In some embodiments, the discovery agent 702 may associate relevant supplemental information with a discovered message log based on keyword analysis of the log. For example, the customer may maintain a knowledgebase 706 on the plant or office network containing knowledgebase records and/or device documentation relating to particular devices or maintenance issues. Upon determining that a message log relates to a troubleshooting session for a particular machine or device, the discovery agent 702 (or discovery component 304) may generate an association between the log and a knowledgebase record or device document relating to that machine or device. Thus, when an event relating to the machine or device is detected, the workflow tracking and identification system 302 system can present, as part of a relevant workflow, a message log outlining steps taken in the past to address a maintenance issue pertaining to the machine/device, with links to relevant knowledgebase articles or device documents to provide supplemental information.”);
determining, by the processing unit, a count of user actions in the plurality of user actions which are similar to the extracted user action (see para [0095], “The system 302 may detect variations of this workflow carried out over multiple instances of the detected alarm condition. For example, some operators may change the order of some steps, or may use a different set of steps for addressing the alarm condition, where the different set of steps may share some common steps with the originally detected workflow. Over time, the workflow generation component can refine the recorded workflow based on these variations, as well as inferences of which workflows provide best results when addressing the alarm conditions (e.g., which operations cause the automation system to recover more quickly, or result in less accumulated downtime for the machine). For example, if multiple different sequences of operations are detected as resulting in recovery of a machine from a particular alarm condition, the workflow generation component may perform comparisons of the total times required for each of the sequences to recover the machine, and select as the preferred workflow that which consistently recovers the machine with the least amount of total downtime.” where multiple detecting of the sequence of operations can be considered to show a count of user actions given broadest reasonable interpretation);
determining, by the processing unit, that the determined count of user actions is lesser than a threshold (see para [0097], “In addition to generating workflows for addressing alarm or downtime issues, the system 302 can also learn workflows for carrying out operational tasks that result in optimal automation system performance or yield preferred production results. For example, the workflow generation component 318 may learn over time that a greatest product output of a particular automation system, or a greatest duration of total system runtime per shift, is achieved when a particular operator methodology is practiced. For example, it may be learned that machine downtime is minimized when the operator sets the system speed to be within a certain range, or when certain operating setpoints are set to be within determined optimal ranges. In another example, the system 302 may determine that a particular operator action—e.g., a manual acknowledgement, removal of a part from a station of the automation system, and adjustment of a setpoint, etc. —when performed at an identified stage of an operating system's cycle, repeatedly results in greater product throughput or lower cycle times relative to performing this operation during another stage of the cycle, or not performing the operation at al. Accordingly, the workflow generation component 318 can generate a workflow for operating the system that incorporates this step at the appropriate stage of the machine's cycle.” showing removal is one of the determinations based on thresholds like system speed or optimal ranges); and
removing, by the processing unit, the user action from the generated workflow based on the determination that the determined count of user actions is lesser than the threshold (see para [0097], “In addition to generating workflows for addressing alarm or downtime issues, the system 302 can also learn workflows for carrying out operational tasks that result in optimal automation system performance or yield preferred production results. For example, the workflow generation component 318 may learn over time that a greatest product output of a particular automation system, or a greatest duration of total system runtime per shift, is achieved when a particular operator methodology is practiced. For example, it may be learned that machine downtime is minimized when the operator sets the system speed to be within a certain range, or when certain operating setpoints are set to be within determined optimal ranges. In another example, the system 302 may determine that a particular operator action—e.g., a manual acknowledgement, removal of a part from a station of the automation system, and adjustment of a setpoint, etc. —when performed at an identified stage of an operating system's cycle, repeatedly results in greater product throughput or lower cycle times relative to performing this operation during another stage of the cycle, or not performing the operation at al. Accordingly, the workflow generation component 318 can generate a workflow for operating the system that incorporates this step at the appropriate stage of the machine's cycle.”).
Claim 20:
Further, Billi-Duran discloses the following limitations:
receiving, by the processing unit, a user request to execute the generated workflow in the multi-user engineering environment, wherein the engineering program is generated based on the received user request (see para [0059], “Once created by the indexing system 506, federated data model 202 can be searched by search component 310 based on search data 502 received from a user's wearable computer or other client device (in the case of explicit manual searches), or by a monitoring component 312 that facilitates automated searching and workflow generation. For example, in the case of manual searches, a user may submit search data 502 to the system 302 requesting a workflow relating to a particular procedure the user wishes to undertake. To this end, the device interface component 316 may be configured to generate and deliver a user interface display to the client device 402 that prompts the user for search criteria that can be used by the search component 310 to identify a desired workflow. In one or more embodiments, the user interface display may provide fields that allow the user to enter an identifier of an industrial system, device, or process; an abnormal condition relating to an industrial process; or other such criteria. Since the federated data model 202 inventories industrial systems and devices throughout the industrial enterprise, some embodiments of the user interface display may present a list of available industrial systems and/or device for selection by the user, and provide these selections to the device interface component 316 as the search data 502.”)
Claim 21:
Further, Billi-Duran discloses the following limitations:
receiving, by the processing unit, a user action performed by the user in the multi-user engineering environment (see para [0072], “In some embodiments, the discovery agent 702 may associate relevant supplemental information with a discovered message log based on keyword analysis of the log. For example, the customer may maintain a knowledgebase 706 on the plant or office network containing knowledgebase records and/or device documentation relating to particular devices or maintenance issues. Upon determining that a message log relates to a troubleshooting session for a particular machine or device, the discovery agent 702 (or discovery component 304) may generate an association between the log and a knowledgebase record or device document relating to that machine or device. Thus, when an event relating to the machine or device is detected, the workflow tracking and identification system 302 system can present, as part of a relevant workflow, a message log outlining steps taken in the past to address a maintenance issue pertaining to the machine/device, with links to relevant knowledgebase articles or device documents to provide supplemental information.”);
determining, by the processing unit, a count of user actions in the plurality of user actions which are similar to the user action (see para [0095], “The system 302 may detect variations of this workflow carried out over multiple instances of the detected alarm condition. For example, some operators may change the order of some steps, or may use a different set of steps for addressing the alarm condition, where the different set of steps may share some common steps with the originally detected workflow. Over time, the workflow generation component can refine the recorded workflow based on these variations, as well as inferences of which workflows provide best results when addressing the alarm conditions (e.g., which operations cause the automation system to recover more quickly, or result in less accumulated downtime for the machine). For example, if multiple different sequences of operations are detected as resulting in recovery of a machine from a particular alarm condition, the workflow generation component may perform comparisons of the total times required for each of the sequences to recover the machine, and select as the preferred workflow that which consistently recovers the machine with the least amount of total downtime.” where multiple detecting of the sequence of operations can be considered to show a count of user actions given broadest reasonable interpretation);
comparing, by the processing unit, the determined count of user action with a threshold (see para [0097], “In addition to generating workflows for addressing alarm or downtime issues, the system 302 can also learn workflows for carrying out operational tasks that result in optimal automation system performance or yield preferred production results. For example, the workflow generation component 318 may learn over time that a greatest product output of a particular automation system, or a greatest duration of total system runtime per shift, is achieved when a particular operator methodology is practiced. For example, it may be learned that machine downtime is minimized when the operator sets the system speed to be within a certain range, or when certain operating setpoints are set to be within determined optimal ranges. In another example, the system 302 may determine that a particular operator action—e.g., a manual acknowledgement, removal of a part from a station of the automation system, and adjustment of a setpoint, etc. —when performed at an identified stage of an operating system's cycle, repeatedly results in greater product throughput or lower cycle times relative to performing this operation during another stage of the cycle, or not performing the operation at al. Accordingly, the workflow generation component 318 can generate a workflow for operating the system that incorporates this step at the appropriate stage of the machine's cycle.” where comparisons are obvious to one of ordinary skill in the art based on determinations based on thresholds like system speed or optimal ranges);
determining, by the processing unit, that the user action is an outlier based the comparison (see para [0097], where an alarm or downtime issue can be considered an outlier given broadest reasonable interpretation); and
training, by the processing unit, the artificial intelligence model to generate another workflow for the determined user profile of the user, wherein the artificial intelligence model is trained based on the determination that the user action is the outlier, and the generated another workflow comprises user action (see para [0096], “Workflow generation component 318 may also incorporate specific user identities into the learned workflows. For example, workflow generation component 318 may learn, based on observation of the plant data and user behavior data, that one or more specific employees congregate at a particular location relative to an automation system when a certain alarm or downtime condition occurs. Accordingly, the workflow generation component 318 may associate the identities of these employees with the workflow for addressing that particular issue. In another scenario, the system 302 may learn, based on analysis of the plant data and user behavior data, that recovery of the automation system occurs more quickly when the issue is addressed by certain identified employees. Accordingly, the system 302 may link the workflow for the particular issue to those preferred user identities, such that the system 302 will subsequently direct notifications to those employees when the downtime condition is detected, or will inform other maintenance personnel that these identified users may be able to provide guidance in connection with addressing the detected issue.”).
Claims 24-26:
Further, Billi-Duran discloses the following limitations:
An engineering system for generation of an engineering program which is specific to each user of a plurality of users in a multi-user engineering environment, wherein the engineering system comprises: one or more processing units; and a memory coupled to the one or more processing unit, wherein the memory comprises an automation module stored in the form of machine-readable instructions executable by the one or more processing units, wherein the automation module is capable of performing a method according to claim 14 (see para [0120], “ Embodiments, systems, and components described herein, as well as industrial control systems and industrial automation environments in which various aspects set forth in the subject specification can be carried out, can include computer or network components such as servers, clients, programmable logic controllers (PLCs), automation controllers, communications modules, mobile computers, wireless components, control components and so forth which are capable of interacting across a network. Computers and servers include one or more processors—electronic integrated circuits that perform logic operations employing electric signals—configured to execute instructions stored in media such as random access memory (RAM), read only memory (ROM), a hard drives, as well as removable memory devices, which can include memory sticks, memory cards, flash drives, external hard drives, and so on.” and see para [0006], [0121]-[0126])
an engineering system as claimed in claim 24; a technical installation comprising one or more physical components; and one or more client devices communicatively coupled to the engineering system via a network, wherein the engineering system is configured to perform a method (Figs 1, 4-5, showing equivalent componetns and functionality)
A computer-program product, having machine-readable instructions stored therein, that when executed by a processing unit, cause the processors to perform a method according to claim 14 (see para [0120], “ Embodiments, systems, and components described herein, as well as industrial control systems and industrial automation environments in which various aspects set forth in the subject specification can be carried out, can include computer or network components such as servers, clients, programmable logic controllers (PLCs), automation controllers, communications modules, mobile computers, wireless components, control components and so forth which are capable of interacting across a network. Computers and servers include one or more processors—electronic integrated circuits that perform logic operations employing electric signals—configured to execute instructions stored in media such as random access memory (RAM), read only memory (ROM), a hard drives, as well as removable memory devices, which can include memory sticks, memory cards, flash drives, external hard drives, and so on.” and see para [0121]-[0126])
Claims 17, 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Billi-Duran, Hedge and Mathews, as applied above, and further in view of Almecija et al. (US 2018/0365025 A1) (hereinafter Almecija).
Claim 17:
Billi-Duran, Hedge and Mathews do not specifically disclose categorizing, by the processing unit, the set of user actions into a cluster of user actions based on the determination that each user action of the set of user actions are of the identical pattern of the plurality of patterns. In analogous art, Almecija discloses the following limitations:
wherein training the artificial intelligence model comprises: categorizing, by the processing unit, the set of user actions into a cluster of user actions based on the determination that each user action of the set of user actions are of the identical pattern of the plurality of patterns (see para [0008], " a method for an adaptive user interface is provided that can include the steps of retrieving user interface interaction history for a user; registering input actions of the user interacting with a first user interface; determining the user experience level of the user based on the user interface interaction history for the user and the registered input actions of the user; adapting a user interface based on the user experience level of the user; and outputting an adapted user interface. The method can include further steps of applying a learning component to the user interface interaction history and registered input actions for the user to assign a user grouping; and wherein the adapted user interface is further based on the user grouping. The user experience level can be determined for the specific software application and user interface paradigm. The user experience level can be further based on user profile information that includes at least one of job title, software access level, training classes taken, and location. The adapted user interface can have less buttons than the first user interface if the user has no user interface interaction history." and see para [0043], " At step 608, user experience system 104, through user experience learning component 142 in an embodiment, applies a learning component to assign and/or update one or more user groupings. This is an initial step in determining how to adapt a user interface. User experience learning component 142 may include deep learning, machine learning, and/or artificial intelligence principles to assign and/or update the user groupings. User experience learning component 142 learns and groups individuals across all of the various software applications and over time. Thus, it learns and develops an understanding of overall usage of the UI to group certain patterns and usages with similar patterns and usages. This is discussed further in relation to FIG. 5 and FIG. 7, among other places within. The user experience learning component 142 can automatically determine changes and patterns in the user's UI behavior and needs. For example, a user may have a similar way of working most sessions, but one session clicking buttons fast and furiously (i.e. shorter intervals between button clicks and faster mouse travel). Thus, the system can learn to group the user into a “speed needed” or “urgent” grouping that may dynamically update the user interface to show only the one anticipated next action or even automate some actions that would normally be clicks by the user in order to save time. Each time user experience learning component 142 is applied, the UI system can improve in its understanding of user needs and preferences, and thus output improved adapted user interfaces."); and
training, by the processing unit, the artificial intelligence model to generate another workflow which is specific to every user of the user profile, wherein the artificial intelligence model is trained by application of the unsupervised learning algorithm on the set of user actions, and the another workflow comprises a sequence of user actions present in the cluster of user actions (see para [0055], "In some embodiments, the user can select which parts of a software application (e.g. which menus, workflows, or processes) will include adaptive UIs and which will not have adaptive UI. For example, if a user uses a software application the same way every time and it is highly optimized, they may turn off or disable the adaptive functionality for that software application. And in other examples with complex software and complex needs, the user may always leave such an adaptive UI on so as to have the most efficient user of the software application. In some embodiments, the user can “pin” certain UI elements they like to be static while the rest of the user interface adapts according to the UI adaptive component 148. In some embodiments, the user has an on-screen option of adapting the user interface they can interact with, which will generate the adapted user interface at that time." and see para [0005]-[0008], [0033]-[0035] and see para [0061], where it would be obvious to one of ordinary skill in the art unsupervised learning would be a type of learning that could be used because para [0061] shows "Grouping includes applying certain input patterns into groups of similar patterns to great broader understandings, such as discussed regarding FIG. 7. Step 222 and 224, separately or in combination, may utilize computer learning (which can include machine learning, deep learning, and/or artificial intelligence modules, models, and/or algorithms).").
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Almecija with Billi-Duran, Hedge and Mathews because categorizing the actions into a cluster enables more effective user workflows where user time can be more efficient and can optimize task processing (see Almecija, para [0002]-[0004]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for adaptive user interfaces as taught by Almecija in the Billi-Duran, Hedge and Mathews 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 22-23:
Billi-Duran, Hedge and Mathews do not specifically disclose generating, by the processing unit, a plurality of user action clusters from the plurality of user actions based on the application of the unsupervised learning algorithm on the plurality of user actions, wherein each user action cluster of the plurality of user action clusters represent a specific logical workflow. In analogous art, Almecija discloses the following limitations:
generating, by the processing unit, a plurality of user action clusters from the plurality of user actions based on the application of the unsupervised learning algorithm on the plurality of user actions, wherein each user action cluster of the plurality of user action clusters represent a specific logical workflow (see para [0055], "In some embodiments, the user can select which parts of a software application (e.g. which menus, workflows, or processes) will include adaptive UIs and which will not have adaptive UI. For example, if a user uses a software application the same way every time and it is highly optimized, they may turn off or disable the adaptive functionality for that software application. And in other examples with complex software and complex needs, the user may always leave such an adaptive UI on so as to have the most efficient user of the software application. In some embodiments, the user can “pin” certain UI elements they like to be static while the rest of the user interface adapts according to the UI adaptive component 148. In some embodiments, the user has an on-screen option of adapting the user interface they can interact with, which will generate the adapted user interface at that time." and see para [0005]-[0008], [0033]-[0035] and see para [0061], where it would be obvious to one of ordinary skill in the art unsupervised learning would be a type of learning that could be used because para [0061] shows "Grouping includes applying certain input patterns into groups of similar patterns to great broader understandings, such as discussed regarding FIG. 7. Step 222 and 224, separately or in combination, may utilize computer learning (which can include machine learning, deep learning, and/or artificial intelligence modules, models, and/or algorithms)." and see para [0008], [0043]).
training an artificial intelligence model to generate the workflow which is specific to each user profile of the plurality of user profiles based on the analysis of the plurality of clusters of user actions of the plurality of users, wherein the workflow generated for each user profile of the plurality of user profiles comprises at least one cluster of user actions associated with the particular user profile (see para [0008], " a method for an adaptive user interface is provided that can include the steps of retrieving user interface interaction history for a user; registering input actions of the user interacting with a first user interface; determining the user experience level of the user based on the user interface interaction history for the user and the registered input actions of the user; adapting a user interface based on the user experience level of the user; and outputting an adapted user interface. The method can include further steps of applying a learning component to the user interface interaction history and registered input actions for the user to assign a user grouping; and wherein the adapted user interface is further based on the user grouping. The user experience level can be determined for the specific software application and user interface paradigm. The user experience level can be further based on user profile information that includes at least one of job title, software access level, training classes taken, and location. The adapted user interface can have less buttons than the first user interface if the user has no user interface interaction history." and see para [0043], " At step 608, user experience system 104, through user experience learning component 142 in an embodiment, applies a learning component to assign and/or update one or more user groupings. This is an initial step in determining how to adapt a user interface. User experience learning component 142 may include deep learning, machine learning, and/or artificial intelligence principles to assign and/or update the user groupings. User experience learning component 142 learns and groups individuals across all of the various software applications and over time. Thus, it learns and develops an understanding of overall usage of the UI to group certain patterns and usages with similar patterns and usages. This is discussed further in relation to FIG. 5 and FIG. 7, among other places within. The user experience learning component 142 can automatically determine changes and patterns in the user's UI behavior and needs. For example, a user may have a similar way of working most sessions, but one session clicking buttons fast and furiously (i.e. shorter intervals between button clicks and faster mouse travel). Thus, the system can learn to group the user into a “speed needed” or “urgent” grouping that may dynamically update the user interface to show only the one anticipated next action or even automate some actions that would normally be clicks by the user in order to save time. Each time user experience learning component 142 is applied, the UI system can improve in its understanding of user needs and preferences, and thus output improved adapted user interfaces.").
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for adaptive user interfaces as taught by Almecija in the Billi-Duran, Hedge and Mathews 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.
Heckel et al. (US 2020/0257259 A1), a system for validating a control program for an automation system, wherein the control program is created in a first controller programming language and is arranged and setup to control an apparatus or device of the automation system when executed by an automation controller of the automation system, where the system includes a controller environment for executing the control program, an equipment simulation environment for executing a simulation program for simulating behavior of the apparatus or device or parts thereof, whereas the equipment simulation environment is communicatively coupled to the controller environment to enable and/or perform a simulation of the automation system, and whereas the simulation program is created in the first controller programming language or a second controller programming language
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624
Prosecution Insights