Generative ui synthesis for ai systems

Abstract

User interfaces (UIs) that display information related to industrial expert applications are typically designed with pre-defined screens. Such interfaces can also define navigation sequences that are rigid, such that the sequences proceed without consideration of different styles, preferences, role-specific workflows of individual users. Methods, systems, and apparatuses can automatically render enriched user interfaces (UIs) and dynamically modify UIs based on user models that are generated via monitoring user behaviors and feedback.

Description

GENERATIVE UI SYNTHESIS FOR Al SYSTEMSBACKGROUND

[0001] Many industrial processes and machinery are monitored and controlled by operators or engineers using human-machine interface (HMI) screens or user interfaces (UIs). UIs can display information related to operational statuses of components. In some cases, HMI screens or UIs can render operational controls so that a user can control one or more monitored processes or components via the HMI screen or UI. UIs that display information related to industrial expert applications are typically designed with pre-defined screens. Such interfaces can also define navigation sequences that are rigid, such that the sequences proceed without consideration of different styles, preferences, role-specific workflows of individual users. It is recognized herein that existing approaches to designing and arranging industrial UIs lack efficiencies and capabilities, thereby limiting productivity and usability of various expert applications.BRIEF SUMMARY

[0002] Embodiments of the invention address and overcome one or more of the described- herein shortcomings by providing methods, systems, and apparatuses that automatically render enriched user interfaces (UIs) and dynamically modify UIs based on user profiles.

[0003] In an example aspect, a computing system defines an industrial expert application. The system can monitor user interactions with the industrial expert application so as to collect data related to a plurality of users. The system can construct a knowledge base based on the data. The knowledge base can include user profiles. The system can determine a first user intent of a first user that is using the industrial expert application. The system can select a first user profile from the user profiles, wherein the first user profile corresponds to the first user. Based on the first user profile and first user intent, the system can render a first user interface of the industrial expert application on a display, wherein the first user interface is tailored to the first user. The system can also determine a plurality of user intent characteristics associated with the first intent. The plurality of user intent characteristics can define a confidence level, an actionability level, and an understandability level. The system can render the first user interface also based on the confidence level, actionability level, and understandability level. The system can monitor a response to the first user interface by the first user. Based on the response, the knowledge base can be updated. The system can also prompt the first user toprovide feedback via the first user interface. Based on the feedback, the knowledge base can be updated.

[0004] In another example aspect, the system can determine a second user intent of a second user that is using the industrial expert application. The system can select a second user profile from the user profiles, wherein the second user profile corresponds to the first user. The system can, based on the second user profile and second user intent, render a second user interface of the industrial expert application that is tailored to the second user. In some cases, the second user interface is equivalent to the first user interface. Alternatively, the second user interface can be different than the first user interface, while the first user intent is equivalent to the second user intent. For example, based on the user intent characteristics associated with the first user intent, the system can generate the first user interface to define less information than the second user interface.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0005] The foregoing and other aspects of the present invention are best understood from the following detailed description when read in connection with the accompanying drawings. For the purpose of illustrating the invention, there is shown in the drawings embodiments that are presently preferred, it being understood, however, that the invention is not limited to the specific instrumentalities disclosed. Included in the drawings are the following Figures:

[0006] FIG. 1 is a block diagram of an example control system that includes a plurality of human-machine interface (HMT) screens, in accordance with an example embodiment.

[0007] FIG. 2 depicts an example computing system that includes an autoencoder configured to learn and generate user interfaces (UIs) in accordance with an example embodiment.

[0008] FIG. 3 is a flow diagram that depicts operations that can be performed by the computing system depicted in FIG. 2, in accordance with an example embodiment.

[0009] FIG. 4 illustrates a computing environment within which embodiments of the disclosure may be implemented.DETAILED DESCRIPTION

[0010] As an initial matter, a human-machine interface (HMI) screen may refer to a panel having one or more physical displays. Thus, unless otherwise specified, display, screen, and panel may be used interchangeably herein, without limitation. A given HMI screen may define a small text-based display or a large panel having key or touch support. Example HMI screens further include, without limitation, PC-based panels and high-resolution displays (e.g., televisions) associated with Supervisory Control and Data Acquisition (SCADA) servers. Furthermore, industrial expert applications can refer to software that has the capability to understand problem-specific knowledge and use various domain knowledge intelligently to suggest alternate paths of actions. Examples include, without limitation, collision detection systems, applications that plan and manage manufacturing processers, and tooling and fixture design applications. By way of further example, a collision detection system for train schedules can identify conflicts in a train schedule of a dispatch system. Conflicts can result in train collisions, so the application needs to find new routing options displayed to a dispatcher. As an example of an expert application in the manufacturing domain, there are applications that maintain the manufacturing line and its control devices (e.g., PLCs, Industrial PCs (IPCs)). Such an expert application can monitor the device status and intelligently analyze issues to suggest repair or maintenance actions even before the production line gets broken due to a failing device. Failures could be in the power supply, in the wear of material, or other properties of sensors that are read and controlled by the devices. In general, expert applications can define an application that is running on an on-premises server or as a web application on a PLC / HMI or tablet, etc.

[0011] It is recognized herein that industrial expert applications typically involve complex and heterogeneous data that is often generated dynamically by artificial intelligence (Al) and other non-determini tic algorithms. As a result, a given user interface (UI) often needs to fit varying data into fixed interfaces, thereby increasing the complexity of navigating such interfaces. It is further recognized herein that such rigid user interfaces (UIs) can also create usability issues that decrease the overall efficiency and effectiveness of the associated application. In particular, for example, inflexible UIs can result in decreased efficiency and productivity. It is further recognized herein that usability issues in expert systems can often go undetected and can negatively impact user satisfaction and productivity. In some examples, usability issues are particularly vulnerable to being undetected during operation when users have limited experience with a given application, thereby making it difficult to determine or justify technical improvements to address those usability issues.

[0012] By way of further background, user intent prediction (UIP) generally involves, in the context of e-commerce or commercial applications, predicting the probability that a user will perform a certain action on a website or in an application program, within a given time. For example, UIP might result in a prediction of which product a user is likely to buy or which word the user will type next in an email reply. User behavior is studied and understood in or order to perform accurate UIP. In some cases, models such as Markov decision processes or partially observable Markov decision processes can be used to represent and predict user behavior. It is recognized that such models are based on the knowledge of the user’s goal or plan. It is further recognized herein, however, that the user’s goal might be unknown especially, for example, at the beginning of a new task when interacting with the UI. Embodiments described herein can define user models that are not dependent on the knowledge of a user’s goal.

[0013] In some examples, machine learning techniques can be employed to perform user intent prediction. It is recognized herein, however, that training such machine learning models often requires knowledge of actions a user can take. Embodiments described herein can classify user intent into different categories or classifiers, such as confidence, understandability, and actionability. User intent can be classified from such classifiers.

[0014] With respect to tailoring a given UI to a specific user, early approaches enabled users to configure the UI design to their preferences. In some cases, users’ uses of existing functionalities was studied to prioritize the functionalities use most often or most recently, such that certain functionalities might be hidden or predominantly positioned on the UI. In various examples described herein, UIs can be automatically generated to adapt to behaviors based on an understanding of the predicted actionability of a user’s intent.

[0015] Referring initially to FIG. 1, an example industrial control system (ICS) 100 includes various HMI screens that can be implemented in accordance with embodiments described herein. The example system 100 includes an office or corporate IT network 102 and an operational plant or production network 104 communicatively coupled to the IT network 102. It will be understood that the ICS 100 is illustrated and simplified as an example, and HMI screens can be implemented in and other domains having other configurations, and all such systems are contemplated as being within the scope of this disclosure. For example, embodiments of the distributed control system can define an operational technology system,energy generation system (e.g., wind parks, solar parks, etc.), or an energy distribution network. 1 should also list OT (Operational Technology).

[0016] The production network 104 can include an abstraction engine 106 that is connected to the IT network 102. The production network 104 can include various production machines configured to work together to perform one or more manufacturing operations. Example production machines of the production network 104 can include, without limitation, robots 108 and other field devices, such as sensors 110, actuators 112, or other machines, which can be controlled by a respective PLC 114. The PLC 114 can send instructions to respective field devices. In some cases, a given PLC 114 can be coupled to one or more human machine interfaces (HMIs) 116.

[0017] The ICS 100, in particular the production network 104, can define a fieldbus portion 118 and an Ethernet portion 120. For example, the fieldbus portion 118 can include the robots 108, PLC 114, sensors 110, actuators 112, and HMIs 116. The fieldbus portion 118 can define one or more production cells or control zones. The fieldbus portion 118 can further include a data extraction node 115 that can be configured to communicate with a given PLC 114 and sensors 110.

[0018] The PLC 114, data extraction node 115, sensors 110, actuators 112, and HMI 116 within a given production cell can communicate with each other via a respective field bus 122. Each control zone can be defined by a respective PLC 114, such that the PLC 114, and thus the corresponding control zone, can connect to the Ethernet portion 120 via an Ethernet connection 124. The robots 108 can be configured to communicate with other devices within the fieldbus portion 118 via a WiFi connection 126. Similarly, the robots 108 can communicate with the Ethernet portion 120, in particular a Supervisory Control and Data Acquisition (SCADA) server 128, via the WiFi connection 126. The Ethernet portion 120 of the production network 104 can include various computing devices communicatively coupled together via the Ethernet connection 124. Example computing devices in the Ethernet portion 120 include, without limitation, a mobile data collector 130, HMIs 132, the SCADA server 128, the abstraction engine 106, a wireless router 134, a manufacturing execution system (MES) 136, an engineering system (ES) 138, and a log server 140. The ES 138 can include one or more engineering workstations. In an example, the MES 136, HMIs 132, ES 138, and log server 140 are connected to the production network 104 directly. The wireless router 134 can also connect to the production network 104 directly. Thus, in some cases, mobile users, for instance themobile data collector 130 and robots 108, can connect to the production network 104 via the wireless router 134. In some cases, by way of example, the ES 138 and the mobile data collector 130 define guest devices that are allowed to connect to the abstraction engine 106. The abstraction engine 106 can be configured to collect or obtain historical user information. The historical user information can include usage information associated with the context of an application, usage time, environment properties, and machine properties.

[0019] Example users of the ICS 100 include, for example and without limitation, operators of an industrial plant or engineers that can update the control logic of a plant. By way of an example, an operator can interact with the HMIs 132, which may be located in a control room of a given plant. Alternatively, or additionally, an operator can interact with HMIs of the ICS 100 that are located remotely from the production network 104. Similarly, for example, engineers can use the HMIs 116 that can be located in an engineering room of the ICS 100. Alternatively, or additionally, an engineer can interact with HMIs of the ICS 100 that are located remotely from the production network 104.

[0020] Referring also to FIG. 2, an example computing system 200 can be configured to dynamically generate UIs for HMIs 114 and HMIs 132, among other interfaces such as, for example and without limitation, the MES 136, ES 138, and the mobile data collector 130. The computing system 200 can include various interfaces for receiving data from external systems. The computing system 200 can define one or more deep neural networks (DNNs) or statistical classification models, for instance a hierarchical attention network (HAN) model 201, that can be configured to capture and predict the dependencies between a user’s actions and a system’s responses. For example, the model 201 can be configured to analyze the user’s actions at different levels of granularity, thereby accurately classifying intents of users.

[0021] The HAN model 201 can derive sub-goals / sub-intents from a sequence of actions, and then derive user intent (main goal) from those sub-intents. But not all sub-intents (user actions and system actions) are important. The HAN model 201 can include a bidirectional RNN and attention model. The bidirectional RNN can learn the meaning of the sequence of actions leading to sub-intents, and can encode actions as vectors. The attention network can then apply weights to each action and identify the most relevant actions that constitutes sub-intent. The same process can be applied to sub-intents and actions again to derive "user intents" (main goal), thereby defining hierarchical. Granularity can refer to multiple levels of sub-intents.The sequence of actions (intents / sub-intents) can be associated with a confidence score, understandability level, and actionability level based on the information from the user profile.

[0022] Thus, a user intent can refer to a user or intention that a user has when using an application. By way of example, a user intent could be "saving a word document." The user may have to navigate to File->Save a copy->Browse file location->Enter name of the file- >Save. Everything that the user does to achieve that goal of saving a document is through a sequence of actions and interactions with the system / application.

[0023] By way of further example, consider a predictive maintenance system for buildings that suggests corrective actions to a user (e.g., facilities manager) based on discovered issues. For example, an issue can arise because a room in a given building is too cold because a heat fan / blower is not working to transfer warm air into the room. The heat fan might not start due to a failing sensor reading which should switch on the heat fan at a certain temperature. In this example, the intent of the facility manager is to fix the issue. The user (facility manager) can select the issue description as an action. To know which fix might be applicable the manager might try to further understand the issue by checking the temperatures at the room, at the fan, etc., which defines a next level of action. Then the manager might set various temperatures for the sensor to see if the fan goes on, which would be a finer level of action. These different actions have different levels of granularity, as the user would not be able to directly check / test different sensor values. Instead the user has first to identify the issue, go into one reading, and therein change a value.

[0024] The computing system 200 can further include one or more processors 205 and a memory 215 storing a plurality of machine-readable instructions executable by the processors 205. The memory 215 may be implemented using any non-transitory medium. The computing system 200 may further include, or be communicatively coupled to, one or more databases 225 configured to store input data and output data associated with the model 201. For example, the databases 225 can define a first or user profile database configured to store and update user profile information, and a second or application database configured to update and store various application data. The user profile database can capture the actual actions that the user is performing to achieve their intent. The application database can contain the possibilities of the application for how (which different ways) the user can reach their intent.

[0025] The model 201 can include a plurality of layers, for instance an input layer 202a configured to receive data, an output layer 203b configured to generate a reconstruction of thedata or class scores associated with the data, and a plurality of intermediate layers connected between the input layer 202a and the output layer 203b. In some cases, the input layer 202a can be configured to receive a plurality of user actions, and the output layer 203b can be configured to generate class scores associated with intent of the user. In an example, the intermediate layers and the input layer 202a can define an encoder 202. In some cases, the encoder 202 can include a plurality of convolutional layers. The intermediate layers and output layer 203b can define a decoder 203. The decoder 203 can include one or more fully connected layers. The encoder 202 can include the input layer 202a configured to receive training and test data. The encoder 202 can further include a final convolutional or last feature layer 202c, and one or more intermediate or second convolutional layers 202b disposed between the input layer 202a and the final convolutional layer 202c. It will be understood that the illustrated model 200 is simplified for purposes of example. In particular, for example, models may include any number of layers as desired, in particular any number of intermediate layers, and all such models are contemplated as being within the scope of this disclosure.

[0026] The decoder 203, which can include a first layer 203a and a second or output layer 203b, can include connections between layers that are fully connected. For example, a neuron in the first layer 203a may communicate its output to every neuron in the second layer 203b, such that each neuron in the second layer 203b will receive input from every neuron in the first layer 203a. It will again be understood that the model is simplified for purposes of explanation, and that the model 201 is not limited to the number or configuration of layers illustrated in the decoder 203. For example, the encoder 202 and the decoder 203 may include layers that are convolutional or fully connected. In contrast to fully connected layers, convolutional layers may be locally connected, such that, for example, the neurons in the intermediate layer 202b might be connected to a limited number of neurons in the final convolutional layer 202c. The convolutional layers 202 can also be configured to share connections strengths associated with the strength of each neuron.

[0027] Still referring to FIG. 2, the input layer 202a of the model 201 can be configured to receive input data 204, and the output layer 203b can be configured to return an output 206 based on the input data 204. In some cases, the input data 204 is retrieved from the database 225. The output 206 can include a classification or arrangement associated with the input 204. For example, the output 206 can include an output vector that indicates a plurality of probabilities or class scores 208 for associated with various classifications (e.g., categories) orarrangements. Thus, the output layer 203b can be configured to generate probabilities (class scores) associated with input data 204.

[0028] Referring now to FIG. 3, example operations 300 can be performed by the computing system 200. At 302, a knowledge base is generated that includes various user profiles and interaction sequences (or user intent trees), which can also be collectively be referred to as user models. To generate the knowledge base that defines the user models, user data is captured so as to define historical usage information. Furthermore, the historical usage information is correlated with various application context and data, such as usage time, environmental factors, and machine properties. In a website example, the system can perform web-analytics so as to collect and analyze user data that is captured, for instance by enabling java scripts on a webpage. More generally, user data can be captured by monitoring the actions that lead to events triggered in the system. Dependencies of actions can lead to data correlation, where one action for instance needs prerequisites to be fulfilled in order to proceed to the next user action. Environmental factors, such as where the application is running, can also be monitored and identified.

[0029] Additionally, information, for instance user actions and system responses, associated with the application roles and system interaction history of various users is collected. Based on this data, an initial user profile is established and generalized to a set of predefined profile categories. Example categories include, without limitation, a novice user, an expert user, building manager, technician, etc. Thus, in some cases, user profile categories indicate the role and / or experience of the user. The user profile for a given user can include various information related to the user’s preferred way of working, and in which environment the user works. Different users have different profiles. An example category might include, for example, the users who work goal-oriented and and thus dislike to have further information provided 4before activating a next step. Such a user profile category might correspond to an expert user / technical facility expert.

[0030] The system 200 can perform sequence mining to analyze the captured user and historical usage data and identify common sequences of actions or patterns of system interactions. These sequences can define an initial knowledge base for future sequence or intent classification using hierarchical attention networks, such as the network 201.

[0031] For example, the network 201 can define a hierarchical attention network configured to capture the dependencies between the user's actions and the responses of the system, forinstance the system 100. The network 201 can define a hierarchical structure configured to analyze the user's actions at different levels of granularity, thereby generating accurate classifications of user intents. The network 201 can perform attention mechanisms to focus on the most relevant parts of the sequence, which helps to reduce noise and increase the accuracy of the classification. These captured action sequences can be further categorized in terms of their understandability, confidence, and actionability properties based on the user's profile or model. In particular, referring again to FIG. 3, at 304, the system can determine user intent characteristics or parameters, based on the user model (e.g., user profile and knowledge base). The user intent characteristics can include a confidence levels, an understandability level, and an actionability level. At 310, the system 200 can determine various user-specific intentions that are associated with the confidence level, understandability level, and actionability level that are based on the user profile.

[0032] The actionability level can refer to different actions as described herein. Confidence levels can define how confident the system 200 is that the user will take a particular action as the next action. The understandability can indicate how well the user is understanding the actions to be taken. For example, referring again to the example predictive maintenance system for buildings, the understandability level might indicate how much information is presented to the facility manager. By way of example, a high level of understandability might inform the system 200 to only present a next action as a button which is labeled accordingly, whereas a lower level of understandability the user might be given an additional hint or text that described what the effect or outcome of activating this button will be.

[0033] The user-specific intent can be dependent on a specific user profile. Because the system continuously learns the user provide, the system can also on how the user behaves and how the user interacts with the system. Based on the understanding of the user and their preferences for interacting with the system, the system can predict or determine user intent, at 310. For example, it can be predicted that a facility manager that has always requested further information / help before performing an action will also prefer to have this information / help for their other intents. Thus, user intents can be derived from behavior even if the behavior or system interactions includes help or actions related to explaining the action of intent.

[0034] With continuing reference to FIG. 3, system 200 can generate (or adjust or augment UIs) based on the user intentions determined at 310, and thus based on the knowledge base, so as to dynamically tailor the presentation of information and features to a specific user's needsand preferences. At 312, the system 200 can render an enriched UI with that displays or indicates various confidence information. For example, if a user’s action sequence is classified as a low confidence, the system 200 may indicate that low confidence on the UI with text, color, symbols, or the like. Additionally, or alternatively, at 312, based on the confidence level, the system 200 may provide additional guidance to help the user better understand the content presented to them, for instance the system 200 can provide additional context that provides more rationale based on the determined confidence level. By way of example, if a user’s action sequence is classified as low confidence, the system 200 might provide additional guidance to help the user better understand the content presented to them, and / or provide additional context that provides further rationale. At 314, based on the user intent and the user profile, confidence, understandability, and actionability, the UI can be dynamically rendered. For example, the system 200 can dynamically modify how information is presented to the user, so as to define dynamic actions. Continuing with the example predictive building maintenance system, dynamic actions can include, for example and without limitation, prefilling values (in the UI) for a sensor temperature check. Additionally, or alternatively, dynamic actions taken at 314 can include combining multiple actions (e.g., button clicks) into a single action, or separation a single action into multiple actions in the UI. Thus, the system 200 can generate suitable user interfaces for a particular user based on the identified user intent and user profile and characteristics.

[0035] Still referring to FIG. 3, at 316, the system can monitor actions of users, for instance continuously monitor actions, so as to identify user actions. In some examples, the system defines an agent that listens for any event that is triggered by a button click, menu selection, or the like. Alternatively, or additionally, the system can prompt the user for explicit feedback related to the UI, and the system can monitor responses, which can define user actions, to the prompts. Based on the identified user actions, the process can return to 302, where the knowledge base can be updated. For example, the user actions identified at 314 can be compared to interaction sequences and associated user intents in a tree defined by the knowledge base. When a user intent is identified, and a user profile is assigned to the user (at 310), the system can use this information to again dynamically modify how information is presented to the user, at 314. By way of example, if the system determines that a given user is an expert, the system might generate a UI that includes various technical data. Alternatively, by way of further example, if the system determines that a given user is a novice, or the action sequence associated with the user is assigned a low confidence (at 304), then the UI might begenerated (at 314) to present the user with a series of screens that only present the minimum information necessary for each stage of a process. Additionally, in some cases, the system can provide suggestions via the UI related to follow-up steps or actions, thereby increasing the efficiency of the user in reaching the identified intent. In various examples, a given user’s responses (user feedback) to these suggestions can be monitored (at 316) and fed back to the knowledge base (at 302), thereby improving the system’s knowledge base by better understanding users’ needs and preferences through explicit feedback provided by the user.

[0036] Without being bound by theory, by continuously evolving and improving the knowledge base through user feedback, the system can provide increasingly accurate and relevant suggestions, ultimately improving user experience and satisfaction. Thus, the operations 300 can define a feedback loop that enables the system to adapt to changing user behaviors and preferences over time, thereby ensuring that the knowledge base, and thus the system itself, remains effective and relevant.

[0037] FIG. 4 illustrates an example of a computing environment within which embodiments of the present disclosure may be implemented. A computing environment 500 includes a computer system 510 that may include a communication mechanism such as a system bus 521 or other communication mechanism for communicating information within the computer system 510. The computer system 510 further includes one or more processors 520 coupled with the system bus 521 for processing the information. HMI screens, panels, or displays may include, or be coupled to, the one or more processors 520.

[0038] The processors 520 may include one or more central processing units (CPUs), graphical processing units (GPUs), or any other processor known in the art. More generally, a processor as described herein is a device for executing machine-readable instructions stored on a computer readable medium, for performing tasks and may comprise any one or combination of, hardware and firmware. A processor may also comprise memory storing machine-readable instructions executable for performing tasks. A processor acts upon information by manipulating, analyzing, modifying, converting or transmitting information for use by an executable procedure or an information device, and / or by routing the information to an output device. A processor may use or comprise the capabilities of a computer, controller or microprocessor, for example, and be conditioned using executable instructions to perform special purpose functions not performed by a general purpose computer. A processor may include any type of suitable processing unit including, but not limited to, a central processingunit, a microprocessor, a Reduced Instruction Set Computer (RISC) microprocessor, a Complex Instruction Set Computer (CISC) microprocessor, a microcontroller, an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), a System-on-a-Chip (SoC), a digital signal processor (DSP), and so forth. Further, the processor(s) 520 may have any suitable micro architecture design that includes any number of constituent components such as, for example, registers, multiplexers, arithmetic logic units, cache controllers for controlling rcad / writc operations to cache memory, branch predictors, or the like. The micro architecture design of the processor may be capable of supporting any of a variety of instruction sets. A processor may be coupled (electrically and / or as comprising executable components) with any other processor enabling interaction and / or communication there-between. A user interface processor or generator is a known element comprising electronic circuitry or software or a combination of both for generating display images or portions thereof. A user interface comprises one or more display images enabling user interaction with a processor or other device.

[0039] The system bus 521 may include at least one of a system bus, a memory bus, an address bus, or a message bus, and may permit exchange of information (e.g., data (including computer-executable code), signaling, etc.) between various components of the computer system 510. The system bus 521 may include, without limitation, a memory bus or a memory controller, a peripheral bus, an accelerated graphics port, and so forth. The system bus 521 may be associated with any suitable bus architecture including, without limitation, an Industry Standard Architecture (ISA), a Micro Channel Architecture (MCA), an Enhanced ISA (EISA), a Video Electronics Standards Association (VESA) architecture, an Accelerated Graphics Port (AGP) architecture, a Peripheral Component Interconnects (PCI) architecture, a PCI-Express architecture, a Personal Computer Memory Card International Association (PCMCIA) architecture, a Universal Serial Bus (USB) architecture, and so forth.

[0040] Continuing with reference to FIG. 4, the computer system 510 may also include a system memory 530 coupled to the system bus 521 for storing information and instructions to be executed by processors 520. The system memory 530 may include computer readable storage media in the form of volatile and / or nonvolatile memory, such as read only memory (ROM) 531 and / or random access memory (RAM) 532. The RAM 532 may include other dynamic storage device(s) (e.g., dynamic RAM, static RAM, and synchronous DRAM). The ROM 531 may include other static storage device(s) (e.g., programmable ROM, erasablePROM, and electrically erasable PROM). In addition, the system memory 530 may be used for storing temporary variables or other intermediate information during the execution of instructions by the processors 520. A basic input / output system 533 (BIOS) containing the basic routines that help to transfer information between elements within computer system 510, such as during start-up, may be stored in the ROM 531. RAM 532 may contain data and / or program modules that are immediately accessible to and / or presently being operated on by the processors 520. System memory 530 may additionally include, for example, operating system 534, application programs 535, and other program modules 536. Application programs 535 may also include a user portal for development of the application program, allowing input parameters to be entered and modified as necessary.

[0041] The operating system 534 may be loaded into the memory 530 and may provide an interface between other application software executing on the computer system 510 and hardware resources of the computer system 510. More specifically, the operating system 534 may include a set of computer-executable instructions for managing hardware resources of the computer system 510 and for providing common services to other application programs (e.g., managing memory allocation among various application programs). In certain example embodiments, the operating system 534 may control execution of one or more of the program modules depicted as being stored in the data storage 540. The operating system 534 may include any operating system now known or which may be developed in the future including, but not limited to, any server operating system, any mainframe operating system, or any other proprietary or non-proprietary operating system.

[0042] The computer system 510 may also include a disk / media controller 543 coupled to the system bus 521 to control one or more storage devices for storing information and instructions, such as a magnetic hard disk 541 and / or a removable media drive 542 (e.g., floppy disk drive, compact disc drive, tape drive, flash drive, and / or solid state drive). Storage devices 540 may be added to the computer system 510 using an appropriate device interface (e.g., a small computer system interface (SCSI), integrated device electronics (IDE), Universal Serial Bus (USB), or FireWire). Storage devices 541, 542 may be external to the computer system 510.

[0043] The computer system 510 may also include a field device interface 565 coupled to the system bus 521 to control a field device 566, such as a device used in a production line. The computer system 510 may include a user input interface or GUI 561, which may comprise oneor more input devices, such as a keyboard, touchscreen, tablet and / or a pointing device, for interacting with a computer user and providing information to the processors 520.

[0044] The computer system 510 may perform a portion or all of the processing steps of embodiments of the invention in response to the processors 520 executing one or more sequences of one or more instructions contained in a memory, such as the system memory 530. Such instructions may be read into the system memory 530 from another computer readable medium of storage 540, such as the magnetic hard disk 541 or the removable media drive 542. The magnetic hard disk 541 (or solid state drive) and / or removable media drive 542 may contain one or more data stores and data files used by embodiments of the present disclosure. The data store 540 may include, but are not limited to, databases (e.g., relational, object-oriented, etc.), file systems, flat files, distributed data stores in which data is stored on more than one node of a computer network, peer-to-peer network data stores, or the like. The data stores may store various types of data such as, for example, skill data, sensor data, or any other data generated in accordance with the embodiments of the disclosure. Data store contents and data files may be encrypted to improve security. The processors 520 may also be employed in a multi-processing arrangement to execute the one or more sequences of instructions contained in system memory 530. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.

[0045] As stated above, the computer system 510 may include at least one computer readable medium or memory for holding instructions programmed according to embodiments of the invention and for containing data structures, tables, records, or other data described herein. The term “computer readable medium” as used herein refers to any medium that participates in providing instructions to the processors 520 for execution. A computer readable medium may take many forms including, but not limited to, non-transitory, non-volatile media, volatile media, and transmission media. Non-limiting examples of non-volatile media include optical disks, solid state drives, magnetic disks, and magneto-optical disks, such as magnetic hard disk 541 or removable media drive 542. Non-limiting examples of volatile media include dynamic memory, such as system memory 530. Non-limiting examples of transmission media include coaxial cables, copper wire, and fiber optics, including the wires that make up the system bus 521. Transmission media may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.

[0046] Computer readable medium instructions for carrying out operations of the present disclosure may be assembler instructions, instruction- set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, statesetting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0047] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, may be implemented by computer readable medium in truction .

[0048] The computing environment 500 may further include the computer system 510 operating in a networked environment using logical connections to one or more remote computers, such as remote computing device 580. The network interface 570 may enable communication, for example, with other remote devices 580 or systems and / or the storage devices 541, 542 via the network 571. Remote computing device 580 may be a personal computer (laptop or desktop), a mobile device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to computer system 510. When used in a networking environment, computersystem 510 may include modem 572 for establishing communications over a network 571, such as the Internet. Modem 572 may be connected to system bus 521 via user network interface 570, or via another appropriate mechanism.

[0049] Network 571 may be any network or system generally known in the art, including the Internet, an intranet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a direct connection or series of connections, a cellular telephone network, or any other network or medium capable of facilitating communication between computer system 510 and other computers (e.g., remote computing device 580). The network 571 may be wired, wireless or a combination thereof. Wired connections may be implemented using Ethernet, Universal Serial Bus (USB), RJ-6, or any other wired connection generally known in the art. Wireless connections may be implemented using Wi-Fi, WiMAX, and Bluetooth, infrared, cellular networks, satellite or any other wireless connection methodology generally known in the art. Additionally, several networks may work alone or in communication with each other to facilitate communication in the network 571.

[0050] It should be appreciated that the program modules, applications, computer-executable instructions, code, or the like depicted in FIG. 4 as being stored in the system memory 530 arc merely illustrative and not exhaustive and that processing described as being supported by any particular module may alternatively be distributed across multiple modules or performed by a different module. In addition, various program module(s), script(s), plug-in(s), Application Programming Interface(s) (API(s)), or any other suitable computer-executable code hosted locally on the computer system 510, the remote device 580, and / or hosted on other computing device(s) accessible via one or more of the network(s) 571, may be provided to support functionality provided by the program modules, applications, or computer-executable code depicted in FIG. 4 and / or additional or alternate functionality. Further, functionality may be modularized differently such that processing described as being supported collectively by the collection of program modules depicted in FIG. 4 may be performed by a fewer or greater number of modules, or functionality described as being supported by any particular module may be supported, at least in part, by another module. In addition, program modules that support the functionality described herein may form part of one or more applications executable across any number of systems or devices in accordance with any suitable computing model such as, for example, a client-server model, a peer-to-peer model, and so forth. In addition, any of the functionality described as being supported by any of the program modules depicted in FIG. 4may be implemented, at least partially, in hardware and / or firmware across any number of devices.

[0051] It should further be appreciated that the computer system 510 may include alternate and / or additional hardware, software, or firmware components beyond those described or depicted without departing from the scope of the disclosure. More particularly, it should be appreciated that software, firmware, or hardware components depicted as forming part of the computer system 510 are merely illustrative and that some components may not be present or additional components may be provided in various embodiments. While various illustrative program modules have been depicted and described as software modules stored in system memory 530, it should be appreciated that functionality described as being supported by the program modules may be enabled by any combination of hardware, software, and / or firmware. It should further be appreciated that each of the above-mentioned modules may, in various embodiments, represent a logical partitioning of supported functionality. This logical partitioning is depicted for ease of explanation of the functionality and may not be representative of the structure of software, hardware, and / or firmware for implementing the functionality. Accordingly, it should be appreciated that functionality described as being provided by a particular module may, in various embodiments, be provided at least in part by one or more other modules. Further, one or more depicted modules may not be present in certain embodiments, while in other embodiments, additional modules not depicted may be present and may support at least a portion of the described functionality and / or additional functionality. Moreover, while certain modules may be depicted and described as sub-modules of another module, in certain embodiments, such modules may be provided as independent modules or as sub-modules of other modules.

[0052] Although specific embodiments of the disclosure have been described, one of ordinary skill in the art will recognize that numerous other modifications and alternative embodiments are within the scope of the disclosure. For example, any of the functionality and / or processing capabilities described with respect to a particular device or component may be performed by any other device or component. Further, while various illustrative implementations and architectures have been described in accordance with embodiments of the disclosure, one of ordinary skill in the art will appreciate that numerous other modifications to the illustrative implementations and architectures described herein are also within the scope of this disclosure. In addition, it should be appreciated that any operation, element, component,data, or the like described herein as being based on another operation, element, component, data, or the like can be additionally based on one or more other operations, elements, components, data, or the like. Accordingly, the phrase “based on,” or variants thereof, should be interpreted as “based at least in part on.”

[0053] Although embodiments have been described in language specific to structural features and / or methodological acts, it is to be understood that the disclosure is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the embodiments. Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments could include, while other embodiments do not include, certain features, elements, and / or steps. Thus, such conditional language is not generally intended to imply that features, elements, and / or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements, and / or steps are included or are to be performed in any particular embodiment.

[0054] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

Claims

CLAIMSWhat is claimed is:

1. A method performed by a computing system that defines an industrial expert application, the method comprising: monitoring user interactions with the industrial expert application so as to collect data related to a plurality of users; constructing a knowledge base based on the data, the knowledge base comprising user profiles; determining a first user intent of a first user that is using the industrial expert application; selecting a first user profile from the user profiles, the first user profile corresponding to the first user; and based on the first user profile and first user intent, rendering a first user interface of the industrial expert application that is tailored to the first user.

2. The method as recited in claim 1, the method further comprising: determining a plurality a user intent characteristics associated with the first intent, the plurality of user intent characteristics defining a confidence level, an actionability level, and an understandability level.

3. The method as recited in claim 2, the method further comprising: rendering the first user interface also based on the confidence level, actionability level, and understandability level.

4. The method as recited in claim 3, the method further comprising: monitoring a response to the first user interface by the first user; and updating the knowledge base based on the response.

5. The method as recited in claim 4, the method further comprising:prompting the first user to provide feedback via the first user interface; and updating the knowledge base based on the feedback.

6. The method as recited in claim 5, the method further comprising: determining a second user intent of a second user that is using the industrial expert application; selecting a second user profile from the user profiles, the second user profile corresponding to the first user; and based on the second user profile and second user intent, rendering a second user interface of the industrial expert application that is tailored to the second user.

7. The method as recited in claim 6, wherein the second user interface is equivalent to the first user interface.

8. The method as recited in claim 6, wherein the second user interface is different than the first user interface, but the first user intent is equivalent to the second user intent.

9. The method as recited in claim 6, the method further comprising: based on the user intent characteristics associated with the first user intent, generating the first user interface to define less information than the second user interface.

10. A computing system comprising: a display; an industrial expert application; a processor; and a memory storing instructions that, when executed by the processor, cause the processor to: monitor user interactions with the industrial expert application so as to collect data related to a plurality of users;construct a knowledge base based on the data, the knowledge base comprising user profiles; determine a first user intent of a first user that is using the industrial expert application; select a first user profile from the user profiles, the first user profile corresponding to the first user; and based on the first user profile and first user intent, rendering a first user interface of the industrial expert application on the display, the first user interface is tailored to the first user.

11. The computing system as recited in claim 10, the memory further storing instructions that, when executed by the processor, further cause the processor to: determine a plurality a user intent characteristics associated with the first intent, the plurality of user intent characteristics defining a confidence level, an actionability level, and an understandability level.

12. The computing system as recited in claim 11, the memory further storing instructions that, when executed by the processor, further cause the processor to: render the first user interface on the display also based on the confidence level, actionability level, and understandability level.

13. The computing system as recited in claim 12, the memory further storing instructions that, when executed by the processor, further cause the processor to: monitor a response to the first user interface by the first user; and update the knowledge base based on the response.

14. The computing system as recited in claim 13, the memory further storing instructions that, when executed by the processor, further cause the processor to: determine a second user intent of a second user that is using the industrial expert application; selecting a second user profile from the user profiles, the second user profile corresponding to the first user; andbased on the second user profile and second user intent, rendering a second user interface of the industrial expert application on the display, the second user interface tailored to the second user.

15. The computing system as recited in claim 14, wherein the second user interface is different than the first user interface, but the first user intent is equivalent to the second user intent.

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