Patent Publication Number: US-2018052696-A1

Title: Providing teaching user interface activated by user action

Description:
BACKGROUND 
     Information collection, management, and analysis have changed work processes and associated application use. Automation and improvements in work processes have expanded scope of capabilities offered by applications. With the development of faster and smaller electronics, execution of mass processes at client and cloud application systems have become feasible. Indeed, feature deployment at client devices and cloud solutions have become common features in modern application environments. Such solutions provide a wide variety of applications such as productivity applications that present training tools to deploy features. Many such applications present training materials to attempt to improve and optimize utilization. User training and preparation also consume significant resources and performance at a promise of improved processes and condensed task flows affecting utilization of productivity applications. 
     User training techniques are becoming ever more important as application complexity grows in proportion to processing capacity across the computer industry. Variety of training techniques are necessary for deployment of multiple features that relate to a multitude of user audiences. There are currently significant gaps within training presentation methods employed when dealing with application feature sets. Lack of easy to use training presentation methods lead to underutilization of application features. 
     SUMMARY 
     This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to exclusively identify key features or essential features of the claimed subject matter, nor is it intended as an aid in determining the scope of the claimed subject matter. 
     Embodiments are directed to providing a teaching user interface (UI) activated by a user action. A productivity service, according to embodiments, may initiate operations to provide the teaching UI upon receiving a notification of a user action from a productivity application. The productivity service may also recognize a trait associated with a user performing the user action. A content associated with a feature of the productivity application may be identified for a presentation in a teaching UI based on the trait and the user action. Furthermore, the content may be provided in the teaching UI for a presentation on the productivity application. 
     These and other features and advantages will be apparent from a reading of the following detailed description and a review of the associated drawings. It is to be understood that both the foregoing general description and the following detailed description are explanatory and do not restrict aspects as claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a conceptual diagram illustrating an example of providing a teaching user interface (UI) activated by a user action, according to embodiments; 
         FIG. 2  is a display diagram illustrating a scheme to provide a teaching UI activated by a user action, according to embodiments; 
         FIG. 3  is a display diagram illustrating example components of a productivity application that provides a teaching UI activated by a user action, according to embodiments; 
         FIG. 4  is a display diagram illustrating components of a scheme to provide a teaching UI activated by a user action, according to embodiments; 
         FIG. 5  is a simplified networked environment, where a system according to embodiments may be implemented; 
         FIG. 6  is a block diagram of an example computing device, which may be used to provide a teaching UI activated by a user action, according to embodiments; and 
         FIG. 7  is a logic flow diagram illustrating a process for providing a teaching UI activated by a user action, according to embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     As briefly described above, a productivity service may provide a teaching user interface (UI) activated by a user action. In an example scenario, the productivity service may receive a notification of a user action from a productivity application. The user action may include a workflow which may indicate an intent of the user to activate a feature of the productivity application. The feature may include a previously unused feature, an underutilized feature, and/or a new feature, among others. 
     The productivity service may recognize a trait associated with the user. The trait may include a user credential, a context associated with the user, and/or similar ones. Next, a content associated with a feature of the productivity application may be identified for a presentation in a teaching UI based on the trait and the user action. The content may include a video stream, an audio stream, and/or a presentation, among others with steps describing how to use the feature. The productivity service may provide the content in the teaching UI for a presentation on the productivity application. 
     In the following detailed description, references are made to the accompanying drawings that form a part hereof, and in which are shown by way of illustrations, specific embodiments, or examples. These aspects may be combined, other aspects may be utilized, and structural changes may be made without departing from the spirit or scope of the present disclosure. The following detailed description is therefore not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents. 
     While some embodiments will be described in the general context of program modules that execute in conjunction with an application program that runs on an operating system on a personal computer, those skilled in the art will recognize that aspects may also be implemented in combination with other program modules. 
     Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that embodiments may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and comparable computing devices. Embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. 
     Some embodiments may be implemented as a computer-implemented process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage medium readable by a computer system and encoding a computer program that comprises instructions for causing a computer or computing system to perform example process(es). The computer-readable storage medium is a physical computer-readable memory device. The computer-readable storage medium can for example be implemented via one or more of a volatile computer memory, a non-volatile memory, a hard drive, a flash drive, a floppy disk, or a compact disk, and comparable hardware media. 
     Throughout this specification, the term “platform” may be a combination of software and hardware components to provide a teaching UI activated by a user action. Examples of platforms include, but are not limited to, a hosted service executed over a plurality of servers, an application executed on a single computing device, and comparable systems. The term “server” generally refers to a computing device executing one or more software programs typically in a networked environment. More detail on these technologies and example operations is provided below. 
     A computing device, as used herein, refers to a device comprising at least a memory and a processor that includes a desktop computer, a laptop computer, a tablet computer, a smart phone, a vehicle mount computer, or a wearable computer. A memory may be a removable or non-removable component of a computing device configured to store one or more instructions to be executed by one or more processors. A processor may be a component of a computing device coupled to a memory and configured to execute programs in conjunction with instructions stored by the memory. A file is any form of structured data that is associated with audio, video, or similar content. An operating system is a system configured to manage hardware and software components of a computing device that provides common services and applications. An integrated module is a component of an application or service that is integrated within the application or service such that the application or service is configured to execute the component. A computer-readable memory device is a physical computer-readable storage medium implemented via one or more of a volatile computer memory, a non-volatile memory, a hard drive, a flash drive, a floppy disk, or a compact disk, and comparable hardware media that includes instructions thereon to automatically save content to a location. A user experience—a visual display associated with an application or service through which a user interacts with the application or service. A user action refers to an interaction between a user and a user experience of an application or a user experience provided by a service that includes one of touch input, gesture input, voice command, eye tracking, gyroscopic input, pen input, mouse input, and keyboards input. An application programming interface (API) may be a set of routines, protocols, and tools for an application or service that enable the application or service to interact or communicate with one or more other applications and services managed by separate entities. 
       FIG. 1  is a conceptual diagram illustrating examples of providing a teaching UI activated by a user action, according to embodiments. 
     In a diagram  100 , a physical server  106  may execute a productivity service  102 . The productivity service  102  may include a cloud based service. The physical server  105  may include a desktop computer, a work station, a data warehouse, a datacenter, and/or similar ones. The physical server  105  may also include a special purpose and/or configured device that is optimized to execute data operations associated with the productivity service  102 . For example, the physical server  105  may include physical components that are custom built to accelerate operations associated with a teaching UI. 
     The physical server  105  may execute the productivity service  102 . The productivity service  102  may initiate operations to provide a teaching UI upon receiving a notification of a user action  112  from a productivity application  103  executed on a client device  104 . The client device  104  may include a mobile device, a notebook computer, a smartphone, and/or a desktop computer, among others. The user action  112  may include operations to activate a feature  108  within a menu  106  of an application UI of the productivity application  102 . The menu  106  may include other features in addition to the feature  108 . Each feature may be configured to execute operations to accomplish a task associated with the productivity application  102 . The productivity service  102  may prompt the productivity application  103  to activate the teaching UI  114  for a variety of conditions that may involve a detected trait of a user  110  such as previous use patterns associated with the productivity application  102 , among others. 
     The trait of the user may include a credential, a context associated with the user, and/or similar ones. The productivity service  102  may identify a content  116  associated with the feature  108  for a presentation in the teaching UI  114 . The content  116  may include a video stream, an audio stream, a presentation, and/or similar ones that describe how to use the feature  108  to the user  110 . The content  116  may be provided for a presentation in the teaching UI  114  to the productivity application  103 . The productivity service  102  may generate the teaching UI  114  and provide the content  116  within the teaching UI for rendering by the productivity application  103 . 
     For example, the productivity service  102  may receive a notification of the user action  112  from the productivity application  103 . The user action may include two or more steps that the user  110  may execute to accomplish a task in the productivity application  103 . The productivity service  102  may match the user action and other traits such as the two or steps to a feature  108  of the productivity application  103  that may accomplish the task in one step. The productivity service  102  may identify a content associated with the productivity service and provide the content to the productivity application  103  for a presentation to the user  110  to show how to use the feature  108 . 
     The physical server  105  may communicate with the client device  104  and other client device(s) or server(s) through a network. The network may provide wired or wireless communications between network nodes such as the computing device  104 , other client device(s) and/or server(s), among others. Previous example(s) to provide the teaching UI  114  activated by the user action  112  are not provided in a limiting sense. Alternatively, the productivity application  103  may be provided as a client interface of the productivity service  102 . In another scenario, the productivity application  103  may process the user action to identify the content  106  and present the content  106  on the teaching UI. In yet another scenario, the teaching UI  114  may be provided to a client application which interacts with the user  110  through other client devices such as a smartphone, among others. 
     The user  110  may interact with the productivity application  102  with a keyboard based input, a mouse based input, a voice based input, a pen based input, and a gesture based input, among others. The gesture based input may include one or more touch based actions such as a touch action, a swipe action, and a combination of each, among others. 
     While the example system in  FIG. 1  has been described with specific components including the physical server  105 , the productivity service  102 , embodiments are not limited to these components or system configurations and can be implemented with other system configuration employing fewer or additional components. 
       FIG. 2  is a display diagram illustrating a scheme to provide a teaching UI activated by a user action, according to embodiments. 
     In a diagram  200 , a productivity application  203  may display application UT components such as a menu  206  that may provide components that execute operations. An example of a component may include a feature  208 . The feature  208  may include an existing feature or a new feature. Use patterns by the user  210  may be analyzed by a productivity service  202  to determine an underutilization or a failure to use the feature  208 . In such a scenario, a user action  212  may be analyzed in relation to the feature  208  by the productivity service  202 . The productivity service  202  may prompt the productivity application  203  to present a teaching UI  214  to render content describing how to use the feature  208 . 
     The productivity service  202  may provide the teaching UI  214  (to the productivity application  203 ) for a presentation a content  216  associated with the feature  208 . The content  216  may include instructions to teach the user  210  how to use the feature  208 . The content  216  may include a video stream, an audio stream, a presentation, and/or similar ones with steps to illustrate utilization of the feature  208 . A trait  218  of the user and the user action  212  may be used to identify the content  216 . The trait  218  may include a credential of the user  210  and/or a context associated with the user  210 . The credential may be used to access information providers such as an organizational service to receive historical information associated with the user  210  such as use patterns of the productivity application  202 . The user patterns may be analyzed to detect a failure to use or underutilization of the feature  208 . 
       FIG. 3  is a display diagram illustrating example components of a productivity application that provides a teaching UI activated by a user action, according to embodiments. 
     In a diagram  300 , an inference engine  324  of a productivity service  302  may process a user action  312  (received from a productivity application  303 ) and a trait  318  of a user  310  to identify a content  316  associated with a feature  308 . The content  316  may include an illustration of how to use the feature  308 . The content  316  may be provided to the user  310  by a rendering engine  322  within a teaching UI  314 . 
     The teaching UI  314  may include controls to display the content  316  which may include a video stream, an audio stream, a presentation, and/or similar ones that describe how to use the feature. The content  316  may include steps to instruct the user  310 . The content  316  may be provided dynamically in response to the user action  312 . As such, training the user  310  on how to use the feature  308  may be automated by providing the content  316  on demand in response to the user action  312 . The demand for the content  316  may be determined by the inference engine  324  based on an analysis of various factors including a user intent, and/or use history, among others associated with the productivity application  303  and the feature  308  rendered by the productivity application  303 . 
     In an example scenario, the inference engine  324  may identify a credential  326  as a trait  318  of the user  310 . The inference engine  324  may query a provider with the credential  326  for a context  328  associated with the user  310 . The context  328  may include a role of the user within an organization, an expertise of the user with the productivity application  302 , a training history of the user with the productivity application  302 , and/or a utilization history of the user with the productivity application, among others. An example of the provider may include a component of the productivity service  302 , an external entity such as an organizational provider, a networking provider, and/or similar ones. For example, the inference engine  324  may query the provider to receive a training history of the user  310  in relation to the feature  308  presented on the productivity application  303 . Upon analysis of the training history, the inference engine  324  may determine that the user  310  was not exposed to training associated with the feature  308 . In such a scenario, the content  316  that describes how to use the feature  308  may be presented to the user  310  on the teaching UT  314 . 
     Alternatively, a role of the user  310  (such as a supervisor) may be identified as the context  328  associated with the user  310 . An organization associated with the user  310  may have rule(s) that mandate any user with the role to receive training on the feature  308 . The user action  312  may be analyzed to detect that the user  310  lacks training on how to use the feature  308  (based on a detected work flow that evades the feature  308 ). In such a scenario, the inference engine  324  may search and locate the content  316  that matches the context associated with the user  310 . For example, the inference engine  324  may customize the content  316  to highlight sections of the content  316  that address users lack of training on the feature  308 . 
       FIG. 4  is a display diagram illustrating components of a scheme to provide a teaching UI activated by a user action, according to embodiments. 
     In a diagram  400 , an inference engine  424  of a productivity service  4012  may identify a content  416  associated with a feature  408  presented on a productivity application. The feature  408  may be a component of the productivity application. Alternatively, the feature  408  may be provided to the productivity application by the productivity service  402 . The content  416  may be presented to a user in a teaching UI  414  to train the user on how to use the feature  408 . 
     In an example scenario, the inference engine  424  may identify a trait  418  of the user. The trait  418  may be used to identify the content when searching for the content based on the trait and the user action. For example, a credential  426  of the user may be identified as the trait  418  of the user. The credential  426  may be used to retrieve a use history of the productivity application by the user. The use history may include past user actions  432  associated with the productivity application (and/or other productivity applications). The inference engine  424  may analyze the use history to identify the feature  408  of the productivity application underutilized or unused by the user. The content  416  that matches the feature  408  may be identified and presented to the user to enhance a utilization of the feature  408  by the user. 
     The inference engine  424  may also analyze the user action  412  to detect an intent of the user to active the feature  408 . In such a scenario, the content  416  (that matches the feature  408 ) may be automatically searched, located, and presented to the user through the teaching UI  414 . Instead of an automated presentation, the inference engine  424  may also analyze a use history (such as the past user actions  432 ) associated with the feature  408  to detect an underutilization or a failure to use the feature  408  (by the user). As a result, the content  416  may be provided for a presentation to the user through the teaching UI  414 . 
     Furthermore, the inference engine  424  may analyze the user action  412  to detect a workflow  434  of the user. The workflow  434  may describe one or more operations and an intent of the user while executing the user action  412 . The workflow  434  may also be detected to have an association with the feature  408 . For example, the user may execute a series of operations that are related to the feature  408 . In such a scenario, the inference engine  424  may infer that the user may intend to activate the feature  408  based on the workflow. The feature  408  may also be detected as an alternative to the workflow  434 . The feature  408  may simplify operations to achieve an end result of the workflow  434 . The inference engine  424  may further automate a presentation of the content  416  by analyzing past user actions  432  to detect an underutilization or a failure to use the feature  408  (by the user). As a result, the content  416  that matches the feature  408  may be searched, located, and provided for a presentation in the teaching UI  414 . 
     The inference engine  424  may further decide when to have a rendering engine provide the teaching UI to the productivity application. In an example scenario, the teaching UI may be provided upon a conclusion of the user action. The teaching UI may also be provided for a presentation in proximity to a section within the productivity application where the user action may conclude. The section (of the productivity application) to display the teaching UI  414  may be selected to keep the user focused on where the user action concludes. The content  416  may also include multiple steps such as a step A  436  and a step B  438  that describe how to use the feature  408 . 
     Furthermore, the past user actions  432  may be processed to detect an error  430  associated with a utilization of the feature  408 . The error  430  may include a number of scenarios such as a wrong utilization of the feature  408 , and/or an incomplete utilization of the feature  408 , among others. The inference engine  424  may detect a number of the error  430  and other errors associated with the utilization of the feature  408  by the user at the productivity application. If the number of the errors exceed a threshold value then the content  416  may be redisplayed on the teaching UI  414  to retrain the user. Alternatively, a section of the content  416  may also be provided that addresses the error  430  and other errors that address the missteps by the user while utilizing the feature  408 . The threshold value used to compare the number of errors may be dependent on an optimum utilization of the feature  408 . The optimum utilization of the feature  408  may be a standard (configured in the productivity service  402 ) that meets use expectations of a consumer of the productivity application or a creating/deploying entity associated with the productivity application. 
     Furthermore, the past user actions  432  associated with the productivity application and other productivity applications may be analyzed to detect underutilization or a failure to use the feature  408  by the user. In such a scenario, the content may be provided to the user through the teaching UI  414 . Alternatively, the past user actions  432  may be analyzed to detect an intent by the user to not use the feature  408  in the productivity application and/or other productivity applications. In such a scenario, the inference engine  424  may prevent an automated presentation of the content  416  through the teaching UI  414 . 
     As discussed above, the productivity service may be employed to provide a teaching UI activated by a user action. An increased user efficiency with the productivity service  102  may occur as a result of processing a user action and a user trait to match a feature of the productivity application to a training content. Additionally, automated training of the user  110  on demand with training content (that describes how to use the feature) by the productivity service  102 , may reduce processor load, increase processing speed, conserve memory, and reduce network bandwidth usage. 
     Embodiments, as described herein, address a need that arises from a lack of efficiency to provide a teaching UI activated by a user action. The actions/operations described herein are not a mere use of a computer, but address results that are a direct consequence of software used as a service offered to large numbers of users and applications. 
     The example scenarios and schemas in  FIG. 1 through 4  are shown with specific components, data types, and configurations. Embodiments are not limited to systems according to these example configurations. Providing a teaching UI activated by a user action may be implemented in configurations employing fewer or additional components in applications and user interfaces. Furthermore, the example schema and components shown in  FIG. 1 through 4  and their subcomponents may be implemented in a similar manner with other values using the principles described herein. 
       FIG. 5  is an example networked environment, where embodiments may be implemented. A productivity service configured to provide a teaching UI activated by a user action may be implemented via software executed over one or more servers  514  such as a hosted service. The platform may communicate with client applications on individual computing devices such as a smart phone  513 , a mobile computer  512 , or desktop computer  511  (‘client devices’) through network(s)  510 . 
     Application(s) executed on any of the client devices  511 - 513  may facilitate communications via service(s) executed by servers  514 , or on individual server  516 . A productivity service may receive a notification of a user action from a productivity application. The productivity service may recognize a trait associated with a user performing the user action. A content associated with a feature may be identified for a presentation in a teaching UI based on the trait and the user action. Next, the content may be provided for the presentation in the teaching UI. The productivity service may store data associated with the feature in data store(s)  519  directly or through database server  518 . 
     Network(s)  510  may comprise any topology of servers, clients, Internet service providers, and communication media. A system according to embodiments may have a static or dynamic topology. Network(s)  510  may include secure networks such as an enterprise network, an unsecure network such as a wireless open network, or the Internet. Network(s)  510  may also coordinate communication over other networks such as Public Switched Telephone Network (PSTN) or cellular networks. Furthermore, network(s)  510  may include short range wireless networks such as Bluetooth or similar ones. Network(s)  510  provide communication between the nodes described herein. By way of example, and not limitation, network(s)  510  may include wireless media such as acoustic, RF, infrared and other wireless media. 
     Many other configurations of computing devices, applications, data sources, and data distribution systems may be employed to provide a teaching UI activated by a user action. Furthermore, the networked environments discussed in  FIG. 5  are for illustration purposes only. Embodiments are not limited to the example applications, modules, or processes. 
       FIG. 6  is a block diagram of an example computing device, which may be used to provide a teaching UI activated by a user action, according to embodiments. 
     For example, computing device  600  may be used as a server, desktop computer, portable computer, smart phone, special purpose computer, or similar device. In an example basic configuration  602 , the computing device  600  may include one or more processors  604  and a system memory  606 . A memory bus  608  may be used for communication between the processor  604  and the system memory  606 . The basic configuration  602  may be illustrated in  FIG. 6  by those components within the inner dashed line. 
     Depending on the desired configuration, the processor  604  may be of any type, including but not limited to a microprocessor (μP), a microcontroller (μC), a digital signal processor (DSP), or any combination thereof. The processor  604  may include one more levels of caching, such as a level cache memory  612 , one or more processor cores  614 , and registers  616 . The example processor cores  614  may (each) include an arithmetic logic unit (ALU), a floating point unit (FPU), a digital signal processing core (DSP Core), or any combination thereof. An example memory controller  618  may also be used with the processor  604 , or in some implementations, the memory controller  618  may be an internal part of the processor  604 . 
     Depending on the desired configuration, the system memory  606  may be of any type including but not limited to volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. The system memory  606  may include an operating system  620 , a productivity service  622 , and a program data  624 . The productivity service  622  may include components such as an inference engine  626  and a rendering engine  627 . The inference engine  626  and the rendering engine  627  may execute the processes associated with the productivity service  622 . The inference engine  626  may receive a notification of a user action from a productivity application. The inference engine  622  may recognize a trait associated with a user performing the user action. A content associated with a feature may be identified for a presentation in a teaching UI based on the trait and the user action. The rendering engine  627  may provide the content in the teaching UI for the presentation on the productivity application. 
     The productivity service  622  may communicate with the productivity application through communication device(s)  666  of the computing device  600 . The communications between the productivity service  622  and the productivity application may include program data  624 . The program data  624  may include, among other data, utilization data  628 , or the like, as described herein. The utilization data  628  may include the feature, the content, the user action, and/or the attribute of the user, among others. 
     The computing device  600  may have additional features or functionality, and additional interfaces to facilitate communications between the basic configuration  602  and any desired devices and interfaces. For example, a bus/interface controller  630  may be used to facilitate communications between the basic configuration  602  and one or more data storage devices  632  via a storage interface bus  634 . The data storage devices  632  may be one or more removable storage devices  636 , one or more non-removable storage devices  638 , or a combination thereof. Examples of the removable storage and the non-removable storage devices may include magnetic disk devices, such as flexible disk drives and hard-disk drives (HDDs), optical disk drives such as compact disk (CD) drives or digital versatile disk (DVD) drives, solid state drives (SSDs), and tape drives, to name a few. Example computer storage media may include volatile and nonvolatile, removable, and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. 
     The system memory  606 , the removable storage devices  636  and the non-removable storage devices  638  are examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVDs), solid state drives, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by the computing device  600 . Any such computer storage media may be part of the computing device  600 . 
     The computing device  600  may also include an interface bus  640  for facilitating communication from various interface devices (for example, one or more output devices  642 , one or more peripheral interfaces  644 , and one or more communication devices  666 ) to the basic configuration  602  via the bus/interface controller  630 . Some of the example output devices  642  include a graphics processing unit  648  and an audio processing unit  650 , which may be configured to communicate to various external devices such as a display or speakers via one or more A/V ports  652 . One or more example peripheral interfaces  644  may include a serial interface controller  654  or a parallel interface controller  656 , which may be configured to communicate with external devices such as input devices (for example, keyboard, mouse, pen, voice input device, touch input device, etc.) or other peripheral devices (for example, printer, scanner, etc.) via one or more I/O ports  658 . An example of the communication device(s)  666  includes a network controller  660 , which may be arranged to facilitate communications with one or more other computing devices  662  over a network communication link via one or more communication ports  664 . The one or more other computing devices  662  may include servers, computing devices, and comparable devices. 
     The network communication link may be one example of a communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and may include any information delivery media. A “modulated data signal” may be a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), microwave, infrared (IR) and other wireless media. The term computer readable media as used herein may include both storage media and communication media. 
     The computing device  600  may be implemented as a part of a general purpose or specialized server, mainframe, or similar computer, which includes any of the above functions. The computing device  600  may also be implemented as a personal computer including both laptop computer and non-laptop computer configurations. 
     Example embodiments may also include methods to provide a teaching UI activated by a user action. These methods can be implemented in any number of ways, including the structures described herein. One such way may be by machine operations, of devices of the type described in the present disclosure. Another optional way may be for one or more of the individual operations of the methods to be performed in conjunction with one or more human operators performing some of the operations while other operations may be performed by machines. These human operators need not be collocated with each other, but each can be only with a machine that performs a portion of the program. In other embodiments, the human interaction can be automated such as by pre-selected criteria that may be machine automated. 
       FIG. 7  is a logic flow diagram illustrating a process for providing a teaching U activated by a user action, according to embodiments. Process  700  may be implemented on a computing device, such as the computing device  600  or another system. 
     Process  700  begins with operation  710 , where the productivity service receives a notification of a user action from a productivity application. At operation  720 , a trait associated with a user performing the user action may be recognized. The trait may include a credential of the user, a context associated with the user, and/or similar ones. 
     At operation  730 , a content associated with a feature of the productivity application may be identified for a presentation in a teaching UI on the productivity application based on the trait and the user action. The feature may include a new or existing feature (with or without updates). The user may be selected for training on how to use the feature if the feature is determined to be underutilized or in demand by the user. The content may include a video stream (among other modalities) that provides instructions on how to use the feature. At operation  740 , the content may be provided in the teaching UI for a presentation on the productivity application. 
     The operations included in process  700  is for illustration purposes. Providing a teaching UI activated by a user action may be implemented by similar processes with fewer or additional steps, as well as in different order of operations using the principles described herein. The operations described herein may be executed by one or more processors operated on one or more computing devices, one or more processor cores, specialized processing devices, and/or general purpose processors, among other examples. 
     In some examples, a physical server to provide a teaching user interface (UI) activated by a user action is described. The physical server includes a memory configured to store instructions associated with a productivity service and processor(s) coupled to the memory. The processor(s) execute the productivity service in conjunction with the instructions stored in the memory. The productivity service includes an inference engine and a rendering engine. The inference engine is configured to receive a notification of a user action from a productivity application, recognize a trait associated with a user performing the user action, identify a content associated with a feature of the productivity application for a presentation in a teaching UI on the productivity application based on the trait and the user action. The rendering engine is configured to provide the content in the teaching UI for a presentation on the productivity application. 
     In other examples, the inference engine is further configured to match the trait and the user action to the feature of the productivity application and locate the content that describes the feature of the productivity application. The inference engine is further configured to detect a credential of the user as the trait associated with the user. The inference engine is further configured to query a provider with the credential for a context associated with the user, wherein the context includes one or more of a role of the user within an organization, an expertise of the user with the productivity application, a training history of the user with the productivity application, a utilization history of the user with the productivity application, receive the context associated with the user from the provider, and search, and locate the content that matches the feature and the context associated with the user. 
     In further examples, the inference engine is further configured to retrieve a use history of the productivity application by the user with the credential, analyze the use history to identify the feature of the productivity application underutilized by the user, and identify the content that matches the feature of the productivity application. The inference engine is further configured to retrieve a use history of the productivity application by the user with the credential, analyze the use history to identify the feature of the productivity application that is previously unused by the user, and identify the content that matches the feature of the productivity application. 
     In other examples, the inference engine is further configured to analyze the user action to detect an intent of the user to activate the feature of the productivity application, search the content that matches the feature, and locate the content that matches the feature. The inference engine is further configured to analyze a use history associated with the feature to detect an underutilization or a failure to use the feature by the user and provide the content to the rendering engine for a presentation to the user. The inference engine is further configured to analyze the user action to detect a workflow of the user within the productivity application and detect the feature of the productivity application associated with the workflow. The inference engine is further configured to analyze one or more past user actions to detect an underutilization or a failure to use the feature by the user, search and locate the content that matches the feature, and provide the content to the rendering engine for a presentation to the user. The inference engine is further configured to provide the teaching UI for the presentation in proximity to a section within the productivity application associated with a conclusion of the user action. 
     In some examples, a method executed on a computing device to provide a teaching user interface (UI) activated by a user action is described. The method includes receiving a notification of the user action from a productivity application, recognizing a trait associated with a user performing the user action, matching the trait and the user action to a feature of the productivity application, searching and locating a content that describes the feature of the productivity application, and providing the content in a teaching UI for a presentation on the productivity application. 
     In other examples, the content includes one or more steps that illustrate how to use the feature. The method further includes processing one or more past user actions associated with the feature to detect one or more errors associated with a utilization of the feature and detecting a number of the one or more errors exceed a threshold value associated with an optimum utilization of the feature. The method further includes in response to matching the one or more errors to a section of the content, providing the section of the content in the teaching UI for the presentation to the user and in response to matching the one or more errors to two or more sections of the content, reloading the content in the teaching UI for another presentation to the user. 
     In further examples, the method further includes querying one or more other productivity applications to detect underutilization or a failure to use the feature on the one or more other productivity applications by the user, analyzing a workflow of the user action to identify a potential to enhance the workflow with the feature, and providing the content for a presentation to the user. The method further includes analyzing one or more past actions by the user on one or more other productivity applications to detect an intent by the user to not use the feature, wherein the one or more other productivity applications include the feature and preventing the presentation of the content to the user. 
     In some examples, a computer-readable memory device with instructions stored thereon to provide a teaching user interface (UI) activated by a user action is described. The instructions include operations that are similar to operations of the method. 
     In some examples, a means for providing a teaching user interface (UI) activated by a user action is described. The means for providing a teaching user interface (UI) activated by a user action includes a means for receiving a notification of a user action from a productivity application, a means for recognizing a trait associated with a user performing the user action, a means for identifying a content associated with a feature of the productivity application for a presentation in a teaching UI on the productivity application based on the trait and the user action, and a means for providing the content in the teaching UI for a presentation on the productivity application. 
     The above specification, examples and data provide a complete description of the manufacture and use of the composition of the embodiments. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims and embodiments.