Patent Application
20170329466
This description generally relates to user interfaces. The description, in particular, relates to systems and techniques for providing a user interface experience for viewing data and information related to multiple software applications.
A user of software applications designed to support processes used by an enterprise often needs to navigate back and forth between multiple (and in many cases different) user interfaces and application instances in order to carry out transactional tasks when making a viable decision for the enterprise. In many cases, the navigation can include viewing data and information related to multiple applications. While navigating between the various user interfaces and application instances, the user may become lost and confused, losing a context of a current transactional task. This can create major usability issues, resulting in the inability of the user to effectively carryout the transactional tasks. In general, the user has a poor user experience using and interacting with the software applications.
In one general aspect, a computer-implemented method for interacting with a digital personal assistant having a user interface includes executing instructions stored on a non-transitory computer-readable storage medium. The method include displaying a user interface for a digital personal assistant and receiving user input through the user interface for the digital personal assistant. The user input includes a user selection of a collection data structure, containing one or more items, from a set of collection data structures. The method includes determining at least a context indicia and a user role, processing at least one item in the selected collection data structure using the context indicia and the user role and generating information for display on the user interface responsive to results of the processing.
Implementations may include one or more of the following features. For example, displaying the user interface may include displaying the user interface as a floating window over an underlying window and determining at least the context indicia may include determining at least the context indicia based on content of the underlying window. Displaying the user interface as the floating window over the underlying window may include continually displaying the user interface as the floating window over changing underlying windows.
Receiving the user input through the user interface may include receiving any combination of one or more of textual data, speech data and visual data as the user input. Determining at least the context indicia and the user role may include using natural language processing to determine at least the context indicia and the user role. The processing may include processing, using natural language processing, at least one item in the selected collection data structure using the context indicia and the user role. The processing may include generating a prediction based on the item, the context indicia and the user role.
The method may further include generating an action based on the prediction. Generating the action may include generating the action based on the prediction and a previous action.
The user input may include one or more of a note, a screenshot, an image, an alert, a task and an activity.
In another general aspect, a system for interacting with a digital personal assistant having a user interface includes at least one memory including instructions and at least one processor that is operably coupled to the at least one memory and that is arranged and configured to execute the instructions that, when executed, cause the at least one processor to implement a user interaction module, a logic engine and a data agent. The user interaction module is configured to cause display of a user interface for a digital personal assistant and receive user input through the user interface for the digital personal assistant, where the user input includes a user selection of a collection data structure, containing one or more items, from a set of collection data structures. The logic engine is configured to determine at least a context indicia and a user role, process at least one item in the selected collection data structure using the context indicia and the user role and generate information for display on the user interface responsive to results of the processing.
Implementations may include one or more of the following features. For example, the user interaction module may be configured to cause the display of the user interface as a floating window over an underlying window and the logic engine may be configured to determine at least the context indicia based on content of the underlying window. The at least one processor may be configured to implement a natural language processing module and the logic engine may be configured to determine at least the context indicia and the user role using information from the natural language processing.
The at least one processor may be configured to implement a natural language processing module that may be configured to use natural language processing to process at least one item in the selected collection data structure using the context indicia and the user role.
In another general aspect, a computer program for interacting with a digital personal assistant having a user interface is tangibly embodied on a non-transitory computer-readable storage medium and includes instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to display a user interface for a digital personal assistant and receive user input through the user interface for the digital personal assistant. The user input includes a user selection of a collection data structure, containing one or more items, from a set of collection data structures. The at least one computing device is configured to determine at least a context indicia and a user role, process at least one item in the selected collection data structure using the context indicia and the user role and generate information for display on the user interface responsive to results of the processing.
Implementations may include one or more of the following features. For example, the instructions that cause the at least one computing device to receive the user input through the user interface may include instructions that cause the at least one computing device to receive any combination of one or more of textual data, speech data and visual data as the user input. The instructions that cause the at least one computing device to process may include instructions that cause the at least one computing device to generate a prediction based on the item, the context indicia and the user role.
The computer program product further may include instructions that cause the at least one computing device to generate an action based on the prediction. The instructions that cause the at least one computing device to generate the action may include instructions that cause the at least one computing device to generate the action based on the prediction and a previous action.
The user input may include one or more of a note, a screenshot, an image, an alert, a task and an activity.
The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features will be apparent from the description and drawings, and from the claims.
Enterprise transactional applications can evolve from large monolithic on-premise software deployments into multiple (e.g., hundreds of, thousands of) small, componentized applications that can execute (run) on various types of computing devices and network platforms. For example, the fifth version of Hypertext Markup Language (HTML5) can be used as a basis for structuring and presenting an application platform that includes a grid matrix-style home screen or dashboard-like user interface. The use of HTML5 can provide an improved user experience, can reduce a user interface complexity, and may increase user productivity across multiple types of computing devices. The computing devices can include, but are not limited to, desktop computers and mobile computing devices such as laptop computers, tablet computers, notebook computers, personal digital assistants (PDAs), smartphones, mobile phones, smart watches, etc.).
In some cases, the grid matrix-style home screen or dashboard-like user interface allows a user to open and access a selected application. In some cases, the grid matrix-style home screen or dashboard-like user interface allows a user to access content within an application. These user interfaces can present user experience (UX) and user interface (UI) shortcomings while attempting to provide optimized usability and user-centric business functionalities related to transactional tasks.
For example, users may need to navigate back and forth between multiple screens that can include home screens, application instances, and drill downs, in order to perform various transactional tasks to make viable business decisions. The user may need to navigate back and forth between multiple screens when viewing related data between various applications.
For example, applications and application instances may not interoperate with each other. Because of this, user navigation and operations may not be minimized. Applications may not readily access relative content without first needing to open an application instance and then perform numerous navigations.
In order to improve a user experience (UX), a user interface can be implemented to intuitively propose relative context or intent to a user. A user interface can be implemented to conform to, recognize, and facilitate ways in which a user may personally work. A user interface can be implemented to help a user remember important tasks. The use of such a “smart” user interface can help a user easily manage and keep track of the context of current tasks and activities when carrying out transactional tasks.
A shortcoming of a UX and a UI can be a lack of relative business contexts related to user roles and expertise that can empower employees to do their job better, faster, and in a more deliberate way. Providing a user with a way to personalize an experience of the user based on a role requirement of the user can result in a better overall user experience. For example, the personalization can result in a UI automatically providing proposals for transactional tasks that the user may need to see. The proposals can be provided at a time when the user wants to see the proposal and on a computing device chosen by the user. The proposals can be provided to the user as a result of a single click or gesture input to the user interface by the user. The UI can be considered a central interface that can provide a user with the ability to communicate, collaborate, initiate, and/or respond to colleagues, managers, and customers without leaving the context of their current activity or application.
Alternatively, a user may have to navigating through multiple applications and user interfaces. A user may easily lose a context of a current task or activity while having to navigate through user interfaces provided in a multiple application environment for an enterprise. The loss of context can affect the ability of the user to effectively carryout a transactional task. In addition, navigating through user interfaces provided in a multiple application environment may not allow a user to readily view, at a glance, live data that may be relevant to personal and professional daily activities, responsibilities and organizational accountabilities of the user. The user may not be provided with a way to efficiently manage hundreds (or thousands) of applications associated with transactional workflows in the enterprise.
Enterprise applications that are associated with transactional workflows in an enterprise can be implemented using a software development technology or foundation (e.g., HTML5/CSS/JS technology) in a particular UI framework (e.g., SAPUI5) in order to provide a beneficial UX and UI. The enterprise applications can be implemented to execute or run on multiple different types of computing devices such as desktop computers and mobile computing devices (e.g., laptop computers, tablet computers, notebook computers, personal digital assistants (PDAs), smartphones, mobile phones, smart watches, etc.).
For example, the UI may use elements of a particular UX (e.g., an SAP Fiori® UX) to provide a user with a personalized, responsive, seamless, and simple user experience across enterprise applications (including legacy and new), across different types of computing devices such as desktop computers and mobile computing devices (e.g., laptop computers, tablet computers, notebook computers, personal digital assistants (PDAs), smartphones, mobile phones, smart watches, etc.), and across all possible deployment options (e.g., on-premise, cloud, as-a-service, etc.).
The particular UX can deploy an array of interactive features and process tools that can be integrated with a suite or set of enterprise applications that have specific functionalities. The particular UX can provide intelligent, contextual-based support for users across an enterprise. The particular UX can use a “push” model that can anticipate tasks (e.g., transactional tasks) for a user based on a role of the user in the enterprise. The particular UX can import real-time data that can be tailored to specific tasks. The software for the UI for the particular UX can be implemented to easily and transparently scale the UI accordingly for display on each display device included in each of the different possible computing devices that can execute (run) the enterprise applications.
The shell main container 104 can display information for use as a main workspace for the UX. In some implementations, multiple different screens can be displayed in the shell main container 104. For example, a login screen, a launchpad screen, and an overview page can alternatively be displayed in the shell main container 104. The viewport 100 can provide a user with a screen orientation that can allow the user access to application information. The viewport 100 can provide the user with a UX and UI the includes the business functionalities and enterprise application-to-application navigations needed by the user without disrupting a context of a current task of the user.
The launchpad 112 can serve as a bracket around (or a base for) a set (or group) of enterprise applications, providing a single point of entry for the set of enterprise applications. In the example shown in
The launchpad 112 can be a role based, personalized, real-time and contextual aggregation point for business applications and analytics. The launchpad 112 can run (execute) on multiple computing devices including, but not limited to, desktop computers and mobile computing devices such as laptop computers, tablet computers, notebook computers, personal digital assistants (PDAs), smartphones, mobile phones, smart watches, etc.). In addition, the launchpad 112 can be deployed on multiple platforms (e.g., Linux, Windows, Windows Phone, MAC®, iOS®, OS X®, Android®, etc.).
The launchpad 112 includes tiles 114a-h. Each tile can display different types of content. For example, tile 114a can be a news and feeds tile that can enhance collaboration by providing a user with information about the enterprise. The tiles 114a-h can be individually color-coded. A color can represent a particular role (e.g., finance, human resources, supply chain management (SCM), customer relationship management (CRM), etc.). The tiles 114a-h can be associated with a group 116. Tile 114f can be a key performance indicator (KPI) tile. Tile 114b can be a basic launch tile. Tile 114d can be a monitoring tile. Tile 114g can display a comparison chart for specific content.
The launchpad 112 includes a link list area 118 that includes links 119a-f. The link list area 118 is an area on the launchpad 112 that can provide links to enterprise applications represented by the tiles 114a-h. For example, a user can select and drag a tile from the tile area on the launchpad 112 into the link list area 118 to create a link to the application associated with (represented by) the tile. In some implementations, the launchpad 112 can include a footer toolbar (e.g., footer toolbar 132 as shown in
In some implementations, the shell toolbar 108 can display a search icon 111 and a CoPilot launch icon 113. A user can select (click on) the CoPilot launch icon 113 to launch a CoPilot UI. A CoPilot UI will be described in more detail with reference to
The overview page 120 can visualize all of the information a user may need for a specific business context (business domain) on a single page or screen. The information can be displayed in one or more variable content packages (VCPs) or cards 122a-i. Each card can be a container of content for organizing large amounts of information on an equal plane within the overview page 120. In some implementations, a user can rearrange the position of the cards 122a-i on the overview page 120. In some implementations, a user define, add, or delete cards included in the overview page 120.
An overview page (e.g., the overview page 120) can be a selectable application (e.g., from the launchpad 112) providing an integrated gateway into enterprise applications and application content included in the launchpad 112. The UI of the overview page (e.g., the overview page 120) can provide a user with a visual summary of data, links, actions, and content that are relevant to a business domain of expertise of a user and relevant to a selected role of the user within the domain. The visual summary can be presented in one or more cards (e.g., the cards 122a-i) that display live content to a user at-a-glance without the user having to open multiple applications and perform multiple drill downs through application content to find and present the content.
In some implementations, the overview page 120 can include a footer toolbar (e.g., footer toolbar 132 as shown in
In some implementations, an enterprise system can determine content displayed on an overview page (e.g., the overview page 120). In addition or in the alternative, a selection of one or more business domains and one or more roles of a user in the business or enterprise can determine content displayed on an overview page (e.g., the overview page 120). In some implementations, a user can make the selection using a settings UI included in a launchpad (e.g., the launchpad 112). In some implementations, a user can select one or more business domains and/or one or more roles of the user in the enterprise by way of an overview page (e.g., the overview page 120). Selecting one or more business domains and/or one or more roles of the user in the enterprise by way of the overview page can maintain absolute relevance to the individual user and the way in which the user works.
In some implementations, the user can personalize the layout and placement of one or more cards (e.g., the cards 122a-i) included in a UI of an overview page (e.g., the overview page 120) and the display of content included in each card. The personalization can enhance the workplace productivity of the user.
The upper section 138 can include sort selections 146a-b. A user can select (click on) a sort selection (e.g., one of the sort selections 146a-b) to determine how the listing of the recent activities included in the lower section 140 will be sorted and displayed.
The lower section 140 of the me area 136 includes a list of recent activities 148a-c. The recent activities 148a-c can include links 156a-c, respectively, that when selected (clicked on) by a user can navigate the user to back to the shell main container 104, opening an application (or function) that corresponds to the link in the shell main container 104. Recent activity items can include, but are not limited to, enterprise applications, triggered searches, co-pilot collections, and co-pilot drafts.
A user can reject a notification by selecting (clicking on) a reject selection (e.g., a reject selection 156a-b). For example, a user can reject the notification 152a by selecting (clicking on) the reject selection 156a. The rejection of the notification 152a (the notification status) can be indicated by content included in (e.g., a color of) a notification indicator 154a. A user can acknowledge a notification by selecting (clicking on) an acknowledge selection (e.g., a acknowledge selection 158a-b). For example, a user can acknowledge the notification 152b by selecting (clicking on) the acknowledge selection 158b. The acknowledgement of the notification 152b (the notification status) can be indicated by content included in (e.g., a color of) a notification indicator 154b.
A user can drill down into a relevant application by selecting (clicking on) a more info selection (e.g., a more info selection 160a-b). In some cases, a user may contact someone directly in response to a notification.
The example CoPilot UI 162 is an example CoPilot start page or start screen. The start screen (the CoPilot UI 162) can be an entry point for CoPilot functionality for an enterprise system.
The CoPilot UI 162 can provide shortcuts to different CoPilot features. For example, as shown in
For example, the selection of a CoPilot shortcut 166a-d can allow a user to create and navigate to a new collection with a specified intention. The selection of a CoPilot create icon 168 located in a CoPilot footer toolbar 170 can create and navigate to a new plain collection. The selection of a CoPilot settings icon 172 located in the CoPilot footer toolbar 170 can allow a user access to CoPilot settings (e.g., display a CoPilot settings UI, open a CoPilot settings application).
Aspects of the CoPilot initiative yield a dynamic, fluid, etc. digital assistant that among other things leverages Natural Language Processing (NLP), learning capabilities, etc. to provide support for seamless conversational interactions.
CoPilot entries can be living, gradually growing artifacts and software entities that can accompany a user from the identification of an issue to a solution for the issue, while providing support in the form of relevant context and actions. CoPilot entries can serve as memory aides while the CoPilot entries can incrementally evolve into valuable transactional tasks and collaborations as they mature in meaningful ways that bridge a gap between predefined application functionality and processes based on personal way of working for a user. Though the example shown in
CoPilot can reference objects in the actual Fiori application and tracks user interaction to provide a list of recently used objects. On the one hand, CoPilot can serve as data provider (sensor) for the user relevance calculation: The tracking of user interaction in CoPilot, which can be quite detailed, can serve as an important source of information. In addition, the recently annotated (NOTE) or collected (COLLECT) items may indicate currently important business objects for the user. The same applies to objects referenced in chats (CHAT). From CHAT, people related to business objects may be retrieved. On the other hand, the user may annotate situation instances (NOTE), may reference to situation instances in collections (COLLECT), or use situations as a basis for chats (CHATS). Also, situations may be used as an initiator of create activities (CREATE). The user interaction tracking used for CoPilot may be available as an independent service, which may also be used for ranking.
CoPilot entries can be made ready for users to use when communicating, collaborating, and creating actionable transactions in desktop or mobile scenarios. For example, CoPilot text entries can be analyzed for recognizing and identifying relevant text related objects. CoPilot text entries can emphasize displayed text, and a CoPilot application can recommend contextual entities for use in a current task. The CoPilot application can understand user context and can intelligently propose selections, autoentries, and user options.
CoPilot may support among other things a dynamic, living ‘sticky note’ that may be associated with for example an item on a screen, an element of an application, etc. such as for example an entry in a list, a display field, a control, etc.
CoPilot is a digital doppelganger in the enterprise. It federates Fiori apps and services to help you accomplish your work, empowers you to make timely decisions or performs tasks for you. By learning individual and organizational behaviors and adjusting to them accordingly, CoPilot will be able to interact with the end user in a revolutionary way. The CoPilot will provide a GUI-less, natural interaction across apps, ready for any input modality and form factor. It will also offer functionality independent of the apps.
CoPilot is highly accessible anywhere, anytime and in any app. CoPilot always stands ready for users to communicate, collaborate, and create actionable transactions in desktop or mobile scenarios. Some examples of CoPilot types and configurations include Personal Notes, Screen Captures, Images, Chat, System Messages and Alerts, Tasks, etc. that can be organized into homogenies, (all of the same type), or heterogeneous (many different types) of contextual collections to further aid in business processes.
CoPilot entries are smart digital sticky-notes, that can be created from everywhere, from a given context or just stand-alone. They can be saved at any time in any condition, thereby working on a copilot entry can easily be halted and later on continued. A CoPilot entry is a living, gradually growing, artifact that accompanies the user on his way from issue to solution, supporting with context and actions as needed. They first serve as a memory aid but easily can be turned into or simply create different object types. As such, the CoPilot functionality is bridging the gap between predefined application functionality and processes and the users personal way of working. When in edit mode the CoPilot floats above the content and can be moved around the screen. While the user navigates through his application, the CoPilot will stay open across different screens and objects, unless the user decides to close it. By appearing as a floating overlay, the user does not have to leave his current context to work on a copilot entry. It shares the screen with the ‘normal’ content and can be moved to another position on the screen or simply be closed if the user needs to change his focus. When being created from a context the CoPilot entry will take over as much information as possible to simplify the users input process. The user still has the option to get rid of some or add additional. A CoPilot can be shared with other users in the business system. While CoPilot entries can be created from every screen, a list of the user's CoPilot entries should also be easily accessible. Contextually filtered lists of CoPilot entries can also be embedded in existing floorplans. With CoPilot the user can create collections of items. Normally these collections are created in relation to a specific topic or a certain problem, the user wants to solve with the help of CoPilot. Each collection consists primarily of a set of items and some meta information.
With SAP CoPilot “LeaH” (Live Enterprise Assistant for HANA) is introduced, which is the first true digital assistant in the enterprise supported by a next-generation user experience. It helps you to get things done at the speed of thought and makes sure you know what you need to know at the very moment you need it.
The conversational (multi-modal) user interface communicates with you using natural human language via text, gesture or voice and enables you to converse with others, within your business context. SAP CoPilot learns over time and can also give you recommendations. It starts out with pre-defined business rules and gradually learns from behavioral data and recommends next best actions to the user. Business Context Awareness offers relevant insights just when you need them based on your role, context and current business situation by recognizing business objects on your screen or within the conversation. Quick actions enable you to take immediate action based on these insights.
LeaH aims to capture a wide functional coverage with limited depth, supporting open-ended questions and enabling traditional applications with natural language input by text or voice as an alternative way to interact.
During conversations with the system, the user might need to involve other users into the conversation. CoPilot provides all the means to facilitate the conversations of the users by the possibility to add notes, screenshots, objects into the conversation. All that information is taken into account by the digital assistant to guide users to the solution of a business problem.
In summary, LeaH is low touch meaning the conversational (multi-modal) user interface communicates with you using natural human language via text, gesture or voice and enables you to converse with others, within your business context. LeaH understands the user and adapts to the user needs. LeaH includes human interaction meaning human like representation of software. The experience of interacting with the assistant will be the user experience. It will be represented by a persona with one personality, memory, and behavior. A relationship between the assistant and the human user will be established and evolved. The assistant can become a coach that explains the system (“her thoughts and reasoning”). Technical functions will be capabilities of the assistant that it will gain, just as humans learn.
LeaH provides assistance and coaching. The digital assistant represents the system, it's capabilities and implemented business logic. It will be natural for the user to expect the assistant to help him understand and use these processes. The assistant will help the user to learn complex business processes, just as a team assistant helps to do the expense report properly. The user will expect the assistant to be able to explain the reasoning and logic behind the business processes it supports and therefore help him gain deeper understanding. In all interactions, the assistant should be able to explain why it gives an answer, recommendation or proposes actions (reasoning). This way she coaches the user and helps him to understand best practices and grow in his profession. The assistant will learn common mistakes a user makes and can prevent them, compensate for weaknesses and help him improve.
LeaH guides the user along best practice. LeaH observes the user and learns new best practices and understands business situations and assists the user to asses situations and proposes solutions best on best practices. LeaH can focus on retrieving information, getting Alerts, executing light-weight tasks. LeaH includes automation through pre-defined rules, human-to-machine &vice versa. The system self-improves human-created rules and includes the ability to recommend follow-up actions based on best practices.
Features for LeaH include Native Copilot apps are available for download on mobile App Stores and integration with standard apps such as email, calendar, contacts, etc. The UI is conversational and enables multi-modal interactions via voice and text. It includes SAP Hana integrations including OData. It contextualizes and analyzes informal unstructured speech to execute actions and present business objects, options and other relevant data in a simple conversational way. It is capable of improving the work life balance of its users by being also aware of the personal habits and data. If allows a user to start a task on your mobile device and continue on your desktop and vice versa. It includes the ability to create bots to augment and personalize standard functionality. It is aware of the user, business situation and relevant context and proactively advises the user. It includes machine learning that anticipates events and recommends actions based on individual and team behavior and proactively pushes content for better decision making.
More specifically, with respect to notifications, CoPilot enables enterprise systems to proactively push notifications to the users. As a result the business users will be reminded of due actions or get recommendations of what should they work on next considering their current situational context. As part of these notifications, the users will get all the relevant information to require to understand and act on the system proposal. To detect situations that warrant notifications to users, the CoPilot will consider the following: Business domain-specific rules (shipped as part of the enterprise system), Rules defined by power users or IT administrators, Rules defined by end user, User context—it includes past situational recommendations ranked as useful by the users themselves, business data recently accessed by the user, interaction history logs, user's business role in the organization, data from “similar” users, location and device type from which the user accessing the system, etc. Note: the business data considered includes structured and unstructured content from heterogeneous sources, and is provided by Data Agents and Plugins, and Business domain-specific machine learning algorithms.
The CoPilot supports this domain-specific detection of situations with a set of predefined best practices, rule engine, a framework that allows to add value via plugins and services such as provisioning of the user context. Next we will use an exemplary scenario to describe in more details the function of the different architectural components when detecting a relevant business situation and delivering it to the relevant business users via notifications.
Referring to
Referring back to
The data agent 304 (e.g. an enterprise business system) contains a set of business domain-specific rules (Rule Based Situation Detection 312). They are configured with parameters by key users (or IT administrators). These rules are evaluated at time intervals by a rule engine optimized for the system's application domains (e.g. discrete manufacturing, utilities, retail, etc). The rule engine is not only integrated with the various business processes running in the system, but also has access to the logical data model (Semantic Network 314), Business Data 316 and a UI vocabulary 318.
If a rule returns a positive evaluation (by analyzing the relevant business data 316 and provided parameters), a notification 332 in the user interaction module 330 gets triggered. In the project management example of
Inside the logic engine 302, the relevance module 306 determines the recipients of the provided information. There are multiple criteria that could be considered. One consideration is the business roles that the business data 316 is relevant to. For example, an overdue purchase order would be of interest to a Purchaser or as in our example, projects exceeding their costs would require the attention of a Project Manager. However, not all PM-s shall be notified about the changes to a particular project (ABC as per our example). The relevance module 306 would also look into the business context part of the notification. As per it, the system could determine the list of PM-s assigned to that project. This is already quite precise user resolution, but the scope could be expanded a bit by considering additional data such as the usage history logs 315. The latter is a data mining operation based of which the system might determine that there are other users who have an interest in the project as they have marked it as a favorite, viewed it frequently, shared project information with others, etc (e.g. the manager of the assigned PM or an important stakeholder). Needless to say, the authorizations of the users will be assessed in order to determine if they are allowed to see such information.
The vocabulary module 304 and pattern module 303 in the data agent 302 would be used to convert the data into a humanly readable response (by mapping to a pattern/template). The natural language plugin 341 would use the data provided by these modules to perform the actual conversion. The input/output plugins 340 would be passed all the required information and “asked” to create textual or voice responses. Some of the template data could actually come from the natural language plugin 341 (for generic scenarios). The input/output plugins 340 include video recognition 342, image recognition 343, voice recognition 344, native language support 345, chat 346, screen scraping 347, mail 348, calendar 349 and native mobile support 350.
To accomplish the notification exemplary scenario, the following language template may be used (part of the patterns and vocabulary):
“The project costs {warning_indicator} the threshold average. The costs {kpi_trend} {kpi_value}”
With the help of the defined rules 305 in the logic engine 302, the natural language plugin 341 will derive the {warning_indicator}, {kpi_trend} and {kpi_value} values and as a result will replace {warning_indicator} with “passed”, {kpi_trend} with “increased by” and {kpi_value} with “more than 50%”.
Next, the Situation Context Plugin 352, which is part of the intelligence plugins 351, could enhance the data context from the data agent 310 and the relevance criteria (e.g. add additional business objects, find actions that can help resolve the situation, include more relevant users). The result is then provided to the Notification service (part of the User Interaction Layer 330). The CoPilot would also pick a proper UI Pattern 333 and use the UI semantics provided by the Data Agent 310 to compose the notification view fragment, for example Quick View panels for business objects, Tables and Charts for analytical data, or as per our example, a simple text view with a visual indicator to show the criticality of the information.
The Notification service pushes the notification 332 and its visual representation to the user, both through the native mobile push channels 360 (immediate availability), as well as through the Copilot browser app 370.
The Copilot application itself also acts as general purpose notification provider. It employs machine learning 353 to e.g. detect situations based on data in Copilot or in other productivity tools (email, calendar), and offers actions and information to the user that typically is relevant in such situations. Thus it enhances the situation detection and context resolution capabilities provided by the backend (Data Agent 310). Example: If the end users references project ABS in email communications with other users, the CoPilot might add additional relevant users to the notification context (as per the email recipients and senders).
As discussed above, another scenario is the conversation and query scenario with CoPilot. The CoPilot supports multi-modal interactions with its users. The latter can request data or ask for help via natural language queries (text or voice commands). The CoPilot will be capable of extracting the business semantics and parameter out of this commands and provide user back with relevant information and suggest proper actions to solve the problem.
Referring to
As shown in
The pattern: “what is going on with the {target} of project {name} {time}?” translates to a business action of type Query 334 for the Project business entity.
As per the vocabulary 304 data the {target} could be the Project's cost, completion %, etc, {name} could be Project's name or ID, {time} can be “this month”, “this week”, “today”, etc
Using the rules module 305, the resolution of the detected actions 335 could be enhanced further. For example, based on the user's role in the organization the same patterns could be translated to different actions. Moreover, based on the user's business role different vocabulary sets could be applied (for example when sales agents refer to an “order” they mean the Sales Order business object, while the employees in procurement would deal with Purchase Orders). Here again the relevance module 306 could help determining what vocabulary and patterns are relevant to the user, as discussed above in the notification scenario.
In the PM example presented earlier the result of the natural language processing could be a JSON like data object similar to:
Some of the natural language processing rules could be automatically derived from the semantic network model 314 (a.k.a. ontology) of the data agent 302. Such automatic rules will be generic in nature and will probably not be utilized when processing data for a specific business domain (the domain rules will extend the generic ones). Being able to derive rules based on an ontology will still be useful as there might be scenarios where domain specific rules do not exist.
The natural language plugins 351 will also require access to the business data 316. The natural language processing is a complex operations and to better “understand” the command, the plugin would require to know what is considered a correct parameter value (e.g. recognize the project name ‘ABC’ value). This is in addition to being able to map the query to a pattern and vocabulary entries. It might be required to take a snapshot of the business data 316 and cache it in the logic engine layer 302 (to improve the performance and reduce network traffic). As a result a new caching module 307 might be added to the logic engine 302 or be implemented as part of the natural language plugin.
The natural language plugins could also consider the conversational context when parsing the text. Some objects and parameters may not be explicitly specified by the user with their questions, but inferred from preceding interactions.
The result would be rooted back to the natural language plugin where the returned data is processed similarly to the notifications scenario and visualized presented to the user in the native mobile clients 360 or the connected browser 370.
Aspects of a CoPilot facility may offer among other things any number of user interaction paradigms, mechanisms, etc. including for example a natural language capability. Such a capability may offer among other things a question-response model using any combination of one or more of the interaction mechanisms text, voice, image, video, etc. Aspects of a CoPilot facility may among other things be integrated into an area of a display screen, application, etc. such as for example a header bar, toolbar, etc. CoPilot may be invoked through an icon, link reference, etc.
CoPilot may function, operate, etc. as something of a dynamic, continuously-learning digital virtual assistant. CoPilot is among other things responsive, flexible, and extensible. Aspects of CoPilot may be supported by any number of different mechanisms including for example workflow engines, rules, events, the rating/ranking/etc. of an event, alerts, prioritizations, the definition/assignment/etc. of user roles, etc. CoPilot may receive, pull, retrieve, etc. information from any number of sources including for example Microsoft Exchange, Microsoft Lync, SMS/MMS/IM/etc. messaging facilities, social media platforms, internal and/or external enterprise systems, web sites, etc. CoPilot items, elements, entries, etc. may be shared, exchanged, etc. through for example any combination of one or more of the sources listed above.
CoPilot may offer a range of flexible, extensible, and dynamically configurable search mechanisms. CoPilot may offer a range of natural language processing services, capabilities, etc. CoPilot may receive, etc. information manually through user input using any combination of one or more of the interaction mechanisms text, voice, image, video, etc. CoPilot may employ any number of different ‘auto learning’ capabilities. CoPilot may continuously, dynamically evaluate a range of data, parameters, etc. to for example identify a universe of information, etc. that is important, relevant, etc. to a user (based on among other things a definition of a rule for a user).
During its processing activities CoPilot may develop any number of contextual understandings, references, etc. During its processing activities CoPilot may examine any number of items including inter alia: ‘Things’ that are on a user's mind and ‘Work’ that is pending within a user's role. During various of its processing activities CoPilot may incorporate, leverage, etc. any number of pieces of data including inter alia biometric information (e.g., from a wearable device such as a Fitbit), location information (e.g., from a GPS or LBS facility), etc. The sizing, placement, location, arrangement, etc. of artifacts on or within CoPilot are among other things responsive, flexible, alterable, and dynamically configurable. A user may optionally edit, customize, etc. aspects of a CoPilot item, element, entry, etc. A user may browse, traverse, navigate, etc. CoPilot items, elements, entries, etc. in any number of ways. While a CoPilot facility may improve, augment, etc. significantly aspects of a Fiori environment, such a facility is not limited to just a Fiori environment and may operate, function, etc. in any number of other environments such as for example standalone, in association with a system, etc.
A CoPilot facility may reside, operate, etc. within a user interface on any target device including for example any combination of one or more of inter alia a desktop, a notebook, a tablet, a smart phone, a smart watch, etc. and may among other things dynamically adjust or adapt aspects of itself to account for any particulars (e.g., display real estate, input mechanism(s), etc.) of a target device. A target device may be any combination of one or more of inter alia a desktop computer, a notebook computer, a laptop computer, a tablet, a smartphone, a smartwatch, etc. In a mobile context aspects of CoPilot may take any number of forms including for example any combination of one or more of web-based, native, hybrid, containerized, etc.
Voice/Conversational and Non-Native Device Interactions, UI and Relevant Content and Contexts
User interactions can be interchangeably one or all input methods—voice, text entry and object selections (Taps), making the UI highly intuitive, flexible, consistent and naturally responsive across all devices. The scope of contextual user input and system return actions (via Logic Engine), navigations, information and contexts are provided based on business domain entities, system, end-user, and other comparable or “Like” users in the system including; communications, collaborations, transactions, data, notifications, forecasted or scheduled alerts, a User's Role Profile, relevance and access permissions, etc. as are defined by the user's personalized Fiori 2.0 apps and services as deployed in the FLP. As such, Copilot is a closely connected “Extension” of the user's specific, unique and familiar Fiori 2.0 experiences and configuration on all devices. User context and experience are completely aligned and synchronized. Numerous Action Types, (e.g. Adding an Object by Object Context, Type, Recent, Recognition, Screenshot (Item or Details), App specific Settings, etc. In addition to the above, a wide range of plugins are made available to the system including device services and accessibilities (email, calendar, phone telephony, camera, photo gallery, bar code scanning etc.), IoT, and SAP and 3rd party intelligence resources and services, etc. CoPilot supports both unstructured (Ad Hoc) and structured way for users for users to interact with the UI. It is completely intuitive to navigate and interact with Copilot functionalities as well as add new Copilot entities, content and actions. CoPilot inherently logically organizes Copilot content (Collections) according to content, context, and users
Functionalities
IT Administrator Configurable Business Domain-Specific Rules and Situation Detection that is integrated with app-specific business domains (Manufacturing, Utilities, etc.). User and System Defined Rules include Semantic Network Modeling (ontology) to create generic rules when Business rules (which take precedence) may not exist. Business Domain-Specific Machine Learning Algorithms provide users with the right data at the right moment (Push vs. Pull) to propose the next, best, and most appropriate action. Structured and Unstructured Business data from heterogeneous sources are accessed. Business-specific relevance Logic Engine mapping to user roles
System provides Situation Contexts combined with Business Objects to present or make recommendations for possible solutions to identified situations.
A copilot entry is a living, gradually growing, artifact that accompanies the user on his way from issue to solution, supporting with context and actions as needed.
They first serve as a memory aid but easily can be turned into or simply create different object types. As such, the copilot functionality is bridging the gap between predefined application functionality and processes, and the user's personal way of working, that can be shared with other users in the Enterprise Business System as well as Copilot Machine learning and Intelligence modules.
Deployment Configurations Options—As a Fiori 2.0 “App” Copilot is platform agnostic without any dependencies to business backend systems. It sits on top of the Fiori architecture but has Cross-Fiori application integrations (Via App Descriptor Target Mapping, etc.) features for all Fiori 2.0 apps running on a user's Fiori Launch Pad (FLP), and so there is no need to implement ANY Copilot-Specific Content.
Deployment Options—HTML5 (Fiori application framework and programming model), across all devices and Screen Sizes. (Per Fiori On-Premise and Cloud Deployments) On Mobile—As a hybrid App and/or As a stand-alone Native App.
Referring to
The Fiori Launchpad can be hosted from three different platforms: HCP 610, Enterprise Portal (Netweaver Java) and SAP ABAP System (NetWeaver ABAP). The Copilot frontend code is platform independent and can be integrated into any Fiori Launchpad implementation (HCP, Corporate Portal (Netweaver Java) and S/4 (Netweaver ABAP), assuming the FLP shell plugin concept is fully supported. In all three frontend deployment scenarios, the Copilot uses its cloud only backend service running on HCP. In other words, the Copilot frontend can run on-premise or in the cloud (Fiori-as-a-Service), but the Copilot backend is cloud only.
If the customer uses multiple Fiori Launchpads that are hosted on different platforms, the FLP configuration (for example to include the Copilot as a plugin and to make the plugin visible to certain user roles) is done on each platform separately. This fact will be mitigated by the Fiori Cloud Services, which will provide a new Fiori Cloud Cockpit and will automate this landscape setup and cloud onboarding.
The Copilot Backend Service includes Java Application implementing the OData 2 Service 620 and persistence layer (JPA), Database Schema(s) on designated HANA instance, Configuration UI implemented with UI5/Fiori. The Copilot OData Service will be deployed as a Java Application onto a Provider account on the productive factory HCP landscape. It will also be integrated into the HCP Service Cockpit as a HCP platform service. The necessary service metadata, which is required by the Service Cockpit, will be provided. This metadata is needed by the Service Cockpit in order to: render the service tile, facilitate the automatic subscription to the app when the service is enabled, display the link to the service documentation, display the link to the service configuration UI, display the link to administration UIs, and display the link to “Service Transparency Page” (Cloud reporting and monitoring).
The Copilot 601 is an integral part of the Launchpad 602. It consists of an application that is responsible for notes, chat messages, or quick creates and quick views. In addition, a plugin is initialized by the Launchpad 602 that loads the Copilot application 601on demand as well as records the users' interactions with other Fiori applications and business entities. The Copilot lifetime is bound to the Launchpad lifetime. If the Copilot is active then it will be notified by the shell 603 about context changes like app navigation 605 or viewport 604 changes. The collections and items in the Copilot can be stored in relation to the context of origin or made related to a specific context in the Launchpad 602 by the user—in most cases to the current active business app. Copilot will store the business context meta-data associated to the created items. This serves as the base for deep linking, navigation, and business context driven item filtering.
The Copilot data may be stored on the Hana Cloud Platform 610 and uses a customer tenant account with subscription to the Copilot service provider application 612. The persistency fulfills all legal requirements and sizing. The protocol to the backend is OData over HTTPS 614 using OData service provisioning 620 and HTTPS provisioning 622 on the fronted server communicating the OData model 624 on the launchpad 602. The OData model 624 includes an HTTPS client 626 and a web socket client 628. To propagate Copilot specific updates to end users, Copilot invokes the Fiori Notification Service API 616. To notify users of Copilot updates to chat messages, collections or collection items while Copilot is still active a Web Sockets 618 implementation that is tightly integrated into the Copilot backend service is used.
Fiori notifications are not to be mixed up with the Copilot entities like chat messages or collection items. These are only represented within Copilot while Fiori notifications are represented within the Fiori notification center. Screenshots are supported via a client-side library and the image binary stored in the HCP document service.
Copilot uses KM documentation which is supported via xRay. Copilot and xRay shall be built within the Launchpad based on the same overlay concept so that they can be displayed side by side. It is possible to have xRay documentation in the Copilot and vice versa that when xRay is open for a business app it must be possible to chat via Copilot. So both z-order variants are supported.
The launchpad 602 includes a CoPilot controller 630, which includes a quick creates controller 632, a quick view controller 634 and entity controller 636. Copilot Quick Creates 638 and Quick Views 640 are regular Fiori applications but typically with a tiny scope. It only uses the minimal required attributes needed to create the business object. To improve app developer's efficiency the Quick Create apps can be based on Smart Templates.
Besides the Quick Create 638 and Quick View 640 applications, no Copilot specific content has to be provided by the app developer. Also, no additional annotations are needed—however, rich semantic annotations can enable the automatic contextual value transfer into e.g. quick creates by prefilling the form.
The following explains the principle set of entities 642 created and managed by the Copilot and a brief description of their meaning and use case. One entity is a collection. Each collection consists primarily of a set of items and some meta-information. The items contained in the collection can be homogeneous (all of the same type), but can as well be heterogeneous, that is, mixed (different item types). The existing collections can be accessed through the collection list which can be filtered and searched. A collection can contain several similar or different items: Note, Object, Chat, Quick Create, Data Snapshot, System Message, Copilot Message and more in the future.
Collections can be private, such as to support memory and the collection and its content are visible only to the owner. As soon as the first participant is invited to a collection, all items are visible to the invitee, as well as all other participants invited thereafter. A clear visual representation and affordance will be provided on collection level and possible content level where relevant to ensure that the user is aware that the collection and contained items is private, that is, especially that the collection and all of the contained items is now visible to participants post invitation.
The following discusses Collection Items. One collection item is a Note Item. The note item type displays text entered by the user that is augmented with system administrative data, such as created on or created by. The text in a note item is parsed; recognized objects are highlighted and can later be added as objects to the collection. Succeeding notes (not interrupted by other item types) are combined and are displayed as one (to a certain time delay limit).
Another collection item is a Business Object Item. The object item type represents a business object. It contains visual representation like an image for product or person, the name or /ID (name preferred), the object type, additional relevant attributes depending on object type like the price, address, or relation and the time stamp. A business object item can be added to a Copilot collection through a feature that discovers visible business entities of the currently running Fiori application, via QuickCreate or text analysis of chat/note messages.
Another collection item is a Chat Message Item. The chat message item type displays message text entered by the user for the purpose of communicating. It differentiates itself from the note text through the visual representation of the intent to communicate. In addition to the written text similar to the note, a chat item type includes the visual representation of chat participants, such as an image or initials, an online/offline marker, the name of the chat participant, the message text, and the timestamp. It is also possible to reference a business object from a chat.
Another collection item is a Quick Create Item. The quick create item type is used to create a business entities (e.g. a Lead or Sales Order) inside the Copilot window. It displays a subset of input fields of the original create screen of the business object. The system can prefill input fields from the context of the current page or items in the Copilot collection. After the object has been created it will be displayed as a regular related business object with a remark that it was created with the help of a quick create.
Another collection item is a Screenshot Item. The screenshot item type displays a thumbnail image of an area of data or Fiori 2.0 screen content that was selected by the user as a snapshot. A full screen view can be triggered from the detail screen of the item as well as the possibility to navigate to the context of origin where authorized.
Another collection item is a System Message. From time to time it makes sense to alert the user about something in the Copilot. Therefore a system message is needed. Some system messages may be clicked to trigger a menu or similar. These messages are triggered by the Copilot itself not by the end user. Copilot system messages are not to be mixed up with Fiori notifications introduced later on in this document.
Other entities may include Augmentative Functions on Item Types. In order for the Copilot to provide flexibility for the user to mix and use items as needed, a set of functions are provided for all item types as relevant for the item type. For example, an item type can be copied and used either to create a new collection based on the item type or Add to an existing collection.
Referring to
The following OData entities are only transient, meaning that the Copilot service does not implement a persistency for them but only provides them for the client to consume:
User—Via this entity the client can query users (for adding participants for example) and get additional information such as first and last names. The user information will be provided by calling the “com.sap.security.um.user.UserProvider” API. This API is accessible at runtime using a JNDI lookup. Please note that for system messages no actual OData entity is required but they will be implemented using Web Sockets, and the Copilot UI will display these socket events according to the UX design.
Referring to
As soon as a new tenant enables the service or accesses it the first time, the database schema will be created automatically for that tenant (e.g., schema Tenant 1804). This should be part of the “enabling” of the Copilot in the HCP service cockpit within a specific consumer account. At runtime dynamic JPA data source lookup will be used to ensure that JPA will persist into the correct DB schema belonging to the current consumer account. In order to connect and map a new tenant DB schema to the Java application, a Neo-SDK console client command is executed. This is automated as part of the service enablement or Fiori Cloud Service onboarding automation.
In order to be able to access and consume the Copilot OData backend service, the customer first of needs an HCP account. Within this consumer account, the Copilot service has to be enabled in the HCP service cockpit. The enablement of the service will create a subscription to the Copilot OData Java application (which technically resides in the Copilot provider account) behind the scenes.
It is planned that this entire setup can be automated by integrating the Copilot into the Fiori Cloud Services and their on-boarding automation from the new Fiori Service Cockpit.
The Copilot frontend JavaScript code will only contain a hardcoded relative URL to pass to the SAPUI5 ODataModel. This relative URL has to be mapped to the absolute service URL (including the consumer account, which will be resolved to the correct tenant ID). Depending on the deployment scenario, this URL mapping is achieved in different ways: FLP runs on-premise and on HCP.
In the case of FLP running on-premise, the relative URL has to be configured in the web-dispatcher to point to the correct HCP service URL. In the case of FLP running on HCP, the URL has to be mapped via an HCP destination. The destination name has to match the assumed destination name in the “neoapp.j son” file and hence has to be documented in the service documentation for the customer to be able to look it up.
Referring back to
Since Copilot is initialized alongside FLP its lifetime is bound to the lifetime of the Launchpad. The Copilot Fiori application provides the main functionality within the floating window container. The floating window container is part of the FLP delivery and is provided to Copilot. It supports three different states: floating, floating collapsed, and docked. When Copilot is active, it uses shell services to get notified about context changes like app navigation or viewport changes. Copilot uses shell APIs to access the currently running application context in order to analyze its business entities and metadata.
Even when Copilot is not running in the foreground, the plugin analyzes the current application context to keep track of the most visited and most recent items the user is interested in.
As mentioned above, Copilot supports different deployment scenarios includes Cloud based HANA Cloud Platform (HCP) and on-premises with cloud based Copilot backend on HCP. The frontend (plugin and application) of Copilot only has one code line for on premises and HANA Cloud Platform installations since Fiori applications must be deployable on the ABAP-based frontend server as well as on HCP without modifications.
For Cloud based HCP Deployment, Copilot uses a specific minimum UI5 version as well as specific versions of the Launchpad and shell services like CrossAppNavigation and other shell APIs. By using the FaaS UI technology stacks it can be assured that these requirements and dependencies are fulfilled. Copilot provides a specific version for each UI technology stack (two maintenance, one innovation stack). These versions are “delivered” via subscriptions to the customers HCP tenants. Cloud based HCP deployment for the frontend of Copilot will follow Fiori as a Service (FaaS) guidelines, which already takes multi-tenancy, configuration, integration, customization into account. FaaS and by extension Copilot supports the following runtime scenario: A central access point where Copilot and all other Fiori applications run on the HANA Cloud Platform. A local access point runs Copilot and Fiori applications through a proxy embedded into the local application landscape. They key difference between a standard Fiori application and Copilot is that the backend service in this runtime scenario is still hosted on HCP.
For on-premise deployment, Fiori Cloud Service enablement is used to deploy Copilot for on premises scenarios. This runtime scenario uses a certain level of user synchronization and session synchronization between the on premises application landscape and the HCP tenant account. These capabilities are provided by the Fiori Cloud Service Enablement infrastructure. The HCP-Java backend service will be implemented in a backward compatible way. For any incompatible changes, a version of the backend service will be maintained to support different versions of the Copilot frontend in parallel. Copilot will provide the necessary steps and artifacts for deployment and configuration on the ABAP-based frontend server until Fiori as a Service Enablement becomes available and supports the on premises onboarding and runtime scenario.
Versioning of Copilot will follow the semantic versioning concept. From an HCP account cockpit perspective, each minor version becomes its own HTML5-application that a customer can subscribe to. The Launchpad points to an App Descriptor which references an HCP HTML5-application's path, which in turn points to the specific minor version of an application. This is synced with specific Launchpad versions, which will eventually be handled by FaaS UI technology stacks.
For authentication, the Copilot Java OData Service is accessible after successful authentication. The main authentication process from a user perspective will be: User opens the Fiori Launchpad (FLP) and authenticates against the hosting system (either Fiori on-premise or Fiori-as-a-Service/HCP). When opening the Copilot, the JavaScript code in the browser will try to call the HCP OData Service. If the FLP runs on-premise, the Web Dispatcher has to be configured in order to forward the request from a relative URL pattern to the right HCP URL. It is then assumed that the Copilot will use XHR authentication. The authentication will validate the user against a trusted Identity Provider, which is setup under “Trust” in the HCP cockpit. Here the customer can add the on-premise backend system, for example.
HCP offers a variety of authentication methods and configurations, which bundle a set of allowed methods. The default authentication configuration used in the Copilot Java OData Service will be “SAML2”, which offers SAML or Application-to-Application SSO.
The configuration of the Copilot service will be possible via an own configuration UI (Fiori/SAPUI5 application) that will be accessible via the service tile in the HCP service cockpit. The configurations will be stored in Copilot specific configuration DB tables. The configuration UI is accessible if the user has a specific user role allowing access to the configuration. Configuration settings for the Copilot will include: Permission to store some business data in Copilot service to enable Copilot search to find collections that contain that data and enabling of functions like converting business object to note text.
Business objects are a specific Copilot collection item type that users can add to their collections. These entities can either be discovered through the currently running Fiori application context, through search, via recent objects or text analysis. Copilot uses shell APIs to access the currently running application context in order to analyze its business entities and metadata. This may be implemented in a generic way called the Contextual Analyzer. The purpose of the Contextual Analyzer is to discover, classify and prioritize business entities that are currently being displayed in the UI. Results are classified and prioritized by how relevant business objects are. As an example, a Sales Order displayed in the Object Page of the Manage Sales Orders application is considered more relevant than a Sales Order bound to a row in a table.
Since business object data can contain sensitive information and may require authorization, Copilot does by default not persist this business data. Optionally, persisted data can improve certain Copilot features and functions such as full text search support for business data (in addition to unstructured notes and chat text) and faster loading of collections. The entity URI of a business object is persisted as part of the item data and is used to perform live loading of the current business object data for display in the collection. This also allows Copilot to perform an authorization check.
The prerequisite to discover business entities from an application screen (for example a Fiori application running in the Launchpad) is a UI5 component which uses data binding, preferably to an OData service. This means that applications do not have to implement any API or provide any configuration. The OData entities including metadata and annotations can be derived from the managed objects of this UI5 component. The Contextual Analyzer uses annotations (Badge, Headerinfo, Identification, Lineltem, SemanticObject) to determine certain business object properties (e.g. title, subtitle, etc.). The way these annotations are used is consistent with how annotations are generally defined and consumed by Fiori 2.0.
If no annotations are present, the Contextual Analyzer tries a set of heuristics to determine these business object properties. For example, the OData attributes are checked for attributes called “title” or “description”. The Title and Subtitle properties are the primary values used to display and persist the Copilot contextual objects and therefore have the most extensive heuristics. A minimal amount of property metadata is captured including property name, label and type, and compound properties are captured by looking at sap:text, sap:unit and sap:semantics; allowing Copilot to use the appropriate formatters to display labels, values and compound properties.
Copilot utilizes the Launchpad Fiori Search Model to provide search capabilities across all supported data sources. Data sources can be searched directly from within Copilot and results can be added as items to a collection. Fiori search data sources may be implemented as REST services and do not define metadata like OData services, and therefore no metadata is provided. Instead, the business object's title and subtitle are provided by the search results. Furthermore, search results do not contain the equivalent of an OData entity URI, so live loading of business object data and authorization checks are not possible.
Copilot allows a user to add business objects they have recently viewed or worked with to their collection. The Contextual Analyzer can discover, classify and prioritize business objects that are currently being viewed within Fiori apps. Copilot subscribes to events from the Launchpad that are invoked when a user navigates between and within apps, allowing Copilot to know what business objects the user is visiting. This allows Copilot to implement Object Usage Tracking across all Fiori apps that are running within the Launchpad, and to persist tracking details such as which business objects have been visited, how often and when. Only objects that are prioritized with a high priority are tracked, such as leading objects (e.g. the selected item in a master-detail floorplan), in order to reduce the amount of noise from related or secondary objects displayed in the app.
Navigation to collection items is supported for entities that were added to a Copilot collection. These items were either discovered by Copilot through the context of an application, via search, added through a QuickCreate or by taking a screenshot.
On the item detail, two different kinds of navigation are available: navigate to origin and navigate to factsheet (open). Navigate to origin allows to return to the application where the collection item was discovered. This is achieved by persisting the browsers location at the time of discovery alongside the collection item. A collection item added from the search results or through text analysis does not support this type of navigation. Navigate to origin is also supported for the collection itself. In this case Copilot navigates to the app where the collection was created.
Navigate to factsheet (open) will open the factsheet application (if deployed and available) associated with this semantic object for this business entity. The navigation follows the UI5 navigation concept for intent-based navigation and uses the CrossAppNavigation shell service. The information for navigation is the semantic object name, action and parameters. This information is available through the OData service metadata and its annotations as well as the data of the OData entity of the collection item itself. The navigate to factsheet option may not be available for screenshots or note/chat collection items. The semantic object name can be found based on the SemanticObject annotation of the Common vocabulary.
If an OData service contains this definition in its metadata, the UI5 control SmartLink and its related classes can identify possible navigation targets via the “CrossApplicationNavigation” service of the unified shell. SmartLink uses the current binding context of the application to construct the necessary parameters for navigation. Copilot will use SmartLink to generate intent-based navigation links. Either already available SmartLink APIs can be used, or a new requirement for UI5 has to be created. The item detail within the Copilot is rendered as Quick View component.
Quick Views show the item detail of business objects within the Copilot itself (when clicking on the object—see the UX specification of Copilot. Technically, Quick Create and Quick Views are very similar; so in the following, “Quick Create” can also be read as “Quick View”.
Conceptually, each semantic business object (Sales Order, Lead, Opportunity, etc.) that wants to support Quick Create (or Quick View) in the Copilot has to provide a designated Quick Create Fiori application, which will then be embedded into the view of the Copilot Quick Create item. There are in principle two ways a Quick Create or Quick View application can be provided:
Copilot will provide a new smart template, which can be used by the application developer for creating these Quick Create applications. The application developer only has to create the application shell (e.g. by using “sap.ui.generic.app”) and use the provided smart template as the root view. The Quick Create smart template will require the underlying Gateway service metadata to contain a new Quick Create OData v4 annotation, which contains the metadata about the input fields, which are supposed to be rendered and to which OData entity fields they are bound to. The controller of the new smart template will support the creation and persistence of both Draft-enabled and non-draft-enabled business objects.
The application developer does not use the new smart template but creates a new Fiori application from scratch, containing the Quick Create UI and controller logic. This would only be required if the provided smart template does not meet the create requirements of the corresponding business object.
In this case, the Quick Create application has to notify the Copilot as soon as the business object was successfully created so that the Copilot knows when to update the item. The provided smart template controller handles this automatically, but in case the Quick Create app is “freestyle”, the application is responsible for this. The suggested way is to fire an event on the used OData model instance of the form:
The Copilot will listen to a successful “POST” event with “createdObj ectContext” as a parameter.
Technically, Quick Create items are created by embedding a SAPUI5 UlComponent into the Copilot item view. The list of valid Quick Create UI components is retrieved at runtime by reading the FLP target mapping configuration and looking for all mappings for semantic action “quickcreate”, or “quickview” vor quick views.
The UIComponent instance is created using the cross-app navigation service provided by the FLP shell. In order to prevent the embedded Quick Create UI component to react to browser URL hash changes (which should control the main Fiori application but not the embedded apps in the Copilot), its UI5 router instance is stopped once the component has been embedded. This also means that QuickCreate apps have to be one-page apps without any internal navigation.
The Quick Create smart template component uses the new Quick Create annotations to generically render a Quick Create UI. The generic UI supports header level fields as well as the creation of item level data. The Quick Create smart template controller extends the generic “sap.suite.ui.generic.template.lib.TemplateViewController” class provided by the smart template framework team. This way it is possible to generically create and persist draft-enabled business objects.
Screenshots in copilot can be used to take snapshot of selected area of an application and display it in the collection. The open source html2canvas library is used for this purpose. The library script traverses through the DOM of the page it is loaded on. It gathers information on all the elements there, which it then uses to build a representation of the page. In other words, it does not actually take a screenshot of the page, but builds a screen representation on HTML Canvas based on the properties it reads from the DOM. The library returns a canvas, which is converted to a base64 string. Also in order for html2canvas to support svg elements like different types of charts, the open source canvg library is used for this purpose. The html2canvas library returns the screenshot image to be displayed on the Copilot collections.
The screenshots in Copilot HCP is persisted using the Document Service provided by SAP HCP. The SAP HCP document service (document service) provides an on-demand content repository for unstructured or semi-structured content. The Copilot backend service running on SAP HANA Cloud Platform can easily consume the document service using the JAVA client library based on Apache Chemistry. The backend service can create a document repository internally in the context of the tenant by providing unique name and repository key. Once a connection is established to the document service, the backend application connects to the repository to get a session. The backend service triggers the document service to add, retrieve and delete the screenshot images from the repository.
The frontend application can retrieve the document content by accessing the URL to the document service. The backend service application can accept incoming requests from the front end and forwards them to the document service. This is achieved using a new servlet that extends from AbstractCmisProxyServlet that acts as a proxy bridge. The proxy bridge is responsible for user authentication. The resources consumed in the document service are billed to the HCP account that deployed this proxy bridge (the Copilot account). The document service supports multitenancy and isolates data between tenants. There is no additional logic required at the application level.
Copilot will provide the end-user an increased value and context awareness of its collaborative features by recognizing and identifying entities out of unstructured data (free text) and offering an easy way to interact with them. Copilot will make use of SAP HANA Text Analysis, especially the feature called entity extraction.
Entity extraction analyzes unstructured text, in multiple languages and from any text data source, and automatically identifies and extracts key entity types, including people, dates, places, organizations, or other information, from the text. An entity is an instance of a thing, can have a meaning and is usually identifiable. Copilot distinguishes between basic entity types (like date/time (absolute/relative), location (city/country names), contact info (phone number, email), etc.), and business object entity types (like product, salesorder, employee, customer, etc.).
By automatically extracting entities from free text, the Copilot UI client could support scenarios like, displaying (business) objects, e.g. as factsheet, providing (semantic) actions on business objects, providing embedded actions on identified basic entities (contacting via email, phone), linking to a map application, etc., providing the option to add identified (business) objects to a collection, and matching identified (business) objects for an auto-fill feature of input fields like “Quick-Create”.
The Copilot Text Analysis component is integrated into the Copilot Service. The Copilot Text Analysis component will analyze and extract free text (like a note or a chat) that is entered by an end-user through the Copilot UI. The text analysis result will be a list of identified (business) object entities with its related (business) data and (semantic) metadata (e.g. business object type, navigation information, actions, rendering (e.g. as a factsheet).
The Copilot Text Analysis component is configurable. It will define, configure and support a default set of basic and business entity types. Entity types should be configurable to extend the components extraction capabilities. SAP HANA Text Analysis already ships predefined basic entity types whereas Copilot needs to define business object entity types. Copilot defines a default content set of dictionaries and rules for business object entities.
The following functional areas define the Copilot Text Analysis component, Text Analysis (Entity Extraction) including (custom) post-processing, Look-up of extracted entities with SAP Fiori Search/Enterprise Search, and Persistence of result set.
The architecture for building and integrating text analysis for Copilot is based on SOA principles. The Copilot backend service will be the main integration hub for these described functional areas. For now, this component will rely on SAP HANA XS engine and the HANA Text Analysis XS API to expose a web service to use the text analysis features. This web service is consumed by the Copilot backend service.
Once a textual collection item is persisted, the text analysis web service is called by the Copilot Backend to discover, recognize and classify related tokens of text of the collection item (e.g. a note or a chat message) as an entity. The result of this web service call will yield a list of extracted entities. Every entity provides information about its entity type, metadata about its location within the text (offset position, sentence number, paragraph number) and if possible, it provides an identification information (like an ID or ID descriptor/variant).
There are 2 approaches available to extract business object entities.
The first approach is to recognize type-qualified business entities with a subsequent ID variant through a rule, like [KEYWORD] [ID]. An example would be ‘salesorder 12345’ or ‘SO #12345’. This is achieved with custom rules rules/rule set (Custom Grouper User Language (CGUL)). The keyword here is a word or variant that describes the business object entity name. Those rules need to be defined.
The second approach is to recognize entities only through an ID or an identification variant without any type-qualified keyword. An identification variant is basically a name or description that can identify an entity. An example here would be a product name or product ID, ‘iPhone 6s’ or ‘HT-1000’, employee name or employee ID or ‘John Doe’ or ‘i123456’.
The difference here is that an end-user in a real-life scenario would not type-qualify those entities in a free text like ‘person i123456’ or ‘product iPhone 6s’. Master data (like product, customer, supplier, employee, org unit, etc.) usually provides the characteristic to identify the entity with an alias like a name or a short description. Text analysis supports this approach by defining custom dictionaries. An idea here is to create those dictionaries semi-automated through scripts by processing master data (through tables or OData endpoints). It requires domain knowledge to create specific variants like abbreviations or misspellings of entity names or IDs supporting multiple languages.
Basic entity types (like dates, phone numbers, emails, location) are already predefined and configured. But those can be extended through dictionaries and rules as well.
The text analysis web service can be extended with (custom) post-processing functionality of its result. Post-processing is usually needed to join, deduplicate, cleanse, enrich and normalize text analysis result sets. Smart Data Quality (SDQ—part of SAP HANA Enterprise Information Management) already provides out-of-the-box cleansing functionality to accomplish those tasks for entity types (like address, person name, organization name, phone number, email address). The SDQ cleanse feature can be used within SQL Script which can be consumed by a HANA XS application.
Cleansing and enriching is usually the process of cleaning up wrong, misspelled text, adding (meta) data, like gender on names, adding address information.
Normalization on the other side does transform a recognized entity into a structured data object, e.g. from recognized text ‘salesorder 12345’ into {entity-type: SALESORDER, identification:12345}.
After the extraction and the post-processing, a lookup web service is called by the Copilot Backend to find the business object and its related data and (semantic) meta data. Copilot will rely on SAP Fiori Search/Enterprise Search as a look-up service for extracted entities. SAP Fiori Search enables the search for business objects across core SAP Business Suite applications. The result of the lookup service will enrich extracted business entities with its data like icon, title, subtitle and additional (semantic) metadata to make use of the identified business object by the Copilot UI (render, navigate, provide actions, etc.). The entity type information is used to narrow down the list of possible (matching) results when searching for an ID or an identification variant. The result set sorted by ranking/relevance is used to check if the look-up found the matching business object.
The results of the text analysis and the look-up will be persisted by the Copilot Backend Service as an optimization for faster responses of READ scenarios (of collection items in a collection). Each collection item can have multiple extracted and identified entities.
On the client side, the Copilot client interprets the entity extraction and look-up data to render the markup of identified entities within the free text (of a collection item) and to enable more UX options (e.g. popover with factsheet, provide navigation to specific application, actions on business object).
Contextual data from the currently open collection can be used to assist the user in entering form values for quick creates and app forms. Auto-filling of forms leverages data that is discovered through text analysis and collection items and intelligently fills form values. Auto-filling looks at all editable input components in a form and analyzes their associated entity metadata in order to intelligently choose corresponding contextual data to fill the input. This includes invoking input value helps if needed.
As an example a collection could contain unstructured text such as, “the supplier will be iTelo and the product price is 295.00 EUR” that can be used to auto-fill some inputs for a Sales Order quick create. The text will be analyzed and “iTelo” will be identified as a potential value for a supplier, and “295.00 EUR” is a potential compound value for price. The Sales Order inputs and their associated metadata will be inspected to see if the form is expecting a supplier, and if so will fill it with the value “iTelo”, including executing and resolving a value list if the target property requires it. Similarly if a price input is found it will be filled with “295.00” including its compound currency property of “USD”.
Simple value persistency is also supported, when quick creates are not draft enabled, allowing users to begin entering values into a quick create and return to complete the form later, or to collaborate on form filling with other users.
Referring to
Since the Copilot service is proposed to become a Cloud service, the Fiori notification service should have an OData interface that Copilot can call through the Cloud Connector. The sequence diagram 900 shows the flow of information, such as Fiori notifications triggered by the addition of participants, as well as by their activity (adding items). In particular the diagram shows differences in the flow based on the status (online or offline) of the participants. The sequence diagram 900 uses the example of chat, because as a communication item type it can be assumed that it will have the highest flow activity. However, the sequence is true for all item types.
In the upper part only user 1, the collection owner, is online in the Copilot and invites user 2 who is currently offline. After that user 2 logs in and gets informed that he was invited to the collection. He then posts an item to the collection while user 1 is still “online”. Copilot supports collaborative features, in which multiple participants can add content to the same collection, and therefore the data model requires real-time push updates. When users add, edit or remove content from a collection, all other users that are viewing the same collection should get an instantaneous real-time update of the change. Push updates not only provide real-time updates of content, but also remove the need to perform backend requests to check if content has changed, such as during navigation. This results in a highly responsive and pleasing User Experience in which the user is not needlessly waiting for the UI to refresh during navigation, and also reduces the amount of network and server load created by the app.
Copilot leverages the standard and widely supported JavaScript Web Socket API to create a persistent socket connection between each client and the backend. A single socket is used to support as many listeners and push updates as are required by the app. Push updates are optimized to only be sent to specific clients that are interested. For example if a collection item is added to a collection only clients that are currently viewing the same collection items will receive a push update.
On the server push updates are implemented via the standard Java Web Socket API in the Copilot persistency container. This allows the backend implementation to instantaneously push updates directly to interested clients when data changes. Updates from the server to the client contain a minimal payload of metadata that allows the client to choose how to present the update to the user, such as immediately requesting the new data via OData, or presenting the user with a message or button to perform the update.
The user can perform full text search on Copilot collection content. This is achieved by enabling search in the corresponding HANA tables. The Copilot UI sends the search string to the copilot backend service, which retrieves search results by calling HANA stored procedure. The backend service provides a function that sends the response back to the client with the resulting entities along with its related entities for consumption on the Copilot UI.
Referring to
Referring to
The floating window 1160 is a container used to extend and enrich the functionality of all apps in the Fiori environment in a loosely coupled way. This cross functionality is not limited to a certain single app or content screen, but is provided throughout all app screens in the Fiori environment and thereby can connect different apps. It provides a way to add functionality to a screen without having to integrate it in the layout of the actual content. The functionality provided inside the container is accessible from every screen and can be used across multiple apps and screens. The container has to part of the shell, as it needs to be omnipresent across apps and Launchpad.
Some high level behaviors of the floating window 1160 include that it is triggered from Shell-Icon, floats above standard content, and is height flexible, width fixed (S size). The structure includes a header (For Navigation, etc.), a content area and a footer (Actions). The floating window 1160 is movable (Drag and Drop) and includes different display modes such as floating, expanded or collapsed mode and docked or undocked from full screen (Left or Right anchor positions depending on user dragging interactions). The floating window 1160 always keeps its position when content is being scrolled and is context aware (Knows the app that is displayed behind the Copilot window and all related business case and user contexts/roles). The floating window 1160 is a drop target meaning the Copilot window remembers the user's last dragged position on any screen (Sticky) and is fully responsive across all devices and screen sizes.
The CoPilot windows 1110 and 1160 include the following structure features and functions. The windows 1110 and 1160 include a header having buttons and controls: Close, Expand/Collapse, Back and other Navigations, Selectable UI elements such as text, Actions, Options, etc. The windows include a content area (flexible height) and a footer (fixed height). For its visual focus structure, the Copilot widow 1110 and 1160 is displayed in a “Front Focus” visual aspect such popups, etc.
The CoPilot windows 1110 and 1160 may be implemented on mobile and other device display structures (Desktop/Tablet/Smartphone/Smartwatch) of all sizes according to responsive/adaptive display behaviors for tablet, smartphone and smartwatches. The content area is flexible in size, behavior, and adding additional containers of any type (Smart Templates, filters, growing text containers, Chat Bar, etc.).
The Copilot windows 1110 and 1160 enable to following container user interactions including automatically launch Copilot from content (e.g. making suggestions to user on how to proceed on a certain action) and closing or launching the copilot window at any time and any entered content is automatically saved without the need for any popups and other user actions. Interactions include non-modal interactions that automatically close the Copilot window upon certain selections such as close Copilot window via “X” button in Header.
For Copilot window size and layering and modes the width of the floating window may be fixed (S). On devices with smaller width than the standard S size, the floating window size will adjust accordingly and the contained controls will adjust respectively to their responsive features. The height is flexible, with min, max and start values. The max=Height of browser window−Fiori shell, the min=Phone height−Fiori shell, the start=Property (in between Min & max). One exception for the height is the collapsed state. The floating window is used to provide functionality across all apps and screens. Therefore, it needs to float above all regular content (including modal dialogs). The only controls/concepts that should be on a higher index level then the floating window are the notifications banners, settings, shell, hierarchical navigation, Me Area, and help.
The following are the CoPilot window modes: Floating 1160 (default) with the floating window not movable outside of the content area, Floating Collapsed—only displays the header of the floating window and removes the navigation except for containing a Close button/icon, expand button, and Title Text with optional navigation, and Docked 111. In contrast to the other states, the docked state does not overlap the screen content under the Copilot window and can be docked on either the right or left side of the screen when dragged there. To save screen space for displaying content, the window may be of a reduced width. The height may always use the full height of the browser window less the height of the Fiori Shell. It is not possible to drag the window “off” the screen.
When the page is being scrolled and the floating window is open, the floating window will keep its position fixed, while the content below will scroll by. This is independent from the state the floating window currently has. While the floating window is closed, it may inform the user about existing floating window entries that where created in or have something to do with the current context. Therefore a small message bubble/window may be used. It may appear upon the page load. Message window contents may include a message text, (Button to directly trigger an action), a close icon, and a checkbox to prevent further message window/bubble. The Copilot Floating Window settings include Use Start Page (Switch On/Off). The start page is used to provide shortcuts and help out first time users. Well versed users might want to switch it off. Other setting include to start with last known state (Switch On/Off) and contextual messages (Switch On/Off). For keyboard operations, the floating window may be moved via arrow keys and floating window may be moved and docked via Tab keys or arrow+Alt keys.
Referring to
The request broker 1210 distributes the request to one or more agents based on the users role, domain or context of the request. The agent execution engine 1212 resolves speech to text (if necessary) and compares the results of agents based on confidence level and other metrics; returns the best match. The agent services 1214 include NLP Tools 1216: HANA TA tools, CoreNLP, Wordnet, etc. The agent services 1214 also includes an intent Classifier/Resolver 1218, which identifies an intent based on NLP output with the help of the Lookup Service 1220 and interfaces with a dialog engine 1221. The Lookup Service 1220 can match parameters and parameter values identified by NLP to an OData endpoint. The agent services 1214 includes an Intent Execution 1222, which can execute a resolved intent, which is either a CRUD operation, a function import call, or collection query with optional filter parameters. The intent repository 1224 is a registry for all provisioned intents.
Referring to
Appendix C through Appendix I of U.S. Provisional Application No. 62/335,879, filed May 13, 2016, provide additional examples, details, implementation examples and architecture examples related to CoPilot, which are herein incorporated by reference in their entirety.
A smart template can provide a framework for generating user interfaces at runtime for an enterprise application. For example, a smart template can be used to generate the UI for the overview page 120 as shown in
The timeline 174 can be used for collaborative communications. The timeline 174 can be configured in multiple different ways depending on use case implementations. For example, the timeline 174 can provide information about changes of an object or about events related to an object. The timeline 174 can provide information about generated entries (e.g., value XY changed from A to B) or about manual entries (e.g., comments from an individual). In some implementations, the latest entry is at the top of a list displayed by a timeline. In some implementations, the timeline 174 can be displayed along with a business object. In some cases, the timeline 174 can be displayed to the right of the business object.
Two versions of a timeline can include a basic timeline and a social timeline. A basic timeline can be a read-only timeline. A social timeline can allow for interaction and collaboration among users.
For example, computing device 206a can be a mobile phone, a smartphone, a personal digital assistant, or other type of mobile computing device. The computing device 206a includes a display device 220. For example, computing device 206b can be a laptop or notebook computer. The computing device 206b includes a display device 222. For example, computing device 206c can be a tablet computer. The computing device 206c includes a display device 224. For example, the computing device 206d can be a wearable device such as a smartwatch. The computing device 206d includes a display device 226. For example, the computing device 206e can be a desktop computer. The computing device 206e can include a display device 228. A user of the computing devices 206a-e can use/interface with the display devices 220, 222, 224, 226, and 228, respectively, when interacting with the enterprise computing system 202. The computing devices 206a-e can display on the display devices 220, 222, 224, 226, and 228 any of the screens and Uls described herein.
The enterprise computing system 202 can include one or more computing devices such as a web management server 214, a frontend server 230, a backend server 208, and a mobile device management server 210. The enterprise computing system 202 can also include a database management computing system 212 that includes a database management server 212a and a database 212b. Though not specifically shown in
In some first implementations, the client computing devices 206a-d (e.g., the mobile computing devices) can communicate with the enterprise computing system 202 (and the enterprise computing system 202 can communicate with the client computing devices 206a-d) by way of the mobile device management server 210. The mobile device management server 210 includes one or more mobile device platform application(s) 216. By using the mobile device platform application(s) 216, the enterprise computing system 202 can deliver cross-platform, secure, and scalable applications to the computing devices 202a-d, independent of the mobile computing device-type (e.g., laptop, notebook, smartwatch, mobile phone, PDA, etc.) and independent of the operating system running on the computing device 206a-d. In these implementations, the mobile device management server 210 can then communicate with the web management server 214.
In some second implementations, the client computing devices 206a-e (both the mobile computing devices (computing devices 206a-d) and the desktop computing device 206e) can communicate with the enterprise computing system 202 (and specifically with the web management server 214), and the enterprise computing system 202 (and specifically with the web management server 214) can communicate with each of the client computing devices202a-e) using the network 204. The web management server 214 includes a web dispatcher application 218. In both the first implementations and the second implementations, the web dispatcher application 218 can act as a “software web switch” accepting or rejecting connections to the enterprise computing system 202.
In some implementations, the network 204 can be a public communications network (e.g., the Internet, cellular data network, dialup modems over a telephone network) or a private communications network (e.g., private LAN, leased lines). In some implementations, the computing devices 206a-e can communicate with the network 204 using one or more high-speed wired and/or wireless communications protocols (e.g., 802.11 variations, WiFi, Bluetooth, Transmission Control Protocol/Internet Protocol (TCP/IP), Ethernet, IEEE 802.3, etc.).
The frontend server 230 can include product specific UI Add-On Applications 232 and a UI infrastructure 234. The UI infrastructure 234 can include a design portion and a runtime portion. The frontend server 230 can decouple a lifecycle of a UI (e.g., design and runtime deployment) from the backend server 208. The decoupling can allow UI applications to interface with a plurality of different databases. The decoupling provides a single point of UI design, access, and maintenance allowing for theming, branding, configuring, and personalizing a UI without a need for development privileges to the backend server 208 (e.g., no need to have backend administrative rights). The decoupling can result in a more secure enterprise computing system. The decoupling can provide for rule-based dispatching of requests in a multi-system landscape (e.g., for approvals including aggregation).
The frontend server 230 includes a gateway 236. The gateway 236 can provide a way to connect devices, environments, and platforms to enterprise software based on market standards. The gateway 236 can enable the development of UIs for use in different environments (e.g., social and collaboration environments). The gateway 236 can enable the development of UIs for use on different types of client computing devices (e.g., client computing devices 206a-e). The gateway 236 can enable the development of UIs for use in internet-based applications.
The backend server 208 can include a bundle (a set) of business applications (e.g., business suite 238). The business applications can be transactional applications. analytical applications, and fact sheet and contextual navigation applications. Transactional applications can allow task-based access to tasks that can include create and change. In addition or in the alternative, transactional applications can allow access to entire processes with guided navigation. Analytical applications can provide a user with a visual overview of complex tasks for monitoring and tracking purposes. Fact sheet applications and contextual navigation applications involve search and explore activities. Fact sheet applications and contextual navigation can allow a user to view essential information about an object and can allow contextual navigation between related objects.
The database management computing system 212 includes a database management server 212a that can run (execute) applications that can manage a database 212b. For example, the database 212b can be an in-memory, column-oriented, relational database (e.g., SAP)HANA®. The database management computing system 212 can include extended application services 240 that can embed a full featured application server, web server, and development environment within the database management computing system 212. The extended application services 240 can include application content 242 and reuse content 244 for use by the enterprise computing system 202 when providing a personalized, responsive, and simple UX across different types of computing devices and deployment options.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” “computer-readable medium” refers to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), and the Internet.
The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
A number of embodiments have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention.
In addition, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. In addition, other steps may be provided, or steps may be eliminated, from the described flows, and other components may be added to, or removed from, the described systems. Accordingly, other embodiments are within the scope of the following claims.
This application claims priority to and the benefit of U.S. Provisional Application No. 62/335,888, filed May 13, 2016, U.S. Provisional Application No. 62/335,892, filed May 13, 2016, U.S. Provisional Application No. 62/335,895, filed May 13, 2016, U.S. Provisional Application No. 62/335,897, filed May 13, 2016, U.S. Provisional Application No. 62/335,899, filed May 13, 2016, U.S. Provisional Application No. 62/335,879, filed May 13, 2016, U.S. Provisional Application No. 62/335,883, filed May 13, 2016, U.S. Provisional Application No. 62/335,886, filed May 13, 2016 and U.S. Provisional Application No. 62/335,887, filed May 13, 2016, each of which is incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 62335892 | May 2016 | US | |
| 62335895 | May 2016 | US | |
| 62335897 | May 2016 | US | |
| 62335899 | May 2016 | US | |
| 62335879 | May 2016 | US | |
| 62335883 | May 2016 | US | |
| 62335886 | May 2016 | US | |
| 62335887 | May 2016 | US | |
| 62335888 | May 2016 | US |
