CONTEXT-BASED NOTIFICATION PROCESSING SYSTEM
In some implementations, a method may involve determining first feature vectors for a plurality of data items accessed by a user of one or more client devices, the first feature vectors representing first contextual data about the one or more client devices at times that respective data items of the plurality of data items were accessed, the plurality of data items including a first data item. A predictive model, configured to classify input feature vectors into context types, may be used to determine that the first feature vector for the first data item is classified as a first context type. A second feature vector representing second contextual data about a first client device operated by the user may be determined and the predictive model may be used to determined that the second feature vector is classified as the first context type. Based at least in part on the first and second feature vectors being classified as the first context type and the first and second data items being of a first data item type, the first client device may be caused to present the second data item.
This application is a divisional of and claims the benefit under 35 U.S.C. § 120 and 35 U.S.C. § 121 to U.S. patent application Ser. No. 17/022,570, entitled CONTEXT-BASED NOTIFICATION PROCESSING SYSTEM, filed Sep. 16, 2020, which is a continuation of and claims the benefit under 35 U.S.C. § 120 and 35 U.S.C. § 365(c) to International Application PCT/CN2020/110931, entitled CONTEXT-BASED NOTIFICATION PROCESSING SYSTEM, with an international filing date of Aug. 25, 2020, the entire contents of each which are incorporated herein by reference for all purposes.
BACKGROUNDVarious systems have been developed that allow client devices to access applications and/or data files over a network. Certain products offered by Citrix Systems, Inc., of Fort Lauderdale, Fla., including the Citrix Workspace™ family of products, provide such capabilities.
SUMMARYThis Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features, nor is it intended to limit the scope of the claims included herewith.
In some of the disclosed embodiments, a method comprises determining first feature vectors for a plurality of data items accessed by a user of one or more client devices, the first feature vectors representing first contextual data about the one or more client devices at times that respective data items of the plurality of data items were accessed, the plurality of data items including a first data item; determining, using a predictive model configured to classify input feature vectors into context types, that the first feature vector for the first data item is classified as a first context type; determining that the first data item is of a first data item type; determining a second feature vector representing second contextual data about a first client device operated by the user; determining, using the predictive model, that the second feature vector is classified as the first context type; determining that a second data item is of the first data item type; and causing, based at least in part on the first and second feature vectors being classified as the first context type and the first and second data items being of the first data item type, the first client device to present the second data item.
In some disclosed embodiments a system comprises at least one processor and at least one computer-readable medium encoded with instructions which, when executed by the at least one processor, cause the system to determine first feature vectors for a plurality of data items accessed by a user of one or more client devices, the first feature vectors representing first contextual data about the one or more client devices at times that respective data items of the plurality of data items were accessed, the plurality of data items including a first data item, to determine, using a predictive model configured to classify input feature vectors into context types, that the first feature vector for the first data item is classified as a first context type, to determine that the first data item is of a first data item type, to determine a second feature vector representing second contextual data about a first client device operated by the user, to determine, using the predictive model, that the second feature vector is classified as the first context type, to determine that a second data item is of the first data item type, and to cause, based at least in part on the first and second feature vectors being classified as the first context type and the first and second data items being of the first data item type, the first client device to present the second data.
In some disclose embodiments, a method comprises generating, by a computing system, at least first and second notifications to be sent to a client device operated by a user, the first and second notifications indicating, respectively, first and second events of first and second applications accessible by the user; receiving, by the computing system from the client device, first data indicative of a current context of the client device; and sending, by the computing system and based at least in part on the first data, the first notification, but not the second notification, to the client device.
Objects, aspects, features, and advantages of embodiments disclosed herein will become more fully apparent from the following detailed description, the appended claims, and the accompanying figures in which like reference numerals identify similar or identical elements. Reference numerals that are introduced in the specification in association with a figure may be repeated in one or more subsequent figures without additional description in the specification in order to provide context for other features, and not every element may be labeled in every figure. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments, principles and concepts. The drawings are not intended to limit the scope of the claims included herewith.
For purposes of reading the description of the various embodiments below, the following descriptions of the sections of the specification and their respective contents may be helpful:
Section A provides an introduction to example embodiments of a context-based notification processing system;
Section B describes a network environment which may be useful for practicing embodiments described herein;
Section C describes a computing system which may be useful for practicing embodiments described herein;
Section D describes embodiments of systems and methods for accessing computing resources using a cloud computing environment;
Section E describes embodiments of systems and methods for managing and streamlining access by clients to a variety of resources;
Section F provides a more detailed description of example embodiments of context-based notification processing system that was introduced above in Section A;
Section G describes example implementations of methods, systems/devices, and computer-readable media in accordance with the present disclosure.
A. Introduction to Illustrative Embodiments of a Context-Based Notification Processing SystemAn intelligent activity feed, such as that offered by the Citrix Workspace™ family of products, provides significant benefits, as it allows a user to respond to application-specific events generated by disparate systems of record, without requiring the user to switch context and separately launch the respective applications to take actions with respect to the different events. An example of a system capable of providing such an activity feed is described in Section E below in connection with
More specifically, and as described in more detail in Section E, a microapp service 528 (shown in
The inventors have recognized and appreciated that even using the available filtering and/or sorting mechanisms, some users may have difficulty locating the notifications that they actually want to access to in a given contextual situation. For example, some users may tend to complete certain tasks not requiring desk space or other computing resources, such as responding to paid time off (PTO) request notifications 546, using their smartphones and/or while on a commuter train. Or some users may tend to review certain “announcement” notifications 546, such as new hire announcements, promotion announcements, announcements concerning company events, etc., using their laptop computers and/or while on their lunch breaks. Still other users may prefer to address notifications 546 relating to certain technical, business, or financial applications, such as Jira, Confluence, or Salesforce, using their desktop computers and/or while at their desks.
Offered is a system that can take a user's historical behavior patterns with respect to accessing notifications 546 (or other data items) in particular contextual situations into account when determining how to present new notifications 546 (or other data items) in a current contextual scenario. With reference to
An example implementation of a context-based notification processing system 100 configured in accordance with some aspects of the present disclosure is shown in
As show in
As indicated by the arrow 112, the machine learning process 110 may be used to train a predictive model 114 to categorize respective input feature vectors 116 into one of the clusters that were identified using the clustering technique. Once it is properly trained, the predictive model 114 may be used to assign labels, referred to herein as “context tags,” to the notification access event records stored in the storage medium(s) 104. In particular, for respective ones of the notification access event records, the stored contextual information for the record may be converted into a feature vector 116, e.g., using one or more encoders, that is then provided to the predictive model 114 for classification into a particular cluster. As illustrated in
In some implementations, the system 100 may periodically (e.g., once per day) evaluate at least some of the recorded notification access event records, including the context tags 118 applied to them by the predictive model 114, to determine “context-based notification forecast scores” for the possible combinations of notification types and context tags that are reflected in the evaluated data sets for respective users 524. In some implementations, for example, the system 100 may use the recorded notification access event records for a set period of time (e.g., the last 20 days) to determine the context-based notification forecast scores for respective users 524.
As indicated by an arrow 124 in
Upon receiving the current contextual data from the client device 202 (e.g., per the arrow 124), the system 100 may encode the contextual data into a context feature vector 116 and provide that context feature vector 116 to the predictive model 114 for determination of a context tag 118. After the context tag 118 has been determined for the current contextual information, the table 120 may be consulted to determine, based on that determined context tag 118, one or more notification types that are to be included in the requested context-based view of the activity feed 544.
In some implementations, the notification types (e.g., as indicated by the notification type IDs in the table 120) that (A) have the same context tag as the current contextual data, and (B) have higher than a threshold context-based notification forecast score, may be selected as the notification types that are to be included in the requested context-based view of the activity feed 544. For example, for the context-based notification forecast scores shown in the table 120, if the threshold score was “2” and the current contextual data was assigned context tag “A,” then “Type A” notifications but not “Type B” notifications would be selected as the notification types that are to be included in the requested context-based view. As another example, for the context-based notification forecast scores shown in the table 120, if the threshold score was “2” and the current contextual data was assigned context tag “B,” then both “Type A” notifications and “Type B” notifications would be selected as the notification types that are to be included in the requested context-based view.
After the pertinent notification type(s) for the user 524 requesting the context-based view of the activity feed 544 have been determined, e.g., based on the entries in the table 120, the system 100 may identify the notifications 546 of the determined type(s) that are currently pending for the user 524, and may construct an activity feed 544 that includes those notifications 546. In some implementations, the context-based notification forecast scores may further be used, either by themselves or together with other scores (e.g., relevance scores assigned by the analytics service 536) to determine the order in which the identified notifications 546 appear in the context-based view of the activity feed 544. For example, the identified notifications 546 having notification type IDs with higher context-based notification forecast scores may, in at least some circumstances, be caused to appear earlier in the activity feed 544 than those having notification type IDs with lower context-based notification forecast scores.
Further, in some implementations, rather than presenting a separate, context-based activity feed 544 that includes only notifications 546 having notification type IDs that match notification type IDs appearing in the table 120, the notification type IDs in the table 120, and/or the context-based notification forecast scores determined for those notification type IDs, may additionally or alternatively be used to enhance the “relevance” scores for some or all of the active notifications 546 in a user's activity feed 544. In some implementations, for example, a weighting value may be applied to relevance scores, e.g., as determined by the analytics service 536 described below, based on whether pending notifications 546 appear in the table 120 and/or the context-based notification forecast scores that were determined for those notification type IDs. Accordingly, the context-based notification forecast scores may additionally or alternatively be used to influence the order in which notifications 546 appear in a user's activity feed 544 when the user selects the “relevance” sorting option, e.g., via the user-interface element 570 shown in
At a step 132 of the routine 128, the server(s) 204 may use a predictive model (e.g., the predictive model 114) to determine that the first feature vector for the first data item (e.g., a notification 546) is classified as a first context type (e.g., has been assigned a particular context tag by the predictive model 114).
At a step 134 of the routine 128, the server(s) 204 may determine that the first data item (e.g., a notification 546) is of a first data item type, e.g., is a particular type of notification, such as a PTO approval request, an expense report approval request, a new hire announcement, etc.
At a step 136 of the routine 128, the server(s) 204 may determine a second feature vector (e.g., a current context feature vector 116) representing contextual information about a first client device (e.g., the client device 202) operated by the user. The second feature vector may, for example, have been determined based on the current contextual data included in a message indicated by the arrow 124.
At a step 138 of the routine 128, the server(s) 204 may determine, using the predictive model (e.g., the predictive model 114), that the second feature vector is classified as the first context type (e.g., has been assigned the same context tag as the first feature vector).
At a step 140 of the routine 128, the server(s) 204 may determine that a second data item (e.g., a pending notification 546 for the user) is of the first data item type.
At a step 142 of the routine 128, the server(s) 204 may cause the first client device (e.g., the client device 202) to present the second data item (e.g., a notification). As indicated, the presentation of the second data item may be based at least in part on the first and second feature vectors being classified as the first context type (e.g., being assigned the same context tag) and the first and second data items being of the first data item type.
Additional details and example implementations of embodiments of the present disclosure are set forth below in Section F, following a description of example systems and network environments in which such embodiments may be deployed.
B. Network EnvironmentReferring to
Although the embodiment shown in
As shown in
A server 204 may be any server type such as, for example: a file server; an application server; a web server; a proxy server; an appliance; a network appliance; a gateway; an application gateway; a gateway server; a virtualization server; a deployment server; a Secure Sockets Layer Virtual Private Network (SSL VPN) server; a firewall; a web server; a server executing an active directory; a cloud server; or a server executing an application acceleration program that provides firewall functionality, application functionality, or load balancing functionality.
A server 204 may execute, operate or otherwise provide an application that may be any one of the following: software; a program; executable instructions; a virtual machine; a hypervisor; a web browser; a web-based client; a client-server application; a thin-client computing client; an ActiveX control; a Java applet; software related to voice over internet protocol (VoIP) communications like a soft IP telephone; an application for streaming video and/or audio; an application for facilitating real-time-data communications; a HTTP client; a FTP client; an Oscar client; a Telnet client; or any other set of executable instructions.
In some embodiments, a server 204 may execute a remote presentation services program or other program that uses a thin-client or a remote-display protocol to capture display output generated by an application executing on a server 204 and transmit the application display output to a client device 202.
In yet other embodiments, a server 204 may execute a virtual machine providing, to a user of a client 202, access to a computing environment. The client 202 may be a virtual machine. The virtual machine may be managed by, for example, a hypervisor, a virtual machine manager (VMM), or any other hardware virtualization technique within the server 204.
As shown in
As also shown in
In some embodiments, one or more of the appliances 208, 212 may be implemented as products sold by Citrix Systems, Inc., of Fort Lauderdale, Fla., such as Citrix SD-WAN™ or Citrix Cloud™. For example, in some implementations, one or more of the appliances 208, 212 may be cloud connectors that enable communications to be exchanged between resources within a cloud computing environment and resources outside such an environment, e.g., resources hosted within a data center of+ an organization.
C. Computing EnvironmentThe processor(s) 302 may be implemented by one or more programmable processors executing one or more computer programs to perform the functions of the system. As used herein, the term “processor” describes an electronic circuit that performs a function, an operation, or a sequence of operations. The function, operation, or sequence of operations may be hard coded into the electronic circuit or soft coded by way of instructions held in a memory device. A “processor” may perform the function, operation, or sequence of operations using digital values or using analog signals. In some embodiments, the “processor” can be embodied in one or more application specific integrated circuits (ASICs), microprocessors, digital signal processors, microcontrollers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), multi-core processors, or general-purpose computers with associated memory. The “processor” may be analog, digital or mixed-signal. In some embodiments, the “processor” may be one or more physical processors or one or more “virtual” (e.g., remotely located or “cloud”) processors.
The communications interfaces 310 may include one or more interfaces to enable the computing system 300 to access a computer network such as a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or the Internet through a variety of wired and/or wireless connections, including cellular connections.
As noted above, in some embodiments, one or more computing systems 300 may execute an application on behalf of a user of a client computing device (e.g., a client 202 shown in
Referring to
In the cloud computing environment 400, one or more clients 202 (such as those described in connection with
In some embodiments, a gateway appliance(s) or service may be utilized to provide access to cloud computing resources and virtual sessions. By way of example, Citrix Gateway, provided by Citrix Systems, Inc., may be deployed on-premises or on public clouds to provide users with secure access and single sign-on to virtual, SaaS and web applications. Furthermore, to protect users from web threats, a gateway such as Citrix Secure Web Gateway may be used. Citrix Secure Web Gateway uses a cloud-based service and a local cache to check for URL reputation and category.
In still further embodiments, the cloud computing environment 400 may provide a hybrid cloud that is a combination of a public cloud and one or more resources located outside such a cloud, such as resources hosted within one or more data centers of an organization. Public clouds may include public servers that are maintained by third parties to the clients 202 or the enterprise/tenant. The servers may be located off-site in remote geographical locations or otherwise. In some implementations, one or more cloud connectors may be used to facilitate the exchange of communications between one more resources within the cloud computing environment 400 and one or more resources outside of such an environment.
The cloud computing environment 400 can provide resource pooling to serve multiple users via clients 202 through a multi-tenant environment or multi-tenant model with different physical and virtual resources dynamically assigned and reassigned responsive to different demands within the respective environment. The multi-tenant environment can include a system or architecture that can provide a single instance of software, an application or a software application to serve multiple users. In some embodiments, the cloud computing environment 400 can provide on-demand self-service to unilaterally provision computing capabilities (e.g., server time, network storage) across a network for multiple clients 202. By way of example, provisioning services may be provided through a system such as Citrix Provisioning Services (Citrix PVS). Citrix PVS is a software-streaming technology that delivers patches, updates, and other configuration information to multiple virtual desktop endpoints through a shared desktop image. The cloud computing environment 400 can provide an elasticity to dynamically scale out or scale in response to different demands from one or more clients 202. In some embodiments, the cloud computing environment 400 may include or provide monitoring services to monitor, control and/or generate reports corresponding to the provided shared services and resources.
In some embodiments, the cloud computing environment 400 may provide cloud-based delivery of different types of cloud computing services, such as Software as a service (SaaS) 402, Platform as a Service (PaaS) 404, Infrastructure as a Service (IaaS) 406, and Desktop as a Service (DaaS) 408, for example. IaaS may refer to a user renting the use of infrastructure resources that are needed during a specified time period. IaaS providers may offer storage, networking, servers or virtualization resources from large pools, allowing the users to quickly scale up by accessing more resources as needed. Examples of IaaS include AMAZON WEB SERVICES provided by Amazon.com, Inc., of Seattle, Wash., RACKSPACE CLOUD provided by Rackspace US, Inc., of San Antonio, Tex., Google Compute Engine provided by Google Inc. of Mountain View, Calif., or RIGHTSCALE provided by RightScale, Inc., of Santa Barbara, Calif.
PaaS providers may offer functionality provided by IaaS, including, e.g., storage, networking, servers or virtualization, as well as additional resources such as, e.g., the operating system, middleware, or runtime resources. Examples of PaaS include WINDOWS AZURE provided by Microsoft Corporation of Redmond, Wash., Google App Engine provided by Google Inc., and HEROKU provided by Heroku, Inc. of San Francisco, Calif.
SaaS providers may offer the resources that PaaS provides, including storage, networking, servers, virtualization, operating system, middleware, or runtime resources. In some embodiments, SaaS providers may offer additional resources including, e.g., data and application resources. Examples of SaaS include GOOGLE APPS provided by Google Inc., SALESFORCE provided by Salesforce.com Inc. of San Francisco, Calif., or OFFICE 365 provided by Microsoft Corporation. Examples of SaaS may also include data storage providers, e.g. Citrix ShareFile from Citrix Systems, DROPBOX provided by Dropbox, Inc. of San Francisco, Calif., Microsoft SKYDRIVE provided by Microsoft Corporation, Google Drive provided by Google Inc., or Apple ICLOUD provided by Apple Inc. of Cupertino, Calif.
Similar to SaaS, DaaS (which is also known as hosted desktop services) is a form of virtual desktop infrastructure (VDI) in which virtual desktop sessions are typically delivered as a cloud service along with the apps used on the virtual desktop. Citrix Cloud from Citrix Systems is one example of a DaaS delivery platform. DaaS delivery platforms may be hosted on a public cloud computing infrastructure, such as AZURE CLOUD from Microsoft Corporation of Redmond, Wash., or AMAZON WEB SERVICES provided by Amazon.com, Inc., of Seattle, Wash., for example. In the case of Citrix Cloud, Citrix Workspace app may be used as a single-entry point for bringing apps, files and desktops together (whether on-premises or in the cloud) to deliver a unified experience.
E. Systems and Methods for Managing and Streamlining Access by Client Devices to a Variety of ResourcesThe client(s) 202 may be any type of computing devices capable of accessing the resource feed(s) 504 and/or the SaaS application(s) 508, and may, for example, include a variety of desktop or laptop computers, smartphones, tablets, etc. The resource feed(s) 504 may include any of numerous resource types and may be provided from any of numerous locations. In some embodiments, for example, the resource feed(s) 504 may include one or more systems or services for providing virtual applications and/or desktops to the client(s) 202, one or more file repositories and/or file sharing systems, one or more secure browser services, one or more access control services for the SaaS applications 508, one or more management services for local applications on the client(s) 202, one or more internet enabled devices or sensors, etc. The resource management service(s) 502, the resource feed(s) 504, the gateway service(s) 506, the SaaS application(s) 508, and the identity provider 510 may be located within an on-premises data center of an organization for which the multi-resource access system 500 is deployed, within one or more cloud computing environments, or elsewhere.
For any of the illustrated components (other than the client 202) that are not based within the cloud computing environment 512, cloud connectors (not shown in
As explained in more detail below, in some embodiments, the resource access application 522 and associated components may provide the user 524 with a personalized, all-in-one interface enabling instant and seamless access to all the user's SaaS and web applications, files, virtual Windows applications, virtual Linux applications, desktops, mobile applications, Citrix Virtual Apps and Desktops™, local applications, and other data.
When the resource access application 522 is launched or otherwise accessed by the user 524, the client interface service 514 may send a sign-on request to the identity service 516. In some embodiments, the identity provider 510 may be located on the premises of the organization for which the multi-resource access system 500 is deployed. The identity provider 510 may, for example, correspond to an on-premises Windows Active Directory. In such embodiments, the identity provider 510 may be connected to the cloud-based identity service 516 using a cloud connector (not shown in
In other embodiments (not illustrated in
The resource feed service 518 may request identity tokens for configured resources from the single sign-on service 520. The resource feed service 518 may then pass the feed-specific identity tokens it receives to the points of authentication for the respective resource feeds 504. The resource feeds 504 may then respond with lists of resources configured for the respective identities. The resource feed service 518 may then aggregate all items from the different feeds and forward them to the client interface service 514, which may cause the resource access application 522 to present a list of available resources on a user interface of the client 202. The list of available resources may, for example, be presented on the user interface of the client 202 as a set of selectable icons or other elements corresponding to accessible resources. The resources so identified may, for example, include one or more virtual applications and/or desktops (e.g., Citrix Virtual Apps and Desktops™, VMware Horizon, Microsoft RDS, etc.), one or more file repositories and/or file sharing systems (e.g., Sharefile®, one or more secure browsers, one or more internet enabled devices or sensors, one or more local applications installed on the client 202, and/or one or more SaaS applications 508 to which the user 524 has subscribed. The lists of local applications and the SaaS applications 508 may, for example, be supplied by resource feeds 504 for respective services that manage which such applications are to be made available to the user 524 via the resource access application 522. Examples of SaaS applications 508 that may be managed and accessed as described herein include Microsoft Office 365 applications, SAP SaaS applications, Workday applications, etc.
For resources other than local applications and the SaaS application(s) 508, upon the user 524 selecting one of the listed available resources, the resource access application 522 may cause the client interface service 514 to forward a request for the specified resource to the resource feed service 518. In response to receiving such a request, the resource feed service 518 may request an identity token for the corresponding feed from the single sign-on service 520. The resource feed service 518 may then pass the identity token received from the single sign-on service 520 to the client interface service 514 where a launch ticket for the resource may be generated and sent to the resource access application 522. Upon receiving the launch ticket, the resource access application 522 may initiate a secure session to the gateway service 506 and present the launch ticket. When the gateway service 506 is presented with the launch ticket, it may initiate a secure session to the appropriate resource feed and present the identity token to that feed to seamlessly authenticate the user 524. Once the session initializes, the client 202 may proceed to access the selected resource.
When the user 524 selects a local application, the resource access application 522 may cause the selected local application to launch on the client 202. When the user 524 selects a SaaS application 508, the resource access application 522 may cause the client interface service 514 to request a one-time uniform resource locator (URL) from the gateway service 506 as well a preferred browser for use in accessing the SaaS application 508. After the gateway service 506 returns the one-time URL and identifies the preferred browser, the client interface service 514 may pass that information along to the resource access application 522. The client 202 may then launch the identified browser and initiate a connection to the gateway service 506. The gateway service 506 may then request an assertion from the single sign-on service 520. Upon receiving the assertion, the gateway service 506 may cause the identified browser on the client 202 to be redirected to the logon page for identified SaaS application 508 and present the assertion. The SaaS may then contact the gateway service 506 to validate the assertion and authenticate the user 524. Once the user has been authenticated, communication may occur directly between the identified browser and the selected SaaS application 508, thus allowing the user 524 to use the client 202 to access the selected SaaS application 508.
In some embodiments, the preferred browser identified by the gateway service 506 may be a specialized browser embedded in the resource access application 522 (when the resource application is installed on the client 202) or provided by one of the resource feeds 504 (when the resource access application 522 is located remotely), e.g., via a secure browser service. In such embodiments, the SaaS applications 508 may incorporate enhanced security policies to enforce one or more restrictions on the embedded browser. Examples of such policies include (1) requiring use of the specialized browser and disabling use of other local browsers, (2) restricting clipboard access, e.g., by disabling cut/copy/paste operations between the application and the clipboard, (3) restricting printing, e.g., by disabling the ability to print from within the browser, (3) restricting navigation, e.g., by disabling the next and/or back browser buttons, (4) restricting downloads, e.g., by disabling the ability to download from within the SaaS application, and (5) displaying watermarks, e.g., by overlaying a screen-based watermark showing the username and IP address associated with the client 202 such that the watermark will appear as displayed on the screen if the user tries to print or take a screenshot. Further, in some embodiments, when a user selects a hyperlink within a SaaS application, the specialized browser may send the URL for the link to an access control service (e.g., implemented as one of the resource feed(s) 504) for assessment of its security risk by a web filtering service. For approved URLs, the specialized browser may be permitted to access the link. For suspicious links, however, the web filtering service may have the client interface service 514 send the link to a secure browser service, which may start a new virtual browser session with the client 202, and thus allow the user to access the potentially harmful linked content in a safe environment.
In some embodiments, in addition to or in lieu of providing the user 524 with a list of resources that are available to be accessed individually, as described above, the user 524 may instead be permitted to choose to access a streamlined feed of event notifications and/or available actions that may be taken with respect to events that are automatically detected with respect to one or more of the resources. This streamlined resource activity feed, which may be customized for individual users, may allow users to monitor important activity involving all of their resources-SaaS applications, web applications, Windows applications, Linux applications, desktops, file repositories and/or file sharing systems, and other data through a single interface, without needing to switch context from one resource to another. Further, event notifications in a resource activity feed may be accompanied by a discrete set of user-interface elements, e.g., “approve,” “deny,” and “see more detail” buttons, allowing a user to take one or more simple actions with respect to events right within the user's feed. In some embodiments, such a streamlined, intelligent resource activity feed may be enabled by one or more micro-applications, or “microapps,” that can interface with underlying associated resources using APIs or the like. The responsive actions may be user-initiated activities that are taken within the microapps and that provide inputs to the underlying applications through the API or other interface. The actions a user performs within the microapp may, for example, be designed to address specific common problems and use cases quickly and easily, adding to increased user productivity (e.g., request personal time off, submit a help desk ticket, etc.). In some embodiments, notifications from such event-driven microapps may additionally or alternatively be pushed to clients 202 to notify a user 524 of something that requires the user's attention (e.g., approval of an expense report, new course available for registration, etc.).
In some embodiments, a microapp may be a single use case made available to users to streamline functionality from complex enterprise applications. Microapps may, for example, utilize APIs available within SaaS, web, or home-grown applications allowing users to see content without needing a full launch of the application or the need to switch context. Absent such microapps, users would need to launch an application, navigate to the action they need to perform, and then perform the action. Microapps may streamline routine tasks for frequently performed actions and provide users the ability to perform actions within the resource access application 522 without having to launch the native application. The system shown in
Referring to
In some embodiments, the microapp service 528 may be a single-tenant service responsible for creating the microapps. The microapp service 528 may send raw events, pulled from the systems of record 526, to the analytics service 536 for processing. The microapp service may, for example, periodically pull active data from the systems of record 526.
In some embodiments, the active data cache service 534 may be single-tenant and may store all configuration information and microapp data. It may, for example, utilize a per-tenant database encryption key and per-tenant database credentials.
In some embodiments, the credential wallet service 532 may store encrypted service credentials for the systems of record 526 and user OAuth2 tokens.
In some embodiments, the data integration provider service 530 may interact with the systems of record 526 to decrypt end-user credentials and write back actions to the systems of record 526 under the identity of the end-user. The write-back actions may, for example, utilize a user's actual account to ensure all actions performed are compliant with data policies of the application or other resource being interacted with.
In some embodiments, the analytics service 536 may process the raw events received from the microapps service 528 to create targeted scored notifications and send such notifications to the notification service 538.
Finally, in some embodiments, the notification service 538 may process any notifications it receives from the analytics service 536. In some implementations, the notification service 538 may store the notifications in a database to be later served in an activity feed. In other embodiments, the notification service 538 may additionally or alternatively send the notifications out immediately to the client 202 as a push notification to the user 524.
In some embodiments, a process for synchronizing with the systems of record 526 and generating notifications may operate as follows. The microapp service 528 may retrieve encrypted service account credentials for the systems of record 526 from the credential wallet service 532 and request a sync with the data integration provider service 530. The data integration provider service 530 may then decrypt the service account credentials and use those credentials to retrieve data from the systems of record 526. The data integration provider service 530 may then stream the retrieved data to the microapp service 528. The microapp service 528 may store the received systems of record data in the active data cache service 534 and also send raw events to the analytics service 536. The analytics service 536 may create targeted scored notifications and send such notifications to the notification service 538. The notification service 538 may store the notifications in a database to be later served in an activity feed and/or may send the notifications out immediately to the client 202 as a push notification to the user 524.
In some embodiments, a process for processing a user-initiated action via a microapp may operate as follows. The client 202 may receive data from the microapp service 528 (via the client interface service 514) to render information corresponding to the microapp. The microapp service 528 may receive data from the active data cache service 534 to support that rendering. The user 524 may invoke an action from the microapp, causing the resource access application 522 to send an action request to the microapp service 528 (via the client interface service 514). The microapp service 528 may then retrieve from the credential wallet service 532 an encrypted Oauth2 token for the system of record for which the action is to be invoked, and may send the action to the data integration provider service 530 together with the encrypted OAuth2 token. The data integration provider service 530 may then decrypt the OAuth2 token and write the action to the appropriate system of record under the identity of the user 524. The data integration provider service 530 may then read back changed data from the written-to system of record and send that changed data to the microapp service 528. The microapp service 528 may then update the active data cache service 534 with the updated data and cause a message to be sent to the resource access application 522 (via the client interface service 514) notifying the user 524 that the action was successfully completed.
In some embodiments, in addition to or in lieu of the functionality described above, the resource management services 502 may provide users the ability to search for relevant information across all files and applications. A simple keyword search may, for example, be used to find application resources, SaaS applications, desktops, files, etc. This functionality may enhance user productivity and efficiency as application and data sprawl is prevalent across all organizations.
In other embodiments, in addition to or in lieu of the functionality described above, the resource management services 502 may enable virtual assistance functionality that allows users to remain productive and take quick actions. Users may, for example, interact with the “Virtual Assistant” and ask questions such as “What is Bob Smith's phone number?” or “What absences are pending my approval?” The resource management services 502 may, for example, parse these requests and respond because they are integrated with multiple systems on the back-end. In some embodiments, users may be able to interact with the virtual assistant through either the resource access application 522 or directly from another resource, such as Microsoft Teams. This feature may allow employees to work efficiently, stay organized, and deliver only the specific information they're looking for.
When presented with such an activity feed 544, the user may respond to the notifications 546 by clicking on or otherwise selecting a corresponding action element 548 (e.g., “Approve,” “Reject,” “Open,” “Like,” “Submit,” etc.), or else by dismissing the notification, e.g., by clicking on or otherwise selecting a “close” element 550. As explained in connection with
The activity feed shown in
In some implementations, the storage medium(s) 610 may be encoded with instructions which, when executed by one or more processors of the client device(s) 202, may cause the client device(s) 202 to perform the functions of the engines 602, 604, 606, and 608 described herein. Similarly, in some implementations, the storage medium(s) 104 may be encoded with instructions which, when executed by one or more processors of the server(s) 204, may cause the server(s) 204 to perform the functions of the services 612, 614, 616, and 618 described herein.
At a high-level, the notification access monitoring engine 604 may monitor a user's interactions with notifications 546 in an activity feed 544 to identify instances in which the user 524 clicks on or otherwise accesses notifications 546, and may create records of such access events in the storage medium(s) 610. As described in more detail below, the notification access monitoring engine 604 may additionally request current contextual data from the context determination engine 606, and may record such contextual data in the storage medium(s) 610 as part of those created records. As noted in Section A, examples of such contextual data that may be so determined and included in the records include (A) a device ID identifying the particular client device 202 used to access the notification 546, (B) the date and/or time the client device 202 was used to access the notification 546, (C) a network ID identifying the network to which the client device 202 was connected at the time the notification 546 was accessed, (D) a location (e.g., latitude and longitude) of the client device 202 at the time it was used to access the notification 546. An example routine 700 that may be performed by the notification access monitoring engine 604 is described below in connection with
The notification data upload engine 602 may be responsible for uploading the new records created by the notification access monitoring engine 604 from the storage medium(s) 610 to the notification access monitoring service 612. As explained below, in some implementations, such record uploads may be performed periodically, e.g., once per day, at a time when the computational load on the client device 202 is low. An example routine 900 that may be performed by the notification data upload engine 602 is described below in connection with
The notification access monitoring service 612 may receive the records, including the contextual data determined by the context determination engine 606, that are uploaded from the notification data upload engine 602, and may write those records to the storage medium(s) 104, e.g., as rows in one or more tables. An example routine 1000 that may be performed by the notification access monitoring service 612 is described below in connection with
The context classifier training service 614 may be responsible for training and/or updating the predictive model 114 that is used by the context-based notification forecasting service 616 and the context-based notification presentation service 618, as explained below. An example routine 1200 that may be performed by the context classifier training service 614 is described below in connection with
The context-based notification forecasting service 616 may be responsible for calculating context-based notification forecast scores that can subsequently be used by the context-based notification presentation service 618 to determine the types of notifications that are to be included in a context-based view of an activity feed 544 generated for a client device 202, based on the current contextual situation of that client device 202. For example, as explained below, in some implementations, the context-based notification forecasting service 616 may periodically (e.g., once per day): (A) select a subset of the data in the table 1100 that is to be used for notification forecasting purposes (e.g., data from the past twenty days), (B) use the predictive model 114 to update the context tags 118 for the respective contextual data samples, and (C) use the selected/updated records to calculate context-based notification forecast scores for the respective context tag/notification type ID combinations in the table 1100. An example routine 1400 that may be performed by the context-based notification forecasting service 616 is described below in connection with
The view determination engine 608 of the client device(s) 202 and the context-based notification presentation service 618 of the server(s) 204 may operate together to present a user 524 of a client device 202 with a context-based view of an activity feed 544. In particular, in some implementations, the view determination engine 608 may determine that a context-based view of the activity feed 544 has been requested (e.g., by detecting selection of a “context-based” option via a user interface element 568, 570). In response to making such a determination, the view determination engine 608 may acquire current contextual data (e.g., from the context determination engine 606) and may send a request for such a view to the context-based notification presentation service 618, together with the determined contextual data.
Upon receiving the request for a context-based view of the activity feed 544 and the current contextual data from the client device 202, the context-based notification presentation service 618 may use the predictive model 114 to determine a context tag 118 for the current contextual data. For example, the context-based notification presentation service 618 may encode the received contextual data into a feature vector 116 and then feed that feature vector 116 to the predictive model 114 to as to yield a context tag 118 based on the current contextual data. Alternatively, in some implementations, the predictive model 114, when generated and/or updated, may be provided to the client device(s) 202, so as to enable the client device(s) 202 to instead determine the context tags 118 for respective contextual data samples. In any event, once context-based notification presentation service 618 has the context tag 118 based on the current contextual data, the context-based notification presentation service 618 may reference the table 1500 to identify the types of pending notifications 546 that are to be included in the context-based activity feed 544, as requested. In some implementations, for example, the notification types (e.g., as indicated by the notification type IDs in the table 1500) that (A) have the same context tag 118 as the current contextual data, and (B) have higher than a threshold context-based notification forecast score, may be selected as the notification types that are to be included in the requested context-based view of the activity feed 544. For example, for the context-based notification forecast scores shown in the table 1500, if the threshold score was “2” and the current contextual data was assigned context tag “C,” then type “NT3” notifications 546, but not types “NT1,” “NT2” or NT4” notifications 546, would be selected as the notification types that are to be included in the requested context-based view of the activity feed 544 for the user 524 with user ID “U1.” As another example, for the context-based notification forecast scores shown in the table 1500, if the threshold score was “3” and the current contextual data was assigned context tag “B,” then both type “NT1” and type “NT3” notifications, but not types “NT2” or “NT4” notifications, would be selected as the notification types that are to be included in the requested context-based view of the activity feed 544 for the user 524 with user ID “U1.”
After the context-based notification presentation service 618 has determined pertinent notification type(s) for the user 524 requesting the context-based view of the activity feed 544, e.g., based on the entries in the table 1500, the context-based notification presentation service 618 may identify the notifications 546 of the determined type(s) that are currently pending for the user 524, and may construct an activity feed 544 that includes those notifications 546. The context-based notification presentation service 618 may then send that activity feed 544 to the client device 202 for presentation as a context-based view of the activity feed 544. In some implementations, the context-based notification forecast scores (e.g., in the table 1500) may further be used, either by themselves or together with other scores (e.g., relevance scores assigned by the analytics service 536) to determine the order in which the identified notifications 546 appear in the context-based view of the activity feed 544. For example, the identified notifications 546 having notification type IDs with higher context-based notification forecast scores in the table 1500 may, in at least some circumstances, be caused to appear earlier in the activity feed 544 than those having notification type IDs with lower context-based notification forecast scores. An example routine 1600 that may be performed by the view determination engine 608 is described below in connection with
As noted above,
At a step 704 of the routine 700, the notification access monitoring engine 604 may determine a notification ID for the accessed notification 546. In some implementations, for example, different notification IDs may be assigned to respective notifications 546 created by the analytics service 536 (described above in connection with
At a step 706 of the routine 700, the notification access monitoring engine 604 may determine a notification type ID for the accessed notification 546. The determined notification type ID may indicate the type of notification 546 that was accessed. In some implementations, the notification type IDs may be names that are given to particular types of notifications, such as “PTO approval request,” “expense report approval request,” “new hire announcement,” etc. In other implementations, the notification type IDs may be identification numbers that are assigned to identify different types of notifications 546. As with the notification IDs, different notification type IDs may be assigned to respective notifications 546 created by the analytics service 536 (described above in connection with
At a step 708, the notification access monitoring engine 604 may request the context determination engine 606 to determine contextual data about the client device 202 at the time the notification 546 was accessed. An example routine 800 that may be employed by the context determination engine 606, as well as examples of contextual data that be determined by that engine, are described below in connection with
At a decision step 710, the notification access monitoring engine 604 may determine whether the requested contextual data has been received from the context determination engine 606. As indicated, the routine 700 may proceed to a step 712 when the requested contextual data has been received.
At the step 712 of the routine 700, the notification access monitoring engine 604 may store a record locally on the client device 202, e.g., in the storage medium(s) 610 shown in
As noted previously,
At a decision step 802 of the routine 800, the context determination engine 606 may determine whether a request for contextual data has been received from another component, such as the notification access monitoring engine 604 (as described above) or the view determination engine 608 (as described below). As indicated, the routine 800 may proceed to a step 804 when such a request is received.
At the step 804, the context determination engine 606 may determine a user ID for the user who is currently operating the client device 202. For example, in some implementations, the user ID may be the user name that the user 524 entered to gain access to resource access application 522. In other implementations, the user ID may be an identification number, separate from such a user name, that is assigned to identify a particular user 524 of the system 100. Since the system 100 may perform context-based notification processing on a user-by-user basis, determining user IDs may allow the system 100 to attribute particular notification access events to specific users 524.
At the step 806 of the routine 800, the context determination engine 606 may determine a device ID of the client device 202 on which the notification access event was detected. As some users 524 access notifications 546 using multiple different client devices 202, e.g., a smartphone, a laptop computer, a desktop computer, etc., the device ID may be used to differentiate amongst access events by different types of client devices 202.
At the step 808, the context determination engine 606 may determine the current date and/or time, e.g., by recording a value of a calendar and/or clock maintained by the client device 202.
At the step 810, the context determination engine 606 may determine a network ID of the network, if any, to which the client device 202 is currently connected. In some implementations, the network IDs may include the names and/or identifiers of specific networks to which client devices 202 are connected. In other implementations, the network IDs may additionally or alternatively indicate particular types of networks, such as 3G, 4G, 5G, wired local area network (LAN), wireless LAN, etc., to which such devices are connected.
At the step 812 of the routine 800, the context determination engine 606 may determine the current location of the client device 202. For example, the client device 202 may obtain the current coordinates (e.g., latitude and longitude) from a global positioning system (GPS) chip or other location determination device or system.
At the step 814, the context determination engine 606 may send the contextual data gathered per the steps 804, 806, 808, 810 and 812 to the component that requested it, e.g., the notification access monitoring engine 604 (as described above) or the view determination engine 608 (as described below).
At the decision step 904, the notification data upload engine 602 may evaluate the current load on the client device 202, such as by determining processing capacity and/or available network bandwidth of the client device 202. As indicated, in some implementations, the notification data upload engine 602 may wait until the load is low, e.g., below a threshold, before proceeding to a step 906, at which it may send the new access event records it has accumulated (since the last time the routine 900 was performed by the client device 202) to the notification access monitoring service 612.
At the step 1204, the context classifier training service 614 may select a subset of the accumulated notification access event records to use for re-training the user's predictive model 114. In some implementations, for example, the context classifier training service 614 may select the user's notification access event records (e.g., as stored in the table 1100) for the prior twenty days for such purpose. At a step 1206, the context classifier training service 614 may use the records selected at the step 1204 to retrain the predictive model 114 for the user 524.
As
As shown in
After identifying clusters of data points within the multi-dimensional feature space, the machine learning process 110 may train the predictive model 114 to classify a given feature vector 1306x into one of the clusters the machine learning process 110 identified. As explained below, in some implementations, a set of contextual data (e.g., either from a notification access record in the table 1100 or from a request for a context-based view of the notification feed 544 received from the view determination engine 608) may be provided as new data 1308 to one or more encoders 1310 (which may be the same as, or operate in the same manner as, the encoder(s) 1304). As shown in
At a step 1404, the context-based notification forecasting service 616 may determine the notification access event records (e.g., from the table 1100) that are to be used to determine the context-based notification forecast stores for the table 1500. In some implementations, for example, the context-based notification forecasting service 616 may identify the notification access event records in the table 1100 that were generated less than a threshold period of time (e.g., 20 days) in the past. The “time” entries 1110 in the table 1100 may, for example, be used for that purpose. In some implementations, the threshold time period used to select notification access event records at the step 1404 may be the same as the threshold time period that is used to determine (at the decision step 1202 of the routine 1200—shown in
At a step 1406, the context-based notification forecasting service 616 may determine and/or update the context tags 118 in the table 1100 (i.e., the “context tag” entries 1116) for the notification access event records selected at the step 1406. With reference to
At a step 1408, the context-based notification forecasting service 616 may generate and/or update the table 1500 of context-based notification forecast scores using the notification access event records that were selected at the step 1404 and for which the context tags 118 were updated at the step 1406. In the illustrated example, the table 1500 shows a set of determined context-based notification forecast scores for the user “U1” (as indicated by the “user ID” entries 1502). Although the example shown includes only twelve possible combinations of notification type IDs and context tags 118, it should be appreciated that, in practice, many more such combinations are likely to occur in the evaluated data set. As noted previously in Section A, in some implementations, the respective context-based notification forecast scores may simply reflect, for the data set being considered, the total number of notifications access event records for a particular type of notification 546 (as indicated by the “notification type ID” entries 1106) that include a particular context tag 118 (as indicated by the “context tag” entries 1116). For example, an entry 1512 in the table 1500 may reflect that, in the notification access event records under consideration, the context tag “C” was assigned to a total of “37” notification access event records that included “NT3” as the “notification type ID” entry 1106. In other implementations, different weights may be applied to different notification access event records when determining the context-based notification forecast scores in the table 1500. For example, if records for the last “X” days are being evaluated, lower weights may be applied to older records, so that the more recent records influence the context-based notification forecast scores more than the less recent ones. In some implementations, for example, an exponential moving average (e.g., a first-order infinite response filter that applies weighting factors that decrease exponentially) may be applied to weight the different notification access event records differently.
At the step 1604, the view determination engine 608 may request the current contextual data from the context determination engine 606 (shown in
Per a decision step 1606, the routine 1600 may proceed to a step 1608 when the contextual data has been received from the context determination engine 606 in response to the request sent at the step 1604.
At the step 1608 of the routine 1600, the view determination engine 608 may send a request for a context-based view of the activity feed 544 to the context-based notification presentation service 618 (shown in
At a decision step 1610, the view determination engine 610 may determine whether a set of notifications 546 to be included in the requested context-based view of the activity feed 544 have been received from the context-based notification presentation service 618. As indicated, the routine 1600 may proceed to a step 1612 when the requested set of notifications 546 has been received.
At the step 1612 of the routine 1600, the view determination engine 608 may cause the client device 202 to present a context-based view of the activity feed 544 that includes the notifications 546 that were received from the context-based notification presentation service 618, as described below.
At the step 1704 of the routine 1700, the context-based notification presentation service 618 may use the predictive model 114 (shown in
At a step 1706 of the routine 1700, the context-based notification presentation service 618 may evaluate the entries in the table 1500 to determine the types of notifications 546 (e.g., notifications having particular notification type IDs) that are to be included in the requested context-based view of the activity feed 544. In particular, in some implementations, the context-based notification presentation service 618 may identify the notification type IDs (per the “notification type ID” entries 1504) for which context-based notification forecast scores in the table are (A) associated with the same context tag as the context tag 118 determined at the step 1704, and (B) greater than a threshold value. For example, for the context-based notification forecast scores shown in the table 1500, if the threshold score was “2” and the current contextual data was assigned context tag “C,” then type “NT3” notifications 546, but not types “NT1,” “NT2” or NT4” notifications 546, would be selected as the notification types that are to be included in the requested context-based notification view for the user 524 with user ID “U1.” As another example, for the context-based notification forecast scores shown in the table 1500, if the threshold score was “3” and the current contextual data was assigned context tag “B,” then both type “NT1” and type “NT3” notifications, but not types “NT2” or “NT4” notifications, would be selected as the notification types that are to be included in the requested context-based notification view for the user 524 with user ID “U1.”
At a step 1708 of the routine 1700, the context-based notification presentation service 618 may determine the pending notifications for the requesting user 524 that have notification type IDs that match any of the notification type IDs determined at the step 1706. In some implementations, the context-based notification presentation service 618 may, for example, be a component of, or operate in conjunction with, the notification service 538 described above in connection with
At a step 1710, after the context-based notification presentation service 618 has selected the pending notifications 546 that are to be included in the request context-based view, the context-based notification presentation service 618 may construct a notification feed 544 that includes those notifications 546, and may send that notification feed 544 to the client device 202 that requested it. In some implementations, the context-based notification forecast scores may further be used, either by themselves or together with other scores (e.g., relevance scores assigned by the analytics service 536) to determine the order in which the identified notifications 546 appear in the context-based view of the activity feed 544. For example, the identified notifications 546 having notification type IDs with higher context-based notification forecast scores may, in at least some circumstances, be caused to appear earlier in the activity feed 544 than those having notification type IDs with lower context-based notification forecast scores.
Further, in some implementations, rather than presenting a separate, context-based view of an activity feed 544 that includes only notifications 546 having notification type IDs that match notification type IDs appearing in the table 1500, the notification type IDs appearing in the table 1500, and/or the context-based notification forecast scores determined for those notification type IDs, may additionally or alternatively be used to enhance the “relevance” scores for other of the active notifications 546 in a user's activity feed 544. In some implementations, for example, a weighting value may be applied to relevance scores, e.g., as determined by the analytics service 536 described below, based on whether pending notifications 546 appear in the table 1500 and/or the context-based notification forecast scores that were determined for those notification type IDs. Accordingly, the context-based notification forecast scores may additionally or alternatively be used to influence the order in which notifications 546 appear in a user's activity feed 544 when the user selects the “relevance” sorting option, e.g., via the user-interface element 570 shown in
G. Example Implementations of Methods, Systems, and Computer-Readable Media in Accordance with the Present Disclosure
The following paragraphs (M1) through (M12) describe examples of methods that may be implemented in accordance with the present disclosure.
(M1) A method may be performed that involves determining first feature vectors for a plurality of data items accessed by a user of one or more client devices, the first feature vectors representing first contextual data about the one or more client devices at times that respective data items of the plurality of data items were accessed, the plurality of data items including a first data item; determining, using a predictive model configured to classify input feature vectors into context types, that the first feature vector for the first data item is classified as a first context type; determining that the first data item is of a first data item type; determining a second feature vector representing second contextual data about a first client device operated by the user; determining, using the predictive model, that the second feature vector is classified as the first context type; determining that a second data item is of the first data item type; and causing, based at least in part on the first and second feature vectors being classified as the first context type and the first and second data items being of the first data item type, the first client device to present the second data item.
(M2) A method may be performed as described in paragraph (M1), and may further involve generating, using at least a first group of the first feature vectors and a clustering process, the predictive model.
(M3) A method may be performed as described in paragraph (M1) or paragraph (M2), and may further involve determining that the first feature vectors for at least a threshold number of the first data items of the first type have been classified as the first context type; wherein causing the first client device to present the second data item may be further based at least in part on the threshold number of the first data items of the first type having been classified as the first context type.
(M4) A method may be performed as described in any of paragraphs (M1) through (M3), and may further involve detecting events of one or more applications; and generating the first and second data items as first and second notifications, respectively, concerning the detected events.
(M5) A method may be performed as described in paragraph (M4), wherein generating the second data item may further comprise causing the second notification to include at least one user interface element enabling the user to take an action with respect to an application to which the second notification relates.
(M6) A method may be performed as described in any of paragraphs (M1) through (M5), and may further involve receiving, from the first client device, a request for a context-based view of an activity feed of notifications, the request including the second contextual data; wherein causing the first client device to present the second data item is performed in response to the request.
(M7) A method may be performed as described in any of paragraphs (M1) through (M6), wherein the second contextual data may comprise at least one of an identifier of the first client device, a current time, a network to which the first client device is connected, or a location of the first client device.
(M8) A method may be performed as described in any of paragraphs (M1) through (M7), and may further involve receiving the first contextual data from the one or more client devices, wherein the first contextual data may comprise one or more identifiers of the one or more client devices, current times at which the plurality of data items were accessed by the user, one or more networks to which the one or more client devices were connected when the plurality of data items were accessed by the user, or locations of the one or more client devices when the plurality of data items were accessed by the user.
(M9) A method may be performed as described in any of paragraphs (M1) through (M8), and may further involve determining a relevance score indicative of a predicted relevance of the second data item to the user, wherein relevance score is based at least in part on the first and second feature vectors being classified as the first context type and the first and second data items being of the first data item type; and determining to cause the first client device to present the second data item based at least in part on the relevance score.
(M10) A method may be performed that involves generating, by a computing system, at least first and second notifications to be sent to a client device operated by a user, the first and second notifications indicating, respectively, first and second events of first and second applications accessible by the user; receiving, by the computing system from the client device, first data indicative of a current context of the client device; and sending, by the computing system and based at least in part on the first data, the first notification, but not the second notification, to the client device.
(M11) A method may be performed as described in paragraph (M10), wherein generating the first and second notifications may further involve causing the first and second notifications to include respective user interface elements enabling the user to take corresponding actions with respect to the first and second applications.
(M12) A method may be performed as described in paragraph (M10) or paragraph (M11), wherein the first data may comprise at least one of an identifier of the client device, a current time, a network to which the client device is connected, or a location of the client device.
The following paragraphs (S1) through (S12) describe examples of systems and devices that may be implemented in accordance with the present disclosure.
(S1) A system may comprise at least one processor, and at least one computer-readable medium encoded with instructions which, when executed by the at least one processor, cause the system to determine first feature vectors for a plurality of data items accessed by a user of one or more client devices, the first feature vectors representing first contextual data about the one or more client devices at times that respective data items of the plurality of data items were accessed, the plurality of data items including a first data item, to determine, using a predictive model configured to classify input feature vectors into context types, that the first feature vector for the first data item is classified as a first context type, to determine that the first data item is of a first data item type, to determine a second feature vector representing second contextual data about a first client device operated by the user, to determine, using the predictive model, that the second feature vector is classified as the first context type, to determine that a second data item is of the first data item type, and to cause, based at least in part on the first and second feature vectors being classified as the first context type and the first and second data items being of the first data item type, the first client device to present the second data item.
(S2) A system may be configured as described in paragraph (S1), wherein the at least one computer-readable medium may be encoded with additional instructions which, when executed by the at least one processor, may further cause the system to generate, using at least a first group of the first feature vectors and a clustering process, the predictive model.
(S3) A system may be configured as described in paragraph (S1) or paragraph (S2), wherein the at least one computer-readable medium may be encoded with additional instructions which, when executed by the at least one processor, may further cause the system to determine that the first feature vectors for at least a threshold number of the first data items of the first type have been classified as the first context type, and to cause the first client device to present the second data item further based at least in part on the threshold number of the first data items of the first type having been classified as the first context type.
(S4) A system may be configured as described in any of paragraphs (S1) through (S3), wherein the at least one computer-readable medium may be encoded with additional instructions which, when executed by the at least one processor, may further cause the system to detect events of one or more applications, and to generate the first and second data items as first and second notifications, respectively, concerning the detected events.
(S5) A system may be configured as described in paragraph (S4), wherein the at least one computer-readable medium may be encoded with additional instructions which, when executed by the at least one processor, may further cause the system to generate the second data item at least part by causing the second notification to include at least one user interface element enabling the user to take an action with respect to an application to which the second notification relates.
(S6) A system may be configured as described in any of paragraphs (S1) through (S5), wherein the at least one computer-readable medium may be encoded with additional instructions which, when executed by the at least one processor, may further cause the system to receive, from the first client device, a request for a context-based view of an activity feed of notifications, the request including the second contextual data, and to cause the first client device to present the second data item in response to the request.
(S7) A system may be configured as described in any of paragraphs (S1) through (S6), wherein the second contextual data may comprise at least one of an identifier of the first client device, a current time, a network to which the first client device is connected, or a location of the first client device.
(S8) A system may be configured as described in any of paragraphs (S1) through (S7), wherein the at least one computer-readable medium may be encoded with additional instructions which, when executed by the at least one processor, may further cause the system to receive the first contextual data from the one or more client devices, wherein the first contextual data may comprise one or more identifiers of the one or more client devices, current times at which the plurality of data items were accessed by the user, one or more networks to which the one or more client devices were connected when the plurality of data items were accessed by the user, or locations of the one or more client devices when the plurality of data items were accessed by the user.
(S9) A system may be configured as described in any of paragraphs (S1) through (S8), wherein the at least one computer-readable medium may be encoded with additional instructions which, when executed by the at least one processor, may further cause the system to determine a relevance score indicative of a predicted relevance of the second data item to the user, wherein relevance score may be based at least in part on the first and second feature vectors being classified as the first context type and the first and second data items being of the first data item type, and to determine to cause the first client device to present the second data item based at least in part on the relevance score.
(S10) A system may comprise at least one processor, and at least one computer-readable medium encoded with instructions which, when executed by the at least one processor, cause the system to generate at least first and second notifications to be sent to a client device operated by a user, the first and second notifications indicating, respectively, first and second events of first and second applications accessible by the user, to receive, from the client device, first data indicative of a current context of the client device, and to send, based at least in part on the first data, the first notification, but not the second notification, to the client device.
(S11) A system may be configured as described in paragraph (S10), wherein the at least one computer-readable medium may be encoded with additional instructions which, when executed by the at least one processor, may further cause the system to generate the first and second notifications at least in part by causing the first and second notifications to include respective user interface elements enabling the user to take corresponding actions with respect to the first and second applications.
(S12) A system may be configured as described in paragraph (S10) or paragraph (S11), wherein the first data may comprise at least one of an identifier of the client device, a current time, a network to which the client device is connected, or a location of the client device.
The following paragraphs (CRM1) through (CRM12) describe examples of computer-readable media that may be implemented in accordance with the present disclosure.
(CRM1) At least one non-transitory computer-readable medium may be encoded with instructions which, when executed by the at least one processor of a computing system, cause the computing system to determine first feature vectors for a plurality of data items accessed by a user of one or more client devices, the first feature vectors representing first contextual data about the one or more client devices at times that respective data items of the plurality of data items were accessed, the plurality of data items including a first data item, to determine, using a predictive model configured to classify input feature vectors into context types, that the first feature vector for the first data item is classified as a first context type, to determine that the first data item is of a first data item type, to determine a second feature vector representing second contextual data about a first client device operated by the user, to determine, using the predictive model, that the second feature vector is classified as the first context type, to determine that a second data item is of the first data item type, and to cause, based at least in part on the first and second feature vectors being classified as the first context type and the first and second data items being of the first data item type, the first client device to present the second data item.
(CRM2) At least one non-transitory computer-readable medium may be configured as described in paragraph (CRM1), and may be encoded with additional instructions which, when executed by the at least one processor, further cause the computing system to generate, using at least a first group of the first feature vectors and a clustering process, the predictive model.
(CRM3) At least one non-transitory computer-readable medium may be configured as described in paragraph (CRM1) or paragraph (CRM2), and may be encoded with additional instructions which, when executed by the at least one processor, further cause the computing system to determine that the first feature vectors for at least a threshold number of the first data items of the first type have been classified as the first context type, and to cause the first client device to present the second data item further based at least in part on the threshold number of the first data items of the first type having been classified as the first context type.
(CRM4) At least one non-transitory computer-readable medium may be configured as described in any of paragraphs (CRM1) through (CRM3), and may be encoded with additional instructions which, when executed by the at least one processor, further cause the computing system to detect events of one or more applications, and to generate the first and second data items as first and second notifications, respectively, concerning the detected events.
(CRM5) At least one non-transitory computer-readable medium may be configured as described in paragraph (CRM4), and may be encoded with additional instructions which, when executed by the at least one processor, further cause the computing system to generate the second data item at least part by causing the second notification to include at least one user interface element enabling the user to take an action with respect to an application to which the second notification relates.
(CRM6) At least one non-transitory computer-readable medium may be configured as described in any of paragraphs (CRM1) through (CRM5), and may be encoded with additional instructions which, when executed by the at least one processor, further cause the computing system to receive, from the first client device, a request for a context-based view of an activity feed of notifications, the request including the second contextual data, and to cause the first client device to present the second data item in response to the request.
(CRM7) At least one non-transitory computer-readable medium may be configured as described in any of paragraphs (CRM1) through (CRM6), wherein the second contextual data may comprise at least one of an identifier of the first client device, a current time, a network to which the first client device is connected, or a location of the first client device.
(CRM8) At least one non-transitory computer-readable medium may be configured as described in any of paragraphs (CRM1) through (CRM7), and may be encoded with additional instructions which, when executed by the at least one processor, further cause the computing system to receive the first contextual data from the one or more client devices, wherein the first contextual data may comprise one or more identifiers of the one or more client devices, current times at which the plurality of data items were accessed by the user, one or more networks to which the one or more client devices were connected when the plurality of data items were accessed by the user, or locations of the one or more client devices when the plurality of data items were accessed by the user.
(CRM9) At least one non-transitory computer-readable medium may be configured as described in any of paragraphs (CRM1) through (CRM8), and may be encoded with additional instructions which, when executed by the at least one processor, further cause the computing system to determine a relevance score indicative of a predicted relevance of the second data item to the user, wherein relevance score may be based at least in part on the first and second feature vectors being classified as the first context type and the first and second data items being of the first data item type, and to determine to cause the first client device to present the second data item based at least in part on the relevance score.
(CRM10) At least one non-transitory computer-readable medium may be encoded with instructions which, when executed by the at least one processor of a computing system, cause the computing system to generate at least first and second notifications to be sent to a client device operated by a user, the first and second notifications indicating, respectively, first and second events of first and second applications accessible by the user, to receive, from the client device, first data indicative of a current context of the client device, and to send, based at least in part on the first data, the first notification, but not the second notification, to the client device.
(CRM11) At least one non-transitory computer-readable medium may be configured as described in paragraph (CRM10), and may be encoded with additional instructions which, when executed by the at least one processor, further cause the computing system to generate the first and second notifications at least in part by causing the first and second notifications to include respective user interface elements enabling the user to take corresponding actions with respect to the first and second applications.
(CRM12) At least one non-transitory computer-readable medium may be configured as described in paragraph (CRM10) or paragraph (CRM11), wherein the first data may comprise at least one of an identifier of the client device, a current time, a network to which the client device is connected, or a location of the client device.
Having thus described several aspects of at least one embodiment, it is to be appreciated that various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this disclosure, and are intended to be within the spirit and scope of the disclosure. Accordingly, the foregoing description and drawings are by way of example only.
Various aspects of the present disclosure may be used alone, in combination, or in a variety of arrangements not specifically discussed in the embodiments described in the foregoing and is therefore not limited in this application to the details and arrangement of components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects described in other embodiments.
Also, the disclosed aspects may be embodied as a method, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.
Use of ordinal terms such as “first,” “second,” “third,” etc. in the claims to modify a claim element does not by itself connote any priority, precedence or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claimed element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.
Also, the phraseology and terminology used herein is used for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having,” “containing,” “involving,” and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.
Claims
1. A method, comprising:
- generating, by a computing system, at least first and second notifications to be sent to a client device operated by a user, the first and second notifications indicating, respectively, first and second events of first and second applications accessible by the user;
- receiving, by the computing system from the client device, first data indicative of a current context of the client device; and
- sending, by the computing system and based at least in part on the first data, the first notification, but not the second notification, to the client device.
2. The method of claim 1, wherein generating the first and second notifications further comprises:
- causing the first and second notifications to include respective user interface elements enabling the user to take corresponding actions with respect to the first and second applications.
3. The method of claim 1, wherein the first data comprises at least one of an identifier of the client device, a current time, a network to which the client device is connected, or a location of the client device.
4. The method of claim 1, further comprising:
- prior to receiving the first data, receiving, by the computing system, second data indicative of one or more other notifications accessed by the user operating one or more client devices, and a context of the one or more client devices when the one or more other notifications were accessed by the user;
- wherein sending the first notification, but not the second notification, to the client device is further based at least in part on the second data.
5. The method of claim 1, further comprising:
- receiving, from the client device, a request for a context-based view of an activity feed of notifications, the request including the first data;
- wherein sending the first notification to the client device is performed in response to the request.
6. The method of claim 1, further comprising:
- retrieving, by the computing system, second data from the first application using first access credentials associated with the user;
- determining, by the computing system, that the second data is indicative of the first event;
- retrieving, by the computing system, third data from the second application using second access credentials associated with the user; and
- determining, by the computing system, that the third data is indicative of the second event.
7. The method of claim 6, wherein:
- the computing system retrieves the second data from first application via a first application programming interface (API) of the first application; and
- the computing system retrieves the third data from second application via a second API of the second application.
8. A system, comprising:
- at least one processor; and
- at least one computer-readable medium encoded with instructions which, when executed by the at least one processor, cause the system to: generate at least first and second notifications to be sent to a client device operated by a user, the first and second notifications indicating, respectively, first and second events of first and second applications accessible by the user, receive, from the client device, first data indicative of a current context of the client device, and send, based at least in part on the first data, the first notification, but not the second notification, to the client device.
9. The system of claim 8, wherein the at least one computer-readable medium is further encoded with additional instructions which, when executed by the at least one processor, further cause the system to:
- cause the first and second notifications to include respective user interface elements enabling the user to take corresponding actions with respect to the first and second applications.
10. The system of claim 8, wherein the first data comprises at least one of an identifier of the client device, a current time, a network to which the client device is connected, or a location of the client device.
11. The system of claim 8, wherein the at least one computer-readable medium is further encoded with additional instructions which, when executed by the at least one processor, further cause the system to:
- prior to receiving the first data, receive second data indicative of one or more other notifications accessed by the user operating one or more client devices, and a context of the one or more client devices when the one or more other notifications were accessed by the user; and
- send the first notification, but not the second notification, to the client device further based at least in part on the second data.
12. The system of claim 8, wherein the at least one computer-readable medium is further encoded with additional instructions which, when executed by the at least one processor, further cause the system to:
- receive, from the client device, a request for a context-based view of an activity feed of notifications, the request including the first data; and
- send the first notification to the client device in response to the request.
13. The system of claim 8, wherein the at least one computer-readable medium is further encoded with additional instructions which, when executed by the at least one processor, further cause the system to:
- retrieve second data from the first application using first access credentials associated with the user;
- determine that the second data is indicative of the first event;
- retrieve third data from the second application using second access credentials associated with the user; and
- determine that the third data is indicative of the second event.
14. The system of claim 13, wherein the at least one computer-readable medium is further encoded with additional instructions which, when executed by the at least one processor, further cause the system to:
- retrieve the second data from first application via a first application programming interface (API) of the first application; and
- retrieve the third data from second application via a second API of the second application.
15. At least one non-transitory computer-readable medium encoded with instructions which, when executed by at least one processor of a system, cause the system to:
- generate at least first and second notifications to be sent to a client device operated by a user, the first and second notifications indicating, respectively, first and second events of first and second applications accessible by the user;
- receive, from the client device, first data indicative of a current context of the client device; and
- send, based at least in part on the first data, the first notification, but not the second notification, to the client device.
16. The at least one non-transitory computer-readable medium of claim 15, further encoded with additional instructions which, when executed by the at least one processor, further cause the system to:
- cause the first and second notifications to include respective user interface elements enabling the user to take corresponding actions with respect to the first and second applications.
17. The at least one non-transitory computer-readable medium of claim 15, wherein the first data comprises at least one of an identifier of the client device, a current time, a network to which the client device is connected, or a location of the client device.
18. The at least one non-transitory computer-readable medium of claim 15, further encoded with additional instructions which, when executed by the at least one processor, further cause the system to:
- prior to receiving the first data, receive second data indicative of one or more other notifications accessed by the user operating one or more client devices, and a context of the one or more client devices when the one or more other notifications were accessed by the user; and
- send the first notification, but not the second notification, to the client device further based at least in part on the second data.
19. The at least one non-transitory computer-readable medium of claim 15, further encoded with additional instructions which, when executed by the at least one processor, further cause the system to:
- retrieve second data from the first application using first access credentials associated with the user;
- determine that the second data is indicative of the first event;
- retrieve third data from the second application using second access credentials associated with the user; and
- determine that the third data is indicative of the second event.
20. The at least one non-transitory computer-readable medium of claim 19, further encoded with additional instructions which, when executed by the at least one processor, further cause the system to:
- retrieve the second data from first application via a first application programming interface (API) of the first application; and
- retrieve the third data from second application via a second API of the second application.
Type: Application
Filed: May 4, 2022
Publication Date: Aug 18, 2022
Inventors: Daowen Wei (Nanjing), Jian Ding (Nanjing), Hengbo Wang (Nanjing)
Application Number: 17/736,230